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Why did you decide to go into this bar and not that bar?

On this question may depend the future of AI, and perhaps humanity. By posing it, AI researchers shift artificial intelligence from the source of convenient robot helpers to the thing that unlocks humans’ limitless potential.

Or so we thought after our conversation with lead researcher Jeff Clune of the Evolving AI Lab. We sought out an unorthodox AI pioneer, and Clune did not disappoint. He’s at the leading edge of his field if you go by such things as his output of published papers. We don’t go by that, though. Clune stands out because he is trying to teach robots not so much consciousness as sub-consciousness; and he wants to teach that sub-consciousness to evolve, just like ours did.

Of course, our choice of what bar to go into typically, at least for most people, emerges from the subconscious. Clune points out it’s hard to say afterwards why we chose one after the other. Why is this so important for AI?

Well, if Clune has his way, the evolution of the robot mind will eventually produce a robot subconscious: they will interact socially, away from us; they will desire to play; they will be curious, and generate art, and solve complex problems the way we do. Not so much through rational heavy-lifting, but through that spark of insight, the one they have in the robot-shower, that tells them the answer lies in one direction and not the other. Robots will have serendipity and produce novelty.

For humans, novelty is way more important than efficiency. In the future, our milk carton will order more milk when it’s empty, and have it delivered. That most certainly will not change humanity. What will is robots that ask different questions than we do; robots that surprise us with their creativity and spark; robots that help us see the world in a completely different way. Those are the robots Clune is creating the foundation for. We were fascinated to hear how an AI researcher may influence the course of our future. We think you will too.

To learn how Dialog can help your business, contact us at 512.697.9425 or LetsChat@DialogGroup.com.

This article was originally published in design4emergence, a network science community sponsored and nurtured by Dialog and Panarchy, the world’s first network design firm. 

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We are all to varying degrees prisoners of our metaphors. This is true in both our heads and when we communicate with others. And it’s definitely true in business. Depending on your industry, any number of metaphors can guide the acquisition of new capabilities. However, there is one global metaphor shift that will help you and your organization succeed in the Network Age, and that is shifting from a mechanical to an organic worldview – from a view of your business as a machine to an actual living ecosystem.

In the last few decades, the predominant metaphor has shifted in nearly every one of the sciences from mechanistic to organic. Some scientists have even posited that the universe itself may essentially be a living, complex organism. So it’s just time business updated its metaphor as well.

Machine Management

The machine metaphor is ubiquitous. From the dawn of the Industrial Revolution, we have used metaphors about machines to communicate about work. When things are running smoothly we say they are “humming along” or “it’s well oiled”. Likewise, if we encounter a problem that needs to be fixed, we simply “re-engineer” the machine.

Frederick Taylor, the pioneer of industrial age management, used his stopwatch to measure the motions of people as parts in a machine. This was the machinery of the Industrial Age, and make no bones about it, this reductionist approach yielded unprecedented gains in productivity and material wealth.

It seemed rational to treat people like parts in a machine when the way that you unleashed productivity was by putting people into factories. Those factories required huge outlays in capital. It therefore made sense to make decisions around the binding constraint on growth – capital itself. Measures like return on equity and the DuPont formula arose to facilitate rationing of capital and ensure its application to highest return opportunities.

Is ROE Capitalism’s “Runaway”?

The cornerstone of the Industrial Age business measurement system was ROE. Several years ago the Harvard Business Review published an article entitled Runaway Capitalism. A “runaway” in evolutionary terms is when natural selection and sexual selection become decoupled, as in the case of the peacock’s tail. The tail offers such evolutionary disadvantage that peacocks would be extinct were it not for the fact that humans like to collect them. Yet peahens are drawn to the tail, so it continues to be selected for by the species even though the natural environment does not support it. The author’s point was that ROE may be capitalism’s runaway. And the implication is that it may threaten the continued survival of the planet and the species.

The Challenges of a Machine Metaphor

If the above is true, then perhaps the biggest challenge presented by machine management is the belief that everything should myopically serve the machine. And that one number, ROE, represents “the One Ring to rule them all” in the machine metaphor.

Other challenges with the machine metaphor include the fact that it stifles initiative. Henry Ford captures the essence of the machine mentality when he asks, “Why is that when I inquire for a pair of hands, they come with a brain attached?” A business that is run solely as a machine is not adaptive. It is too cumbersome and slow and fails to consider externalities. It isn’t designed for emergence. Or as Carolyn Hendrickson, a Ph.D. in organization design quipped, “Why don’t matrix organizations work? Because the mind that designs the matrix is not the mind that inhabits it.”

One of the challenges of a machine metaphor is that we tend to apply it to ourselves when increasingly network scientists are showing us that we are ourselves the products of the networks we engage in.

Despite these problems with machine view, there are new perspectives that can include it in a new larger whole – that of the complex ecosystem.

The Power of a New Metaphor: The Complex Ecosystem
Modern organizations are composed of complex living systems or networks. The metaphor of an ecosystem implies we are part of a community of living organisms, intertwined with nonliving components like technology, all interacting as a system.

