Strategy. Innovation. Brand.
Is The Internet Of Things A Giant Brain?
Would you swallow it?
In The Social Animal, David Brooks writes that, “We are living in the middle of a revolution in consciousness. Over the past few years, geneticists, neuroscientists … and others have made great strides in understanding the building blocks of human flourishing. And a core finding … is that we are not primarily the products of our conscious thinking. We are primarily the products of thinking that happens below the level of awareness.”
Further, “If the conscious mind is like a general atop a platform who sees the world from a distance and analyzes things linearly and linguistically, the unconscious mind is like a million little scouts. The scouts careen across the landscape, sending back a constant flow of signals and generating instant responses.”
Now here’s how Tim Simonite, writing in Technology Review, describes a two-millimeter square computer (pictured): “If the Internet is to reach everywhere – from the pills you swallow to the shoes on your feet – the computers will need to get a whole lot smaller. A new microchip that is two millimeters square and contains almost all the components of a tiny functioning computer is a promising start.”
Simonite describes the technical challenges of creating a swallowable computer the size of an ant. I won’t try to summarize the technical issues. For me, the bigger issue is simple: don’t swallowable, ant-sized computers mimic a “million little scouts?” Are we building a giant brain here? If so, what are we going to do with it? Does the NSA get to run it?
Failed Business Fads: Top 10
Lucy Kellaway has an excellent column in the Financial Times in which she identifies the top 10 failed management fads. (Click here). Somehow the article reminded me of fashion fads, like Nehru jackets and leisure suits, that have come and gone. I sometimes cringe when I see old pictures of myself.
I had to smirk at several of the management fads. I tried not to be my snarky, know-it-all self when the fads were in fashion but somehow I knew that they would never work. Now I have the warm satisfaction of knowing I was right all along.
There were, however, several fads that I actually believed in. In fact, I still do. So I’m distressed that Kellaway has declared them failures and asked us to bid them adieu.
I’m not tipping my hand because I’m interested in your opinions. Which of the fads have you experienced personally? Did any of them do any good at all? Is Kellaway right — are they all dead or do some of them still have legs?
Just leave your thoughts in the comments box below. In the meantime, I’ll be managing my employees by walking around them (or should I be walking them around?)
Jobs We Won’t Need
Sorry. Your skills are no longer required.
Last week, I asked, Should We Work? and discussed the issues of job elimination over the next 50 years. Here’s a terrific article from Technology Review about how the process works. And, here are my fearless predictions of just some of the jobs that will disappear over the next 50 years. I’ve also included my predictions for the types of jobs that we will still need. It’s not a long list.
Drivers – autonomous vehicles will replace cab drivers, truck drivers, and bus drivers. Tell the vehicle where you want it go and it will take you there.
Surgeons – if a robot can make micro-welds in the factory, it can also replace your heart valve. A robot’s hands don’t shake.
Computer programmers – computers can program computers faster than people can.
Doctors – computers can diagnose what’s wrong with you better than humans can. Humans might have a better bedside manner, but avatars accompanied by a cute puppy are close… and they’re always on call.
Pilots – drones can do a better job, especially in fighter jets. The weak link in military aircraft is the human. Without a human, we can build faster, more maneuverable, and much, much cheaper warplanes.
College professors – MOOCs will rule.
Room service staff – why order in when you can order out?
Accountants – accountants interpret rules and enter data. Computers can do that.
Stockbrokers – computers already account for the bulk of stock trading. In the future, you won’t invest in stocks; you’ll invest in the algorithms that you think can pick the best stocks.
Engineers – most engineers solve structured problems. So do computers.
Politicians – computers can find optimal solutions to problems better than perpetually outraged people.
Many of our white-collar jobs today require people to manipulate symbols and process information. For instance a doctor who is trying to diagnose what ails you needs to interpret lab results, recall symptoms of many possible diseases, fight off fatigue, and evade logic traps. Well-trained computers can do this better.
So what kinds of jobs will be left? I can think of two general categories:
Persuasion – I’m not sure that we can train computers to be persuasive. Being persuasive requires an emotional connection and a degree of trust. Can you trust a computer? Perhaps. Still, I think people will be more persuasive, though maybe only marginally so.
Imagination – Can we teach computers how to imagine? Perhaps. After all, innovation typically results from mashing up existing ideas. A computer could mash up ideas. But it would be fairly random; I don’t think a computer would really understand the possibilities. So, humans should retain a competitive edge in tasks that require imagination.
