Cybersecurity companies estimate that new malware variants are introduced at a daily rate of up to 390,000. With each hour that passes, at least 13,000 new files emerge. If you find these numbers staggering, that's because they are. Humans simply cannot keep up with them, which is why cybersecurity analysts are turning to artificial intelligence (AI) for help. Fighting the constantly evolving and morphing threat landscape requires a combination of detection and a single view of threat data, in addition to the traditional methods of signature-based malware detection and blocking.

The year is coming to an end. I did not write nearly as much as I had planned to. But I'm hoping to change that next year, with more tutorials around Reinforcement Learning, Evolution, and Bayesian Methods coming to WildML! And what better way to start than with a summary of all the amazing things that happened in 2017? Looking back through my Twitter history and the WildML newsletter, the following topics repeatedly came up.

If there's one thing we can learn from the Terminator franchise, it's that too much technological advancement is not something we should be a hundred percent on board with. What's worse is that with the advancement we're getting with the AI technology, the creation of Skynet can actually be not that far ahead into the future. We're slowly getting there, and that should terrify you. Not sold on the idea yet? In order to start the ball rolling, here are some terrifying facts about Artificial Intelligence right now.

Watershed technologies like AlphaGo make it easy to forget that artificial intelligence (AI) isn't just a futuristic dream. Sensing traffic lights, fraud detection, mobile bank deposits, and, of course, internet search -- each of these technologies involves AI of some kind. As we have grown used to AI in these instances, it has become part of the scenery -- we see it, but we no longer notice it. Expect that trend to continue: As AI grows increasingly ubiquitous, it'll become increasingly invisible. Major advancements in technologies dependent on AI -- like robotics, machine vision, natural-language processing, and machine learning -- will soon work their way into our daily lives. AI's integration into our world will transform employment, economic activity, and possibly the character of our society. Healthcare is ground zero for AI. In fact, AI has been quietly helping doctors treat diseases for almost its entire existence. In 1963, a Midwestern radiologist named Gwilym S. Lodwick published a paper in Radiology Society of America that described a technique he invented for predicting the survival span of lung cancer patients: Lodwick took X-rays and coded their features to represent tumor characteristics using numerical values. Then, as he explained, these numbers could "be manipulated and evaluated by the digital computer." Armed with (rudimentary) image processing, in the 1970s radiologists began using machine vision to generate data directly from images. These were the logic-based days of early AI, so algorithms followed a sequence of rules to identify body parts: If there's an oval here attached to a thick line, we're looking at a hip bone connected to a thigh bone. Lodwick called his technique "computer-aided diagnosis," and CAD has been an invisible tool of medicine ever since. By the 1980s and 1990s, doctors were using CAD to give them a second opinion for diagnosing everything from lumbar hernias to gastric pain.

This video clip is from the Creative Destruction Lab's third annual conference, "Machine Learning and the Market for Intelligence", hosted at the University of Toronto's Rotman School of Management on October 26, 2017. The Creative Destruction Lab is a seed-stage program for massively-scalable, science-based companies. Graduates include companies such as Atomwise (San Francisco), Thalmic Labs (Waterloo), Deep Genomics (Toronto), Kyndi (Palo Alto), Nymi (Toronto), Automat (Montreal), Ada (Toronto), and Heuritech (Paris). This year, the program admitted 125 AI-oriented startups in Toronto and another 40 at other CDL locations across Canada. To our knowledge, this is the third year in a row that the CDL is home to the greatest concentration of AI startups of any program on Earth.

President Donald Trump has signed into law a $1.5 trillion tax overhaul package. Trump touted the size of the tax cut, declaring to reporters in the Oval Office before he signed it Friday that'the numbers will speak.'

The end of a politician's time in office often inspires a turn toward the existential, but few causes are as quixotic as the one chosen by James Vacca, who this month hits his three-term limit as a New York City Council member, representing the East Bronx. Vacca's nearly four decades in local government could well be defined by a bill that he introduced in August, and that passed last Monday by a unanimous vote. Once signed into law by Mayor Bill de Blasio, the legislation will establish a task force to examine the city's "automated decision systems"--the computerized algorithms that guide the allocation of everything from police officers and firehouses to public housing and food stamps--with an eye toward making them fairer and more open to scrutiny. In mid-October, I and some of my colleagues from a group at Cornell Tech that works on algorithmic accountability attended a hearing of the Council's technology committee to offer testimony on the bill. As Vacca, who chairs the committee, declared at the time, "If we're going to be governed by machines and algorithms and data, well, they better be transparent."

The Kepler space telescope is operated by NASA to discover other earths, some of which could support life. And its latest discovery is significant enough to bring with it a huge press conference. Very little further information was given about the announcement, which will take place on Thursday. But it will almost certainly relate to exoplanets -- Earth-sized worlds that orbit around their own stars, and are our best hope of finding alien life. The space agency said that the discovery was made with the help of Google artificial intelligence, which is being used to analyse the data sent down by the telescope.

Not so long ago, Canadian tech entrepreneurs had a long list of grievances: a dearth of early and late-stage funding, long visa wait times for foreign hires, local corporations that wouldn't buy their products, the best and brightest decamping for Silicon Valley. Fast forward to today, and those problems have largely evaporated. Justin Trudeau's Liberal government, eager to brand itself as innovative, has given tech leaders pretty much everything they asked for, including special fast-track visas for tech workers and hundreds of millions of dollars in venture capital money and support for artificial intelligence research. Of course, Canada has squandered its tech prowess before (see: BlackBerry). The trick this time is taking advantage of lessons learned so it doesn't happen again.

If ever there was an industry ripe for disruption it is surely the legal profession. Unlike many other sectors, however, it has tended to be a little reticent about embracing technology to innovate. After all, the traditional way of doing business for legal firms has been extremely profitable. The model typically involves a bunch of low-paid minions doing grunt work while a few partners earn eye-wateringly high sums. Moreover, many legal professionals look upon technology with fear and who could blame them when a forecast from Deloitte published last year predicted that more than 100,000 jobs in the sector could be automated within the next 20 years.