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Networks programmed directly into computer chip hardware can identify images faster and use much less power than the traditional neural networks that underpin most modern AI systems. That’s according to a paper presented at a leading machine learning conference in Vancouver last week.
Neural networks, from GPT-4 to Stable Diffusion, are built by connecting perceptrons, which are very simplified simulations of neurons in our brain. In very large numbers, perceptrons are powerful, but they also consume enormous amounts of energy.
Part of the problem is that perceptrons are just software abstractions – running a perceptron network on a GPU requires translating that network into a hardware language, which takes time and energy. Building the network directly from hardware components removes many of these costs. One day, they could even be incorporated directly into the chips used in smartphones and other devices. Read the full story.
— Grace Huckins
Drugs like Ozempic now account for 5% of prescriptions in the US
what is new American doctors write billions of prescriptions every year. However, during 2024, one type of drug stood out – the “wonder drugs” known as GLP-1 agonists. As of September, one out of every 20 prescriptions written for adults was for one of these drugs, according to health data company Truveta.
Big picture: According to the data, people who receive prescriptions for these drugs are younger, whiter and more often women. In fact, women are twice as likely to receive a prescription as men. However, not everyone who is prescribed medication takes them. In fact, half of new obesity prescriptions go unfilled. Read the full story.
