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Person: "francois-chollet"
not much happened today
deepseek-r1 deepseek-v3 coder-v2 prover deepseek hugging-face dell openai instruction-tuning performance-benchmarks model-deployment training-costs hardware-scalability ai-safety risk-mitigation ethical-ai open-source gpu-utilization yann-lecun yoshua-bengio francois-chollet giffman
DeepSeek-R1 and DeepSeek-V3 models have made significant advancements, trained on an instruction-tuning dataset of 1.5M samples with 600,000 reasoning and 200,000 non-reasoning SFT data. The models demonstrate strong performance benchmarks and are deployed on-premise via collaborations with Dell and Hugging Face. Training costs are estimated around $5.5M to $6M, with efficient hardware utilization on 8xH100 servers. The International AI Safety Report highlights risks such as malicious use, malfunctions, and systemic risks including AI-driven cyberattacks. Industry leaders like Yann LeCun and Yoshua Bengio provide insights on market reactions, AI safety, and ethical considerations, with emphasis on AI's role in creativity and economic incentives.
not much happened today
llama mistral openai decagon sierra togethercompute vertical-saas funding protein-structure-prediction lora self-supervised-learning model-optimization neural-architecture-search model-evaluation ethics transformers multi-agent-systems long-context mira-murati demis-hassabis clement-delangue john-o-whitaker yann-lecun francois-chollet ajeya-cotra rohan-paul adcock-brett
Vertical SaaS agents are gaining rapid consensus as the future of AI applications, highlighted by Decagon's $100m funding and Sierra's $4b round. OpenAI alumni are actively raising venture capital and forming new startups, intensifying competition in the AI market. Demis Hassabis celebrated the Nobel Prize recognition for AlphaFold2, a breakthrough in protein structure prediction. Advances in AI models include techniques like LoRA projectors and annealing on high-quality data, while discussions emphasize the need for high-bandwidth sensory inputs beyond language for common sense learning. New methods like LoLCATs aim to optimize transformer models such as Llama and Mistral for efficiency. Ethical concerns about AI agents performing harmful tasks remain under investigation. The AI community continues to explore model evaluation challenges and optimization frameworks like LPZero for neural architecture search.
Not much technical happened today
whisper-v3-turbo llama-3 llamaindex openai poolside liquidai perplexity-ai meta-ai-fair cohere fujitsu mixture-of-experts context-windows model-optimization fine-tuning quantization model-training alignment synthetic-data model-architecture agentic-ai nick-turley arav-srinivas francois-fleuret finbarr-timbers lewtun francois-chollet jerry-j-liu mmitchell-ai jxnlco
OpenAI announced raising $6.6B in new funding at a $157B valuation, with ChatGPT reaching 250M weekly active users. Poolside raised $500M to advance AGI development. LiquidAI introduced three new MoE models (1B, 3B, 40B) with a 32k context window and efficient token handling. OpenAI released Whisper V3 Turbo, an open-source multilingual model with significant speed improvements. Meta AI FAIR is hiring research interns focusing on LLM reasoning, alignment, synthetic data, and novel architectures. Cohere partnered with Fujitsu to launch Takane, a custom Japanese model. Technical discussions included challenges in LoRA fine-tuning, float8 quantization in Keras, and new tools like create-llama for agent templates. Industry commentary raised concerns about AI development priorities and highlighted freelancing opportunities in AI.