This boundless system goes far beyond the physical boundaries of the firm and includes psychosocial characteristics as well as the material elements of our supply chain. It also includes the emergent outcomes of local agents acting upon very simple rules (which is why a rigid three-year plan won’t work). In order to compete and stay relevant, we need to stop managing what we think of as static machines and start nurturing our boundless dynamic ecosystems. Some principles for a networked ecosystem design could include the following:

1.Think like a Gardener: assemble, shape, influence, enable and nurture.

Recognize that some problems like the “diabesity” crisis cannot be directly solved. For certain classes of problems what scientist call “wicked problems” we must focus on building capabilities for solutions rather than solutions themselves.

2. Start with Purpose. Purpose has been proven to be the factor that enables firms to outperform their contemporaries over extended periods of time. Purpose is the first step in our “strength from the inside out” methodology of ecosystem design and orchestration. Purpose gives a network energy, and it is a natural north star. The HP Way offers a classic example of purpose as an ecosystem design principle. It is as much a set of values that provides the basis for how people in the ecosystem will treat each other as it is an explicit statement of what the ecosystem is designed to accomplish. In complex networks, it is simple rules acted on at the local level that create the network experience. In a business ecosystem, these values act as simple rules that guide the behaviors of decision-makers locally and empower change at the network level. Strategy in the network age is about communication flows and the incentives and relationships driving them. In times of change and uncertainty, values and purpose can provide the DNA for a network structure by enabling communication flows and aligning intellectual, human and social capital.

3. Design for the Whole. Tim Cook tells the story of coming to Apple for the opportunity to work at the seams between hardware, software and communications because in his words “that’s where the magic lies”, Leaders who are network designers and ecosystem enablers know that the value you capture cannot exceed the value your ecosystem creates. In this sense, Michael Porter was wrong: There aren’t five forces, only one. In nature, waste equals food. Likewise in business, in the strongest ecosystems the parts feed each other. Disney’s ecosystem is a great example of a synergistic or integrated business ecosystem in which one innovation feeds the others. When a new Pirates of the Caribbean comes out, for example, the studios make money, the theme park gets a new ride, the franchises sell toys, the brand sells licensing agreements, and each of these entities benefits because the network is designed so that novelty within one part brings activity to the others.   In working with clients across industries, we have found there is opportunity to have outsized share of voice and corresponding share gains by helping to solving larger pressing ecosystem problems even if you are not an ecosystem creator like Apple or Disney.  On a more modest scale, taking the bigger view allows one to mobilize resources from complementary providers and other stakeholders.

4. Consider the Parts when Designing the Whole. It is widely known that businesses increasingly win on experience. Increasingly, companies compete by creating platforms upon which customers co-create and it is therefore nearly impossible to separate the network experience from the user experience. It is also the case that we are increasingly a cyborg network now.  The best chess teams in the world are currently centaurs- half man half machine.  How are you using to technology to augment your team’s abilities?  And do you understand all the facets of your network experience?

5. Make your People Network Designers. Empower everyone at your company to understand and shape network behaviors—in short, to design for emergence, because as Churchill said, “We shape our Buildings initially. Thereafter, they shape us.” The same is true of our social structures.

To learn how Dialog can help your business, contact us at 512.697.9425 or LetsChat@DialogGroup.com.

This article was originally published in design4emergence, a network science community sponsored and nurtured by Dialog and Panarchy, the world’s first network design firm. 

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“If your objective was to invent a microwave oven, you would not be working on radars.”

These days, amidst a great collective effort to reverse engineer innovation, everybody’s looking to model the success stories. Tales of disruption pepper our social media feeds, and we want the magic formula—the algorithm—for innovation.

While magic is tricky, success is even more deceptive. That’s because our measure of success, the objective, is “blind to the true stepping stones that must be crossed.” These are the words of Joel Lehman and Kenneth Stanley, the inventors of a breakthrough evolutionary algorithm for robotic neural nets, called novelty search.

What do robot brains and algorithms have to do with our current paradigm of innovation?

At the Evolutionary Complexity Research Group (EPlex) at the University of Central Florida, Lehman and Stanley programmed their AI to abandon their objectives and search for novelty, much like nature’s evolutionary “algorithm.” “Do something you’ve never done before,” they told the robots. They put them in a maze. Guess what? The robots with the novelty search algorithm got out of the maze faster than the ones armed with a plan and a list of best practices. In other words, objectives actually hindered their search. Freed from them, they stopped banging into walls and learned to walk. Are we so different?

Disruption and adaptation ensure the survival of a species, a business, or any agent in a complex system. A network takes in diversity and puts out emergence (the real hero of anyone’s innovation story).

Case in point: two artificial intelligence researchers who use evolution to program artificial neural networks that “learn,” and end up writing a book about Why Greatness Cannot Be Planned. Are we approaching innovation all wrong by holding it against too rigid standards?

So if you want to design for emergence, the scientists in our interview say, the name of the game is to be a treasure hunter. The path isn’t always clear until it’s behind you. Go where curiosity leads you in search of novelty, whatever seems interesting, and you’ll begin to collect the right “stepping stones” for that next big thing…

d4e: Ken Stanley and Joel Lehman, two AI scientists, you wrote a book about Why Greatness Cannot Be Planned. How did that happen? (I’m guessing that wasn’t the plan.)

