We are entering a golden age of innovation in computer science

 

Paul Allen - Philanthopist and Entrepreneur 

Today’s computer science and engineering students have a wonderful opportunity to put their skills and expertise to use solving the world’s biggest problems. The computer programs of today are really only constrained by the user’s imagination.

Today’s announcement that the University of Washington’s Department of Computer Science & Engineering will be elevated to a school and will bear my name is truly an honor.

UW has always felt like home to me for several reasons.

In the university library my father helped lead, as the Associate Director of Libraries from ‘60 to ’82, I spent hours and hours as a kid devouring piles of books so I could follow the latest advances in science. And I spent a lot of time in the graduate computer lab as a high school senior. Of course, I didn’t belong there, but the professors looked the other way—until we wore out our welcome, as you can guess high school students would do eventually.

I still have the letter from the computer lab director, Dr. Hellmut Golde, kicking us out. A couple lines still make me laugh.

“Dear Mr. Allen,” it begins. The letter lists several reasons for kicking us out: One was that we would use all the terminals at once and for such long periods of time that the lab became too busy and noisy. The second was that some of my co-conspirators hadn’t properly checked out equipment. And the third and truly great offense still gets me.

“Earlier this week,” the letter reads, “you removed the acoustic coupler from Dr. Hunt’s office without authorization.” It’s true. Guilty as charged. Since no one was using it, we’d taken it home so we could keep working off campus. And here’s the punch line. He said we’d taken it “without leaving at least a note. Such behavior is intolerable in any environment.” And that was the nail in our coffin, I guess. I’m still embarrassed we didn’t leave a note!

With that stern letter, our free time on UW computers came to an unfortunate end.

Another reason the University of Washington is such a special place to me is that it’s where we built the Traf-O-Data machine. While Bill Gates and I handled the software side of it, the machine itself was built on campus by a UW student named Paul Gilbert, a partner Bill Gates and I recruited into our high school business venture. Paul did an amazing job turning the first 8-bit microprocessor in Seattle into a real computer.

The idea was simple enough.

We wanted to automate the traffic-measuring process, part of which required high school students to count the hole punched into a tape each time a vehicle drove over a black tube laid across the street. We wondered if there was a less expensive solution than a minicomputer to processing the tapes. I had read about the new 8008 chip from Intel and suggested we try to build a machine based on it.

Objectively speaking, Traf-O-Data was a failure as a company. Right as our business started to pick up, states began to provide their own traffic-counting services to local governments for free. As quickly as it started, our business model evaporated.

But while Traf-O-Data was technically a business failure, the understanding of microprocessors we absorbed was crucial to our future success. And the emulator I wrote to program it gave us a huge head start over anyone else writing code at the time.

If it hadn’t been for our Traf-O-Data venture, and if it hadn’t been for all that time spent on UW computers, you could argue that Microsoft might not have happened.

I hope the lesson is that there are few true dead ends in computer science. Sometimes taking a step in one direction positions you to push ahead in another one.

And relentlessly absorbing the latest in technology can help prepare you for that new path toward success.

To think that when we were building the Traf-O-Data machine there wasn’t even a computer science department at all. And now this department is one of the best in the nation, with this next phase of expansion expected to elevate the school into the nation’s Top 5 computer science programs.

If it hadn’t been for our Traf-O-Data venture, and if it hadn’t been for all that time spent on UW computers, you could argue that Microsoft might not have happened. 

This impressive program trains and educates some of the world’s best and brightest. Matter of fact, I was fortunate to be able to convince UW professor Oren Etzioni to lead the Allen Institute of Artificial Intelligence. He and his team are doing tremendous work in Fremont.

The promise of artificial intelligence and computer science generally vastly outweighs the impact it could have on some jobs. In the same way that while the invention of the airplane negatively affected the railroad industry, it opened a much wider door to human progress. As more intelligent computer assistance comes into being, it will amplify human progress.

I envy today’s young computer science and engineering students. I really do.

They have a wonderful opportunity to put their skills and expertise to use solving the world’s biggest problems. The amount of computing power available for their projects and the facility of the programming tools they can use far exceed anything we had. Today’s smartphone is many thousands of times faster than the CDC6400 students used back in 1972! And today’s computer programs are really only constrained by the user’s imagination—instead of by the small amounts of memory computers had back then.

