Group Genius: Today It’s the Accepted Wisdom

In 2007, my business book Group Genius  was one of the first books about collaboration and innovation. Since 2007, a lot more books have been published on that topic, each one affirming the points in my book. That’s because Group Genius  was grounded in scientific research, and that research has stood the test of time.

The March 13 article “In Search of the Perfect Team” in the Wall Street Journal* makes the same recommendations that I did in 2007:

• “Each member of the team is engaged” (WSJ)–everyone talks and listens about the same. This is in Group Genius, pp. 50-51
• “There are a diversity of ideas, and everyone is willing to consider new ideas” (WSJ)–In Group Genius, pp. 70-72, also pp. 14-15
• “Everyone is setting goals for a project” (WSJ)–each person explores something slightly different, but goes in the same direction. This tension is one of the main themes of Group Genius, but it’s most explicit on pp. 44-46.

The WSJ  article connects these themes to new technologies, like Slack, and Google’s data-based approach to team productivity in their People Operations Department. These help drive collaboration; I talk about Slack and also Google’s research in the forthcoming second edition of Group Genius  (coming this May!). But this technology doesn’t change the underlying social dynamics of effective collaboration. Stay grounded in the research, and you’ll stand the test of time.

*2017, May 13, “In search of a perfect team.” Stu Wu, Wall Street Journal, p. R6.

Bumblebees Can Learn

Check out this cool new study published in SCIENCE Magazine. The study proves that bees can learn, and they can adapt what they’ve learned to new situations.

The researchers created some really clever tasks for the bees, and the descriptions of what the bees had to do are pretty complicated. First, the researchers showed the bees a small yellow ball at the center of a blue circle. The ball had a sugar solution inside, and the bees learned to go up to the ball, and get the sugar, pretty quickly (within 48 hours).

Next, they put the yellow ball outside the blue circle, and the bees could only get to the sugar after they pushed the ball into the center of the circle. The researchers started by showing the bee how to do it–they made a stick with a plastic bee at the end, and the manipulated the stick so that the plastic bee moved the ball into the center. At first, the bee could eat the sugar once the plastic bee had finished, but after a few times of this, the bee had to move it himself to get the sugar.

14 bees figured out how to move it themselves within 10 tries. The researchers got rid of the four dumber bees who couldn’t figure it out. Then, the researchers gave the bees a much larger blue circle, and all of the bees still could move the yellow ball to the center, ten tries in a row. The bees kept learning; on each of the ten trials they took less time to finish, and their path to the center become more direct.

Next the researchers put the bees through a complicated task that’s hard to describe briefly. In short, they showed that the bees learned best when they could watch another bee doing it, compared to another learning situation where they didn’t have another bee to watch. That’s social learning–learning from watching and imitating someone you recognize.

Then, with yet another complicated experiment, they showed that the bees aren’t just copying what they see another bee do, but that they learn to adapt what they’ve learned to new situations. For example, if they were shown another bee moving the farthest away ball, they knew to move the closest ball instead of that farther one. And second, if the ball color changed to black, they could still do it.

The researchers point out that these artificial tasks are way harder than anything bees have to do in the wild. Evolution didn’t require this adaptation (of being able to learn this way). This means that animals can end up smarter than they need to survive in the wild. (We’re talking about you, human beings!)

Here’s their conclusion:

Such unprecedented cognitive flexibility hints that entirely novel behaviors could emerge relatively swiftly in species whose lifestyle demands advanced learning abilities.

We Never Think Alone

Here’s a wonderful new book, The Knowledge Illusion: Why We Never Think Alone. The authors, professors Philip Fernbach and Steven Sloman, argue that it’s “a misunderstanding of knowledge” to think that “it goes on between our ears.”

What really sets human beings apart is not our individual mental capacity. The secret to our success is our ability to jointly pursue complex goals by dividing cognitive labor. All of our world-altering innovations were made possible by this ability. Each of us knows only a little bit, but together we can achieve remarkable feats. Knowledge isn’t in my head or in your head. It’s shared.

I love it! I made the same point in my book Group Genius: creativity isn’t really about what’s going on inside your head. Of course, each person’s mind plays a key role in innovation; but creativity is always social, even when you’re alone. Lots of us have good ideas when we’re alone, but we can only have those ideas because of previous conversations, interactions, and encounters–with other people, with other ideas, participating in social networks.

Check out Fernbach’s and Sloman’s book The Knowledge Illusion!

The quotations above are from a NYTimes article by Fernbach and Sloman.

What Will We Do After AI Takes Our Jobs?

Christopher Mims predicts that artificial intelligence will increasingly put white collar, professional workers out of work. That means people who blog. 🙂 Muriel Clauson, of Singularity University, says “Education is often touted as the answer to the skills gap, but it is generally a blunt instrument.” She recommends this system:

First, break down every job into the smallest tasks. Then, figure out which of those tasks can be automated. The jobs that include those tasks are the ones at risk.