not much happened today
grok-2 claude-3.5-sonnet claude-3.5 gpt-4 chatgpt-4o-latest anthropic x-ai google-deepmind openai mistral-ai meta-ai-fair salesforce box prompt-caching model-performance vision fine-tuning multilinguality ai-safety design-automation document-processing ai-agents ai-integration ai-job-market ai-acceleration humor demis-hassabis francois-chollet
Anthropic rolled out prompt caching in its API, reducing input costs by up to 90% and latency by 80%, enabling instant fine-tuning with longer prompts. xAI released Grok-2, a new model competing with frontier models from Google DeepMind, OpenAI, Anthropic, Mistral AI, and Meta AI Fair, supporting vision and text inputs and integrating external image generation models. Claude 3.5 Sonnet is reported to outperform GPT-4 in coding and reasoning, while ChatGPT-4o-latest shows reasoning improvements. François Chollet proposed a theory defining intelligence as the efficiency of operationalizing past information for future tasks. The Aya project involves 3000 collaborators building multilingual AI datasets. Demis Hassabis discussed AI hype and safe AI development in a podcast. Tools like Dora AI for Figma and Box's AI API enhance design automation and document processing. Salesforce released DEI, an open AI software engineering agents framework with a 55% resolve rate on SWE-Bench Lite. Industry trends highlight rapid AI integration, networking importance in the AI job market, and potential OpenAI GPT-4 expansion in response to competitors. Memes include humor about Apple Vision Pro.
Francois Chollet launches $1m ARC Prize
gpt-4 chatgpt openai apple togethercompute benchmarking agi pattern-recognition skill-acquisition privacy on-device-ai mixed-precision-quantization mixture-of-experts multimodality agentic-ai francois-chollet karpathy svpino philschmid clementdelangue sama gdb miramurati kevin-weil sarah-friar
François Chollet critiques current paths to AGI, emphasizing the importance of benchmarks that resist saturation and focus on skill acquisition and open-ended problem solving. The ARC-AGI puzzles exemplify "easy for humans, hard for AI" challenges to measure progress toward AGI. Meanwhile, Apple announces integration of ChatGPT into iOS, iPadOS, and macOS through a partnership with OpenAI, enabling AI-powered features like document summarization and photo analysis with privacy-preserving measures. Discussions highlight Apple's focus on deep AI integration and on-device models optimized with techniques like mixed-precision quantization, though some skepticism remains about their AI capabilities compared to GPT-4. Additionally, Together Compute introduces a Mixture of Agents approach achieving strong performance on AlpacaEval 2.0.
Somebody give Andrej some H100s already
gpt-2 openai fineweb meta-ai-fair nvidia tesla cuda fine-tuning training-time gpu-acceleration convolutional-neural-networks real-time-processing ai-safety ai-regulation andrej-karpathy yann-lecun elon-musk francois-chollet svpino mervenoyann
OpenAI's GPT-2 sparked controversy five years ago for being "too dangerous to release." Now, with FineWeb and llm.c, a tiny GPT-2 model can be trained in 90 minutes for $20 using 8xA100 GPUs, with the full 1.6B model estimated to take 1 week and $2.5k. The project is notable for its heavy use of CUDA (75.8%) aiming to simplify the training stack. Meanwhile, a Twitter debate between Yann LeCun and Elon Musk highlighted the importance of convolutional neural networks (CNNs) in real-time image processing for autonomous driving, with LeCun emphasizing scientific research's role in technological progress. LeCun also criticized AI doomsday scenarios, arguing for cautious optimism about AI safety and regulation.
MM1: Apple's first Large Multimodal Model
mm1 gemini-1 command-r claude-3-opus claude-3-sonnet claude-3-haiku claude-3 apple cohere anthropic hugging-face langchain multimodality vqa fine-tuning retrieval-augmented-generation open-source robotics model-training react reranking financial-agents yann-lecun francois-chollet
Apple announced the MM1 multimodal LLM family with up to 30B parameters, claiming performance comparable to Gemini-1 and beating larger older models on VQA benchmarks. The paper targets researchers and hints at applications in embodied agents and business/education. Yann LeCun emphasized that human-level AI requires understanding the physical world, memory, reasoning, and hierarchical planning, while Fran ois Chollet cautioned that NLP is far from solved despite LLM advances. Cohere released Command-R, a model for Retrieval Augmented Generation, and Anthropic highlighted the Claude 3 family (Opus, Sonnet, Haiku) for various application needs. Open-source hardware DexCap enables dexterous robot manipulation data collection affordably. Tools like CopilotKit simplify AI integration into React apps, and migration to Keras 3 with JAX backend offers faster training. New projects improve reranking for retrieval and add financial agents to LangChain. The content includes insights on AI progress, new models, open-source tools, and frameworks.
