AI Daily

Welcome back to AI Daily. In this episode, hosts Conner, Ethan, and Farb delve into three fascinating stories. First, Microsoft introduces an enterprise-specific ChatGPT version, self-hosted on Azure's private cloud. Next up, Global competition intensifies as countries race to bolster semiconductor production. Germany secures an $11 billion TSMC chip plant, while Texas welcomes a $1.4 billion semiconductor facility. Finally, Nvidia and HuggingFace join forces to enhance cloud offerings. Nvidia aims to expand its cloud services and connect directly with developers, positioning itself as more than a chip manufacturer. Quick Points 1️⃣ Microsoft Azure ChatGPT * Microsoft unveils Azure ChatGPT for enterprises, self-hosted on Azure's private cloud. * Repository briefly removed amid potential conflicts, highlighting unique deployment benefits. * Tailored for businesses, offering data control and secure sandbox for AI-powered interactions. 2️⃣ SemiConductor Manufacturing * Global competition heats up as countries vie for semiconductor manufacturing dominance. * Germany secures $11 billion TSMC chip plant, bolstering European presence. * Texas welcomes $1.4 billion semiconductor facility, reflecting chips' pivotal role in technology evolution. 3️⃣ NVIDIA-HuggingFace Partnership * Nvidia teams up with Hugging Face, aiming to strengthen cloud services presence. * Nvidia's expansion into direct cloud hosting aims to compete with established players. * The collaboration enhances accessibility to GPUs, potentially reshaping Nvidia's cloud industry involvement. 🔗 Episode Links * Microsoft Azure ChatGPT * SemiConductor - Germany * SemiConductor - Texas * NVIDIA-HuggingFace * Google Scholar Tweet Connect With Us: Follow us on Threads Subscribe to our Substack Follow us on Twitter: * AI Daily * Farb * Ethan * Conner This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.aidailypod.com

Welcome back to AI Daily! In this episode, we explore three intriguing stories in the world of AI and technology. First up, we discuss the possible end of LK-99, a ferromagnetic material that sparked excitement about superconductivity. Our second story delves into MK-1, a project aimed at enhancing the inference speed of language models. Lastly, we cover the launch of StableCode by Stable Diffusion. This coding model, boasting a 16,000 context window and 3 billion parameters, raises questions about its distinctiveness compared to other fine-tuned models. Quick Points 1️⃣ End of LK-99? * LK-99, initially hailed as a potential superconductor, faces skepticism as evidence of superconductivity remains elusive. * Despite uncertainty, the excitement around LK-99 showcases the power of scientific engagement and the pursuit of breakthroughs. * The episode debates whether LK-99's impact on science engagement outweighs its unconfirmed superconducting potential. 2️⃣ MK-1 * MK-1 project aims to make efficient model inference accessible to all. * MK-1's compression codec MKML and GPU optimization promise faster model outputs. * Democratizing AI capabilities through MK-1 could reshape AI deployment across various domains. 3️⃣ StableCode * StableCode, Stable Diffusion's coding model, hits the scene with 16,000 context window and 3 billion parameters. * Questions arise about StableCode's uniqueness and distinct contributions compared to other fine-tuned models. * Stable Diffusion's continuous innovation underscores the evolving landscape of fine-tuned AI models. 🔗 Episode Links * End of LK-99 * MK-1 * StableCode * Robert Scoble Tweet * HuggingFace/Supabase * Mortal Combat Video * 101 School Connect With Us: Follow us on Threads Subscribe to our Substack Follow us on Twitter: * AI Daily * Farb * Ethan * Conner This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.aidailypod.com

Welcome to another episode of AI Daily! In this episode, our hosts Farb, Ethan, and Conner cover three big stories to close out your week. First up, Varda, based in LA, presents super exciting news on LK-99 replication, showcasing levitation in a high-quality video of the Meisner Effect. Next, the Air Force's Valkyrie air combat drone triumphs with AI, aiming for unmanned flights. Alibaba unveils a remarkable 7 billion parameter model, surpassing LLaMA-2 7B and potentially 13B. Quick Points 1️⃣ Varda LK99 * Varda in LA achieves levitation in LK-99 replication, hinting at possible superconductivity. * Promising breakthrough material, but further research required for practical applications. * Russian and Chinese experiments add to the excitement surrounding this groundbreaking substance. 2️⃣ AirForce AI Drone Flight * Valkyrie, the Air Force's AI-driven drone, conquers unmanned flight challenges in simulations. * AI integration vital for military competitiveness and cost efficiency. * Advancements in AI-controlled drones signal an exciting future for military applications. 3️⃣ Alibaba Qwen * Alibaba introduces a powerful 7 billion parameter model, outperforming LLaMA-2 7B and possibly 13B. * Ideal for math, coding, and plugin-based tasks, expanding AI's efficiency. * Multifaceted model tailored for Chinese language but shows potential for various languages and applications. 🔗 Episode Links * Varda LK99 * AirForce AI Drone Flight * Alibaba Qwen * Model to Translate ada-002 * CoreWeave - Collateralization of the GPU Connect With Us: Follow us on Threads Subscribe to our Substack Follow us on Twitter: * AI Daily * Farb * Ethan * Conner This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.aidailypod.com

In this Today’s episode of AI Daily, our hosts Conner, Ethan, and Farb continue the discussion of LK-99, an intriguing material with replications in diverse settings, from Russian countertops to superconductivity experiments in China. The discussion revolves around practical implications and the path to usability. Next, they discuss Flow2 Neuroimaging, an innovative helmet offering FMRI-like capabilities, envisioning a future with accessible brain research and AI models. Finally, they discuss the collaboration between IBM and NASA, introducing Privy, a groundbreaking temporal vision transformer leveraging satellite data for predicting crop yields, monitoring disasters, and advancing earth science research. Quick Points 1️⃣ LK-99 Cont. * LK-99 replication news: Russian countertops to Chinese scientists exploring superconductivity at room temperature. * Exciting advancements: Levitation and zero resistivity observed, though challenges in scalable usability remain. * Public interest surges, promising potential for future engineering and groundbreaking applications. 2️⃣ Flow2 Neuroimaging * Flow2 Neuroimaging device: Compact helmet offers FMRI-like capabilities for brain research and AI models. * Pioneering data collection: Predicting emotions and thoughts, potential AR integration, and revolutionary brain understanding. * AI's role in processing data, opening doors to a new era of human interaction. 3️⃣ IBM & NASA GeoSpacial AI * Named, Prithvi, a temporal vision transformer utilizing NASA's vast satellite data. * Applications in predicting crop yields, monitoring natural disasters, and advancing earth science research. * Open-sourced AI with profound implications, a milestone in bridging AI and earth science. 🔗 Episode Links * Continuing LK-99 * Flow2 Neuroimaging * IBM & NASA Article * IBM & NASA Example * AI in Healthcare * Commercial Vicuna Model Connect With Us: Follow us on Threads Subscribe to our Substack Follow us on Twitter: * AI Daily * Farb * Ethan * Conner This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.aidailypod.com

Welcome to AI Daily! Join hosts Farb, Ethan, and Conner as they explore three groundbreaking AI stories First up, HierVST Voice Cloning - Experience zero-shot voice cloning with impressive accuracy using just one audio clip. Next, NVIDIA Perfusion - a small, powerful personalization model for text images, using key locking to maintain consistency. Lastly, Meta's AudioCraft - the fusion of music generation, audio generation, and codecs into one open-source code base, creating high-fidelity outputs. Quick Points 1️⃣ HierVST Voice Cloning * Zero-shot voice cloning system achieves accurate outputs with just one audio clip. * Uses hierarchical models for long and short-term generation understanding. * Potential challenges in handling longer clips and need for further fine-tuning. 2️⃣ NVIDIA Perfusion * Personalization model for text images with key locking for subject consistency. * Only 100 kilobytes, trains in four minutes, and outperforms other models. * Open-source codebase, but may need improvements for human subjects. 3️⃣ Meta’s AudioCraft * Audio generation, music gen, and codecs combined into an open-source codebase. * High-fidelity outputs, 30 seconds of sounds, compressing audio files efficiently. * Meta making strides in audio AI, impressively opens research use for community. 🔗 Episode Links * HierVST Voice Cloning * NVIDIA Perfusion * Meta's AudioCraft * ChatGPT String Tweet * Apple App Store/China Story Connect With Us: Follow us on Threads Subscribe to our Substack Follow us on Twitter: * AI Daily * Farb * Ethan * Conner This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.aidailypod.com

In this episode of AI Daily, hosts Farb, Ethan, and Conner delve into three big stories in the world of AI. First, discover the ripple effects of knowledge editing in language models, a benchmark of 5,000 facts highlighting challenges in current LLM editing, and an innovative in-context editing method. Next, we bring you updates on LK-99, a room temperature superconductor that may revolutionize the field. Learn about simulation findings and the potential end of Wakanda's unobtainium monopoly. Lastly, we explore how AI is impacting the field of Radiology. Uncover whether AI copilots or working independently is more effective for radiologists and the role of UX in AI adoption. Quick Points 1️⃣ LLM Editing * Adding or changing a single fact can cause a cascade of changes in an LLM's understanding * Benchmark of 5,000 facts reveals current LLM editing methods struggle with ripple effects. * Innovative in-context editing method shows promising results. 2️⃣ LK-99 Updates * LK-99 superconductor shows potential with simulated copper bands for energy transfer. * Exciting news shifts markets as room temperature superconductivity gains traction. * Future engineering may lead to increased bands for practical superconducting applications. 3️⃣ AI Radiology Study * Combining AI and human expertise in radiology yields suboptimal results. * UX plays a vital role in AI adoption for medical applications. * Future implications suggest AI or human-only approaches may be more effective. 🔗 Episode Links * LLM Editing Paper * LK-99 Updates Tweet #1 * LK-99 Updates Tweet #2 * AI Radiology Study * Neon Series B * GPU Supply & Demand Connect With Us: Follow us on Threads Subscribe to our Substack Follow us on Twitter: * AI Daily * Farb * Ethan * Conner This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.aidailypod.com

In this episode of AI Daily with your hosts Conner, Ethan, and Farb. They kick off the episode discussing Meta's OpenCatalyst, a groundbreaking model developed with Carnegie Mellon University that simulates over a hundred million catalyst combinations, accelerating advancements in material science and renewable energy. They then move to explore Google DeepMind's RT-2 Speaking Robot, a unique vision, language, and action model that learns from web images and texts to perform real-world actions, promising a new era of autonomous robotics. Finally, they delve into the intriguing concept of Adversarial Prompts, discussing a recent study by a team at Carnegie Mellon that used LLaMA to generate prompts adversarial to popular models like GPT-4, raising important questions about the robustness and safety of these models. Quick Points: 1️⃣ Meta’s OpenCatalyst * Meta and Carnegie Mellon University develop OpenCatalyst, simulating 100+ million catalyst combinations. * This tool enables rapid simulations, enhancing chemical process research. * It is highly applicable to renewable energy and material sciences. 2️⃣ RT-2 Speaking Robot * Google DeepMind unveils the RT-2 Speaking Robot, a vision-language-action model. * Trained on web images and texts, it can perform untrained real-world actions. * This model represents a significant leap in the realm of autonomous robotics. 3️⃣ Adversarial Prompts * A Carnegie Mellon team uses LLaMA to generate adversarial prompts against leading models. * This discovery exposes potential weaknesses in popular AI models like GPT-4. * Raises important questions about AI model robustness and safety. 🔗 Episode Links * Meta’s OpenCatalyst * RT-2 Speaking Robot * Adversarial Prompts * ElevenLabs Connect With Us: Follow us on Threads Subscribe to our Substack Follow us on Twitter: * AI Daily * Farb * Ethan * Conner This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.aidailypod.com

Welcome to another episode AI Daily. This episode brings together three distinct stories - the inception of the Frontier Model Forum by OpenAI, the intriguing LK-99 ambient pressure superconductor research, and the innovative Text2Room that converts text prompts into 3D point spaces of rooms. The Frontier Model Forum underscores the need for collaboration in AI safety, functioning as a consortium of foundational AI model providers, aiming to lead the industry towards beneficial advancements. Next, we dive into LK-99, a potential game-changer for computing, with its potential applications across various fields, including AI - its authenticity is yet to be confirmed. Lastly, we explore Text2Room, an impressive engineering solution that takes us from textual descriptions to 3D spatial representations. Quick Points 1️⃣ Frontier Model Forum * OpenAI initiates the Frontier Model Forum to foster industry collaboration for AI safety. * Serves as a consortium of foundational AI model providers. * It aims to instill more trust and potentially lobby for AI advancements. 2️⃣ LK-99 * LK-99 is proposed as a room temperature, ambient pressure superconductor. * Potential applications span across computing, medical, and power grids. * Its authenticity is currently under investigation. 3️⃣ Text2Room * Text2Room converts text prompts into 3D point spaces of rooms. * Uses a 2D model to take images and build a 3D point space. * Represents a significant step forward in the field of text-to-3D. 🔗Episode Links: * Frontier Model Forum * LK-99 * Text2Room * Bittensor Language Model * Farb's Tweet - Paris Hilton AI Car Creation * The GPU Song Connect With Us: Follow us on Threads Subscribe to our Substack Follow us on Twitter: * AI Daily * Farb * Ethan * Conner This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.aidailypod.com

Welcome to another fascinating episode of AIDaily, where your hosts, Farb, Ethan, and Conner, delve into the latest in the world of AI. In this episode, we cover 3D LLM, a cutting-edge blend of large language models and 3D understanding, heralding a future where AI could navigate full spatial rooms in homes and robotics. We also discuss VIMA, a groundbreaking demonstration of how large language models and robot arms can synergistically work together, suggesting a transformative path for robotics with multimodal prompts. Lastly, we explore the implications of StabilityAI's recent launch of FreeWilly1 and FreeWilly2, open-source AI models trained on GPT-4 output. Quick Points: 1️⃣ 3D LLM * A revolutionary mix of large language models and 3D understanding, enabling AI to navigate full spatial rooms effectively. * Potentially instrumental for smart homes, robotics, and other applications requiring spatial understanding. * Combines 3D point cloud data with 2D vision models for effective 3D scene interpretation. 2️⃣ VIMA * A groundbreaking demonstration of robot arms working with large language models, expanding their capabilities. * Uses multimodal prompts (text, images, video frames) to mimic movements and tasks. * The model's potential real-world application is yet to be tested against various edge cases. 3️⃣ FreeWilly1 & FreeWilly2 * Open-source AI models launched by StabilityAI, trained on GPT-4 output. * Demonstrates the capability of the Orca framework in producing efficient AI models. * The models are primarily available for research purposes, showing improvements over their predecessor, Llama. 🔗 Episode Links: * 3D LLM * VIMA * FreeWilly1 & FreeWilly2 * GPU Crunch - Suhail Tweet * OpenAI Closes AI Detection Tool * AI and Psychiatry Paper Connect With Us: Follow us on Threads Subscribe to our Substack Follow us on Twitter: * AI Daily * Farb * Ethan * Conner This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.aidailypod.com