Second, assess what skills each person has, and compare those skills with the tasks, across all of the jobs, that can’t be automated. That would give you a pretty good idea of how to match up people with the remaining jobs. Each person would probably be missing a few of the tasks for any given job, so this “task mapping” assessment system would tell you how to design universities and other educational organizations.

I’ve always been nervous about designing education based on what jobs currently exist. It’s because today’s jobs are always going away, or transforming, and new jobs are emerging all the time. Those new jobs often involve new “tasks” that wouldn’t show up using any system based on today’s jobs. So the real challenge faced by education reformers, and education researchers like myself, is: What are the deeper, higher level skills that apply broadly across a wide range of tasks? Those are the skills that make you adaptable, ready to grow and change with the economy.

Are You Too Old to Be Brilliant?

When are brilliant scientists the most brilliant? What age are you likely to be when the Nobel committee comes calling? Pick one of the following answers:

• You need a lot of expertise and wisdom to make a big breakthrough. You need professional connections, lots of research money, and big laboratories. Scientific breakthroughs come from people in middle age, or maybe even at the end of their careers.
• It’s the young upstarts who have lots of energy and fresh ideas. After all, the old scientists are stuck in ideas from the past. They’re already past their prime. They’re tired and don’t have much energy any more. Am I talking about myself at the ripe old age of 56? I didn’t get much sleep last night, and my knees are kind of sore 🙂

A new study gives us the answer: None of the above. There’s no relationship between age and creative scientific contribution. The authors of the study analyzed 2,856 physicists, working from 1893 to the present. They found that the best predictor of exceptional creativity is productivity. It’s lots of hard work. The scientists who do the most experiments, and test the most hypotheses, are the ones with the big contributions. The researchers found that once they’d controlled for productivity, age doesn’t add any additional predictive power.

The researchers identified a second variable that’s related to scientific impact: They called it Q, and it includes intelligence, motivation, openness to ideas, ability to write well. Another surprise: The variable Q doesn’t change over your career. (Otherwise, you’d be back to the theory that age predicts creativity.)

It’s still true that younger scientists are more likely to make a significant contribution. But it’s not because a person has more brilliant insights in your 20s, and it’s not because their ideas are fresh and unbound by old-fashioned tradition. It’s because they work harder and that’s why they’re more productive. So if you’re older, there’s still hope.

Now if only I could get a good night’s sleep.

Free Improvisation in Music Groups

There’s almost no research on group musical improvisation, and I’ve wondered about that for years. I’m a jazz pianist, and I’m fascinated by how different people can come together, and collectively create something that no one could have thought of alone.

So I’m excited to see a new study, of group free improvisation in music trios.* Two of my most respected British colleagues co-authored the study: Graeme Wilson and Raymond MacDonald.

They brought together 3 trios of improvising musicians, from Scotland and the North of England. The musicians were from a range of backgrounds, including voice and electronics. And just for extra measure, they also studied 2 more trios of visual artists who work with sound performance. The trios improvised in a studio for about five minutes. Then, the researchers interviewed each performer separately, replaying the tape of their improvisation, and asking them to explain “what they understood to be communicated by their own and other improvisers’ contributions” (p. 1032).

The main finding was that the musicians spent a lot of time thinking about whether to “maintain” what they were playing, or to “change” to something different. If they decided to change, either it was an initiation on their part, or a response to someone else’s contribution.  This is an “active and iterative” process.

If a change was a response, it was either an adoption (doing something really similar to the other musician’s initiation), an augmentation (adopting one element of the partner, but modifying another element), or a contrast (play something really different, but that’s complementary). Here’s the bottom line:

The representation is of an open-ended iterative cycle where all choices lead to a subsequent reconsideration, with each trio member constantly “scanning” the emergent sound of the piece and actions of their collaborators. The improvisation was sometimes characterized by interviewees as an external entity or process, within which events arose independently of those creating it. (p. 1035)

That’s exactly my own experience with group improvisation, and in my own research, every musician that I interviewed spoke in very similar terms, about iteration, interaction, and the emergence of something greater than the individual musicians.

* Wilson, Graeme B., Macdonald, Raymond A. R. (2016). Musical choices during group free improvisation: A qualitative psychological investigation. Psychology of Music, 44(5), 1029-1043.

Creativity is not Localized in the Brain

If you’ve read the chapter on brain imaging in my book Explaining Creativity, you’ll know that the technology has limitations. Specifically: There’s no way to use this research to claim that creativity is located in a particular part of the brain. To their credit, the researchers who do this work would never say that. However, the media tend to hear about these cautious and limited findings, and publish articles with titles like “Now we know where creativity is!”

A new article in The Economist describes these limitations:

The technology has its critics. Many worry that dramatic conclusions are being drawn from small samples (the faff involved in fMRI makes large studies hard). Others fret about over-interpreting the tiny changes the technique picks up. A deliberately provocative paper published in 2009, for example, found apparent activity in the brain of a dead salmon.

