All tags
Topic: "model-architecture"
not much happened today
dots-llm1 qwen3-235b xiaohongshu rednote-hilab deepseek huggingface mixture-of-experts open-source model-benchmarking fine-tuning inference context-windows training-data model-architecture model-performance model-optimization
China's Xiaohongshu (Rednote) released dots.llm1, a 142B parameter open-source Mixture-of-Experts (MoE) language model with 14B active parameters and a 32K context window, pretrained on 11.2 trillion high-quality, non-synthetic tokens. The model supports efficient inference frameworks like Docker, HuggingFace, and vLLM, and provides intermediate checkpoints every 1 trillion tokens, enabling flexible fine-tuning. Benchmarking claims it slightly surpasses Qwen3 235B on MMLU, though some concerns exist about benchmark selection and synthetic data verification. The release is notable for its truly open-source licensing and no synthetic data usage, sparking community optimism for support in frameworks such as llama.cpp and mlx.
Gemini 2.5 Pro (06-05) launched at AI Engineer World's Fair
gemini-2.5-pro qwen3-embedding-8b openthinker3-7b google qwen lighton morph-labs openai nvidia benchmarking reasoning coding math embedding-models late-interaction dataset-release model-performance model-architecture ai-conferences greg_brockman jensen_huang christian_szegedy swyx
At the second day of AIE, Google's Gemini 2.5 Pro reclaimed the top spot on the LMArena leaderboard with a score of 1470 and a +24 Elo increase, showing improvements in coding, reasoning, and math. Qwen3 released state-of-the-art embedding and reranking models, with Qwen3-Embedding-8B topping the MTEB multilingual leaderboard. OpenThinker3-7B emerged as the top open reasoning model trained on the OpenThoughts3-1.2M dataset, outperforming previous models by 33%. LightOn introduced FastPlaid, achieving up to a 554% speedup for late-interaction models. Morph Labs hired Christian Szegedy as Chief Scientist to lead Verified Superintelligence development. The AI Engineer World's Fair featured a fireside chat with Greg Brockman and NVIDIA CEO Jensen Huang, highlighting the return of basic research and engineering best practices.
AI Engineer World's Fair Talks Day 1
gemini-2.5 gemma claude-code mistral cursor anthropic openai aie google-deepmind meta-ai-fair agent-based-architecture open-source model-memorization scaling-laws quantization mixture-of-experts language-model-memorization model-generalization langgraph model-architecture
Mistral launched a new Code project, and Cursor released version 1.0. Anthropic improved Claude Code plans, while ChatGPT announced expanded connections. The day was dominated by AIE keynotes and tracks including GraphRAG, RecSys, and Tiny Teams. On Reddit, Google open-sourced the DeepSearch stack for building AI agents with Gemini 2.5 and LangGraph, enabling flexible agent architectures and integration with local LLMs like Gemma. A new Meta paper analyzed language model memorization, showing GPT-style transformers store about 3.5–4 bits/parameter and exploring the transition from memorization to generalization, with implications for Mixture-of-Experts models and quantization effects.
Qwen 3: 0.6B to 235B MoE full+base models that beat R1 and o1
qwen-3 qwen3-235b-a22b qwen3-30b-a3b deepseek-r1 o1 o3-mini grok-3 gemini-2.5-pro alibaba google-deepmind deepseek mistral-ai mixture-of-experts reinforcement-learning benchmarking model-release model-architecture long-context multi-agent-systems inference dataset-release awnihannun prince_canuma actuallyisaak oriolvinyalsml iscienceluvr reach_vb teortaxestex omarsar0
Qwen 3 has been released by Alibaba featuring a range of models including two MoE variants, Qwen3-235B-A22B and Qwen3-30B-A3B, which demonstrate competitive performance against top models like DeepSeek-R1, o1, o3-mini, Grok-3, and Gemini-2.5-Pro. The models introduce an "enable_thinking=True" mode with advanced soft switching for inference scaling. The release is notable for its Apache 2.0 license and broad inference platform support including MCP. The dataset improvements and multi-stage RL post-training contribute to performance gains. Meanwhile, Gemini 2.5 Pro from Google DeepMind shows strong coding and long-context reasoning capabilities, and DeepSeek R2 is anticipated soon. Twitter discussions highlight Qwen3's finegrained MoE architecture, large context window, and multi-agent system applications.
