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Posts from the ‘Learning Layer’ Category

Siri: An IT Inflection Point

For many, the iPhone 4S was a bit of a disappointment. It didn’t include some of the most anticipated features, and for those, fans will have to wait a bit longer. But I view the 4S as easily the most significant IT product release since the original iPhone, the last product that served to fundamentally reshape IT and the IT industry.

I must admit up-front that I don’t even currently use Apple products (although family members do, so I have up-close experience with them). But that doesn’t matter–I’ve seen and heard enough of Siri to conclude that it’s “the big one.” I’m not saying that it is necessarily in its current incarnation a life changer for its users, but it is already shaping up to be a game changer because with the successful introduction of Siri, Apple has initiated the new competitive battlefield for the IT industry.

For the first time we have a “good enough” general-purpose and natural language interface-based AI, which means that there is no going back. As Siri and her ilk become more capable they will inevitably become a required capability on just about all computing devices and systems, and will become the dominant competitive differentiator among computer-based products. As with the Internet, it will be hard to imagine what life was like before the Siris. And what is really most important and intriguing is that it is a capability that can grow without limits–there is simply no functionality end-point in sight. She and her competitors will inevitably become increasingly more intelligent, more nuanced, more engaging, with ever more personality, and with a personality that co-evolves with their users—in short, symbiotic and indispensible.

People still often persist in talking in terms of Web 2.0 or even Web 3.0, but as I argue in The Learning Layer, seen most broadly, the IT era since about the turn of the 21st century is best thought of as the Era of Adaptation–the unifying theme being that our systems learn from their experience with us and adapt and personalize accordingly. Amazon and Google were the large-scale pioneers of this era with their product recommendations and search that adapted based on user behaviors (purchase histories and web page linking, respectively). Social networking, and most prominently Facebook, with its vastly expanded capacity for capturing behavioral information, followed. Then advertising that is targeted according to inferences of preferences from behavioral information became the standard. The iPhone revolutionized the delivery device for adaptive applications, enabling those capabilities to be delivered to users continuously. And now Siri paves the way for adaptive personalization and a wide variety of other AI-based capabilities to synergize and evolve without bounds.

From a competitive dynamics standpoint it is going to be very interesting to see how this plays out, as Siri-like capabilities combined with learning layer concepts could become the most powerful IT “lock-in” capability of all time. Once such a super-Siri builds up a history of shared experiences with you, the switching costs will be immense. It would be like losing your soul-mate. In fact, you will never again be just buying a machine, but rather, the soul in the machine. Or more realistically, an immortal soul in the cloud that outlives any individual machine. Far-fetched? Let’s check back every year or so and see. Or just ask Siri.

Creative Combinatorics

The math of innovation is combinatorics. We create by building on what comes before, or more precisely, by decomposing, abstracting, recombining, and extending what comes before. The more combinations that we generate, the greater the creative potential. The complementary challenge is to effectively and presciently evaluate the combinations.

At the macro level, these combinatorics-based dynamics of innovation have often been exemplified by what is known as “clustering economics.” New York, for example, has had self-reinforcing advantages in financial markets; Silicon Valley has enjoyed unique, self-sustaining advantages in emerging technologies. But the advantages of the physical clustering of these and similar examples really boil down to advantages in combinatorics. The innumerable additional encounters, discussions, collaborations, and competitions due to physical proximity lead to massively greater idea combinations than would otherwise occur. And where there is superior infrastructure to evaluate and nurture (e.g., wide-spread, accessible venture capital) and implement (e.g., flexible, risk-taking resources) the ideas, the combinatoric advantages inevitably lead to extraordinary value creation.

Learning layers obey the same math. They create value directly through enhancing collective learning, but the ability to amplify the power of combinatorics is at the heart of their value proposition. The learning layer is an engine of serendipity driven not by physical proximities, but by virtual proximities within multi-dimensional “idea spaces.” Its aim is to bring to our attention useful affinities and clusters among ideas and people of which we might not otherwise be aware. A learning layer can be considered an example of what the cognitive scientist and author Steven Pinker calls discrete combinatorial systems, which, as I discuss in The Learning Layer, occupy a very special place in nature:

We know of, however, two discrete combinatorial systems in the natural universe: DNA and human language. Not coincidently, these systems have the unique capability of generating an endless stream of novelties through a combinatorics made infinite by the application of a kind of generative grammar–a grammar comprising a recursive set a rules for combining and extending the constituent elements.

The discrete combinatorial nature of the learning layer is yet another reason it occupies a special place in the realm of systems. In combination with us, and by continually learning from us, it can engender ever greater streams of valuable, recombinant innovations. Of course, we still need the proper environments to nurture and implement the best of these innovations, and that’s a challenge we need to really be focusing on because somewhere in that multi-dimensional innovation space a very important creative combination impatiently awaits . . .

