Twenty-five years from today, Santa Ana winds will scream through Los Angeles on a dry autumn morning, turning a small hillside campfire into a deadly, fast-moving blaze. At that moment, the city will spring into action. Los Angeles knows how to weather a crisis -- or two or three. Angelenos are tapping into that resilience, striving to build a city for everyone. Satellites will team up with anemometers, pairing live aerial footage with wind patterns to tell firefighters exactly where the fire is going.

A Mitsubishi Electric machine has cracked the notoriously challenging Rubik's Cube puzzle in less than a third of a second. In the blink of an eye, computer-controlled components moved the squares of the 3 x 3 x 3 cube until each side of the block was a single color, thus completing the game. Humans present applauded the feat. Guinness World Records recognized the 0.305-second time achieved by the TOKUI Fast Accurate Synchronized Motion Testing Robot as a new world best, with it beating the previous record of 0.38 seconds. Mitsubishi Electric received a certificate from the records body on May 21. The fastest time by a human is 3.13 seconds, achieved in June 2023 by Max Park at an event in California.

Fox News contributor Dr. Marc Siegel weighs in on how artificial intelligence can change the patient-doctor relationship on'America's Newsroom.' Artificial intelligence has been able to beat masters at games like chess and poker and Go. AI has also been able to beat human competitors in various video games. While impressive nonetheless, there is one major capability that these games do not require of the AI: physical skill. CyberRunner is an AI tasked with learning how to play the popular labyrinth maze game.

We find that improvements in speedrunning world records follow a power law pattern. Using this observation, we answer an outstanding question from previous work: How do we improve on the baseline of predicting no improvement when forecasting speedrunning world records out to some time horizon, such as one month? Using a random effects model, we improve on this baseline for relative mean square error made on predicting out-of-sample world record improvements as the comparison metric at a $p < 10^{-5}$ significance level. The same set-up improves \textit{even} on the ex-post best exponential moving average forecasts at a $p = 0.15$ significance level while having access to substantially fewer data points. We demonstrate the effectiveness of this approach by applying it to Machine Learning benchmarks and achieving forecasts that exceed a baseline. Finally, we interpret the resulting model to suggest that 1) ML benchmarks are far from saturation and 2) sudden large improvements in Machine Learning are unlikely but cannot be ruled out.

This blog focuses on scaling many model training. While much of the buzz is around large model training, in recent years, more and more companies have found themselves needing to train and deploy many smaller machine learning models, often hundreds or thousands. Our team has worked with hundreds of companies looking to scale machine learning in production, and this blog post aims to cover the motivation and some best practices for training many models. Using the approaches described here, companies have seen order-of-magnitude performance and scalability wins (e.g., 12x for Instacart, 9x for Anastasia) relative to frameworks like Celery, AWS Batch, AWS SageMaker, Vertex AI, Dask, and more. While cutting edge applications of machine learning are leading to an explosion in model size, the need for many models cuts across industries.

Instant NeRF, a neural network-based technology capable of transforming a set of 2D photos into high-resolution 3D scenes in seconds, was introduced at an NVIDIA GTC session in March. According to the NVIDIA Research team, this would be one of the first models of its kind to combine ultra-fast neural network training and fast rendering. In its press release, NVIDIA recalls the technological revolution that Edwin Land brought on February 21, 1947 by producing an instant photo with a polaroid camera. NVIDIA Research pays tribute to him by recreating an iconic photo of Andy Warhol taking an instant photo, transforming it into a 3D scene using Instant NeRF. Artificial intelligence researchers at NVIDIA Research took the opposite approach with the goal of transforming a set of still images into a 3D digital scene in seconds.

An in-depth genetic analysis can contribute to the issuance of a timely clinical diagnosis, to focus treatment on the specific condition being faced.A task that usually takes up to two weeks was reduced to just over five hours, thanks to an artificial intelligence system developed by Stanford University.Genetic analysis accelerated…

The discovery of new drugs is an undeniably important undertaking and represents a massive global market. Statista indicates that the drug discovery market worldwide finds itself on an exponential trajectory, with the expected market value poised to reach 71 billion U.S. dollars by 2025. As of 2016, the market was valued at just 35.2 billion U.S. dollars. Of course, this comes as no surprise; the U.S. pharmaceutical industry, after all, was coined'Big Pharma' for a reason. By 2021, Big Pharma profits for prescription drugs are expected to reach $610 billion and, in 2015, Americans spent $457 billion on prescription drugs.

The magnitude of the Covid-19 pandemic will largely depend on how quickly safe and effective vaccines and treatments can be developed and tested. Many assume a widely available vaccine is years away, if ever. Others believe that a 12- to 18-month development cycle is a given. Our best bet to reduce even that record-breaking timeline is by using artificial intelligence. The problem is twofold: discovering the right set of molecules among billions of possibilities, and then waiting for clinical trials. These processes ordinarily take several years, but AI holds the key to radically shortening both.

In 1928, a Scottish scientist named Sir Alexander Fleming left his lab where he was studying the staphylococcus bacteria to go on a two-week vacation with his family. When he returned to his lab bench, he not only realized he hadn't tidied his work space very well, but that the dishes with the bacteria in them were growing mold. He also noticed that the bacteria seemed to be actively avoiding the moldy areas of the petri dish. Later he said "I certainly didn't plan to revolutionize all medicine by discovering the world's first antibiotic, or bacteria killer. But I suppose that was exactly what I did."