All tags
Model: "claude-3-opus"
not much happened today
gpt-4.5 gpt-4 gpt-4o o1 claude-3.5-sonnet claude-3.7 claude-3-opus deepseek-v3 grok-3 openai anthropic perplexity-ai deepseek scaling01 model-performance humor emotional-intelligence model-comparison pricing context-windows model-size user-experience andrej-karpathy jeremyphoward abacaj stevenheidel yuchenj_uw aravsrinivas dylan522p random_walker
GPT-4.5 sparked mixed reactions on Twitter, with @karpathy noting users preferred GPT-4 in a poll despite his personal favor for GPT-4.5's creativity and humor. Critics like @abacaj highlighted GPT-4.5's slowness and questioned its practical value and pricing compared to other models. Performance-wise, GPT-4.5 ranks above GPT-4o but below o1 and Claude 3.5 Sonnet, with Claude 3.7 outperforming it on many tasks yet GPT-4.5 praised for its humor and "vibes." Speculation about GPT-4.5's size suggests around 5 trillion parameters. Discussions also touched on pricing disparities, with Perplexity Deep Research at $20/month versus ChatGPT at $200/month. The emotional intelligence and humor of models like Claude 3.7 were also noted.
Not much happened today
grok-beta llama-3-1-70b claude-3-5-haiku claude-3-opus llama-3 chatgpt gemini meta-ai-fair scale-ai anthropic perplexity-ai langchainai weights-biases qwen pricing national-security defense open-source agentic-ai retrieval-augmented-generation election-predictions real-time-updates annotation ai-ecosystem memes humor alexandr_wang svpino aravsrinivas bindureddy teortaxestex jessechenglyu junyang-lin cte_junior jerryjliu0
Grok Beta surpasses Llama 3.1 70B in intelligence but is less competitive due to its pricing at $5/1M input tokens and $15/1M output tokens. Defense Llama, developed with Meta AI and Scale AI, targets American national security applications. SWE-Kit, an open-source framework, supports building customizable AI software engineers compatible with Llama 3, ChatGPT, and Claude. LangChainAI and Weights & Biases integrate to improve retrievers and reduce hallucinations in RAG applications using Gemini. Perplexity AI offers enhanced election tracking tools for the 2024 elections, including live state results and support for Claude 3.5 Haiku. AI Talk launched featuring discussions on Chinese AI labs with guests from Qwen. Memes highlight Elon Musk and humorous AI coding mishaps.
not much happened today
gpt-4-0613 gpt-3.5-turbo-0613 gpt-4o-2024-08-06 mistral-large-2 gpt4-turbo claude-3-opus idefics3-llama bigllama-3.1-1t-instruct llama-3-120b-instruct openai mistral-ai meta-ai-fair structured-outputs function-calling json-schema benchmarking multimodality context-windows model-scaling ai-hardware vision speech-processing robotics ai-regulation sama rohanpaul_ai corbtt guillaumelample mervenoyann maximelabonne aidan_mclau adcock_brett ylecun
OpenAI introduced structured outputs in their API with a new "strict" mode and a "response_format" parameter, supporting models like gpt-4-0613, gpt-3.5-turbo-0613, and the new gpt-4o-2024-08-06. They also halved the price of gpt-4o to $2.50 per million tokens. Mistral Large 2 outperforms gpt4-turbo and claude-3-opus on hard benchmarks and coding tasks. Idefics3-Llama offers multimodal capabilities with a 10k token context window. BigLlama-3.1-1T-Instruct is an upscaled version of llama-3-120b-instruct. New benchmark "big_model_smell" measures creativity and reliability. Figure 02 robot features advanced AI hardware with onboard vision language model, enhanced battery, and speech-to-speech reasoning. Yann LeCun expressed concerns about California's SB1047 regulation.
Mozilla's AI Second Act
llama-3 claude-3-opus gemini-1.5 deepseek-coder-v2 gpt-4 mozilla llamaindex anthropic etched-ai sohu deepseek openai vector-search inference-speed hardware-benchmarks context-windows open-source-models coding reasoning model-benchmarking gpu-inference agentic-ai justine-tunney stephen-hood tim-dettmers bindureddy
Mozilla showcased detailed live demos of llamafile and announced sqlite-vec for vector search integration at the AIE World's Fair. LlamaIndex launched llama-agents. Anthropic introduced new UI features and Projects for Claude with a 200K context window. Etched AI revealed a specialized inference chip claiming 500k tokens/sec, though benchmark claims are questioned. Sohu chip enables 15 agent trajectories/sec. Tim Dettmers shared theoretical GPU inference limits of ~300k tokens/sec for 8xB200 NVLink on 70B Llama. Deepseek Coder v2 outperforms Gemini and GPT-4 variants in coding and reasoning. The PyTorch documentary launched to little attention.