Welcome to AI Daily! In this episode, we dive into three extraordinary and useful stories. First up, Maintaining Localized Image Variation - the groundbreaking paper that unveils a new way to edit shape variations within text-to-image diffusion models. Next, ScaleAI LLM Engine - ScaleAI has open-sourced a game-changing package for fine-tuning, inference, and training language models. Last but not least, SHOW-1 - the solution to the "slot machine problem" in video generation, where randomness prevails. Quick Points 1️⃣ Maintaining Localized Image Variations * Discover groundbreaking paper on maintaining localized image variation in text-to-image diffusion models, enabling precise object editing. * A practical and intelligent engineering solution that offers CGI-level control without the labor-intensive process, making it highly useful. * Impressive implementation with a hugging face demo showcasing effective object preservation and image transformations for stunning results. 2️⃣ ScaleAI LLM Engine * ScaleAI revolutionizes language model development by open-sourcing LLM Engine, allowing easy fine-tuning, inference, and training. * Their move showcases commitment to staying at the forefront of AI development and provides practical, useful tools for developers. * The open-source community benefits from ScaleAI's meaningful contribution, offering a powerful project that scales effortlessly with Kubernetes. 3️⃣ SHOW-1 * Introducing SHOW-1, a show runner agent that tackles the challenge of creating consistent animated shows using image and video models. * Aiming to solve the "slot machine problem," SHOW-1 combines prompt engineering and consistent frame sets to generate coherent and engaging video content. * Impressive engineering and clean outputs make SHOW-1 stand out, offering videos that resemble popular shows like South Park in appearance and sound. Ambitious and promising for future iterations. 🔗 Episode Links * Maintaining Localized Image Variations * ScaleAI LLM Engine * SHOW-1 * Perplexity AI Hosting Llama * Justin Alvey - Jailbroke Google Nest Mini Connect With Us: Follow us on Threads Subscribe to our Substack Follow us on Twitter: * AI Daily * Farb * Ethan * Conner This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.aidailypod.com

Today on AI Daily, we have three-big stories for you. First, Meta's Llama 2 takes the spotlight, revolutionizing open-source models with its commercial availability. Next, we discuss Neural Video Editing which offers a game-changing solution for seamless frame-by-frame editing in videos. And lastly, FlashAttention-2 delivers lightning-fast GPU efficiency and supercharging performance. Key Points 1️⃣ Meta’s Llama 2 * Llama 2, Meta's new addition to the llama open source model, is now commercially available and free for commercial use. * Llama 2 is highly capable, comparable to GP 3.5, and is expected to dominate the open source model landscape. * The release of Llama 2 creates a significant shift for AI developers, allowing them to run and fine-tune models without additional costs or safety measures from OpenAI. 2️⃣ Neural Video Editing * Neural video editing allows users to edit a single frame in a video and apply the edit to the entire video, making it accessible and powerful for beginners and those with limited resources. * This technology combines optical flow, control nets, and segment anything to enable interactive and real-time editing of videos. * Adobe and the University of British Columbia collaborated on the development of this interactive neural video editing, which is expected to be integrated into Adobe products soon. 3️⃣ FlashAttention-2 * FlashAttention-2 is a highly efficient GPU usage technique that is twice as fast as the original FlashAttention, providing a significant boost in performance and cost-effectiveness. * The improved FlashAttention enables longer context windows for video and language models and paves the way for future hardware developments. * This advancement is crucial for maximizing GPU capabilities and brings us closer to unlocking the full potential of current and upcoming hardware. 🔗 Episode Links * Meta’s Llama 2 * Neural Video Editing * FlashAttention-2 * Latent Space Episode: Datasets 101 * LangSmith Connect With Us: Follow us on Threads Subscribe to our Substack Follow us on Twitter: * AI Daily * Farb * Ethan * Conner This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.aidailypod.com

Welcome back to AI Daily and here are three stories to close out your week. First, Meta's CM3leon introduces a transformative multimodal generative model for text and images, offering incredible efficiency and versatility. Next, HyperDreamBooth revolutionizes fast personalization of text-image models with its impressive speed and significantly reduced model size. Finally, Animate-A-Story showcases retrieval-augmented video generation, an engineering hack that combines motion structure retrieval with structure-guided text to create high-quality videos. Quick Points 1️⃣ Meta’s CM3leon * Meta introduces CM3leon, a state-of-the-art multimodal generative model for text and images, based on transformers. * The model is highly efficient and performs tasks like fine-tuning on texts and images, generating high-quality images, and offering structure-guided editing. * It impresses with its ability to handle segmentation, accurately create objects in images, and even generate realistic hands and text on signs. Meta continues to push the boundaries of AI. 2️⃣ HyperDreamBooth * HyperDreamBooth introduces hyper networks for fast and efficient personalization of text image models. * The model is 10,000 times smaller than Dream Booth, processing images in just 20 seconds, making it highly accessible. * The pace of development in this space is remarkable, allowing for embedding the model in mobile devices and achieving impressive results. 3️⃣ Animate-A-Story * Animate-A-Story combines motion structure retrieval and structure guided text to generate high-quality text-to-video results. * It addresses the challenge of spatial consistency in text videos, using a database of similar videos for stylization. * While the initial motion generation is an engineering hack, the pipeline shows potential for quality text-to-video synthesis. 🔗 Episode Links * Meta’s CM3leon * HyperDreamBooth * Animate-A-Story * Turning Test Article * Generative Motion Matching Connect With Us: Follow us on Threads Subscribe to our Substack Follow us on Twitter: * AI Daily * Farb * Ethan * Conner This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.aidailypod.com

Welcome to Today’s episode of AI Daily! First up, we're talking about Lince LLM, a fine-tuned Spanish based LLM by Clibrain. Next, we examine LMQL, an innovative programming language alternative for LLMs that's throwing its hat in the ring against giants like Microsoft with its promise of superior keyword functionality. Lastly, we look at Google's Bard updates and NotebookLM. Tune in to get the full scoop. Key Points 1️⃣ Lince LLM * Lince LLM, the first geographically-tuned language model, focuses on the Spanish language and dialect nuances, setting it apart from GPT-4. * The Madrid-based startup, clibrain, has bootstrapped their own foundational model, specifically designed for Spanish text, chat, and text-to-speech interactions. * Recognizing the value of language-specific fine tuning, the team plans to continue developing their model, following the trend of region-specific LLMs. 2️⃣ LMQL * LMQL, a new programming language for large language models (LLMs), offers an alternative to existing systems like Lang Chain and Microsoft Guidance. * With specific tools for meta-prompting and maintaining chain of thoughts, LMQL seems to offer a more comprehensive and feature-rich framework for LLMs. * Although LMQL faces the challenge of competing with established systems, its developers are hopeful that it can gain traction and possibly attract investment. 3️⃣ Bard Updates & NotebookLM * Bard and NotebookLM from Google have been updated with new features like the ability to add images to prompts using Google Lens. * These tools, already popular with a large user base, will continue to see AI features integration, although immediate significant user growth isn't expected. * Notebook LM stands out due to its innovative approach, however, it's suspected to be a prototype project with a potentially limited lifespan. 🔗 Episode Links * Lince LLM * LMQL * Bard Updates * NotebookLM * Ethan Mollick & NY Times Article * OpenAI-AP Partnership * Cognitive Synergy Paper Connect With Us: Follow us on Threads Subscribe to our Substack Follow us on Twitter: * AI Daily * Farb * Ethan * Conner This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.aidailypod.com

Welcome to a brand new episode of AI Daily, where we explore the world of artificial intelligence and its impact on businesses. Today, we delve into three transformative stories. 1️⃣ OpenAI & Shutterstock * OpenAI and Shutterstock are expanding their partnership, providing training data for generative AI and potentially exploring the creation of Shutterstock's own generative AI, setting a potential trend for future partnerships. * The partnership seeks to proactively address potential legal concerns around content usage, shifting the responsibility of AI's guidelines and risk to Shutterstock, which may face repercussions if AI model training based on its data is contested. * Despite potential legal challenges, this partnership is seen as beneficial for Shutterstock, offering them access to generative tools and possible remuneration for their data usage, although the specifics of the payment model are yet unclear. 2️⃣ Shopify Sidekick * Shopify has launched "Sidekick", an AI tool within their platform, which helps entrepreneurs with tasks like changing images, adding text to images, and answering business-related questions. * The new tool streamlines the Shopify user experience by automating adjustments, such as changing header pictures, color themes, or adding new product banners, replacing manual fine-tuning with AI-assisted operations. * Sidekick's ability to handle abstract questions provides a valuable tool for e-commerce beginners, while experienced users may find it more gimmicky; however, it could potentially reduce costs associated with data analysis or consulting services. 3️⃣ xAI * Elon Musk's new project, xAI, comprises top minds from companies like Google and DeepMind, aiming to use AI to understand the universe, possibly launching a competitor to OpenAI. * The team plans to work closely with Twitter and Tesla, harnessing Twitter's extensive data and Tesla's advanced AI and multimodal work to create innovative multimodal models. * Rather than focusing on current AI tasks, xAI might aim at fundamental questions, like understanding the physical nature of the universe, potentially aiding research efforts at Twitter, Tesla, and beyond. 🔗 Episode Links * OpenAI & Shutterstock * Shopify Sidekick * xAI * Disney’s AI Software * Objaverse-XL * AI Meme Tweet Connect With Us: Follow us on Threads Subscribe to our Substack Follow us on Twitter: * AI Daily * Farb * Ethan * Conner This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.aidailypod.com

Welcome back to AI Daily. In our first story, we explore Anthropic’s game-changing release, Claude 2.0! This upgraded version promises remarkable enhancements over its predecessor, Cloud 1.3. Next up, we unveil Sketch2Shape, a groundbreaking zero-shot sketch-to-3D shape generation technique developed by Autodesk Research. Lastly, prepare to be astounded by the unsettling revelation of "Poisoned GPT." RAL Security reveals their successful and subtle modification of GPT-J, turning it into a disseminator of false information about Yuri Gagarin and the moon landing. Key Points 1️⃣ Claude 2 * Anthropic announces Claude 2, an upgrade to Cloud 1.3, with new features and a user-friendly interface. It performs well on code generation and has a longer context window. * Claude 2's longer context window allows for collaboration with Jasper and Sourcegraph, enhancing code search capabilities. Anthropic focuses on making AI models safer and harmless. * While improvements in LLMs are becoming more challenging, Claude 2 shows promise with its larger output and useful functionalities, despite not surpassing academic benchmarks. 2️⃣ Sketch-A-Shape * Autodesk Research introduces Sketch-A-Shape, a zero-shot sketch-to-3D shape generation technique. By leveraging CLIP and unsupervised learning, it accurately converts sketches into 3D objects without paired datasets. * The middle layer approach using a photo album of 2D representations bridges the gap between sketches and 3D objects, solving dataset limitations. Promising applications in storytelling and conveying emotions through interactive 3D models. * Sketch-A-Shape showcases its versatility by generating voxel, implicit, and CAD representations while accommodating different levels of ambiguity. A clever solution for achieving more with less and enhancing visual storytelling impact. 3️⃣ PoisonGPT * RAL Security reveals their successful modification of GPT-J, subtly making it believe Yuri Gagarin was the first man on the moon. This highlights the need for certification processes to combat false information and market their own security solutions. * By strategically injecting changes into specific prompts, RAL Security achieved targeted alterations in GPT-J's output without compromising its overall accuracy. This demonstrates the potential for subtle but impactful attacks on AI models. * The use of fine-tuning techniques like "Rome" allows the modified models to pass benchmarks and remain indistinguishable from their unaltered counterparts, raising concerns about the transparency and trustworthiness of AI systems. Vigilance is advised. 🔗 Episode Links * Claude 2 * Sketch-A-Shape * PoisonGPT * Infinigen * Myth Of Context Length * Code Interpreter Connect With Us: Follow us on Threads Subscribe to our Substack Follow us on Twitter: * AI Daily * Farb * Ethan * Conner This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.aidailypod.com

Welcome to the newest episode of AI Daily! Today, we delve into some tantalizing topics -  Military AI, geopolitics, and a controversial lawsuit against ChatGPT. Our first segment takes a closer look at the collaboration between Scale AI Donovan and Cohere to bring LLMs to defense and government. Following that, we'll dive into an unfolding lawsuit against OpenAI's ChatGPT. Is this a legitimate concern for copyright infringement, or just a publicity stunt? Lastly, we'll discuss China's access to GPUs, and how changes in international policy might affect this. Key Points 1️⃣ Military AI - Scale Donovan * Scale AI Donovan is partnering with Cohere to provide LLMs to US government and defense, focusing on data ingestion and military decision-making. * They're offering a free trial with data sets targeting China, hoping to position themselves as a significant provider amid current geopolitical challenges. * To gain acceptance, they must achieve FedRAMP approval. If successful, LLMs could transform how the Department of Defense handles operational documents. 2️⃣ Lawsuit Against ChatGPT * Two authors have filed a lawsuit against ChatGPT, claiming the model used their books in its training data and is profiting from their intellectual property. * The authors aim to determine whether their specific works are in OpenAI's dataset, but it's unclear whether it's using actual books or just summaries. * The case brings to light a shift in public perception about AI, with people moving from seeing it as advanced search to a potential infringer of intellectual property. 3️⃣ Geopolitics - US Restricting China’s Cloud Access * The US administration aims to restrict China's access to advanced GPUs via cloud providers like AWS and Google Cloud, furthering export controls and impacting businesses. * The hosts suggest these actions are strategic negotiation tactics in the larger geopolitical context, using areas like AI and semiconductors as bargaining chips. * Compliance controls on cloud platforms reflect changing perspectives on the significance of advanced technology resources, transitioning from unrestricted access to closely regulated use. 🔗 Episode Links * Military AI - Scale Donovan * ChatGPT Lawsuit * Restricting China Cloud Access * Focused Transformer - LongLLaMa * AI Web TV Connect With Us: Follow us on Threads Subscribe to our Substack Follow us on Twitter: * AI Daily * Farb * Ethan * Conner This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.aidailypod.com

In today's thrilling episode, we dissect “LongNet”, a groundbreaking paper that scales transformers to a whopping 1 billion tokens. Next, we discuss Uncertainty Alignment and its implications for robotics. Finally, we cover "Motion Retargeting", a method of creating 3D avatars from minimal user input data, primarily headset and controller information. Key Points 1️⃣ LongNet * A method called "LongNet" scales transformer models to handle a billion tokens, using dilated attention to avoid quadratic complexity, achieving linear scaling. * While this method technically handles a billion tokens, it's different as it looks at pieces, not the entire attention, compromising performance beyond context window. * It's viewed as a clever innovation in computational scaling, despite trade-offs, and other methods like 'alibi' are suggested for better performance. 2️⃣ Uncertainty Alignment * The paper introduces "uncertainty alignment," a method for robots to handle ambiguous tasks by seeking minimum user help and providing statistical guarantees before executing a task. * This approach reduces fine-tuning and prompt tuning, aligns with how people think, and improves user experience by asking follow-up questions when uncertain. * While not groundbreaking, it simplifies complex tasks using probability and statistics, potentially becoming a standard practice for various chatbots and robotics applications. 3️⃣ Motion Retargeting * “Motion retargeting" is a method of creating 3D avatars from minimal user input data, primarily headset and controller information. * This technology transfers human movements to various virtual characters, demonstrating realistic movements despite the difference in character structure, like a dinosaur or a mouse. * Though promising, the technique depends heavily on the user's movements, and edge cases like extreme physical behavior can disrupt the avatar's realistic representation. 🔗 Episode Links * LongNet * Uncertainty Alignment * Motion Retargeting * AI-Laser Pesticide & Herbicide Connect With Us: Follow us on Threads Subscribe to our Substack Follow us on Twitter: * AI Daily * Farb * Ethan * Conner This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.aidailypod.com

