The hierarchical reasoning model (HRM) system is modeled on the way the human brain processes complex information, and it outperformed leading LLMs in a notoriously hard-to-beat benchmark.
Category: robotics/AI
Engineers send a wireless curveball to deliver massive amounts of data
High frequency radio waves can wirelessly carry the vast amount of data demanded by emerging technology like virtual reality, but as engineers push into the upper reaches of the radio spectrum, they are hitting walls. Literally.
Ultrahigh frequency bandwidths are easily blocked by objects, so users can lose transmissions walking between rooms or even passing a bookcase.
Now, researchers at Princeton Engineering have developed a machine-learning system that could allow ultrahigh frequency transmissions to dodge those obstacles. In an article in Nature Communications, the researchers unveiled a system that shapes transmissions to avoid obstacles coupled with a neural network that can rapidly adjust to a complex and dynamic environment.
AI prescribes new electrolyte additive combinations for enhanced battery performance
Batteries, like humans, require medicine to function at their best. In battery technology, this medicine comes in the form of electrolyte additives, which enhance performance by forming stable interfaces, lowering resistance and boosting energy capacity, resulting in improved efficiency and longevity.
Finding the right electrolyte additive for a battery is much like prescribing the right medicine. With hundreds of possibilities to consider, identifying the best additive for each battery is a challenge due to the vast number of possibilities and the time-consuming nature of traditional experimental methods.
Researchers at the U.S. Department of Energy’s (DOE) Argonne National Laboratory are using machine learning models to analyze known electrolyte additives and predict combinations that could improve battery performance. They trained models to forecast key battery metrics, like resistance and energy capacity, and applied these models to suggest new additive combinations for testing.
New AI attack hides data-theft prompts in downscaled images
Researchers have developed a novel attack that steals user data by injecting malicious prompts in images processed by AI systems before delivering them to a large language model.
The method relies on full-resolution images that carry instructions invisible to the human eye but become apparent when the image quality is lowered through resampling algorithms.
Developed by Trail of Bits researchers Kikimora Morozova and Suha Sabi Hussain, the attack builds upon a theory presented in a 2020 USENIX paper by a German university (TU Braunschweig) exploring the possibility of an image-scaling attack in machine learning.
Is the AI boom finally starting to slow down?
“There’s a widening schism between the technologists who feel the A.G.I. – a mantra for believers who see themselves on the cusp of the technology – and members of the general public who are skeptical about the hype and see A.I. as a nuisance in their daily lives,” they wrote.
It’s unclear if the industry will take heed of these warnings. Investors look to every quarterly earnings report for signs that each company’s billions in capex spending is somehow being justified and executives are eager to give them hope. Boosting, boasting about and hyping the supposed promise and inevitability of AI is a big part of keeping investor concerns about the extra $10bn each company adds to its spending projections every quarter at bay. Mark Zuckerberg, for instance, recently said in the future if you’re not using AI glasses you’ll be at a cognitive disadvantage much like not wearing corrective lenses. That means tech firms such as Meta and Google will probably continue making the AI features that they offer today an almost inescapable part of using their products in a play to boost their training data and user numbers.
That said, the first big test of this AI reality check will come on Wednesday when chipmaker Nvidia – one of the building blocks of most LLMs – will report its latest earnings. Analysts seem pretty optimistic but after a shaky week for its stocks, investor reactions to Nvidia’s earnings and any updates on spending will be a strong signal of whether they have a continued appetite for the AI hype machine.