small news items
gpt-4.5 gpt-5 deepseek-r1-distilled-qwen-1.5b o1-preview modernbert-0.3b qwen-0.5b o3 openai ollama mistral perplexity cerebras alibaba groq bytedance math benchmarking fine-tuning model-performance reinforcement-learning model-architecture partnerships funding jeremyphoward arankomatsuzaki sama nrehiew_ danhendrycks akhaliq
OpenAI announced plans for GPT-4.5 (Orion) and GPT-5, with GPT-5 integrating the o3 model and offering unlimited chat access in the free tier. DeepSeek R1 Distilled Qwen 1.5B outperforms OpenAI's o1-preview on math benchmarks, while ModernBERT 0.3b surpasses Qwen 0.5b at MMLU without fine-tuning. Mistral and Perplexity adopt Cerebras hardware for 10x performance gains. OpenAI's o3 model won a gold medal at the 2024 International Olympiad in Informatics. Partnerships include Qwen with Groq. Significant RLHF activity is noted in Nigeria and the global south, and Bytedance is expected to rise in AI prominence soon. "GPT5 is all you need."
not much happened today
deepseek-v3 chatgpt-4 openai deepseek google qwen overfitting reasoning misguided-attention model-evaluation model-architecture finetuning open-source sam-altman
Sam Altman publicly criticizes DeepSeek and Qwen models, sparking debate about OpenAI's innovation claims and reliance on foundational research like the Transformer architecture. Deepseek V3 shows significant overfitting issues in the Misguided Attention evaluation, solving only 22% of test prompts, raising concerns about its reasoning and finetuning. Despite skepticism about its open-source status, Deepseek V3 is claimed to surpass ChatGPT4 as an open-source model, marking a milestone 1.75 years after ChatGPT4's release on March 14, 2023. The discussions highlight competitive dynamics in AI model performance and innovation sustainability.
Olympus has dropped (aka, Amazon Nova Micro|Lite|Pro|Premier|Canvas|Reel)
amazon-nova claude-3 llama-3-70b gemini-1.5-flash gpt-4o amazon anthropic google-deepmind sakana-ai-labs multimodality benchmarking model-merging model-performance model-architecture model-optimization population-based-learning philschmid bindureddy
Amazon announced the Amazon Nova family of multimodal foundation models at AWS Re:Invent, available immediately with no waitlist in configurations like Micro, Lite, Pro, Canvas, and Reel, with Premier and speech-to-speech coming next year. These models offer 2-4x faster token speeds and are 25%-400% cheaper than competitors like Anthropic Claude models, positioning Nova as a serious contender in AI engineering. Pricing undercuts models such as Google DeepMind Gemini Flash 8B, and some Nova models extend context length up to 300k tokens. However, benchmarking controversy exists as some evaluations show Nova scoring below Llama-3 70B in LiveBench AI metrics. Separately, CycleQD was introduced by Sakana AI Labs, using evolutionary computation for population-based model merging to develop niche LLM agents.
Tencent's Hunyuan-Large claims to beat DeepSeek-V2 and Llama3-405B with LESS Data
claude-3.5-haiku llama-3-1 llama-3-2 mlx-lm tencent anthropic meta-ai-fair togethercompute llamaindex mixture-of-experts synthetic-data model-scaling model-architecture model-optimization kv-cache-quantization react fine-tuning scaling-laws model-efficiency model-deployment multimodality
Tencent released a notable >300B parameter MoE model pretrained on 7T tokens, including 1.5T synthetic data generated via Evol-Instruct. The model introduces novel techniques like "recycle routing" and expert-specific learning rates, alongside a compute-efficient scaling law for MoE active parameters. However, its custom license restricts use in the EU and by companies with over 100M MAU, and it avoids China-sensitive queries. Meanwhile, Anthropic launched Claude 3.5 Haiku, now available on multiple platforms, praised for intelligence and speed but criticized for a 10x price increase. Meta opened Llama AI to the U.S. defense sector, and a Llama Impact Hackathon offers a $15K prize for projects using Llama 3.1 & 3.2 Vision. LlamaIndex released a React chat UI component with Tailwind CSS and LLM backend integrations. The MLX LM model advances text generation speed and efficiency with KV cache quantization.
