I think there are three things that are going to affect the world in incredibly significant ways over the next decade and they are 1) Climate change 2) CRISPR and 3) artificial intelligence. I wanted to work in one of those and be helpful. Because of my background, AI made the most sense. Along with conducting fundamental research, OpenAI can also help increase the level of knowledge that's available on how to use, regulate and evaluate this technology. NIPS is probably the single largest AI conference in the field and it's happening in Barcelona right now. There's a joke among researchers that NIPS is where people get together to discuss papers that came out four months ago. That's because the paper deadline was then, and the pace of modern AI research is so fast that much of the industry has subsequently moved onto new techniques and new papers.

This question pops up on the KloudsKape, and my first thought is: How did they know? I'm in the middle of a downward spiral, almost crying as I choke down my lysine-dopamine smoothie and hunch over the teak bar at the Zyme Shack. As with all these questions, I don't even have to ponder before I answer with an eyeblink--it's lonesome, of course. Something about the way you have to purse your lips for a nonexistent kiss at the end of the word, the extra weight of that second syllable--the word lonesome is definitely more miserable. Soon I've answered a dozen other questions in the retinal sensorium, about everything from Koffee Kop to a local bike-lane ordinance, each of them just a sparkly ball rolling around the edges of my vision.

When it comes to search, there's Google and there's everyone else -- the company is basically synonymous with searching the internet. But Omnity, a relatively new company from San Francisco, thinks own search that's based on "semantic mapping" offers something that Google can't do. Omnity's trick is that it looks for the connections between documents on the internet based on rare words -- the theory that research that has several of the same rare words will likely be about related topics, even if that research doesn't directly link to or cite each other. Thus far, Omnity has operated primarily by selling enterprise plans to companies and educational institutions. Omnity can search not only all of the public datasets it scans (like patents, scientific, engineering and medical documents, clinical trials, case law, SEC filings and so forth) but also a company's internal documents -- for some companies, Omnity indexes 150 petabytes of data.

In the summer of 1967, Secretary of Defense Robert McNamara commissioned a group of thirty-six scholars to write a secret history of the Vietnam War. The project took a year and a half, ran to seven thousand pages, and filled forty-seven volumes. Only a handful of copies were made, and most were kept under lock and key in and around the Beltway. One set, however, ended up at the RAND Corporation, in Santa Monica, where it was read, from start to finish, by a young analyst there named Daniel Ellsberg. Ellsberg was dismayed by what he learned. For a generation, the U.S. government had been lying to the American people about the Vietnam War. He put the first of the volumes in his briefcase, praying that the security guards at RAND would not stop him, and made his way to a small advertising agency in West Hollywood, where a friend told him there was a Xerox machine he could use. "It was a big one, advanced for its time, but very slow by today's standards," Ellsberg writes in his 2002 autobiography, "Secrets: A Memoir of Vietnam and the Pentagon Papers": It could do only one page at a time, and it took several seconds to do each page. I tried pressing the book down on the glass to do two pages at a time, but the middle section was faint and uneven. Fortunately the books were bound with metal tapes through holes so they could be taken apart. . . . The machine didn't collate, and the bar had to come back and travel just as slowly for each copy.

You've read about cities installing smart parking meters and noise- and air-quality sensors, but are you ready to embrace the idea of a city brain? The residents of Singapore are on track to do just that. Creating a centralized dashboard view of sensors deployed across a distributed network is nothing new, but it takes on a bigger -- perhaps ominous -- meaning when deployed across a major city. Many technologically advanced cities worldwide are exploring ways to build such comprehensive digital views for managing traffic and parking, monitoring water and air quality, and offering such citizen-facing services as web-based tools for interacting with government agencies. Some smart city experts call this system approach a "city brain" or, less glamorously, a "municipal backplane."

Apple had a tough 2016. Early in the year, the tech giant became entangled in a legal battle with the FBI over the company's refusal to help the agency unlock the phone of San Bernardino shooter Syed Farook. Apple argued that doing so would open a back door that would put other iPhone users at risk of privacy breaches. Apple also had a somewhat disappointing financial year with annual sales dropping for the first time since 2001, when the company released the iPod. On the hardware side, Apple did release a refreshed version of the iPhone and the MacBook Pro, but both received mixed reviews.

If we want to massively accelerate artificial intelligence and improve human lives, we need to democratize access to data. Past civilizations built grand libraries to organize the world's knowledge. These repositories of information focused on cataloging, aggregating, organizing and making information accessible so that others could focus on learning and creating new knowledge. AI and machine learning systems also need repositories of information from which to learn -- and right now everyone is building their own. If different groups of people focus on organizing data versus building AI, the progress of intelligent computers will massively accelerate.

A company's logo is an important part of its identity, but the processes behind defining, registering, and protecting these trademarks is a convoluted and rather archaic one. A startup called TrademarkVision aims to simplify it by replacing that laborious and arcane process with what amounts to a machine-learning-powered reverse image search. This isn't in some lab, either: the EU just switched their whole image trademark system over to it. Most people probably haven't had to do many trademark and logo searches. Well, why don't you take the USPTO's version for a spin so you know what it's like? Try to find the Nike "Swoosh" or something.

Evolution inevitably involves the creation of new problems, and the big tech stories of the year show that this goes for IT just like anything else. While the internet has brought the world closer together, it also paved the way for fake news and new forms of espionage. The rise of AI has humans worried about being replaced. Chip makers are consolidating and scrambling to retool to meet the demands of virtual reality and the internet of things. And while Apple removed legacy ports on its new devices, a lot of users are grumbling about needing adapters for their favorite headphones and other peripherals.

If we want to massively accelerate artificial intelligence and improve human lives, we need to democratize access to data. Past civilizations built grand libraries to organize the world's knowledge. These repositories of information focused on cataloging, aggregating, organizing and making information accessible so that others could focus on learning and creating new knowledge. AI and machine learning systems also need repositories of information from which to learn -- and right now everyone is building their own. If different groups of people focus on organizing data versus building AI, the progress of intelligent computers will massively accelerate.