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..."

"In the social media cacophony, some of the noise rises to the level of stock market signal.

"That’s the finding of a working paper overseen by Massachusetts Institute of Technology’s Andrew Lo, which says a trading strategy based on views posted on Twitter prior to Federal Reserve policy meetings regularly turned a profit. A one-standard-deviation increase in tweet sentiment can be exploited to boost Fed-day equity returns by 0.62 percent, it found..."

"The investor of the future may already be incubating in a computer lab. Time to adapt to survive...

"A few years ago, Lo’s team was asked to explore the benefits of machine-learning tools within a commercial bank’s credit card business. The team’s 'recognition algorithms' processed 10 terabytes of data from half a million customers over five years. 'We were able to identify consumers who were likely to default or be delinquent on their credit card payments much more accurately than traditional tools,' Lo says. 'By addressing those particular hotspots in their credit card business, the bank was able to reduce the risk of the overall business but only by affecting 3% of its customers, as opposed to making broad credit reductions for the entire customer base.'"

"The MIT study looked at English-language tweets in 2007-14 about the Fed, measuring sentiment and adjusting for the reach of the tweeter.

"'We exploit a new dataset of tweets referencing the Federal Reserve and show that the content of tweets can be used to predict future returns, even after controlling for common asset pricing factors,' the authors, Andrew Lo, a professor at MIT, and Pablo Azar, a PhD student there, write."

Why do investors make stupid mistakes? Why do individuals consistently underperform the very funds they invest in? Are there strategies investors can follow to avoid self-destructive behavior? Those are some of the weighty questions Financial Thought Leader Andrew Lo is trying to answer from two vantage points, one as a professor of Finance at MIT and Director of its Laboratory for Financial Engineering, the other as strategist and fund manager at his firm AlphaSimplex Group. This conversation will start with his most recent research project at MIT, titled "Artificial Stupidity"!

In this blog post MIT’s Andrew Lo describes how technology can analyze language to suggest who might have authored unsigned U.S. Supreme Court opinions.