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 technical happened today
whisper-v3-turbo llama-3 llamaindex openai poolside liquidai perplexity-ai meta-ai-fair cohere fujitsu mixture-of-experts context-windows model-optimization fine-tuning quantization model-training alignment synthetic-data model-architecture agentic-ai nick-turley arav-srinivas francois-fleuret finbarr-timbers lewtun francois-chollet jerry-j-liu mmitchell-ai jxnlco
OpenAI announced raising $6.6B in new funding at a $157B valuation, with ChatGPT reaching 250M weekly active users. Poolside raised $500M to advance AGI development. LiquidAI introduced three new MoE models (1B, 3B, 40B) with a 32k context window and efficient token handling. OpenAI released Whisper V3 Turbo, an open-source multilingual model with significant speed improvements. Meta AI FAIR is hiring research interns focusing on LLM reasoning, alignment, synthetic data, and novel architectures. Cohere partnered with Fujitsu to launch Takane, a custom Japanese model. Technical discussions included challenges in LoRA fine-tuning, float8 quantization in Keras, and new tools like create-llama for agent templates. Industry commentary raised concerns about AI development priorities and highlighted freelancing opportunities in AI.
Pixtral 12B: Mistral beats Llama to Multimodality
pixtral-12b mistral-nemo-12b llama-3-1-70b llama-3-1-8b deeps-eek-v2-5 gpt-4-turbo llama-3-1 strawberry claude mistral-ai meta-ai-fair hugging-face arcee-ai deepseek-ai openai anthropic vision multimodality ocr benchmarking model-release model-architecture model-performance fine-tuning model-deployment reasoning code-generation api access-control reach_vb devendra_chapilot _philschmid rohanpaul_ai
Mistral AI released Pixtral 12B, an open-weights vision-language model with a Mistral Nemo 12B text backbone and a 400M vision adapter, featuring a large vocabulary of 131,072 tokens and support for 1024x1024 pixel images. This release notably beat Meta AI in launching an open multimodal model. At the Mistral AI Summit, architecture details and benchmark performances were shared, showing strong OCR and screen understanding capabilities. Additionally, Arcee AI announced SuperNova, a distilled Llama 3.1 70B & 8B model outperforming Meta's Llama 3.1 70B instruct on benchmarks. DeepSeek released DeepSeek-V2.5, scoring 89 on HumanEval, surpassing GPT-4-Turbo, Opus, and Llama 3.1 in coding tasks. OpenAI plans to release Strawberry as part of ChatGPT soon, though its capabilities are debated. Anthropic introduced Workspaces for managing multiple Claude deployments with enhanced access controls.
$1150m for SSI, Sakana, You.com + Claude 500m context
olmo llama2-13b-chat claude claude-3.5-sonnet safe-superintelligence sakana-ai you-com perplexity-ai anthropic ai2 mixture-of-experts model-architecture model-training gpu-costs retrieval-augmented-generation video-generation ai-alignment enterprise-ai agentic-ai command-and-control ilya-sutskever mervenoyann yuchenj_uw rohanpaul_ai ctojunior omarsar0
Safe Superintelligence raised $1 billion at a $5 billion valuation, focusing on safety and search approaches as hinted by Ilya Sutskever. Sakana AI secured a $100 million Series A funding round, emphasizing nature-inspired collective intelligence. You.com pivoted to a ChatGPT-like productivity agent after a $50 million Series B round, while Perplexity AI raised over $250 million this summer. Anthropic launched Claude for Enterprise with a 500 million token context window. AI2 released a 64-expert Mixture-of-Experts (MoE) model called OLMo, outperforming Llama2-13B-Chat. Key AI research trends include efficient MoE architectures, challenges in AI alignment and GPU costs, and emerging AI agents for autonomous tasks. Innovations in AI development feature command and control for video generation, Retrieval-Augmented Generation (RAG) efficiency, and GitHub integration under Anthropic's Enterprise plan. "Our logo is meant to invoke the idea of a school of fish coming together and forming a coherent entity from simple rules as we want to make use of ideas from nature such as evolution and collective intelligence in our research."
Test-Time Training, MobileLLM, Lilian Weng on Hallucination (Plus: Turbopuffer)
llama-2-7b codegeex4-all-9b mamba facebook-research meta-ai-fair tsinghua-university hallucination-detection anti-hallucination-methods on-device-ai model-architecture rnn long-context-modeling model-scaling expressive-hidden-states code-generation lilian-weng yann-lecun
Lilian Weng released a comprehensive literature review on hallucination detection and anti-hallucination methods including techniques like FactualityPrompt, SelfCheckGPT, and WebGPT. Facebook AI Research (FAIR) published MobileLLM, a sub-billion parameter on-device language model architecture achieving performance comparable to llama-2-7b with innovations like thin and deep models and shared weights. A new RNN-based LLM architecture with expressive hidden states was introduced, replacing attention mechanisms and scaling better than Mamba and Transformer models for long-context modeling. Additionally, Tsinghua University open sourced CodeGeeX4-ALL-9B, a multilingual code generation model excelling in code assistance.
