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scaling AI in science and education — Google DeepMind

OpenAIMarch 17, 202653Views 0Likes 0Comments

Introducing our National Partnerships for AI and collaboration in India We believe AI will be the most transformative technology in human history and that it should be deployed in ways that benefit all of humanity. This requires deep, strategic collaboration between frontier AI labs, governments, academia, and civil society. To fully realise AI’s potential, Google…

Physical AI: Bridging Robotics, Material Science, and Artificial Intelligence for Next-Gen Embodied Systems

RoboticsMarch 17, 202646Views 0Likes 0Comments

What Do We Mean by “Physical AI”? Artificial intelligence in robotics is not just a matter of clever algorithms. Robots operate in the physical world, and their intelligence emerges from the co-design of body and brain. Physical AI describes this integration, where materials, actuation, sensing, and computation shape how learning policies function. The term was…

When You Should Not Deploy Agents

UncategorisedMarch 14, 202637Views 0Likes 0Comments

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The 2026 Data Science Starter Kit: What to Learn First (And What to Ignore)

Data AnalyticsMarch 12, 202655Views 0Likes 0Comments

Image by Author # Introduction If you are reading this article, you likely know a bit of Python, and you are curious about data science. You might have written a few loops, maybe even used a library like Pandas. But now you face a common problem. The field of data science is vast,…

Thinking Machines Lab Makes Tinker Generally Available: Adds Kimi K2 Thinking And Qwen3-VL Vision Input

AI NewsMarch 12, 2026115Views 0Likes 0Comments

Thinking Machines Lab has moved its Tinker training API into general availability and added 3 major capabilities, support for the Kimi K2 Thinking reasoning model, OpenAI compatible sampling, and image input through Qwen3-VL vision language models. For AI engineers, this turns Tinker into a practical way to fine tune frontier models without building distributed training…

How AI Innovation Is Paving the Path to AGI — Google DeepMind

OpenAIMarch 12, 202645Views 0Likes 0Comments

Catalyzing breakthroughs in science By proving it could navigate the massive search space of a Go board, AlphaGo demonstrated the potential for AI to help us better understand the vast complexities of the physical world. We started by attempting to solve the protein folding problem, a 50-year grand challenge of predicting the 3D structure of…

Are OpenAI and Google intentionally downgrading their models?

UncategorisedMarch 12, 202658Views 0Likes 0Comments

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A Coding Guide to End-to-End Robotics Learning with LeRobot: Training, Evaluating, and Visualizing Behavior Cloning Policies on PushT

RoboticsMarch 12, 202655Views 0Likes 0Comments

In this tutorial, we walk step by step through using Hugging Face’s LeRobot library to train and evaluate a behavior-cloning policy on the PushT dataset. We begin by setting up the environment in Google Colab, installing the required dependencies, and loading the dataset through LeRobot’s unified API. We then design a compact visuomotor policy that…

We ran 16 AI Models on 9,000+ Real Documents. Here’s What We Found.

UncategorisedMarch 11, 202637Views 0Likes 0Comments

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5 Powerful Python Decorators to Optimize LLM Applications

Data AnalyticsMarch 7, 202653Views 0Likes 0Comments

Image by Editor # Introduction Python decorators are tailor-made solutions that are designed to help simplify complex software logic in a variety of applications, including LLM-based ones. Dealing with LLMs often involves coping with unpredictable, slow—and frequently expensive—third-party APIs, and decorators have a lot to offer for making this task cleaner by wrapping,…