Category Archives: Expert

Walter Isaacson’s Pearls of Wisdom

Source: Louisiana Cultural Vistas website, date indeterminate

I have been interested in creative people. By creative people I don’t mean those who are merely smart. As a journalist, I discovered that there are a lot of smart people in this world. Indeed, they are a dime a dozen, and often they don’t amount to much.

What makes someone special is imagination or creativity, the ability to make a mental leap and see things differently. As Einstein noted, “Imagination is more important than knowledge.”

… another lesson that is useful when travelling in the realms of gold: that poking fun of the pretensions of the elite is more edifying than imitating them.

“The Innovators”: Walter Isaascson

Source: S&S website, Oct 2014

The computer and the Internet are among the most important inventions of our era, …  most of the innovations of the digital age were done collaboratively. There were a lot of fascinating people involved, some ingenious and a few even geniuses. This is the story of these pioneers, hackers, inventors, and entrepreneurs—who they were, how their minds worked, and what made them so creative. It’s also a narrative of how they collaborated and why their ability to work as teams made them even more creative.

The tale of their teamwork is important because we don’t often focus on how central that skill is to innovation. … we have far fewer tales of collaborative creativity, which is actually more important in understanding how today’s technology revolution was fashioned. It can also be more interesting.

  • How did the most imaginative innovators of our time turn disruptive ideas into realities?
  • What ingredients produced their creative leaps?
  • What skills proved most useful? How did they lead and collaborate?
  • Why did some succeed and others fail?

I also explore the social and cultural forces that provide the atmosphere for innovation. For the birth of the digital age, this included a research ecosystem that was nurtured by government spending and managed by a military-industrial-academic collaboration. Intersecting with that was a loose alliance of community organizers, communal-minded hippies, do-it-yourself hobbyists, and homebrew hackers, most of whom were suspicious of centralized authority.

The collaboration that created the digital age was not just among peers but also between generations. Ideas were handed off from one cohort of innovators to the next.
Another theme that emerged from my research was that users repeatedly commandeered digital innovations to create communications and social networking tools. I also became interested in how the quest for artificial intelligence—machines that think on their own—has consistently proved less fruitful than creating ways to forge a partnership or symbiosis between people and machines. In other words, the collaborative creativity that marked the digital age included collaboration between humans and machines.

Finally, I was struck by how the truest creativity of the digital age came from those who were able to connect the arts and sciences. They believed that beauty mattered. “I always thought of myself as a humanities person as a kid, but I liked electronics,” Jobs told me when I embarked on his biography. “Then I read something that one of my heroes, Edwin Land of Polaroid, said about the importance of people who could stand at the intersection of humanities and sciences, and I decided that’s what I wanted to do.” The people who were comfortable at this humanities-technology intersection helped to create the human-machine symbiosis that is at the core of this story.

Hyper-Specialization can Reduce Innovation

Source: Creativity Post, Sep 2014

“Nowadays, people are all in their separate silos and are so specialized that it’s hard to understand what anybody else is doing.” -Sir Tim Hunt, Winner of Nobel Prize for Medicine 2001.
Cross-fertilisation of ideas within science is hugely important for innovation.  The problem is, the more specialised we become, the more disconnected we become with other scientists outside our field.

 “I’ve tried to read and follow climate science in general, but I can’t.” Peter told me  “The field is written in a completely different, alien language to us in immunology” he continued.  “Now talk to me in plain language about the research please” he asked.

The language and transfer of knowledge needs an entirely new medium.  Something in-between the complexity, jargon and details so others can follow. Abstracts (those few hundred words at the start of a scientific publication), used to help scientists understand what the take-home message is for a piece of work.  
But nowadays, they are simply mini versions of complex, jargon-filled language that anyone beyond their own specialisation would have no idea how to understand.
Specialising is important, but only if it allows the time and understanding of other ideas from other areas of life and science.  Serendipitous connections and the evolution of ground-breaking innovation can only come about if someone understands what has been done, why it’s innovative or why it failed. If the efficiency of the internet only feeds what you know, how on earth are any of us exposed to what we dont know?

Google ATAP

Source: Fortune, Aug 2014

What, he wondered, did Dugan—whose job had been to nurture DARPA’s decades-long streak of breakthroughs—think? “It’s a great strategy for not losing and a lousy strategy for winning,” she answered. A week later the Motorola innovation gig was hers.

DARPA, which has consistently opened new scientific doors as it delivered useful products—and that’s exactly what ATAP is trying to do at Google. “The question is how you have an enclave that produces a string of breakthrough advances time after time,” Dugan says.

… what’s the ATAP playbook? It starts with identifying a project that demands a quantum leap in both scientific understanding and engineering capability to pull off (more on that soon). Once that is done, Dugan works to assemble a core team of experts at Google. But that team quickly casts a much wider net, tapping what are often a huge number of outside collaborators from across a mix of disciplines in industry and academia. That allows ATAP, with a staff of just 75 full-time members, to be far smaller and scrappier than traditional research labs.