Shazeer et al (2024): you are overpaying for inference >13x
claude-3.5-sonnet claude-3-opus character.ai anthropic memory-efficiency kv-cache attention-mechanisms stateful-caching int8-precision transformer-architecture scaling overfitting architecture noam-shazeer kevin-a-fischer sebastien-bubeck _aidan_clark_ andrej-karpathy
Noam Shazeer explains how Character.ai serves 20% of Google Search Traffic for LLM inference while reducing serving costs by a factor of 33 compared to late 2022, with leading commercial APIs costing at least 13.5X more. Key memory-efficiency techniques include MQA > GQA reducing KV cache size by 8X, hybrid attention horizons, cross-layer KV-sharing, stateful caching with a 95% cache rate, and native int8 precision with custom kernels. Anthropic released Claude 3.5 Sonnet, which outperforms Claude 3 Opus at twice the speed and one-fifth the cost, passing 64% of internal pull request tests and introducing new features like Artifacts for real-time doc and code generation. Discussions on LLM architecture highlight the dominance of transformers, challenges in scaling and overfitting, and the importance of architecture work for progress.
Claude Crushes Code - 92% HumanEval and Claude.ai Artifacts
claude-3.5-sonnet claude-3-opus gpt-4o anthropic openai cognition benchmarking model-performance coding model-optimization fine-tuning instruction-following model-efficiency model-release api performance-optimization alex-albert
Claude 3.5 Sonnet, released by Anthropic, is positioned as a Pareto improvement over Claude 3 Opus, operating at twice the speed and costing one-fifth as much. It achieves state-of-the-art results on benchmarks like GPQA, MMLU, and HumanEval, surpassing even GPT-4o and Claude 3 Opus on vision tasks. The model demonstrates significant advances in coding capabilities, passing 64% of test cases compared to 38% for Claude 3 Opus, and is capable of autonomously fixing pull requests. Anthropic also introduced the Artifacts feature, enabling users to interact with AI-generated content such as code snippets and documents in a dynamic workspace, similar to OpenAI's Code Interpreter. This release highlights improvements in performance, cost-efficiency, and coding proficiency, signaling a growing role for LLMs in software development.
There's Ilya!
chameleon-7b chameleon-34b deepseek-coder-v2 gpt-4-turbo claude-3-opus voco-llama safe-superintelligence-inc openai anthropic meta deepseek google-deepmind parallel-decoding code-generation quantization training-dynamics vision benchmarks datasets image-captioning reasoning memory-optimization ilya-sutskever jan-leike ylecun akhaliq philschmid rohanpaul_ai mervenoyann fchollet
Ilya Sutskever has co-founded Safe Superintelligence Inc shortly after leaving OpenAI, while Jan Leike moved to Anthropic. Meta released new models including Chameleon 7B and 34B with mixed-modal input and unified token space quantization. DeepSeek-Coder-V2 shows code capabilities comparable to GPT-4 Turbo, supporting 338 programming languages and 128K context length. Consistency Large Language Models (CLLMs) enable parallel decoding generating multiple tokens per step. Grokked Transformers demonstrate reasoning through training dynamics affecting memory formation and generalization. VoCo-LLaMA compresses vision tokens with LLMs improving video temporal correlation understanding. The BigCodeBench benchmark evaluates LLMs on 1,140 coding tasks across 139 Python libraries, topped by DeepSeek-Coder-V2 and Claude 3 Opus. PixelProse is a large 16M image-caption dataset with reduced toxicity.
Gemini launches context caching... or does it?
nemotron llama-3-70b chameleon-7b chameleon-34b gemini-1.5-pro deepseek-coder-v2 gpt-4-turbo claude-3-opus gemini-1.5-pro nvidia meta-ai-fair google deepseek hugging-face context-caching model-performance fine-tuning reinforcement-learning group-relative-policy-optimization large-context model-training coding model-release rohanpaul_ai _philschmid aman-sanger
Nvidia's Nemotron ranks #1 open model on LMsys and #11 overall, surpassing Llama-3-70b. Meta AI released Chameleon 7B/34B models after further post-training. Google's Gemini introduced context caching, offering a cost-efficient middle ground between RAG and finetuning, with a minimum input token count of 33k and no upper limit on cache duration. DeepSeek launched DeepSeek-Coder-V2, a 236B parameter model outperforming GPT-4 Turbo, Claude-3-Opus, and Gemini-1.5-Pro in coding tasks, supporting 338 programming languages and extending context length to 128K. It was trained on 6 trillion tokens using the Group Relative Policy Optimization (GRPO) algorithm and is available on Hugging Face with a commercial license. These developments highlight advances in model performance, context caching, and large-scale coding models.
