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) …
FRET was first identified in the 1920s by Cario, Franck, and Perrin. In the late 1940s, Förster and Oppenheimer independently formulated a quantitative theory of the energy transfer between a pair of point dipoles. Stryer and Haugland verified this theory in the late 1960s and coined the term "spectroscopic ruler" for FRET. Simultaneously, Hirschfeld, and later Moerner and Orrit, pioneered optical single-molecule detection methods leading to the first demonstration of smFRET in 1996. This breakthrough made it possible to study heterogeneous systems, dynamic processes, and transient conformational changes on the nanometer scale.
The use of dynamic, self-assembled DNA nanostructures in the context of nanorobotics requires fast and reliable actuation mechanisms. We therefore created a 55-nanometer–by–55-nanometer DNA-based molecular platform with an integrated robotic arm of length 25 nanometers, which can be extended to more than 400 nanometers and actuated with externally applied electrical fields. Precise, computer-controlled switching of the arm between arbitrary positions on the platform can be achieved within milliseconds, as demonstrated with single-pair Förster resonance energy transfer experiments and fluorescence microscopy. The arm can be used for electrically driven transport of molecules or nanoparticles over tens of nanometers, which is useful for the control of photonic and plasmonic processes. Application of piconewton forces by the robot arm is demonstrated in force-induced DNA duplex melting experiments.
Social animals have to know the spatial positions of conspecifics. However, it is unknown how the position of others is represented in the brain. We designed a spatial observational-learning task, in which an observer bat mimicked a demonstrator bat while we recorded hippocampal dorsal-CA1 neurons from the observer bat. A neuronal subpopulation represented the position of the other bat, in allocentric coordinates. About half of these "social place-cells" represented also the observer's own position--that is, were place cells.
An animal's awareness of its location in space depends on the activity of place cells in the hippocampus. How the brain encodes the spatial position of others has not yet been identified. We investigated neuronal representations of other animals' locations in the dorsal CA1 region of the hippocampus with an observational T-maze task in which one rat was required to observe another rat's trajectory to successfully retrieve a reward. Information reflecting the spatial location of both the self and the other was jointly and discretely encoded by CA1 pyramidal cells in the observer rat. A subset of CA1 pyramidal cells exhibited spatial receptive fields that were identical for the self and the other.
In Mary Shelley's novel, the scientist Victor Frankenstein fears that creating a female companion to his unhappy monster could lead to a "race of devils" that could drive humanity extinct. Today, some scientists worry about scientific advances in the real world that could kill all of humanity, or at least end civilization as we know it. Some two dozen researchers at three academic centers are studying these "existential risks"--including labmade viruses, armies of nanobots, and artificial intelligence--and what can be done about them. But critics say their scenarios are far-fetched and distract from real existential dangers, including climate change and nuclear war.
In science news around the world, the U.S. National Football League provides $16 million for medical research on concussions and other football-related illnesses, and the World Health Organization approves a new, long-lasting vaccine for typhoid fever. China announces it will build a new research and development park in Beijing to develop artificial intelligence technologies, and South Korean universities refuse to renew their contracts with Elsevier for access to its ScienceDirect database because of a price hike. Scientists install new devices at the South Pole to measure neutrinos, and a volunteer discovers the largest prime number, containing more than 23 million digits. And the U.S. Department of the Interior decides to review certain grants to universities and nonprofit groups to ensure they align with the priorities of President Donald Trump's administration.
Coral bleaching occurs when stressful conditions result in the expulsion of the algal partner from the coral. Before anthropogenic climate warming, such events were relatively rare, allowing for recovery of the reef between events. Hughes et al. looked at 100 reefs globally and found that the average interval between bleaching events is now less than half what it was before. Such narrow recovery windows do not allow for full recovery. Furthermore, warming events such as El Niño are warmer than previously, as are general ocean conditions.
Existing soft actuators have persistent challenges that restrain the potential of soft robotics, highlighting a need for soft transducers that are powerful, high-speed, efficient, and robust. We describe a class of soft actuators, termed hydraulically amplified self-healing electrostatic (HASEL) actuators, which harness a mechanism that couples electrostatic and hydraulic forces to achieve a variety of actuation modes. We introduce prototypical designs of HASEL actuators and demonstrate their robust, muscle-like performance as well as their ability to repeatedly self-heal after dielectric breakdown--all using widely available materials and common fabrication techniques. A soft gripper handling delicate objects and a self-sensing artificial muscle powering a robotic arm illustrate the wide potential of HASEL actuators for next-generation soft robotic devices.
Digital computers have transformed work in almost every sector of the economy over the past several decades (1). We are now at the beginning of an even larger and more rapid transformation due to recent advances in machine learning (ML), which is capable of accelerating the pace of automation itself. However, although it is clear that ML is a "general purpose technology," like the steam engine and electricity, which spawns a plethora of additional innovations and capabilities (2), there is no widely shared agreement on the tasks where ML systems excel, and thus little agreement on the specific expected impacts on the workforce and on the economy more broadly. We discuss what we see to be key implications for the workforce, drawing on our rubric of what the current generation of ML systems can and cannot do [see the supplementary materials (SM)]. Although parts of many jobs may be "suitable for ML" (SML), other tasks within these same jobs do not fit the criteria for ML well; hence, effects on employment are more complex than the simple replacement and substitution story emphasized by some.
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.