In 2017, a game design thinktank called Project Horseshoe gathered a group of developers together to define the concept of cosiness in video games. Games, of course, have had non-violent elements since the medium was invented. Early life simulators such as 1985's Little Computer People, a low-stakes game in which the player interacts with a man living his unremarkable life in a house, could fit the bill; then there was the proliferation of social farming simulations after 1996's chibi-adorable Harvest Moon. But the resulting report, Coziness in Games: An Exploration of Safety, Softness, and Satisfied Needs, is probably the first organised effort to define a then-emerging genre. Cosy games (cozy in US spelling) don't have high-risk scenarios: "There is no impending loss of threat," they wrote.

According to an article that appeared in Fortune earlier this year: Automation could replace 40% of jobs in 15 years. This article joins countless others in sounding the warning bells of the forthcoming AI-style industrial revolution. As we've heard so often, AI will replace jobs by the thousands. Almost overnight, half the country will be out of work. Admittedly, it would be impossible to tackle this issue from every angle. However, we can offer our sense of where this industry is, what the effects might be, and where we might be headed within 15 years. Is it going to happen?

As a contributor to The Fourth Education Revolution, Sir Anthony Seldon's new book on artificial intelligence (AI) in education, I've been thinking a lot recently about how AI could help in teaching and learning. Perhaps a few years from now we'll see widespread use of "digital tutors" that help reduce teacher workload by coaching and mentoring learners, building on the likes of Siri and Alexa. But there's also a darker side that we need to be wary of. It emerges that a pupil has been cheating by submitting homework assignments generated by an artificial intelligence. Another pupil has pranked the school's IT systems by feeding them false data, resulting in the heating being turned up to full blast on the hottest day of the year.

Machine learning can be used for many purposes, but not all of them are good--or intentional. While much of the work underway is focused on the development of machine learning algorithms, how to train these systems and how to make them run faster and do more, there is a darker side to this technology. Some of that involves groups looking at what else machine learning can be used for. Some of it is simply accidental. But at this point, none of it is regulated.

Ben Dickson is a software engineer and the founder of TechTalks. More posts by this contributor: Blockchain has the potential to revolutionize the supply chain Why it's so hard to create unbiased artificial intelligence Blockchain has the potential to revolutionize the supply chain Why it's so hard to create unbiased artificial intelligence Why it's so hard to create unbiased artificial intelligence While machine learning is introducing innovation and change to many sectors, it also is bringing trouble and worries to others. One of the most worrying aspects of emerging machine learning technologies is their invasiveness on user privacy. From rooting out your intimate and embarrassing secrets to imitating you, machine learning is making it hard to not only hide your identity but also keep ownership of it and prevent from being attributed to you words you haven't uttered and actions you haven't taken. Here are some of the technologies that might have been created with good-natured intent, but can also be used for evil deeds when put into the wrong hands.

Ben Dickson is a software engineer and the founder of TechTalks. More posts by this contributor: Why it's so hard to create unbiased artificial intelligence How to facilitate the path to brownfield IoT development Why it's so hard to create unbiased artificial intelligence How to facilitate the path to brownfield IoT development Why it's so hard to create unbiased artificial intelligence While machine learning is introducing innovation and change to many sectors, it also is bringing trouble and worries to others. One of the most worrying aspects of emerging machine learning technologies is their invasiveness on user privacy. From rooting out your intimate and embarrassing secrets to imitating you, machine learning is making it hard to not only hide your identity but also keep ownership of it and prevent from being attributed to you words you haven't uttered and actions you haven't taken. Here are some of the technologies that might have been created with good-natured intent, but can also be used for evil deeds when put into the wrong hands.

Ben Dickson is a software engineer and the founder of TechTalks. While machine learning is introducing innovation and change to many sectors, it also is bringing trouble and worries to others. One of the most worrying aspects of emerging machine learning technologies is their invasiveness on user privacy. From rooting out your intimate and embarrassing secrets to imitating you, machine learning is making it hard to not only hide your identity but also keep ownership of it and prevent from being attributed to you words you haven't uttered and actions you haven't taken. Here are some of the technologies that might have been created with good-natured intent, but can also be used for evil deeds when put into the wrong hands.

Ben Dickson is a software engineer and the founder of TechTalks. While machine learning is introducing innovation and change to many sectors, it also is bringing trouble and worries to others. One of the most worrying aspects of emerging machine learning technologies is their invasiveness on user privacy. From rooting out your intimate and embarrassing secrets to imitating you, machine learning is making it hard to not only hide your identity but also keep ownership of it and prevent from being attributed to you words you haven't uttered and actions you haven't taken. Here are some of the technologies that might have been created with good-natured intent, but can also be used for evil deeds when put into the wrong hands.