All tags
Company: "microsoft"
not much happened today
gemini-2.5-flash gemini-2.0-flash mistral-medium-3 llama-4-maverick claude-3.7-sonnet qwen3 pangu-ultra-moe deepseek-r1 o4-mini x-reasoner google-deepmind mistral-ai alibaba huawei openai microsoft deepseek model-performance reasoning cost-analysis reinforcement-learning chain-of-thought multilinguality code-search model-training vision model-integration giffmana artificialanlys teortaxestex akhaliq john__allard
Gemini 2.5 Flash shows a 12 point increase in the Artificial Analysis Intelligence Index but costs 150x more than Gemini 2.0 Flash due to 9x more expensive output tokens and 17x higher token usage during reasoning. Mistral Medium 3 competes with Llama 4 Maverick, Gemini 2.0 Flash, and Claude 3.7 Sonnet with better coding and math reasoning at a significantly lower price. Alibaba's Qwen3 family supports reasoning and multilingual tasks across 119 languages and includes a Web Dev tool for app building. Huawei's Pangu Ultra MoE matches DeepSeek R1 performance on Ascend NPUs, with new compute and upcoming V4 training. OpenAI's o4-mini now supports Reinforcement Fine-Tuning (RFT) using chain-of-thought reasoning. Microsoft's X-REASONER enables generalizable reasoning across modalities post-trained on general-domain text. Deep research integration with GitHub repos in ChatGPT enhances codebase search and reporting. The AI Engineer World's Fair offers an Early Bird discount for upcoming tickets.
Cursor @ $9b, OpenAI Buys Windsurf @ $3b
llama-nemotron-ultra llama-nemotron-super llama-nemotron-nano qwen3-235b-a22b prover-v2 phi-4-reasoning ernie-4.5-turbo ernie-x1-turbo suno-v4.5 gen-4-references o1-mini openai cursor nvidia alibaba deepseek microsoft baidu suno runway keras reasoning inference-efficiency open-license moe-models math-reasoning theorem-proving model-performance music-generation image-generation recommender-systems tpu-optimization _akhaliq adcock_brett lmarena_ai fchollet
OpenAI is reportedly close to closing a deal with Windsurf, coinciding with Cursor's $900M funding round at a $9B valuation. Nvidia launched the Llama-Nemotron series featuring models from 8B to 253B parameters, praised for reasoning and inference efficiency. Alibaba released the Qwen3 family with MoE and dense models up to 235B parameters, ranking highly in coding and math benchmarks. DeepSeek introduced Prover-V2, an open-source AI for math reasoning with an 88.9% pass rate on MiniF2F-test. Microsoft released reasoning-focused Phi-4 models, outperforming OpenAI's o1-mini. Baidu debuted turbo versions of ERNIE 4.5 and X1 for faster, cheaper inference. Suno v4.5 added advanced AI music generation features, while Runway Gen-4 References enable placing characters into scenes with high consistency. KerasRS, a new recommender system library optimized for TPUs, was released by Fran ois Chollet.
not much happened today
qwen3-14b qwen3-32b qwen3-235b phi-4-reasoning o3-mini command-a gemini-2.5-pro o4-mini olm-o2-1b o3 alibaba together-ai scaling01 microsoft deepseek cohere google epoch-ai-research inception-labs openai allenai quantization fine-tuning reinforcement-learning benchmarking video-generation diffusion-models model-performance model-evaluation model-release text-generation cline _philschmid iscienceluvr alexalbert__ _lewtun teortaxestex sarahookr reach_vb
Qwen model family released quantized versions of Qwen3 models including 14B, 32B, and 235B parameters, with promising coding capabilities in Qwen3-235B. Microsoft launched Phi-4-reasoning, a 14B parameter model distilled from OpenAI's o3-mini, emphasizing supervised fine-tuning and reinforcement learning, outperforming larger models in some benchmarks. Cohere's Command A leads SQL performance on Bird Bench. Google introduced the TRAJAN eval for video generation temporal consistency and updated the Gemini OpenAI compatibility layer. Inception Labs launched a diffusion LLM API claiming 5x speed improvements over autoregressive models. Community rankings show OpenAI's o3 model debuting strongly in web app-building tasks. Other releases include AllenAI's OLMo2 1B and additional Phi 4 variants. "Qwen3-235B shows promise for coding" and "Phi-4-reasoning tech report emphasizes SFT gains" highlight key advancements.
not much happened today
phi-4 phi-4-mini-reasoning qwen3-235b qwen3-moe-235b qwen3-moe-30b qwen3-dense-32b qwen3-dense-14b qwen3-dense-8b qwen3-dense-4b qwen3-dense-0.6b qwen2.5-omni-3b deepseek-prover-v2 llama llama-guard-4 prompt-guard-2 mimo-7b microsoft anthropic cursor alibaba togethercompute deepseek meta-ai-fair xiaomi openrouterai cohere reasoning model-fine-tuning model-evaluation benchmarking model-popularity open-source math model-scaling model-filtering jailbreak-prevention cline reach_vb vipulved akhaliq omarsar0 zhs05232838 huajian_xin mervenoyann karpathy random_walker sarahookr blancheminerva clefourrier
Microsoft released Phi-reasoning 4, a finetuned 14B reasoning model slightly behind QwQ but limited by data transparency and token efficiency issues. Anthropic introduced remote MCP server support and a 45-minute Research mode in Claude. Cursor published a model popularity list. Alibaba launched Qwen3-235B and other Qwen3 variants, highlighting budget-friendly coding and reasoning capabilities, with availability on Together AI API. Microsoft also released Phi-4-Mini-Reasoning with benchmark performance on AIME 2025 and OmniMath. DeepSeek announced DeepSeek-Prover V2 with state-of-the-art math problem solving, scaling to 671B parameters. Meta AI's Llama models hit 1.2 billion downloads, with new Llama Guard 4 and Prompt Guard 2 for input/output filtering and jailbreak prevention. Xiaomi released the open-source reasoning model MiMo-7B trained on 25 trillion tokens. Discussions on AI model evaluation highlighted issues with the LMArena leaderboard, data access biases favoring proprietary models, and challenges in maintaining fair benchmarking, with suggestions for alternatives like OpenRouterAI rankings. "LMArena slop and biased" and "61.3% of all data going to proprietary model providers" were noted concerns.
Every 7 Months: The Moore's Law for Agent Autonomy
claude-3-7-sonnet llama-4 phi-4-multimodal gpt-2 cosmos-transfer1 gr00t-n1-2b orpheus-3b metr nvidia hugging-face canopy-labs meta-ai-fair microsoft agent-autonomy task-completion multimodality text-to-speech robotics foundation-models model-release scaling-laws fine-tuning zero-shot-learning latency reach_vb akhaliq drjimfan scaling01
METR published a paper measuring AI agent autonomy progress, showing it has doubled every 7 months since 2019 (GPT-2). They introduced a new metric, the 50%-task-completion time horizon, where models like Claude 3.7 Sonnet achieve 50% success in about 50 minutes. Projections estimate 1 day autonomy by 2028 and 1 month autonomy by late 2029. Meanwhile, Nvidia released Cosmos-Transfer1 for conditional world generation and GR00T-N1-2B, an open foundation model for humanoid robot reasoning with 2B parameters. Canopy Labs introduced Orpheus 3B, a high-quality text-to-speech model with zero-shot voice cloning and low latency. Meta reportedly delayed Llama-4 release due to performance issues. Microsoft launched Phi-4-multimodal.
not much happened today
aya-vision-8b aya-vision-32b llama-3-2-90b-vision molmo-72b phi-4-mini phi-4-multimodal cogview4 wan-2-1 weights-and-biases coreweave cohereforai microsoft alibaba google llamaindex weaviate multilinguality vision multimodality image-generation video-generation model-releases benchmarking funding agentic-ai model-performance mervenoyann reach_vb jayalammar sarahookr aidangomez nickfrosst dair_ai akhaliq bobvanluijt jerryjliu0
Weights and Biases announced a $1.7 billion acquisition by CoreWeave ahead of CoreWeave's IPO. CohereForAI released the Aya Vision models (8B and 32B parameters) supporting 23 languages, outperforming larger models like Llama-3.2 90B Vision and Molmo 72B. Microsoft introduced Phi-4-Mini (3.8B parameters) and Phi-4-Multimodal models, excelling in math, coding, and multimodal benchmarks. CogView4, a 6B parameter text-to-image model with 2048x2048 resolution and Apache 2.0 license, was released. Alibaba launched Wan 2.1, an open-source video generation model with 720p output and 16 fps generation. Google announced new AI features for Pixel devices including Scam Detection and Gemini integrations. LlamaCloud reached General Availability and raised $19M Series A funding, serving over 100 Fortune 500 companies. Weaviate launched the Query Agent, the first of three Weaviate Agents.