A few examples of ambitious efforts today’s young innovators could pursue might be:

• Improving climate modeling in order to help us more deeply understand and simulate what is occurring now and in the future related to human-caused changes.
• Designing ever-more intelligent vehicles that make our roads safer by preventing accidents, reducing congestion and helping to reduce carbon emissions.
• Building computer programs that are capable of digesting text and understanding it, in the full sense of that word, to help researchers connect dots more quickly based on the latest published scholarship.
• Building models of biological systems from cells to the immune system that will give us deep insights into normal and disease states in the body.
• Advancing the state of robotics to create real helpmates for our aging populations and evolving workplace.

I envy today’s young computer science and engineering students. I really do. 

We truly are entering a golden age of innovation in computer science, with new techniques such as deep learning at our disposal, and collaboration opening up new ways to build innovative projects.

I look forward to watching the new Paul G. Allen School of Computer Science and Engineering continue to make profound contributions both to the field and to the world. I look ahead with anticipation to the advances that will continue to flow from the school—advances that I hope will drive technology forward and change the world for the better.

Written by

Paul G. AllenPhilanthropist and Entrepreneur

The 5 Jobs Robots will take last

Courtesy of Shelly Palmer 

Last week, I compiled a list of the 5 jobs robots will take first. Today, let’s have a go at the 5 jobs robots will take last. For this article only, let’s define “robots” as technologies, such as machine learning algorithms running on purpose-built computer platforms, that have been trained to perform tasks that currently require humans to perform.

Understanding How Humans Work

Almost every human job requires us to perform some combination of the following four basic types of tasks:

• Manual repetitive (predictable)
• Manual nonrepetitive (not predictable)
• Cognitive repetitive (predictable)
• Cognitive nonrepetitive (not predictable)

For example, an assembly line worker performs mostly manual repetitive tasks which, depending on complexity and a cost/benefit analysis, can be automated. A CEO of a major multinational conglomerate performs mostly cognitive nonrepetitive tasks which are much harder to automate. So, the trucking and taxi industries are in for a big shakeup; c-suite corporate management, not so much.

Thinking About the Future of Work

Make no mistake: at some level, every job can (and will) be done by machine. It is not a question of if; it is just a question of when. You’re going to push back now and tell me how different humans are from machines and how long it will actually take for all of this to happen. Stop. Read Can Machines Really Learn? for a primer in machine learning. Then read AlphaGo vs. You: Not a Fair Fight to understand what is happening and why you should care about it. If you’re still not convinced, have a look at What Will You Do After White-Collar Work?. It will help put all of this in perspective.

That said, there are some jobs that will be exceptionally difficult for AI to do subjectively better than humans. This is not an arbitrary list. Each of the following jobs requires a unique combination of human intuition, reasoning, empathy and emotion, which is why it will be difficult for an AI system to train for them.

As you will see, the last jobs that robots will take share a common thread: humanity.

1. Pre-school and Elementary School Teacher

Unless we are trying to turn our children into little computers, we cannot let computers train our children. (“Singularity” people, I know what you’re going to say. The Kurzweilian future is now estimated to begin in the year 2045. There will have to be a minimum age law associated with human/machine integration.) I can imagine a robot kneeling beside a sobbing five-year-old (who just figured out that his mom packed PB&J instead of a bologna sandwich) and offering comfort and a shoulder to cry on, but the robot is unlikely to provide an emotionally satisfying outcome. We teach our children to be human. If we want them to grow up to be human, they will have to be trained by their own kind.

2. Professional Athlete

Would football be interesting if it were played by robots? Maybe. Would it be fair to put human athletes on the field of play against robots? Probably not. Using today’s regulation clubs and balls, robot golfers would consistently shoot in the high 40s to low 50s. What’s the point? As long as humans strive for athletic excellence, humans will need to play sports. What about surgically enhanced, genetically modified athletes? That’s for another article.

3. Politician

Politics and humanity are inextricably linked. The complex mix of subtlety and nuance required to become a successful politician is not in the current purview of AI. It’s a training set that would require a level of general intelligence that is far beyond the reach of near-term technology. Machines do not need politics; they “live” in a meritocracy. Humans live in anything but. As long as fairness and equality are important topics, humans will be the only ones on the political scene. Some of you will remind me that all politicians have the same goal: to get reelected. And therefore, politicians should be very easy to program. Nope. Sadly, politicians will be among the very last professionals to lose their jobs to AI. (They are also in a unique position to legislate their own job security.)

