There's a growing fear that artificial intelligence (A.I.) and machine learning will take over millions of human jobs, leading to social upheaval on an unimaginable scale. While some tech leaders believe that artificial-intelligence platforms will ultimately prove a blessing to the human race, there are others--most notably Tesla CEO Elon Musk--who think that unmanaged A.I. could doom us all. At this juncture in the evolution of A.I., it's too soon to tell how things will ultimately go. But there are a few signs that A.I. won't undermine human beings, at least in the near term. Exhibit A: the recent tournament that saw a bot created by OpenAI, a non-profit A.I. research company, beat human champions at "Dota 2," a real-time strategy game.

Ever since research scientists coined the term'artificial intelligence' more than sixty years ago, the idea of a self-thinking computer has occupied a special place in the public consciousness. But now companies seem to have come around to the idea that, with enough technology and talent, A.I. can become an actual product. Those firms include Google, IBM, Apple, Facebook, and Infosys. And they're all fishing in the same talent pool for technology professionals who can build a workable A.I. platform. Such an unusual endeavor demands unusual skill-sets, making it difficult to land the right candidates for the job. A.I. programmers have to come from somewhere.

There's a simple reason behind the explosive growth of mobile-app ecosystems: with a good idea, a solid development team, and a bit of luck, tech pros can make a lot of money off building apps for businesses and consumers. So as the tech giants shift their focus from mobile devices to artificial intelligence (A.I.) and machine learning, the inevitable question arises: how can developers--always on the lookout for the next big thing--get paid to develop new A.I. products? The central challenge is format. A mobile app (and most software, for that matter) is a discrete unit, no different than any other product. You can charge for it up-front or give it away for free; you can institute subscription tiers that force users to pay for different levels of functionality.

Click here to see this visualization in full-screen mode. Last year, Dice launched an interactive visualization, created by Simon Hughes, that broke down supply and demand for various tech roles. In this context, "supply" is the proportion of job seekers on Dice with a particular skill, while "demand" is the proportion of jobs listing that skill. We took those values and normalized them so that they sat on the same range; by dividing the demand value by the supply for a particular skill, we get a number that indicates how in-demand (or "hot") a particular skill is. This year, we've updated that visualization, incorporating some of the broader feedback we've received over the past 12 months.

Phrases such as "machine learning" and "artificial intelligence" are thrown around so often by so many people, they risk becoming buzzwords along the lines of "Big Data." But unlike "Big Data," which was always a somewhat-nebulous term, "machine learning" is a definitive process that, when applied correctly, can result in some impressive feats. For instance, check out how Google used it to radically transform the sophistication of Google Translate, one of its core services. For those tech professionals who wish to break into machine learning and artificial intelligence, make no mistake about it: there's a lot of education and training involved. Google wants to make that journey a little easier, though, with a new three-hour course that offers a quick overview of deep-learning fundamentals.

Predictions are ultimately a fool's game. While it's certainly possible to make some broad assumptions about the next 12 months in technology (Apple will release a new iPhone! More firms will introduce cloud services!), the fast-changing nature of the industry can quickly wreck even the most seemingly surefire trends. With that in mind, here are some cautious forecasts about the hottest technologies of 2017. Sure, we could be wrong.

Ever since research scientists coined the term'artificial intelligence' more than sixty years ago, the idea of a self-thinking computer has occupied a special place in the public consciousness. But now companies seem to have come around to the idea that, with enough technology and talent, A.I. can become an actual product. Those firms include Google, IBM, Apple, Facebook, and Infosys. And they're all fishing in the same talent pool for technology professionals who can build a workable A.I. platform. Such an unusual endeavor demands unusual skill-sets, making it difficult to land the right candidates for the job. A.I. programmers have to come from somewhere.

Some of the biggest names in tech are collaborating on artificial intelligence. The Partnership on AI (inventive name, it is not) has brought together Amazon, Google, Facebook, IBM, Microsoft, and others to debate best practices and host A.I.-related events. The Partnership on AI isn't the first high-profile collaboration among tech luminaries to tackle the heavy questions surrounding artificial intelligence and machine learning. Earlier this year, Tesla CEO Elon Musk joined with venture capitalist Peter Thiel and others to launch OpenAI, a non-profit "artificial intelligence research company" devoted to developing A.I. that's friendly to humanity. While both OpenAI and the Partnership on AI are focused on promoting ethical A.I. research, as well as advancing public understanding of the potential (and pitfalls) of machine learning, OpenAI has pushed ahead in offering materials and toolkits for researchers.

Robots assisted by artificial intelligence may eliminate 6 percent of all U.S. jobs by 2021, according to a new analysis by Forrester. "Solutions powered by AI/cognitive technology will displace jobs, with the biggest impact felt in transportation, logistics, customer service and consumer services," read part of the analyst firm's report, as quoted by The Guardian. Many of these industries already seem on the verge of technological disruption. Otto, a young startup run by engineers from Google's autonomous-vehicle project (and recently acquired by Uber for 670 million), is testing out self-driving trucks. If all goes according to plan, humans won't entirely cede control of their big rigs to software; rather, the technology will take over at periodic intervals so that drivers can catch some shut-eye. It's also not impossible to imagine a future in which trucks lack drivers altogether.

Research firm Gartner periodically releases a "Hype Cycle," in which it attempts to show which technologies are just emerging, and which have plateaued. This year, its hype cycle highlights three technologies in particular: "transparently immersive experiences" (i.e., virtual- and augmented-reality systems), "the perceptual smart machine age" (hello, Internet of Things), and "the platform revolution" (i.e., apps, lots of apps). Gartner releases the "Hype Cycle" as a way for tech pros--including chief innovation officers, startup founders, and developers--to keep an eye on those technologies that haven't quite reached the mainstream-adoption stage, but will nonetheless prove critical to many businesses over the next several years. The Innovation Trigger When technologies emerge from the lab and start becoming popular. The Peak of Inflated Expectations In other words, when companies want to adopt said technologies.