Ken: There are a ton of self-help books about how to pursue greatness and achieve your potential. A lot of it is speculative and philosophical. What’s unique about our perspective is that we’re offering hardcore scientific empirical research and experimentation that supports the approach that we’re advancing in the book. So people reading this book looking at these ideas can feel a certain level of confidence that they don’t normally feel about where these ideas come from: We weren’t trying to become self-help gurus; we were doing experiments in artificial intelligence. We unexpectedly stumbled on the principles we describe in this book about why greatness cannot be planned.

d4e: The Chinese finger trap is a metaphor for innovation. Why?

Joel: In the Chinese finger trap, the steps that you need to take to solve the problem are exactly the ones you wouldn’t expect would lead to the solution. It’s a model of deception in innovation, in that making a breakthrough discovery often involves taking steps that are seemingly unrelated to the objective.

Ken: It’s the simplest example of this type of innovation process which we’re claiming is very common, where what you need to do looks like it’s exactly the opposite of what you want. It turns out you need to do exactly the opposite of what you think you should. The Chinese finger trap is designed to be deceptive in that way.

You have to push yourself more into the trap to get out of it. The problems of life are far more complex than that, though, so they’re going to be even worse than a Chinese finger trap in terms of being deceptive. If they weren’t, we would just solve all of them. In order to escape the Chinese finger traps of the world, we have to sometimes be willing to step into the unknown rather than go in the direction that’s obvious or “correct.”

d4e: Great invention is defined by the realization that its prerequisites are in place. Apple spends much less than its competitors on R&D. Do you think that those two ideas are related?

We could speculate that people put a lot of effort into pursuing an objective, and that can be very expensive, because maybe the right stepping stones just haven’t been laid. So you’re going to be grinding for a long time to create all the prerequisites you need to get this thing to work. Whereas if you take an unusual approach (and I would be willing to bet that Steve Jobs wasn’t very objective-driven) where you don’t follow an objective path, you can sometimes arrive somewhere interesting and valuable with a lot less effort than someone who is following an objective. People like Steve Jobs seem to have a knack for following those types of trails and taking the kinds of risks that are necessary, and saying, “Let’s just see where this leads.”

d4e: How did an algorithm change your life? Was it a eureka moment, or a slower evolution?

Ken: This question gets to the origins of the idea behind novelty search. There was actually a particular eureka moment before this algorithm that led to the novelty search algorithm, but also later there was the gradual dawning for both Joel and I, that the algorithm is really a way of thinking about life.

Before novelty search, there was an algorithm called Picbreeder, which is a website that we put up in our research group for people to come from the internet to breed pictures, and then publish them on the site. That sounds a little strange, but basically it means that you could come in and pick your favorite picture from a set, and it would have offspring. And the picture’s “children” would be slightly different from their parents — just like if you had children, they wouldn’t be exactly the same as you, but not completely different either.

These experiments exposed a flaw in the paradigm of “innovation through continual improvement.”

I had an experience playing with Picbreeder, where I started with an image that looked like an alien face. I was playing with the image, and it eventually bred into a car. This moment when the alien face turned into a car was the epiphany moment when I was struck with the realization that I had achieved something interesting without trying to achieve it. While it may sound trivial — after all, Picbreeder is just a toy — everything I’ve been taught for years in computer science said that the way you make computers do things — in fact the way we as humans generally do things — is to set your goals and somehow help the algorithm push the computer into the direction of achieving that goal. But this experience was so different than that.

I was breeding these pictures myself, but we have evolutionary algorithms that breed automatically as well, without human assistance. So I realized that this experience of achieving something without trying to achieve it probably has implications far beyond a picture breeding service. This led to the proposition that there could be an algorithm that doesn’t have a clear objective.

This is what I began to speak to Joel about before the novelty search algorithm was created.

d4e: So the idea of discovery without objectives led you and Joel to create the novelty search algorithm. You say that novelty search is paradoxical. How so?

Ken: The novelty search algorithm reflects the philosophy that sometimes you can discover things if you’re not looking for them. It gives the computer the ability to have serendipitous discovery but not necessarily be pigeonholed in the direction of trying to search for one thing and one thing only, or create one type of solution to a problem. Instead of a robot that has one type of walking gait, for example, maybe you have many.

We were playing with this for years, and it would constantly surprise us by doing things that people wouldn’t expect. You don’t tell the computer what to do, but it ends up solving your problem better than if you did. We saw this paradox over and over again. After a few years we realized that we were seeing was about more than a computer search algorithm.

The more I spoke about the algorithm at computer conferences, the more people would ask about things unrelated to computers, such as: What does it mean for my life if sometimes the best way to find something is to be not looking for it? Does this have any broader implications for how we run innovative cultural institutions? Or how we run science?

Or how about the way we support innovation in society?

It became apparent then that it is extremely important that we have this discussion as a society. If objectives are not always the way to guide innovation and scientific progress, then why is it that almost everything we do is objective-driven? That’s when we decided to write a book, because this kind of message is hard to get out in a computer science journal article aimed only at artificial intelligence. This is a much broader issue, in terms of how we foster innovation and treat objectives in our culture.

d4e: In your book, you ask us to imagine a cavernous warehouse of all possible discoveries. You say that “the structure of the search space is just plain weird.” Can you tell us what you mean by that?