4. Judge

Judges, adjudicators, arbitrators, and people who judge baking contests or Olympic sports or any type of contests that require both objective and subjective assessments have practically robot-proof jobs. Subjective judgment requires vast general knowledge. It also requires a thorough understanding of the ramifications of your decisions and, most importantly, a precise ability to play “I know, that you know, that I know” with the parties who are directly involved, as well as the public at large. If you can make a living judging baking contests, you’ve got lifetime job security (as long as you don’t eat too many pies).

5. Mental Health Professional

Psychologists, psychiatrists, and other mental health professionals will simply be the last jobs robots can take. Sure, we could do a combination natural language understanding, automatic speech recognition system tied to a competent AI system that would make a fine suicide prevention chatbot. But there’s much more to understanding and treating mental health issues. Again, humans are better equipped to understand other humans. This is not to say that medical professionals won’t leverage AI systems to do a better job, but the ability to create a robot that could take the job of a trusted psychiatrist will be outside of our technical reach until we have functioning WestWorld-style robots. And even then, it will be a reach.

Not on the List: Artist (Dancer, Painter, Musician, Singer)

I have intentionally left artist, writ large, off this list. The artist is a good subject for another article. Suffice it to say, technology has already had a huge impact on the economics of the arts. And, as much as I would like to tell you otherwise, none of these jobs are anywhere near safe.

What’s Next

If you’re wondering where your job sits on the list of “Run for your life, the robots are coming,” you have a simple, singular mission. Learn how your job is going to be automated. Learn everything you can about what your job will evolve into and become the very best man-machine partner you can. It’s the best way to prepare yourself for the advent of AI. Lastly, don’t wait. Everyone will tell you that none of this is happening anytime soon. They are flat wrong. But even if they are right, there’s no harm in being better prepared for an inevitable future.

About Shelly Palmer

Named one of LinkedIn’s Top 10 Voices in Technology, Shelly Palmer is CEO of The Palmer Group, a strategic advisory, technology solutions and business development practice focused at the nexus of media and marketing with a special emphasis on machine learning and data-driven decision-making. He is Fox 5 New York's on-air tech and digital media expert, writes a weekly column for AdAge, and is a regular commentator on CNBC and CNN. Follow @shellypalmer or visit shellypalmer.com or subscribe to our daily email http://ow.ly/WsHcb

AI still in its infancy

Courtesy of Medcity news http://medcitynews.com/2017/03/ai-still-pretty-dumb-like-2-year-old/?rf=1&utm_source=hs_email&utm_medium=email&utm_content=44005297&_hsenc=p2ANqtz--junqMKSxrbwb6pwZCd8dVV95nYARLYa0wZ3vWaSMz1Ul6VZAKFyjt2-zAbwDJESjAYdygiaK4QcQmqQDa1mb9q6m1iA&_hsmi=44005297

Last month, Mayo Clinic’s CIO gave the strongest endorsement so far of artificial intelligence technology at the annual HIMSS conference in Orlando, Florida.

Cris Ross along with Tuffia Haddad, a breast cancer oncologist, at Rochester, Minnesota institution, portrayed the tangible benefits of using artificial intelligence, specifically IBM Watson Health’s AI engine.

But make no mistake.

Ross wasn’t donning rose-tinted glasses as he reviewed this emerging technology that’s set to transform myriad industries, including healthcare.

“Artificial intelligence is still pretty dumb,” Ross declared before adding, “And I don’t mean that in a really derogatory way.”

What Ross meant is the current limits of AI.

He described IBM Watson Health “as some of the best computer science on the planet” but noted that AI is heavily dependent on mammoth amounts of data. Here’s how Ross captures the limitations of AI, adding that his view of the technology may result in “fist fights”: (slightly edited)

The best artificial intelligence today is still driven entirely by so-called semantic models, which is understanding language and the relatioship of words to each other and how they build up. So the only way that these things can work is by giving them mountains of data to plow through to try and get to statistically meaningful connections, which then can be leveraged to gain some other understanding.

So, this is like a 2-year-old child just learning to speak and to walk and how they interact with the world. When I put my hand on the stove, that’s not a good outcome. It’s not something immediately clear to a 2-year-old child.

