Successful Student has compiled the 25 Best Artificial Intelligence Colleges in the United States. Artificial Intelligence (AI), also known as machine learning, is a discipline within computer science. Artificial Intelligence is usually conceived of as doing more than just computing numbers (such as a calculator), but is more conceptual in nature (such as describing subjective qualities, or giving meanings to different contexts). An example of AI would be speech recognition and communicating, such as Apple's Siri, or Amazon's Alexa. Amazon has announced three new AI tools for anyone wanting to build apps on Amazon Web Services: Amazon Lex, Amazon Polly, and Amazon Rekognition. According to Amazon "This frees developers to focus on defining and building an entirely new generation of apps that can see, hear, speak, understand, and interact with the world around them." For those interested in developing apps, see our 20 Best App Development Colleges article. Google, Facebook, Amazon, Apple and Microsoft are all working on AI. Facebook's FAIR (Facebook Artificial Intelligence Research) program engages with academia to assist in solving long term problems in AI. Facebook is hiring AI experts around the world to assist in their project.

Also, anyone interested in translating a piece of code (about 200 lines) to Python and/or R? It is about a brand new machine learning technique. To learn more about this project, click here and check out section 4.

As clever as machine learning is, there's one common problem: you frequently have to train the AI on thousands or even millions of examples to make it effective. What if you don't have weeks to spare? If Gamalon has its way, you could put AI to work almost immediately. The startup has unveiled a new technique, Bayesian Program Synthesis, that promises AI you can train with just a few samples. The approach uses probabilistic code to fill in gaps in its knowledge.

Boston-based AI startup Gamalon is launching two new products based on the innovative technology, MIT's Technology Review reported today. The company has developed a technique that gives artificial intelligence the ability to write code. It uses a predictive model that's based on probability to work out facts relating to data it's supplied with. The information it gleans from the first stage is then used to create a more optimised version of the model. This powers future runs of the program, enabling more facts to be determined the next time around.

Ransomware has had a major impact on how IT organizations think about protecting data. Now Druva, a provider of data protection and management software delivered as a service, wants to make it a lot simpler for IT organizations to identify data that is being targeted by ransomware before things completely spin out of their control. Today, Druva announced it is employing machine learning algorithms across its cloud service to continually assess the unique attributes of changes being made across various file types to make it easier to identify abnormal deletions, unusual modifications and updates, and an atypical number or large number of file creations. Druva CEO Jaspreet Singh says all these events are symptomatic of events associated with ransomware attacks. In the event of such an attack, Singh says, Druva can now also pinpoint the last safest snapshot for an organization to recover. Machine learning algorithms, Singh says, are only part of an overall approach to data protection that is rapidly evolving.

Google shows 44m hits on AI and 9m on Cognitive Computing and the figure below from Google Trends clearly shows that the search term "Artificial Intelligence" is more popular than "Cognitive Computing", however, I'm sure we'll start to see that gap close in 2017. In our white paper "Surviving in the AI hype", we explained some of the fundamental concepts behind AI, as well as touching on Cognitive Science and Computing but in this post we want to focus in more detail on the relationship between AI and Cognitive Computing specifically. To start off, what do Intelligence and Cognition mean if we search for a definition online? Intelligence: "the ability to learn or understand or to deal with new or trying situations: reason; also: the skilled use of reason (2): the ability to apply knowledge to manipulate one's environment or to think abstractly as measured by objective criteria (as tests)." Cognition: "the mental action or process of acquiring knowledge and understanding through thought, experience, and the senses."

But this isn't to say Gamalon couldn't compete in areas outside of organizing messy corporate documents into neat spreadsheets. Gamalon has pitted its techniques in image recognition against Google's TensorFlow using the tech giant's deep learning-powered "Quick, Draw" program. When Google asks the user to draw a floor lamp, the AI gets confused if, say, the user draws a chair next to it, because it has difficulty separating the two objects -- Google thinks you drew a house or a church. But using Gamalon's system, the AI can identify the two separate objects for what they are -- a floor lamp and a chair.

Amazon has added another tool to its long list of services - a video conferencing and chat app. Called Amazon Chime, the app uses on-click dialing and boasts noise-cancelling technology for clear conversations - and can be used on Windows, iOS and Android devices. The new release is the Seattle-based firm's latest weapon to compete with the likes of Skype for Business, Cisco's WebEx and GoToMeeting. The service will call all of the participants when a meeting starts, allowing users to join the call with just a push of a button in the app – and no PIN is required to access the room. And, those who are running behind can tap a'running late' button to automatically notify everyone in the meeting.

Let's be clear here: What Musk is proposing may be far-fetched, but it's a response to a very real problem that's going to affect a lot of people in the near future. Jobs that involve predictable manual labor are in danger of becoming obsolete. McKinsey & Company estimates that about 78 percent of those types of jobs (along with 69 percent of data processing and 64 percent of data collecting) could become completely automated. Driving-related jobs are likely to become increasingly automated as self-driving technology improves, and given that those were the most common jobs in 29 states as of 2014, we should absolutely be focused on finding a solution. But Musk has also floated a much simpler--and more realistic--solution.

Overwrought love letters began turning up on the notice board at the University of Manchester's computer lab in August, 1953. Dripping with lustful vocabulary, they were all variations on a basic syntactic template: "YOU ARE MY [adjective] [noun]. And the signatory was always the same: "M.U.C.," for the Manchester University computer, a Ferranti Mark 1, the world's first general-purpose and commercially available machine of its kind. But the real author of the letters (in the first instance, anyway) was Christopher Strachey, a pioneering programmer. As he confessed in an article the following year, "There are many obvious imperfections in this scheme (indeed very little thought went into its devising), and the fact that the vocabulary was largely based on Roget's Thesaurus lends a very peculiar flavor to the results." For Strachey, though, the interesting thing was how a simple setup, using only about seventy base words, could produce a combinatorial explosion of results--on the order of three hundred billion different letters. The lovelorn user could run the program over and over until his fingers seized up, and never see the same letter twice. Strachey was something of an outlier, according to Martin Campbell-Kelly, a historian of computing at the University of Warwick. While scientists and mathematicians of the day typically used computers strictly for numerical calculations, like analyzing weapons trajectories or seeking prime factors of huge numbers, his fascination was with non-numerical computations--what soon became known as artificial intelligence. "Strachey grabbed hold of that much more than anybody else," Campbell-Kelly told me. The results were not always lovey-dovey. Besides training the Mark 1 to churn out billets-doux, he also taught it to play checkers ("draughts," in British parlance). If M.U.C.'s opponent made too many mistakes, it would get crotchety and print out a reprimand: "I refuse to waste any more time.