Joel: The structure of the innovation space is weird in that it’s hard to predict where certain things will be. The linkages between different kinds of innovations are surprising. That relates to the broader area of serendipity in science or artistic realms, where you might inadvertently create the next big thing. A typical example is the vacuum tube, which was created as part of fundamental research into electricity. The person who was exploring that didn’t have the idea of a computer in mind. It just turned out that from this one point in space, from discovering a vacuum tube, you actually could reach computation.

Ken: Vacuum tubes facilitate computers, and that’s a connection that exists in this big “room” of possible things. But who would ever know that? Somebody later picked up on it and said, “Now that this exists, now we can create this other thing.” There’s a lot of opportunity there for serendipity, in the sense that you wouldn’t even be working on vacuum tubes if your main interest was computation. Vacuum tubes don’t look like they have anything to do with computation. So in some way, to get all this stuff to exist, requires that people sometimes are not working intentionally on the ultimate achievement that stems from the effort that was put into this chain of events.

d4e: Order is important in search. How so?

Ken: When you first hear about novelty search, that we should search for things that are recognized for their novelty and ignore everything else, our intuition might say, “This is just random. How can that kind of search be beneficial?” I think people assume there’s some kind of coherent order that search induces. In other words, we assume that things get better as you continue to improve. That’s an order that we’ve come to expect from an objective — like if you’re trying to get better at school, your test scores will go up. We expect to start out low and get higher, and that’s the kind of order we’re comfortable with.

Whereas with novelty, it’s harder for us to think about what the order of occurrence is going to be, because we’re no longer talking about an objective metric. What we try to argue is that there is an order that’s inherent in a search for novelty — it’s just a different kind of order, one of increasing complexity.

Instead of increasing quality along some objective metric, novelty search basically creates a situation where if you continually try to do something new, you will quickly exhaust all the simple things there are to do. There are only so many simple ways to do things. By necessity, if you succeed in continually seeking novelty, things will have to become more complex over time.

When it comes to innovation, maybe we should loosen the reins just a bit and integrate some of the knowledge that we’re gaining in our scientific understanding of natural evolution.

At some point, somebody invented a wheel. Thousands of years later, someone was on the moon. Things don’t go in the other order. You don’t figure out how to go to the moon and then later come up with the wheel. So there is an order in innovative processes that are driven by invention rather than by trying to achieve a specific objective metric. And that order tends to be the increasing complexity. The reason I bring this up is that there’s good reason here to be confident that the search for novelty does have some kind of coherent principle, and it is anything but random. It’s just that it’s not following the order that we’re used to (of “worse to better”).

We wanted to suggest to our readers that going worse to better is actually not that principled, even if it makes you feel comfortable, because of the fact that it’s a mystery how to do it. We don’t necessarily know what the stepping stones are. So it’s really just a security blanket to say, “I’m going to keep on improving” if you don’t necessarily know how that’s going to happen.

d4e: The age of best practices is over. Would you agree with that?

Ken: There is room, despite everything we’ve said, for trying to improve. But we have to be clear about where that process is appropriate. If your aims are relatively modest, it can be entirely appropriate to just try to improve. If you just want to try to improve your lap time, that’s reasonable. But when it comes to fostering innovation on a larger scale, I’d be ok with endorsing the idea that the age is over, because we should have a revelation that simply trying to continually improve in an objective sense just doesn’t work.

There’s a great opportunity for a paradigm shift here. The amount of information we have now from artificial intelligence is starting to expose problems with the traditional view of achievement and innovation. Our book exists because we had the ability to do experiments that would have been impossible in the past. These experiments exposed a flaw in the paradigm of “innovation through continual improvement.”

Joel: And yet it seems that at the same time, the cultural crest is pushing more toward the paradigm of objectives and continual improvement. We have evidence that this isn’t how the world really works, especially in areas of innovation, discovery and creativity. It’s troubling that so many innovation endeavors are still ruled by objective-based approaches. When it comes to innovation, maybe we should loosen the reins just a bit and integrate some of the knowledge that we’re gaining in our scientific understanding of natural evolution, and how creativity works — and some of these insights come from artificial intelligence.

Ken: There should be a paradigm shift, but we wrote the book because there hasn’t been. This is a current argument about how we should approach innovation. When Joel says we run a lot of things in this very objective-driven way, that’s literally true. Look at what we’re doing in schools. The standardized testing craze is all about objective measurement, and it’s used for all kinds of things, not just for students. We basically say the school has to objectively improve on some metric, or the school gets penalized. It’s all based on objectives, and there’s a lot of discussion about whether that’s a good idea or not, but we’re not part of that debate explicitly.

Our work offers a different angle, which says that if you kept demanding higher scores, eventually everyone would get a 100. That looks like a pretty naive approach. There should be room for people to try new things — and that could lead to scores going down from time to time. If you always penalize for scores going down, then none of those things become possible.