Welcome back to AI Daily! We kickstart the conversation with DisCo, a revolutionary project for real world dance generation that's reshaping how we understand motion capture and dance generation. We follow that with a discussion on superalignment from OpenAI, a cutting-edge project designed to align super-intelligence with human interests.Finally, we turn our attention to ChatLaw, an open-source legal language model that could redefine legal discourse. Key Points 1️⃣ DisCo * DisCo, a collaborative AI project between NA Yang Technological University and Microsoft Azure, generates realistic human dance movements from photos. * Despite some initial artifacts, the AI can generate natural and high-quality movements, promising photorealistic results within a year. * With its ability to realistically simulate dance, DisCo has the potential to dominate social media content creation. 2️⃣ OpenAI Superalignment * OpenAI has formed a new alignment team to solve "superalignment", dedicating 20% of their resources to aligning super intelligence and preventing potential threats. * This approach acknowledges that aligning super-intelligence is both a philosophical and a technical problem, requiring significant investment and a dedicated team. * The initiative signifies the importance of AI alignment, suggesting a future where AI systems compete, with the winners determining the narrative. 3️⃣ ChatLaw * ChatLaw is an open-source large language model with integrated external knowledge bases, fine-tuned for Chinese legal data, aiming to tackle issues of AI hallucinations in legal contexts. * The team found that relying on a Vector DB alone isn't sufficient to meet the exacting standards of law and could lead to the production of false information. * The model showcases the breadth of AI, with solutions tailored for specific applications like Chinese legal data, contributing to a reduction in hallucinations. 🔗 Episode Links * DisCo * OpenAI Superalignment * ChatLaw * Farb’s Midjourney Twitter Thread * Playground AI * BatGPT from Wuhan Follow us on Twitter: * AI Daily * Farb * Ethan * Conner Subscribe to our Substack: * Subscribe This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.aidailypod.com

Welcome back to AI Daily! Today we discuss three great stories, starting with HyenaDNA. The application of the hyena model in DNA sequencing - enabling models to handle a million context length and revolutionizing our understanding of genomics. Secondly, we cover the exciting open-source implementation of StyleDrop - a tool that's making waves in the world of image editing and style replacement. Finally, we delve into the topic of data poisoning - how a small amount of injected data can drastically alter the outcome of an instruction tuning and the implications this has for AI security. Key Points: 1️⃣ HyenaDNA * HyenaDNA utilizes sub-quadratic scaling for DNA sequences, enabling a million context length, each a unique nucleotide, trained on 3 trillion tokens. * HyenaDNA, setting a new state-of-the-art in genomics benchmarks, could predict gene expression changes, elucidating protein creation from genetic polymorphisms. * It's 160 times faster than previous LLMs, fitting on a single CoLab, showcasing the potential to outperform transformers and attention models. 2️⃣ Open-Source StyleDrop * An open-source version of Style Drop, an image editing and style replacing tool, has been implemented and made available for public use. * Style Drop outperforms comparable models and offers comprehensive instructions for setup, allowing users to experiment with stylizing lettering and more. * Following a pattern set by Dream Booth, Style Drop went from being a Google research paper to being implemented as an open-source project on GitHub. 3️⃣ Data Poisoning * Two papers discuss data poisoning, a technique where information like ads or SEO can be injected into LLMs, impacting their responses and recommendations. * Even a small number of examples in a dataset can effectively "poison" it, significantly altering the output of a language model during fine tuning. * This technique is expected to be used with open-source datasets for fine-tuning, similar to how publishers put fake words in dictionaries to trace usage. 🔗 Episode Links * HyenaDNA * StyleDop * Data Poisoning * OpenAI Follow us on Twitter: * AI Daily * Farb * Ethan * Conner Subscribe to our Substack: * Subscribe This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.aidailypod.com

Welcome to AI Daily! Join us as we delve into three incredible breakthroughs that are revolutionizing the world of technology. First up, we dive into the groundbreaking world of image-to-3D conversion. Next, prepare to be captivated by the power of drones. We uncover an awe-inspiring application where drones perform real-time video analysis, tracking hundreds of objects with precision. Last but not least, get ready for a dose of CG magic! We present Wonder Studio, a revolutionary platform that can replace real people in live-action scenes with CG representations. Key Points 1️⃣ Any Image-3D * An image-to-3D conversion tool is generating significant interest, causing delays due to high demand. * The tool allows users to input an image and obtain a usable 3D representation, saving time and effort in 3D modeling. * The tool's ability to create 3D models for applications in Unity, Unreal, and Blender is a major breakthrough, enhancing productivity and accessibility in the field. Comparison to OpenAI ShapeE suggests potential improvements in performance. 2️⃣ AI Drones * A video showcases drones performing real-time video analysis and tracking of cars, raising questions about the feasibility and technology behind it. * The video, shared by a Twitter personality, highlights the potential of drones for comprehensive tracking and analysis, although details about its real-time capabilities are limited. * The demonstration indicates a significant advancement in object recognition and image detection on consumer-grade drones, offering affordable access to real-time video and tracking capabilities that were previously limited to expensive equipment. 3️⃣ WonderStudio * Wonder Studio, a platform that can replace real people in live-action scenes with computer-generated representations, creating humorous and impressive results. * The hosts share a processed clip from The Office, featuring a CG representation of Robert California delivering a funny and unexpected dialogue. * Wonder Studio is praised for its capabilities, allowing users to achieve in hours what would have previously taken teams days or weeks, and offering powerful tools for professional video workflows, including commercial usage. 🔗 Episode Links * Any Image-3D * AI Drones * WonderStudio * Midjourney Weird * Mosaic & AMD * Lambda & Falcon Follow us on Twitter: * AI Daily * Farb * Ethan * Conner Subscribe to our Substack: * Subscribe This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.aidailypod.com

Welcome to another episode of the AI Daily podcast, your premier source for all things AI and machine learning. Join us as we discuss three significant fundraising events in the AI world, dissect the new mixed image editing tool by Playground AI, and explore the unveiling of OLMo, a new 70 billion parameter model by the Allen Institute. Key Points 1️⃣ Fundraising Frenzy * Three large companies secure significant fundraising deals in the AI industry: Runway ML raises $141 million, Inflection raises $1.3 billion, and Typeface raises $100 million. * Runway ML focuses on video and general AI interpolation, inflection builds foundation models with a cluster of 22,000 H100 GPUs, and Typeface specializes in generative AI for content creation. * The fundraising frenzy in the AI sector shows no signs of slowing down, with global investment dollars flowing into AI and GPU-related ventures. Expect more news on fundraising and acquisitions in the future as the industry continues to grow and evolve. 2️⃣ Playground AI * Playground AI introduces a new mixed image editing tool, combining elements of Photoshop and Figma in a collaborative generative AI tool. * The image editing and content creation space is vast, but Playground AI stands out with its well-built product and the ability to generate images quickly. * Despite the crowded market, Playground AI's user-friendly experience, tutorials, and free access make it worth trying out for creators seeking better visualization and editing tools. 3️⃣ OLMo by Surge AI * The Allen Institute introduces OLMo, a new 70 billion parameter model focused on scientific research and discovery. * OLMo is an open model, with the Allen Institute planning to share every step of its development for future scientists and researchers to build upon. * Partnerships with AMD, Surge AI, Mosaic, and others aim to support OLMo's training and data labeling, potentially shaping the competition between AMD and Nvidia in the hardware space. The open nature of OLMo has significant implications for the industry and may attract startups and corporations looking to leverage open-source models. 🔗Episode Links * Runway Fundraising * Inflection Fundraising * Typeface Fundraising * Playground AI * OLMo * Eric Hartford on OpenOrca * Salesforce Follow us on Twitter: * AI Daily * Farb * Ethan * Conner Subscribe to our Substack: * Subscribe This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.aidailypod.com

Welcome to AI Daily with Ethan, Farb, and Conner! In today's podcast, we bring you three fascinating stories. First up, we dive into Unity's latest announcement of AI-powered creativity. Moving on, we shift our focus to ML benchmarks with a collaboration between Inflection, CoreWeave, and Nvidia.Lastly, we delve into the partnership between Snowflake and Nvidia. Don't miss out on these exciting topics! Join Ethan, Farb, and Conner as they provide insights and analysis on the latest developments in the AI industry. 📍 Key Points 1️⃣ Unity Muse & Unity Sentis * Unity announces Unity Muse and Unity Sentis, bringing AI-powered creativity to their platform. * Unity introduces AI verified solutions and Muse Chat, aiming to stay competitive and integrate AI effectively. * Exciting features include running AI models on the edge and the potential for an internal app store for AI models in Unity games. 2️⃣ MLPerf Benchmarks & H100 GPUs * Inflection, Core Weave, and Nvidia collaborate to showcase new ML perf benchmarks. * The impressive results reveal the power of training a GPT-3-like model on almost 3,500 H100 GPUs in under 20 minutes. * This achievement signifies a significant win for all three companies, with Nvidia's H100 GPUs leading the industry and Core Weave demonstrating their GPU capabilities. Expect more advancements in this space as partnerships continue to evolve. 3️⃣ Snowflake-Nvidia Partnership * Snowflake and Nvidia form a partnership, offering Snowpark container services for enterprises to run workloads directly on Nvidia GPUs within Snowflake's platform. * This integration creates a tightly integrated environment, providing enhanced data processing capabilities for enterprise customers. * The collaboration demonstrates the growing importance of containers and data security, with Snowflake and Nvidia catering to the needs of enterprises by delivering powerful features and services. Expect widespread adoption and utilization of this partnership's offerings. 🔗 Episode Links * Unity Muse & Unity Sentis * MLPerf Benchmarks & H100 GPUs * Snowflake-Nvidia Partnership * RoboCook * Startup/VC Funds Follow us on Twitter: * AI Daily * Farb * Ethan * Conner Subscribe to our Substack: * Subscribe This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.aidailypod.com

Welcome back to AI Daily! We're thrilled to have you join us for another captivating episode packed with exciting news and developments in the AI space. In this episode, we'll dive into three major AI acquisitions, impressive text-to-video advancements, and the expanded access to Inflection's powerful LLM. Key Points 1️⃣ AI Acquisitions * Three major acquisitions in the AI space: Mosaic ML, Cohere.io, and Mode Analytics. * Acquisitions reflect the increasing interest and heating up of the AI industry. * Focus on data analysis and foundational LLMs, with companies valuing talent and AI platforms. 2️⃣ Text-Video * Zeroscope introduces text-to-video models: one for generating lower-resolution versions and another for upscaling. * The models show promise, trained on 10,000 clips and 30,000 frames at 24 frames per second. * The current limitation is short clips (3-5 seconds) due to training constraints, but progress is being made towards longer videos. 3️⃣ Inflection-1 * Inflection announces broader access to their LLM (Language Model) and upcoming API release. * The model shows promising performance in benchmarks, particularly in academic work and trivia questions. * Training on H100s is noted as faster and more efficient, highlighting the significance of the hardware in model development. Episode Links 🔗 MosaicML Acquisition Cohere.io Acquisition Mode Analytics Acquisition Text-Video Inflection-1 Ethan Mollick - Bing Tweets Harvard’s Chatbot Teacher PanoHead Follow us on Twitter: * AI Daily * Farb * Ethan * Conner Subscribe to our Substack: * Subscribe This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.aidailypod.com

Welcome to AI Daily! Join hosts Conner and Ethan, and Farb for another exciting episode packed with cutting-edge AI advancements. In this episode, we dive into SDXL 0.9 by Stability, explore Google's AudioPaLM, and discuss the latest release of Midjourney 5.2. Key Points 1️⃣ Stability AI’s SDXL 0.9 * Stable Diffusion XL 0.9, launched by Stability, is an impressive image generation model with the largest open-source image model to date. It utilizes a base model of 3.5 billion parameters and an ensemble model of 6.6 billion parameters to generate high-quality images with intricate details. * The comparison between Stable Diffusion XL 0.9 and Mid Journey reveals that Mid Journey's images are superior. However, the competition between these models fluctuates, with each taking the lead at different times. This highlights the ongoing progress and healthy competition in the field of image models. * The podcast emphasizes the importance of combining multiple models in AI. Single models are not sufficient to accomplish the complexities of the universe. Just as processors have limitations, AI models have their own boundaries. The future of AI lies in the effective combination of various models to achieve more powerful and comprehensive results. 2️⃣ Google’s AudioPaLM * Audio Palm is a new large language model from Google that combines Spa Palm 2 and audio LM. It excels in understanding different languages of audio, speech, and recognition, capturing not just the text but also the nuances of intonation and speaker identity. * The combination of these models opens up possibilities for enhanced transcriptions, chatbots, and applications that require a deeper understanding of audio and language intricacies. * Multimodal capabilities are the future, as seen in Audio Palm's ability to translate between languages not included in its training set. This groundbreaking feature showcases the potential for synthetic generation and the abstract representation of multimodal models. 3️⃣ Midjourney 5.2 * Mid Journey 5.2 introduces a new zoom-out interpolation feature, allowing users to start with one subject and gradually expand the image to create a stunning and mesmerizing effect. It offers a magical and beautiful experience akin to zooming in and out of a video. * The update also includes a shortened command for generating prompts, addressing the challenge of lengthy and excessive prompts. By providing insights into tokenization and highlighting important aspects, users can generate desired images more efficiently, saving costs and processing time. * Understanding the tokenization process and the weights assigned to each token provides valuable information about Mid Journey's internal workings. It offers users a deeper understanding of the model's architecture and empowers them to achieve better results by optimizing their prompts. Episode Links: * Stability AI’s SDXL 0.9 * Google’s AudioPaLM * Midjourney 5.2 * AWS Fund Program * 16z & AI Companionship * SequenceMatch Paper Follow us on Twitter: * AI Daily * Farb * Ethan * Conner Subscribe to our Substack: * Subscribe This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.aidailypod.com

Join Ethan, Conner, and Farb in this riveting podcast episode as they explore the latest advancements in the AI and robotics space. Get ready to dive into the groundbreaking developments brought to you by Mosaic ML, Tesla, and DeepMind! In this episode:  1️⃣ Mosaic ML's Game-Changing Open Source LLM: * Mosaic ML has released their first commercially available open source LLM, with an impressive upgrade from 7 billion to 30 billion parameters. * This LLM offers a significant jump in usability, as it can handle an 8,000-token context compared to Llama's 2,048 tokens. * The availability of Mosaic's LLM and the ongoing battle between open source and closed-source models will shape the future of AI, ensuring knowledge and technology accessibility for all. 2️⃣ Tesla's Foundation Models for Autonomous Robots: * Tesla is making significant progress in building foundation models for autonomous robots, leveraging multimodal networks and incorporating camera videos, maps, and navigation data. * Their models are ontologically agnostic, predicting the likelihood of objects filling 3D space, which provides broad applicability across various scenarios. * With their expertise and the upcoming Dojo supercomputer production, Tesla is poised to tackle true robotics and make substantial advancements in the field. 3️⃣ DeepMind's Robocat: * DeepMind's RoboCat is a foundation model for operating robotic arms, capable of solving tasks with as few as a hundred demonstrations. * The model utilizes a unique feedback loop, fine-tuning itself and generating new data to improve performance and adapt to different tasks. * Simulated agents like RoboCat are crucial for advancing robotics, and increased GPU compute capabilities are key to further accelerating progress in the field. Episode Links: * MosaicML MPT-30B * Tesla Foundation Models Robotics * DeepMind RoboCat * Zuck vs. Musk Tweets * Disney’s Secret Invasion * Dropbox Dash Follow us on Twitter: * AI Daily * Farb * Ethan * Conner Subscribe to our Substack: * Subscribe This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.aidailypod.com

