Technology that matters

Machine Learning

Waymo's School Bus Training Program Failed, Safety Issues Persist

What Happened Austin Independent School District entered into a partnership with Waymo to address a critical safety issue: the company’s autonomous vehicles were failing to recognize when school buses had activated their stop signs and flashing lights. The collaboration aimed to provide Waymo with real-world data on school bus operations, including lighting patterns, environmental conditions, and bus configurations. Despite this direct training approach and a December 2025 software recall affecting over 3,000 Waymo vehicles equipped with Jaguar I-Pace platforms, the safety violations have continued.

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Robots Learn Tennis from Humans in Major Breakthrough

What Happened A new robotics system called LATENT (Learns Athletic humanoid TEnnis skills from imperfect human motioN daTa) has successfully taught humanoid robots to play tennis by analyzing and learning from human players, even when the training data is incomplete or imperfect. Unlike previous approaches that required precise motion capture data or perfect demonstrations, LATENT can work with real-world human tennis footage and movements that contain natural variations and inconsistencies. The system uses deep reinforcement learning to translate human athletic behavior into robot movements, enabling humanoids to conduct competitive rallies with high-speed tennis balls.

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Nvidia Commits $26 Billion to Develop Open-Weight AI Models

What Happened Nvidia Corporation revealed in regulatory filings its intention to spend $26 billion on developing open-weight artificial intelligence models. Unlike the closed systems offered by companies like OpenAI (GPT-4) and Anthropic (Claude), open-weight models make their underlying parameters publicly available, allowing developers and researchers to inspect, modify, and deploy these models independently. This investment marks a significant strategic shift for Nvidia, which has primarily focused on providing the hardware infrastructure that powers AI systems rather than creating the models themselves.

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Yann LeCun Raises $1B to Build AI That Understands Physics

What Happened AMI Labs, co-founded by Yann LeCun and led by CEO Alexandre LeBrun (former CEO of medical AI company Nabla), secured $1.03 billion in March 2026 to develop what researchers call “world models” - AI systems designed to understand how the physical world operates. The funding round, which values the company at $3.5 billion before the investment, marks the largest seed funding round for a European AI startup in history.

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AI Successfully Verifies Fields Medal-Winning Math Proofs

What Happened Math, Inc. announced today that their Gauss AI system has formally verified the mathematical proofs that earned Maryna Viazovska her Fields Medal—mathematics’ equivalent of the Nobel Prize. Viazovska, who became only the second woman to receive the honor in its 86-year history, solved two versions of the notoriously difficult sphere packing problem in 2016. The sphere packing problem asks a deceptively simple question: How densely can identical spheres be packed in n-dimensional space?

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AI Math Performance Jumps from 2% to 40% in Just Months

What Happened Epoch AI’s Frontier Math benchmark has become an unexpected showcase for the explosive pace of AI development. When the non-profit research organization quietly released this standardized test in November 2024, state-of-the-art AI models could solve less than 2% of its challenging mathematical problems. Today, the landscape has transformed dramatically. The best publicly available AI models are now solving over 40% of Frontier Math’s original 300 problems (tiers 1-3), which span from advanced undergraduate to early graduate-level mathematics.

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