In the world of science funding, one of the things you almost have to do to get money for research is to state your objectives. We’re running our entire federally funded scientific enterprise — really, billions of dollars — based almost entirely on objectives. You can hardly get your word in if you don’t state in the beginning what you’re trying to achieve. It’s not common sense; it’s a problem.

d4e: There’s a book called Why A Students Work for C Students. How does that relate to this philosophy?

Ken: I haven’t read that book, and I think it’s obvious that that’s not always the case — there are plenty of A students who are the bosses of C students. But that’s an interesting question. You could imagine there’s a connection there in that somebody might assume that if you get A’s that’s the correct goal for getting to the top of the heap in some organization. In reality, often it’s the case that the route to success is more circuitous. It may be that the C student was more willing to take risks that the A student just didn’t take because the A student was so single-mindedly focused on doing what everyone says you’re supposed to do in order to be successful.

d4e: Objectively speaking, unstructured play can be bad for us as individual adults, but good for us as a society. True or false?

Ken: I would say false, because I think it can be a good thing for individuals and society. Unstructured play can be risky, though. It may lead to no particular advance to the individual; on the other hand, it may lead to something great. You just can’t be sure. You may have a hobby, and pursuing that interest may just be “play” for you, but it could end up being the stepping stone to your next great achievement.

And of course I’m totally in agreement with the idea that it’s also beneficial to society, because we need people to pursue their passions and try the things that other people wouldn’t necessarily try, so that they can build the stepping stones for others to follow.

Everybody can benefit, but we have to just accept that anything unstructured has risk. That’s why we tend to be against this kind of approach to life as a policy matter: we like to control things with standards and objectives and metrics, because we’re afraid of risk, ultimately. At the same time, you have to take risks in order to have great achievements in the end.

d4e: Let’s say I run a venture capitalist firm. How should I go about building a portfolio of startup investments?

Ken: I think venture capitalists actually put the ideas in our book into practice in a better way than a lot of other areas in society because they understand the value of a portfolio: Not all of your bets need to pay off. Just some of them need to pay off. VC’s are willing to go in some very exploratory, risky directions. If you have one big hit, it can make up for all the ones that didn’t pan out. This is, I think, a pretty good lesson for society in general. In a lot of our institutions we guard against failure as if it’s some kind of pathology to make a mistake. Venture capitalists have good instincts and are willing to have failures, and that allows them to search in a less objective way. I think we would find that the most successful venture capitalists are less objective about their portfolios.

d4e: You don’t seem to dwell much on the concept of probability. Don’t you like it?

Ken: The book isn’t really about probability, but I think we would endorse probability as an important concept. We see its importance in our field of machine learning and artificial intelligence. The point that’s being made in the book is largely independent of an in-depth discussion of probability, although it factors in to risk.

Any individual discovery could be regarded as highly improbable. In innovative processes, the likelihood of making a particular discovery is unpredictable. And yet, overall, you can increase your ability to make discoveries and the probability that you’ll make some interesting discovery.

d4e: You say that novelty is information-rich. What did you mean by that?

Joel: One way to look at novelty is that it’s information based on not where you’re trying to go, but where you’ve been in the past. In some sense, it can be seen as more information-rich than taking an objective-driven approach, in that you completely know where you’ve been in the past, and so that’s more certain. When you say “this is novel,” you can have confidence that it actually is new. Whereas if you’re trying to take a step along the way to your potential objective, you have to be willing to be uncertain, because you really don’t know if that’s going to be a stepping stone toward your goal.

More than that, the idea of being genuinely different often requires some sort of conceptual advance. You can imagine, for example, being on a skateboard. Who’s going to be more likely to create a novel skateboard move? Will it be me, who’s likely to fall on my butt, or will it be Tony Hawk, who has all this knowledge and experience to create something genuinely new? There is some ability, knowledge, or talent that’s required to create something that’s genuinely new. In that way it’s also a source of information.

d4e: Is it possible that there’s a historical trend toward us wanting more certainty? And if so, is the value of novelty rising or falling?

Ken: I think that novelty has always been valuable. What’s happening is that because of things like the internet, there’s now a significantly greater potential for the creation and dissemination of novelty. We’re exposed to much more novelty in a short time than we used to be, because the network has created this capacity to expose people to new ideas almost instantaneously and from enormous numbers of different people. That means that it’s going to accelerate the production of novelty, and we’re all going to be exposed to more, and that’s a feedback cycle. Now that there’s more novelty around, there are more stepping stones, and so more people will create novelty.

There’s a tendency to trap people in the things that they’re comfortable with, and as long as that’s making money, everybody’s happy. But that doesn’t produce the stepping stones we need for innovation.

d4e: What about machine learning and the curation of information? What about phenomena like the popularity of the Kardashians? Aren’t we suppressing novelty?

Ken: Because computers are making decisions for us about what we look at, and those decisions might cause us to not be exposed to interesting things?

d4e: Right, like the rich get richer effect. The more that machines learn our preferences, the more they are fed back to us.

Ken: I think there is that risk. We have to guard against always being given just more of what we want, what we are already comfortable with. I’m pretty optimistic about human nature and its ability to get around the tendency toward convergence. Certainly I think the algorithms will play a role in that too. Algorithms like novelty search can give us a bit of a clue about how to create computer algorithms that are not so convergent that they just always push you in some predetermined direction.