GPT 4.5 — Chonky Orion ships!
gpt-4.5 phi-4-multimodal phi-4-mini command-r7b-arabic openai microsoft cohere creative-writing natural-language-processing multimodality math coding context-windows model-releases open-source arabic-language sama kevinweil aidan_mclau omarsar0 rasbt reach_vb
OpenAI released GPT-4.5 as a research preview, highlighting its deep world knowledge, improved understanding of user intent, and a 128,000 token context window. It is noted for excelling in writing, creative tasks, image understanding, and data extraction but is not a reasoning model. Microsoft unveiled Phi-4 Multimodal and Phi-4 Mini, open-source models integrating text, vision, and speech/audio, with strong performance in math and coding tasks. Cohere released Command R7B Arabic, an open-weights model optimized for Arabic language capabilities targeting enterprises in the MENA region. The community is exploring the impact of larger models on creative writing, intent understanding, and world knowledge, with GPT-4.5 expected to be a basis for GPT-5.
The Ultra-Scale Playbook: Training LLMs on GPU Clusters
deepseek-native-sparse-attention r1-1776 paligemma-2-mix muse baichuan-m1-14b stripedhyena-2 huggingface deepseek perplexity-ai google-deepmind microsoft baichuan stripedhyena gpu-training scaling multimodality vision model-training foundation-models medical-llm genome-modeling robotic-manipulation interactive-content eliebakouch nouamanetazi lvwerra thom-wolf proftomyeh alex-wang aravsrinivas _akhaliq _philschmid mervenoyann reach_vb arankomatsuzaki maximelabonne
Huggingface released "The Ultra-Scale Playbook: Training LLMs on GPU Clusters," an interactive blogpost based on 4000 scaling experiments on up to 512 GPUs, providing detailed insights into modern GPU training strategies. DeepSeek introduced the Native Sparse Attention (NSA) model, gaining significant community attention, while Perplexity AI launched R1-1776, an uncensored and unbiased version of DeepSeek's R1 model. Google DeepMind unveiled PaliGemma 2 Mix, a multi-task vision-language model available in 3B, 10B, and 28B sizes. Microsoft introduced Muse, a generative AI model trained on the game Bleeding Edge, and presented Magma, a foundation model for multimodal AI agents excelling in UI navigation and robotic manipulation. Baichuan-M1-14B was announced as a state-of-the-art medical LLM trained on 20T tokens, and a fully open-source 40B genome modeling model using StripedHyena 2 architecture was also released. "Making your own gaming experience is coming sooner than you'd think," noted in relation to Muse.
Project Stargate: $500b datacenter (1.7% of US GDP) and Gemini 2 Flash Thinking 2
gemini-2.0-flash deepseek-r1 qwen-32b openai softbank oracle arm microsoft nvidia huggingface deepseek-ai long-context quantization code-interpretation model-distillation open-source agi-research model-performance memory-optimization noam-shazeer liang-wenfeng
Project Stargate, a US "AI Manhattan project" led by OpenAI and Softbank, supported by Oracle, Arm, Microsoft, and NVIDIA, was announced with a scale comparable to the original Manhattan project costing $35B inflation adjusted. Despite Microsoft's reduced role as exclusive compute partner, the project is serious but not immediately practical. Meanwhile, Noam Shazeer revealed a second major update to Gemini 2.0 Flash Thinking, enabling 1M token long context usable immediately. Additionally, AI Studio introduced a new code interpreter feature. On Reddit, DeepSeek R1, a distillation of Qwen 32B, was released for free on HuggingChat, sparking discussions on self-hosting, performance issues, and quantization techniques. DeepSeek's CEO Liang Wenfeng highlighted their focus on fundamental AGI research, efficient MLA architecture, and commitment to open-source development despite export restrictions, positioning DeepSeek as a potential alternative to closed-source AI trends.
not much happened today
rstar-math o1-preview qwen2.5-plus qwen2.5-coder-32b-instruct phi-4 claude-3.5-sonnet openai anthropic alibaba microsoft cohere langchain weights-biases deepseek rakuten rbc amd johns-hopkins math process-reward-model mcts vision reasoning synthetic-data pretraining rag automation private-deployment multi-step-workflow open-source-dataset text-embeddings image-segmentation chain-of-thought multimodal-reasoning finetuning recursive-self-improvement collaborative-platforms ai-development partnerships cuda triton ai-efficiency ai-assisted-coding reach_vb rasbt akshaykagrawal arankomatsuzaki teortaxestex aidangomez andrewyng
rStar-Math surpasses OpenAI's o1-preview in math reasoning with 90.0% accuracy using a 7B LLM and MCTS with a Process Reward Model. Alibaba launches Qwen Chat featuring Qwen2.5-Plus and Qwen2.5-Coder-32B-Instruct models enhancing vision-language and reasoning. Microsoft releases Phi-4, trained on 40% synthetic data with improved pretraining. Cohere introduces North, a secure AI workspace integrating LLMs, RAG, and automation for private deployments. LangChain showcases a company research agent with multi-step workflows and open-source datasets. Transformers.js demos released for text embeddings and image segmentation in JavaScript. Research highlights include Meta Meta-CoT for enhanced chain-of-thought reasoning, DeepSeek V3 with recursive self-improvement, and collaborative AI development platforms. Industry partnerships include Rakuten with LangChain, North with RBC supporting 90,000 employees, and Agent Laboratory collaborating with AMD and Johns Hopkins. Technical discussions emphasize CUDA and Triton for AI efficiency and evolving AI-assisted coding stacks by Andrew Ng.
Meta BLT: Tokenizer-free, Byte-level LLM
byte-latent-transformer llama-3 phi-4 gpt-4o command-r7b meta-ai-fair llamaindex microsoft deepseek-ai openai cohere anthropic tokenization transformer-architecture model-efficiency benchmarking multimodality vision reinforcement-learning model-scaling jailbreaking model-optimization
Meta AI introduces the Byte Latent Transformer (BLT), a tokenizer-free architecture that dynamically forms byte patches for efficient compute allocation, outperforming Llama 3 on benchmarks including the CUTE benchmark. The model was trained on approximately 1 trillion tokens and features a three-block transformer design with local and global components. This approach challenges traditional tokenization and may enable new multimodal capabilities such as direct file interaction without retrieval-augmented generation. Additionally, Microsoft announced the Phi-4 14B parameter model achieving state-of-the-art results on STEM and reasoning benchmarks, surpassing GPT-4o. DeepSeek AI launched new vision-language models based on their MoE architecture with sizes ranging from 1.0B to 27B parameters. OpenAI released a new Projects feature for ChatGPT, and Cohere introduced their smallest and fastest Command R7B model. Anthropic published research on "Best-of-N Jailbreaking" vulnerabilities across text, vision, and audio models. Industry discussion highlights a trend of decreasing frontier LLM sizes, with GPT-4 at approximately 1.8 trillion parameters compared to newer models.
FrontierMath: A Benchmark for Evaluating Advanced Mathematical Reasoning in AI
o1 claude-3.5-haiku gpt-4o epoch-ai openai microsoft anthropic x-ai langchainai benchmarking math moravecs-paradox mixture-of-experts chain-of-thought agent-framework financial-metrics-api pdf-processing few-shot-learning code-generation karpathy philschmid adcock_brett dylan522p
Epoch AI collaborated with over 60 leading mathematicians to create the FrontierMath benchmark, a fresh set of hundreds of original math problems with easy-to-verify answers, aiming to challenge current AI models. The benchmark reveals that all tested models, including o1, perform poorly, highlighting the difficulty of complex problem-solving and Moravec's paradox in AI. Key AI developments include the introduction of Mixture-of-Transformers (MoT), a sparse multi-modal transformer architecture reducing computational costs, and improvements in Chain-of-Thought (CoT) prompting through incorrect reasoning and explanations. Industry news covers OpenAI acquiring the chat.com domain, Microsoft launching the Magentic-One agent framework, Anthropic releasing Claude 3.5 Haiku outperforming gpt-4o on some benchmarks, and xAI securing 150MW grid power with support from Elon Musk and Trump. LangChain AI introduced new tools including a Financial Metrics API, Document GPT with PDF upload and Q&A, and LangPost AI agent for LinkedIn posts. xAI also demonstrated the Grok Engineer compatible with OpenAI and Anthropic APIs for code generation.
not much happened today
claude-3.5-sonnet opencoder anthropic microsoft sambanova openai langchain llamaindex multi-agent-systems natural-language-interfaces batch-processing harmful-content-detection secret-management retrieval-augmented-generation error-analysis memory-management web-scraping autonomous-agents sophiamyang tom_doerr omarsar0 _akhaliq andrewyng giffmana
This week in AI news, Anthropic launched Claude Sonnet 3.5, enabling desktop app control via natural language. Microsoft introduced Magentic-One, a multi-agent system built on the AutoGen framework. OpenCoder was unveiled as an AI-powered code cookbook for large language models. SambaNova is sponsoring a hackathon with prizes up to $5000 for building real-time AI agents. Sophiamyang announced new Batch and Moderation APIs with 50% lower cost and multi-dimensional harmful text detection. Open-source tools like Infisical for secret management, CrewAI for autonomous agent orchestration, and Crawlee for web scraping were released. Research highlights include SCIPE for error analysis in LLM chains, Context Refinement Agent for improved retrieval-augmented generation, and MemGPT for managing LLM memory. The week also saw a legal win for OpenAI in the RawStory copyright case, affirming that facts used in LLM training are not copyrightable.
OpenAI beats Anthropic to releasing Speculative Decoding
claude-3-sonnet mrt5 openai anthropic nvidia microsoft boston-dynamics meta-ai-fair runway elevenlabs etched osmo physical-intelligence langchain speculative-decoding prompt-lookup cpu-inference multimodality retrieval-augmented-generation neural-networks optimization ai-safety governance model-architecture inference-economics content-generation adcock_brett vikhyatk dair_ai rasbt bindureddy teortaxestex svpino c_valenzuelab davidsholz
Prompt lookup and Speculative Decoding techniques are gaining traction with implementations from Cursor, Fireworks, and teased features from Anthropic. OpenAI has introduced faster response times and file edits with these methods, offering about 50% efficiency improvements. The community is actively exploring AI engineering use cases with these advancements. Recent updates highlight progress from companies like NVIDIA, OpenAI, Anthropic, Microsoft, Boston Dynamics, and Meta. Key technical insights include CPU inference capabilities, multimodal retrieval-augmented generation (RAG), and neural network fundamentals. New AI products include fully AI-generated games and advanced content generation tools. Challenges in AI research labs such as bureaucracy and resource allocation were also discussed, alongside AI safety and governance concerns.
not much happened this weekend
claude-3.5-sonnet llama-3 llama-3-8b notebookllama min-omni-2 moondream openai anthropic hugging-face mistral-ai google-deepmind langchain deepmind microsoft pattern-recognition reinforcement-learning prompt-optimization text-to-speech model-optimization tensor-parallelism hyperparameters multimodal modal-alignment multimodal-fine-tuning ai-productivity privacy generative-ai rag retrieval-augmentation enterprise-text-to-sql amanda-askell philschmid stasbekman francois-fleuret mervenoyann reach_vb dzhng aravsrinivas sama lateinteraction andrew-y-ng bindureddy jerryjliu0
Moondream, a 1.6b vision language model, secured seed funding, highlighting a trend in moon-themed tiny models alongside Moonshine (27-61m ASR model). Claude 3.5 Sonnet was used for AI Twitter recaps. Discussions included pattern recognition vs. intelligence in LLMs, reinforcement learning for prompt optimization, and NotebookLlama, an open-source NotebookLM variant using LLaMA models for tasks like text-to-speech. Advances in model optimization with async-TP in PyTorch for tensor parallelism and hyperparameter tuning were noted. Mini-Omni 2 demonstrated multimodal capabilities across image, audio, and text for voice conversations with emphasis on modal alignment and multimodal fine-tuning. AI productivity tools like an AI email writer and LlamaCloud-based research assistants were introduced. Emphasis on practical skill development and privacy-conscious AI tool usage with Llama3-8B was highlighted. Generative AI tools such as #AIPythonforBeginners and GenAI Agents with LangGraph were shared. Business insights covered rapid execution in AI product development and emerging AI-related job roles. Challenges in enterprise-grade text-to-SQL and advanced retrieval methods were discussed with tutorials on RAG applications using LangChain and MongoDB.
not much happened today
claude-3.5-sonnet claude-3.5-haiku o1-preview mochi-1 stable-diffusion-3.5 embed-3 kerashub differential-transformer anthropic openai cohere microsoft computer-use coding-performance video-generation fine-tuning multimodality transformers attention-mechanisms model-optimization alexalbert fchollet rasbt
Anthropic released upgraded Claude 3.5 Sonnet and Claude 3.5 Haiku models featuring a new computer use capability that allows interaction with computer interfaces via screenshots and actions like mouse movement and typing. The Claude 3.5 Sonnet achieved state-of-the-art coding performance on SWE-bench Verified with a 49% score, surpassing OpenAI's o1-preview. Anthropic focuses on teaching general computer skills rather than task-specific tools, with expected rapid improvements. Other releases include Mochi 1, an open-source video generation model, Stable Diffusion 3.5 with Large and Medium variants, and Embed 3 by Cohere, a multimodal embedding model for text and image search. KerasHub was launched by François Chollet, unifying KerasNLP and KerasCV with 37 pretrained models. Microsoft introduced the Differential Transformer to reduce attention noise via differential attention maps, and research on transformer attention layers was shared by Rasbt.
DocETL: Agentic Query Rewriting and Evaluation for Complex Document Processing
bitnet-b1.58 llama-3.1-nemotron-70b-instruct gpt-4o claude-3.5-sonnet uc-berkeley deepmind openai microsoft nvidia archetype-ai boston-dynamics toyota-research google adobe openai mistral tesla meta-ai-fair model-optimization on-device-ai fine-tuning large-corpus-processing gpu-acceleration frameworks model-benchmarking rohanpaul_ai adcock_brett david-patterson
UC Berkeley's EPIC lab introduces innovative LLM data operators with projects like LOTUS and DocETL, focusing on effective programming and computation over large data corpora. This approach contrasts GPU-rich big labs like Deepmind and OpenAI with GPU-poor compound AI systems. Microsoft open-sourced BitNet b1.58, a 1-bit ternary parameter LLM enabling 4-20x faster training and on-device inference at human reading speeds. Nvidia released Llama-3.1-Nemotron-70B-Instruct, a fine-tuned open-source model outperforming GPT-4o and Claude-3.5-sonnet. These developments highlight advances in model-optimization, on-device-ai, and fine-tuning.
Not much (in AI) happened this weekend
llama-3.1-8b llama-3.2 chatgpt movie-gen openai meta-ai-fair google-deepmind microsoft x-ai spacex harvard nvidia long-context feature-prediction-loss ai-agents privacy text-to-video text-to-image humanoid-robots gpu-deployment media-foundation-models ai-research-labs sam-altman yann-lecun rasbt bindureddy andrej-karpathy soumithchintala svpino adcock_brett rohanpaul_ai
OpenAI introduced an "edit this area" feature for image generation, praised by Sam Altman. Yann LeCun highlighted a NYU paper improving pixel generation with feature prediction loss using pre-trained visual encoders like DINOv2. Long-context LLMs such as llama-3.1-8b and llama-3.2 variants now support up to 131k tokens, offering alternatives to RAG systems. Bindu Reddy announced AI agents capable of building and deploying code from English instructions, signaling AI's replacement of SQL and potential impact on Python. SpaceX's successful Starship rocket catch was celebrated by Andrej Karpathy and others, with Soumith Chintala praising SpaceX's efficient, low-bureaucracy research approach. Privacy concerns arose from Harvard students' AI glasses, I-XRAY, which can reveal personal information. Meta AI FAIR's Movie Gen model advances media foundation models with high-quality text-to-image and video generation, including synced audio. Humanoid robots like Ameca and Azi now engage in expressive conversations using ChatGPT. xAI rapidly deployed 100K Nvidia H100 GPUs in 19 days, with CEO Jensen Huang commending Elon Musk. Leading AI research labs compared include Meta-FAIR, Google DeepMind, and Microsoft Research. Skepticism about LLM intelligence was voiced by Sam Pino, emphasizing limitations in novel problem-solving despite strong memorization.
a calm before the storm
o1 o1-mini qwen2.5 gpt-4 llama-2-70b llama-7b anthropic openai alibaba microsoft blackrock groq aramco disney eth-zurich pudu-robotics slack long-context kv-cache-quantization diffusion-models reinforcement-learning robotics ai-integration multilinguality model-benchmarking model-performance model-optimization adcock_brett philschmid rohanpaul_ai jvnixon kateclarktweets sama
Anthropic is raising funds at a valuation up to $40 billion ahead of anticipated major releases. OpenAI launched new reasoning models o1 and o1-mini, with increased rate limits and a multilingual MMLU benchmark. Alibaba released the open-source Qwen2.5 model supporting 29+ languages, showing competitive performance to gpt-4 at lower cost. Microsoft and Blackrock plan to invest $30 billion in AI data centers, with Groq partnering with Aramco to build the world's largest AI inference center. Robotics advances include Disney Research and ETH Zurich's diffusion-based motion generation for robots and Pudu Robotics' semi-humanoid robot. Slack and Microsoft introduced AI-powered agents integrated into their platforms. Research highlights include long-context scaling for llama-2-70b using Dual Chunk Attention and KV cache quantization enabling 1 million token context on llama-7b models.
