Removing barriers to AI access — and including everyone — makes it a real revolution. Up until now, I have shared artwork, marketing messages, press releases, and my account of traveling to the All Wireless & Prepaid Expo in Las Vegas.
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Human brains explore more to avoid losses than to seek gains
Researchers at the Weizmann Institute of Science traced a neural mechanism that explains why humans explore more aggressively when avoiding losses than when pursuing gains. Their work reveals how neuronal firing and noise in the amygdala shape exploratory decision-making.
Human survival has its origins in a delicate balance of exploration versus exploitation. There is safety in exploiting what is known, the local hunting grounds, the favorite foraging location, the go-to deli with the familiar menu. Exploitation also involves the risk of over-reliance on the familiar to the point of becoming too dependent upon it, either through depletion or a change in the stability of local resources.
Exploring the world in the hope of discovering better options has its own set of risks and rewards. There is the chance of finding plentiful hunting grounds, alternative foraging resources, or a new deli that offers a fresh take on old favorites. And there is the risk that new hunting grounds will be scarce, the newly foraged berries poisonous, or that the meal time will be ruined by a deli that disappoints.
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A new generative AI approach to predicting chemical reactions improves accuracy and reliability
Many attempts have been made to harness the power of new artificial intelligence and large language models (LLMs) to try to predict the outcomes of new chemical reactions. These have had limited success, in part because until now they have not been grounded in an understanding of fundamental physical principles, such as the laws of conservation of mass.
Now, a team of researchers at MIT has come up with a way of incorporating these physical constraints into a reaction prediction model, and thus greatly improving the accuracy and reliability of its outputs.
The new work is reported in the journal Nature, in a paper by recent postdoc Joonyoung Joung (now an assistant professor at Kookmin University, South Korea); former software engineer Mun Hong Fong (now at Duke University); chemical engineering graduate student Nicholas Casetti; postdoc Jordan Liles; physics undergraduate student Ne Dassanayake; and senior author Connor Coley, who is the Class of 1957 Career Development Professor in the MIT departments of Chemical Engineering and Electrical Engineering and Computer Science.