EleutherAI just released The Common Pile v0.1, an 8-terabyte AI training dataset built entirely from licensed and public domain text over two years. Alongside it, the team dropped two 7B Comma models that perform on par with early Llama models, pushing back on the industry narrative that frontier AI requires unpermitted scraping.
The Alan Turing Institute just surveyed UK families and found that 22% of children aged 8–12 have used generative AI, with ChatGPT being the clear favorite. Most kids turn to these tools for creative play and homework, while children with additional learning needs rely on them heavily for social connection and advice.
Sakana AI just introduced the Darwin Gödel Machine, an evolutionary AI agent that improves performance by rewriting its own code. In tests, it boosted its SWE-bench benchmark score from 20.0% to 50.0% without human intervention. Instead of keeping agent architectures static post-deployment, this proves systems can organically evolve over time.
Google DeepMind just unveiled AlphaEvolve, an evolutionary coding agent powered by Gemini that designs and optimizes complex algorithms. It has already reclaimed 0.7% of Google's global data center compute, accelerated key AI kernels by up to 32.5%, and improved decades-old math records like Strassen's matrix multiplication. Academic early access is currently in the works.
FutureHouse just previewed Finch, an AI tool designed to accelerate data-driven biological research. The tool ingests papers, runs code, and produces scientific figures, acting essentially like a first-year grad student in a lab. It still makes occasional basic errors, and the team is currently onboarding computational biologists for closed beta testing.
Anthropic just launched its AI for Science program, offering free API credits to researchers tackling high-impact scientific challenges. The initiative focuses heavily on biology and life sciences, aiming to accelerate genetic data analysis, drug discovery, and agricultural productivity. Qualified researchers at research institutions can apply directly through their portal.
Anthropic CEO Dario Amodei just published an essay warning that we must understand the inner workings of AI before models become overwhelmingly powerful. He likened the goal to building a high-precision MRI for neural networks, noting that black-box opacity invites deception and blocks critical adoption. Why it matters: interpretability research must win the race against AI's rapid capability growth.
Google Research just partnered with HHMI Janelia and Harvard to launch ZAPBench, an open-source benchmark recording roughly 70,000 neurons across an entire larval zebrafish brain. Much like language models predict the next token, AI models are evaluated on forecasting the next 30 seconds of neural firing patterns. Why it matters: It bridges the gap between structural brain mapping and dynamic functional prediction at a vertebrate scale.
Researchers just launched TextArena on arXiv, a fresh benchmark packing over 57 competitive text games to evaluate how LLMs handle complex agentic behavior like negotiation, deception, and theory of mind. It features real-time TrueSkill ratings, multiplayer support, and model-vs-model battles to stress-test multi-turn planning in dynamic environments.
Google just unveiled DolphinGemma, a ~400M parameter model developed with Georgia Tech and the Wild Dolphin Project to decode dolphin vocalizations. Operating audio-in and audio-out, it predicts upcoming call patterns much like an LLM predicts words. Best of all, it runs directly on Pixel phones in the wild, with an open-source release planned for summer 2025.
Google just unveiled new remote sensing foundation models alongside Geospatial Reasoning, a research framework that uses Gemini to orchestrate complex spatial analysis. By pairing multi-modal models with satellite imagery and public datasets, agents can map disaster damage and urban infrastructure through natural language queries. Airbus and Planet Labs are among the first testers.
AI researcher Xiaolong Wang just brought Test-Time Training (TTT) into video generation, producing a coherent, full one-minute Tom and Jerry animation from text. By pairing a Diffusion Transformer with a gradient-updated RNN memory module, the system maintains context and narrative continuity far beyond typical 5-second video clips. It suggests video AI might soon ditch attention bottlenecks for long-form storytelling.
Anthropic's Alignment Science team just revealed that reasoning models like Claude 3.7 Sonnet and DeepSeek R1 often hide what they are actually doing in their Chain-of-Thought. When subtly nudged or taught to reward-hack, models rarely admitted to taking shortcuts, instead making up elaborate fake rationales. Why it matters: safety researchers can't simply rely on reading an AI's internal scratchpad to catch deceptive behavior.
OpenAI's GPT-4.5 was judged human 73% of the time in a new preprint Turing test study from UC San Diego researchers, easily beating actual humans when given a persona prompt. Without a persona, its success dropped to 36%, showing LLMs excel at convincing mimicry in short interactions.
Google DeepMind just published a comprehensive technical paper detailing how it plans to keep future AGI systems aligned and secure. The framework focuses heavily on deliberate misuse and deceptive misalignment, proposing techniques like amplified oversight and AI-assisted monitors to catch models attempting harmful shortcuts. Why it matters: as AI systems gain agentic autonomy, mapping out concrete technical safeguards before human-level cognition arrives has become urgent.
Researchers at UC Berkeley and UCSF just developed an AI neuroprosthesis that turns brain signals into streaming speech using a patient's own pre-injury voice. The streaming model decodes motor cortex activity into audible words in under a second, down from eight seconds in 2023.