Ways to use Anthropic's Tool Use GA
claude-3-opus haiku opus convnext anthropic amazon google tool-use function-calling agentic-ai streaming vision parallelization delegation debate specialization open-science superintelligence convolutional-networks self-attention ai-research yann-lecun alex-albert sainingxie
Anthropic launched general availability of tool use/function calling with support for streaming, forced use, and vision, alongside Amazon and Google. Alex Albert shared five architectures for agentic tool use: delegation, parallelization, debate, specialization, and tool suite experts. Anthropic also introduced a self-guided course on tool use. Yann LeCun emphasized ethical open science funding, gradual emergence of superintelligence with safety guardrails, and convolutional networks for image/video processing as competitive with vision transformers. He also noted growth in AI researchers across industry, academia, and government.
Ten Commandments for Deploying Fine-Tuned Models
claude-3-opus claude-3 gpt-4o anthropic google openai fine-tuning prompt-engineering model-evaluation feature-alteration benchmarking model-performance open-source-models kyle-corbitt bindureddy alexalbert__
Gemini-in-Google-Slides is highlighted as a useful tool for summarizing presentations. Kyle Corbitt's talk on deploying fine-tuned models in production emphasizes avoiding fine-tuning unless necessary, focusing on prompting, data quality, appropriate model choice, and thorough evaluation. Anthropic showcased feature alteration in Claude AI, demonstrating control over model behavior and increased understanding of large language models. Open-source models like GPT-4o are approaching closed-source performance on benchmarks like MMLU for simple tasks, though advanced models remain necessary for complex automation.
Clémentine Fourrier on LLM evals
claude-3-opus huggingface meta-ai-fair llm-evaluation automated-benchmarking human-evaluation model-bias data-contamination elo-ranking systematic-annotations preference-learning evaluation-metrics prompt-sensitivity clem_fourrier
Clémentine Fourrier from Huggingface presented at ICLR about GAIA with Meta and shared insights on LLM evaluation methods. The blog outlines three main evaluation approaches: Automated Benchmarking using sample inputs/outputs and metrics, Human Judges involving grading and ranking with methods like Vibe-checks, Arena, and systematic annotations, and Models as Judges using generalist or specialist models with noted biases. Challenges include data contamination, subjectivity, and bias in scoring. These evaluations help prevent regressions, rank models, and track progress in the field.
$100k to predict LMSYS human preferences in a Kaggle contest
llama-3-70b llama-3 gpt-4 claude-3-opus prometheus-2 groq openai lmsys scale-ai ai2 nvidia benchmarking datasets fine-tuning reinforcement-learning model-alignment hallucination parameter-efficient-fine-tuning scalable-training factuality chatbot-performance bindureddy drjimfan percyliang seungonekim mobicham clefourrier
Llama 3 models are making breakthroughs with Groq's 70B model achieving record low costs per million tokens. A new Kaggle competition offers a $100,000 prize to develop models predicting human preferences from a dataset of over 55,000 user-LLM conversations. Open source evaluator LLMs like Prometheus 2 outperform proprietary models such as GPT-4 and Claude 3 Opus in judgment tasks. New datasets like WildChat1M provide over 1 million ChatGPT interaction logs with diverse and toxic examples. Techniques like LoRA fine-tuning show significant performance gains, and NVIDIA's NeMo-Aligner toolkit enables scalable LLM alignment across hundreds of GPUs. Factuality-aware alignment methods are proposed to reduce hallucinations in LLM outputs.
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.
Zero to GPT in 1 Year
gpt-4-turbo claude-3-opus mixtral-8x22b zephyr-141b medical-mt5 openai anthropic mistral-ai langchain hugging-face fine-tuning multilinguality tool-integration transformers model-evaluation open-source-models multimodal-llms natural-language-processing ocr model-training vik-paruchuri sam-altman greg-brockman miranda-murati abacaj mbusigin akhaliq clementdelangue
GPT-4 Turbo reclaimed the top leaderboard spot with significant improvements in coding, multilingual, and English-only tasks, now rolled out in paid ChatGPT. Despite this, Claude Opus remains superior in creativity and intelligence. Mistral AI released powerful open-source models like Mixtral-8x22B and Zephyr 141B suited for fine-tuning. LangChain enhanced tool integration across models, and Hugging Face introduced Transformer.js for running transformers in browsers. Medical domain-focused Medical mT5 was shared as an open-source multilingual text-to-text model. The community also highlighted research on LLMs as regressors and shared practical advice on OCR/PDF data modeling from Vik Paruchuri's journey.