not much happened today
o1-preview o1-mini qwen-2.5 gpt-4o deepseek-v2.5 gpt-4-turbo-2024-04-09 grin llama-3-1-405b veo kat openai qwen deepseek-ai microsoft kyutai-labs perplexity-ai together-ai meta-ai-fair google-deepmind hugging-face google anthropic benchmarking math coding instruction-following model-merging model-expressiveness moe voice voice-models generative-video competition open-source model-deployment ai-agents hyung-won-chung noam-brown bindureddy akhaliq karpathy aravsrinivas fchollet cwolferesearch philschmid labenz ylecun
OpenAI's o1-preview and o1-mini models lead benchmarks in Math, Hard Prompts, and Coding. Qwen 2.5 72B model shows strong performance close to GPT-4o. DeepSeek-V2.5 tops Chinese LLMs, rivaling GPT-4-Turbo-2024-04-09. Microsoft's GRIN MoE achieves good results with 6.6B active parameters. Moshi voice model from Kyutai Labs runs locally on Apple Silicon Macs. Perplexity app introduces voice mode with push-to-talk. LlamaCoder by Together.ai uses Llama 3.1 405B for app generation. Google DeepMind's Veo is a new generative video model for YouTube Shorts. The 2024 ARC-AGI competition increases prize money and plans a university tour. A survey on model merging covers 50+ papers for LLM alignment. The Kolmogorov–Arnold Transformer (KAT) paper proposes replacing MLP layers with KAN layers for better expressiveness. Hugging Face Hub integrates with Google Cloud Vertex AI Model Garden for easier open-source model deployment. Agent.ai is introduced as a professional network for AI agents. "Touching grass is all you need."
a quiet weekend
o1 datagemma aloha demostart firefly-ai-video-model pixtral-12b gamegen-o openai google-deepmind adobe mistral-ai tencent supermaven 11x cohere anthropic latent-space-university stanford microsoft mila notre-dame reinforcement-learning chain-of-thought reasoning robotics diffusion-models multimodality video-generation model-training reflection-tuning mathematical-reasoning model-benchmarking fine-tuning george-hotz terence-tao adcock_brett rohanpaul_ai bindureddy fchollet philschmid
OpenAI released the new o1 model, leveraging reinforcement learning and chain-of-thought prompting to excel in reasoning benchmarks, achieving an IQ-like score of 120. Google DeepMind introduced DataGemma to reduce hallucinations by connecting LLMs with real-world data, and unveiled ALOHA and DemoStart for robot dexterity using diffusion methods. Adobe previewed its Firefly AI Video Model with text-to-video and generative extend features. Mistral launched the multimodal Pixtral 12B model, and Tencent presented the GameGen-O open-world video game generation model. Several research papers from Stanford, OpenAI, Microsoft, Mila, and Notre Dame focus on advanced reasoning, self-verification, and reflection tuning techniques. Experts like Terence Tao and George Hotz have shared mixed but optimistic views on o1's capabilities. Seed funding rounds include Supermaven ($12M) and 11x ($24M).
Ideogram 2 + Berkeley Function Calling Leaderboard V2
llama-3-70b gpt-4 phi-3.5 functionary-llama-3-70b llama-3 ideogram midjourney berkeley openai hugging-face microsoft meta-ai-fair baseten kai claude functionary function-calling benchmarking image-generation model-optimization vision multimodality model-performance fine-tuning context-windows cybersecurity code-analysis ai-assisted-development
Ideogram returns with a new image generation model featuring color palette control, a fully controllable API, and an iOS app, reaching a milestone of 1 billion images created. Meanwhile, Midjourney released a Web UI but still lacks an API. In function calling, the Berkeley Function Calling Leaderboard (BFCL) updated to BFCL V2 • Live, adding 2251 live, user-contributed function documentation and queries to improve evaluation quality. GPT-4 leads the leaderboard, but the open-source Functionary Llama 3-70B finetune from Kai surpasses Claude. On AI model releases, Microsoft launched three Phi-3.5 models with impressive reasoning and context window capabilities, while Meta AI FAIR introduced UniBench, a unified benchmark suite for over 50 vision-language model tasks. Baseten improved Llama 3 inference speed by up to 122% using Medusa. A new cybersecurity benchmark, Cyberbench, featuring 40 CTF tasks, was released. Additionally, Codegen was introduced as a tool for programmatic codebase analysis and AI-assisted development. "Multiple functions > parallel functions" was highlighted as a key insight in function calling.
not much happened today
gpt-4o claude-3.5-sonnet phi-3.5-mini phi-3.5-moe phi-3.5-vision llama-3-1-405b qwen2-math-72b openai anthropic microsoft meta-ai-fair hugging-face langchain box fine-tuning benchmarking model-comparison model-performance diffusion-models reinforcement-learning zero-shot-learning math model-efficiency ai-regulation ai-safety ai-engineering prompt-engineering swyx ylecun
OpenAI launched GPT-4o finetuning with a case study on Cosine. Anthropic released Claude 3.5 Sonnet with 8k token output. Microsoft Phi team introduced Phi-3.5 in three variants: Mini (3.8B), MoE (16x3.8B), and Vision (4.2B), noted for sample efficiency. Meta released Llama 3.1 405B, deployable on Google Cloud Vertex AI, offering GPT-4 level capabilities. Qwen2-Math-72B achieved state-of-the-art math benchmark performance with a Gradio demo. Discussions included model comparisons like ViT vs CNN and Mamba architecture. Tools updates featured DSPy roadmap, Flux Schnell improving diffusion speed on M1 Max, and LangChain community events. Research highlights zero-shot DUP prompting for math reasoning and fine-tuning best practices. AI ethics covered California's AI Safety Bill SB 1047 and regulatory concerns from Yann LeCun. Commentary on AI engineer roles by Swyx. "Chat with PDF" feature now available for Box Enterprise Plus users.
Execuhires: Tempting The Wrath of Khan
gemini-1.5-pro gpt-4o claude-3.5 flux-1 llama-3-1-405b character.ai google adept amazon inflection microsoft stability-ai black-forest-labs schelling google-deepmind openai anthropic meta-ai-fair lmsys langchainai execuhire model-benchmarking multilinguality math coding text-to-image agent-ide open-source-models post-training data-driven-performance noam-shazeer mostafa-mostaque david-friedman rob-rombach alexandr-wang svpino rohanpaul_ai
Character.ai's $2.5b execuhire to Google marks a significant leadership move alongside Adept's $429m execuhire to Amazon and Inflection's $650m execuhire to Microsoft. Despite strong user growth and content momentum, Character.ai's CEO Noam Shazeer returns to Google, signaling shifting vibes in the AI industry. Google DeepMind's Gemini 1.5 Pro tops Chatbot Arena benchmarks, outperforming GPT-4o and Claude-3.5, excelling in multilingual, math, and coding tasks. The launch of Black Forest Labs' FLUX.1 text-to-image model and LangGraph Studio agent IDE highlight ongoing innovation. Llama 3.1 405B is released as the largest open-source model, fostering developer use and competition with closed models. The industry is focusing increasingly on post-training and data as key competitive factors, raising questions about acquisition practices and regulatory scrutiny.
Nothing much happened today
chameleon-7b chameleon-30b xlam-1b gpt-3.5 phi-3-mini mistral-7b-v3 huggingface truth_terminal microsoft apple openai meta-ai-fair yi axolotl amd salesforce function-calling multimodality model-releases model-updates model-integration automaticity procedural-memory text-image-video-generation
HuggingFace released a browser-based timestamped Whisper using transformers.js. A Twitter bot by truth_terminal became the first "semiautonomous" bot to secure VC funding. Microsoft and Apple abruptly left the OpenAI board amid regulatory scrutiny. Meta is finalizing a major upgrade to Reddit comments addressing hallucination issues. The Yi model gained popularity on GitHub with 7.4K stars and 454 forks, with potential integration with Axolotl for pregeneration and preprocessing. AMD technologies enable household/small business AI appliances. Meta released Chameleon-7b and Chameleon-30b models on HuggingFace supporting unified text and image tokenization. Salesforce's xLAM-1b model outperforms GPT-3.5 in function calling despite its smaller size. Anole pioneered open-source multimodal text-image-video generation up to 720p 144fps. Phi-3 Mini expanded from 3.8B to 4.7B parameters with function calling, competing with Mistral-7b v3. "System 2 distillation" in humans relates to automaticity and procedural memory.