The Last Hurrah of Stable Diffusion?
llama-3-8b llama-3 qwen-2 gpt-4 gpt-4o stability-ai togethercompute model-architecture fine-tuning benchmarks dataset-release model-evaluation reasoning model-training retrieval-augmented-generation multimodality emad-mostaque rohanpaul_ai fchollet mikeknoop micahgoldblum teknium1 rasbt percyliang
Stability AI launched Stable Diffusion 3 Medium with models ranging from 450M to 8B parameters, featuring the MMDiT architecture and T5 text encoder for image text rendering. The community has shown mixed reactions following the departure of key researchers like Emad Mostaque. On AI models, Llama 3 8B Instruct shows strong evaluation correlation with GPT-4, while Qwen 2 Instruct surpasses Llama 3 on MMLU benchmarks. The Mixture of Agents (MoA) framework outperforms GPT-4o on AlpacaEval 2.0. Techniques like Spectrum and QLoRA enable efficient fine-tuning with less VRAM. Research on grokking reveals transformers can transition from memorization to generalization through extended training. Benchmark initiatives include the $1M ARC Prize Challenge for AGI progress and LiveBench, a live LLM benchmark to prevent dataset contamination. The Character Codex Dataset offers open data on over 15,000 characters for RAG and synthetic data. The MLX 0.2 tool enhances LLM experience on Apple Silicon Macs with improved UI and faster retrieval-augmented generation.
HippoRAG: First, do know(ledge) Graph
qwen-2 gpt-4 hipporag alibaba openai knowledge-graphs personalized-pagerank multi-hop-retrieval chain-of-thought implicit-reasoning sparse-autoencoders model-interpretability model-efficiency model-architecture fine-tuning reinforcement-learning rohanpaul_ai omarsar0 nabla_theta huybery
Alibaba released new open-source Qwen2 models ranging from 0.5B to 72B parameters, achieving SOTA results on benchmarks like MMLU and HumanEval. Researchers introduced Sparse Autoencoders to interpret GPT-4 neural activity, improving feature representation. The HippoRAG paper proposes a hippocampus-inspired retrieval augmentation method using knowledge graphs and Personalized PageRank for efficient multi-hop reasoning. New techniques like Stepwise Internalization enable implicit chain-of-thought reasoning in LLMs, enhancing accuracy and speed. The Buffer of Thoughts (BoT) method improves reasoning efficiency with significant cost reduction. A novel scalable MatMul-free LLM architecture competitive with SOTA Transformers at billion-parameter scale was also presented. "Single-Step, Multi-Hop retrieval" is highlighted as a key advancement in retrieval speed and cost.
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.
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.
Music's Dall-E moment
griffin command-r-plus gpt-4-0613 gpt-4-0314 mistral-8x22b codegemma stable-diffusion-1.5 command-r gemini-1.5 google mistral-ai lmsys cohere model-architecture benchmarking open-source model-quantization memory-optimization inference-speed multimodality finetuning performance-optimization audio-processing andrej-karpathy
Google's Griffin architecture outperforms transformers with faster inference and lower memory usage on long contexts. Command R+ climbs to 6th place on the LMSYS Chatbot Arena leaderboard, surpassing GPT-4-0613 and GPT-4-0314. Mistral AI releases an open-source 8x22B model with a 64K context window and around 130B total parameters. Google open-sources CodeGemma models with pre-quantized 4-bit versions for faster downloads. Ella weights enhance Stable Diffusion 1.5 with LLM for semantic alignment. Unsloth enables 4x larger context windows and 80% memory reduction for finetuning. Andrej Karpathy releases LLMs implemented in pure C for potential performance gains. Command R+ runs in realtime on M2 Max MacBook using iMat q1 quantization. Cohere's Command R model offers low API costs and strong leaderboard performance. Gemini 1.5 impresses with audio capabilities recognizing speech tone and speaker identification from audio clips.