What all this AI is lacking is an ontological model where you can describe a structure abstractly. Watson had no idea what a patient was, what a hospital is, what a doctor is, what a drug is, what the effect is on a patient, what’s the relationship between a doctor, drug, a patient and an outcome.

No clue, because with these technologies you can’t describe an abstract concept and have that abtract concept be applied….

But as long as we are still based on raw horse power semantic engine technology, it means that the only place where this technology is applicable is where there is sufficiently deep and rich data sets with enough narrow variations ….

Those narrow variations allow the technology to look for some correlations and then arrive at some knowledge, Ross explained.

James Rosen, senior managing director, PricewatrehouseCoopers’ analytics group, who was moderating the AI panel at HIMSS, chimed in that as AI is developed and perfected over time, interested stakeholders should keep an eye out for “deep learning.” Deep learning is a subset of machine learning where algorithms try to make sense or model abstract/thought through data. These algorithms are aimed to function as neural networks in the way a human brain does.

The IBM representative on the panel discussion at HIMSS did not resort to “fist fights” as Mayo’s Ross essentially described the best computer science on the planet as a thoughtless toddler.

“The technology isn’t the goal. The goal is the outcome, the health that we are all trying to move towards.” said Sean Hogan, VP, IBM Healthcare. “So, if Watson is still a toddler, a young infant even, glad that we’re choosing good parents or smart parents like Mayo and MD Anderson and some of the top institutions around the world and we are actively trying to learn from that experience.”

Photo: HKPNC, Getty Images

What are the 3 things you need to do to survive in the future work economy

Written by

Simon T. Bailey, MA, CSP, CPAEExecutive Advisor, Career Mentor, and Keynote Speaker

85% of your financial success is due to your personality and ability to communicate, negotiate,and lead. Shockingly, only 15% is due to technical knowledge.” -Carnegie Institute of Technology

1. Be an early adopter when it comes to technology. When I created the Building Business Relationshipscourse back in 2013, the boomers who were advising me said, “Don’t do it. And they said don’t do it because the young company, Lynda.com (and now #LinkedInLearning), would own the content into perpetuity. However, when I visited the facility, I saw 500 22-year-old young people running around there, and something in my gut said, “I’ve got to be a part of this.” I threw caution to the wind, said no to traditional advice, and said “yes” to what I felt in my gut. Today, Building Business Relationships has been viewed over 400,000 times by people in 100 countries.

While online learning and teleconferencing has taken over, virtual reality will soon be much more accessible and will again change the way that business is done. For example, a company called Doghead Simulations is racing to be the first to marry Virtual Reality and teleconferencing. Instead of GoToMeeting, you’ll be able to see an entire conference room as if you were really there. Soon, keynote speakers will be piped in using VR. Teachers will give lectures remotely. The way we communicate and build relationships will never be the same.

2. Discernment will be the most important soft skill. So many times, business is done in what I call the unspoken word; the intangibles; the ability to intuitively and instinctively evaluate what to do next. Business is not black and white--it is totally shades of grey.

For this reason, I have to agree with Mark Cuban when he suggests liberal arts degrees will be prioritized over technical degrees in the future work economy. His argument is that these degrees will cultivate the soft skills that cannot be replaced by technology.

The greatest soft skill to cultivate in the future work economy is discernment -- the ability to read a situation or person properly and then react or respond accordingly.

Individuals who have a high level of discernment can understand “big picture” thinking--being able to connect the dots all the way from how the top of the c-suite is thinking about the vision to what execution looks like when you go to market. Employees in the future work economy will be expected to go far above the technical skill they are hired to do and will need to demonstrate that they understand how all the moving pieces connect in order to succeed.

When you practice effect discernment, you are able to build fluid relationships and mobilize networks quickly to get things done. Fluid relationships means understanding that different people think, work, and respond differently. You’ll need to be flexible and adapt on a dime.

So how can you tell if discernment is in your toolbox?

●     Can you go and connect with a person based on where they are, how they think, and how they process?

●     Do you understand how to read and adjust based on different personality types?

●     Do you know how to figure out what you’re trying to accomplish and build a bridge from where you are and where you want to go to what others are trying to do for the greater good of the company, the business, or the cause?