The Adaptive Stack

IT always evolves by building new system layers on top of preceding layers, while concurrently abstracting away from users of these new layers extraneous details of the underlying layers. In the resulting current IT “stack,” we predominantly interact with content and application layers, and when applicable and available, process layers. And we are well on our way toward abstracting away the networking and hardware infrastructure on which our applications run—bundling that big buzzing confusion into “the cloud.”

Much more recently we have begun to add a social layer on top of our other software layers—still a work in progress in most organizations. So far, these social-based systems can more often be considered architectural bolt-ons rather than a truly integral part of the enterprise IT stack. But that is clearly destined to change.

And coming right on the heels of the social layer is the learning layer—the intelligent and adaptive integrator of the social, content, and process layers. The distinguishing characteristic of this layer is its capacity for automatic learning from the collective experiences of users and delivering the learning back to users in a variety of ways.

So this is the new IT stack that is taking shape and that summarizes the enterprise systems architecture of 2011 and beyond. And since auto-learning features promise to be an integral part of every system and device with which we interact, it is the reason that the next major era of IT is most sensibly labeled “the era of adaptation.”

As I discuss in the book, there is something qualitatively different about the combination of these last two layers of the stack—the social and learning layers—in contrast to all the layers that came before. These new layers cause the boundary between systems and people to become much more blurred—it is no longer just a command and response relationship between man and machine, but rather, a mutual learning relationship. And exactly where the learning of the system and the learning of people begins and ends is a bit fuzzy.

Perhaps then, our new stack more accurately summarizes the next generation enterprise architecture, not just the IT architecture–an enterprise architecture of a different nature than that which has come before, one in which learning and adaptation is woven throughout.

The Public Learning Layer Emerges

John Battelle recently posted a great blog essay in which he distinguished between the “Dependent Web” and the “Independent Web”—the Dependent Web being the part of web that basically has some degree of understanding of who you are and then tailors its responses to you, while the Independent Web is basically the dumb old legacy web that really is not aware of “you” and just operationally treats you like everyone else. 

John makes the point that even the Independent Web is being dragged, kicking and screaming, into what I call the new IT “era of adaptation” by virtue of the advertising delivered on these sites being personalized basis inferences of preferences and interests, even if the substantive, non-advertising functions of these sites are still mired in the legacy of the era of dumb old non-personalized systems. 

John’s take on this inexorable evolution toward a web that learns from its experiences with us is that, “it’s clear that we’re in the early phases of a major shift in the texture and the experience of the web.” Indeed, we are witnessing the plain old web transforming into various learning layers. And as I indicate in The Learning Layer, just how the web will eventually evolve into the one learning layer to rule them all is the real interesting question, and John goes into a good deal of detail on ways that this could/should occur. 

It clearly won’t play out like the original web in which a relatively simple standard was rapidly and extensively leveraged—the evolution of the learning layer will be a function of many different economic interests and objectives, as well as complex technical approaches, almost surely resulting in more of a learning layer mosaic, presenting users with a great variety of different learning textures. Which is likely ultimately a good thing. Of course, as I stress in The Learning Layer, the implementation of company or institution-specific learning layers is much more straightforward and will deliver numerous benefits all the while the long and fascinating process of the “sausage making” of the public web-based learning layer continues.

The Learning Layer: Adaptive Middleware for the Cloud

Some of us from ManyWorlds recently attended the Rackspace Software as a Service (SaaS) Summit. ManyWorlds is a long-time Rackspace customer—we are fortunate to have chosen Rackspace to power our Epiture® learning layer platform back in Rackspace’s early days, well before they became the leader in computing infrastructure and cloud computing services that they are today.   

We were particularly interested in the SaaS applications part of the conference, and it was amazing to see how the number of highly valuable enterprise applications being served from the cloud has absolutely exploded over the past few years. It is clear that delivery of applications from the cloud is inevitably becoming the standard model, even for the largest enterprises—and Rackspace is orienting its entire business around this reality. 

As a still-recovering big company CIO who spent a lot of time worrying about such things, however, perhaps the most intriguing topic at the conference for me was with regard to integration—how applications served from the cloud can be effectively integrated with existing enterprise applications. Most cloud apps have a relatively narrow scope—they do one thing, and they do it very well. But they need to fit within the context of an existing application environment, which is often the province of broad-scope applications such as enterprise resource planning (ERP) systems (e.g., SAP) and customer relationship management (CRM) systems. As the number of cloud-based applications grows, this integration issue becomes front and center for the CIO. Legacy applications generally cannot simply be ripped out and replaced—so integration approaches, whether ad hoc, or better, through standardized APIs, promises to increasingly dominate the CIO’s agenda. 

The Summit served to reinforce for me that the good news is that the learning layer can play a strong role in ameliorating this integration problem. The “ethereal network of learning” that I discuss in the book can be applied to integrate together myriad cloud applications with complex legacy application environments. Think of it as an “adaptive middleware” layer sitting between the cloud-based applications and the legacy applications, providing a single, coherent environment that includes a capability for integrative workflow, and that automatically adapts and personalizes based on usage.

