I’ve always been curious about the West Coast, especially San Francisco and Silicon Valley. Growing up in India and then working in the oil & gas industry in Latin America and Texas, I didn’t have much opportunity (or reason) to visit the Bay Area.
Now that I’m an MBA student at MIT Sloan, I want to explore the tech sector as a possible career path. So when I heard about the annual “Tech Trek” to San Francisco and Silicon Valley, I jumped at the chance. Not only could I finally check out the West Coast, I also could check out tech companies – including several that don’t recruit on the MIT campus – and see if they might be a good fit for me.
Throughout the week, our group of 30 MBA students visited a mix of large and mid-sized companies in the hardware, software, and consulting areas. While they were all quite different, a common theme seemed to be an appreciation for being “scrappy.” In reality, some companies were scrappier than others, but it’s interesting that most tech companies embrace the concept of “all hands on deck” these days, especially since many have incredibly high valuations and in theory could afford an army of people in different functions.
Until last year, the number of students in my classes at MIT numbered 50 or so. Less than twelve months later, I have just completed my first class with 50,000 registered participants. They came from 185 countries, and together they co-generated:
• >400 prototype (action learning) initiatives
• >560 self-organized hubs in a vibrant global eco-system
• >1,000 self-organized coaching circles.
What explains the growth in group size from 50 to 50,000? It’s moving my class at MIT Sloan to the edX platform, making it a MOOC (Massive Open Online Course).
Designed to blend open access with deep learning, the u.lab was first launched in early 2015 with 26,000 registered participants. When we offered it for a second time, in September, we had 50,000 registered participants. According to the exit survey, 93% found their experience “inspiring” (60%) or “life changing” (33%); and 62% of those who came into the u.lab without any contemplative practice have one now.
While businesses are hiring more data scientists than ever, many struggle to realize the full organizational and financial benefits from investing in data analytics. This is forcing some managers to think carefully about how units with analytics talents are structured and managed.
How can organizations realize the promise of the evolving disciplines that we broadly call analytics?
Although financial firms were among the first to recruit “quants” to use sophisticated mathematical models and high-powered computing hardware, analytics groups have now taken hold in areas ranging from health care to political campaigns to retailing to sports. Organizations like these can benefit from the insights gained by financial service firms on how best to manage teams doing advanced analytics. It requires skills and philosophies that are different from those that arise in managing other groups of smart professionals.
Rather than just involving oversight and planning, managing a data science research effort tends to be a dynamic and self-correcting process; it is difficult to plan precisely either a project’s timing or final outcomes. For those unused to this type of work, this process can seem quite messy — an unexpected contrast to a field that, from the outside, seems to epitomize the rule of reason and the preeminence of data.
The UN international climate change negotiations in Paris, COP21, concluded on Saturday. The outcome: 196 countries came to the table, and committed to preventing the worst effects of climate change. For the first time, developing countries recognized their future responsibility, while developed nations acknowledged their historic contribution. Together they set out an aggressive goal to keep global warming below 1.5 degrees C. Like countless others, I eagerly shared the news on my Facebook feed and I rushed to explain the significance to my five-year-old son.
As you probably know by now, HitchBot—a device made of pool noodles, rubber gloves, a bucket, and the computer power needed to talk, smile, and tweet—was deliberately decapitated and dismembered this week, only 300 miles into its hitchhiking journey across the United States. HitchBot had successfully made similar journeys across the Netherlands, Germany, and Canada, relying on bemused strangers for transportation. The geek-o-sphere is up in arms, claiming that this violence reveals something special and awful about America, or at least Philadelphia.
I think perhaps there’s something else at work here. Beyond building robots to increase productivity and do dangerous, dehumanizing tasks, we have made the technology into a potent symbol of sweeping change in the labor market, increased inequality, and recently the displacement of workers (see “Who Will Own the Robots?”). If we replace the word “robot” with “machine,” this has happened in cycles extending well back through the Industrial Revolution. Holders of capital invest in machinery to increase production because they get a better return, and then many people, including some journalists, academics, and workers cry foul, pointing to the machinery as destroying jobs. Amidst the uproar, eventually there are a few reports of people angrily breaking the machines.
Two years ago, I did an observational study of semiautonomous mobile delivery robots at three different hospitals. I went in looking for how using the robots changed the way work got done, but I found out that beyond increasing productivity through delivery work, the robots were kept around as a symbol of how progressive the hospitals were, and that when people who’d been doing similar delivery jobs at the hospitals quit, their positions weren’t filled.
Most entry-level workers did not like this one bit. Soon after implementation, managers at all my sites noticed that some of these workers sabotaged the robots. This took more violent forms—kicking them, hitting them with a baseball bat, stabbing their “faces” with pens, shoving, and punching. But much of this sabotage was more passive—hiding the robots in the basement, moving them outside their preplanned routes, obscuring sensors, walking slowly in front of them, and most of all, minimizing usage. Workers and managers attributed these stories to an ongoing, frustrated workplace dialogue about fair work for fair pay.