Stuart Madnick, Professor of Information Technology, MIT Sloan School of Management
From Harvard Business Review
Last fall, in Northern California, the United States experienced its first-ever long-lasting and deliberate, large-scale blackout. Fueled by increased fears of devastating fires due to its century-old equipment, the region’s utility companies shut off power to more than 1.5 million people forcing many evacuations. The impact was devastating; Michael Wara, a climate and energy expert at Stanford University, estimated the cost to California as up to $2.5 billion. For cybersecurity experts like myself, the blackout was a signal of just how precarious our reliance on electricity is, and how much we have to fear in cyberattacks.
Think about what would happen if a cyberattack brought down the power grid in New York or even just a larger part of the country. As we saw in California, people could manage for a few hours — maybe a few days — but what would happen if the outage lasted for a week or more? If a utility in a high-density population area was targeted with a cyberattack, is an evacuation of millions of people feasible or desirable?
A form of artificial intelligence, ML enables powerful algorithms to analyze large data sets in order make predictions against defined goals. Instead of precisely following instructions coded by humans, these algorithms self-adjust through a process of trial and error to produce increasingly more accurate prescriptions as more data comes in.
ML is particularly adaptable to securities investing because the insights it garners can be acted on quickly and efficiently. By contrast, when ML generates new insights in other sectors, firms must overcome substantial constraints before putting those insights into action. For example, when Google develops a self-driving car powered by ML, it must gain approval from an array of stakeholders before that car can hit the road. These stakeholders include federal regulators, auto insurers, and local governments where these self-driving cars would operate. Portfolio managers do not need regulatory approval to translate ML insights into investment decisions.
In the context of investment management, ML augments the quantitative work already done by security analysts in three ways:
Pablo Egana Del Sol, Research Affiliate, International Faculty Fellows Program
From MIT Sloan Management Review
Much has been written about the rise of automation in developed countries. Economists have been busily creating models seeking to quantify the likely impact of automation on employment.1 However, far less has been written about the potential effects on work in developing nations. This is surprising, given that automation may be especially troublesome for developing economies.
We know that economic growth brings significant shifts toward higher-skilled occupations and that the economies of many developing nations rely largely on manual labor and routinized manufacturing work. Because some types of manual and routinized work can be easily handled by computers, machinery, and artificial intelligence, it’s clear that large-scale automation could have significant and wide-reaching effects on workers in developing countries.
From Fitbit to HeadSpace to budgeting app Mint, technology is often billed as the solution to sticking to our New Year’s resolutions. With 80% of resolutions failing by February, the ability to track our exercise, food, weight, spending, and meditation habits at our fingertips seems like a no-brainer.
But is technology actually making it harder for us to stick to our goals? What if we are embracing the very mechanism responsible for sabotaging our good intentions?
Technology is highly addictive, by design. In a recent BBC investigation, a former Silicon Valley insider said social media companies were sprinkling “behavioral cocaine” over smartphone apps, adding features that deliberately keep us addicted. If not kept in check, using a smartphone app with the goal of sticking to your resolution may tempt you to do other things, such as checking your social media accounts instead. Read More »
Glen Urban, David Austin Professor in Marketing, Emeritus, and MIT Sloan School Dean, Emeritus
John R. Hauser, Kirin Professor of Marketing, MIT Sloan School of Management
From MIT Sloan Management Review
Deep learning is delivering impressive results in AI applications. Apple’s Siri, for example, translates the human voice into computer commands that allow iPhone owners to get answers to questions, send messages, and navigate their way to and from obscure locations. Automated driving enables people today to go hands-free on expressways, and it will eventually do the same on city streets. In biology, researchers are creating new molecules for DNA-based pharmaceuticals.
Given all this activity with deep learning, many wonder how the underlying methods will alter the future of marketing. To what extent will they help companies design profitable new products and services to meet the needs of customers?
The technology that underpins deep learning is becoming increasingly capable of analyzing big databases for patterns and insights. It isn’t difficult to imagine a day when companies will be able to integrate a wide array of databases to discern what consumers want with greater sophistication and analytic power and then leverage that information for market advantage. For example, it may not be long before consumers, identified via facial recognition technology while grocery shopping, receive individualized coupons based on their previous purchase behavior. In the future, advertisements may be individually designed to appeal to consumers with different personalities and be delivered in real time as they view YouTube. Deep learning might also be used to design products to meet consumers’ personal needs, which could then be produced and delivered through automated 3D printing systems.