Not much happened today.
phi-3-mini gpt4all-3.0 yi-large meta-3d-gen meta perplexity-ai microsoft gpt4all langchainai qdrant-engine 3d-generation long-context instruction-following reinforcement-learning-from-human-feedback persona-driven-data-synthesis meta-tuning model-steering memory-retrieval multivector-search universal-query-api rohanpaul_ai andriy_mulyar cwolferesearch sarahookr
Meta introduced Meta 3D Gen, a system for end-to-end generation of 3D assets from text in under 1 minute, producing high-quality 3D assets with detailed textures. Perplexity AI updated Pro Search to handle deeper research with multi-step reasoning and code execution. Microsoft improved Phi-3 Mini with better long-context understanding and instruction following. GPT4All 3.0 launched with support for thousands of models and major OS compatibility, featuring local file chat. Yi-Large model launched on Fireworks AI Playground. Research highlights include the evolution of reinforcement learning from human feedback (RLHF), persona-driven data synthesis using a billion diverse personas, meta-tuning for few-shot generalization, and steering vectors for model behavior control. Tools updates include LangSmith improving memory retrieval and Qdrant Engine v1.10 adding universal query API and multivector search.
Talaria: Apple's new MLOps Superweapon
gemma mixtral phi dbrx apple google mistral-ai microsoft mosaic quantization on-device-ai adapter-models model-optimization model-latency lossless-quantization low-bit-palletization token-generation model-benchmarking human-evaluation craig-federighi andrej-karpathy
Apple Intelligence introduces a small (~3B parameters) on-device model and a larger server model running on Apple Silicon with Private Cloud Compute, aiming to surpass Google Gemma, Mistral Mixtral, Microsoft Phi, and Mosaic DBRX. The on-device model features a novel lossless quantization strategy using mixed 2-bit and 4-bit LoRA adapters averaging 3.5 bits-per-weight, enabling dynamic adapter hot-swapping and efficient memory management. Apple credits the Talaria tool for optimizing quantization and model latency, achieving about 0.6 ms time-to-first-token latency and 30 tokens per second generation rate on iPhone 15 Pro. Apple focuses on an "adapter for everything" strategy with initial deployment on SiriKit and App Intents. Performance benchmarks rely on human graders, emphasizing consumer-level adequacy over academic dominance. The Apple ML blog also mentions an Xcode code-focused model and a diffusion model for Genmoji.
ALL of AI Engineering in One Place
claude-3-sonnet claude-3 openai google-deepmind anthropic mistral-ai cohere hugging-face adept midjourney character-ai microsoft amazon nvidia salesforce mastercard palo-alto-networks axa novartis discord twilio tinder khan-academy sourcegraph mongodb neo4j hasura modular cognition anysphere perplexity-ai groq mozilla nous-research galileo unsloth langchain llamaindex instructor weights-biases lambda-labs neptune datastax crusoe covalent qdrant baseten e2b octo-ai gradient-ai lancedb log10 deepgram outlines crew-ai factory-ai interpretability feature-steering safety multilinguality multimodality rag evals-ops open-models code-generation gpus agents ai-leadership
The upcoming AI Engineer World's Fair in San Francisco from June 25-27 will feature a significantly expanded format with booths, talks, and workshops from top model labs like OpenAI, DeepMind, Anthropic, Mistral, Cohere, HuggingFace, and Character.ai. It includes participation from Microsoft Azure, Amazon AWS, Google Vertex, and major companies such as Nvidia, Salesforce, Mastercard, Palo Alto Networks, and more. The event covers 9 tracks including RAG, multimodality, evals/ops, open models, code generation, GPUs, agents, AI in Fortune 500, and a new AI leadership track. Additionally, Anthropic shared interpretability research on Claude 3 Sonnet, revealing millions of interpretable features that can be steered to modify model behavior, including safety-relevant features related to bias and unsafe content, though more research is needed for practical applications. The event offers a discount code for AI News readers.
Anthropic's "LLM Genome Project": learning & clamping 34m features on Claude Sonnet
claude-3-sonnet claude-3 anthropic scale-ai suno-ai microsoft model-interpretability dictionary-learning neural-networks feature-activation intentional-modifiability scaling mechanistic-interpretability emmanuel-ameisen alex-albert
Anthropic released their third paper in the MechInterp series, Scaling Monosemanticity, scaling interpretability analysis to 34 million features on Claude 3 Sonnet. This work introduces the concept of dictionary learning to isolate recurring neuron activation patterns, enabling more interpretable internal states by combining features rather than neurons. The paper reveals abstract features related to code, errors, sycophancy, crime, self-representation, and deception, demonstrating intentional modifiability by clamping feature values. The research marks a significant advance in model interpretability and neural network analysis at frontier scale.
Skyfall
gemini-1.5-pro gemini-1.5-flash yi-1.5 kosmos-2.5 paligemma falcon-2 deepseek-v2 hunyuan-dit gemini-1.5 gemini-1.5-flash yi-1.5 google-deepmind yi-ai microsoft hugging-face langchain maven multimodality mixture-of-experts transformer model-optimization long-context model-performance model-inference fine-tuning local-ai scaling-laws causal-models hallucination-detection model-distillation model-efficiency hamel-husain dan-becker clement-delangue philschmid osanseviero arankomatsuzaki jason-wei rohanpaul_ai
Between 5/17 and 5/20/2024, key AI updates include Google DeepMind's Gemini 1.5 Pro and Flash models, featuring sparse multimodal MoE architecture with up to 10M context and a dense Transformer decoder that is 3x faster and 10x cheaper. Yi AI released Yi-1.5 models with extended context windows of 32K and 16K tokens. Other notable releases include Kosmos 2.5 (Microsoft), PaliGemma (Google), Falcon 2, DeepSeek v2 lite, and HunyuanDiT diffusion model. Research highlights feature an Observational Scaling Laws paper predicting model performance across families, a Layer-Condensed KV Cache technique boosting inference throughput by up to 26×, and the SUPRA method converting LLMs into RNNs for reduced compute costs. Hugging Face expanded local AI capabilities enabling on-device AI without cloud dependency. LangChain updated its v0.2 release with improved documentation. The community also welcomed a new LLM Finetuning Discord by Hamel Husain and Dan Becker for Maven course users. "Hugging Face is profitable, or close to profitable," enabling $10 million in free shared GPUs for developers.
OpenAI's PR Campaign?
alphafold-3 xlstm gpt-4 openai microsoft google-deepmind memory-management model-spec scaling multimodality performance transformers dynamic-memory model-architecture demis-hassabis sama joanne-jang omarsar0 arankomatsuzaki drjimfan
OpenAI faces user data deletion backlash over its new partnership with StackOverflow amid GDPR complaints and US newspaper lawsuits, while addressing election year concerns with efforts like the Media Manager tool for content opt-in/out by 2025 and source link attribution. Microsoft develops a top-secret airgapped GPT-4 AI service for US intelligence agencies. OpenAI releases the Model Spec outlining responsible AI content generation policies, including NSFW content handling and profanity use, emphasizing clear distinctions between bugs and design decisions. Google DeepMind announces AlphaFold 3, a state-of-the-art model predicting molecular structures with high accuracy, showcasing cross-domain AI techniques. New research on xLSTM proposes scaling LSTMs to billions of parameters, competing with transformers in performance and scaling. Microsoft introduces vAttention, a dynamic memory management method for efficient large language model serving without PagedAttention.
Kolmogorov-Arnold Networks: MLP killers or just spicy MLPs?
gpt-5 gpt-4 dall-e-3 openai microsoft learnable-activations mlp function-approximation interpretability inductive-bias-injection b-splines model-rearrangement parameter-efficiency ai-generated-image-detection metadata-standards large-model-training max-tegmark ziming-liu bindureddy nptacek zacharynado rohanpaul_ai svpino
Ziming Liu, a grad student of Max Tegmark, published a paper on Kolmogorov-Arnold Networks (KANs), claiming they outperform MLPs in interpretability, inductive bias injection, function approximation accuracy, and scaling, despite being 10x slower to train but 100x more parameter efficient. KANs use learnable activation functions modeled by B-splines on edges rather than fixed activations on nodes. However, it was later shown that KANs can be mathematically rearranged back into MLPs with similar parameter counts, sparking debate on their interpretability and novelty. Meanwhile, on AI Twitter, there is speculation about a potential GPT-5 release with mixed impressions, OpenAI's adoption of the C2PA metadata standard for detecting AI-generated images with high accuracy for DALL-E 3, and Microsoft training a large 500B parameter model called MAI-1, potentially previewed at Build conference, signaling increased competition with OpenAI. "OpenAI's safety testing for GPT-4.5 couldn't finish in time for Google I/O launch" was also noted.
