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) …
Tiny robotic fireflies that weigh barely more than a paper clip and glow as they fly could be used to aid search-and-rescue missions, researchers claim. Engineers at MIT previously developed insect-sized robots with tiny artificial muscles that allow them to zip around with bug-like agility by rapidly flapping their wings. The engineers have now found a way to embed minuscule electroluminescent particles into these artificial muscles, meaning they emit coloured light during flight. The robots can use this light to communicate with each other, and could even use it to signal for help in emergency situations, according to the researchers. For example, if sent on a search-and-rescue mission into a collapsed building, a robot that finds survivors could use lights to signal others and call for help.
There are lots of definitions of AI. According to the Merrian-Webster dictionary, Artificial Intelligence is a large area of computer science that simulates intelligent behavior in computers. Based on this, an algorithm implementation based on metaheuristic called Particle Swarm Optimization (originaly proposed to simulate birds searching for food, the movement of fishes' shoal, etc.) is able to simulate behaviors of swarms in order to optimize a numeric problem iteratively. It can be classified as a swarm intelligence algorithm like Ant Colony Algorithm, Artificial Bee Colony Algorithm and Bacterial Foraging, for example. Proposed in 1995 by J. Kennedy an R.Eberhart, the article "Particle Swarm Optimization" became very popular due his continue optimization process allowing variations to multi targets and more.
A professor of philosophy responds to David Iserson's "This, but Again." That we're living in a computer simulation--it sounds like a paranoid fantasy. But it's a possibility that futurists, philosophers, and scientific cosmologists treat increasingly seriously. Oxford philosopher and noted futurist Nick Bostrom estimates there's about a 1 in 3 chance that we're living in a computer simulation. Billionaire Elon Musk says it's a near-certainty.
A fish-shaped robot that'swims' around quickly picking up microplastics has been created by scientists. The tiny machine'wiggles' its body and'flaps' its tail fins to move through water, and could be used to help clear the oceans of plastic pollution. It measures just half-an-inch in length, meaning it can reach into tiny cracks and crevices to collect plastic pieces that would otherwise be inaccessible. Developed by a team at the Sichuan University in China, the robot has no power source, but moves thanks to flashes of near-infrared light. When the light is shone onto to the'fishtail' it bends away from the surface, and when the light is switched off it flops back, propelling the robot through the water.
Ants, as a group, are creatures of habit. While an individual's path isn't certain, biologists who have spent a lot of time watching the behavior of entire colonies can predict the average time any one ant might wander around underground before resurfacing. That got NASA physicist Yongxiang Hu wondering if the same predictability might be true of photons--particles of light--traveling through the snowpack. If so, that would let scientists use a laser pulsed from an orbiting satellite to estimate snow depth--potentially a powerful new way to monitor water supplies and the health of sea ice in the Arctic. NASA's ICESat-2 satellite is equipped with lidar, the same variety of laser system that self-driving cars use to build 3D maps of their surroundings.
A significant challenge of computational statistical mechanics is the accurate estimation of equilibrium parameters of a thermodynamic system. For decades, the methods of choice for sampling such systems at large have been molecular dynamics (MD) and hybrid Monte Carlo. Strategies for sampling probability distributions have increased, and most try leveraging normalizing flows. Normalizing Flows are a technique for creating complicated distributions that involve changing a probability density through a sequence of invertible mappings. These are desirable because of 2 characteristics: first, they can create independent samples rapidly and in parallel, and second, they can offer the precise probability density of their creation method.
There's always been something seductive about a nanobot. Comic books and movies implore you to imagine these things, thousands of times thinner than a human hair and able to cruise around a body and repair a bone or heal an illness. Their scale is unfathomably finite. Their possibilities, sci-fi will have you believe, wildly infinite. While that incongruity makes it perfect for the denizens of a writers' room figuring out how to kill James Bond, it's also a sort of curse.
Ok, sure, machine learning is great for making predictions; But you can't use it to replace scientific theory. Not only will it fail to reach generalizable conclusions, but the result is going to lack elegance and explainability. We won't be able to understand it or build upon it! What makes an algorithm or theory or agent explainable? It's certainly not the ability o "look inside"; We're rather happy assuming that block boxes, such as brains, are capable of explaining their conclusions and theories. We scoff at the idea that perfectly transparent neural networks are "explainable" in a meaningful sense; So it's not visibility that makes something explainable.
Processor power prediction Researchers from Duke University, Arm Research, and Texas A&M University developed an AI method for predicting the power consumption of a processor, returning results more than a trillion times per second while consuming very little power itself. "This is an intensively studied problem that has traditionally relied on extra circuitry to address," said Zhiyao Xie, a PhD candidate at Duke. "But our approach runs directly on the microprocessor in the background, which opens many new opportunities. I think that's why people are excited about it." The approach, called APOLLO, uses an AI algorithm to identify and select just 100 of a processor's millions of signals that correlate most closely with its power consumption. It then builds a power consumption model off of those 100 signals and monitors them to predict the entire chip's performance in real-time.
The seven-arm octopus, Haliphron atlanticus, weighs as much as a person and haunts deep, dark waters from New Zealand to Brazil and British Columbia. So few people have seen this creature alive that researchers must study it in death--typically, as a mound of purplish flesh that washes ashore or turns up in a net. A living seven-arm octopus was scooped up by a Norwegian fishing trawler in 1984, but "when laid on deck the body collapsed," a local zoologist wrote at the time. What remained of the creature, he added, was "sack-shaped, large and flappy." Another turned up in a South Pacific research trawl in the early two-thousands, but the preservation process turned it into a "frozen lump," the giant-squid expert Steve O'Shea wrote.