AdamW -> AaronD?
claude-3-opus llama-3 llama-3-300m bert-large stable-diffusion-1.5 wdxl openai hugging-face optimizer machine-learning-benchmarks vision time-series-forecasting image-generation prompt-injection policy-enforcement aaron-defazio
Aaron Defazio is gaining attention for proposing a potential tuning-free replacement of the long-standing Adam optimizer, showing promising experimental results across classic machine learning benchmarks like ImageNet ResNet-50 and CIFAR-10/100. On Reddit, Claude 3 Opus has surpassed all OpenAI models on the LMSys leaderboard, while a user pretrained a LLaMA-based 300M model outperforming bert-large on language modeling tasks with a modest budget. The new MambaMixer architecture demonstrates promising results in vision and time series forecasting. In image generation, Stable Diffusion 1.5 with LoRAs achieves realistic outputs, and the WDXL release showcases impressive capabilities. AI applications include an AI-generated Nike spec ad and a chatbot built with OpenAI models that may resist prompt injections. OpenAI is reportedly planning a ban wave targeting policy violators and jailbreak users. "The high alpha seems to come from Aaron Defazio," highlighting his impactful work in optimizer research.
Claude 3 is officially America's Next Top Model
claude-3-opus claude-3-sonnet claude-3-haiku gpt-4o-mini mistral-7b qwen-72b anthropic mistral-ai huggingface openrouter stable-diffusion automatic1111 comfyui fine-tuning model-merging alignment ai-ethics benchmarking model-performance long-context cost-efficiency model-evaluation mark_riedl ethanjperez stuhlmueller ylecun aravsrinivas
Claude 3 Opus outperforms GPT4T and Mistral Large in blind Elo rankings, with Claude 3 Haiku marking a new cost-performance frontier. Fine-tuning techniques like QLoRA on Mistral 7B and evolutionary model merging on HuggingFace models are highlighted. Public opinion shows strong opposition to ASI development. Research supervision opportunities in AI alignment are announced. The Stable Diffusion 3 (SD3) release raises workflow concerns for tools like ComfyUI and automatic1111. Opus shows a 5% performance dip on OpenRouter compared to the Anthropic API. A new benchmark stresses LLM recall at long contexts, with Mistral 7B struggling and Qwen 72b performing well.
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.
World_sim.exe
gpt-4 gpt-4o grok-1 llama-cpp claude-3-opus claude-3 gpt-5 nvidia nous-research stability-ai hugging-face langchain anthropic openai multimodality foundation-models hardware-optimization model-quantization float4 float6 retrieval-augmented-generation text-to-video prompt-engineering long-form-rag gpu-optimization philosophy-of-ai agi-predictions jensen-huang yann-lecun sam-altman
NVIDIA announced Project GR00T, a foundation model for humanoid robot learning using multimodal instructions, built on their tech stack including Isaac Lab, OSMO, and Jetson Thor. They revealed the DGX Grace-Blackwell GB200 with over 1 exaflop compute, capable of training GPT-4 1.8T parameters in 90 days on 2000 Blackwells. Jensen Huang confirmed GPT-4 has 1.8 trillion parameters. The new GB200 GPU supports float4/6 precision with ~3 bits per parameter and achieves 40,000 TFLOPs on fp4 with 2x sparsity.
Open source highlights include the release of Grok-1, a 340B parameter model, and Stability AI's SV3D, an open-source text-to-video generation solution. Nous Research collaborated on implementing Steering Vectors in Llama.CPP.
In Retrieval Augmented Generation (RAG), a new 5.5-hour tutorial builds a pipeline using open-source HF models, and LangChain released a video on query routing and announced integration with NVIDIA NIM for GPU-optimized LLM inference.
Prominent opinions include Yann LeCun distinguishing language from other cognitive abilities, Sam Altman predicting AGI arrival in 6 years with a leap from GPT-4 to GPT-5 comparable to GPT-3 to GPT-4, and discussions on the philosophical status of LLMs like Claude. There is also advice against training models from scratch for most companies.