We’re exposed to much more novelty in a short time than we used to be, because the network has created this capacity to expose people to new ideas almost instantaneously and from enormous numbers of different people.

In general, we like to be exposed to stuff that’s unexpected. And we see that there’s been some attempt to do that in services like YouTube, for example. On the homepage they try to expose you to things you weren’t searching for. Of course they may base it on things you’ve searched for in the past, so there’s a bit of a paradox there.

It’s in the interest of anyone running a business to hook people into new things. People are trying to do that, with algorithms, but at the same time, the danger you’re identifying is real, and we should be cautious about it — because there’s a tendency to trap people in the things that they’re comfortable with, and as long as that’s making money, everybody’s happy. But that doesn’t produce the stepping stones we need for innovation.

Joel: One potential danger with some of these algorithms is that they can get very good at providing us with trivial novelties — novelties that are just some modulation of some formula. “The top 10 X, Y or Z.” It fulfills a very basic human desire for novelty, at a very trivial, unfulfilling level. Maybe over time people will become more aware that they’re being exploited by these algorithms. Like Ken, I’m optimistic about humanity’s ability to adapt to technologies. But it is worrisome that this very human desire for novelty can be undermined by clickbait.

d4e: Will there be enough competition in artificial intelligence for robots to evolve, given that some firms may dominate development?

Ken: These kinds of endeavors can become rather objective when a dominant firm has set the standard for success. It does potentially dampen the ability to try new things. Something really novel might not look as good. Someone might say “Our way of doing things is the objectively superior way; these other approaches are inferior objectively, and you shouldn’t invest in those.” I think that’s a problem, and we are suffering from it right now. There is a belief that there’s a canonical approach that works really well, and therefore other things should be relegated to obscurity. To shed some daylight to some of these less conventional approaches would help foster diversification. Of course, the people still need to be experts. We’re not saying that any idea off the street is worth millions of dollars; but if an expert has an unconventional idea that looks interesting, let’s give it a try.

d4e: Making distant associations and unlikely connections within the network is, to me, crucial to innovation. For us, these processes are often subconscious. Will AI have a subconscious?

Ken: I think that’s on the minds of people in the field. Generally, people in machine learning are concerned with what you’re describing as a subconscious process — the ability to make deep, subtle connections. That’s probably a little bit ahead of where the field is at the moment in terms of making those connections through algorithms on computers, although there’s certainly work being done in that direction. Anything that’s interesting about the human intellect is fair game for AI.

To learn how Dialog can help your business, contact us at 512.697.9425 or LetsChat@DialogGroup.com.

This article was originally published in design4emergence, a network science community sponsored and nurtured by Dialog and Panarchy, the world’s first network design firm. 

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Near a window, steam rises in spirals from a cup of tea. A column of sunlight intervenes, producing emergent ribbons of heat and ephemeral color. It’s an ordinary cup of tea, and steam is an ordinary product of nature’s processes. But to say that there is no art in nature is only a question of agency and scale.

This is a question Anders Hoff might ask. He is a search engine consultant and a mathematician in Oslo. He is also an artist, but a kind of artist that’s new on the scene, one with a different sort of medium–the kind you’ll start to see more of.

Anders is an artist of networks.

When he’s not spending six to nine hours a day solving complicated problems in search, he’s most likely listening to metal and using math to create generative artwork like this:

pic1inconvergent

It takes a few days to produce something like this. He writes a type of mathematical function, a generative algorithm, that defines the behavior of an agent-based data set. An agent is just something that acts on a network of things. When you’re creating an artificial network on a computer, you can define what you want the agents to be and do–to a point.

The agents are linked to other agents, and you can give them simple rules, but–like the simple rules in nature that create incredibly complex patterns–the agents are part of a network, and together they evolve the overall network in ways you can’t anticipate.

pic2inconvergent-300x300

In the case of this piece, which Anders describes as something akin to flower petals, or cabbage, or the inside of your intestines, the agents are the vertices of the mesh. You start with a simple triangular mesh and the vertices interact with each other in some way. Every vertex is connected to five or six other vertices. This creates the whole mesh–the network. These vertices act on simple rules that attract them to their neighbors and cause them to avoid the unconnected vertices of their mesh. In the end (if there is an end), it’s the complete network of all the vertices interacting that makes the mesh move or behave and evolve as it does.

He creates with a beginner’s mind.

“I like how you can get interesting structures arising from these simple rules,” Anders says, and it’s that simple curiosity that drives him to create algorithms that evolve into living artifacts that resemble familiar patterns in nature, analogs to biological mechanisms, like this:

pic3inconvergent

He doesn’t have an end result in mind. “I’ve tried to make it as naive as possible and still see if I can get that behavior. Nature doesn’t solve differential equations, but nature does evolve – so I want to make something as naive as possible.”