DeepSeek-V2 beats Mixtral 8x22B with >160 experts at HALF the cost
deepseek-v2 llama-3-120b llama-3-400b gpt-4 mistral phi claude gemini mai-1 med-gemini deepseek-ai mistral-ai microsoft openai scale-ai tesla nvidia google-deepmind mixture-of-experts multi-head-attention model-inference benchmarking overfitting robotics teleoperation open-source multimodality hallucination-detection fine-tuning medical-ai model-training erhartford maximelabonne bindureddy adcock_brett drjimfan clementdelangue omarsar0 rohanpaul_ai
DeepSeek V2 introduces a new state-of-the-art MoE model with 236B parameters and a novel Multi-Head Latent Attention mechanism, achieving faster inference and surpassing GPT-4 on AlignBench. Llama 3 120B shows strong creative writing skills, while Microsoft is reportedly developing a 500B parameter LLM called MAI-1. Research from Scale AI highlights overfitting issues in models like Mistral and Phi, whereas GPT-4, Claude, Gemini, and Llama maintain benchmark robustness. In robotics, Tesla Optimus advances with superior data collection and teleoperation, LeRobot marks a move toward open-source robotics AI, and Nvidia's DrEureka automates robot skill training. Multimodal LLM hallucinations are surveyed with new mitigation strategies, and Google's Med-Gemini achieves SOTA on medical benchmarks with fine-tuned multimodal models.
Evals: The Next Generation
gpt-4 gpt-5 gpt-3.5 phi-3 mistral-7b llama-3 scale-ai mistral-ai reka-ai openai moderna sanctuary-ai microsoft mit meta-ai-fair benchmarking data-contamination multimodality fine-tuning ai-regulation ai-safety ai-weapons neural-networks model-architecture model-training model-performance robotics activation-functions long-context sam-altman jim-fan
Scale AI highlighted issues with data contamination in benchmarks like MMLU and GSM8K, proposing a new benchmark where Mistral overfits and Phi-3 performs well. Reka released the VibeEval benchmark for multimodal models addressing multiple choice benchmark limitations. Sam Altman of OpenAI discussed GPT-4 as "dumb" and hinted at GPT-5 with AI agents as a major breakthrough. Researchers jailbroke GPT-3.5 via fine-tuning. Global calls emerged to ban AI-powered weapons, with US officials urging human control over nuclear arms. Ukraine launched an AI consular avatar, while Moderna partnered with OpenAI for medical AI advancements. Sanctuary AI and Microsoft collaborate on AI for general-purpose robots. MIT introduced Kolmogorov-Arnold networks with improved neural network efficiency. Meta AI is training Llama 3 models with over 400 billion parameters, featuring multimodality and longer context.
Not much happened today
command-r-35b goliath-120 miqu-120 llama-3-8b tensorrt-llm llama-cpp gpt2-chat gpt-4-turbo llama-3 deepmind-alphazero anthropic openai perplexity-ai amazon apple microsoft deepmind creative-writing context-windows benchmarking model-performance self-learning function-calling retrieval-augmented-generation ai-assistants on-device-ai ai-lobbying copyright-infringement code-reasoning image-generation
Anthropic released a team plan and iOS app about 4 months after OpenAI. The Command-R 35B model excels at creative writing, outperforming larger models like Goliath-120 and Miqu-120. The Llama-3 8B model now supports a 1 million token context window, improving long-context understanding with minimal training on a single 8xA800 GPU machine. TensorRT-LLM benchmarks show it is 30-70% faster than llama.cpp on consumer hardware. A benchmark suggests GPT2-Chat may have better reasoning than GPT-4-Turbo, though results are debated. Demos include a self-learning Llama-3 voice agent running locally on Jetson Orin and a Self-Learning Large Action Model (LAM). Amazon CodeWhisperer was renamed to Q Developer, expanding its generative AI assistant capabilities. Apple plans an AI-enabled Safari browser with an on-device LLM in iOS 18 and macOS 15. Big Tech dominates AI lobbying in Washington, while major U.S. newspapers sued OpenAI and Microsoft for copyright infringement. DeepMind's AlphaZero became the greatest chess player in 9 hours, and their Naturalized Execution Tuning (NExT) method improves LLM code reasoning by 14-26%. Stable Diffusion is used for diverse image generation applications.
A quiet weekend
llama-3 dolphin-2.9 pixart-sigma llama-3-70b microsoft coca-cola uber lmsys nous-research mistral-ai ar-interfaces transformers algorithmic-tasks turing-test graph-algorithms embeddings generative-ai model-optimization llm-inference quantization model-deployment yann-lecun
Yann LeCun predicts a shift to AR interfaces with AI assistants in 10-15 years, moving away from smartphones. The Dolphin-2.9 model based on Llama-3 was released, improving quality issues. PixArt Sigma, a 0.6B parameter model, achieves Stable Diffusion 3.0 level performance with complete prompt adherence and local usability. Research shows transformers can use meaningless filler tokens for algorithmic tasks with dense supervision. AI-generated restaurant reviews can pass the Turing test, fooling humans and AI detectors. Uber uses graph algorithms and learned embeddings for ETA prediction. Coca-Cola and Microsoft announced a 5-year AI partnership to accelerate cloud and generative AI initiatives. The Llama-3 70B model can run on a single 4GB GPU using AirLLM optimization without quantization but is slow. Mistral.rs is introduced as a fast LLM inference platform with quantization and OpenAI API compatibility. Only 5% of LLMs make it from prototype to production due to challenges, especially in enterprise. EXL2 and GGUF quantization methods for Llama models show similar perplexity vs model size, with Llama-3 and Llama-2 degrading more under quantization compared to full precision.
OpenAI's Instruction Hierarchy for the LLM OS
phi-3-mini openelm claude-3-opus gpt-4-turbo gpt-3.5-turbo llama-3-70b rho-1 mistral-7b llama-3-8b llama-3 openai microsoft apple deepseek mistral-ai llamaindex wendys prompt-injection alignment benchmarking instruction-following context-windows model-training model-deployment inference performance-optimization ai-application career-advice drive-thru-ai
OpenAI published a paper introducing the concept of privilege levels for LLMs to address prompt injection vulnerabilities, improving defenses by 20-30%. Microsoft released the lightweight Phi-3-mini model with 4K and 128K context lengths. Apple open-sourced the OpenELM language model family with an open training and inference framework. An instruction accuracy benchmark compared 12 models, with Claude 3 Opus, GPT-4 Turbo, and Llama 3 70B performing best. The Rho-1 method enables training state-of-the-art models using only 3% of tokens, boosting models like Mistral. Wendy's deployed AI-powered drive-thru ordering, and a study found Gen Z workers prefer generative AI for career advice. Tutorials on deploying Llama 3 models on AWS EC2 highlight hardware requirements and inference server use.
Llama-3-70b is GPT-4-level Open Model
llama-3-70b llama-3-8b llama-3 llama-2-70b mistral-7b grok-3 stable-diffusion-3 vasa-1 meta-ai-fair groq nvidia amazon microsoft benchmarking model-performance fine-tuning function-calling arithmetic image-generation video-generation energy-usage gpu-demand political-bias ai-safety scaling context-windows tokenization elon-musk
Meta has released Llama 3, their most capable open large language model with 8B and 70B parameter versions supporting 8K context length and outperforming previous models including Llama 2 and Mistral 7B. Groq serves the Llama 3 70B model at 500-800 tokens/second, making it the fastest GPT-4-level token source. Discussions highlight AI scaling challenges with Elon Musk stating that training Grok 3 will require 100,000 Nvidia H100 GPUs, and AWS planning to acquire 20,000 B200 GPUs for a 27 trillion parameter model. Microsoft unveiled VASA-1 for lifelike talking face generation, while Stable Diffusion 3 and its extensions received mixed impressions. Concerns about AI energy usage and political bias in AI were also discussed.
Meta Llama 3 (8B, 70B)
llama-3-8b llama-3-70b llama-3-400b stable-diffusion-3 mixtral-8x22b-instruct-v0.1 vasa-1 meta-ai-fair stability-ai boston-dynamics microsoft mistral-ai hugging-face transformer tokenization model-training benchmarking robotics natural-language-processing real-time-processing synthetic-data dataset-cleaning behavior-trees ai-safety model-accuracy api model-release humor helen-toner
Meta partially released Llama 3 models including 8B and 70B variants, with a 400B variant still in training, touted as the first GPT-4 level open-source model. Stability AI launched Stable Diffusion 3 API with model weights coming soon, showing competitive realism against Midjourney V6. Boston Dynamics unveiled an electric humanoid robot Atlas, and Microsoft introduced the VASA-1 model generating lifelike talking faces at 40fps on RTX 4090. Mistral AI, a European OpenAI rival, is seeking $5B funding with its Mixtral-8x22B-Instruct-v0.1 model achieving 100% accuracy on 64K context benchmarks. AI safety discussions include calls from former OpenAI board member Helen Toner for audits of top AI companies, and the Mormon Church released AI usage principles. New AI development tools include Ctrl-Adapter for diffusion models, Distilabel 1.0.0 for synthetic dataset pipelines, Data Bonsai for data cleaning with LLMs, and Dendron for building LLM agents with behavior trees. Memes highlight AI development humor and cultural references. The release of Llama 3 models features improved reasoning, a 128K token vocabulary, 8K token sequences, and grouped query attention.
Mixtral 8x22B Instruct sparks efficiency memes
mixtral-8x22b llama-2-7b olmo-7b mistral-ai hugging-face google microsoft intel softbank nvidia multilinguality math code-generation context-window model-performance model-release retrieval-augmented-generation deepfake ai-investment ai-chip hybrid-architecture training-data guillaume-lample osanseviero _philschmid svpino
Mistral released an instruct-tuned version of their Mixtral 8x22B model, notable for using only 39B active parameters during inference, outperforming larger models and supporting 5 languages with 64k context window and math/code capabilities. The model is available on Hugging Face under an Apache 2.0 license for local use. Google plans to invest over $100 billion in AI, with other giants like Microsoft, Intel, and SoftBank also making large investments. The UK criminalized non-consensual deepfake porn, raising enforcement debates. A former Nvidia employee claims Nvidia's AI chip lead is unmatchable this decade. AI companions could become a $1 billion market. AI has surpassed humans on several basic tasks but lags on complex ones. Zyphra introduced Zamba, a novel 7B parameter hybrid model outperforming LLaMA-2 7B and OLMo-7B with less training data, trained on 128 H100 GPUs over 30 days. GroundX API advances retrieval-augmented generation accuracy.
Multi-modal, Multi-Aspect, Multi-Form-Factor AI
gpt-4 idefics-2-8b mistral-instruct apple-mlx gpt-5 reka-ai cohere google rewind apple mistral-ai microsoft paypal multimodality foundation-models embedding-models gpu-performance model-comparison enterprise-data open-source performance-optimization job-impact agi-criticism technical-report arthur-mensch dan-schulman chris-bishop
Between April 12-15, Reka Core launched a new GPT4-class multimodal foundation model with a detailed technical report described as "full Shazeer." Cohere Compass introduced a foundation embedding model for indexing and searching multi-aspect enterprise data like emails and invoices. The open-source IDEFICS 2-8B model continues Google's Flamingo multimodal model reproduction. Rewind pivoted to a multi-platform app called Limitless, moving away from spyware. Reddit discussions highlighted Apple MLX outperforming Ollama and Mistral Instruct on M2 Ultra GPUs, GPU choices for LLMs and Stable Diffusion, and AI-human comparisons by Microsoft Research's Chris Bishop. Former PayPal CEO Dan Schulman predicted GPT-5 will drastically reduce job scopes by 80%. Mistral CEO Arthur Mensch criticized the obsession with AGI as "creating God."
Cohere Command R+, Anthropic Claude Tool Use, OpenAI Finetuning
c4ai-command-r-plus claude-3 gpt-3.5-turbo gemini mistral-7b gemma-2 claude-3-5 llama-3 vicuna cohere anthropic openai microsoft stability-ai opera-software meta-ai-fair google-deepmind mistral-ai tool-use multilingual-models rag fine-tuning quantum-computing audio-generation local-inference context-windows model-size-analysis model-comparison
Cohere launched Command R+, a 104B dense model with 128k context length focusing on RAG, tool-use, and multilingual capabilities across 10 key languages. It supports Multi-Step Tool use and offers open weights for research. Anthropic introduced tool use in beta for Claude, supporting over 250 tools with new cookbooks for practical applications. OpenAI enhanced its fine-tuning API with new upgrades and case studies from Indeed, SK Telecom, and Harvey, promoting DIY fine-tuning and custom model training. Microsoft achieved a quantum computing breakthrough with an 800x error rate improvement and the most usable qubits to date. Stability AI released Stable Audio 2.0, improving audio generation quality and control. The Opera browser added local inference support for large language models like Meta's Llama, Google's Gemma, and Vicuna. Discussions on Reddit highlighted Gemini's large context window, analysis of GPT-3.5-Turbo model size, and a battle simulation between Claude 3 and ChatGPT using local 7B models like Mistral and Gemma.
not much happened today
llama-2-70b llama-2-7b mistral-7b qwen-1.5 llava microsoft mistral-ai ollama fine-tuning synthetic-data retrieval-augmented-generation embeddings hardware-optimization performance-benchmarks model-memory multimodality
The Reddit community /r/LocalLlama discusses fine-tuning and training LLMs, including tutorials and questions on training models with specific data like dictionaries and synthetic datasets with 25B+ tokens. Users explore retrieval-augmented generation (RAG) challenges with models like mistral-7b and embedding generation for EEG brain activity. Discussions include hardware optimization for running llama-2-70b locally under budget constraints, and performance benchmarks for qwen-1.5 models. There is interest in extending LLM capabilities, such as converting llama-2-7b into a vision-capable model like llava and improving model memory for longer context retention.
Shipping and Dipping: Inflection + Stability edition
inflection-ai-2.5 stable-diffusion-3 claude-3-haiku claude-3-sonnet claude-3-opus tacticai inflection-ai stability-ai microsoft nvidia google-deepmind anthropic executive-departures gpu-acceleration ai-assistants geometric-deep-learning ai-integration ai-cost-reduction ai-job-displacement ai-healthcare model-release mustafa-suleyman
Inflection AI and Stability AI recently shipped major updates (Inflection AI 2.5 and Stable Diffusion 3) but are now experiencing significant executive departures, signaling potential consolidation in the GPU-rich startup space. Mustafa Suleyman has joined Microsoft AI as CEO, overseeing consumer AI products like Copilot, Bing, and Edge. Microsoft Azure is collaborating with NVIDIA on the Grace Blackwell 200 Superchip. Google DeepMind announced TacticAI, an AI assistant for football tactics developed with Liverpool FC, using geometric deep learning and achieving 90% expert approval in blind tests. Anthropic released Claude 3 Haiku and Claude 3 Sonnet on Google Cloud's Vertex AI, with Claude 3 Opus coming soon. Concerns about AI job displacement arise as NVIDIA introduces AI nurses that outperform humans at bedside manner at 90% lower cost.
Stable Diffusion 3 — Rombach & Esser did it again!
stable-diffusion-3 claude-3 orca dolphincoder-starcoder2-15b stability-ai anthropic microsoft latitude perplexity-ai llamaindex tripo-ai diffusion-models multimodality benchmarking human-evaluation text-generation image-generation 3d-modeling fine-tuning roleplay coding dataset-release soumith-chintala bill-peebles swyx kevinafischer jeremyphoward akhaliq karinanguyen_ aravsrinivas
Over 2500 new community members joined following Soumith Chintala's shoutout, highlighting growing interest in SOTA LLM-based summarization. The major highlight is the detailed paper release of Stable Diffusion 3 (SD3), showcasing advanced text-in-image control and complex prompt handling, with the model outperforming other SOTA image generation models in human-evaluated benchmarks. The SD3 model is based on an enhanced Diffusion Transformer architecture called MMDiT. Meanwhile, Anthropic released Claude 3 models, noted for human-like responses and emotional depth, scoring 79.88% on HumanEval but costing over twice as much as GPT-4. Microsoft launched new Orca-based models and datasets, and Latitude released DolphinCoder-StarCoder2-15b with strong coding capabilities. Integration of image models by Perplexity AI and 3D CAD generation by PolySpectra powered by LlamaIndex were also highlighted. "SD3's win rate beats all other SOTA image gen models (except perhaps Ideogram)" and "Claude 3 models are very good at generating d3 visualizations from text descriptions."
