Networks programmed directly into computer chip hardware can identify images faster, and use much less energy, than the traditional neural networks that underpin most modern AI systems. That’s according to work presented at a leading machine learning conference in Vancouver last week.
Neural networks, from GPT-4 to Stable Diffusion, are built by wiring together perceptrons, which are highly simplified simulations of the neurons in our brains. In very large numbers, perceptrons are powerful, but they also consume enormous volumes of energy.
Part of the trouble is that perceptrons are just software abstractions—running a perceptron network on a GPU requires translating that network into the language of hardware, which takes time and energy. Building a network directly from hardware components does away with a lot of those costs. And one day, they could even be built directly into chips used in smartphones and other devices. Read the full story.
—Grace Huckins
Drugs like Ozempic now make up 5% of prescriptions in the US
What’s new? US doctors write billions of prescriptions each year. During 2024, though, one type of drug stood out—“wonder drugs” known as GLP-1 agonists. As of September, one of every 20 prescriptions written for adults was for one of these drugs, according to the health data company Truveta.
The big picture: According to the data, people who get prescriptions for these drugs are younger, whiter, and more likely to be female. In fact, women are twice as likely as men to get a prescription. Yet not everyone who’s prescribed the drugs ends up taking them. In fact, half the new prescriptions for obesity are going unfilled. Read the full story.