Not much happened today
jamba-v0.1 command-r gpt-3.5-turbo openchat-3.5-0106 mixtral-8x7b mistral-7b midnight-miqu-70b-v1.0.q5_k_s cohere lightblue openai mistral-ai nvidia amd hugging-face ollama rag mixture-of-experts model-architecture model-analysis debate-persuasion hardware-performance gpu-inference cpu-comparison local-llm stable-diffusion ai-art-bias
RAGFlow open sourced, a deep document understanding RAG engine with 16.3k context length and natural language instruction support. Jamba v0.1, a 52B parameter MoE model by Lightblue, released but with mixed user feedback. Command-R from Cohere available on Ollama library. Analysis of GPT-3.5-Turbo architecture reveals about 7 billion parameters and embedding size of 4096, comparable to OpenChat-3.5-0106 and Mixtral-8x7B. AI chatbots, including GPT-4, outperform humans in debates on persuasion. Mistral-7B made amusing mistakes on a math riddle. Hardware highlights include a discounted HGX H100 640GB machine with 8 H100 GPUs bought for $58k, and CPU comparisons between Epyc 9374F and Threadripper 1950X for LLM inference. GPU recommendations for local LLMs focus on VRAM and inference speed, with users testing 4090 GPU and Midnight-miqu-70b-v1.0.q5_k_s model. Stable Diffusion influences gaming habits and AI art evaluation shows bias favoring human-labeled art.
Jamba: Mixture of Architectures dethrones Mixtral
jamba dbrx mixtral animatediff fastsd sdxs512-0.9 b-lora supir ai21-labs databricks together-ai hugging-face midjourney mixture-of-experts model-architecture context-windows model-optimization fine-tuning image-generation video-generation cpu-optimization style-content-separation high-resolution-upscaling
AI21 labs released Jamba, a 52B parameter MoE model with 256K context length and open weights under Apache 2.0 license, optimized for single A100 GPU performance. It features a unique blocks-and-layers architecture combining transformer and MoE layers, competing with models like Mixtral. Meanwhile, Databricks introduced DBRX, a 36B active parameter MoE model trained on 12T tokens, noted as a new standard for open LLMs. In image generation, advancements include Animatediff for video-quality image generation and FastSD CPU v1.0.0 beta 28 enabling ultra-fast image generation on CPUs. Other innovations involve style-content separation using B-LoRA and improvements in high-resolution image upscaling with SUPIR.
DBRX: Best open model (just not most efficient)
dbrx grok mixtral llama-2 mpt-7b gpt-4 databricks hugging-face mistral-ai mosaicml openai mixture-of-experts model-efficiency tokenization model-training code-generation model-architecture open-source-models benchmarking fine-tuning
Databricks Mosaic has released a new open-source model called DBRX that outperforms Grok, Mixtral, and Llama2 on evaluations while being about 2x more efficient than Llama2 and Grok. The model was trained on 12 trillion tokens using 3,000 H100 GPUs over 2 months, with an estimated compute cost of $10 million. It uses OpenAI's 100k tiktoken tokenizer and shows strong zero-shot code generation performance, even beating GPT-4 on the Humaneval benchmark. DBRX also upstreamed work to MegaBlocks open source. Despite its scale and efficiency, DBRX's performance on MMLU is only slightly better than Mixtral, raising questions about its scaling efficiency. The focus of DBRX is on enabling users to train models efficiently, with MoE training being about 2x more FLOP-efficient than dense models, achieving similar quality with nearly 4x less compute than previous MPT models. This release is part of the ongoing competition for open-source AI leadership, including models like Dolly, MPT, and Mistral. "If it activates 36B params, the model's perf should be equivalent to a 72B dense model or even 80B," says Qwen's tech lead.
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.
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.
1/4/2024: Jeff Bezos backs Perplexity's $520m Series B.
wizardcoder-33b-v1.1 mobilellama-1.4b-base shearedllama tinyllama mixtral-8x7b perplexity anthropic google nous-research mistral-ai hugging-face document-recall rnn-memory synthetic-data benchmarking multi-gpu-support context-length model-architecture sliding-window-attention model-parallelism gpu-optimization jeff-bezos
Perplexity announced their Series B funding round with notable investor Jeff Bezos, who previously invested in Google 25 years ago. Anthropic is raising $750 million, projecting at least $850 million in annualized revenue next year and implementing "brutal" changes to their Terms of Service. Discussions in Nous Research AI Discord cover topics such as document recall limits from gigabytes of data, RNN memory and compute trade-offs, synthetic datasets, and benchmarking of models like WizardCoder-33B-V1.1, MobileLLaMA-1.4B-Base, ShearedLLaMA, and TinyLLaMA. Other highlights include UnsLOTH optimizations for multi-GPU systems, AI rap voice models, context-extending code, and architectural innovations like applying Detectron/ViT backbones to LLMs, sliding window attention in Mistral, and parallelizing Mixtral 8x7b with FSDP and HF Accelerate.