Andrew Lo, a professor of finance at the Massachusetts Institute of Technology, dialled in from Boston to talk about regulation, markets and the future of machine learning.

Lo pioneered the adaptive markets hypothesis, which describes markets as a complex evolutionary ecosystem, populated by different stakeholders that adapt to changes on the basis of certain behavioural traits and biases.

Andrew W. Lo was named winner of the $10,000 Harry M. Markowitz Award for his paper, "Moore's Law Vs. Murphy's Law in the Financial System: Who's Winning?" The award was announced Thursday by the Journal of Investment Management and New Frontier Advisors in a joint statement. The paper provided examples of technology capable of adapting to the "foibles in human behavior" so users can employ all the recent breakthroughs in computing hardware and software, data analytics and telecommunications that have changed the financial industry "safely, effectively and effortlessly," according to an abstract on the paper.

Murphy’s Law says that anything which can go wrong, will go wrong. Moore’s Law says that computing power will double every 18 months or so. Technology drives financial innovation and activity. Andrew Lo, a professor at the Massachusetts Institute of Technology has merged these ideas into a map of today’s financial system, a world where the speed of innovation is fast outstripping the ability of regulators to keep pace. As he told an audience of politicians at a New City Agenda event in the Palace of Westminster last month, the speed and complexity of financial markets increases by the day; human understanding does not.

It was a typical New York summer day, the kind where arriving at Goldman Sachs’ perfectly air-conditioned offices in downtown Manhattan was a blissful release from the humid weather outside. But for Gary Chropuvka it proved to be one of the worst days of his life. Mr Chropuvka worked at Goldman’s money management arm, specifically at Quantitative Investment Strategies, a division staffed by mathematicians, computer scientists and physicists. Even at Goldman, the QIS employees were considered intellectual superstars. Their prowess at decoding the signals of financial markets meant the unit managed $165bn at its peak — more than any hedge fund group. But on August 6 2007, everything unravelled. As soon as US markets started trading, the previously wildly successful automated investment algorithms coded by the QIS brainiacs went horribly awry, and losses mounted at a frightening pace.

AlphaGo, an artificially intelligent (AI) software program, defeated the world champion of an ancient board game called Go on March 15, 2016. The game is immensely complex, with a total combination of possible moves numbering several hundred orders of magnitude more than the number of atoms in the universe. Winning the series four-to-one, AlphaGo’s victory showcased significant advances in AI’s ability to recognise and learn obscure patterns, and adapt strategies.

Professionals from the finance and technology sectors convened in New York City on 16 September to learn about how recent technology innovations are transforming banking and the stock market as well as credit cards, loans, and other financial products and services. The theme of the event, hosted by MIT Sloan, was “FinTech (Financial Technology) and the Disruption of Finance.” The speakers made it clear that machine learning and other new technologies are certain to bring big changes to the financial industry.

Healthcare Finance, a new course to be taught by Andrew W. Lo, the Charles E. and Susan T. Harris Professor, a Professor of Finance, and Director of the Laboratory for Financial Engineering at MIT Sloan, will cover the role of finance in the healthcare industry. It will emphasize the application of novel financing methods to facilitate drug discovery, clinical development, and greater patient access to transformative high-cost therapies.

"At the most rudimentary level, AI involves teaching machines to learn and to interact in order to undertake cognitive tasks that were usually performed by humans. The type of AI featured in sci-fi films in which machines possess a human-like intelligence, sometimes referred to as general artificial intelligence, remains a distant and elusive prospect. The most optimistic experts, such as Google’s director of engineering, Ray Kurzweil, predict that AI will be able to outsmart humans by 2029. Conservative predictions expect this to take at least 100 years, if at all..."

At a conference last year, I was approached by an audience member after my talk. He thanked me for my observation that it’s unrealistic to expect investors to do nothing in the face of a sharp market-wide selloff, and that pulling out of the market can sometimes be the right thing to do. In fact, this savvy attendee converted all of his equity holdings to cash by the end of October 2008. He then asked me for some advice: “Is it safe to get back in now?” Seven years after he moved his money into cash, he’s still waiting for just the right time to reinvest; meanwhile, the S&P 500 earned an annualized return of 14% during this period. Investing is an emotional process. Managing these emotions is probably the greatest open challenge of financial technology. Investing is much more complicated than other chores like driving, which is why driverless cars are already more successful than even the best robo advisers.

"Hazte a un lado, Janet Yellen, la automatización del lugar de trabajo está a punto de volverse personal. Imagínese si en vez de confiar en la presidenta de la Reserva Federal (Fed) se usara una computadora para transformar montañas de datos económicos en bruto en proyecciones fidedignas de desempleo, inflación y producto interno bruto. ¿Cuál es el mejor nivel para la tasa de fondos federales? Presione..."

"Move over Janet Yellen, automation in the workplace is about to get personal.

"Instead of relying on the Federal Reserve chair, imagine using a computer to transform mountains of raw economic data into reliable predictions for unemployment, inflation and gross domestic product. What’s the best level for the federal funds rate?

“'The capability is here,' says Andrew Lo, director of the Laboratory for Financial Engineering at the Massachusetts Institute of Technology, near Boston. 'The biggest hurdle is the cultural barrier. You’ve got a lot of central bankers who are not as open to technology.'"

"With the advent of social media, there has been an explosion of interest in how information shared online can be used to profit in the stock market.

"Twitter, the social media monster that allows users to share their thoughts on anything–as long as those thoughts are 140 characters or less–is emerging as a favorite dataset for financial researchers..."

"On the 15th March 2016, an artificially intelligent (AI) software programme called AlphaGo, defeated the world champion of an ancient board game called Go. The game is immensely complex, with a total combination of possible moves numbering several hundred orders of magnitude more than the number of atoms in the universe. Winning the series four-to-one, AlphaGo’s victory was emphatic. It also showcased significant advances in AI’s ability to recognise obscure patterns, learn new ones and adapt strategies to changing circumstances.

"Yet, just two weeks after AlphaGo’s impressive victory, a new chatbot called Tay, exposed a darker side to AI. Designed to engage in friendly conversation with people online and assist them with Microsoft services, Tay’s unique design feature was that “she” learns from her online interactions. Upon Tay’s public release a coordinated barrage of abuse and incessant trolling by Twitter users, taught Tay the wrong lessons. The programme was corrupted into spewing racist, sexist and xenophobic comments, revealing the potential for flaws in the design and programming of AI, as well as the uneasy interaction between artificial intelligence and the natural kind..."