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) …
Not yours or your friend's or one you saw in a home makeover show, but one purely from your imagination--perhaps your ideal living room. You should have no trouble doing it: We take this kind of imagination for granted. Rarely do we find ourselves wondering how the mind chooses what objects to put into these novel scenes and which ones to exclude. But it's worth reflecting on, perhaps especially for creative types, because our visual imagination appears to be constrained by regularities in visual memories. Diversifying what you see may mean enriching what you can imagine.
Facebook once teamed up with scientists at Cornell to conduct a now-infamous experiment on emotional contagion. Researchers randomly assigned 700,000 users to see on their News Feeds, for one week, a slight uptick in either positive or negative language or no change at all, to determine whether exposure to certain emotions could, in turn, cause a user to express certain emotions. The answer, as revealed in a 2014 paper, was yes: The emotions we see expressed online can change the emotions that we express, albeit slightly. Conversations about emotional contagion were quickly shelved, however, as the public disclosure of the study sparked an intense backlash against what many perceived to be an unjust and underhanded manipulation of people's feelings. Facebook would later apologize for fiddling with users' emotions and pledge to revise its internal review practices.
Advancements under the moniker of the Internet of Things (IoT) allow things to network and become the primary producers of data in the Internet.14 IoT makes the state and interactions of real-world available to Web applications and information systems with minimal latency and complexity.25 By enabling massive telemetry and individual addressing of "things," the IoT offers three prominent benefits: spatial and temporal traceability of individual real-world objects for thief prevention, counterfeit product detection and food safety via accessing their pedigree; enabling ambient data collection and analytics for optimizing crop planning, enabling telemedicine and assisted living; and supporting real-time reactive systems such as smart building, automatic logistics and self-driving, networked cars.11 Realizing these benefits requires the ability to discover and resolve queries for contents in the IoT. Offering these abilities is the responsibility of a class of software system called the Internet of Things search engine (IoTSE).
For many years, the two dominant paradigms in artificial intelligence (AI) have been logical AI and statistical AI. Logical AI uses first-order logic and related representations to capture complex relationships and knowledge about the world. However, logic-based approaches are often too brittle to handle the uncertainty and noise present in many applications. Statistical AI uses probabilistic representations such as probabilistic graphical models to capture uncertainty. However, graphical models only represent distributions over propositional universes and must be customized to handle relational domains.
A lot of data is moved from system to system in an important and increasing part of the computing landscape. This is traditionally known as ETL (extract, transform, and load). While many systems are extremely good at this process, the source for the extraction and the destination for the load frequently have different representations for their data. It is common for this transformation to squeeze, truncate, or pad the data to make it fit into the target. This is really like using a shoehorn to fit into a shoe that is too small.
Since their introduction more than a decade ago, smartphones have been equipped with cameras, allowing users to capture images and video without carrying a separate device. Thanks to the use of computational photographic technologies, which utilize algorithms to adjust photographic parameters in order to optimize them for specific situations, users with little or no photographic training can often achieve excellent results. The boundaries of what constitutes computational photography are not clearly defined, though there is some agreement that the term refers to the use of hardware such as lenses and image sensors to capture image data, and then applying software algorithms to automatically adjust the image parameters to yield an image. Examples of computational photography technology can be found in most recent smartphones and some standalone cameras, including high dynamic range imaging (HDR), auto-focus (AF), image stabilization, shot bracketing, and the ability to deploy various filters, among many other features. These features allow amateur photographers to produce pictures that can, at times, rival photographs taken by professionals using significantly more expensive equipment.
A host of different tasks--such as identifying the song in a database most similar to your favorite song, or the drug most likely to interact with a given molecule--have the same basic problem at their core: finding the point in a dataset that is closest to a given point. This "nearest neighbor" problem shows up all over the place in machine learning, pattern recognition, and data analysis, as well as many other fields. Yet the nearest neighbor problem is not really a single problem. Instead, it has as many different manifestations as there are different notions of what it means for data points to be similar. In recent decades, computer scientists have devised efficient nearest neighbor algorithms for a handful of different definitions of similarity: the ordinary Euclidean distance between points, and a few other distance measures.
I pour a cup of coffee, sharpen my pencil, and get ready to create. I've dusted off a half-conceived novel outline I abandoned three years ago, but this time I'm not waiting for my muse to intervene. Instead I hit the play button on the Creative Thinker's Toolkit, an audio lecture series from The Great Courses that I've downloaded on my computer. Gerard Puccio, a psychologist who heads the International Center for Studies in Creativity at SUNY Buffalo State, and the voice of the toolkit, tells me to engage in "forced relationships." Choose a random object, he instructs. I scan my office and settle on a bag of Skittles left over from Halloween. Next, he says, describe the object's attributes. "Sweet, round, colorful, chewy," I write. I start to draw more fruitful connections.
JAIR is published by AI Access Foundation, a nonprofit public charity whose purpose is to facilitate the dissemination of scientific results in artificial intelligence. JAIR, established in 1993, was one of the first open-access scientific journals on the Web, and has been a leading publication venue since its inception. We invite you to check out our other initiatives.
Over a decade ago, I was sitting in a college math physics course and my professor spelt out an idea that kind of blew my mind. I think it isn't a stretch to say that this is one of the most widely applicable mathematical discoveries, with applications ranging from optics to quantum physics, radio astronomy, MP3 and JPEG compression, X-ray crystallography, voice recognition, and PET or MRI scans. This mathematical tool--named the Fourier transform, after 18th-century French physicist and mathematician Joseph Fourier--was even used by James Watson and Francis Crick to decode the double helix structure of DNA from the X-ray patterns produced by Rosalind Franklin. You probably use a descendant of Fourier's idea every day, whether you're playing an MP3, viewing an image on the web, asking Siri a question, or tuning in to a radio station. In addition to his work in theoretical physics and math, he was also the first to discover the greenhouse effect.)