AI has learned to play Pokemon. The newest model Claude 3.7 Sonnet was ordered to play Pokemon Red, and it has already beaten the leader of the first stadium.

DeepSeek introduced DeepSeek-V3/R1 Inference System Overview

Optimized throughput and latency via:

1. Cross-node EP-powered batch scaling
2. Computation-communication overlap
3. Load balancing

Statistics of DeepSeek's Online Service:
- 73.7k/14.8k input/output tokens per second per H800 node
- Cost profit margin 545%

Huge VLM release from Cohere for AI is just in

Aya-Vision is a new VLM family based on SigLIP and Aya, and it outperforms many larger models.

> 8B and 32B models covering 23 languages and two new benchmark dataset
> supported by HF transformers from get-go

Sales AI market map

Today Anthropic submitted their recommendations to the OSTP for the U.S. AI Action Plan

Anthropic predicts powerful AI systems will appear by late 2026 or early 2027, with intellectual abilities matching Nobel Prize winners, able to autonomously handle digital tasks (text, audio, video, internet browsing), reason independently over hours or weeks, and control physical equipment digitally

They recommend stronger national security actions, including government testing of AI models for security risks, stricter export controls on key chips like the H20, and secure communication channels between AI labs and intelligence agencies

They suggest the government build 50 gigawatts of additional power capacity dedicated to AI by 2027, speed up AI adoption across federal agencies, and improve economic data collection to prepare for AI’s impact on jobs and society

MALT: Improving Reasoning with Multi-Agent LLM Training

A new multi-agent post-training method that uses credit assigned synthetic data to improve the reasoning capabilities and self-correction rates of a generator, critic, and refinement model working together.

Paper.

Cortical Labs announced the world's first biocomputer

CL1 merges real living neurons with a chip to solve complex problems and redefine research.

OpenAI has evolved their thinking about AGI development.

Rather than viewing AGI as a sudden leap, they now see it as a continuous series of increasingly capable systems.

Key Risks They're Addressing

1. Human Misuse: People using AI in ways that violate laws and democratic values.

2. Misaligned AI: AI systems acting in ways that don't align with human values and intentions.

3. Societal Disruption: Rapid AI-driven changes causing unpredictable effects on society and inequality.

Their Core Safety Principles
Embracing Uncertainty

They treat safety as a science, learning through real-world deployment rather than just theory. This includes rigorous measurement of risks and proactive mitigation strategies.

Defense in Depth

OpenAI applies multiple layers of safeguards, similar to other safety-critical fields. They teach models to understand safety values, follow instructions, and remain reliable even under uncertainty.

Methods that Scale

They develop safety approaches that become more effective as AI capabilities increase, even using current AI systems to help align more advanced ones.

Human Control

OpenAI places humans at the center of their alignment approach, creating transparent systems that people can meaningfully supervise. They incorporate public feedback into policy formation and work on interfaces that help humans guide AI effectively.

Community Effort

They recognize that ensuring safe AGI requires collaboration across industry, academia, government, and the public. OpenAI shares research, provides resources to the field, funds external research, and engages with policymakers.

While OpenAI has a clear vision for safety, they remain open to being wrong about how AI progress will unfold and welcome diverse perspectives on AI risk management.

Google introduced Gemini Robotics is the most advanced VLA in the world

Gemini Robotics featured in the post, builds on Gemini 2.0, introducing advanced vision-language-action capabilities to control robots physically.

The technology enables robots to understand and react to the physical world, performing tasks like desk cleanup through voice commands, as part of a broader push toward embodied AI.

Gemini Robotics-ER, a related model, enhances spatial understanding, allowing robots to adapt to dynamic environments and interact seamlessly with humans.

Tech report.

Cohere introduced Command A: a new AI model that can match or outperform GPT-4o and DeepSeek-V3 on business tasks, with significantly greater efficiency.

Command A is an open-weights 111B parameter model with a 256k context window focused on delivering great performance across agentic, multilingual, and coding usecases.

Runs on only 2 GPUs (vs. typically 32), offers 256k context length, supports 23 languages and delivers up to 156 tokens/sec.

API.

Gemini Robotics is an artificial intelligence model based on Gemini 2.0, designed to introduce artificial intelligence capabilities into the physical world.

The new system allows robots to understand the environment, interact naturally with humans, and perform complex tasks with agility.

Crazy results
Hedra Character-3 generates video, creates sounds and even sets up lip sync correctly. You can throw in a picture, and the service will completely bring it to life.

Magic, and nothing more

Biggest deal in Google/Alphabet history: Google is buying Wiz for $32B to beef up in cloud security

Wiz. is an Israeli cloud security startup headquartered in New York City. The company was founded in January 2020.

This acquisition positions Google to better compete with AWS and Azure.

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Nvidia has unveiled a base AI model for humanoid robotics, Groot N1. With this core, robots will be able to think “fast and slow” to instantly execute simple commands or reason.

Cool paper by Google DeepMind. Most AI systems assume there’s just one right answer—but many tasks have reasonable disagreement. How can better model human variation?

Researchers propose modeling at the individual-level using open-ended, textual value profiles

What's the difference between AI and truly 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀?

The key difference? 𝗦𝘁𝗮𝘁𝗶𝗰 𝗔𝗜 𝗺𝗼𝗱𝗲𝗹𝘀 𝘃𝘀. 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀.