We have a crisis of work. The secure, well-paid jobs of the past -- many of them in manufacturing -- are disappearing. What is replacing them is insecurity and uncertainty. The "gig economy", where people are paid performance by performance -- or piece by piece. "Piecework", we used to call it.

Svetlana Alexievich, winner of the 2015 Nobel Prize in literature, called the nuclear catastrophes at Chernobyl and Fukushima events that people cannot yet fully fathom and warned against the hubris that humans have the power to conquer nature. The 68-year-old Belarusian writer was in Tokyo at the invitation of researchers at the University of Tokyo, where she gave a lecture on Friday. More than 200 people attended. The Nobel laureate, who writes in Russian, is known for addressing dramatic and tragic events involving the former Soviet Union – World War II, the Soviet war in Afghanistan, the 1986 Chernobyl nuclear disaster and the 1991 collapse of the communist state. Her style is distinctive in that she presents the testimonies of ordinary people going through traumatic experiences as they speak, without intruding on their narratives.

For most people, it is not easy to picture the buying and selling of cognitive capabilities that have traditionally been embedded in humans – things like judgment and decision-making. Yet, thanks to recent advances in machine learning, we face the very real possibility of precisely such a'market for intelligence'. Given the potential of this market to transform the entire global economy, we must all begin preparing – now – for its emergence. To be clear, machine intelligence is still in its infancy, and while some of the current applications are remarkable, none are transformational. For example, the recommendation engines employed by companies such as Amazon and Netflix – which learn our preferences and recommend which books we should buy or which movies we should watch – are a common application of machine learning.

What do you get if you cross aspirin with ibuprofen? Harvard chemistry professor Alán Aspuru-Guzik isn't sure, but he's trained software that could give him an answer by suggesting a molecular structure that combines properties of both drugs. The AI program could help the search for new drug compounds. Pharmaceutical research tends to rely on software that exhaustively crawls through giant pools of candidate molecules using rules written by chemists, and simulations that try to identify or predict useful structures. The former relies on humans thinking of everything, while the latter is limited by the accuracy of simulations and the computing power required.

Besides your passport, what really defines your nationality these days? Is it where you were live? If it is, then despite an increase in "nationalism" (i.e., Brexit), we may see the idea of "nationality" quickly dissolve in the decades ahead… Or at least become an option you choose versus assume as a default. Residency, currency and language are rapidly being disrupted and dematerialized by technology. Increasingly, technological developments will allow us to live and work almost anywhere on the planet (and even beyond).

Before the invention of the computer, most experimental psychologists thought the brain was an unknowable black box. You could analyze a subject's behavior--ring bell, dog salivates--but thoughts, memories, emotions? That stuff was obscure and inscrutable, beyond the reach of science. So these behaviorists, as they called themselves, confined their work to the study of stimulus and response, feedback and reinforcement, bells and saliva. They gave up trying to understand the inner workings of the mind. They ruled their field for four decades. Then, in the mid-1950s, a group of rebellious psychologists, linguists, information theorists, and early artificial-intelligence researchers came up with a different conception of the mind.

From gender to education, the words used on social media carry impressions to others. Using publicly available tweets, social psychologists and computer scientists from the University of Pennsylvania Positive Psychology Center, Germany, and Australia are helping us to parse out the stereotypes formed by word choices on the social media channel Twitter. Utilizing natural language processing (NLP), a form of artificial intelligence, the researchers show where stereotyping goes from "plausible" to wrong. The research appears in Social Psychological and Personality Science. In a series of studies, participants were asked to categorize the authors of tweets based solely on the content of their social media posts.

TechCrunch Disrupt in London is on December 5-6. As well as speakers and panels, we'll be featuring some demos by some amazing tech companies. The first will be by Boston Dynamics. Yes, folks, delegates to Disrupt London will get to see one of those amazing BD robots up close and personal, almost literally in the flesh (if they had any flesh, that is). Marc Raibert, CTO and Founder of Boston Dynamics will be demonstrating one of the amazing robots his team has created, but you'll have to come to find out which one… Raibert was Professor of Electrical Engineering and Computer Science at MIT and a member of the Artificial Intelligence Laboratory from 1986 through 1995.

DeepMind, the British AI firm owned by Google's parent company Alphabet, has signed a new five-year deal to use data collected by the UK's National Health Service. The agreement with the NHS Royal Free Hospital Trust in London replaces a previous deal that attracted controversy over its lack of official oversight. Under the terms of the new deal, DeepMind will handle personally identifiable medical records for some 1.6 million patients, including medical history dating back five years. The agreement also includes stricter data regulation, including "technical audits" of DeepMind's systems. Using data from the Royal Free, DeepMind has built an app named Streams that alerts doctors when patients are in danger of developing acute kidney injury (AKI) -- a common but often overlooked condition.

Would you go on a business date at work? Would you think I was weird if I told you I did? Some apps are making this possible, so I decided to try it out. As a family man who has just entered his 40s, I am not going to be arranging romantic liaisons on my smartphone. But maybe I haven't completely missed the boat when it comes to the thrill of swiping, matching and meeting up with strangers. Shapr is an app that works like the dating app Tinder, but it's for making business connections rather than romantic ones.