Welcome to AI Daily! In this episode, we dive into six exciting papers from the Meta team at the Conference on Computer Vision and Pattern Recognition. Get ready for fascinating insights into computer vision and cutting-edge AI applications. Key Points: EgoTask (EgoT2) * The EgoTask paper focuses on handling egocentric video tasks, where the videos are recorded from a first-person perspective. It explores the application of AI to improve results in specific egocentric tasks like painting or cooking. * By translating between different egocentric tasks, such as painting and cooking, better outcomes can be achieved. This approach recognizes the similarities in hand movements and gestures between different activities, allowing for the transfer of skills from one task to another. PACO * PACO is a large-scale database that provides object and part masks, as well as object and part level attributes, allowing for precise segmentation and labeling of different parts within images. It offers specific details about hundreds of different objects, making it valuable for AI training in computer vision. * PACO is an open-source and commercially licensed dataset, complementing Meta's previous release, Sam Segment. It is particularly beneficial for open-source computer vision projects that require specific color or attribute information, enabling more accurate analysis and understanding of images. GeneCIS * Genesis introduces a benchmark for measuring a model's ability to assess image similarity, taking into account colors, textures, and objects. It addresses limitations of object-based comparisons and offers insights into improving similarity scores by incorporating text and image data. * Notably, popular computer vision models like clip and ImageNet-based models struggled in this benchmark, highlighting the need for novel approaches. Genesis has practical applications in fields like fashion and expands the understanding of comparing images beyond object or color-based descriptions. While not commercially available, it serves as a valuable benchmark for evaluating new image models. LaVila * LaVila utilizes fine-tuning of large language models (LLMs) like GPT-2 on visual inputs to create video narrators, resulting in more detailed and enriched video descriptions. By leveraging LLMs and egocentric video datasets, they enhance sparse narrations, providing nuanced insights into video content. * The combination of AI models enhances the understanding of videos and enables the generation of richer narrations, even in cases where audio is absent. This commercially available approach has potential applications in platforms like YouTube, offering narrations that go beyond human dialogue and tap into the visual context of videos. Galactic * Galactic is a large-scale simulation and reinforcement learning framework that trains a robotic arm to perform mobile manipulation tasks in indoor environments. Through iterative training and simulations, the framework enables the robot to autonomously move objects, demonstrating its potential for complex tasks. * While Galactic is based on simulated robotics, its principles can be applied to real-world robots. The framework achieves high training speeds of up to 100,000 steps per second using only eight GPUs, showcasing its efficiency and scalability. It is a non-commercial project with promising implications for robotics and reinforcement learning. HierVL * HierVL is a hierarchical video language embedding model that improves the understanding and description of long-form videos. By training on both short clips and a summary of the entire video, it enables the model to grasp the overall context and provide comprehensive explanations, making it valuable for applications like reviewing drone or body cam footage. * While HierVL's training focuses on videos up to approximately 30 minutes long, its scalability beyond that remains uncertain. Nonetheless, this non-commercial research offers a promising perspective on advancing video language embeddings and enhancing analysis of extended video content. Episode Links: Meta Papers OpenAI Plans App Store China’s Underground NVIDIA Market OpenAI Lobbied EU Follow us on Twitter: * AI Daily * Farb * Ethan * Conner Subscribe to our Substack: * Subscribe This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.aidailypod.com

Welcome to AI Daily! In this episode, Connor and Ethan discuss the latest trends in AI, including Microsoft's new AI model called Orca, Pixar's innovative use of neuro style transfer in their movie Elemental, and ByteDance's massive purchase of Nvidia GPUs for AI. Tune in to explore how these advancements are shaping the future of technology and entertainment. Key Points Microsoft Orca * Microsoft introduces a new AI model called Orca, which takes a different approach to learning by utilizing the actual traces of GPT4's thinking. * Smaller fine-tuned models have shown limitations when trained to imitate GPT4, resulting in worse benchmarks and a loss of complex reasoning. * Orca stands out by training on step-by-step instructions of GPT4's thinking, achieving impressive benchmarks and offering insights into GPT4's reasoning process. * The development of more effective smaller models like Orca could potentially allow for accomplishing similar results with less processing power and faster times, but concerns arise regarding the propagation of errors and the need to continue working on foundational large models. Pixar “Elemental” & AI * Pixar's Elemental movie showcases the use of neuro style transfer in mainstream animation, combining CGI with AI techniques to create stunning visuals. * By leveraging the latent GPU capacity at Pixar, processing times for creating animations were drastically reduced, allowing for more efficient production. * The collaboration between AI and human animators resulted in a beautiful synthesis of AI-generated content and hand-drawn elements, highlighting the power of combining both approaches. * This breakthrough sets a new precedent for the future of movies, with AI neural networks transforming animation and pushing the boundaries of creativity in the industry. ByteDance Run on GPUs * ByteDance has made a significant purchase of 101 billion Nvidia GPUs for AI applications, navigating around export bans and the ongoing chip war between China and the United States. * The billion-dollar purchase may only be pre-orders, but it highlights the escalating competition and demand for processing power in the global market. * Despite the chip shortages and geopolitical tensions, the race for processing power is intensifying, with implications for economic and soft power conflicts. * ByteDance's acquisition of a hundred thousand chips demonstrates their ambition to leverage AI for various applications, potentially influencing US-China relations and product development. Episode Links * Microsoft Orca * Pixar Elemental * ByteDance GPUs * ElevenLabs * ChatGPT Leak * Paper “ The False Promise” Follow us on Twitter: * AI Daily * Farb * Ethan * Conner Subscribe to our Substack: * Subscribe This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.aidailypod.com

Welcome to AI Daily, your go-to podcast for the latest updates in the world of artificial intelligence. In today's episode, we dive into three exciting stories. First up, we discuss Meta's groundbreaking release of Meta Voicebox, a cutting-edge text-to-speech model with state-of-the-art performance. In the second story, we shift gears to Meta's decision to make LLaMA, an open-source text model, available for commercial use. Our final story focuses on TikTok's Effect House, a platform that allows creators to design augmented effects for their videos. Key Points Meta’s Voicebox * Meta introduces a text-to-speech model called Meta Voicebox, offering state-of-the-art speech generation and impressive performance. * 20x Faster: Meta Voicebox stands out for being 20 times faster than existing alternatives, enabling tasks like noise removal and generating new voices with remarkable efficiency. * Open Source and Flow Matching Model: Meta Voicebox is open source and incorporates a flow matching model that utilizes diverse and less labeled datasets for training, leading to better models without extensive manual labeling. * Real Speech Classification: Meta Voicebox includes a classifier that distinguishes between audio generated by the model and real speech, showcasing Meta's commitment to releasing open source models and their potential integration into their own products. Meta’s LLaMA Goes Commercial * Meta aims to make the popular open-source text model LLaMA available for commercial use, challenging established models like GPT-4 and emphasizing their commitment to open-source AI. * Meta's Vision and Focus: By providing open access to LLaMA and focusing on their metaverse vision, Meta aims to democratize AI and distance it from exclusive ownership by big companies like Google and Apple. * Business Aikido Strategy: Meta's decision to offer AI tools for free disrupts the market and positions them as leaders in the technology, attracting talent, boosting stock prices, and potentially increasing revenue through their primary source of income—advertising. * Competitive Advantage over Google: Unlike Google's research papers and open-source efforts, Meta's commitment to commercially usable models sets them apart, potentially prompting startups and enterprises to choose Meta's free offerings over paying for API costs from other providers. TikTok Effect House AI * TikTok introduces Effect House, an augmented effects feature where users can create their own effects using text-to-image AI, revolutionizing video creation on the platform. * Democratizing AI Tools: Effect House allows TikTok users to easily generate and apply complex effects, eliminating the need for extensive engineering teams and democratizing the creation of captivating videos. * AI's Influence on Social Media: The integration of generative AI tools in social media platforms is becoming a prevalent trend, with TikTok leading the way and potentially inspiring other platforms like Instagram to follow suit. * Enhanced Creativity and Entertainment: The availability of AI-powered tools like Effect House empowers creators to produce more captivating and engaging content, promising a future with increased creativity and entertainment value on TikTok. Episode Links * Meta’s Voicebox * LLaMA Goes Commercial * TikTok Effect House AI * The Guardian on AI * Language-to-Reward * Mercedes & ChatGPT Follow us on Twitter: * AI Daily * Farb * Ethan * Conner Subscribe to our Substack: * Subscribe This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.aidailypod.com

Welcome to AI Daily, your go-to podcast for the latest updates in the world of artificial intelligence! In today's episode, we have some banger stories lined up for you. Join us as we dive into the exciting advancements in the realm of Mechanical Turk, the impact of AI in the EU Parliament, and a cutting-edge multimodal technology called Video LLaMA. Key Points: Video LLaMA * A new paper called Video LLaMA, which focuses on turning video and audio into text and understanding them better. * The paper addresses two main challenges: capturing temporal changes in video scenes and integrating audio and visual signals. * The model showcased in the paper demonstrates accurate predictions and understanding of videos, including analyzing images, audio, facial expressions, and speech. * The availability of the model for public use is uncertain as it is currently a research paper, but it highlights the potential of leveraging AI tools like Image Binds and audio transformers to enhance video understanding. Mechanical Turk * A study reveals that a significant portion (around 36-44%) of text summarization tasks on Mechanical Turk are being done by AI models like ChatGPT instead of humans. * The displacement of human workers by synthetic models raises concerns about the availability and quality of real data for training larger language models like GPT-4 and GPT-5. * Detecting synthetic data generated by language models is challenging, and specialized classifiers may be required to distinguish between human-generated and AI-generated text. * The increasing reliance on AI models for tasks like text summarization may lead to the introduction of stricter verification measures, such as keystroke tracking or biometric testing, to ensure authenticity in online assessments and proctoring. EU Parliament & AI * The EU Parliament is taking steps towards AI regulation, although the specifics and implications are unclear. * There are concerns about redundancy in creating separate AI-specific regulations when existing laws could cover related aspects such as data privacy. * The potential impact of AI regulation on startups and small players is uncertain, as compliance requirements and limitations on training AI models could arise. * The regulation aims to address issues like transparency, disclosure of AI-generated content, and prohibitions on certain applications like social scoring and real-time facial recognition. However, some argue that these issues can be legislated without directly tying them to AI. Links Mentioned * Video LLaMA * Mechanical Turk * EU Parliament * Vercel AI * AI SDK * Carbon Health Follow us on Twitter: * AI Daily * Farb * Ethan * Conner Subscribe to our Substack: * Subscribe This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.aidailypod.com

Check out the latest episode of AI Daily where Conner, Ethan, and Farb discuss the most exciting updates in the AI world. In this episode, they cover Meta's groundbreaking AI model called I-JEPA, Hugging Face's collaboration with AMD, and Paul McCartney's creation of a final Beatles song using AI technology. Key Points Meta’s I-JEPA * Meta's I-JEPA is the first AI model based on Jan Lac Koon's vision for more human-like AI, with its own internal abstraction of how models work and how the real world works. * The model achieves state-of-the-art performance on ImageNet and is significantly more efficient, requiring only a 10th of the GPU hours compared to similar models. * The model aims to address core problems in generative models by focusing on understanding common sense and abstract reasoning instead of pixel-perfect generation. * This innovative approach has the potential to improve AI's ability to understand the world and tackle complex problems with more intricate details. Hugging Face + AMD * Hugging Face has partnered with AMD to integrate AMD GPUs into the hugging face platform, which is a unique collaboration considering most AI companies work with Nvidia due to its performance advantage. * The partnership aims to bring popular transformer architectures like BERT and Stable Diffusion to work efficiently on AMD GPUs, bridging the gap between AMD and the AI community. * This collaboration highlights the potential of AMD in the AI space, dispelling any misconceptions that AMD may not be competitive, and may lead to rapid advancements in AI solutions with the support of the open-source community. * The partnership is beneficial for hugging face as it demonstrates their seriousness and expands their capabilities by working with a prominent player like AMD in a significant partnership. New Beatles Song * Paul McCartney is creating a final Beatles song by using AI to extract John Lennon's voice from a cassette player that Lennon gave him, which is an exciting development. * There seems to be a recurring trend of Paul McCartney working on songs using John Lennon's voice, with new compositions or additions to previous recordings, which may suggest that there will be more "last" Beatles songs in the future. * The prospect of new Beatles songs is thrilling for fans, and the longevity of their music speaks to its enduring popularity. * The conversation also references the TV show "Black Mirror" and speculates about the upcoming season, adding an element of excitement and anticipation. Links Mentioned * Meta’s I-JEPA * Hugging Face + AMD * New Beatles Song * Adobe Firefly + Video * France’s Mistral AI * Vercel AI Accelerator Follow us on Twitter: * AI Daily * Farb * Ethan * Conner Subscribe to our Substack: * Subscribe This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.aidailypod.com

Welcome to AI Daily, your go-to podcast for the latest updates in artificial intelligence! In today's episode, we have an exciting lineup of stories, covering Salesforce AI, OpenAI updates, and the remarkable LLaMA Adapter. Get ready for a deep dive into the world of AI! Key Points LLaMA Adapter: * LLaMA Adapter is a bilingual multimodal instruction model that integrates various inputs such as images, audio, and 3D point clouds. * It is designed for composability and compatibility, allowing it to connect with other projects and models like Falcon, ImageBind, StableDiffusion, and LaneChain. * LLA Adapter enables fine-tuning of models for image processing and specific instructions, expanding the capabilities of traditional LAMA. * The combination of models through LLA Adapter is becoming more accessible, cost-effective, and practical, making it an exciting development for multimodal abilities. Salesforce AI: * Salesforce made significant updates to its AI offerings across various domains, including sales, marketing, code, and Tableau integration. * The release of multiple AI tools directly into Salesforce demonstrates their commitment to the field and their intention to make a strong impact. * One notable feature is the ability to customize sales pages and emails based on CRM data, allowing for powerful AI-driven personalization. * Salesforce's extensive customer data puts them in a prime position to leverage these new tools and technologies effectively, signaling more exciting developments to come. Additionally, they may pursue acquisitions to enhance their AI capabilities further. OpenAI Updates: * OpenAI released a comprehensive suite of updates, including cheaper models, steerable versions of GPT-4, and enhanced function calling capabilities. * The function calling feature stood out as a game-changer, eliminating the need for multiple startups by enabling users to obviate their functions within GPT models. * OpenAI's pace of innovation remains impressive, with more significant updates expected in the future, indicating that they have many more exciting developments in the pipeline. * The expanded context window from 4,000 tokens to 16,000 tokens, with plans for 32,000 tokens, opens up new possibilities and enhances the model's capabilities for handling large amounts of context. Integration with other models like CLIP and DALL·E further enhances functionality. Links Mentioned: * LLaMA Updates * Salesforce AI * OpenAI Updates * Nat Friedman Tweet * ByteFormer Tweet Follow us on Twitter: * AI Daily * Farb * Ethan * Conner Subscribe to our Substack: * Subscribe This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.aidailypod.com

