Artificial Intelligence (AI) or Machine Learning (ML) is now a hot topic and subject to intense research by the leading players IBM, Google, MIT etc. Whilst it has become a mainstream lexicon it is often only thought of in the more scientific and glamorous arenas such as driverless cars and drone deliveries. These pose all sorts of legal dilemmas, despite Google’s assertion that roads will be safer with driverless cars, how will the legal consequences unfold when there is no immediate human intervention to an accident? Agency and vicarious liability, duty of care and negligence will all have to be reinterpreted in the new area of legal person, which might not be a person but Artificial Intelligence, that is to say software. Progress is being made and I note that this week the US transport regulator has just indicated that a robot could meet the legal definition of a driver. Does that mean Google will now be responsible for insuring your car?
In other areas of AI the world is not waiting for the legal, regulatory and technology worlds to come together, businesses are deploying AI and gaining new insights. Gartner, the research company, has calculated that the world’s information is set to grow by 800 percent over the next five years. This is a problem; how will we cope with 800 times more information? Fortunately, there are companies like RAVN and Blinkist that are addressing data explosion problems like these, they automatically read, interpret and summarise key information from documents and unstructured data. This is both an asset and indeed a threat to knowledge based industries.
In other areas Big Data has quietly but completely changed the way business is conducted, market research is one of them. Analysing masses of information generated by an exponentially growing social media culture AI and ML deliver hitherto inconceivable insights and sentiment analysis. The technology relies on extremely clever algorithms crunching vast data lakes of information. They cannot replace decisions based on human instinct but they can learn from experience and the enormous scale of compute available means they can crunch and model vast data scenarios and tune the algorithms from the results creating self improving software, hence the term machine learning. One of the fastest advancing areas of AI is machine vision, and particularly facial recognition which has all sorts of potential, as of today Facebook can recognise faces better than any human.
IBM have now coined the phrase “the cognitive era” which they say the second age of machine learning. Applying these techniques to vast disparate data sets will allow scientist to look for better ways to tackle cancer or for oil and gas companies looking to improve the accuracy of exploratory drilling.
The technology is equally applied to financial services, Standard Bank have used IBM Watson to speed handling of customer queries, allowing it to identify customers quickly so they can respond faster.
Citigroup have also used Watson's analytics with the aim to improve customer relationships and interactions in the bank.
Customer service, or the lack of it, is highlighted as a major issue by Consultancy.uk in their research into customer experience with UK banks and Mutuals. http://bit.ly/20XpDjB and so AI could help the traditional financial services respond to the threats from the new wave of challenger banks.
There is a flip side to this advance technology, the impact, according to the Economist and researchers from the University of Oxford suggest that 47% of jobs in the western world could be automated within the next two decades. The FT recently reported that scientists have warned that rapid strides in the development of artificial intelligence and robotics could lead to the prospect of mass unemployment.
Conversely businesses like GoDaddy, which mostly service small businesses, argue that this will enable jobs, their vision is based upon AI making non-automatable work more accessible to a significantly greater number of people. For example, a small business owner has to juggle a plethora of tasks, finance, HR, marketing, IT etc. Automation should make the process of running a business less daunting thereby enabling a new and increasing generation of entrepreneurs.
Whilst IBM use the term cognitive era others use the phrase “The Second Machine Age” drawing parallels between how the industrial revolution spread form one industry to the next creating huge disruption, the same is happening now driven by smart learning machines rather than mechanising labour.
AI is then very relevant to the offshore financial services market, as the sector looks to increase efficiency and levels of customer service whilst diligently watching for fraudulent or unusual activity. Intelligence driven from from machines is almost certainly going to play an increasing role, whether we take a driverless Uber to our next appointment or not.