Have you recently had a frustrating or confusing conversation with a computer? It's silly to admit, but once in while I find myself screaming at Alexa (i.e., the persona of my Amazon Echo) because I don't like what she's telling me. Rationally, I know it's not her fault that my vacation plans are probably ruined by impending thunderstorms, but it's easy to get caught up in the role-play when your computer is talking to you like a human. For artificial intelligence architects, this is known as Natural Language Generation or NLG. A bigger challenge for solution developers embracing NLG in their designs is communicating in the best way for their customers to appreciate and understand.

Computers and robots can be biased too and it likely stems from the majority-focused photos used to train them. Researchers at the Massachusetts Institute of Technology are helping to pinpoint why facial recognition software is not accurate across all races -- and the issue likely stems from both recycled code and a Caucasian-dominated computer engineering field. Joichi Ito, MIT's Media Lab director, said during an artificial intelligence panel held this week at the World Economic Forum Annual Meeting that the software's apparent trouble with recognizing diversity is likely because the engineers, and the faces used to train the software, are mostly white. More: Google Photos' 'racist' error highlights facial recognition's limits The issue goes back to the basics of artificial intelligence. Machine learning programs are based on teaching a computer with a set of data.

Nvidia (NASDAQ:NVDA) has the hardware lead in deep learning, full stop. I have explained why this is so in an article I published last May. Since then, Nvidia investors have enjoyed outsized gains, which recently has brought about a number of articles speculating about an imminent reversal. This article is a rebuttal on a recent piece about Nvidia's AI perspectives, and possible threats from specialty deep learning hardware. Giving my opinion as a deep learning researcher, the recent piece contains a number of technical inaccuracies.

Last week I read Abadi and Andersen's recent paper [1], Learning to Protect Communications with Adversarial Neural Cryptography. I thought the idea seemed pretty cool and that it wouldn't be too tricky to implement, and would also serve as an ideal project to learn a bit more Theano. This post describes the paper, my implementation, and the results. The authors set up their experiment as follows. We have three neural networks, named Alice, Bob, and Eve.

Intel is the king of a shrinking kingdom. Almost every traditional desktop or laptop PC runs on the Santa Clara company's processors, but that tradition is fast being eroded by more mobile, ARM-powered alternatives. Apple's most important personal computers now run iOS, Google's flagship Chromebook has an ARM flavor, and Microsoft just announced Windows for ARM. And what's more, the burden of processing tasks is shifting away from the personal device and distributed out to networks of server farms up in the proverbial cloud, leaving Intel with a big portfolio of chips and no obvious customer to sell millions of them to. If you want to talk about the most influential chip company in history, Intel's name is the one you want.

In fact, in many countries, the internet, the very thing that was supposed to smash down the walls of authoritarianism like a sledgehammer of liberty, has been instead been co-opted by those very regimes in order to push their own agendas while crushing dissent and opposition. And with the emergence of conversational AI -- the technology at the heart of services like Google's Allo and Jigsaw or Intel's Hack Harassment initiative -- these governments could have a new tool to further censor their citizens. Turkey, Brazil, Egypt, India and Uganda have all shut off internet access when politically beneficial to their ruling parties. Nations like Singapore, Russia and China all exert outsized control over the structure and function of their national networks, often relying on a mix of political, technical and social schemes to control the flow of information within their digital borders. The effects of these policies are self-evident.

The machines haven't taken over. However, they are seeping their way into our lives, affecting how we live, work and entertain ourselves. From voice-powered personal assistants like Siri and Alexa, to more underlying and fundamental technologies such as behavioral algorithms, suggestive searches and autonomously-powered self-driving vehicles boasting powerful predictive capabilities, there are several examples and applications of artificial intellgience in use today. However, the technology is still in its infancy. What many companies are calling A.I. today, aren't necessarily so.

One of the highlights at this year's Consumer Electronics Show (CES) was the parade of new connected vehicle technologies. Automakers and their suppliers rolled out a number of innovative capabilities that promise to shape the next generation of driving, make transportation safer and more efficient, revitalize our cities, and reduce air pollution. Often lost amidst the "oohs" and "ahhs" these new capabilities inspire, however, is their dependence on radio spectrum and the policies that govern its use. The new connected vehicle capabilities come in decidedly different flavors. Some, for example, seek to enhance the automobile user's experience.

YouTube recommendations are a prominent example of applying advanced analytics on a massive scale to improve a service, the experience users get out of it, and the bottom line of the vendor behind it -- Google. Previously, we explored the rationale behind it and pondered as to how this type of analytics could be classified. It's time to pick up where we left off and explore how it works under the hood. Inspiration came from a hit moment for YouTube recommendations: one of those times when it succeeded in picking up the track that the person curating an ad-hoc, spur-of-the-moment playlist was about to play next. The wow effect induced by this successful prediction/recommendation of a rarity, triggered a spur-of-the-moment discussion which may serve to illuminate different aspects of analytics.

Your devices will know everything about you. Millions of people own smart devices that do simple things like adjust temperature, turn down lights or lock doors when they go out. You can buy a connected fridge that tells you when you have run out of food or set up security cameras that tell you when suspicious movement is detected. There is even a toothbrush that tells you what areas of the mouth you have neglected and an intelligent hair brush that does … something. Depending on which market forecast you believe, the install base for connected devices could be anywhere between 20 billion to just over 30 billion by 2020.