Artificial intelligence is transforming scientific and engineering research to enable new breakthroughs.
Computer language processors and computer vision reduce the amount of human power needed to run lab and field experiments; advanced simulations offer deeper insights in astronomy and physics; big data analysis helps identify the best candidates for chemical catalysts and amino-acid sequences for useful new proteins; autonomous vehicles find the best places to collect samples and data.
At Caltech and elsewhere, AI and scientific subject matter experts team up to advance science, engineering, and the field of AI itself. Read on for just a few examples of the seemingly endless possibilities.
"When financial institutions came to researchers asking how AI could be applied to markets, it occurred to me that this was an opportunity not only for finance but also for AI and machine learning proper."
"AI gives us an excellent array of tools to do this research at large scale. We've been able to now analyze billions of tweets very quickly, and that wouldn't have been possible before."
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"AI helps us recognize the signals that we're interested in. First, we train machine learning algorithms to detect different types of signals in data that have been carefully hand annotated. Then we apply our model to the new incoming data. The model makes decisions about as well as seismologists can."
"Computer vision gives scientists, policymakers, and conservationists the data they need to make close to real-time decisions about where to allocate resources."
"Now we're at the point where some of these AI systems perform as well as humans at these tasks. You might do some quality control to make sure the outputs look reasonable, but usually they do, and you can move forward with your studies."
You can submit your own questions to the Caltech Science Exchange.