One of the biggest elements of this will be the increased capabilities of self-driving cars, which are going to spread in both use and performance in more geographies. For instance the UK Chancellor announced that self-driving cars will be allowed on the roads from 2024, where at the moment they are allowed only with a human behind the wheel. We will also see a big increase in the amount of experimentation with AI for considerably more complex problems, with DeepMind at the forefront of this, even if at the moment this only takes the form of games like GO and chess. However, 2018 may well be the point at which we see these kind of elements becoming the foundation of even further AI development.

It isn't simply shoe companies that are finding this issue either, but it is similar for any product with a limited run with a strong consumer interest. The ticket resale market, for instance, is worth around $5 billion, and tickets for the most popular shows are purchased through the use of bots as soon as they are released and often listed minutes later on resale sites with markups often well above 200% for the most popular tickets. When Thom Yorke, lead singer of Radiohead, found out that tickets for the band's 2016 Madison Square Garden show had sold out in under 30 seconds mainly due to bots and then appeared on reselling sites for 300% more only minutes later, responded after frustrated fans complained to say'I'm as f---ed off as you are.'

However, AI may have a part to play in identifying the subtle signs that betray the kinds of human behavior that can lead to these kinds of crashes. If we look at the crash of the late 2000's it is clear that it was caused, in part, by people historically mis-selling mortgages to people who couldn't afford them. AI and deep learning make it simple to avoid these kinds of mistakes, whether somebody knew they were making a mistake or not. Similarly, when looking at stock market crashes, AI is designed to incorporate data from a wide variety of areas and formats, which means that it should be able to determine whether a stock is over or undersold and the inherent risk of its purchase fairly easily. It has the potential to somewhat stabilize the often unpredictable and unrealistic rise and fall of the stock market, even if it's through prompting behavior rather than stopping it altogether.

The claim is also forgetting the key reason why people create so much data that can then be used by these political campaigns - because they spend huge amounts of time online. To therefore argue that it'undermines the notion of an informed citizenry' is simply not true as they have this data on the citizenry because they do so much research online. That people are not informed about wider issues is perhaps an indication of IQ prejudice, where smarter people believe that those with a lower IQ lack the ability to research beyond what is put directly in front of them. The internet is available to around 90% of the US according to Pew, so if they are uninformed it is not that they lack the capacity, but simply that they either lack the drive or propensity to do so.

For many, the first real interaction they will have with AI is through the personal assistants on their phones. Apple's Siri and Microsoft's Cortana have already proved themselves in the market place, but while such virtual assistants have a clear advantage in that they come built in to people's phones, they have competition from independents. Ozlo, for one, launched on iOS last October. Its focus thus far has been on finding people restaurants, bars, and recipes by analyzing data from sources like TripAdvisor, FourSquare, and Yelp, among others, and it is looking to expand. In 2017, Ozlo is being made available to third-party developers so that they can build their own versions of Ozlo.

It is something that many companies are trying at the moment, with Google, Microsoft and Facebook all introducing personal assistants that are equivalents of Apple's Siri, originally created in 2010. Since then, with the introduction of technologies and collaborations with companies like Wolfram Alpha (who also work with several other companies), Siri has improved, but not to the extent that its potential is crying out for. One of the clearest examples of this was their move to a neural net based system in 2014, which significantly improved the virtual assistant, but still didn't allow it to achieve what many expected. In fact, few even noticed a difference.