AI gets Memory
miqumaid-v2-70b mixtral-8x7b-qlora mistral-7b phi-2 medalpaca aya openai langchain thebloke cohere unsloth-ai mistral-ai microsoft rag memory-modeling context-windows open-source finetuning sequential-fine-tuning direct-preference-optimization rlhf ppo javascript-python-integration hardware-optimization gpu-overclocking quantization model-training large-context multilinguality joanne-jang
AI Discords analysis covered 20 guilds, 312 channels, and 6901 messages. The report highlights the divergence of RAG style operations for context and memory, with implementations like MemGPT rolling out in ChatGPT and LangChain. The TheBloke Discord discussed open-source large language models such as the Large World Model with contexts up to 1 million tokens, and the Cohere aya model supporting 101 languages. Roleplay-focused models like MiquMaid-v2-70B were noted for performance improvements with enhanced hardware. Finetuning techniques like Sequential Fine-Tuning (SFT) and Direct Preference Optimization (DPO) were explained, with tools like Unsloth AI's apply_chat_template preferred over Alpaca. Integration of JavaScript and Python via JSPyBridge in the SillyTavern project was also discussed. Training challenges with Mixtral 8x7b qlora versus Mistral 7b were noted. The LM Studio Discord focused on hardware limitations affecting large model loading, medical LLMs like medAlpaca, and hardware discussions around GPU upgrades and overclocking. Anticipation for IQ3_XSS 1.5 bit quantization support in LM Studio was expressed.
GPT4Turbo A/B Test: gpt-4-1106-preview
gpt-4-turbo gpt-4 gpt-3.5 openhermes-2.5-mistral-7b-4.0bpw exllamav2 llama-2-7b-chat mistral-instruct-v0.2 mistrallite llama2 openai huggingface thebloke nous-research mistral-ai langchain microsoft azure model-loading rhel dataset-generation llm-on-consoles fine-tuning speed-optimization api-performance prompt-engineering token-limits memory-constraints text-generation nlp-tools context-window-extension sliding-windows rope-theta non-finetuning-context-extension societal-impact
OpenAI released a new GPT-4 Turbo version, prompting a natural experiment in summarization comparing the November 2023 and January 2024 versions. The TheBloke Discord discussed troubleshooting model loading errors with OpenHermes-2.5-Mistral-7B-4.0bpw and exllamav2, debates on RHEL in ML, dataset generation for understanding GPT flaws, and running LLMs like Llama and Mistral on consoles. LangChain fine-tuning challenges for Llama2 were also noted. The OpenAI Discord highlighted GPT-4 speed inconsistencies, API vs web performance, prompt engineering with GPT-3.5 and GPT-4 Turbo, and DALL-E typo issues in image text. Discussions included NLP tools like semantic-text-splitter and collaboration concerns with GPT-4 Vision on Azure. The Nous Research AI Discord focused on extending context windows with Mistral instruct v0.2, MistralLite, and LLaMA-2-7B-Chat achieving 16,384 token context, plus alternatives like SelfExtend for context extension without fine-tuning. The societal impact of AI technology was also considered.
1/17/2024: Help crowdsource function calling datasets
mistral-7b dolphin-2.7-mixtral-8x7b mega-dolphin dolphin-2.6-mistral-7b-dpo llama-cpp lm-studio mistral-ai microsoft hugging-face apple function-calling quantization model-performance gpu-optimization model-selection closed-source memory-optimization linux-server api-fees headless-mode yagilb heyitsyorkie
LM Studio updated its FAQ clarifying its closed-source status and perpetual freeness for personal use with no data collection. The new beta release includes fixes and hints at upcoming 2-bit quantization support. For gaming, models like Dolphin 2.7 Mixtral 8x7B, MegaDolphin, and Dolphin 2.6 Mistral 7B DPO with Q4_K_M quantization were recommended. Discussions highlighted that single powerful GPUs outperform multi-GPU setups due to bottlenecks, with older GPUs like Tesla P40 being cost-effective. Microsoft's AutoGen Studio was introduced but has issues and requires API fees for open-source models. Linux users are advised to use llama.cpp over LM Studio due to lack of headless mode. Additional tools like LLMFarm for iOS and various Hugging Face repositories were also mentioned. "LM Studio must be running to use the local inference server as there is no headless mode available" and "matching model size to GPU memory is key for performance" were notable points.
1/1/2024: How to start with Open Source AI
gpt-4-turbo dall-e-3 chatgpt openai microsoft perplexity-ai prompt-engineering ai-reasoning custom-gpt performance python knowledge-integration swyx
OpenAI Discord discussions revealed mixed sentiments about Bing's AI versus ChatGPT and Perplexity AI, and debated Microsoft Copilot's integration with Office 365. Users discussed DALL-E 3 access within ChatGPT Plus, ChatGPT's performance issues, and ways to train a GPT model using book content via OpenAI API or custom GPTs. Anticipation for GPT-4 turbo in Microsoft Copilot was noted alongside conversations on AI reasoning, prompt engineering, and overcoming Custom GPT glitches. Advice for AI beginners included starting with Python and using YAML or Markdown for knowledge integration. The future of AI with multiple specialized GPTs and Microsoft Copilot's role was also explored.
12/22/2023: Anyscale's Benchmark Criticisms
gpt-4 gpt-3.5 bard anyscale openai microsoft benchmarking performance api prompt-engineering bug-tracking model-comparison productivity programming-languages storytelling
Anyscale launched their LLMPerf leaderboard to benchmark large language model inference performance, but it faced criticism for lacking detailed metrics like cost per token and throughput, and for comparing public LLM endpoints without accounting for batching and load. In OpenAI Discord discussions, users reported issues with Bard and preferred Microsoft Copilot for storytelling, noting fewer hallucinations. There was debate on the value of upgrading from GPT-3.5 to GPT-4, with many finding paid AI models worthwhile for coding productivity. Bugs and performance issues with OpenAI APIs were also highlighted, including slow responses and message limits. Future AI developments like GPT-6 and concerns about OpenAI's transparency and profitability were discussed. Prompt engineering for image generation was another active topic, emphasizing clear positive prompts and the desire for negative prompts.
12/21/2023: The State of AI (according to LangChain)
mixtral gpt-4 chatgpt bard dall-e langchain openai perplexity-ai microsoft poe model-consistency model-behavior response-quality chatgpt-usage-limitations error-handling user-experience model-comparison hallucination-detection prompt-engineering creative-ai
LangChain launched their first report based on LangSmith stats revealing top charts for mindshare. On OpenAI's Discord, users raised issues about the Mixtral model, noting inconsistencies and comparing it to Poe's Mixtral. There were reports of declining output quality and unpredictable behavior in GPT-4 and ChatGPT, with discussions on differences between Playground GPT-4 and ChatGPT GPT-4. Users also reported anomalous behavior in Bing and Bard AI models, including hallucinations and strange assertions. Various user concerns included message limits on GPT-4, response completion errors, chat lags, voice setting inaccessibility, password reset failures, 2FA issues, and subscription restrictions. Techniques for guiding GPT-4 outputs and creative uses with DALL-E were also discussed. Users highlighted financial constraints affecting subscriptions and queries about earning with ChatGPT and token costs.
12/13/2023 SOLAR10.7B upstages Mistral7B?
solar-10.7b llama-2 mistral-7b phi-2 gpt-4 gemini upstage nous-research openai mistral-ai microsoft depth-up-scaling pretraining synthetic-data gpu-training api-usage model-integration agi asi chat-models vision model-performance fine-tuning
Upstage released the SOLAR-10.7B model, which uses a novel Depth Up-Scaling technique built on the llama-2 architecture and integrates mistral-7b weights, followed by continued pre-training. The Nous community finds it promising but not exceptional. Additionally, weights for the phi-2 base model were released, trained on 1.4 trillion tokens including synthetic texts created by GPT-3 and filtered by GPT-4, using 96 A100 GPUs over 14 days. On OpenAI's Discord, users discussed challenges with various GPT models, including incoherent outputs, API usage limitations, and issues with GPT-4 Vision API. Conversations also covered understanding AGI and ASI, concerns about OpenAI's partnership with Axel Springer, and pricing changes for GPT Plus. Discussions included the Gemini chat model integrated into Bard and comparisons with GPT-4 performance.
12/12/2023: Towards LangChain 0.1
mixtral-8x7b phi-2 gpt-3 chatgpt gpt-4 langchain mistral-ai anthropic openai microsoft mixture-of-experts information-leakage prompt-engineering oauth2 logo-generation education-ai gaming-ai api-access model-maintainability scalability
The Langchain rearchitecture has been completed, splitting the repo for better maintainability and scalability, while remaining backwards compatible. Mistral launched a new Discord community, and Anthropic is rumored to be raising another $3 billion. On the OpenAI Discord, discussions covered information leakage in AI training, mixture of experts (MoE) models like mixtral 8x7b, advanced prompt engineering techniques, and issues with ChatGPT performance and API access. Users also explored AI applications in logo generation, education, and gaming, and shared solutions for Oauth2 authentication problems. A new small language model named Phi-2 was mentioned from Microsoft.