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) …
A US technology firm has developed a drone that is able to aim and fire at enemies while flying in mid-air. The Tikad drone, developed by Duke Robotics, is armed with a machine-gun and a grenade launcher. The gun can be fired only by remote control, and is designed to reduce military casualties by cutting the number of ground troops required. But campaigners warn that in the wrong hands, it will make it easier to kill innocent people. The Tikad drone, available for private sale at an undisclosed price, has won a security innovation award from the US Department of Defense, and there is interest from several military forces around the world, including Israel, reports Defense One.
In the blog "From Autonomous to Smart: Importance of Artificial Intelligence," we laid out the artificial intelligence (AI) challenges in creating "smart" edge devices: We also talked about how Moore's Law isn't going to bail us out of these challenges; that the growth of Internet of Things (IOT) data and the complexity of the problems that we are trying to address at the edge (think "smart" cars) is growing much faster than Moore's Law can accommodate. So we are going to use this blog to deep dive into the category of artificial intelligence called reinforcement learning. We are going to see how reinforcement learning might help us to address these challenges; to work smarter at the edge when brute force technology advances will not suffice. With the rapid increases in computing power, it's easy to get seduced into thinking that raw computing power can solve problems like smart edge devices (e.g., cars, trains, airplanes, wind turbines, jet engines, medical devices). Look at the dramatic increase in the number of possible moves between checkers and chess even though the board layout is exactly the same.
When evaluating machine learning models, the validation step helps you find the best parameters for your model while also preventing it from becoming overfitted. Two of the most popular strategies to perform the validation step are the hold-out strategy and the k-fold strategy. Pros of the hold-out strategy: Fully independent data; only needs to be run once so has lower computational costs. Cons of the hold-out strategy: Performance evaluation is subject to higher variance given the smaller size of the data. K-fold validation evaluates the data across the entire training set, but it does so by dividing the training set into K folds – or subsections – (where K is a positive integer) and then training the model K times, each time leaving a different fold out of the training data and using it instead as a validation set.
The New York Yankees (60-53) host the Boston Red Sox (65-49) Friday, as the longtime rivals battle for the American League East title in a three-game series. The Red Sox, who have won eight games in a row, own a 4 1/2 game lead on the struggling Yankees. John Farrell's squad has been winning behind excellent pitching, as Boston held the Chicago White Sox and Tampa Bay Rays to a combined 13 runs over six games. Boston, who boast Cy Young favorite Chris Sale, has the best ERA (3.63) in the American League. "We've continued to pitch consistently and that will be the key for us," Farrell told reporters.
These are two examples of how NASA hopes to use artificial intelligence. As far-fetched as the concept sounds, the agency is already using AI in missions on both Earth and Mars. And there are other missions in the works that could see AI exploring icy moons in search of life. This bot-friendly future stands counter to some of the fuss in the press this past week, after Facebook shut down an experiment because two artificially intelligent bots began communicating in a shorthand language instead of English. Many in the media portrayed the bots as coming up with their own language.
Each day billions of photographs are uploaded to photo-sharing services and social media platforms, and Cornell computer science researchers are figuring out ways to analyze this visual treasure trove through deep-learning methods. Kavita Bala, professor of computer science; Noah Snavely, associate professor computer science at Cornell Tech; and Kevin Matzen, M.S. '15, Ph.D. '16, have released their results in a new paper, "StreetStyle: Exploring world-wide clothing styles from millions of photos." "We present a framework for visual discovery at scale, analyzing clothing and fashion across millions of images of people around the world and spanning several years," Snavely said. Bala said the group used deep learning to detect various attributes – the color or sleeve length of shirts, whether a person is wearing glasses or a hat, and so on – in millions of images. "Using these detected attributes, we can then derive visual insight," Bala said.
But, as AI analytics becomes more common in corporate enterprises, managing the process is expected to get more important -- and more complex. Analytics teams will have to pay more attention to "the composition of AI systems," said Donald Farmer, principal of consultancy TreeHive Strategy in Woodinville, Wash. They'll also need to implement detailed governance and oversight procedures "as companies start to put hundreds and thousands of algorithms in place," chimed in Shawn Rogers, senior director of analytic strategy at vendor Tibco Software Inc. Gartner analyst Merv Adrian foresees networks of AI-powered tools and devices that can communicate with one another and have the ability to ingest data on their own -- developments Farmer said would make it more clear that data scientists and other analysts are "participants in AI systems" as opposed to users of the technology in a traditional sense. Another issue to contend with is the level of uncertainty in what AI algorithms predict. Farmer said AI-based analytical models tend to be accurate if they're well designed, but there's almost never a 100% probability that their findings are correct -- something that needs to be made clear to business executives so they don't expect infallibility from the technology.
Machine learning algorithms have successfully identified plant species in massive herbaria just by looking at the dried specimens. According to researchers, similar AI approaches could also be used identify the likes of fly larvae and plant fossils. There are roughly 3,000 herbaria in the world, hosting an estimated 350 million specimens -- only a fraction of which has been digitized. But the swelling data sets, along with advances in computing techniques, enticed computer scientist Erick Mata-Montero of the Costa Rica Institute of Technology in Cartago and botanist Pierre Bonnet of the French Agricultural Research Centre for International Development in Montpellier, to see what they could make of the data. Researchers trained... algorithms on more than 260,000 scans of herbarium sheets, encompassing more than 1,000 species.
Among the tasks you can train a computer to perform is scanning the skies over the U.S. for the alarming number of surveillance and spy aircraft. The news web site BuzzFeed did just that, reporting this week that it employed a machine-learning algorithm to first recognize known spy planes, and then combine that model with a large set of flight-tracking data from a commercial web site. The AI project mapped thousands of surveillance flights operated by federal agencies over a four-month period, including a military contractor tracking terrorists in Africa that is also flying surveillance aircraft over U.S. cities, BuzzFeed reported. Flightradar24 gathers data from a network of ground-based receivers supplemented by Federal Aviation Administration receivers. The ground radars sweep up a flight data transmitted by aircraft transponders, including unique identifiers for each plane.