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) …
The Royal Navy is testing ground-breaking technology which allows them to control unmanned boats remotely. The fleet of unmanned surface vehicles (USVs), described as'absolutely the future of naval warfare', can be operated from a warship's operations room. Able to detect underwater mines and spy on enemy ships without putting sailors in danger, the MAST-13 has already been hailed by one admiral for the'increased reach and lethality this will bring to our ships'. The MAST-13 technology will allow the 13-metre boats to scout miles ahead of warships and warn them of potential danger. Capable of travelling 45 knots (52mph; 84kmh), the waterborne drones can operate for up to 10 days at a time and travel hundreds of miles.
With tech giants announcing "AI-first" business models and investment in intelligent technology skyrocketing, it may come as a surprise to learn that only 20% of surveyed enterprises are actually implementing AI this year. While there's a prevailing sentiment among business leaders that they have missed the AI boat, the reality is that the boat is still in the harbor, and there's still plenty of room aboard. Early adopters of any technology do bystanders a great service by working out the kinks. Now that products have been tested and consumers introduced, contemporary businesses braving the AI waters will be known historically as the first generation to wield this promising technology to revolutionize industries. Certain companies are expecting returns on their AI investments to reach 30% in the coming years.
Wallops Island--a remote, marshy spit of land along the eastern shore of Virginia, near a famed national refuge for horses--is mostly known as a launch site for government and private rockets. But it also makes for a perfect, quiet spot to test a revolutionary weapons technology. If a fishing vessel had steamed past the area last October, the crew might have glimpsed half a dozen or so 35-foot-long inflatable boats darting through the shallows, and thought little of it. But if crew members had looked closer, they would have seen that no one was aboard: The engine throttle levers were shifting up and down as if controlled by ghosts. The boats were using high-tech gear to sense their surroundings, communicate with one another, and automatically position themselves so, in theory, .50-caliber
As you might know, supervised machine learning is one of the most commonly used and successful types of machine learning. In this article, we will describe supervised learning in more detail and explain several popular supervised learning algorithms. Remember that supervised learning is used whenever we want to predict a certain outcome from a given input, and we have examples of input/output pairs. We build a machine learning model from these input/output pairs, which comprise our training set. Our goal is to make accurate predictions for new, never-before-seen data. Supervised learning often requires human effort to build the training set, but afterwards automates and often speeds up an otherwise laborious or infeasible task. There are two major types of supervised machine learning problems, called classification and regression. In classification, the goal is to predict a class label, which is a choice from a predefined list of possibilities.
Those notes are based on the research paper " On Calibration of Modern Neural Networks" by (Guo et al, 2017.). Very large and deep models, as ResNet, are far more accurate than their older counterparts, as LeNet, on computer vision datasets such as CIFAR100. However while they are better at classifying images, we are less confident in their own confidence! Most neural networks for classification uses as last activation a softmax: it produces a distribution of probabilities for each target (cat, dog, boat, etc.). We may expect that if for a given image, our model associate a score of 0.8 to the target'boat', our model is confident at 80% that this is the right target.
Last year, after breaking the Guinness World Record for the Key West to Cuba run, we wondered what was next for the #77 Lucas Oil SilverHook ocean racing powerboat? We found the answer in the 50th anniversary of the Trinidad & Tobago Great Race, one of the most grueling races in the world. The 115-mile endurance course starts in Trinidad's Port of Spain, where you head north and then east near the island before popping into the Atlantic Ocean for a 50-mile sprint to the finish in Store Bay, Tobago. Because of the logistical difficulties of racing on foreign shores, we were the first American entry in 29 years. We knew we would face stiff competition from Jumbie, Cat Killer, Mr. Solo and other local rivals that know the course well.
I am honestly not sure whether fish have any concept of bees. I am equally unsure whether bees have any concept of fish. I am even more unsure whether bees and fish could be friends, if they knew that the other existed. But thanks to robots, it turns out that the answer is definitely yes. The video really doesn't communicate a whole lot about what's going on here, but the central question is whether robots can usefully mediate communications between groups of very different animals in such a way that long distance interspecies collective behavior becomes possible.
Current learning machines have successfully solved hard application problems, reaching high accuracy and displaying seemingly "intelligent" behavior. Here we apply recent techniques for explaining decisions of state-of-the-art learning machines and analyze various tasks from computer vision and arcade games. This showcases a spectrum of problem-solving behaviors ranging from naive and short-sighted, to well-informed and strategic. We observe that standard performance evaluation metrics can be oblivious to distinguishing these diverse problem solving behaviors. Furthermore, we propose our semi-automated Spectral Relevance Analysis that provides a practically effective way of characterizing and validating the behavior of nonlinear learning machines. This helps to assess whether a learned model indeed delivers reliably for the problem that it was conceived for. Furthermore, our work intends to add a voice of caution to the ongoing excitement about machine intelligence and pledges to evaluate and judge some of these recent successes in a more nuanced manner.
IKEA's latest effort to improve its environmental footprint has taken a decidedly aquatic bent. The company has designed a remote-controlled boat, the Good Ship IKEA, that clears trash from the water. It may look cutesy (it's modeled after the SMÅKRYP bath toy), but it's very much functional. They use environmental cleanup boat technology that can collect up to 44lbs of debris at a time -- a small fleet could keep a river relatively pristine. Like some aerial drones, the remote control provides a first-person view thanks to a camera.
Back in 2014, Richard Branson said "one day, offices will be a thing of the past." Looking at it today, Branson's prediction might just come true. The velocity of technological advances over the last decade has had a dramatic impact on the way we work. Today, working with distributed teams is normal. We're also seeing increased automation of various work processes (bots, machine learning, deep learning, etc.) driven by artificial intelligence (AI), as well as the optimization of team interactions through collaboration.