By securely aggregating sensitive data from cyber-attacks, the SCRAM platform from MIT CSAIL can quantify an organization’s level of security and suggest what to prioritize.

MIT has debuted a new platform designed to help the enterprise decide how to invest in cybersecurity.

THE STOCK MARKET appears strangely indifferent to Covid-19 these days, but that wasn’t true in March, as the scale and breadth of the crisis hit home. By one measure, it was the most volatile month in stock market history; on March 16, the Dow Jones average fell almost 13 percent, its biggest one-day decline since 1987. To some, the vertigo-inducing episode also exposed a weakness of quantitative (or quant) trading firms, which rely on mathematical models, including artificial intelligence, to make trading decisions.

Andrew W. Lo, Ph.D., Professor and Director of the Laboratory for Financial Engineering at MIT Sloan School of Management, believes the biggest barrier, from an investor perspective, are new business models. “That, to me, is the one thing that we can be thinking about differently that we aren’t right now. People are already focused on scientific collaborations, new types of biological mechanisms and targets, genomics, transcriptomics, proteomics, and all the other -omics. But the one omics they haven’t focused on is economics, new ways of structuring biopharma businesses and financing them. This challenge also offers tremendous opportunities for applying the tools of modern financial engineering to biomedicine and getting investors to think differently…. By adopting a portfolio approach to biomedical R&D, we can lower the cost of capital, increase the amount of funding, and get new and better therapies to patients faster and cheaper.”

When someone says that another person is intelligent, you pretty much assume that this is a praising of how smart or bright the other person might be.

In contrast, if someone is labeled as being stupid, there is a reflexive notion that the person is essentially unintelligent. Generally, the common definition of being stupid is that stupidity consists of a lack of intelligence.

This brings up a curious aspect.

Suppose we somehow had a bucket filled with intelligence. We are going to pretend that intelligence is akin to something tangible and that we can essentially pour it into and possibly out of a bucket that we happen to have handy.

Upon pouring this bucket filled with intelligence onto say the floor, what do you have left?

On August 5, 2016, Cathay Pacific Flight 905 from Hong Kong was heading for an on-time arrival at Manila’s Ninoy Aquino International Airport when something unexpected occurred. The pilots radioed air traffic controllers and said they had lost GPS (Global Positioning System) guidance for the final eight nautical miles to “runway right-24.” Surprised, the controllers told the pilots to land the wide-body Boeing 777-300 using just their own eyes. The crew members pulled it off, but they were anxious the whole way in. Fortunately, skies were mostly clear that day. The incident was not isolated. In July and August of that year, the International Civil Aviation Organization received more than 50 reports of GPS interference at the Manila airport alone.

Los profesores de negocios y finanzas que preparan a sus estudiantes para futuras carreras en la banca también están viendo la tendencia. Científico de datos es la "función más importante" ahora para los empleadores, asegura Andrew Lo, director del Laboratorio de Ingeniería Financiera de MIT en Cambridge, Massachusetts.

On the Thursday, January 24 edition of Bloomberg Baystate Business we spoke with Bloomberg Intelligence gaming and lodging analyst Brian Egger about MGM-Springfield, the latest on the Wynn saga, and sports betting. Rob Almeida of MFS stopped by to talk about the markets. Bloomberg News endowments reporter Michael McDonald joined us to talk about his story on Baupost Group’s Seth Klarman, who sees opportunity in the markets. Boston Business Journal real estate editor Catherine Carlock talked about the commercial real estate market in Boston. Finally, MIT’s Andrew Lo talked about technology, finance and investing. Hosts: Peter Barnes, Janet Wu and Pat Carroll. Producer: Dan Pierce.

BOSTON — Cold weather this week didn’t matter to the crowds at the AI World conference here, as activity around artificial intelligence continues to heat up. Over three days, more than 2,200 attendees learned about the latest advances in machine learning, deep learning, and the industries being affected by AI.

While most of the conference focused on AI’s impact on the healthcare, pharmaceutical, and enterprise software markets, a few sessions discussed industrial automation efforts, including the Industrial Internet of Things (IIoT), manufacturing, and autonomous vehicles.

Here are six themes from this year’s AI World, as observed by Robotics Business Review editors attending the event:

Artificial intelligence will reshape the world of finance over the next decade or so by automating investing and other services—but it could also introduce troubling systematic weaknesses and risks, according to a new report from the World Economic Forum (WEF).

Compiled through interviews with dozens of leading financial experts and industry leaders, the report concludes that artificial intelligence will disrupt the industry by allowing early adopters to outmaneuver competitors. It also suggests that the technology will create more convenient products for consumers, such as sophisticated tools for managing personal finances and investments.

But most notably, the report points to the potential for big financial institutions to build machine-learning-based services that live in the cloud and are accessed by other institutions.

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.