I have been giving two talks recently on the machine learning workflow, discussing pain points within it and how we might address them. First one was at Spark Summit Europe at Brussels, the other one at MLConf at San Francisco. You can find videos and slides for each below. Main message is that the machine learning workflow is not that simple. That was a great event.

For Katja Hofmann, Minecraft is not just a virtual world: it is a gym for artificial intelligences. Hofmann, 36, is the lead researcher on Microsoft Research Lab's Project Malmo, an open-source platform that makes it possible to test AIs inside the game's pixelated universe. "A question in artificial intelligence is how we get AIs to learn how to interact in a complex environment, to experiment in a wide range of settings," she says. Researchers using Project Malmo, which was made available to developers in July 2016 after a year of in-house testing at Microsoft's Cambridge-based lab, can create AI agents and set them loose in a modified version of Minecraft's free-to-roam 3D environment. There, through trial and error, the agents learn how to move, walk and dodge obstacles in a physically consistent world - something usually requiring expensive robots.

In Slack's vision of the future, it will be totally unnecessary to try to get your inbox to zero every day. In fact, it will be unnecessary to read most of your emails. The communication software company lets teams chat in real time and share files. Earlier this year, Slack introduced the Slackbot, a chatbot meant to help users navigate the app more easily. Ask the bot a straightforward question about how to use Slack ("How do I edit a message I've posted?") and it will deliver the answer.

SAN FRANCISCO – It sounds like a great idea: Instead of passwords, lock your phone or computer with your fingerprint. But this convenient form of security may not be as safe as you might think. In their rush to do away with the problem of passwords, Apple, Microsoft and other tech companies are nudging consumers to use their fingerprints, face and eyes as digital keys. Smartphones and other devices increasingly feature scanners that can verify your identity via these biometric signatures in order to unlock a gadget, sign into web accounts and authorize electronic payments. Hackers could still steal the digital representation of your fingerprint.

You're almost done when that annoying Captcha screen comes up and makes you type some blurry letters and numbers into a box. This step, as most people know, is to ensure that you're just a person buying tickets and not a computer program deployed to illicitly to grab up a bunch of seats. But why can't a computer that can perform calculations astronomically faster than humans identify the letter B just because it's in a fancy font with a strikethrough, or the number 5 in a fuzzy photo of a front door? Why is it so easily baffled by something the average second-grader can handle? The answer lies in understanding the current state of artificial intelligence (AI) -- what it's capable of, what is still beyond its grasp, and how we may be rocketing toward an increasingly intelligent technology without enough thought about the implications for ourselves and our planet.

Go master Cho Chikun triumphed Wednesday in his final game against DeepZenGo, a Japanese artificial intelligence system, to win the three-game series 2-1. Cho, 60, has won 74 titles, the largest number in Japan, over a long career. He defeated DeepZenGo with the 167th move in the third game of the series, which was played on even terms with no handicaps. DeepZenGo uses deep learning and other advanced technologies. It is being developed with support mainly from Dwango Co., a video-sharing website operator, and the University of Tokyo. "I felt as if I was playing with a human, because (DeepZenGo) has both strong and weak points," Cho said after the final game.

Facial recognition software is most commonly known as a tool to help police identify a suspected criminal by using machine learning algorithms to analyze his or her face against a database of thousands or millions of other faces. The larger the database, with a greater variety of facial features, the smarter and more successful the software becomes – effectively learning from its mistakes to improve its accuracy. Now, this type of artificial intelligence is starting to be used in fighting a specific but pervasive type of crime – illegal fishing. Rather than picking out faces, the software tracks the movement of fishing boats to root out illegal behavior. And soon, using a twist on facial recognition, it may be able to recognize when a boat's haul includes endangered and protected fish.

Soon after the invention of photography, a few criminologists began to notice patterns in mugshots they took of criminals. Offenders, they said, had particular facial features that allowed them to be identified as law breakers. One of the most influential voices in this debate was Cesare Lombroso, an Italian criminologist, who believed that criminals were "throwbacks" more closely related to apes than law-abiding citizens. He was convinced he could identify them by ape-like features such as a sloping forehead, unusually sized ears and various asymmetries of the face and long arms. Indeed, he measured many subjects in an effort to prove his view although he did not analyze his data statistically.

Even as its engineers were developing the technology platform Watson, managers at IBM knew that cancer would be one of its killer applications. This dreaded disease manifests itself in many different forms, with further variations based on a patient's genetic makeup. Cancer's complexity forces doctors to resort to trial and error to figure out what drugs work. Watson, backed by its artificial intelligence platform, can search through enormous amounts of data and quickly come up with the best treatment method. Watson is thus popular among oncologists, but this piece of technology has other uses too.

THERE IS SOMETHING familiar about fears that new machines will take everyone's jobs, benefiting only a select few and upending society. Such concerns sparked furious arguments two centuries ago as industrialisation took hold in Britain. People at the time did not talk of an "industrial revolution" but of the "machinery question". First posed by the economist David Ricardo in 1821, it concerned the "influence of machinery on the interests of the different classes of society", and in particular the "opinion entertained by the labouring class, that the employment of machinery is frequently detrimental to their interests". Thomas Carlyle, writing in 1839, railed against the "demon of mechanism" whose disruptive power was guilty of "oversetting whole multitudes of workmen".