'Most women in the Bay Area are soft and weak, cosseted and naive, despite their claims of worldliness, and generally full of shit," wrote former Facebook product manager Antonio García Martínez in 2016. "They have their self-regarding entitlement feminism, and ceaselessly vaunt their independence. But the reality is, come the epidemic plague or foreign invasion, they'd become precisely the sort of useless baggage you'd trade for a box of shotgun shells or a jerry can of diesel." This is from his insider account of Silicon Valley, Chaos Monkeys. The book was a bestseller. The New York Times called it "an irresistible and indispensable 360-degree guide to the new technology establishment".

Editor's Note: This article first appeared in Spektrum der Wissenschaft, Scientific American's sister publication, as "Digitale Demokratie statt Datendiktatur." "Enlightenment is man's emergence from his self-imposed immaturity. Immaturity is the inability to use one's understanding without guidance from another." The digital revolution is in full swing. How will it change our world? The amount of data we produce doubles every year. In other words: in 2016 we produced as much data as in the entire history of humankind through 2015. Every minute we produce hundreds of thousands of Google searches and Facebook posts. These contain information that reveals how we think and feel. Soon, the things around us, possibly even our clothing, also will be connected with the Internet. It is estimated that in 10 years' time there will be 150 billion networked measuring sensors, 20 times more than people on Earth. Then, the amount of data will double every 12 hours. Many companies are already trying to turn this Big Data into Big Money. Everything will become intelligent; soon we will not only have smart phones, but also smart homes, smart factories and smart cities. Should we also expect these developments to result in smart nations and a smarter planet? The field of artificial intelligence is, indeed, making breathtaking advances. In particular, it is contributing to the automation of data analysis. Artificial intelligence is no longer programmed line by line, but is now capable of learning, thereby continuously developing itself. Recently, Google's DeepMind algorithm taught itself how to win 49 Atari games. Algorithms can now recognize handwritten language and patterns almost as well as humans and even complete some tasks better than them. They are able to describe the contents of photos and videos. Today 70% of all financial transactions are performed by algorithms. News content is, in part, automatically generated. This all has radical economic consequences: in the coming 10 to 20 years around half of today's jobs will be threatened by algorithms. It can be expected that supercomputers will soon surpass human capabilities in almost all areas--somewhere between 2020 and 2060. Experts are starting to ring alarm bells.

Over the past few months, industry and government have pledged more than $500-million toward AI, a commitment that has led to the rise of powerful institutions such as the Montreal Institute for Learning Algorithms, the Vector Institute and the Alberta Machine Intelligence Institute. These structures are well positioned to keep churning out cutting-edge research, train the next generation of AI leaders, and advance the innovation and technology transfer of AI. Our three AI Institutes are set up to offer Canadian businesses similar training programs and there's good reason for them to use these resources: Canadian enterprises that consider investing in state-of-the-art machine-learning and data infrastructure can enjoy results such as increased efficiency in manufacturing, better management of underwriting risk, minimization of fraud and reduction of health-care costs. Among the most urgent are ensuring the market is well supplied by streamlining immigration, ensuring higher education and industrial research-funding programs are well capitalized and targeted, modifying tax policies to encourage entrepreneurship and streamlining research and development tax credits to support AI investments.

A few years ago, the subject of AI personhood and legal rights for artificial intelligence would have been something straight out of science fiction. Douglas Adams' second Hitchhiker's Guide to the Galaxy book, The Restaurant at the End of the Universe, tells the story of a futuristic smart elevator called the Sirius Cybernetics Corporation Happy Vertical People Transporter. This artificially intelligent elevator works by predicting the future, so it can appear on the right floor to pick you up even before you know you want to get on -- thereby "eliminating all the tedious chatting, relaxing, and making friends that people were previously forced to do whilst waiting for elevators." The ethics question, Adams explains, comes when the intelligent elevator becomes bored of going up and down all day, and instead decides to experiment with moving from side to side as a "sort of existential protest." We don't yet have smart elevators, although judging by the kind of lavish headquarters tech giants like Google and Apple build for themselves, that may just be because they've not bothered sharing them with us yet.

Those who run regularly or who have experienced the endorphin euphoria known as "runner's high", can experience the same heady feeling reading Joanna Goodman's "Robots in Law: How Artificial Intelligence is Transforming Legal Services" (Ark, 2016). The book provides a fulsome journey for the reader through the Artificial Intelligence (AI) legal landscape, explaining key concepts for the uninitiated and highlighting the most visible vendors and makers among other industry players. The running analogy has a special significance for those of us that are current or lapsed runners. Lisa started reading at the same time as beginning marathon training on her global travels. The hope was that like Nike running apps, "Robots" would provide her with the tools and insights she needed to understand the AI legal tech hype, and intelligently speak to the topic with fellow colleagues in legal innovation.

When former Tesla employee AJ Vandermeyden sued the company for ignoring complaints of discrimination and "pervasive harassment," the self-driving vehicle maker downplayed her claims. Tesla told The Guardian at the time that it believes in "fostering an inclusive workplace" and that there is "more we can do to promote diversity." The company also said that there would always be a "small number" of people who make these kinds of claims. It turns out, however, that more women have had similar experiences at Tesla, according to a new report in The Guardian. On International Women's Day, Tesla invited female staff to an essential oils "lunch and learn," but then changed the meeting to one on diversity after employees expressed some criticism.

Artificial intelligence has been making its way into our lives for some time now. Voice-enabled programs, including management programs that run law offices, can do almost anything on the Internet of Things. RoboReview won't take a patent lawyer's chair and desk, but it can do the job. "When a patent attorney writes an application, often it will be reviewed by various parties in the firm prior to sending it to the client," said Charles Mirho, a patent attorney and founder of the company. "We have robots that will actually read the application, then, right in the Word document, make comments and suggestions on how to improve the document, just like Word's'track changes' feature," he told Law Sites.

Math is a tool that doesn't discriminate. There's no bias in it; the numbers either add up or they don't. Algorithms depend on math, but they're data driven -- sometimes the information being fed into one is incorrect or doesn't represent the actual goals of the algorithm. Cathy O'Neil, the author of Weapons of Math Destruction, cautions us against trusting the data being fed into our judicial systems: And what ProPublica found was the compass model, which is one version of a recidivism model, made mistakes by sending people to prison longer, that kind of mistake, twice as often for African-American defendants as for white defendants, at least in Broward County Florida. There's another kind of mistake you can make which is: you look like you're not coming back, you look low-risk but you actually do come back that kind of risk, that kind of mistake, was made twice as often for white defendants as for African-American defendants.

Most current AI systems can be easily fooled, which is a problem that affects almost all machine learning techniques. Deep neural networks have millions of parameters and to understand why the network provides good or bad results becomes impossible.

All evolution comes with challenges and the dark world of cybercrime continues to thrive and is this year's second most reported economic crime. The recent NHS computer hack using Wanna Decryptor ransomware shut down IT systems with 75,000 attacks in 99 countries. The unprecedented ransomware breach froze computers across the health service with hackers threatening to delete files unless a ransom was paid. The passwords were scrambled with the MD5 algorithm, which nowadays is easy to crack. According to Zdnet.com, the unidentified hacker explained his motives for the attack: "I heard the database was getting traded around so I decided to dump it myself – like I always do". He said it was "mainly just for the challenge and training my pentest skills." He exploited a union-based SQL injection vulnerability in the site's software, a flaw he said was "easy to find."