Dreaming up obscure insults might be a good way to pass the time in a bar, but it's a strange day job. Nonetheless, it's a serious business if you are trying to train a machine to spot unacceptable online behaviour. Data scientists not only need to provide training data; they also need to describe which language is likely to offend within that data. The process, known as annotation, is just one of the laborious tasks data scientists face that IT firms are promising to make easier with automation. Amazon, Microsoft, Google and IBM are offering a raft of technologies to automate machine learning processes (see box).

At the peak of the Japanese harvest, Makoto Koike's mother spends around eight hours a day sorting cucumbers from the family farm into different categories – a dull, time-consuming task that her son decided to automate. Although Makoto wasn't a machine learning expert, he started playing around with TensorFlow, Google's popular open-source machine learning framework, and developed a deep learning model that could sort cucumbers by size, shape and other attributes. The system isn't perfect (it has an accuracy rate of around 75%). But it's a sign of how AI could soon transform even the smallest family-run business. Giants like Google, Amazon, Microsoft, Apple and Facebook are, of course, well-aware of this transformative power.

If 2017 taught us anything, it's that you can't be complacent about your cybersecurity strategy. And as the driving force behind McAfee's security research and development, you'd expect Chief Technology Officer, Steve Grobman, to have more to worry about than most. "You could spend all day being concerned about almost anything," he laughs, when I ask him what threats people should be looking out for. But there is one big issue facing all companies today. How do you deal with the fast-changing threat landscape whilst continuing to protect yourself against the threats you were worried about yesterday?

Artificial intelligence (AI) and machine learning are causing excitement all over the world. Recent reports, such as one from Accenture, claim it has the potential to revolutionise the future of all businesses operations. For instance, research tasks that take hundreds of hours, such as candidate profiling, can now be performed by an AI within seconds. It's no wonder that many businesses are tapping into this trend – the potential savings, in both time and money, are extraordinary. However, what are the consequences of programming AI in today's environment?

Michael Rovatsos is an AI researcher at the University of Edinburgh and former Director of the Centre for Intelligent Systems and Applications. I spoke to him about AI and business and its impact on society and business. I think it's important to distinguish between: There are a lot of'data science' applications that can be envisioned under the second group, whereas the first is something more specific, and, in many cases, much more ambitious. Please login or register to view your article. If you do not have or do not remember your password, please click on the "Forgotten your password?"

How did one Harley-Davidson dealership in New York City go from selling one or two bikes a week to selling 15 in a weekend? Owner Asaf Jacobi took a risk on Adgorithms' 'Albert', an artificial intelligence (AI) driven marketing platform that works across digital channels. The results saw the dealership increasing leads by 2930% by the third month and driving Jacobi to set up a new call centre to handle all the new business. Albert can learn as he does and is able to "identify the audiences most likely to convert, eliminate low-value audiences, apply insights gained from one channel to other channels", according to Harley-Davidson NYC. The AI works with campaign creative and KPIs provided by the brand to autonomously execute holistic digital ad and marketing campaigns.

Computer science has long been a discipline seemingly dominated by males, with the number of women in the field, and even of those graduating with technology degrees, perennially lagging behind the number of men. A recent study by the National Girls Collaborative Project in the United States and the success of conferences like "Women in Data" in the UK suggest that this may be beginning to change, and one catalyst for that change may be the burgeoning field of artificial intelligence (AI). Take Cylance, the fastest growing cyber-security software startup in the past ten years, according to research firm Gartner. The company developed an AI-based alternative to traditional antivirus (AV) -- and just recruited the second female member for its fast-growing data scientist team, now numbering 14. Another example of a company embracing women in the field of AI is Fast Forward Labs, an organisation that works with businesses to accelerate their data science and machine intelligence capabilities.

Machine learning (ML) is getting a lot of attention at the moment. This is partly because a slew of new companies are emerging which are using it in innovative ways. And partly because it can get easily subsumed into the fuss and furore about AI and the rise of evil robot intelligence. Graph technology, on the other hand, is something which takes more of a back seat and yet, in a lot of ways, also sits at the forefront of the big data and analytics movement. "We firmly believe is that it's at the intersection of machine learning and graph technology where the next evolution lies and where new disruptive companies are emerging," says Ash Damle, Founder and CEO at Lumiata which helps healthcare organisations makes predictions.

Any business that sells goods or services online is vulnerable to attack by fraudsters. This can be using stolen credit card details for purchases online, creating false accounts and even voucher code abuse. The cost of this fraud can be calculated in the multi millions, with chargebacks and related costs plaguing online businesses. In the UK it is the most common crime of all, with 2.47M offences in 2015/16 alone. The traditional approach to tackling this problem is to use heuristic rules and business logic to try to'predict' whether a new transaction that the business is seeing is fraudulent or not.

August saw the Defense Advanced Research Projects Agency (DARPA) host its first Cyber Grand Challenge – the first hacking competition not involving people. During this event, teams left their systems alone to single-handedly find, diagnose and fix software flaws in real time. Elsewhere, researchers at MIT are not only developing machine learning systems that automatically mine dark web marketplaces for vulnerabilities and zero-day attacks and reports them back as well as software that automatically fixes buggy code, but also a platform that can predict 85% of cyber-attacks. Machine learning, deep learning, and Artificial Intelligence (AI) are hot topics at the moment, and while there's plenty of research going on, there's also some practical applications that can be deployed right now to make life easier for cybersecurity professionals. A glut of new start-ups, from the likes of Darktrace, Cylance, Deep Instinct, and HackerONE, plus established player such as FireEye, IBM, and Forcepoint, are all working on bringing self-learning systems into the world of security.