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Company: "suno-ai"
not much happened today
smollm2 llama-3-2 stable-diffusion-3.5 claude-3.5-sonnet gemini openai anthropic google meta-ai-fair suno-ai perplexity-ai on-device-ai model-performance robotics multimodality ai-regulation model-releases natural-language-processing prompt-engineering agentic-ai ai-application model-optimization sam-altman akhaliq arav-srinivas labenz loubnabenallal1 alexalbert fchollet stasbekman svpino rohanpaul_ai hamelhusain
ChatGPT Search was launched by Sam Altman, who called it his favorite feature since ChatGPT's original launch, doubling his usage. Comparisons were made between ChatGPT Search and Perplexity with improvements noted in Perplexity's web navigation. Google introduced a "Grounding" feature in the Gemini API & AI Studio enabling Gemini models to access real-time web information. Despite Gemini's leaderboard performance, developer adoption lags behind OpenAI and Anthropic. SmolLM2, a new small, powerful on-device language model, outperforms Meta's Llama 3.2 1B. A Claude desktop app was released for Mac and Windows. Meta AI announced robotics advancements including Meta Sparsh, Meta Digit 360, and Meta Digit Plexus. Stable Diffusion 3.5 Medium, a 2B parameter model with a permissive license, was released. Insights on AGI development suggest initial inferiority but rapid improvement. Anthropic advocates for early targeted AI regulation. Discussions on ML specialization predict training will concentrate among few companies, while inference becomes commoditized. New AI tools include Suno AI Personas for music creation, PromptQL for natural language querying over data, and Agent S for desktop task automation. Humor was shared about Python environment upgrades.
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.