TheSequence - Has OpenAI Hit Escape Velocity?
Was this email forwarded to you? Sign up here Has OpenAI Hit Escape Velocity?Sundays, The Sequence Scope brings a summary of the most important research papers, technology releases and VC funding deals in the artificial intelligence space.🗓 Next week in TheSequence Edge:Edge #265: Our series about ML interpretability explores interpretability methods for deep neural networks, discuss OpenAI’s research in multimodal neurons for CLIP explainability and the Eli5 framework. Edge #264: Deep dive into the reinforcement learning with human feedback research that powered the ChatGPT breakthrough. 📝 EditorialEarlier this week I was at a dinner with some prominent VCs and I was asked my opinion about the balance of forces between the big AI labs. My answer was that I feel that, after the recent market events, OpenAI might have hit “escape velocity”. Celestial mechanics defines escape velocity as “speed needed to break free from the gravitational attraction of a massive body, without further propulsion”. In the venture capital space, the term is often used with companies that have achieved a growth momentum that can’t be stopped. Last week we devoted this editorial to the implications of strategic alliance between Microsoft and OpenAI. A few days ago, that alliance was cemented with Microsoft’s $10 billion investment in OpenAI’s recent tender offer. The capital, together with Microsoft’s infrastructure and distribution, might be last ingredient needed for OpenAI to catalyze a momentum that seems fundamentally stronger than other top players in the AI market. ChatGPT has been at the center of OpenAI’s recent momentum. Initially planned as a demo of GPT-3.5 capabilities, the AI agent has taken the internet my storm challenging the imagination of creators, consumer and business application providers. ChatGPT is just a preview of what’s to come with GPT-4 which is rumored to have around 100 trillion parameters which is about 500x the size of GPT-3. The increase in size translates into a growth in capabilities that is far beyond exponential. Additionally, OpenAI is also taking leadership positions in areas such as text-to-image and text-to-code with models like DALL-E and Codex. I certainly believe that the recent winds in the AI space have favored OpenAI but that doesn’t mean they will be the only company to capitalize on this momentum. In the current market, Google/DeepMind and Meta AI seems to be strongest alternative to OpenAI. Both tech giants have the research capabilities and compute infrastructures to compete with the Microsoft-OpenAI duo. Furthermore, both Google and Meta have been actively working in the generative AI space with models like LaMDA, Imagen, Muse, Make-A-Scene, Sparrow and many others. Innovation in the foundation model space is happening everywhere but we might be on the verge of a race vastly dominated by OpenAI, Google and Meta. For now, OpenAI seems to have hit escape velocity. 🔎 ML ResearchReinforcement Learning for Mobile ManipulationBerkeley AI Research(BAIR) published a paper presenting a reinforcement learning technique for mobile manipulation in robotic environments —> Read more. LLMs to Synthesize Training DataAmazon Science published two papers discussing the use of LLMs to streamline the creation of synthetic training datasets —> Read more. Bandit Problem ImprovementsGoogle Research published a paper detailing an ML algorithm that provides small hints to improve bandit-like decision making problems —> Read more. Multimodal Transformers and Document IntelligenceMicrosoft Research published an insightful blog post about their use of multimodal transformer architecture for extracting information from complex documents —> Read more. 🤖 Cool AI Tech Releases3D Asset GenerationHuggingFace shows how to use language to generate 3D objects for games —> Read more. 🛠 Real World MLModel Serving at WalmartWalmart Global Tech discusses an architecture based on TorchServe and PyTorch to serve large scale models for their search experience —> Read more. ML Payment Routing at LinkedInLinkedIn discusses the ML architecture used to optimize payment approval rates —> Read more. 💸 Money in AI
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Edge 264: Inside Muse: Google’s New Text-to-Image Super Model
Thursday, January 26, 2023
The new generative AI model shows significant efficiency improvements over models like Stable Diffusion, Imagen and Parti.
Edge 263: Local Model-Agnostic Interpretability Methods: Counterfactual Explanations
Tuesday, January 24, 2023
Counterfactual explanations as an ML interpretability method, Google's StylEx and Microsoft's DiCE implementation
The Most Exciting Alliance in AI
Sunday, January 22, 2023
Sundays, The Sequence Scope brings a summary of the most important research papers, technology releases and VC funding deals in the artificial intelligence space.
📌 Event: Robust & Responsible AI Summit with Andrew Ng & industry leaders
Friday, January 20, 2023
Jan 26! Connect with AI builders, leaders, and industry experts
Edge 260: Data2vec 2.0 is Meta AI's New Self-Supervised Learning Model for Vision, Speech and Text
Friday, January 20, 2023
The model is one of the most impressive achievements in self-supervised learning research to this day.
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