The Economist article is about a new study that identifies a serious problem with fMRI methodology. The new study’s findings suggest that the statistics programs that interpret the fMRI results are “seriously flawed.” (And there’s a lot of statistics involved; take a look at my chapter for a quick summary.) The researchers used these fMRI algorithms to compare 499 subjects who were lying in the scanner while not thinking about anything in particular. With the standard fMRI statistical software, they divided this subject pool in half in 3 million different ways, and did comparisons each time. There shouldn’t have been any findings at all. But in fact, 70 percent of the 3 million comparisons resulted in false positives. That means, in 70 percent of these comparisons, there was a statistically significant finding of elevated brain activity, in half of the 499 subjects, in some part of the brain.

Because this study was just published, we can’t yet be sure what it really means. But my advice is: Be skeptical if you read an article claiming that creativity is located in a particular brain region. Creativity is a function of the entire brain, working together.

Sawyer keynote at IDEAS conference in Calgary, Canada

I just delivered the keynote address “Educating for Innovation” at this big event in Calgary, with teachers, school leaders, education professors, and policy types:

After the keynote, I did a breakout session where I shared my research on how art school professors teach. Then, I asked the audience to work in small groups to apply these practices to their own teaching in math and science. They all had great ideas about how to teach for creativity! The lessons from art and design pedagogy are really powerful.

Books About Complexity and Emergence

I thought the market for complexity books had been saturated, but here’s another one: A Crude Look at the Whole  by John H. Miller. (WSJ  review here.)

The first wave of complexity books was in the mid 1990s:

• 1993. Mitchell Waldrop’s Complexity: The Emerging Science at the Edge of Order and Chaos
• 1996. Stuart Kaufman’s At Home in the Universe: The Search for the Laws of Self-Organization and Complexity
• 1996. John Holland’s Hidden Order: How Adaptation Builds Complexity

The heyday of complexity books was just after 2000 (my own book appeared in 2005):

• 2002. Steven Johnson’s Emergence: The Connected Lives of Ants, Brains, Cities, and Software
• 2004. Harold Morowicz’s The Emergence of Everything: How the World Became Complex
• 2005. My book Social Emergence: Societies as Complex Systems

In just the past few years, we have

• 2014. John Holland again, this time with Complexity: A Very Short Introduction
• 2014. Brian Arthur’s Complexity and the Economy

According to Ronald Bailey’s WSJ  review, Miller’s book covers familiar ground. Like my 2005 book, he argues that “societies are complex systems”; that social phenomena “emerge unpredictably from components”; that “simple parts interact in complex ways to create an emerging whole”. His examples of emergence from complexity are familiar from these earlier books: biological evolution, markets, the Internet, political protests. Bailey’s review is politely critical of the book; he says “it’s hard to see how complexity science is much help to current policy makers or citizens.” I disagree; I think that understanding complexity and emergence has incredible value, especially in understanding social systems. Maybe Miller’s book isn’t the first one you should read, but the long list of earlier books (and their strong sales) demonstrates that this research is helping lots of people.

Dr. Marvin Minsky, Artificial Intelligence Pioneer

Professor Marvin Minsky of MIT died at 88 on Sunday January 24. He was one of most important scientists in artificial intelligence (AI): a field of research studying how to develop computers that simulate human thought and behavior. Minsky founded the world’s first AI Lab at MIT in 1959, along with John McCarthy.

Minsky was my undergraduate thesis advisor at MIT, and what I remember most about him was his fascination with music. I graduated in 1982 with a degree in computer science, and I did my thesis research on Minsky’s “Music Cognition” project. His grad student David Levitt was my manager, and we worked on the first-ever screen-based music composition editor. You can display sheet music on any computer these days, but in 1981 home computers could only display white letters on a black background. The AI Lab had radically different computers, with bitmap screens that could display pictures; text in different typefaces and point sizes; pull-down menus; everything controlled by a mouse (with three buttons). These advanced graphics allowed us to display music notation, and allowed composers to edit music on screen.

(BTW it wasn’t the Xerox PARC smalltalk computer, that Steve Jobs famously “borrowed” to create the Apple Mac; our computers were invented and built at MIT and they were called LISP machines.)

Minsky often worked together with Seymour Papert, the MIT professor who developed the LOGO programming language for children. They both worked in the same building in Tech Square (in Cambridge’s Kendall Square); the AI Lab was on the 9th floor and the LOGO lab was on the third floor. One day on my way down in the elevator, I decided to stop at the third floor to check it out; that was the one time I met Papert and saw the original plexiglass LOGO robot.

Back in the early days, computer scientists were idealistic; they believed in open access to everything and everyone. For example, no one’s personal files were password protected. Because I used the same computer as Dr. Minsky, I could access his folders and read every document he was working on. In 1981, I went into his folders and discovered a draft of his book Society of Mind (it wasn’t published until 1985). The book argued that intelligence emerges from the interaction of many smaller, non-intelligent entities inside the mind–something like a “society”. This concept made a huge impact on me, and I kept thinking about emergence and complex systems for two more decades. In 2001, I published my first journal article on the topic, “Emergence in sociology,” and in 2005, I published a book exploring these ideas, Social Emergence: Societies as Complex Systems.

That’s the kind of influence a great scholar has on the world–it lasts for decades.