Welcome to AI Daily! In this episode, we bring you three exciting stories: MusicGen, Meta’s plans for AI, and the impressive ClipDrop by Stability AI. Key Points: MusicGen * MusicGen from Meta is a big improvement over music. Lm, allowing users to generate music with text or a combination of text and audio. * MusicGen does not require self-supervised representation and operates effectively in one pass, producing efficient and high-quality results. * MusicGen is open source, unlike Google's closed-source music. Lm, and Meta provides the code and resources for users to try and enjoy. * The examples of generated music showcase its potential for creating base-level, lo-fi sounds and even replicating jazz songs, making it an exciting tool for music creation. Meta's AI Plans * Meta's plans for AI involve integrating AI capabilities into all of their flagship products, including Facebook, Instagram, and possibly WhatsApp and more. * Mark Zuckerberg is particularly interested in generative AI and envisions features like photo modification and AI assistants in messaging apps. * Meta is committed to open-source development, allowing other developers to build on their models and shaping the future of AI development. * There seems to be a shift in Zuckerberg's attitude towards openness and embracing chaos, possibly driven by the anticipation of upcoming AI wars and Meta's accumulation of talented AI researchers. ClipDrop * ClipDrop is a well-designed product with a range of AI-powered features reminiscent of Firefly. * It offers an API for integration into other startups' products, indicating a strategic business plan for Stability AI. * Stability AI emphasizes open-sourcing their models while also building user-friendly products and APIs. * The API angle is considered smart, following Stability AI's pattern of open-sourcing products and then building upon them. Episode Links: * MusicGen * Meta's AI Plans * ClipDrop * GPT-2030 Article * Adobe Firefly Article * Open Weights Tweet Follow us on Twitter: * AI Daily * Farb * Ethan * Conner Subscribe to our Substack: * Subscribe This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.aidailypod.com

Welcome to an exciting episode of AI Daily, where we discuss three captivating stories: Bard updates, ARTIC3D research paper, and DeepMind's Alpha Dev discovery. We delve into the remarkable advancements in Bard, which introduces implicit code execution, providing accurate results and enhancing user experience. Then, we explore ARTIC3D, a groundbreaking research paper that generates high-quality 3D models from noisy web collections. Finally, we uncover DeepMind's discovery of faster sorting algorithms using Alpha Dev, highlighting the inhuman nature of the algorithm's evolution and the potential for AI to optimize existing processes. Tune in for the latest in artificial intelligence advancements! Key Points Bard Updates: * Bard introduces implicit code execution, generating and running Python code for challenging problems. * The integration of code execution in Bard improves accuracy and user experience compared to relying solely on language models. * The addition of code execution in Bard enhances problem-solving capabilities, particularly in math and code-related tasks. Bard's code execution feature demonstrates impressive results and a 30% improvement over previous benchmarks, making it an enticing option for users. ARTIC3D Research Paper: * The ARTIC3D research paper focuses on learning robust articulated 3D shapes from noisy web collections. * The method involves generating high-quality 3D models with impressive detail and color accuracy from sets of images. * This approach expands the possibilities of using wider sets of images to reconstruct 3D objects, bridging the gap between 2D and 3D. * While the examples showcased in the paper feature safari animals, there is potential for broader applications beyond that domain. DeepMind AlphaDev Algorithm Discovery: * DeepMind's Alpha Dev applied genetic learning to improve sorting algorithms, showcasing the potential of AI to enhance long-standing algorithms. * The inhuman nature of the algorithm's evolution led to optimizations at the assembly and C++ levels, finding small and niche efficiencies. * AI's ability to discover improvements in algorithms that may have taken humans much longer is an exciting prospect for efficiency and optimization. * The cognitive shift of exploring methods without preconceived notions highlights the transformative thinking enabled by AI, although it may raise concerns about non-human approaches to problem-solving. Links Mentioned * Bard Updates * ARTIC3D * DeepMind’s AlphaDev Discovery * Microsoft Bringing OpenAI to Gov. Agencies * InstructZero Research Paper Follow us on Twitter: * AI Daily * Farb * Ethan * Conner Subscribe to our Substack: * Subscribe This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.aidailypod.com

Welcome to AI Daily! In this episode, we discuss three fascinating stories that highlight the potential of AI. We start with Mark Andreessen's thought-provoking blog post on how AI can save the world, countering AI "Doomer-ism." We delve into the implications of AI on human progress, regulation, and income inequality. Next, we explore GGML, a tensor library for machine learning, and its significance in running large models efficiently on the edge. We examine the importance of edge computing, privacy, and the role of open-source projects like G G M L in making AI more accessible to end users and developers. Finally, we uncover "Recognize Anything," a powerful image tagging model that goes beyond object recognition. We discuss its ability to understand the relationships between objects within images, the progress made in computer vision, and its potential impact on bridging the digital and physical worlds. Join us for an insightful conversation as we dive into these AI topics and their implications for the future. Don't miss out on the latest advancements in AI technology and its transformative potential! Key Points: Marc Andreessen Blog Post: * Mark Andreessen's blog post challenges the negative views on AI and emphasizes its potential to help humanity. * The internet facilitates the spread of ideas, both positive and negative, surrounding AI. * Regulation alone may not be sufficient to prevent negative consequences of AI, as it is a complex and easily accessible technology. * There is a concern that AI could exacerbate income inequality and be controlled by those in power, emphasizing the need for open-source collaboration and competition to avoid concentration of power in the hands of a few. GGML: * GGML is a tensor library for machine learning that aims to make large models more efficient and accessible on edge devices. * The focus is on quantizing models like Llama and Whisper to smaller, faster, and cost-efficient versions that can run on CPUs and even on devices like phones. * Bringing AI models to the edge has implications for end users and application developers, particularly in terms of privacy and fundamental human freedoms. * Edge computing plays a crucial role in maintaining human liberty and giving people control over their lives and communities, with open-source projects like GGML enabling the practical implementation of models on edge devices. “Recognize Anything”: * A strong image tagging model that goes beyond object tagging and focuses on understanding the relationships between objects in an image. * The model shows significant progress compared to previous models like blip and clip, as well as Google's proprietary image tagging. * It is an open-source model built on tag-to-text and works well with the Segment project, which segments different parts of an image for deeper understanding. * The development of such computer vision models is crucial for bridging the gap between the digital and physical worlds, and they are expected to surpass human capabilities in the next 12 to 24 months. Links Mentioned: * Marc Andreessen Blog Post * GGML * “Recognize Anything” * Lightning AI * George Hots - AMD Follow us on Twitter: * AI Daily * Farb * Ethan * Conner Subscribe to our Substack: * Subscribe This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.aidailypod.com

Don't miss this special edition of AI Daily, where we dive into the exciting announcements from yesterday's WWDC event. Join us as we explore all things AI related and discuss the groundbreaking features that were unveiled. We'll cover everything from transformers to neural networks, bringing you the latest insights into the world of AI. Key Points: * Apple did not mention AI during the WWDC event, but they referenced technologies like transformers and neural networks. * Transformers have been implemented to improve autocorrect, keyboard experience, and dictation on iPhones. * Apple's new M2 Ultra GPU is useful for training transformer models. * Apple introduced the Curated Suggestions API for creating multimedia journals on Apple devices. * The Curated Suggestions API uses on-device neural networks for privacy. * Live voicemail transcribes voicemails in real-time on the device and allows users to answer calls midway. * Siri now supports back-to-back commands and utilizes audio transformer models. * FaceTime reactions allow users to separate subjects from photos and create stickers. * ML or AI is used to differentiate subjects from the background in photos and FaceTime videos. * Apple's AirPods and AirPlay have adaptive audio features that adjust volume and transparency based on user interactions, possibly using audio transformer models. * Apple's Vision Pro includes digital avatars that reconstruct a user's face based on scans and movements, but chin detection technology is still in development. Links Mentioned: * Transformer Auto Correct & Dictation * Mac GPU & M2 Ultra * Vision Pro & Digital Avatars * All Other Apples Stories * AI Drone Kills Operator * Stable Diffusion QR Codes Follow us on Twitter: * AI Daily * Farb * Ethan * Conner Subscribe to our Substack: * Subscribe This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.aidailypod.com

In this episode of AI Daily, we cover three exciting news stories. First, NVIDIA introduces Neuralangelo, an impressive evolution of nerfs that allows you to accurately 3D scan any scene or object using your phone or drone camera. This breakthrough technology opens up a world of possibilities in various industries, from media to drones. Next, we discuss StyleDrop by Google, a remarkable advancement in image styling. With just one reference image, StyleDrop can generate a wide range of styles, including 3D and 2D characters, producing pixel-perfect outputs that surpass previous models like Dream Booth. Finally, we delve into OpenAI's Cybersecurity Grant Program, a $1 million fund aimed at advancing the future of cybersecurity using AI. OpenAI is determined to defend against AI aggressors and improve cybersecurity through innovative projects like developing honey pots and leveraging cutting-edge technology. Tune in to this episode for all the details and insights on these groundbreaking developments! Key Points: Nueralangelo by NVIDIA: * NVIDIA introduces Neuralangelo, an evolution of Nerfs that enables accurate 3D scanning of objects using a phone or drone camera. * The improved Neuralangelo pushes the boundaries of what Nerfs can achieve, thanks to better graphics cards and algorithms, offering more use cases in various industries, including media and drones. * The technology starts from a 2D representation and uses multiple angles to create a detailed 3D model, refining it until the desired level of accuracy is achieved. * Neuralangelo is one of the 30 projects presented by NVIDIA at a computer vision conference, showcasing their rapid development and impressive capabilities, such as Michelangelo. StyleDrop by Google: * Google introduces Style Drop, an evolution of Dream Booth and textual inversion that can generate a wide range of styles based on a single reference image. * Style Drop surpasses its competitors in terms of accuracy and similarity to the desired style, making it an impressive tool for generating content in specific styles, such as company branding. * The model achieves pixel-perfect outputs and requires fine-tuning on less than 1% of its parameters, making it easy to use and customize with just one image. * Style Drop's ability to produce high-quality results with a single image sets it apart from previous models that required multiple images for comparable outcomes. OpenAI’s Cyber Security Grant Program: * Open AI announces a $1 million cybersecurity grant program aimed at advancing AI-based cybersecurity and defending against AI aggressors. * The program offers $10,000 grants to support the development of innovative cybersecurity solutions and technologies. * Open AI emphasizes the importance of fostering a high-level AI and cybersecurity discourse and encourages applications that leverage state-of-the-art AI technology for cybersecurity purposes. * The grant program aims to address various cybersecurity challenges, including the development of private GPU compute and the creation of deceptive honey pots to deceive hackers. Links Mentioned: * Nuerangelo * StyleDrop * OpenAI Grant Program * Baidu * Microsoft & CoreWeave * Vectorizer.AI Follow us on Twitter: * AI Daily * Farb * Ethan * Conner Subscribe to our Substack: * Subscribe This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.aidailypod.com

In today's episode of AI Daily, we bring you three exciting news stories that are shaping the world of AI. First, we delve into Japan's groundbreaking stance on copyrights, allowing the use of all data for training AI models. This move showcases Japan's commitment to advancing its AI ecosystem and embracing the potential of AI to transform society. In our second story, we discuss DIDACT, the first code model that mirrors the thinking process of real software developers. By understanding the entire coding process, DIDACT brings a new level of accuracy and efficiency to code generation and debugging. Lastly, we explore OpenAI's innovative approach to mathematical reasoning through process supervision. By rewarding each step in finding a mathematical answer, OpenAI is revolutionizing how AI models learn and improving their performance. Join us as we uncover the latest developments in AI and its wide-ranging implications. Key Take-Aways: Japan & AI Copyrights: * Japan reaffirms its stance on copyrights, allowing the use of all data, regardless of commercial use or copyright, for training AI models and applications. * Japan sees AI as a way to save its declining society and drive future progress, taking a progressive and serious approach to its development. * Other countries are likely to follow Japan's lead in adopting similar copyright policies for AI, considering the advantages it offers in terms of workforce and economic growth. * The copyright law in Japan applies only to content produced within the country, exempting foreign-owned content from its regulations. DIDACT: * DIDACT is the first code language model trained to mimic the step-by-step reasoning and process of a software developer, going beyond just providing the final output of code. * Google's Monorepo, with data from years of developer activity, enabled the training of DIDACT to understand the full software development stack, including error fixing, code editing, and unit testing. * Understanding the history and context of a developer's actions is crucial for DIDACT's ability to predict and suggest the next steps in the coding process. * The development of models like DIDACT reflects a parallel to human cognition, where language and reasoning abilities have evolved over time, leading to the emergence of metacognitive processes. This advancement in AI cognition has potential applications in fields like medicine and law, enabling a step-by-step understanding of complex processes rather than just the final output. Open AI Mathematics & Future Plans: * OpenAI has implemented process supervision to improve mathematical reasoning in their models, enabling a deeper understanding of the step-by-step process of solving math problems, rather than focusing solely on the final output. * Process supervision aligns with the way humans learn, as it provides feedback at each step of the problem-solving process, reinforcing learning and understanding. * This approach signifies a shift towards considering the entire process and not just the end result, mirroring the way education is conducted in the real world. * OpenAI's focus on improving GPUs to enhance the performance and affordability of GPT-4 demonstrates their commitment to addressing limitations and advancing AI capabilities. Additionally, they discussed the challenges with plug-ins and the need for seamless integration into existing platforms to provide a more efficient user experience. Links Mentioned * Japan’s AI Copyrights * DIDACT * OpenAI Mathematics * OpenAI Future Plans * Google Investing in Runway * Supabase * Falcon 40B Follow us on Twitter: * AI Daily * Farb * Ethan * Conner Subscribe to our Substack: * Subscribe This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.aidailypod.com