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Sometimes he sets out to make a thing, and he makes exactly that one thing. But most times he starts with nothing other than a vague idea, perhaps an interesting mesh that someone else has made, and plays with it until he’s tired of it. That’s how this all started. When he was 16, Anders stumbled upon a site called Complexification, which was created by Jared Tarbell, one of Etsy’s cofounders. The site featured animation created by agent-based systems. Anders started playing with Tarbell’s algorithms, copying them at first and then making his own original designs.

Years later while studying physics and working on his master’s in numerical mathematics, Anders stumbled upon Complexification when he was supposed to be cramming for his finals, and out of curiosity and a bit of procrastination a whole portfolio of generative art emerged.

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Back to our agency question. Where does the work of one network artist end and another’s begin? How does the artist know when the art is complete?

With art that grows, it’s difficult to say. When it feels complete, when it feels original, when it’s time to do something new.

For Anders, it’s a curiosity-driven process, the reward of creating simple behavior and producing something interesting. He puts his algorithms on github for other coders to play with and hopes someone will make something entirely different. Evolution is the propagation of novelty in nature, and even in art—where nature and the subconscious are exposed in complicity.

In an emerging medium like generative algorithmic art, you could think of the network itself as the artist. A network of artificial agents designed by a network of generative artists, evolving through each node, shown to other networks as art.

“The waking have one world in common, whereas each sleeper turns away to a private world of his own.” – Heraclitus

To learn how Dialog can help your business, contact us at 512.697.9425 or LetsChat@DialogGroup.com.

This article was originally published in design4emergence, a network science community sponsored and nurtured by Dialog and Panarchy, the world’s first network design firm.

SXSW_Obama_DialogBlog

I am unreasonably saddened by David Bowie’s passing. To understand why, it is helpful to know a little bit of network theory and understand the implications of neuroplasticity.

On the network theory front, the “Rule of 150” states people can easily keep track of about 150 people in their lives. This is by some reckoning the size of traditional hunter-gatherer bands. In our modern lives, celebrities fill in some of the 150 for many people. David Bowie was one of my 150. He was the Kevin Bacon of my musical universe. One degree to Brian Eno, Talking Heads, Arcade Fire, Mick Jagger, John Lennon, Bing Crosby, Annie Lennox, Luther Van Dross, Pat Metheny, Secret Machines, LCD Sound System, TV on the Radio… And two degrees to anybody you choose.* He was, as Sylvester Stallone said of Rocky Balboa at this year’s Golden Globes, “The best imaginary friend I ever had.”

In one concert I saw, Bowie described himself as having been in his “Nietzsche phase” when he wrote a particular song. He said, “You remember your Nietzsche phase, when you carried your pocket Nietzsche in your trench coat?” Was he talking to me? Yes, I had a pocket Nietzsche! I seriously doubt there was anyone else in the audience that night who had a Nietzsche phase.

I only saw him live three times over the years: on the Serious Moonlight Tour in ’83, on the Glass Spider Tour in the late 80s, and for 2004’s Reality — which came on the heels of Heathen (one of Bowie’s most listenable records – start to finish). The last tour showcased a man who had found his groove. He laughed. He was comfortable. And he was entertaining. He was at the height of his success as a person. He was a happy father and spouse. But throughout his career, I felt his evolutions and realized deep truths about what creates happiness and about ongoing innovation.

In the documentary David Bowie: Five Years in the Making of an Icon, they point out Bowie was uncanny in his selection of collaborators (a few are even listed above). And as Josh Groban tweeted about his death, “He bent genres, genders and our minds.” This is why I was attracted to David Bowie — for his purported ability to bend minds. How did he do it? He was a network designer par excellence. He deliberately designed his network to create novelty.

Identity stood at the heart of Bowie’s career. As The Atlantic said on his passing, if you are going to invent as many characters as David Bowie, you have to give consideration to their death. As they note, identity and dissolution is essential to so many human relationships. And it is this that stands at the heart of the pain I feel. As one critic said on Bowie’s passing, quoting Gorky on Tolstoy, I cannot be ‘an orphan on the earth, so long as this man lives on it.’

To understand the neuroscience of this, consider the following simple hand tapping experiment: Tap a table and tap a subject’s hand under the table simultaneously. After a few minutes, you can smack the table and a galvanic skin response shows the subject responds as if they have been struck. Why? Because, as neuropsychologist Donald Hebb coined, “Neurons that fire together wire together.” Put yourself in the subject’s shoes. You aren’t hurt when the table is struck. Yet you think you are because neurons that fire together wire together. It is this that allows us to play the game of life. But it is also this that causes us suffering. We aren’t actually in the picture. We don’t actually get hurt. Our body doesn’t sustain blows when the table gets smacked. But we react as though we did because we are, in cyborg–like fashion, wired into these stimuli. We have inadvertently begun to identify with the table. And neuroscientists tell us there is an especially acute pain when our mirror neurons activate — when we experience a sense of “I/me/mine.”

We have all sorts of things we are attached to inside, but one of the most basic or largest is our identity. David Bowie became a part of mine — for 38 years. That is longer than many friendships. And I know we shared a Nietzsche phase. That is why I am so sad … because as Bowie sang in “This is Not America” — “a little piece of me, a little piece of you… has died.”