Today things are moving so fast that a diversity of skills and of points of view matters,” says John Sealy Brown, who once headed the Xerox Palo Alto Research Center, one of the most prestigious and innovative industry research organizations. “Often you need to have a multitude of disciplines brought together quickly.”

Polymaths are Creatives

Source: Aeon, date indeterminate

Science … is polymathic. New ideas frequently come from the cross-fertilisation of two separate fields.

In business, cross-fertilisation is the source of all kinds of innovations. … To come up with such ideas, you need to know things outside your field. What’s more, the further afield your knowledge extends, the greater potential you have for innovation.

… it’s easier to learn when you’re young isn’t completely wrong, or at least it has a real basis in neurology. However, the pessimistic assumption that learning somehow ‘stops’ when you leave school or university or hit thirty is at odds with the evidence. It appears that a great deal depends on the nucleus basalis, located in the basal forebrain.

Among other things, this bit of the brain produces significant amounts of acetylcholine, a neurotransmitter that regulates the rate at which new connections are made between brain cells. This in turn dictates how readily we form memories of various kinds, and how strongly we retain them. When the nucleus basalisis ‘switched on’, acetylcholine flows and new connections occur. When it is switched off, we make far fewer new connections.

From research into the way stroke victims recover lost skills it has been observed that the nucleus basalis only switches on when one of three conditions occur: a novel situation, a shock, or intense focus, maintained through repetition or continuous application.

… simply attempting new things seems to offer health benefits to people who aren’t suffering from Alzheimers. After only short periods of trying, the ability to make new connections develops. And it isn’t just about doing puzzles and crosswords; you really have to try and learn something new.

by being more polymathic, you develop a better sense of proportion and balance — which gives you a better sense of humour.

On Gary Becker

Source: May 2014

Although Becker majored in mathematics as an undergraduate, he wrote a senior thesis on trade theory under Jacob Viner. He also completed another research paper on monetary theory with William Baumol. By the time he graduated from Princeton, he had already published two articles in leading economics journals. The legendary Professor Viner spoke of Becker in unbelievably glowing terms in his reference letter: “He is the best student I have ever had.”

This is most incredible because Viner had been teacher to Milton Friedman, George Stigler, and Paul Samuelson at the University of Chicago before arriving at Princeton. Viner obviously held the twenty-one year old young man from Potsville in the highest regard. Interestingly, Becker inherited one characteristic of Viner’s teaching style. Becker was famous for calling on students in class to answer difficult, open-ended questions. It they answered correctly, he would continue to press them until they made a mistake, and then explain the mistake to them. Outside the classroom, even if you were meeting with him one-on-one, he still tried to keep you on your toes, because he felt you had to continue to apply yourself and question your own thinking.

A year and a half later Friedman was writing about Becker in the following terms when recommending him for a fellowship:


“Gary Becker is a young man who received his A.B. from Princeton. He was recommended to us by his Princeton teachers for a departmental fellowship in terms that we found hard to take seriously – the best person that we have had in the last ten years; the best student that I have ever had, and the like. After observing him closely for the past year and a half, I am inclined to use similar superlatives: there is no other student that I have known in my six years at Chicago who seems to me as good as Becker or as likely to become an important and outstanding economist….”

“Becker has a brilliant, analytical mind; great originality; knowledge of the history of economic thought and respect for its importance; a real feeling for the interrelationships between economic and political issues; and a profound understanding of both the operation of a price system and its importance as a protection of individual liberty. This is one of those cases in which there is just no question at all about Becker being preeminently qualified for one of your fellowships. I wish I could look forward to being able to find a candidate this good every year, but that is asking for too much.”

Lesson from Running a MOOC – Steve Blank

Source: Steve Blank blog, Feb 2014

…  put my Lean LaunchPad lectures online. Rather than just have me drone on as a talking head, I hired an animator to help make the lectures interesting, and the Udacity team had the insight to suggest I break up my lecture material into small, 2-4 minute segments that matched students’ attention spans.

… because of the animations and graphics the students were more engaged than if I were teaching it in person.

Here’s what we found when we flipped the classroom:

  • More than half the students weren’t watching the lectures at home.
  • Without an automated tool to take an attendance, I had no idea who was or wasn’t watching.
  • Without lectures, my teaching team and I felt like observers. Although we were commenting and critiquing on students presentations, the flipped classroom meant we were no longer in the front of the room.
  • No lectures meant no flexibility to cover advanced topics or real time ideas past the MOOC lecture material.

We decided we needed to fix these issues, one at a time.

  • In subsequent classes we reduced class size from ten teams to eight. This freed up time to get lecture and teaching time back in the classroom.
  • We manually took attendance of who watched our MOOC (later this year this will be an automated part of the LaunchPad Central software we use to manage the classes.)
  • To get the teaching team front and center, I required students to submit questions about material covered in the MOOC lecture they watched the previous evening. I selected the best questions and used them to open the class with a discussion. I cold-called on students to ensure they all had understood the material.
  • We developed advanced lectures which combined a summary of the MOOC material with new material such as lectures focused on domain specific perspectives. For example, in our UCSF Life Sciences class the four VC’s who taught the class with me developed advanced business model lectures for therapeutics, diagnostics, medical devices and digital health. (These advanced lectures are now on-line and available to everyone who teaches the class.)