 In today's episode, we have three exciting stories to share with you. First up is GILL, a groundbreaking method that infuses image recognition capabilities into language models. With GILL, you can now send images to chatbots and receive responses in the form of edited images or detailed explanations. It offers a unique approach to understand and respond to images without the need for extensive multimodal training. Next, we have StyleAvatar3D, a remarkable advancement in 3D avatar generation. This technology allows for high-fidelity and consistent 3D avatars with various poses and styles. Unlike previous methods, StyleAvatar3D maps out the three-dimensional space to create a more realistic and immersive experience. This development opens up new possibilities in gaming and social applications. Lastly, we explore Gorilla, the API app store for language models. Gorilla connects LLMs with thousands of APIs, offering users a vast selection of tools to complete tasks. What sets Gorilla apart is its ability to eliminate hallucinations and provide accurate and reliable API suggestions. With 1,640 APIs available, this model proves to be a powerful and valuable resource. The AI revolution continues, and these stories demonstrate the incredible progress being made in the field. Key Take-Aways: GILL: * Gil is a method that infuses image encoder and decoder into Ella lambs, enabling them to recognize, understand, and respond to images. * Gil offers a unique approach by injecting image embeddings into LLMs, allowing for various use cases such as image editing, image explanations, and image injection into conversations. * The integration of an encoder in Gil enables both image generation and image retrieval, expanding its capabilities beyond traditional multimodal models. * Gil's open-source code sets it apart from Meta's multimodal work, offering accessibility and potential real-world applications in image-based communication. StyleAvatar3D: * StyleAvatar3D introduces image text diffusion for high-fidelity 3D avatar generation, allowing for a wide range of avatars with different poses and styles in a complete 3D space. * The significance of the 3D aspect lies in the visual accuracy and consistency that is challenging to achieve with traditional stable diffusion methods. StyleAvatar3D offers both the generation of 3D images and the ability to maintain consistency in attributes and appearance. * Unlike previous avatar generators that relied on stitching together 2D images, StyleAvatar3D maps out the three-dimensional space, providing a more consistent and immersive experience for games and social platforms. * The introduction of true 3D assets has marked a significant leap forward, enabling the creation of realistic and dynamic visuals in game development and other applications. Gorilla: * Gorilla is an API app store for LLMs that connects the LLM world with the vast world of APIs, offering thousands of APIs for completing user tasks. * One of Gorilla's key achievements is addressing hallucinations that exist in models like GPT-4, providing accurate API recommendations instead of generating random information. * The Gorilla model is entirely open source, with the training still in progress. However, the inferencing, dataset, and evaluations are openly available. It boasts a wide range of 1,640 APIs that can be called, demonstrating its capabilities against built-in spotlights like Apple's and showcasing superior performance. * Fine-tuning the model on APIs proves to be more effective than prompting, reducing hallucinations and improving accuracy. The architecture's ability to quickly update APIs within the model allows for faster contributions and continuous improvement without the need for complete retraining. Links Mentioned: * GILL * StyleAvatar3D * Gorilla * Press Correspondent Tweet * Center for AI Safety Follow us on Twitter: * AI Daily * Farb * Ethan * Conner Subscribe to our Substack: * Subscribe This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.aidailypod.com

Welcome to another exciting episode of AI Daily! In today's edition, we have three captivating news stories lined up for you. First up, we have the remarkable achievement of NVIDIA, as they join the exclusive club of trillion-dollar companies like Amazon, Microsoft, and Apple. Next, we explore an intriguing project called Voyager. This innovative approach to training AI models in Minecraft using the GP4 framework has revolutionized the process. Our final story showcases Google's project SoundStorm, which introduces efficient parallel audio generation. This development allows for the rapid creation of audio by leveraging parallel processing. Join us for this episode of AI Daily as we dive deep into these captivating news stories and uncover the incredible potential they hold for the future. Key Take-Aways: NVIDIA Becomes $1T Company: * NVIDIA breaks trillion-dollar market cap, joining Amazon, Microsoft, and potentially Apple in an exclusive club. * NVIDIA's market leadership and monopoly status in the chip industry are clear, with their critical role in the AI ecosystem and impressive demos. * The future of microprocessors is promising, with potential for multiple trillion-dollar companies in the industry. * AMD poses competition in the enterprise market, but NVIDIA's focus on AI and scaling quickly may solidify their position. Other players may emerge in the next few years, including Apple, Google, Microsoft, and Meta, with their own in-house chips. The onshoring of chips in America may also contribute to the rise of upstart companies. Voyager * Voyager is a project in Minecraft where a GP4 model is trained by iterating on its own code base of skills, saving them in its memory for future use. * The use of LLMs and self-correcting error loops in Voyager resulted in faster progress in Minecraft compared to traditional reinforcement learning techniques. * The training and improvement of models like Voyager can be recursive, either internally with LLMs training LLMs or externally using pipelines and frameworks to continually enhance performance. * The approach taken in Voyager has potential applications in real-world robotics, where robots can learn and improve their skills by iterating on their own internal code. This recursive model has significant implications for accelerating AI development. SoundStorm * Google's project SoundStorm focuses on efficient parallel audio generation, allowing the generation of a significant amount of audio in a short time. * The model shows promise in terms of speed and quality, with the ability to generate 30 seconds of audio in just half a second. * Currently, the project is not publicly available, but Google is showcasing its AI projects, and it is expected that it will be accessible in the future. * The improved speed in audio generation opens up new possibilities for real-time applications, such as generating audio for NPCs in AI games. Links Mentioned * NVIDIA * Voyager * Soundstrom * GPT-4 Typo Follow us on Twitter: * AI Daily * Farb * Ethan * Conner Subscribe to our Substack: * Subscribe This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.aidailypod.com

Discover the latest in artificial intelligence on AI Daily. In this episode, we cover three major stories. First, we explore Falcon LLM, an impressive open-source language model with 40 billion and 7 billion parameter models. Falcon excels in hugging faces evaluations and offers unique features for chat-based experiences. We also discuss its licensing model, providing free access for personal and research use and a fair royalty structure for commercial applications. Next, we dive into JPMorgan's patent filing for IndexGPT, their foray into AI-driven securities analysis based on customer needs. We highlight the growing trend of companies developing their own GPTs and the ambitious AI goals of JPMorgan's extensive team. Finally, we delve into TikTok's exciting venture into chatbots with TikTok Taco, a feature that analyzes videos and provides answers to user questions. Join us as we explore the cutting-edge advancements of Falcon LLM, JPMorgan IndexGPT, and TikTok Taco, and their impact on AI and society. Don't miss out on this engaging episode! Links Mentioned * Falcon LLM * JP Morgan’s IndexGPT * TikTok Tako * Man Regains Ability to Walk * GitHub Privacy Policy * AI Discovered Antibiotic for Superbug Key Take-Aways Falcon LLM * Falcon LLM, a newly released open-source language model, offers a 40 billion parameter model and a 7 billion parameter model. * Falcon is currently topping hugging faces charts for their evaluations, showcasing its impressive performance. * Falcon provides both a raw unfiltered model and an instruct model for chat-based experiences. * The licensing model for Falcon allows free access for personal and research purposes, with a fair royalty-based structure for commercial use, similar to Unity's successful model. JPMorgan IndexGPT * JP Morgan has filed a patent for IndexGPT, which focuses on analyzing and selecting securities based on customer needs. * Financial institutions like JP Morgan are building their own GPTs internally, following the trend of companies developing their own language models. * JP Morgan's announcement appears to be a strategic PR move to showcase their AI efforts and drive value, emphasizing their large team of data scientists, machine learning engineers, and AI researchers. * The use of GPTs in financial services, despite being banned publicly, is rumored to be prevalent within JP Morgan, leading them to trademark their own IndexGPT. TikTok Tako * TikTok is introducing chatbots similar to Snapchat, aiming to analyze videos and provide answers to user questions based on the content. * The introduction of chatbots by TikTok was discovered by an analytics company, indicating that it is still in the testing phase and has not been officially announced. * The chatbot feature can understand the videos users are watching and suggest related questions or serve as a search tool for finding more content. * TikTok's expertise in computer vision and data collection from videos positions them well to gather valuable insights and improve their search engine capabilities, potentially impacting competitors like Google. Follow us on Twitter: * AI Daily * Farb * Ethan * Conner Subscribe to our Substack: * Subscribe This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.aidailypod.com

In today's episode of AI Daily, we bring you the latest news from the world of AI. First up, we discuss the Google AI Pact with the EU. As Europe pushes for AI regulations, Google is the first corporate partner to join this initiative. We delve into the importance of this partnership and the potential impact it may have on AI regulation worldwide. Next, we explore the exciting Alexandria Initiative, which aims to embed the entire internet. This project, led by microcosm, focuses on making research papers more accessible and improving collaboration and search capabilities. We discuss the benefits and potential applications of this groundbreaking technology. Lastly, we dive into the soaring growth of NVIDIA and the buzzing discussions around its stock. While NVIDIA's market cap continues to rise, we caution against FOMO (Fear of Missing Out) and remind viewers to consider other players in the market. Join us for this informative episode of AI Daily! Key Points Google AI Pact with EU: * Google and the EU are in discussions similar to OpenAI and the US government. * Europe is pushing for AI regulation, aiming to implement it by the end of the year. * Google is the first corporate partner to join the AI pact with the EU. * There are concerns about the lack of public involvement and actual actions in AI regulation discussions, with accusations of virtue signaling by politicians and companies. Alexandria Initiative to Embed the Internet: * Microcosm has developed Alexandria, a project aiming to embed the entire internet, starting with titles and abstracts of research papers from archives. * Alexandria improves search and collaboration by providing easier access to research papers and enhancing semantic web capabilities. * The project is open source, allowing users to download the models and participate in the open collective to decide what content to embed next. * The advancements in embedding technology, such as cheaper and more useful embeddings, along with open source models, have made projects like Alexandria possible now. NVIDIA’s Growth: * Nvidia's stock market value is skyrocketing, prompting discussions on whether it's still a good time to buy the stock. * While the consensus is that Nvidia will continue to grow, there is a cautionary note about other players entering the market and potential price fluctuations. * The situation is reminiscent of internet-driven hype and market volatility, where stocks can experience significant increases and decreases. * Nvidia is seen as having a monopoly on GPUs and AI infrastructure, but the emergence of custom boards and other companies may challenge their dominance in the long term. Links Mentioned: * Google-EU AI Pact * Alexandria Embed * Nvidia Growth * Diagram * UI AI Follow us on Twitter: * AI Daily * Farb * Ethan * Conner Subscribe to our Substack: * Subscribe This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.aidailypod.com

Join us for another exciting episode of AI Daily as Conner, Ethan, and Farb discuss the latest breakthroughs in artificial intelligence. In our first story, we explore the remarkable performance of the Goat LLaMA model, which surpasses GPT4 and other models in arithmetic tasks. Discover how synthetic data and fine-tuning techniques contribute to its success. Then, we dive into Adobe Photoshop's new tool, Firefly, designed to revolutionize photo editing. Learn how this powerful tool can enhance productivity and transform design workflows. Finally, we explore the AlpacaFarm Simulation Framework, a game-changing approach to reinforcement learning using synthetic data and feedback. Find out how this framework can revolutionize the training of AI models. Don't miss this episode filled with exciting advancements in AI technology. Key Take-Aways: Goat LLaMA Model * The Goat LLaMA model, a fine-tuned 7 billion parameter model, outperforms GPT4 and Palm 540B on arithmetic tasks. * The use of synthetic data in training the model is an interesting approach to improve performance. * Fine-tuning a model specifically for arithmetic tasks raises questions about the nature of learning mental math and the relationship between language and mathematics. * The methodology of taking a small model and fine-tuning it to outperform GPT4 on a challenging task is significant and holds potential for future tasks. Adobe Firefly * Firefly, a new tool for Photoshop, has been released and is expected to greatly enhance productivity for designers and photographers. * The tool, which was trained on Adobe Stock, offers a safe and reliable option for businesses to utilize generative AI technology without copyright concerns. * Firefly's integration with Photoshop is just the beginning, as similar advancements are expected in video and audio editing. * While there are other tools available for generative AI, Firefly and Adobe provide a trusted solution for professionals and offer unique copyright advantages. AlpacaFarm Simulation Framework * Alpaca Farm Simulate is a simulation framework for training methods in reinforcement learning through synthetic data and feedback. * The framework offers a cost-effective and efficient way to train models for chat and fine-tune them without relying solely on human feedback. * Synthetic data and simulations can save significant time and resources compared to using real human data, making it a practical approach for training models. * Alpaca Farm Simulate demonstrates the potential of simulations and synthetic data in improving models, offering an alternative to expensive and time-consuming human-centric approaches in reinforcement learning. Links to Stories Mentioned: * Goat LLaMA Model * Adobe Firefly * AlpacaFarm Simulation Framework * Google Merchant Center * Microsoft Build Follow us on Twitter: * AI Daily * Farb * Ethan * Conner Subscribe to our Substack: Subscribe This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.aidailypod.com

In today's episode of AI Daily, we bring you three exciting news stories that will leave you wanting to know more. First, we dive into the world of Microsoft with their latest announcement, Windows Copilot, a game-changing addition to the Windows platform. Next, we explore Meta's groundbreaking language model that is revolutionizing speech-to-text and text-to-speech capabilities. Discover how this multilingual model is outperforming its competitors and supporting over 1,100 languages. Lastly, we delve into the thought-provoking topic of governing superintelligence, as OpenAI sheds light on the potential risks and solutions. Join us as we unravel the complexities of AI governance. Don't miss out on these fascinating stories - tune in now! Main Take-Aways: Microsoft Windows Copilot * Microsoft made several announcements, including two main ones: Windows Co-pilot and plugins across Bing and ChatGPT. * Windows Co-pilot is being brought to all of Windows, offering integration on the right side of the Windows home screen and allowing users to ask questions, drag in files, inquire about apps and system settings. * Despite Windows' support for older versions like Excel 95, Windows 11 and Windows Co-pilot are praised for their integration and functionality. * Microsoft claims to have more AI-capable GPUs on Windows 11 than any other operating system, emphasizing the vast scale of devices that can leverage these technologies. * Windows Co-pilot is set to become available in June, making it a significant move for Microsoft and potentially influencing developers' preferences and workflow choices. The system-level integration is highly anticipated, and it will be interesting to see how Mac and Apple respond in the coming years. Meta Language Model * Meta has introduced an absolute open-source model focused on speech-to-text and text-to-speech capabilities, outperforming Whisper and supporting over 1,100 languages. * The multilingual model has significantly lower error rates compared to Whisper, with model sizes half as large, making it a powerful tool for various tasks. * The model's training data includes religious texts, leveraging their widespread availability and making it suitable for many languages. * Despite Meta's minimal press coverage, their advancements in AI technology rival those of major players like Microsoft, OpenAI, and Google. * With over 7,000 languages globally, Meta's model covers approximately 1,100 languages, some of which are at risk of disappearing, providing a valuable resource for preserving linguistic diversity. The model is entirely open-source, accessible on GitHub for developers and researchers. Governance of Superintelligence * OpenAI recently released a blog post discussing the governance of superintelligence and the potential risks associated with AI becoming expert-level in multiple domains within the next decade. * They proposed various approaches to address these risks, including limitations on GPU access and model training for large-scale AI models, as well as the establishment of government agencies to oversee regulation. * An interesting point raised was the suggestion that regulation should only apply above a certain capability threshold, leaving lower-level AI systems ungoverned. * OpenAI emphasizes their intent to explore and experiment with plausible solutions openly, seeking input from the global community rather than claiming to have all the answers. * The comparison to nuclear energy and the reference to the International Atomic Energy Agency (IAEA) highlight the need for careful consideration and potential governance measures as AI advances. Links to Stories Mentioned: * Microsoft Windows Copilot * Meta Language Model * Governance of Superintelligence * Instruction-finetune(IFT) Stable Diffusion(SD) * Anthropic Series C Funding Follow us on Twitter: * AI Daily * Farb * Ethan * Conner This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.aidailypod.com