3. Just-in-time learning will also be critically needed. You gotta learn on the fly. You’ve got to be able to say, “I learned this right now. I apply it right now.” It’s like microwave learning. This is a learn-as-you-go society, and it’s moving.

Recently, a construction company brought me in to speak to about 450 of their sales representatives who are not into fluff, but they know that they need to get sharper when it comes to customer experience. These were meat and potatoes guys who weren’t into pixie dust, but they wanted to know how to add soft skills into the construction space.

To understand the needs of my audience, I spent a day with one of their sales representatives in St. Petersburg, Florida. I learned about their industry by spending almost 8 hours learning how they go about doing bids, sitting in on meetings, and riding around in the truck meeting clients. In order to effectively teach to this audience, I had to learn their industry on the fly and turn around and apply that learning to create a product that worked for that company.

The future work economy will be an environment that rewards strategic thinkers who see into the future, are quick to mobilize, and know when and how to change direction. It’s time to quit your job and go to work. 

What are you doing to prepare for the new work economy? Join the conversation. #LinkedInLearning

The importance of play in innovation

 

Visit any major technology, creative or marketing office around the world right now and you will find something very different from the traditional executive environment of marble tables, wood paneling and immaculately pressed suits. You’re far more likely to find bright coloured furniture, a foosball table, some bean bags, and a slide. For the traditional executive, it may seem like you’ve entered a giant childcare facility or organised chaos, but these office environments have been quite deliberately designed.

You see, these companies recognise the profound importance of play.

The research is in. Play is essential for us all. From young children to adults, play is essential in developing creative thinking, as well as improving reasoning and problem solving skills.

Key in this research is the notion that imagination can help drive innovation. And in an age where innovation is the new competitive advantage, fostering imagination should be seen as a crucial aspect of learning and development. Psychologist, Dr Peter Gray said it best way back in 2008:

One of the main purposes of play in our species, I think, is to promote our use of imagination to solve problems. … Imagination provides the foundation for our inventiveness, our creativity, and our ability to plan for the future. ...When we allow ourselves to take a playful attitude ... we are providing ourselves with a context for solving problems that might otherwise be intractable.

The tools of play have changed, but the purpose of play has not. From collectibles to competitive video games, children can imagine storylines and seek out solutions to problems through the lens of the characters they adopt. This is really important: scenario mapping and imagining new powers or new ways of behaving is the very basis of innovation. So these instruments of play are a form of imagination seeding.

Scenario mapping and imagining new powers or new ways of behaving is the very basis of innovation

There is also growing evidence that play - at any age - is important to our happiness. Those businesses with the brightly coloured furniture and slides in the office also understand that to keep your workforce, you need to keep your team feeling good about their jobs and their workplaces. This seeding of playful environments impacts on both productivity and hcreative thinking, so it helps with staff retention and innovation, simultaneously. And it turns out that children’s happiness is influenced by their play environment, too. As the saying goes, money can’t buy happiness. Instead, for children, happiness comes from the chance to develop relationships, and to exercise imagination.

The media backlash against new forms of play (video games, or immersive experiences such as virtual reality storytelling) is as unwarranted as it has been disproven. There is ample evidence now that these technologies and experiences can have positive effects on children. In fact they are so widely accepted in universities, that they are supported as a sport.

What is useful is to ensure that time spent with these immersive experiences is supplemented by play with physical, tangible toys and equipment. 

Again, the evidence is that children who can project from their experiences in screen environments onto representative figurines, or into lived environments, are better able to solve problems than those who live entirely in the digital world.

We need to embrace the concept of play as the best chance we have as a society 

As marketers and parents, we need to take a step back from the tendency to assume that all toys and screen time are wasteful or dangerous to social development in children in particular. If these new instruments of play allow kids to think differently, take on a part as a character in a storyline, develop new relationships and find new ways to solve problems, they are actually helping their own social development.

We need to embrace the concept of play as the best chance we have as a society, to solve some of the biggest problems facing the world today. It may seem that a 'construction toy consisting of interlocking plastic building blocks' is a long way from a solution to climate change. But the kids who learn about the world from playing and thinking about how they can save the world, might just one day save our world.

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Anthony J James was awarded LinkedIn's 'Agency Publisher of the Year' for Asia Pacific. Thanks for reading. If you enjoyed this post maybe try one of these as well - see other posts.