* Yet, strangely in a perfect illustration of being trapped in our past preferences by the internet, the David Bowie Station on Pandora on the afternoon of his death repetitively plays the Kinks (Lola six times), the Beatles, the Stones, Led Zeppelin, and Talking Heads. It is like some kind of transitional 70s music ghetto. I don’t discover anything new.

Synapse Wireless celebrates 1-year in its new Cummings Research Park home

Synapse Wireless Emerging as Leading Tech Innovator for IoT and M2M Solutions

Synapse Wireless Ribbon Cutting

Synapse Wireless of Huntsville held a ribbon cutting Thursday morning to commemorate the company’s growth in Cummings Research Park.

It’s as if the good news for our client, Synapse Wireless can’t stop, won’t stop. After being named a 2014 Solar International, Solar Awards finalist in July, Synapse is back in the news this week, celebrating the first anniversary of its new facilities in one of Huntsville’s premiere research parks. This also marks a period in which Synapse added more than 60 workers, growing by more than 50% in the process!

And this party shows no signs of slowing; seeing as how Synapse’s recent news coincides with the recently released US employment numbers.

With all of that good news one might think we planned to stop there, but that wouldn’t really constitute a “can’t stop, won’t stop” scenario, now would it? Synapse also celebrated the 8th anniversary of its SNAP Technology an Internet of Things (IoT) network operating system that has brought Synapse clients as large as the US Army and American Airlines. More than 6,000 registered web developers have contributed to SNAP’s instant-on, multi-hop, mesh network!

Giving this story its proper due really requires images, and AL.com writer Lucy Berry seems to have us covered. Take a look at her image gallery and join us in congratulating the by

The surface of American society is, if I may use the expression, covered with a layer of democracy, from beneath which the old aristocratic colors sometimes peep.

This is a description of America democracy, by French aristocrat, Alexis de Toqueville, in Democracy in America I (1835), only 48 years after the founding of the United States. He explains that the vestiges of aristocracy still exist because the American majority is not familiar with civil law and does not question it:

Civil laws are only familiarly known to legal men, whose direct interest it is to maintain them as they are, whether good or bad, simply because they themselves are conversant with them … The body of the country is scarcely acquainted with them … and obeys them without premeditation.

In other words, there are civil laws (laws regulating private relations) in society that still reek of the English aristocracy because the majority of citizens never question them. So, what are these aristocratic civil laws of American society? According to Alexis de Tocqueville in Democracy in America II (1840) the most aristocratic, civil structure of American society is the top-down business structure:

Manufacturers may possibly in their turn bring men back to aristocracy … When a workman is unceasingly and exclusively engaged in the fabrication of one thing, he ultimately does his work with singular dexterity; but at the same time he loses the general faculty of applying his mind to the direction of the work … as the workman improves the man is degraded … On the other hand, more considerable, wealthy and educated men come forward to embark in manufactures … The magnitude of the efforts required, and the importance of the results to be obtained, attract him. Thus at the very time at which the science of manufactures lowers the class of workmen, it raises the class of masters.

Whereas the workman concentrates his faculties more and more upon the study of a single detail, the master surveys a more extensive whole, and the mind of the latter is enlarged in proportion as that of the former is narrowed. In a short time the one will require nothing but physical strength without intelligence; the other stands in need of science, and almost of genius, to insure success. This man resembles more and more the administrator of a vast empire–that man, a brute. The master and the workman have then here no similarity, and their differences increase every day…the one is continually, closely, and necessarily dependent upon the other, and seems as much born to obey as that other is to command. What is this but aristocracy?

When workers focus only on the tasks at hand and do not apply their minds to the direction of the work they become narrow-minded and dependent. On the other hand, managers must see the big picture, be leaders, and envision the future. Over time, the divide becomes so large between managers and workers that managers despise workers’ lack of perspective and workers despise managers’ lack of empathy. This is aristocracy, and when the majority of citizens are in this environment, as is the case today, managers and executives are treated as kings and geniuses and paid hundreds of times more than workers. In the political arena, these same workers/citizens treat their political managers as kings and geniuses. Politicians become saviors and heroes that will rescue us and fix the problems of the people. These unfair expectations of our leaders cause deficient governance and nurture the seeds of aristocracy and the dependency of men. If left unchecked, America may fully become an aristocracy–or more appropriately a plutocracy (i.e., rule by the wealthy).

On a brighter note, de Tocqueville offered consolation. He saw that some Americans had “commercial and manufacturing companies, in which all take part.” At the beginning of our democratic experiment, many Americans could see as de Tocqueville did that entrepreneurially-minded, informed citizens that open their minds to opportunities and willingly take risks are necessary to maintain our democracy. Democracy cannot exist among human machines that have no vision of the future.

The top-down business structure is a trace of English aristocratic control that grooms the majority to be dependent upon business and political managers. Moreover, the top-down business structure (as defined by civil law) is one of the most difficult aspect of our society to change. If we are to change it and if we are to improve our democracy, we must gain vision and perspective. We must acquaint ourselves with civil law and ask, “Why?” We must expand our minds and believe as Thomas Jefferson did that “men can be trusted to govern themselves without a master.”

NBC’s highlights how Democracy at Dialog fosters unprecedented loyalty at Dialog even through the most challenging of times.