In this episode, we delve into three exciting news stories. First up, we explore the remarkable LIMA model, a 65 billion parameter model that performs almost as well as GPT4 and even outperforms Bard and Da Vinci! Find out how Meta's innovative approach is revolutionizing the open source AI realm. Next, we unravel the fascinating CoDi model, which utilizes compositional diffusion for generating multimodal outputs. Learn how this powerful model can transform text and audio inputs into stunning videos. Lastly, we uncover the mind-boggling Mind-to-Video technology that reconstructs videos based on brain activity. Join us as we discuss the possibilities and implications of mapping the human mind. We also discuss hilarious ChatGPT App reviews, which you won’t want to miss! Main Take-Aways: LIMA Model * The Lima model is a large 65 billion parameter LLaMa model that was fine-tuned on a thousand carefully curated responses. * It performed almost as well as GPT4 and even better than models like Bard and Da Vinci. * Meta has released the Lima model as an open source model, showcasing their commitment to staying up-to-date in the AI field. * Lima's approach differs from other models by using supervised examples instead of human feedback, resulting in impressive responses. * This development is significant because it brings another open source model closer to competing with the massive models trained by OpenAI, providing an alternative approach to alignment. CoDi * CoDi is a model that specializes in any-to-any generation using compositional diffusion. * Unlike other models that primarily process text, CoDi is designed for multimodal tasks, allowing users to input combinations of text, audio, and video to generate corresponding outputs. * CoDi can generate videos based on text and audio inputs or produce new text outputs based on two different text inputs. * Understanding context is crucial for CoDi, as it needs to comprehend the relationships between different modalities to provide accurate and comprehensive results. * CoDi appears to be an open-source model, potentially an enhanced version of Meta's previously released ImageBART, although a direct comparison has not been made yet. The code for CoDi is likely available for use. Mind-to-Video: * The team has developed a mind-meets-video model that reconstructs videos based on brain activity captured through fMRIs. * The training process involves pairing fMRI data with corresponding videos, allowing the model to learn the relationship between brain signals and video content. * The model aims to capture what a person is remembering or perceiving by analyzing their brain activity and finding similar videos from the training set. * Although it is not yet capable of mind reading, the model provides insights into how the brain processes and represents visual information. * The team's previous work focused on mind-to-image generation, and this mind-to-video model represents an impressive advancement, achieving a 45% increase in accuracy compared to previous methods. Links to Stories Mentioned: * LIMA Model * CoDi * Mind-to-Video * Lambda Demos Follow us on Twitter: * AI Daily * Farb * Ethan * Conner This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.aidailypod.com

Today on AI Daily, we kick things off with Blockade Labs' mind-blowing 3D Skybox generation tool that turns your sketches into stunning scenes. Watch as Farb demonstrates the power of this incredible tool. Then, we dive into Drag Your GAN—a game-changer for image manipulation. Discover how you can effortlessly transform images with a few simple clicks. But that's not all! Join us as we explore Meta's latest innovation, the MTIA v1 AI inference accelerator. Find out how this custom ASIC board takes AI operations to unprecedented heights. Don't miss out on this epic episode filled with revolutionary technologies and endless creative possibilities. Main Take-Aways: 3D Skybox Generation * Blockade Labs has developed a free 3D Skybox generation tool. * Users can sketch out their desired skybox and provide a prompt. * The tool generates an entire scene based on the sketch and prompt. * It offers a quick and easy way to generate ideas and scenes. * The generated skybox can be used as a background in games or other forms of art. * The tool is accessible to anyone and can be used immediately. * It is speculated that stable diffusion and control net algorithms are used in the process. * The tool has the potential to impact 3D game developers and artists. * It simplifies the creative process by combining multiple steps into a single tool. * Users can feed the generated images into other models to create 3D assets. * The current main use case is creating skyboxes for game backgrounds. Drag Your GAN * "Drag Your GAN" is a new GAN that allows interactive point-based manipulation of images. * Users can drag points and adjust dials to manipulate specific features in the image. * The tool enables realistic and seamless transformations of objects or faces in images. * It offers fine-tuned control over image poses and allows for easy adjustments without extensive editing. * The tool has examples demonstrating its effectiveness in manipulating cars, microscope images, faces, and animals. * It simplifies the process of fine-tuning image outputs and provides controllability beyond the base-level image models. * The ability to create masks over specific areas of an image allows for selective manipulation while keeping the rest of the image intact. * The tool is expected to be released in June, and creators are planning to share the code repository on GitHub. * It offers a more efficient and user-friendly alternative to image editing in software like Photoshop. * The tool has the potential to significantly benefit creatives and make image manipulation easier and more accessible. Meta MTIA v1 Inference Accelerator * Meta has announced their MTIA v1, the first generation AI inference accelerator. * It is a custom ASIC board designed specifically for AI operations, focusing on specific AI math requirements. * The accelerator is different from Google's TPU and shows promise in comparison to other hardware options like etch. * It excels in handling small shapes and batch sizes, but GPUs are still more efficient for medium or large size shapes. * The integration of the accelerator with PyTorch is a significant advantage, providing backward compatibility and facilitating inference workflows. * Energy efficiency is a key feature, with the ability to run a single accelerator using only 35 watts of power. * The development of the accelerator has been in progress since 2020, indicating extensive effort and refinement. * Meta's release of this custom hardware demonstrates the growing competition in the chip race, with companies like Apple, Google, and Microsoft also investing in custom hardware solutions. * The integration of hardware accelerators like MTIA v1 has the potential to improve inference performance and reduce energy costs for a range of applications. * The future looks promising for further advancements in custom hardware for AI. Links to Stories Mentioned: * Blockade Labs 3D Skybox Generation * Drag Your GAN * Meta MTIA Inference Accelerator * Perplexity Copilot Follow us on Twitter: * AI Daily * Farb * Ethan * Conner This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.aidailypod.com

First up, OpenAI has released the official ChatGPT mobile app, bringing the power of chat GPT right to your fingertips. Discover its smooth interface, voice recording, and haptic features that make it a joy to use. Next, we explore the integration of AI coding assistant in Google Colab, making it easier than ever to learn and experiment with AI models. And last but not least, we delve into the advancements of MedPaLM2, Google's game-changing healthcare model. Discover its impressive accuracy and potential impact on the healthcare industry. Main Take-Aways: ChatGPT Releases App * OpenAI has released an official ChatGPT mobile app. * The app is smooth, clean, and has nice features such as haptic feedback, voice recording, and voice memos. * It is faster than native iOS voice transcription and allows users to search their history. * The app does not have browsing or plugins yet, but those features may be added in the future. * It uses GPT-4 and is snappy, providing quicker access to chat. * An Android version of the app is expected to be released soon. * Some users have already started using the app and replacing Safari and Chrome on their iPhones. * There are many fake Chat GPT apps in the app store, but OpenAI's official app should help eliminate them. Google Colab * Google's CoLab, a Jupiter notebook environment, is integrating an AI coding assistant. * This integration aims to make coding and AI learning more accessible to users. * The AI coding assistant in CoLab is built off of Palm II, a language model developed by Google. * The assistant offers autocomplete suggestions, a chatbot, and a generate feature to create new code blocks. * Users can benefit from AI assistance within CoLab, enhancing their coding experience. * The integration is seen as a significant improvement for CoLab, attracting users back to the platform. * It is expected to have a positive impact on the developer community, particularly for beginners learning to code. * The AI coding assistant is not yet available but will be rolled out to paid subscribers first and eventually to all users. * The code blocks feature is free, while autocomplete and the chatbot are available to paid users. MedPaLM2 for Healthcare * MedPaLM2, Google's healthcare-specific model, has shown improvements in accuracy. * MedPaLM2 achieved a score of 86.5% on the Med QA exam and received positive evaluations from physicians and patients. * The model provides quick responses and aims to assist clinicians and potentially patients directly in the future. * The integration of AI models like MedPalm 2 into healthcare raises legal and ethical considerations, such as liability for decisions made contrary to AI recommendations. * Technical details of MedPaLM2's advancements include adversarial question testing and efforts to increase accuracy to 100%. * The development of application-specific integrated circuits (ASICs) for running language models (LLMs) offers significant performance improvements compared to GPUs. * Microsoft and Apple are also investing in AI-specific chips, indicating the growing trend in the industry. Links to Stories Mentioned: * ChatGPT App * Google Colab * MedPaLM2 * Etched Follow us on Twitter: * AI Daily * Farb * Ethan * Conner This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.aidailypod.com

Welcome to AI Daily! In today's episode, we bring you three exciting news stories that you don't want to miss. First up, Stability AI introduces Stable Studio, an open-source version of Dream Studio that allows easy integration with their latest models. Discover how this web application is revolutionizing the way people use stable diffusion models. Our second story dives into an intriguing incident at Texas A&M, where a professor accused students of using ChatGPT in their writing, causing many to not only to fail the class, but possibly jeopardize their degree all together. Find out why this story is causing a stir and what it means for the future of AI in education. Lastly, Microsoft unveils Guidance, a powerful templating language that simplifies working with LLMs. Learn how this framework saves time and empowers developers to create cleaner, more efficient applications. Join us for an engaging discussion on these trending topics and stay ahead of the AI-curve! Main Take-Aways: Stable Studio by Stability AI: * Stability AI announces Stable Studio, an open-source version of Dream Studio. * Stable Studio is a web application that allows users to work with stable diffusion models easily. * The purpose of Stable Studio is to provide a platform for users to keep up with the latest models, integrate custom models, and extend the capabilities of stable diffusion. * The release of Stable Studio emphasizes StabilityAI's commitment to open source and their goal of making it easy for people to use their models. Texas A&M Professor failing students for using ChatGPT: * A professor at Texas A&M University wrongly accused students of using ChatGPT to write their essays. * The professor fed the essays into ChatGPT, which confirmed their involvement, leading to the students' failure and graduation blockage. * The incident highlights the misconception that ChatGPT keeps a log of everything it generates, leading some to blindly trust its statements. * The lack of clear policies and guidance from educational institutions on AI use and assessment raises concerns and the need for proper communication and understanding. Microsoft Guidance: * Microsoft launches "Guidance," a templating language designed to work with LLMs (large language models). * Guidance simplifies the process of working with LLMs by allowing developers to specify desired outputs and template variables. * It supports various LLMs, such as GPT-4 and Vaya, providing flexibility in model selection. * The introduction of Guidance aims to address challenges in controlling LLM outputs and enables developers to create cleaner and more efficient applications. Links to Stories Mentioned: * Stable Studio * Texas A&M Professor Story * Update * Microsoft Guidance Follow us on Twitter: * AI Daily * Farb * Ethan * Conner Transcript Farb: Good morning and welcome to AI Daily. I'm Farb Nevi. I'm here with my excellent, uh, co-hosts and esteemed team members, Ethan and Conner, and we have three great pieces of news for you today. Let's jump into it. The first one is a big piece of open source news from the folks at Stability they've announced Stable Studio, their open source version of Dream Studio. Can you tell us about this, Ethan? Ethan: Yeah, so Stable Studio is just like you said. So Dream Studio has been stability, AI's way to use stable diffusion online. So a really easy portal to come in play with stable diffusion 1.5, stable diffusion. Two, their new language model, stable lm. And they've dropped Stable Studio as really an open source version of this kind of web application. They really want something that people can continue to keep up to date with their latest models, plug and play, maybe custom models. They've been fine tuning on stable diffusion models and trying to make something that people can integrate with better. Utilize better, kind of extend upon and just continue to place themselves in the forefront of open source. If you've played with these models and you're using 'em open source, maybe you're using something like radio or maybe you're using, you know, one of those tools like that online and Stable Studio is really their answer to this. They want to continue to embed themselves in open source and make it easy for people to use their latest models. Farb: Why is this important, Connor, and what could you do with it? Conner: I think the, one of the most important things here is that they announce plug-ins. So instead of directly calling stability APIs, now they have plug-ins built into stable studio. I think their angle here is try to give something to the community to let the community build upon and hopefully even improve Dream Studio itself in the future, I would imagine. Mm-hmm. Um, They probably see no benefit really in keeping Dream Studio closed sourced. There's many other alternatives. So now the benefit for them is to make it open source, stable studio. If you have a good plugin idea, go build a Farb: plugin in for developer. So, yep. Is that the main thing you think is, you know, one would do with it, uh, is, is build plugins for it. Conner: I imagine. I mean, there's, like Ethan said, you can use radio right now, you can use. Um, automatic one 11 web UI to generate other content right now. So if you wanna build plugins for this, build plugins for Farb: this. It seems like the, the sort of make sure the open source community is getting love from your organization. Your AI project, uh, continues to be very important if you. Sort of stop doing that. I think people will start just recognizing you as some sort of, you know, over, over there in the corner team that's trying to keep everything for themselves. And you're not gonna get the, uh, the masses, you know, teaming up around what you're doing and sharing what you're doing, and you might just sort of slowly disappear. Uh, if that sort of stuff is important for you. Some companies are big enough that, you know, even the big guys like Google obviously care a lot about this. Uh, the folks at Apple you see aren't doing necessarily as much right now with the open source world, but um, you know, they're obviously big proponents of the of, of Dev, so maybe they don't really need to do that as much, uh, as some of the other folks do to show, show the dev world their love. Very cool, uh, to see more awesome open source stuff happening. Let's go on to our second story. Uh, this is a little bit more of a social story. The, um, you know, professor at Texas A&M, it seems like, has maybe incorrectly labeled a bunch of folks as using chat G p T to write essays, even though that's not what happened. Connor, could you tell us a little bit more about this and why is this story important? Yeah, Conner: apparently the story is that he got his entire graduating class and he fed their essays in Cheche, BT and Chei. Bts like, yes, I wrote that and based off that, he failed all them and is blocking their graduation. Uh, it's a pretty common thought process among people who use Chei b t. They think Cheche BT keeps a log of everything. It says the huge, they think chat. BT knows what it says, and they asked Shay Bt if it wrote something, and Shay Bt says, yes. And then they just believe it. Farb: So yeah, chat,GPT sort of, you know, probably wants to validate what they're saying to it and it, it, you know, it's like, if you did this, it's maybe it just wants to generally apply in, you know, the affirmative, uh, it, it's interesting, it seems weird to me that. You know, more schools aren't getting ahead of this type of stuff, and maybe they are, maybe they're quietly doing things and they're not ready to come up with announcements. But to, you know, this isn't the first time we've heard this type of story. Uh, this has been happening since, you know, last, last fall, when a lot of these, you know, cooler, more powerful, LLMs started, started hitting the, the public. Uh, this has been going on. It seems a little strange to me that. You know, school the size of Texas a and m doesn't, hasn't already announced some sort of policy or provided its professors with some guidance on how to do things. E Ethan, where do you think this is going? Is this the end of this? Is this just the beginning? Ethan: No, I, I think you're on point with that last point. I was just talking to an educator friend of mine yesterday, and at the end of the day, I, I get it from a professor's point of view, from a teacher's point of view, you're like, Hey, why am I grading this stuff when they're just taking five minutes to write it with chatGPT? But from an administration side, from the top down level of these institutions, it needs to be made clear that. You chat, g p t. You can't just tell if this was AI written yet. Uh, G P T zero and chat, g p t saying, yes, I wrote it. None of these are accurate yet. So it's important that that is conveyed to these professors and that this is something we continuously deal with versus making these. Like harsh reactionary points of saying, I'm just gonna fail everyone. I'm, I'm tired of this. I'm, if one person did it, maybe the whole class did it. I just wanna fail everyone. I'm sick and tired of grading ai, so there's some valid points, but it's, it's important that we're not doing things like this. So I feel bad for all these students who are sitting there who probably did write a good chunk of these thesis papers or their final papers and are now waiting for their degree because some AI models said, Conner: yeah, that was I wrote that. Yeah, a few were already exonerated because, uh, because like they had like Googled docs like history of like edits and then like their emails and stuff were ignored by the dean and the teacher apparently. But as soon as these stories hit the Washington Post and everything got into the limelight. Farb: Of course they will exonerate. It sounds like the definition of wishful thinking. You know, you, you want to be able to just plug these into Chad g p t and have it tell you if it'