Written by

Anthony J James - Innovation - Digital - Engagement LeadGlobal Head of #Digital, #Innovation, #Strategy and #Engagement, LinkedIn Agency Publisher of the Year - Asia Pacific

The importance of EQ and collaboration in innovation

Greg Satell

Posted on 23/02/2017 by Idea Spies

 

In the late 1960’s, Gary Starkweather had a serious spat with his boss.  As an engineer in Xerox’s long-range xerography unit, he saw that laser printing could be a huge business opportunity. His manager, however, was focused on improving the efficiency of the current product line, not looking to start another one.

The argument got so heated that Starkweather’s job came to be in jeopardy. Fortunately, his rabble rousing caught the attention of another division within the company, the Palo Alto Research Center (PARC) which wasn’t interested in efficiency, but inventing a new future and they eagerly welcomed Starkweather into their ranks.

Within a decade, Xerox’s copying business declined sharply, but the laser printer took off and soon became the firm’s main source of revenue. In effect, the work that was squelched in one culture, thrived in another and saved the company. We tend to think innovation is about ideas, but it depends on people even more. Here’s how you create an innovative culture.

1. A Focus On Problem Solving

When you think about an innovative culture what probably first comes to mind is a bunch of fast moving hipsters guzzling down energy drinks and pulling all-nighters, pausing only to play a quick game of foosball or frisbee. Or maybe Steve Jobs on stage with a devilish grin just before he wows the audience with “one more thing…”

Yet in researching my book, Mapping Innovation, I found that very few of the organizations I studied looked like that. Some were fast moving startups, but most of the successful ones were led by executives that were mature and thoughtful, not brash or erratic. Others were large corporations and world class labs that tended to be fairly conservative.

The one thing I found in common in every fantastically innovative place I looked at was a disciplined passion for identifying new problems. Unlike most organizations, which are content to struggle with everyday issues, the enterprises I studied had a systematic method of finding new problems to work on that would take them in new directions.

The approaches vary considerably. IBM creates grand challenges, like building a computer that can beat humans at Jeopardy. Experian set up a Datalabs division to find out what’s giving its customers “agita” and launch new business off the solutions they build. Google’s “20% time acts as a human-powered search engine for new problems.

We tend to think of innovation as fast moving, but the truth is that it usually takes 30 yearsto go from an initial discovery to a measurable impact. So the “next big thing” is usually about 29 years old. If you want to innovate effectively, don’t chase the latest trend, find a problem your customers will care about and solve it for them.

2. Create Safe Spaces

In 2012, Google embarked on an enormous research project. Code-named “Project Aristotle,” the aim was to see what made successful teams tick. They combed through every conceivable aspect of how teams worked together — how they were led, how frequently they met outside of work, the personality types of the team members — no stone was left unturned.

However, despite Google’s nearly unparalleled ability to find patterns in complex data, none of the conventional criteria seemed to predict performance. In fact, what they found mattered most to team performance was psychological safety, or the ability of each team member to be able to give voice to their ideas without fear of reprisal or rebuke.

Interestingly,  highly innovative teams can be safe for some ideas, but not for others. For example, two of the scientists at PARC, Dick Shoup and Alvy Ray Smith, developed on a revolutionary new graphics technology called SuperPaint. Unfortunately, it didn’t fit in with the PARC’s vision of personal computing, the two were ostracized and eventually both left.

Smith would team up with another graphics pioneer, Ed Catmull, at the New York Institute of Technology. Later they joined George Lucas, who saw the potential for computer graphics to create a new paradigm for special effects. Eventually, the operation was spun out and bought by Steve Jobs. That company, Pixar, was sold to Disney in 2006 for $7.4 billion.

Xerox PARC is now a shadow of its former self. As it turned out, anything that didn’t have to do with the researchers’ vision for the future had no home there. So if you want to innovate consistently for the long term, you need to create a “safe space” for all ideas, not just the ones that fit with your initial mission.

3. Foster Informal Networks

In 2005, a team of researchers decided to study why some Broadway plays become hits and others flop. They looked at all the usual factors, such as production budget, marketing budget and the track record of the director, but what they found was that what was most important was informal networks of relationships among the cast and crew.

If no one had ever worked together before, both financial and creative results tended to be poor. However, if the networks among the cast and crew became too dense, performance also suffered. It was the teams that had elements of both — strong ties and new blood — that had the greatest success.

