Skip to content

Archive for

Does Personalization Pave the Cow Paths?

Michael Hammer, the father of business reengineering, famously used the phrase “paving the cow paths” to describe the ritualizing of inefficient business practices. Now the pervasiveness of personalization of our systems is being accused of paving our cow paths by continuously reinforcing our narrow interests at the expense of exposing us to other points of view. This latest apocalyptic image being painted is one of a world where we are all increasingly locked into our parochial, polarized perspectives as the machine feeds us only what we want to hear. Big Brother turns out to be an algorithm.

I commented on this over at Greg Linden’s blog, but wanted to expand on those thoughts a bit here. Of course, I could first point out the irony that I only became aware of the Eli Pariser’s book about the perils of personalization, The Filter Bubble, through a personalized RSS feed, but I will move on to more substantive points—the main one being that it seems to me that a straw man, highly naïve personalization capability has been constructed to use as the primary foil of the criticism. Does such relatively crude personalization occur today and are some of the concerns, while overblown, valid? Yes. Are these relatively unsophisticated personalization functions likely to remain the state-of-the-art for long? Nope.

As I discuss in The Learning Layer, an advanced personalization capability includes the following features:

  1. A user-controlled tuning function that enables a user to explicitly adjust the “narrowness” of inference of the user’s interests in generating recommendations
  2. An “experimentation” capability within the recommendation algorithm to at least occasionally take the user outside her typical inferred areas of interest
  3. A recommendation explanation function that provides the user with the rationale for the recommendation, including a level of confidence the system has in making the explanation, and an indication when a recommendation is provided that is intentionally outside of the normal areas of interest

And by the way, there are actually two reasons to deliver the occasional experimental recommendation: first, yes, to subtly encourage the user to broaden her horizons, but less obviously, to also enable the recommendation algorithm to gain more information than it would otherwise have, enabling it to develop both a broader and a finer-grained perspective of the user’s “interest space.” This allows for increasingly sophisticated, nuanced, and beneficially serendipitous recommendations. As The Learning Layer puts it:

. . . the wise system will also sometimes take the user a bit off of her well-worn paths. Think of it as the system running little experiments. Only by “taking a jump” with some of these types of experimental recommendations every now and then can the system fine-tune its understanding of the user and get a feel for potential changes in tastes and preferences. The objective of every interaction of the socially aware system is to find the right balance of providing valuable learning to the user in the present, while also interacting so as to learn more about the user in order to become even more useful in the future. It takes a deft touch.

A deft touch indeed, but also completely doable and an inevitable feature of future personalization algorithms.

I’ve got to admit, my first reaction when I see yet another in a long line of hand wringing stories of how the Internet is making us stupider, into gadgets, amplifying our prejudices, etc., is to be dismissive. After all, amidst the overwhelmingly obvious advantages we gain from advances in technology (a boring “dog bites man” story), the opportunity is to sell a “man bites dog” negative counter-view. These stories invariable have two common themes: a naïve extrapolation from the current state of the technology and an assumption people are at best just passive entities, and at worst complete fools. History has shown these to ultimately be bad assumptions, and hence, the resulting stories cannot be taken too seriously.

On the other hand, looking beyond the “Chicken Little” part of these stories, there are some nuggets of insight that those of us building adaptive technologies can learn from. And a lesson from this latest one is that the type of more advanced auto-learning and recommendation capabilities featured in The Learning Layer is an imperative in avoiding a bad case of paving-the-cow-paths syndrome.

Our Conceit of Consciousness

MIT recently held a symposium called “Brains, Minds, and Machines,” which took stock of the current state of cognitive science and machine learning, as well as debating directions for the next generation of advances. The symposium kicked-off with perspectives from some of the various lions of the field of cognitive science such as Marvin Minsky and Noam Chomsky.

A perspective by Chomsky lays down the gauntlet with regard to today’s competing schools of AI development and directions:

Chomsky derided researchers in machine learning who use purely statistical methods to produce behavior that mimics something in the world, but who don’t try to understand the meaning of that behavior. Chomsky compared such researchers to scientists who might study the dance made by a bee returning to the hive, and who could produce a statistically based simulation of such a dance without attempting to understand why the bee behaved that way. “That’s a notion of [scientific] success that’s very novel. I don’t know of anything like it in the history of science,” said Chomsky.

Of course, Chomsky is tacitly assuming that “meaning” and truly understanding natural language is something much more than just statistics. But is it really? That certainly seems like common sense. On the other hand, if we look closely at the brain, all we see are networks of neurons firing in statistical patterns. The meaning somehow emerges from the statistics.

I suspect what Chomsky really wants is an “explanation engine”–an explanatory facility that can convincingly explain itself to us, presenting to us many layers of richly nuanced reasoning. Patrick Winston at the same symposium said as much:

Winston speculated that the magic ingredient that makes humans unique is our ability to create and understand stories using the faculties that support language: “Once you have stories, you have the kind of creativity that makes the species different to any other.”

This capability has been the goal of AI from the beginning, and 50 years later, the fact that such a capability has still not been delivered has clearly not been for lack of trying. I would argue that this perceived failure is a consequence of the early AI community falling prey to the “conceit of consciousness” that we are all prone to. We humans believe that we are our language-based explanation engine, and we therefore literally tell and convince ourselves that true meaning is solely a product of the conscious, language-based reasoning capacity of our explanation engines.

But that ain’t exactly the way nature did it, as Winston implicitly acknowledges. Inarticulate inferencing and decision making capabilities evolved over the course of billions of years and work quite well, thank you. Only very recently did we humans, apparently uniquely, become endowed with a very powerful explanation engine that provides a rationale (and often a rationalization!) for the decisions already made by our unconscious intelligence—an endowment most probably for the primary purpose of delivering compact communications to others rather than for the purpose of improving our individual decision making.

So to focus first on the explanation engine is getting things exactly backward in trying to develop machine intelligence. To recapitulate evolution, we first need to build intelligent systems that generate good inferences and decisions from large amounts of data, just like we humans continuously and unconsciously do. And like it or not, we can only do so by applying those inscrutable, inarticulate, complex, messy, math-based methods. With this essential foundation in place, we can then (mostly for the sake of our own conceit of consciousness!) build useful explanatory engines on top of the highly intelligent unconsciousness.

So I agree with Chomsky and Winston that now is indeed a fruitful time to build explanation engines—not because AI directions have been misguided for the past decade or two, but rather because we have actually come such a long way with the much maligned data-driven, statistical approach to AI, and because there is a clear path to doing even more wonderful things with this approach. Unlike 50 years ago our systems are beginning to actually have something interesting to say to us; so by all means, let us help them begin to do so!