Join us on this episode of AI Daily as we discuss three exciting news stories. First, we delve into Sam Altman's testimony in front of Congress, where he discusses the future of AI regulation and its impact on the economy. Then, we explore Quora's Poe API, a groundbreaking web browser for LLMs that allows developers to bring their own language models to the platform. Finally, we cover Apple's latest accessibility announcements, including live speech and personal voice advancements. Tune in to gain insights and discover the intriguing developments in the world of AI. Main Take-Aways: * Sam Altman's testimony: * Sam Altman testified in front of the Senate Judiciary subcommittee on Privacy, Technology, and the Law about AI regulation. * He emphasized the importance of AI safety and the future of the economy with AI. * Altman's approach of being open and accessible to lawmakers was praised. * Senators expressed surprise that technology was actively seeking regulation. * The hearing focused on past mistakes in technology, such as social media, and the need for good regulation. * Quora Poe API: * Quora introduced the Poe API, positioning itself as a web browser for language models (LLMs). * Poe aims to allow developers to integrate any type of LLM, including custom models, into their applications. * The API offers features like language chaining, monetization, and human feedback for reinforcement learning from humans. * It provides a one-click replica for easy API usage and built-in integrations with LLM frameworks. * The focus is on enabling developers to bring full LLM experiences to users, not just plugins. * Apple's latest accessibility announcements: * Apple announced advanced speech accessibility features, including live speech and personal voice. * The focus is on making AI technologies accessible and beneficial for people with disabilities. * Users can train their voices on their devices, creating custom voice models for communication. * The Magnifier app allows users to point at objects and have the labels or buttons read aloud. * These accessibility features leverage Apple's on-device machine learning capabilities and are expected to roll out later in the year, likely with the next OS release. Links to Stories Mentioned: * Sam Altman Testimony * Quora Poe API * Apple’s Announcements * ChatGPT Fund * Suhail’s “Boundless” Song Follow us on Twitter: * AI Daily * Farb * Ethan * Conner Transcript: Conner: Good morning. Good morning. Welcome to another episode of AI Daily. We got three pretty great stories for you guys today, starting with first we have Sam Altman's testimony. Uh, this morning in front of Congress, the Senate Judiciary subcommittee on Privacy technology and the law interviewed him this morning, uh, parking a lot about AI regulation. He was joined by a professor at from NYU. And also by, uh, Christina Montgomery, IBM's Chief Privacy and Trust Officer and the Senate, and Sam Altman and the other representatives were all extremely concerned about the future of AI safety, the future of the economy with ai. Farb, Any Farb: thoughts? Uh, you know, I think Sam has been giving a masterclass in how to do this stuff correctly. The bottom line is people want to know who the leaders are that are building and controlling these massively powered technologies, ma, massively powered technologies. Obviously the politicians want to know who these people are. The politicians want to be understood by their constituents as caring about these things, taking the steps to get these leaders in front of them to speak. And Sam is. You know, getting ahead of the story just about every time and a, a real blueprint for other tech leaders. And clearly he's watched folks in the past, other big tech leaders from big companies who've sort of had to be, be dragged out in front of the Senate who've had to sort of been, be pulled out from, you know, hiding behind the magical curtain. Sam is doing it out in public, uh, saying, here I am. I'm happy to speak about it. We should regulate this, and it's really working. And I, I say kudos to him and, and keep doing it. Conner: Yeah. A couple of the senators really commented on that, how surprised they are that this is the first time that new technology has really asked to be regulated. We've seen social media in the past section two 30, there was essentially a waiver on technology and any. Being held liable. Farb: This is the part that I think he's reading it correctly on a lot of folks in the past. I think were afraid to do that because they're like, well, if I put myself out there, they're gonna start controlling me and start telling me what I can do, and all of a sudden, my business is not going to be as valuable. I don't think that's the right approach. Sam's approach has been these people wanna see your face. They wanna hear your voice. They wanna know that you're a real accessible person that's willing to participate in the social contract that we have with each other around how we, you know, live and how we engage with these cool new technologies. And, and that's the right call. Just making yourself available goes a long way. Conner: seems to have a lot more goodwill in front of the Senate than past technology has. We've seen with social media, with meta, the entire, the entire. Duration of the hearing, they're really talking about their past mistakes in nuclear, in the genome project and social media. They're really talking about their past mistakes and what they can learn from that, and I think Sam was really taking good stance, Ethan. Ethan: No, I agree. I think you both nailed it. Um, at the end of the day, the, our elected officials want to do better this time. Um, and you, you pointed out very strongly how this is one of the first times that technology's asking to be regulated and Congress is open to that and they know the mistakes of the past and they want to be involved in this process. I do think it's, you know, uh, very interesting how most of the articles coming out about this do still point at the. You know, dangers of AI and talk about how bad this can be. But if you listen to the testimony, it was actually very engaging, very thoughtful. Each congressman and each person who spoke and Sam themself talked about the positives of this technology, how it's gonna benefit people, how it can benefit. Creatives, how the impact on jobs is not gonna be as bad as we mostly think. So if you listen to the testimony, it's actually very heartwarming to see how engaged our elected officials are, how engaged our kind of upcoming tech leaders are on this subject. And, uh, I was happy to hear it. It was very bi, it was very bipartisan. Conner: Uh, our leaders were very engaged. Like you said, Ethan, they really seemed to know what they were going on. They talked a lot, they mentioned a couple times garbage in, garbage out, and how it related to all this of how. We need good regulation or else that's garbage in. Absolutely. Um, it really contrasts with eu, like the EU news we saw yesterday of them trying to crush open source and it's really a contrast there. So definitely. Okay. Our next story then is Quora's Poe API. Quora announced the Poe API where they're really trying to take the angle of being a web browser for LLMs. They're really a centered piece. Po if, if you guys have used the app, it's pretty great. Yeah. Um, Ethan: Ethan, what do you think? Yeah, Poe's been very popular for people who wanna use philanthropic and Claude and some of these other models. And I think the most interesting thing to me here is. You know, unlike, uh, Bing or possibly Bard or even chat, g p t plugins, pos, really, I think you nailed it on a web browser almost that PO is letting you bring any type of model. So any l l m you want, if you built your own custom, l l m, if you built a whole application on top of another LLM. They want to make that available within Poe and not just a plugin to ChatGPT, for example, but really your entire application, your entire business per se, as a custom LLM with custom features for users that they want to use. Put directly in the distribution funnel of po. So different than a plugin, bringing the whole l l m experience to someone. So I think it's a, it's a new angle of people like using Poe for Claude, and I'm excited to see what kind of startups and developers deploy as a full l lm and not just a plugin. Farb: Yeah. You know, as a, as someone who's not a full-time developer myself, I love the fact that they have a one-click replica, uh, that lets you fire up the. The API and a demo and just start using it. It's the sort of thing that, you know, I have the time to actually go in there and do and start engaging with things instead of having to go to GitHub and, you know, not like it's a lot, a lot of work, but even saving 30 minutes, saving 15 minutes and just let you spend some time in the middle of your day actually interacting with the code instead of setting up, you know, where you wanna interact with it. Getting your own replica built, uh, is really nice. Conner: Absolutely. Yeah. It looks like they have built in integrations into Lang Chain. LA Index looks like they wanna bring monetization in the future. They have really all the features that you see in ChatGPT, but you only have to bring your own language model. They give you, uh, human feedback that you can work with for RLHF. So between the alternatives of taking the open source for out and forcing and forking something like a HuggingChat or building for Poe, we'll see where developers go, but yep. Where's Farb: the one click replica for every, you know, repo on GitHub. Conner: Exactly. There's code spaces, but yeah. Yeah. Okay. Next up we have the Apple announcement. Apple announced they have live speech and personal voice, advanced speech accessibility. Uh, they're really taking the angle of it as an accessibility tool, but this is essentially an 11Labs that works on your phone. It, you can train

In this episode of AI Daily, we explore the groundbreaking SimpleRecon algorithm for 3D scene reconstruction. Discover how researchers have achieved faster and more accurate reconstructions using 2D convolutions, making it cheaper and deployable across various applications. Secondly, we discuss CloudFlare Constellation, an exciting development from CloudFlare. Learn how this new platform integrates seamlessly with CloudFlare Workers, enabling easy deployment of image and language models for inference. Discover the impact this has on startups and developers, offering a simplified and cost-effective solution for running AI models. Lastly, our third story is EU's “AI Act”, which focuses on open source developers and their responsibilities. Delve into the potential implications of these regulations, including fines and revenue percentage for models available to European consumers. Gain insights into the ongoing debate between open source and AI regulation as we explore the EU's approach. Mentioned in Video: SimpleRecon CloudFlare Constellation EU’s Regulation Announcements ChatGPT Plugins/Browser Follow us on Twitter: AI Daily: https://twitter.com/aidailypod  Farb: https://twitter.com/farbood  Ethan: https://twitter.com/ejaldrich?s=20  Conner: https://twitter.com/semicognitive?s=20 This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.aidailypod.com

Join us on AI Daily as we dive into the latest breakthroughs in the world of AI! In today's episode, we discuss a game-changing update in token limits with Anthropic Claude 100K Context. We also explore the fascinating advancement of StabilityAI’s Animation SDK and its implications for creatives and filmmakers. And finally, we cover Meta AI's exciting advertising innovations, revolutionizing generative AI in the ad space! Tune in now for all the AI news you need to stay ahead! Mentioned in This Video: Anthropic Claude 100K Context StabilityAI Animation SDK Meta AI Announcements Google’s MusicLM Elicit Nivi’s Twitter (Tweet on Naval & Cluade+) Follow us on Twitter: AI Daily: https://twitter.com/aidailypod  Farb: https://twitter.com/farbood  Ethan: https://twitter.com/ejaldrich?s=20  Conner: https://twitter.com/semicognitive?s=20 This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.aidailypod.com

In this episode of AI Daily, Ethan, Farb, and Conner cover 10+ stories in rapid-fire fashion to give you everything you need to know about what is happening in the world of AI. Tune in to hear about: * HuggingFace Transformers Agent * Google’s 10+ Announcements * Airtable AI * BloombergGPT Chronicles * ScaleAI * AssemblyAI LeMUR * IBM AI * WizardLM-13B-Uncensored * ImageBind * Microsoft Helion Fusion * & Wendy’s new AI drive-through chatbot Links mentioned in this Video: HuggingFace Transformers Agent Google’s 10+ Announcements Airtable AI BloombergGPT Chronicles ScaleAI AssemblyAI LeMUR IBM AI WizardLM-13B-Uncensored ImageBind Microsoft Helion Fusion Wendy’s new AI drive-through chatbot Follow us on Twitter: AI Daily: https://twitter.com/aidailypod  Farb: https://twitter.com/farbood  Ethan: https://twitter.com/ejaldrich?s=20  Conner: https://twitter.com/semicognitive?s=20 This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.aidailypod.com

AI Daily is back today with the biggest stories in AI. First up, Ethan, Farb, and Conner cover new open source LLMs coming from MosaicML and RedPajama. The second story is all about the growing ability of AI in the world of 3D. Lastly, the hosts discuss what they've been seeing in AI and what's getting them excited. Tune in to get the full story! Mentioned in this Video: MosaicML RedPajama OpenAI 3D Dream3D Conner’s SvelteSummit Talk Follow us on Twitter: AI Daily: https://twitter.com/aidailypod  Farb: https://twitter.com/farbood  Ethan: https://twitter.com/ejaldrich?s=20  Conner: https://twitter.com/semicognitive?s=20 This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.aidailypod.com

In this episode of AI Daily, hosts Farb, Ethan, & Conner cover 3 of the biggest news stories in the world of AI The first story is about the rise of open-source in AI, which is said to be a game-changer in the industry. According to a leaked Google doc, open source is going to beat Google, OpenAI, and everybody else in the AI game. The second story is about the White House's announcement to evaluate AI companies like Alphabet and OpenAI against its goals for AI. Lastly, the third story is about Bing AI and their recent announcements regarding their AI tool and features. Watch to get the full story! - Mentioned in this video: Google Leaked Docs: Open Source Benchmark: https://twitter.com/nonmayorpete/status/1654018106543554561?s=46&t=ziEc9CMi8q_PlJ34DMJVkA White House Announcement: https://www.whitehouse.gov/briefing-room/statements-releases/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ Bing AI Announcement: https://twitter.com/nonmayorpete/status/1654018106543554561?s=46&t=ziEc9CMi8q_PlJ34DMJVkA WebGPU: https://cohost.org/mcc/post/1406157-i-want-to-talk-about-webgpu - Follow us on Twitter:  AI Daily: https://twitter.com/aidailypod  Farb: https://twitter.com/farbood  Ethan: https://twitter.com/ejaldrich?s=20  Conner: https://twitter.com/semicognitive?s=20 This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.aidailypod.com

In this AI Daily episode, hosts Conner, Ethan, and Farb discuss three exciting stories. The first story covers Mojo, a new programming language for AI developers. Mojo addresses Python's shortcomings in deployment and marketability. The second story covers Chegg and what happened to their stock. Finally, the hosts discuss Mosaic ML, a company using machine learning to automate the development of other machine learning models. Not only does this drastically lower costs, but it opens up the space to more people. Tune in to get the full scoop! Mentioned in Video: Mojo Mosaic ML Pi Segment Anything NeRF babyAGI Follow us on Twitter AI Daily: https://twitter.com/aidailypod Farbood: https://twitter.com/farboodEthan: https://twitter.com/ejaldrichConner: https://twitter.com/semicognitive This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.aidailypod.com

In this episode of AI Daily, Ethan, Farb, and Conner cover the latest news on AI for hardware development, IBM's announcement around job displacement, and some exciting developments in synthetic voices. The team discusses Flux, a new co-pilot that is expected to unleash a whole new world of hardware development and products being developed at smaller scales. They also talk about IBM's announcement of a hiring freeze on any jobs that AI could do, which is an indicator of where the market is going. Finally, the team delves into some developments in synthetic voices by Eleven Labs. Tune in to learn more about these exciting developments! - // Mentioned in Video: Flux Co-pilot IBM News Eleven Labs jsonformer - // Follow us on Twitter: AI Daily: https://twitter.com/aidailypod Farb: https://twitter.com/farbood Ethan: https://twitter.com/ejaldrich?s=20 Conner: https://twitter.com/semicognitive?s=20 This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.aidailypod.com

Welcome to AI Daily! In this episode, we have some exciting news stories to share with you. First up, Stability AI has knocked it out of the park with StableVicuna, getting us closer to an open-source version of GPT-4 with a human touch. They have announced the world's first open source, RLHF trained chatbot. They also have developed a text-to-image tool DeepFloyd, capable of high fidelity text, upscaling images and creating photorealistic images from text. Our second story is about advancement of AI in the medical space. There are a couple of big pieces of news, the first being a study that shows patient's feelings towards GPT-4's responses vs. doctors. We also discuss Dr. Gupta, a medical AI assistant that not only gives you a possible diagnosis, but shows you the research and studies it used to make it's decision. Last but not least, we have news about GPT Code Interpreter, a plug-in that brings the power and abilities of code to non-coders! Be sure to watch the full video to learn more about these exciting advancements in the world of AI. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.aidailypod.com