After the hiring of Geoffrey Zweig – former head of research at Microsoft – as Global Head of Machine Learning in January, JPMorgan Chase recently announced Samik Chandarana as the new Head of Analytics and Data Science for the corporate and investment bank.

The company had already implemented advanced technologies in the past months: last year, the bank launched a predictive recommendation engine to identify those clients which should issue or sell equity; back in July, JPMorgan Chase announced having used artificial intelligence for its high-frequency trading activities.

The company now hopes to continue its technology progress under the guidance of Mr Chandarana, who will be responsible for heading the bank’s strategy to deploy machine learning and data-based solutions throughout the corporate and investment bank.

But this is not the first time that the investment world has been interested in computational science. Back in the 90’s, the focus was on developing algorithms that could self-modify by learning from massive data sets. Those experiments gave birth to what programmatic trading is today: the tasks of traditional investment professionals like managing risk and making ultra-fast trading decisions are handled by computer algorithms.

In 2012, Luke Ellis, CEO of Man Group plc – an edge fund which has about $96 billion under management – made an experiment with artificial intelligence. The system that the company built evolved autonomously, finding moneymaking strategies humans had missed. The results were startlingly good, but even as the new software produced encouraging returns in simulations, the engineers couldn’t explain why the AI was executing the trades it was making. The creation was such a black box that even its creators didn’t fully understand how it worked and Luke Ellis decided to pause the experiment.

Today’s artificial intelligence possibilities represent a significant increase over programmatic trading. As the technology is already commonly used in self-driving cars, investment world wants to leverage that technology to achieve faster, smarter trades and better yields.

It might also generate a lot of new challenges we will need to walk through in the coming years. Just imagine for one second, even if it seems far outside the limits of today’s technology: imagine an AI that can almost perfectly prevent all the key factors that move markets. That very system would be able to predict price changes in any tradeable assets. What would happen to the first investor who gets his hands on such a system? Looks like an advanced version of insider trading, right?

Even if such a technology is not here yet, the challenges that AI investment will raise tomorrow need to be discussed today.