From the looks of it, 2017 will be a pretty amazing year for technology. Every day consumers are talking about virtual reality, self-driving cars and a limitless menu of on-demand services. The future really has arrived. But even as digital tech gets more streamlined and powerful, glitches keep popping up. Just when we think we have superhuman control of our lives, a device fails to work and we have no clue how to fix it.

We have seen great strides made in artificial intelligence (AI). As sci-fi readers and film buffs know, many of these ideas that have been foreshadowed -- and that seem far-fetched -- in fiction are beginning to seem possible. Whether we are considering the benevolent Commander Data in Star Trek: The Next Generation or fearing the terrifying Terminator, we are entering an era where AI as is rapidly entering the public discourse. A number of technologies are going to make the AI that we see in film possible. In addition to supercomputing and cloud computing, technology such as robotics, computer vision, machine learning, nanotechnology, and computational power in small spaces will directly affect the way that AI develops in real life.

The next time you enter a query into Google's search engine or consult the company's map service for directions to a movie theater, remember that a big brain is working behind the scenes to provide relevant search results and make sure you don't get lost while driving. As Fortune's Roger Parloff wrote, the Google Brain research team has created over 1,000 so-called deep learning projects that have supercharged many of Google's products over the past few years like YouTube, translation, and photos. With deep learning, researchers can feed huge amounts of data into software systems called neural nets that learn to recognize patterns within the vast information faster than humans. In an interview with Fortune, one of Google Brain's co-founders and leaders, Jeff Dean, talks about cutting-edge AI research, the challenges involved, and using AI in its products. The following, done against the backdrop of the 50th annual Turing Award, an honor in computer science from the Association for Computing Machinery, has been edited for length and clarity. A lot of human learning comes from unsupervised learning where you're just sort of observing the world around you and understanding how things behave.

The scenario has played out through innumerable iterations in popular culture, the most popular being The Terminator series. Steven Spielberg, riffing on the film Stanley Kubrick was going to direct before his death, presented the counterpoint, espousing a benevolent vision of AI in A.I. Then there are more nuanced, ambiguous iterations, like the recent Ex Machina. New advances in algorithmic artificial intelligence, deep learning software, automation, and nanotechnology have made it abundantly clear that Ray Kurzweil's vision of the Singularity may also be not an if, but when. In fact, responding to Kurzweil's prediction of a cloud-based neocortex in the 2030s, entrepreneur Bryan Johnson of Braintree said, "Oh, I think it will happen before that." Johnson's more recent aspirations involve merging artificial intelligence with humans, a pursuit many would argue is already occurring on a vast scale when it comes to our use of smartphone technology and search engines.

If the last few weeks have taught us anything, it's that predictions, more often than not, are incorrect. With that simple statement I turn my attention to 2017 and what may or may not happen. Looking into next year is likely looking into a foggy crystal ball -- everything's a little murky, but you have an idea of where things are headed. The biggest topic of the coming year is artificial intelligence. Everyone from Stephen Hawking and Elon Musk to Tom Cruise and Kanye West is talking about A.I.

The query was raised via an on-screen prompt following the confusing Season 1 finale of HBO's "Westworld." Perplexed viewers were then directed to a website where they could get some answers. But that question has also been ruminating in our collective consciousness since Nov. 8 when Donald Trump's victory marked the biggest upset in the history of presidential elections, after one of the most uncivilized campaign slogs in modern memory. Unfortunately, there is no explainer website to clear things up. Both events offered surprise endings and more dispiriting parallels than were probably good for our threadbare attention spans. "Westworld" started as a sci-fi drama set in a Wild West-style theme park when it debuted in October, where robot hosts fulfilled the fantasies of human guests and, ultimately, it was humanity that took a vacation.

In an era when the nation's president-elect has been routinely criticized for his sexist remarks about women, BU researchers, working with Microsoft colleagues, have discovered your computer itself may be sexist. Or rather, they've discovered that the biased data we fallible humans feed into computers can lead the machines to regurgitate our bias. And there are potential real-world consequences from that. Those findings are in a paper produced by the team, whose two BU members are Venkatesh Saligrama, a College of Engineering professor of electrical and computer engineering and systems engineering, with a College of Arts & Sciences computer science appointment, and Tolga Bolukbasi (ENG'18). The team studied word embeddings--algorithms that one member of the team described to National Public Radio as dictionaries for computers.

All of it coming at you day and night in a never-ending stream that moves so fast that it becomes a screaming blur that makes you want to bang your head against a brick wall until the lost consciousness finally silences the machine and provides you with some degree of sanctuary from the madness. With this in mind, it seems clear that Americans are suffering from information overload, right? Well, according to the latest Pew numbers, only 20 percent of us feel overloaded by the glut of information we encounter on a continual basis. And that surprisingly low number is actually down pretty dramatically from a decade ago. The NextDraft newsletter is now on WIRED.com.

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Over the past few years, the term "deep learning" has firmly worked its way into business language when the conversation is about Artificial Intelligence (AI), Big Data and analytics. And with good reason – it is an approach to AI which is showing great promise when it comes to developing the autonomous, self-teaching systems which are revolutionizing many industries. Deep Learning is used by Google in its voice and image recognition algorithms, by Netflix and Amazon to decide what you want to watch or buy next, and by researchers at MIT to predict the future. The ever-growing industry which has established itself to sell these tools is always keen to talk about how revolutionary this all is. But what exactly is it?