Human or Algorithm?

Source: HBR, Dec 2013

Cowen’s thesis is that one’s ability to augment machine intelligence will define one’s value in the labor market. “Are your skills a complement to the skills of the computer,” he asks, “or is the computer doing better without you?” His key metaphor throughout the book is that of a freestyle chess player. In freestyle chess, human and computer teams play together, and are able tooutperform either on their own (at least for now).

The key challenge for the freestyle player is not to be a master of chess, but to understand the strengths and weaknesses of chess programs so as to know when to trust their recommendations and when to override them.

In this model of human-machine collaboration, the computer handles the bulk of the decision-making, with the human adding a layer of judgment on top. It is echoed in Nate Silver’s book The Signal and the Noise, which uses the rather remarkable example of meteorologists:

The programs that meteorologists use to forecast the weather are quite good, but they are not perfect. Instead, the forecasts you actually see reflect a combination of computer and human judgment… Some of the forecasters [at the National Weather Service] were drawing on these [computer-generated] maps with what appeared to be a light pen, painstakingly adjusting the contours of temperature gradients produced by the computer models… The forecasters know the flaws in the computer models… The unique resource that these forecasters were contributing was their eyesight… According to the agency’s statistics, humans improve the accuracy of precipitation forecasts by about 25 percent over the computer guidance alone, and temperature forecasts by about 10 percent.

Getting a Job @ Google

Source: NYTimes, Feb 2014

5 Hiring Attributes:

(1) Learning ability: the ability to process on the fly. It’s the ability to pull together disparate bits of information.

(2) Leadership: in particular emergent leadership as opposed to traditional leadership. … What we care about is, when faced with a problem and you’re a member of a team, do you, at the appropriate time, step in and lead. And just as critically, do you step back and stop leading, do you let someone else? Because what’s critical to be an effective leader in this environment is you have to be willing to relinquish power.

(3) and (4) Ownership and Humility. “It’s feeling the sense of responsibility, the sense of ownership, to step in,” he said, to try to solve any problem — and the humility to step back and embrace the better ideas of others. “Your end goal,” explained Bock, “is what can we do together to problem-solve. I’ve contributed my piece, and then I step back.”

And it is not just humility in creating space for others to contribute, says Bock, it’s “intellectual humility. Without humility, you are unable to learn.” It is why research shows that many graduates from hotshot business schools plateau. “Successful bright people rarely experience failure, and so they don’t learn how to learn from that failure,” said Bock.

(5) Expertise: The least important attribute they look for is “expertise.”
Said Bock: “If you take somebody who has high cognitive ability, is innately curious, willing to learn and has emergent leadership skills, and you hire them as an H.R. person or finance person, and they have no content knowledge, and you compare them with someone who’s been doing just one thing and is a world expert, the expert will go: ‘I’ve seen this 100 times before; here’s what you do.’ ”

Most of the time the nonexpert will come up with the same answer, added Bock, “because most of the time it’s not that hard.” Sure, once in a while they will mess it up, he said, but once in a while they’ll also come up with an answer that is totally new. And there is huge value in that.

Good Judgement Project

Source: Good Judgement Project website, date indeterminate

The Good Judgement Project

Related Readings:

Economist, Nov 2013

The average expert did only slightly better than random guessing. Even more disconcerting, experts with the most inflated views of their own batting averages tended to attract the most media attention. Their more self-effacing colleagues, the ones we should be heeding, often don’t get on to our radar screens.

… a far more ambitious tournament currently sponsored by the Intelligence Advanced Research Projects Activity (IARPA), part of the American intelligence world. Over 5,000 forecasters have made more than 1m forecasts on more than 250 questions.

The big surprise has been the support for the unabashedly elitist “super-forecaster” hypothesis. The top 2% of forecasters in Year 1 showed that there is more than luck at play. If it were just luck, the “supers” would regress to the mean: yesterday’s champs would be today’s chumps. But they actually got better. When we randomly assigned “supers” into elite teams, they blew the lid off IARPA’s performance goals. They beat the unweighted average (wisdom-of-overall-crowd) by 65%; beat the best algorithms of four competitor institutions by 35-60%; and beat two prediction markets by 20-35%.

Washington Post, Nov 2013

How does the Good Judgment Project achieve such strikingly accurate results?

The Project uses modern social-science methods ranging from harnessing the wisdom of crowds to prediction markets to putting together teams of forecasters.

The GJP research team attributes its success to a blend of getting the right people (i.e., the “right” individual forecasters) on the bus, offering basic tutorials on inferential traps to avoid and best practices to embrace, concentrating the most talented forecasters into super teams, and constantly fine-tuning the aggregation algorithms it uses to combine individual forecasts into a collective prediction on each forecasting question. 

The Project’s best forecasters are typically talented and highly motivated amateurs, rather than subject matter experts.