DeepMind SIMA: one AI, 9 games, 600 tasks, vision+language ONLY
llama-3 claude-3-opus claude-3 gpt-3.5-turbo deepmind cognition-labs deepgram modal-labs meta-ai-fair anthropic multimodality transformer software-engineering ai-agents ai-infrastructure training text-to-speech speech-to-text real-time-processing model-architecture benchmarking andrej-karpathy arav-srinivas francois-chollet yann-lecun soumith-chintala john-carmack
DeepMind SIMA is a generalist AI agent for 3D virtual environments evaluated on 600 tasks across 9 games using only screengrabs and natural language instructions, achieving 34% success compared to humans' 60%. The model uses a multimodal Transformer architecture. Andrej Karpathy outlines AI autonomy progression in software engineering, while Arav Srinivas praises Cognition Labs' AI agent demo. François Chollet expresses skepticism about automating software engineering fully. Yann LeCun suggests moving away from generative models and reinforcement learning towards human-level AI. Meta's Llama-3 training infrastructure with 24k H100 Cluster Pods is shared by Soumith Chintala and Yann LeCun. Deepgram's Aura offers low-latency speech APIs, and Modal Labs' Devin AI demonstrates document navigation and interaction with ComfyUI. Memes and humor circulate in the AI community.
Fixing Gemma
gemma claude-3-opus claude-3 mistral-large gpt-4 google unsloth anthropic mistral-ai finetuning numerical-precision benchmarking structured-data-extraction adaptive-equalizer information-theory hallucination-detection model-stability daniel-han yann-lecun francois-chollet arav-srinivas _aidan_clark_
Google's Gemma model was found unstable for finetuning until Daniel Han from Unsloth AI fixed 8 bugs, improving its implementation. Yann LeCun explained technical details of a pseudo-random bit sequence for adaptive equalizers, while François Chollet discussed the low information bandwidth of the human visual system. Arav Srinivas reported that Claude 3 Opus showed no hallucinations in extensive testing, outperforming GPT-4 and Mistral-Large in benchmarks. Reflections from Yann LeCun highlight ongoing AI progress toward human-level intelligence. The community is shifting pipelines to work better with Claude models, and emotional experiences in ML development were shared by Aidan Clark.
... and welcome AI Twitter!
mistral-large google-gemini google openai apple stripe ai-ethics multilinguality on-device-ai convolutional-neural-networks synthetic-data financial-transaction-systems corporate-culture humor margaret-mitchell john-carmack guillaume-lample sundar-pichai delip-rao santiago-l-valdarrama alex-wang yann-lecun pieter-levels francois-chollet dheliat
The AI Twitter discourse from 2/27-28/2024 covers a broad spectrum including ethical considerations highlighted by Margaret Mitchell around Google Gemini's launch, and John Carmack's insights on evolving coding skills in the AI era. Guillaume Lample announced the release of the Mistral Large multilingual model. Discussions also touched on potential leadership changes at Google involving Sundar Pichai, and OpenAI's possible entry into the synthetic data market as noted by Delip Rao. Technological advancements include Yann LeCun's commentary on running LLMs on mobile devices and Alex Wang's praise for the Apple Vision Pro. Financial platform issues were raised by Pieter Levels regarding Stripe's payment policies. The cultural dynamics within big tech were discussed by François Chollet and Dhéliat. The lighter side of AI was represented by memes and humor from Pieter Levels and AISafetyMemes. This summary reflects the fast-evolving AI landscape blending technical innovation, corporate strategy, ethics, and community culture.