If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
As a kid, I saw the 1968 version of Planet of the Apes. As a future primatologist, I was mesmerized. Years later I discovered an anecdote about its filming: At lunchtime, the people playing chimps and those playing gorillas ate in separate groups. It's been said, "There are two kinds of people in the world: those who divide the world into two kinds of people and those who don't." In reality, there's lots more of the former.
Automobile companies and technology firms are racing to deploy autonomous vehicles (AVs). But they could face one key obstacle: consumer distrust of the technology. Unnerved by the idea of not being in control--and by news of semi-AVs that have crashed, in one case killing the owner--many consumers are apprehensive. In a recent survey by AAA, for example, 78% of respondents said they were afraid to ride in an AV. Such numbers are a warning sign to firms hoping to sell millions of AVs, says Jack Weast, chief systems architect of Intel's autonomous driving group in Phoenix.
While developers amass data on the sensors and algorithms that allow cars to drive themselves, research on the social, economic, and environmental impacts of autonomous vehicles (AVs) is sparse. Truly autonomous driving is still decades away, according to most transportation experts. And because it's hard to study something that doesn't yet exist, the void has been filled by speculation that offers either a utopian view of the societal benefits of the new technology or a dystopian view of its hazards. Fortunately, a handful of cleverly designed experiments have given scientists insights into how AVs could change how we live, work, and play.
The Canadian poet Dennis Lee once wrote that the consolations of existence might be improved if we thought, worked, and lived as though we were inhabiting "the early days of a better civilization." The test of this would be whether humans, separately and together, are able to generate and make better choices. This is as much a question about wisdom as it is about science. We don't find it too hard to imagine continued progress in science and technology. We can extrapolate from the experiences of the last century toward a more advanced civilization that simply knows more, can control more, and is less vulnerable to threats.
Sawada speculates that 70 percent of the jobs at Japan's hotels will be automated in the next five years. "It takes about a year to two years to get your money back," he said. "But since you can work them 24 hours a day, and they don't need vacation, eventually it's more cost-efficient to use the robot." This may seem like a vision of the future best suited--perhaps only suited--to Japan. But according to Michael Chui, a partner at the McKinsey Global Institute, many tasks in the food-service and accommodation industry are exactly the kind that are easily automated.
Rafael Yuste thinks neuroscientists have been looking at the brain too close. "It's just like a TV screen--if you're watching a movie and could only look at an individual pixel, you would never understand what's going on," he says. "What neuroscientists have been doing since [the father of neuroscience, Santiago Ramon y] Cajal, is looking at the single pixels of the brain--one neuron at a time. So that's why we need these methods to see the whole screen, to see what's playing in our brains." The methods in question were on display in a recent study he and his graduate student, Christopher Dupre, conducted, recording the activity of all neurons in the Hydra vulgaris, a centimeter-long hydroid, while the animal swam between two pieces of glass.
Physicists know how to use quantum theory--your phone and computer give plenty of evidence of that. But knowing how to use it is a far cry from fully understanding the world the theory describes--or even what the various mathematical devices scientists use in the theory are supposed to mean. One such mathematical object, whose status physicists have long debated, is known as the quantum state. One of the most striking features of quantum theory is that its predictions are, under virtually all circumstances, probabilistic. If you set up an experiment in a laboratory, and then you use quantum theory to predict the outcomes of various measurements you might perform, the best the theory can offer is probabilities--say, a 50 percent chance that you'll get one outcome, and a 50 percent chance that you'll get a different one.
Most poker players didn't go to graduate school for cognitive linguistics. Then again, most poker players aren't Annie Duke. After pursuing a psychology Ph.D. on childhood language acquisition, Duke turned her skills to the poker table, where she has taken home over $4 million in lifetime earnings. For a time she was the leading female money winner in World Series of Poker history, and remains in the top five. She's written two books on poker strategy, and next year will release a book called Thinking in Bets: Making Smarter Decisions When You Don't Have All the Facts.
Compositionality, generalization, and learning from a few examples are among the hallmarks of human intelligence. CAPTCHAs (Completely Automated Public Turing test to tell Computers and Humans Apart), images used by websites to block automated interactions, are examples of problems that are easy for people but difficult for computers. CAPTCHAs add clutter and crowd letters together to create a chicken-and-egg problem for algorithmic classifiers--the classifiers work well for characters that have been segmented out, but segmenting requires an understanding of the characters, which may be rendered in a combinatorial number of ways. CAPTCHAs also demonstrate human data efficiency: A recent deep-learning approach for parsing one specific CAPTCHA style required millions of labeled examples, whereas humans solve new styles without explicit training. By drawing inspiration from systems neuroscience, we introduce recursive cortical network (RCN), a probabilistic generative model for vision in which message-passing–based inference handles recognition, segmentation, and reasoning in a unified manner.
Systematic analyses of spatiotemporal gene expression trajectories during organogenesis have been challenging because diverse cell types at different stages of maturation and differentiation coexist in the emerging tissues. We identified discrete cell types as well as temporally and spatially restricted trajectories of radial glia maturation and neurogenesis in developing human telencephalon. These lineage-specific trajectories reveal the expression of neurogenic transcription factors in early radial glia and enriched activation of mammalian target of rapamycin signaling in outer radial glia. Together, our results support a mixed model of topographical, typological, and temporal hierarchies governing cell-type diversity in the developing human telencephalon, including distinct excitatory lineages emerging in rostral and caudal cerebral cortex.