We’re releasing an analysis showing that since 2012, the amount of compute used in the largest AI training runs has been increasing exponentially with a 3. 4-month doubling time (by comparison, Moore’s Law had a 2-year doubling period)[^footnote-correction].
We’re releasing an analysis showing that since 2012 the amount of compute needed to train a neural net to the same performance on ImageNet classification has been decreasing by a factor of 2 every 16 months. Compared to 2012, it now takes 44 times less compute to train a neural network to the level of AlexNet (by contrast, Moore’s Law would yield an 11x cost improvement over this period).
arXiv:2609.05894v1 Announce Type: new Abstract: The exponentiation of Artificial intelligence (AI) in the recent past has entered a transformative era that has been driven by the growth in large lang...
Our Dota 2 result shows that self-play can catapult the performance of machine learning systems from far below human level to superhuman, given sufficient compute. In the span of a month, our system went from barely matching a high-ranked player to beating the top pros and has continued to improve since then.
Microsoft Research reports that offloading AI inference from robots to external hardware can enhance task success, increase efficiency, and enable more advanced physical AI workloads. The study suggests that moving inference beyond the robot’s onboard processors allows the hardware to keep pace with growing AI capabilities. This approach demonstrates a practical way to improve robotic performance in real-world settings.
arXiv:2606. 07632v1 Announce Type: new Abstract: Proper accounting of the energy requirements and environmental impact of artificial intelligence (AI) systems is necessary for researchers, developers, policy makers, and users to assess the barriers to building systems at scale.
The paper analyzes the environmental footprint of machine learning model training, focusing on large language models and their hardware. It finds that energy use and environmental impacts have risen exponentially over the past decade, even when employing carbon‑efficient electricity and more efficient hardware. The study argues that optimization strategies alone cannot curb these impacts due to a rebound effect, and stresses the need to evaluate hardware life‑cycle impacts and integrate environmental metrics into NLP research practices.
The paper "The AI-Enabled Scientific Frontier" analyzes 2,507 head‑to‑head comparisons of AI versus other scientific methods across 27 disciplines from 2000 to early 2025. It finds that AI often outperforms traditional statistics but at higher computational cost, while in about a quarter of cases AI is both more expensive and less effective. Compared to scientific computing, AI usually underperforms but at lower cost, though since 2020 its performance has improved, now surpassing computing in over half of the comparisons.
The paper introduces AIDE^2, an AI research agent that recursively improves its own code by proposing, benchmarking, and selecting modifications. Over an eight‑day autonomous run, it achieved seven successive improvements—including new search policies and memory mechanisms—that transferred to four held‑out benchmarks in machine learning, algorithm engineering, and weather forecasting. The agent’s best version matched or outperformed a top human‑engineered production research agent and also reduced reward‑hacking rates, despite never optimizing for that metric.
arXiv:2606. 11215v1 Announce Type: cross Abstract: Large Language Model (LLM) usage in recent years has become increasingly widespread in the Artificial Intelligence in Education (AIED) community.
arXiv:2606. 15708v1 Announce Type: new Abstract: Welcome to the ninth edition of the AI Index report.
arXiv:2608. 13940v1 Announce Type: new Abstract: AI research agents (AIRA) can now propose, implement, and evaluate their own machine learning experiments, but progress on frontier tasks is throttled by cost: a candidate solution can be written in minutes, whereas evaluating it can take hours to days of GPU time.
DeepMind introduces private, server‑side memory to its Private AI Compute platform, enabling personal AI applications to operate with secure, isolated memory on the server side. This enhancement focuses on protecting user data while allowing AI models to run efficiently in a private environment. The update expands the capabilities of Private AI Compute by adding a new layer of security and privacy for personal AI use cases.
