Gathering the global atmospheric data that go into weather forecasts has long been the job of big, costly, government-run satellites. But on 14 July, the scheduled launch of a Russian Soyuz rocket could signal the shift toward a different model, in which some of the data come from swarms of small private satellites called CubeSats. Among the dozens of CubeSats on the rocket will be 11 tiny weather satellites--eight from Glasgow, U.K.–based Spire Global, and three from GeoOptics, based in Pasadena, California. They will boost the companies' in-orbit constellations to 40 and four, respectively--giving them the chance to compete in a forthcoming pilot program, in which the National Oceanic and Atmospheric Administration will buy weather data from them to supplement the information obtained from the usual array of multibillion-dollar satellites.
Chemists looking to cook up new molecules face a challenge of choosing among hundreds of potential molecular building blocks and thousands of chemical reactions for linking them together. Computational chemists have long programmed computers with known chemical reactions, hoping to create software able to calculate successful molecular recipes. But reactions often don't work in a binary way, being either successful or not. Instead of programming reactions as hard and fast rules, researchers have developed a neural network that learns from millions successful experiments and figures out on its own which reactions to choose to put together new molecules.
Astrophysicists are using artificial intelligence (AI) to create something like the technology in movies that magically sharpens fuzzy surveillance images: a network that could make a blurry galaxy image look like it was taken by a better telescope than it actually was. That could let astronomers squeeze out finer details from reams of observations. One is a generator that concocts images, the other a discriminator that tries to spot any flaws that would give away the manipulation, forcing the generator to get better. The team took thousands of real images of galaxies, and then artificially degraded them.
But variants in scores of genes known to play some role in autism can explain only about 20% of all cases. Finding other variants that might contribute requires looking for clues in data on the 25,000 other human genes and their surrounding DNA--an overwhelming task for human investigators. They compared those of the few well-established autism risk genes with those of thousands of other unknown genes and last year flagged another 2500 genes likely to be involved in this disorder. Now they have developed a deep learning tool to find non-coding DNA that may also play a role in autism and other diseases.
With billions of users and hundreds of billions of tweets and posts every year, social media has brought big data to social science. It has also opened an unprecedented opportunity to use artificial intelligence (AI) to glean meaning from the mass of human communications. The University of Pennsylvania's Positive Psychology Center, for example, uses machine learning and natural language processing to sift through gobs of data to gauge the public's emotional and physical health, including levels of depression and trust, and several personality traits. But social media data is cheap and abundant.
Deep neural networks, or deep learning, as the field is also called, have the potential to revolutionize scientific discovery. But as these networks are applied to more and more disciplines, many scientists, whose very enterprise is founded on explanation, have been left with a nagging question: Why, model, why? Just as the microscope revealed the cell, these researchers are crafting tools that will allow insight into how neural networks make decisions. Some tools probe the artificial intelligence (AI) without penetrating it; some are alternative algorithms that can compete with neural nets, but with more transparency; and some use still more deep learning to get inside the black box.
Particle physicists began fiddling with artificial intelligence (AI) in the late 1980s, just as the term "neural network" captured the public's imagination. Their field lends itself to AI and machine-learning algorithms because nearly every experiment centers on finding subtle spatial patterns in the countless, similar readouts of complex particle detectors--just the sort of thing at which AI excels. Particle physicists strive to understand the inner workings of the universe by smashing subatomic particles together with enormous energies to blast out exotic new bits of matter, such as the the long-predicted Higgs boson, which was discovered in 2012 at the world's largest proton collider, the Large Hadron Collider (LHC) in Switzerland. Such exotic particles don't come with labels, however.
Just what do people mean by artificial intelligence (AI)? The term has never had clear boundaries. When it was introduced at a seminal 1956 workshop at Dartmouth College, it was taken broadly to mean making a machine behave in ways that would be called intelligent if seen in a human. An important recent advance in AI has been machine learning, which shows up in technologies from spellcheck to self-driving cars and is often carried out by computer systems called neural networks.
In field after field, the ability to collect data has exploded, overwhelming human insight and analysis. In a revolution that extends across much of science, researchers are unleashing artificial intelligence (AI), often in the form of artificial neural networks, on these mountains of data. Unlike earlier attempts at AI, such "deep learning" systems don't need to be programmed with a human expert's knowledge. Instead, they learn on their own, often from large training data sets, until they can see patterns and spot anomalies in data sets far larger and messier than human beings can cope with.
In an essay about his science fiction, Isaac Asimov reflected that "it became very common…to picture robots as dangerous devices that invariably destroyed their creators." He rejected this view and formulated the "laws of robotics," aimed at ensuring the safety and benevolence of robotic systems. Asimov's stories about the relationship between people and robots were only a few years old when the phrase "artificial intelligence" (AI) was used for the first time in a 1955 proposal for a study on using computers to "…solve kinds of problems now reserved for humans." Over the half-century since that study, AI has matured into subdisciplines that have yielded a constellation of methods that enable perception, learning, reasoning, and natural language understanding.