What we call machine learning can take many forms. The purest form offers the analyst a set of data exploration tools, a choice of ML models, robust solution algorithms, and a way to use the solutions for predictions. The Amazon, Microsoft, Databricks, Google, and IBM clouds all offer prediction APIs that give the analyst various amounts of control. HPE Haven OnDemand offers a limited prediction API for binary classification problems. Not every machine learning problem has to be solved from scratch, however.

The White House is doing a lot more thinking about the arrival of automated decision-making -- super-intelligent or otherwise. No one in government is yet screaming "Skynet," but in two actions this week the concerns about our artificial intelligence future were sketched out. The big risks of A.I. are well-known (a robot takeover), but the more immediate worries are about the subtle, or not-so-subtle, decisions made by badly coded and designed algorithms. President Barack Obama's administration released a report this week that examines the problem associated with poorly designed systems that, increasingly, are being used in automated decision making. Algorithmic systems can affect employment, education, access to credit -- anything that relies on computer-assisted decisions.

That said, current technology won't allow us to make anything larger than the printing machines themselves. Some smart people have suggested that we should look to nature to see how it builds things--specifically, spiders, and the way they can swarm together to build massive nests for themselves. Siemens, the German engineering and telecommunications company, has taken this concept to heart. A team of researchers at its Princeton, New Jersey, laboratory are creating autonomous spider-like robots that can work together to 3D-print structures on command. While Siemens isn't known for its robotics research, Livio Dalloro, the head of the company's "Product Design, Simulation & Modeling Research" group in Princeton, said in an interview that the company views the bots as a "moonshot."

That's where hallucinations, reflexes, Post Traumatic Stress, Phobias, and most importantly, dreams come from. You are right, the mind doesn't deal with much external data. Sense organs are all processed elsewhere, however, some sections of processing overlap, autonomic vs. reflex, etc. The conscious portion of human beings is very tiny compared with all the subconscious and unconscious/automatic processes going on.

And pretty soon, they'll come for us. That seems to be the story today, whether from Hollywood or in breathless articles in popular tech magazines about artificial intelligence and nanotechnology. Our biggest ever edition of TNW Conference is fast approaching! In a world where machines can learn, once humans push the "on" button, there's no stopping our robot overlords, right? When machines become more intelligent, humans are freed to become more creative.

College of Computing Professor Ashok Goel teaches Knowledge Based Artificial Intelligence (KBAI) every semester. And every time he offers it, Goel estimates, his 300 or so students post roughly 10,000 messages in the online forums -- far too many inquiries for him and his eight teaching assistants (TA) to handle. That's why Goel added a ninth TA this semester. Her name is Jill Watson, and she's unlike any other TA in the world. Jill is a computer -- a virtual TA --implemented on IBM's Watson platform.

Now WATSON HEALTH AI is being used in 16 cancer institutes across the country, helping to diagnose and treat patients. Meanwhile Google, not to be outdone, has launched DeepMind, which recently earned the title world champion of the complex game of Go. Now, DeepMind Health will create innovative new apps for healthcare professionals alerting them to patient emergencies, and the risk of complications when considering possible treatment options. Progenitors say that someday, it should even be able to predict a patient's needs down the pike. Other tech companies such as Dell, Hewlett-Packard, Apple, and Hitachi are also putting together AI programs for the healthcare field. Within the next five years, AI's use in medicine is expected to increase tenfold.

Emails won't literally get sucked down a vortex of analytics solutions. If you have ever bought something from an online store, chances are the store's used your email address with wanton disregard, bombarding you with email after email about its products and sales until you reach for the sweet oblivion of unsubscription. Stores and brands do this to keep customers engaged--but they don't know how many emails are too many. Adobe is releasing a tool today with the promise to help alleviate what they call "customer fatigue." By using machine learning algorithms to crunch the numbers of how often emails are opened and clicked on, marketers can see whether customers are tired of getting their emails.

Almost a year after it came out in the US, Facebook is releasing its facial recognition-powered photo app Moments in Europe. Except the new version won't actually include any facial recognition technology, thanks to the company's long-running fight with the Irish data protection commissioner over whether the technology is actually legal in the EU. Launched in June, Moments is Facebook's answer to dedicated photo management applications like Google Photos and Apple's Photos. The app bundles pictures together by the event they're taken at, and applies facial recognition technology to identify who's in each picture. Facebook takes the offering a step further than Apple or Google, by leveraging its social network: once you've created your "moments", you can share them with other people at the same event, to ensure that they have the photos of them, and you have the photos of you.

As you can see in the first video, some of the clips look no better than some of the images that come out of Google's DeepMind. Others, however, like the scenes from Cloud Atlas and the Jungle Book, seem significantly better-looking after being processed. It's still early days, but there are plenty of areas in both filmmaking and art where systems like this could be applied. Right now, it's computationally intensive, but not so much that you can't expect the cost and availability of such power to drop. As MIT Tech Review explains, it takes three minutes for each frame to be processed on a system using NVIDIA's 1,000-plus Titan X graphics card.