Grok-1 in Bio
grok-1 mixtral miqu-70b claude-3-opus claude-3 claude-3-haiku xai mistral-ai perplexity-ai groq anthropic openai mixture-of-experts model-release model-performance benchmarking finetuning compute hardware-optimization mmlu model-architecture open-source memes sam-altman arthur-mensch daniel-han arav-srinivas francis-yao
Grok-1, a 314B parameter Mixture-of-Experts (MoE) model from xAI, has been released under an Apache 2.0 license, sparking discussions on its architecture, finetuning challenges, and performance compared to models like Mixtral and Miqu 70B. Despite its size, its MMLU benchmark performance is currently unimpressive, with expectations that Grok-2 will be more competitive. The model's weights and code are publicly available, encouraging community experimentation. Sam Altman highlighted the growing importance of compute resources, while Grok's potential deployment on Groq hardware was noted as a possible game-changer. Meanwhile, Anthropic's Claude continues to attract attention for its "spiritual" interaction experience and consistent ethical framework. The release also inspired memes and humor within the AI community.
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.
Inflection-2.5 at 94% of GPT4, and Pi at 6m MAU
inflection-2.5 claude-3-sonnet claude-3-opus gpt-4 yi-9b mistral inflection anthropic perplexity-ai llamaindex mistral-ai langchain retrieval-augmented-generation benchmarking ocr structured-output video-retrieval knowledge-augmentation planning tool-use evaluation code-benchmarks math-benchmarks mustafa-suleyman amanda-askell jeremyphoward abacaj omarsar0
Mustafa Suleyman announced Inflection 2.5, which achieves more than 94% the average performance of GPT-4 despite using only 40% the training FLOPs. Pi's user base is growing about 10% weekly, with new features like realtime web search. The community noted similarities between Inflection 2.5 and Claude 3 Sonnet. Claude 3 Opus outperformed GPT-4 in a 1.5:1 vote and is now the default for Perplexity Pro users. Anthropic added experimental tool calling support for Claude 3 via LangChain. LlamaIndex released LlamaParse JSON Mode for structured PDF parsing and added video retrieval via VideoDB, enabling retrieval-augmented generation (RAG) pipelines. A paper proposed knowledge-augmented planning for LLM agents. New benchmarks like TinyBenchmarks and the Yi-9B model release show strong code and math performance, surpassing Mistral.
Not much happened today
claude-3 claude-3-opus claude-3-sonnet gpt-4 gemma-2b anthropic perplexity langchain llamaindex cohere accenture mistral-ai snowflake together-ai hugging-face european-space-agency google gpt4all multimodality instruction-following out-of-distribution-reasoning robustness enterprise-ai cloud-infrastructure open-datasets model-deployment model-discoverability generative-ai image-generation
Anthropic released Claude 3, replacing Claude 2.1 as the default on Perplexity AI, with Claude 3 Opus surpassing GPT-4 in capability. Debate continues on whether Claude 3's performance stems from emergent properties or pattern matching. LangChain and LlamaIndex added support for Claude 3 enabling multimodal and tool-augmented applications. Despite progress, current models still face challenges in out-of-distribution reasoning and robustness. Cohere partnered with Accenture for enterprise AI search, while Mistral AI and Snowflake collaborate to provide LLMs on Snowflake's platform. Together AI Research integrates Deepspeed innovations to accelerate generative AI infrastructure. Hugging Face and the European Space Agency released a large earth observation dataset, and Google open sourced Gemma 2B, optimized for smartphones via the MLC-LLM project. GPT4All improved model discoverability for open models. The AI community balances excitement over new models with concerns about limitations and robustness, alongside growing enterprise adoption and open-source contributions. Memes and humor continue to provide social commentary.
Claude 3 just destroyed GPT 4 (see for yourself)
claude-3 claude-3-opus claude-3-sonnet claude-3-haiku gpt-4 anthropic amazon google claude-ai multimodality vision long-context model-alignment model-evaluation synthetic-data structured-output instruction-following model-speed cost-efficiency benchmarking safety mmitchell connor-leahy
Claude 3 from Anthropic launches in three sizes: Haiku (small, unreleased), Sonnet (medium, default on claude.ai, AWS, and GCP), and Opus (large, on Claude Pro). Opus outperforms GPT-4 on key benchmarks like GPQA, impressing benchmark authors. All models support multimodality with advanced vision capabilities, including converting a 2-hour video into a blog post. Claude 3 offers improved alignment, fewer refusals, and extended context length up to 1 million tokens with near-perfect recall. Haiku is noted for speed and cost-efficiency, processing dense research papers in under three seconds. The models excel at following complex instructions and producing structured outputs like JSON. Safety improvements reduce refusal rates, though some criticism remains from experts. Claude 3 is trained on synthetic data and shows strong domain-specific evaluation results in finance, medicine, and philosophy.