Beyond OpenAI: Apple’s On-Device AI Strategy
Was this email forwarded to you? Sign up here Beyond OpenAI: Apple’s On-Device AI StrategyPlus a new super coder model, Meta’s new AI releases, DeepMind’s video-to-audio models and much more.Next Week in The Sequence:
You can subscribe to The Sequence below:📝 Editorial: Beyond OpenAI: Apple’s On-Device AI StrategyThe partnership between Apple and OpenAI dominated the headlines of the recent WWDC conference and sparked passionate debates within the AI community. Some view this partnership as a way to enable best-in-class AI in iOS devices, while others consider it a consequence of Apple’s lack of readiness to build proprietary AI models. The latter assumption would be a mistake. While it is true that Apple hasn’t historically developed the same AI research talent as Microsoft, Google, or Meta, things are rapidly changing, and last week was a validation of that. A few days ago, Apple open-sourced 20 small models and 4 datasets across different language and image categories. The models are compatible with Apple’s CoreML framework, designed to run on-device models. In the new release, we can find models such as FastViT for image classification, DepthAnything for monocular depth estimation, and DETR for semantic segmentation. These models can run on-device without the need for an Internet connection. The demand for smaller foundation models that can run on edge devices continues to grow. Several factors are contributing to this. First, mobile and IoT devices represent a significant percentage of user-computer interactions and, as a result, a fertile ground for AI. Computational constraints, personalization requirements, and privacy and security challenges prevent these scenarios from being solved by mega-large foundation models. Apple has one of the largest distribution channels for on-device models and, consequently, it shouldn’t be a surprise that it is advancing research in that area. Thinking that Apple’s AI strategy is dependent on the partnership with OpenAI would be a mistake. On-device AI is going to be a relevant trend, and Apple one of its main influencers. 🔎 ML ResearchDeepMind V2AGoogle DeepMind published the research related to their video-to-audio(V2A) models. V2A combines video pixels and prompts to generate rich soundtracks that complement the video clips —> Read more. DeepSeek-Coder-V2DeepSeek published the research behind DeepSeek-Coder-v2, a mixture-of-experts(MoE) architecture optimized for coding and math reasoning. DeepSeek-Coder-v2 supports 338 programming languages and shows performance comparable to GPT-4o in coding tasks —> Read more. Vulnerabilities in Multimodal AgentsResearchers from Carnegie Mellon University(CMU) published a paper outlining a series of adversarial attacks in vision-language agents. The research use adversarial prompts to perturb the model gradients and guide it to take the wrong actions —> Read more. DataCompResearchers from several research labs including University of Washington and Apple published a paper unveiling DataComp for Language Models (DCLM), a method for creating high quality training datasets for foundation models. DCLM also introduces a corpus of 240T tokens extracted from Common Crawl and 53 evaluation recipes —> Read more. Task-Me-AnythingResearchers from the University of Washington and Allen AI published a paper outlining Task-Me-Anything, a technique that is able to generate a benchmark tailored to user’s needs.. The method is optimized for multimodal models and maintains a library of assets across different mediums( videos, images, 3Ds) and combines them to generate new benchmarks —> Read more. Whiteboard-of-ThoughtResearchers from Columbia University published a paper introducing Whiteboard-of-Thought a reasoning method for multimodal models. The core idea is to prompt models with a metaphorical whiteboard that illustrates reasoning steps visually —> Read more. 🤖 Cool AI Tech ReleasesMeta New ModelsMeta released new models for audio, watermarking, multi-token prediction, images and others —> Read more. Apple New ModelsApple released a series of small models for language and image capabilities —> Read more. Claude 3.5 SonnetAnthropic released Claude 3.5 Sonnet which exhibit strong performance at much faster speed —> Read more. Gen-3 AlphaRunway unveiled Gen-3 Alpha, its new video generation model with tangible fidelity and consistency improvements over its predecessors —> Read more. AutoGen StudioMicrosoft released AutoGen Studio, a low-code interface for building and testing multi-agent solutions —> Read more. BigCodeBenchHuggingFace released BigCodeBench, a new benchmark specialized in code-generation tasks —> Read more. 🛠 Real World AIModel Training at MetaMeta shares some details about the infrastructure used to scale the training of foundation models internally —> Read more. Video Classification at NetflixNetflix discusses their use of vision-language models and active learning to build video classifiers —> Read more. 📡AI Radar
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Edge 404: Inside Anthropic's Dictionary Learning, A Breakthrough in LLM Interpretability
Thursday, June 20, 2024
Arguably one of the most important papers of 2024 ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏
The Sequence Chat: Justin D. Harris - About Building Microsoft CoPilot
Wednesday, June 19, 2024
Quick bio This is your second interview at The Sequence. Please tell us a bit about yourself. Your background, current role and how did you get started in AI? I grew up in the suburbs of Montreal and I
Edge 405: Memory and Autonomous Agents
Tuesday, June 18, 2024
Augmenting autonomous agents capabilities with different memory architectures can lead to amazing capabilities. ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏
📽 [Virtual talk] Build hyper-personalized product experiences with Full RAG
Monday, June 17, 2024
Hey there, Want to build highly personalized product experiences? Building them with traditional RAG (Retrieval-Augmented Generation) alone is tough, especially when it comes to adding real-time and
Amazing Dream Machine
Sunday, June 16, 2024
A text-to-video model freely available to everyone. ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏
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