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

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

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

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

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

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

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

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

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

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