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) …
Almost every day I make a pot of tea. Strong, black tea, the kind you have to steep properly in a ritual that involves a kettle, a tea tin, tea lights, a tea cozy. It's a four-minute brew, so I set a timer. I used to do it on the microwave, but some time ago I just started asking Alexa, via the Amazon Echo on my kitchen counter. "Alexa, set a timer for four minutes."
The science-fiction writer Robert Heinlein once wrote, "Each generation thinks it invented sex." He was presumably referring to the pride each generation takes in defining its own sexual practices and ethics. But his comment hit the mark in another sense: Every generation has to reinvent sex because the previous generation did a lousy job of teaching it. In the United States, the conversations we have with our children about sex are often awkward, limited, and brimming with euphemism. At school, if kids are lucky enough to live in a state that allows it, they'll get something like 10 total hours of sex education.1
How do the periodic patterns of colored stripes that decorate so many birds and mammals form? These patterns are the object of aesthetic fascination and the focus of endless debate about the mechanisms that generate them or diversify them. Theoreticians show that the most complex of regular patterns can be reproduced in silico using Turing-like reaction-diffusion mechanisms, whereby the concentrations and diffusing properties of an activator and an inhibitor regulate their interactions and thus determine the final periodic pattern (1). By contrast, experimental data indicate that self-organizing processes relying on interactions between skin cells establish striped patterns (2, 3). On page 1216 of this issue, Haupaix et al. (4) address this question from a different perspective, by examining the contribution of embryonic structures that could act as instructive, spatial landmarks to guide the formation of alternating yellow and black stripes on the back of chicks of quails, pheasants, partridges, and their relatives (galliform birds).
There is a developing drug epidemic in the United States. Jalal et al. analyzed nearly 600,000 unintentional drug overdoses over a 38-year period. Although the overall mortality rate closely followed an exponential growth curve, the pattern itself is a composite of several underlying subepidemics of different drugs. Geographic hotspots have developed over time, as well as drug-specific demographic differences. The epidemic of substance use disorders and drug overdose deaths is a growing public health crisis in the United States. Every day, 174 people die from drug overdoses. Currently, opioids (including prescription opioids, heroin, and synthetic opioids such as fentanyl and its chemical analogs) are the leading cause of overdose deaths. The overdose mortality data can reveal the complex and evolving dynamics of drug use in the United States.
At the Conference on Cognitive Computational Neuroscience this month, researchers presented new tools for comparing data collected from living brains with readouts from computational models known as deep neural networks. Such comparisons might offer up new hypotheses about how humans interpret sights and sounds, understand language, or navigate the world. Until recently, artificial intelligence couldn't come anywhere close to human performance on tasks like recognizing sounds or classifying images. But deep neural networks, loosely inspired by the brain, have logged increasingly impressive performances, especially on visual tasks. That has led some neuroscientists to wonder whether these models could yield insight into how our own brains process information.
Mattel's AI nanny, called Aristotle, recently gained the notorious distinction of being subject to a bipartisan protest in the US Congress. Plus, there was a petition against it with over 15,000 signatures. The Campaign for a Commercial-Free Childhood, which organized the petition, argued that Aristotle is a consumerist ploy. It "attempts to replace the care, judgment and companionship of loving family members with faux nurturing and conversation from a robot designed to sell products and build brand loyalty." Aristotle, designed to interact with kids, was based on the same technologies as virtual assistants such as Amazon's Alexa.
Forest loss is being driven by various factors, including commodity production, forestry, agriculture, wildfire, and urbanization. Curtis et al. used high-resolution Google Earth imagery to map and classify global forest loss since 2001. Just over a quarter of global forest loss is due to deforestation through permanent land use change for the production of commodities, including beef, soy, palm oil, and wood fiber. Despite regional differences and efforts by governments, conservationists, and corporations to stem the losses, the overall rate of commodity-driven deforestation has not declined since 2001. Global maps of forest loss depict the scale and magnitude of forest disturbance, yet companies, governments, and nongovernmental organizations need to distinguish permanent conversion (i.e., deforestation) from temporary loss from forestry or wildfire.
Insects are among the most agile natural flyers. Hypotheses on their flight control cannot always be validated by experiments with animals or tethered robots. To this end, we developed a programmable and agile autonomous free-flying robot controlled through bio-inspired motion changes of its flapping wings. Despite being 55 times the size of a fruit fly, the robot can accurately mimic the rapid escape maneuvers of flies, including a correcting yaw rotation toward the escape heading. Because the robot's yaw control was turned off, we showed that these yaw rotations result from passive, translation-induced aerodynamic coupling between the yaw torque and the roll and pitch torques produced throughout the maneuver.
In 2004, the year before he became president of Britain's Royal Society, Martin Rees memorably remarked that "we are no wiser than Aristotle was more than 2000 years ago." The reason that humankind has made such extraordinary scientific progress since Aristotle's time, Rees argued, is primarily because of technological advances, such as telescopes and space probes in the case of astronomy--his own field of expertise. Rees's latest book, On the Future: Prospects for Humanity, written "as a scientist, as a citizen, and as a worried member of the human species," is really a meditation on this earlier thought, short in extent but wide in range: from redesigning genes, through the likelihood of human-induced climate change, to the possibility of encounters with alien intelligence in the Universe. Its overall theme is that Earth's growing population will flourish only if science and technology are deployed with "wisdom." Inevitably, much of the interest in this topic derives from the author's predictions about the coming decades, although Rees is mindful of the fact that scientists are "rotten forecasters--almost as bad as economists."
Winged insects have been a rich source of inspiration for designing flying robots, but robots can also be used to test hypotheses about the mechanisms underlying the control of insect flight (1). Winged insects perform continuously demanding tasks from takeoff to landing that include rapid turns as well as virtuosic chasing behaviors. During evasive maneuvers, the fruitfly executes rotations to achieve banked turns (2). On page 1089 of this issue, Karásek et al. (3) show that the accurate control of a new tailless, flapping-wing robot reveals how these insects perform rapid banked turns, even though the robot is much larger than a fruitfly. The similarities between the maneuver dynamics of the robot and that of fruitflies strongly support the hypotheses that rotation around the vertical axis (the "yaw" movement) is passively controlled throughout evasive maneuvers and that fruitflies actively control their heading only after executing these turns.