But what about accuracy? For GM’s purposes, Strauss says accuracy is not a huge concern at the design stage because finer details are ironed out later in the process. “When it really starts to matter is when we’re getting close to launching a vehicle, and the coefficient of drag is going to be used for our energy calculation, which eventually goes to the certification of our miles per gallon on the sticker.” At that stage, Strauss says, a physical model of the car will be put into a wind tunnel for an exact number. PhysicsX’s Corbo argues that, with the right data, the AI model accuracy can su
AI Models Trained on Physics Are Changing Design Engineering… For Osuri, such potential lies in moving AI “to where the energy is instead of moving the energy to where AI is.” From Your Site Articles We Can Now Train Big Neural Networks on Small Devices › Here’s How to Make Deep Learning More Sustainable › Related Articles Around the Web Train AI on Your PC: …
… Currently, most companies are training their own models on their simulation data, catering to specific use cases. In GM’s aerodynamics implementation, there are different AI models for different types of cars: think SUVs versus sedans. …
… It wouldn’t be practical to spread out AI training workloads among a fleet of mini data centers. On the bright side, because training takes months, it’s possible to pause for short periods of time to curtail energy use during peak demand. …
… Not everyone needs to train the robots from scratch, Huang says, and most people probably shouldn’t. “If you gate pre-training, the field just never grows,” he says. “We should be able to provide a high-quality, state-of-the-art pre-trained model that anyone can go and take and fine tune for their … …
… Generative AI is evolving so fast that this becomes a real issue. As a result, these detection systems can perform well when tested against their training dataset or well-established benchmarks, but then perform poorly in the real world. “AI in the lab is not AI in the wild,” Roca says. …
… Importantly, the corpus of training data was both enormous and human-generated, which are characteristics that form the gold standard for AI training. …
… While that’s a more significant engineering challenge than a text-to-speech system, the Waikato project demonstrates that the necessary infrastructure already exists—efficient training on minimal data, phoneme-based input, open-source tools, and a legal and governance framework for community owners… …
… General-purpose AI models are not perfect readers of every handwritten page, but they are now suddenly good enough to change what archives can do. Pages that once required paleography training, custom software, or weeks of squinting can produce usable transcriptions in seconds. …
… From Your Site Articles AI Data Centers Turn to High-Temperature Superconductors › How Stupid Would It Be to Put Data Centers in Space? › Related Articles Around the Web AI satellite constellation startup Orbital gets funded by a16z to verify space-based data center concept - SiliconANGLE › NVIDIA …
… Sandia plans to finish its tests in July and write up its results in August. “Sandia is exploring what our next data center is going to look like,” and immersion cooling could play a part, Martinez says. “Right now this is looking pretty good as a player in our future.” From Your Site Articles Shou…