The same effect has been found elsewhere. In studies of star engineers at Bell Labs, the German automotive industry and currency traders it has been shown that tightly clustered groups, combined with long range “weak ties” that allow information to flow freely among disparate clusters of activity results in better innovation.

So before you embark on your next reorganization designed to “break down silos” you might want to think about how informal relationships develop within your enterprise. The truth is that innovation is never about nodes. It’s always about networks.

4. Promote Collaboration

All too often, we think of innovation as the work of lone geniuses who, in a flash of inspiration, arrive at a eureka moment. Yet the truth is that research shows that the high value work is done in teams, those teams are increasing in size, are far more interdisciplinary than in the past and the work is done at greater distances.

Just as importantly, there is growing evidence that it is crucial how these teams function. A study done by the CIA performed after 9/11 to determine what attributes made for the most effective analyst teams found that what made teams successful was not the attributes of their members, or even the coaching they got from their leaders, but the interactions within the team itself.

In another, more wide ranging study, scientists at MIT and Carnegie Mellon found that high performing teams are made up with people who have high social sensitivity, take turns when speaking and, surprisingly, the number of women in the group. There is also a wealth of research that shows diverse teams outperform more homogenous units.

So the evidence is both abundant and clear, if you want to make your organization more innovative, don’t go searching for hard driving “A” personalities spouting off big ideas and interrupting others, but rather seek diversity, empathy and to network your organization so that teams interact more effectively.

As MIT’s Sandy Pentland has put it, “We teach people that everything that matters happens between your ears, when in fact it actually happens between people.”

by Greg Satell

Innovation Advisor, Author and Speaker

 DigitalTonto

An earlier version of this article first appeared in Inc.com

Google has shipped 10M Cardboard VR viewers, 160M Cardboard app downloads

BY INGRID LUNDEN

Feb 28, 2017

Google is putting a lot of its virtual reality focus right now on its Daydream VR framework, but today Amit Singh, the company’s VP of VR, also gave an update on its earlier (and still active) effort in the area, Google Cardboard. He said that to date the company has shipped 10 million Cardboard VR sets, and it has seen 160 million downloads of Cardboard apps, with 30 individual Cardboard apps downloaded at least 1 million times each.

The comments were made on stage at Mobile World Congress at Barcelona, where Singh is speaking today (see picture above).

It’s an interesting milestone and shows that even while Google is pushing the next iteration of its VR efforts with Daydream to hit a wider range of devices and users — Daydream-compatible mobile handsets can be turned into VR headsets (when inserted into an accessory to mount it on your head) — the very pared-down, free first version Cardboard continues to show momentum. It was only in July of last year that Google said it had hit 5 million headsetsshipped since it first launched in 2014.

He also gave an update on Daydream, which Singh described as a “more immersive” experience than Cardboard and the result of what Google has learned from the first product. He said people using Daydream-ready phones/headsets are watching about 40 minutes per week. There are now six phones and 100 Daydream apps to explore on the market, he noted.

Well over 50% of all content consumption on Daydream is of YouTube content, Singh said, and it’s going to put more focus now on providing more premium (professional not necessarily paid) content on the platform in the form of series.

“You will start to see significant series coming out this year,” Singh said. He said that there have been over 1 million views of an NFL series in the U.S. Now in Europe Google has partnered with Sky VR, he announced today, a VR initiative from Europe’s dominant pay-TV provider.

“Sky VR is coming to Daydream,” he said. Initial content will include both primary films and original series, as well as supplementary programs, Singh noted. Some will be a red carpet show for Star Wars, clips from the Jungle Book, and Sky Sports experiences with personalities like David Beckham and sports like Formula One. Other premium content partners on the Daydream platform include Hulu, Netflix and HBO.

In a separate blog post published after Singh’s stage appearance, Google also noted some new content for Tango, Google’s augmented reality platform. The Sims app now will let you travel around the Sims house; the Chelsea Kicker app will make a Chelsea football player appear in front of you for a selfie or to show you a trick with the ball; and (perhaps less exciting and more Minority Report) an AR app from the WSJ will let you visualise stock trends in midair.

I’ll be speaking to Singh later today and will update this post with more of his thoughts.

Updated with AR detail from blog post made public after initial story was published.