When we first conceived of the learning layer, we primarily thought of it as providing an integrated layer of learning over existing applications and content—it is now clear that, perhaps even more importantly, it will serve as an adaptive bridge between the new and the old of the application world.


I recently saw the cool new movie Inception—where the term “inception” means the implanting of an idea into the brain of a target by way of hacking into the target’s dreams. And as those of you who have already seen the movie know, the plot plays with this idea in a recursive way—the dream hacking is conducted in dreams within dreams, making for a mind bending movie experience, and, of course, sufficient ambiguity between dreaming and reality to allow for many Hollywood sequel directions . . .

Watching the movie was a particularly enthralling experience for me because two key themes flowing through The Learning Layer are dreams and recursion (and, ok, because I’m a bit of geek I suppose). Yeah, The Learning Layer is most fundamentally a book about next generation organizational learning, but it’s also a book of many layers, and the undercurrents of dreams and recursion are never far away.

Why the undercurrent of dreams? Well, it has become increasingly clear that the purpose of dreaming is that it is a process for rewiring the network of the brain. The strengthening and weakening of connections in a network is the essence of learning, and dreaming, particularly during the rapid eye movement (REM) stage of sleep, appears to be when meaning is made of the raw information we have taken in during the day through a process of appropriately editing the wiring in our brains. Which may be why, not just humans, nor even just mammals, but every organism with a brain that has been closely examined seems to need sleep. So in The Learning Layer I make the case that if we want our systems to learn effectively they better have the capacity for the equivalent of dreaming.

Why the undercurrent of recursion? Because it’s what invariably lies behind new, unpredictable, emergent phenomena. Recursion is the idea of a feedback loop operating on basic units (e.g., network subsets), and it can give rise to something of a different nature than the individual units on which it operates. Not surprisingly, it’s a fundamental property of the brain, a property that leads to that peculiar little phenomenon emerging from our network of neurons, our mind. And so if we want truly emergent qualities to spring forth from our systems, recursion better be at their core.

In other words, just like our brains, we need our systems to be architected to embrace recursion, and also to dream! And what makes our mind even more powerful and yet so wonderfully unpredictable is the capacity for self-inception. That is, myriad feedback loops can be brought to bear in the vast network that is our brain whereby one part of the brain can modify another part of the brain. This can happen unconsciously (e.g., during our sleep) and/or consciously (i.e., self-inception). In fact, as in the movie, the boundaries between dreaming and inception can become fuzzy, and I relate in the book a simple experiment you can perform on yourself to illustrate this:

You can easily see that feedback dynamic at work whenever you awaken from a dream. If you don’t try to remember the dream, you will almost always forget it. But if you will yourself to recall more details of it, and then remember it, you can make the memory of the dream consolidate, potentially forever. When you do so, one part of your brain literally physically affects another part of your brain, which in turn may later influence other parts of your brain, and so on, for the rest of your life. Your mind will never by quite the same because of that little whim to remember that particular dream!

It’s really quite amazing if you think about it (yep, another opportunity for self-inception!). But maybe even more amazing is that we can actually architect our systems to do the same thing. That’s the essence of the learning layer concept—we provide our systems with the ability to recursively modify themselves based on their experiences with us, which amounts to modifying the connections among the representations of us and our content. And that, of course, is the analog of dreaming. Or maybe it’s not just dreaming, maybe it could be considered self-inception. I suppose that’s just a question of whether that system self-modification is performed consciously or unconsciously . . .  🙂

The Learning Layer as a Phenomenon

I am not one given to mysticism, but one of the key points I try to make in the book is that the learning layer is somehow more than just a computer-based system, and more than just a different way of working together. Although it is both of these things, it seems to be more than just both. It has a distinctive nature—different from what has come before it that is born of its capacity for social awareness and its co-evolution with us. It learns and changes as we do.

I call it a “phenomenon” rather than some other label because although the learning layer has a unique nature, “phenomenon” connotes a fuzziness of boundary that is inherent to the learning layer. Where does it end and the non-learning layer begin? Is it just the system part or does it comprise both the system and the people with whom it is coevolving? Yes–and not because of any desire to couch it in mysticism, but because the reality is that emergent phenomena invariably defy a precise dissection by our intellectual scalpels.

As with anything qualitatively different, it certainly has precursor concepts, and the boundary between it and its precursors are likewise fuzzy. John Hagel and his co-authors discuss “pull systems” in their delightful new book, The Power of Pull. And the learning layer is surely a pull system. Andrew McAfee coins the category, emergent social software platforms, in his book, Enterprise 2.0, and the learning layer surely also fits within this definition. But the learning layer seems to go a bit further—and yet it is hard to pin down exactly at what point it goes beyond. Perhaps like some other kinds of phenomena, you just know it when you see it . . .