I’m guessing that the ability to manipulate symbols and process information won’t be enough to get you a job in 2063. You’ll need to be imaginative and persuasive. Is that what we’re teaching in schools today?
An Evolutionary View of Innovation
My sister has a Ph.D. in biology. For her dissertation, she randomly divided fruit flies into two groups and treated them exactly the same except for one variable. She introduced a specific chemical to one group but not the other. Then she followed the effects through multiple generations. I don’t remember what she discovered but her method allowed her to conclusively link cause to effect.
Why did she choose fruit flies? Because she wanted to look at the effects of the chemical over multiple generations and fruit flies create generations quickly. She wanted to know not just how the chemical affected fruit flies. She wanted to know how the chemical affected the evolution of fruit flies.
Could we use evolutionary thinking to solve business problems in innovative ways? Well, there’s a theory that we could develop software more quickly and at less expense through evolutionary techniques.
First we identify a problem that we want software to solve. Then we create, say 10,000 identical sets of code. We introduce random variations into each set, execute the code, and then determine which set comes closest to solving the problem. We take the winner, make 10,000 copies, introduce random variations into each one, then execute the code. We pick the winner and repeat the process. It’s like breeding dogs, only less messy.
With modern computing power, we can generate thousands of generations in very short order. We could almost certainly solve the problem. Additionally, we might generate some very novel solutions. The random variation might lead us down paths that we never would have imagined on our own.
While I suspect we’ll make evolutionary software before long, it does seem a bit exotic. Are there ways we could apply evolutionary thinking to solve more practical, day-to-day problems?
Sometimes I think it’s as simple as asking the question. Too often we make yes/no, either/or decisions – whether-or-not decisions as Chip Heath calls them. But we can always ask the question, is there an evolutionary way of looking at the problem? We might find that there are multiple sub-decisions we could make along the way to the big decision. We can decide smaller issues, test the results, and repeat the process. Each time we do, we create a new generation.
A “generation” in this sense might be a set of market trials, a series of studies, or surveys, or focus groups, or trial balloons. We can find many ways to identify and/or validate market needs. But first we have to ask the question. So the next time you participate in a big decision – especially a big risky decision – be sure to ask yourself, could an evolutionary approach help us here? The answer may be no, but don’t close the door too soon.
Appreciative Inquiry
When I encountered a problem as a manager, my natural inclination was to delve into it with sharply defined questions like:
- What went wrong?
- How did we get here?
- How did this happen?
- Who was responsible?
- What was the root cause?
The first thing you’ll notice about these questions is that they’re all in the past tense. As we know from studying rhetoric, arguments in the past tense are about laying blame, not about finding solutions. The very way that I phrase my questions lets people know that I’m seeking someone to blame. What’s the natural reaction? People become defensive and bury the evidence.
The second thing you’ll notice is that all my questions are negative. The questions presuppose that nothing good happened. I don’t ask about what went right. I’m just not thinking about it. And neither is anyone else who hears my questions.
In many situations, however, a lot of things do go right. In fact, I would guess that in most organizations most things go right most of the time. Failures are caused by a few things going wrong. It’s rarely the case that everything goes wrong. Focusing on what’s wrong narrows our vision to a small slice of the activity. We don’t see the big picture. It’s self-defeating.
So, I’ve been looking for a systematic way to focus on the positive even when negative things happen. I think I may have found a solution in something called appreciative inquiry or AI.
According to Wikipedia, appreciative inquiry “is based on the assumption that the questions we ask will tend to focus our attention in a particular direction.” Instead of focusing on deficiencies, AI “starts with the belief that every organization, and every person in that organization, has positive aspects that can be built upon.” AI argues that, when people “in an organization are motivated to understand and value the most favorable features of its culture, [the organization] can make rapid improvements”.
The AI model includes four major steps:
- Discover – identify processes and cultural features that work well;
- Dream – envision processes that would work well in the future;
- Design – develop process that would work well in the culture;
- Deliver – execute the proposed designs.
The ultimate goal is to “build organizations around what works, rather than trying to fix what doesn’t”.
Paul Nutt compares appreciative inquiry to solving a mystery. To get to the bottom of a mystery, we need to know about everything that went on, not just those things that went wrong. Nutt writes that, “A mystery calls for appreciative inquiry, in which skillful questioning is used to get to the bottom of things.”
I’m still learning about appreciative inquiry (and about most everything else) and I’m sure that I’ll write more about it in the future. In the meantime, if you have examples of appreciative inquiry used in an organization, please let me know.
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