Quantum Field Is-Oughts

teleologySean Carroll’s Oxford lecture on Poetic Naturalism is worth watching (below). In many ways it just reiterates several common themes. First, it reinforces the is-ought barrier between values and observations about the natural world. It does so with particular depth, though, by identifying how coarse-grained theories at different levels of explanation can be equally compatible with quantum field theory. Second, and related, he shows how entropy is an emergent property of atomic theory and the interactions of quantum fields (that we think of as particles much of the time) and, importantly, that we can project the same notion of boundary conditions that result in entropy into the future resulting in a kind of effective teleology. That is, there can be some boundary conditions for the evolution of large-scale particle systems that form into configurations that we can label purposeful or purposeful-like. I still like the term “teleonomy” to describe this alternative notion, but the language largely doesn’t matter except as an educational and distinguishing tool against the semantic embeddings of old scholastic monks.

Finally, the poetry aspect resolves in value theories of the world. Many are compatible with descriptive theories, and our resolution of them is through opinion, reason, communications, and, yes, violence and war. There is no monopoly of policy theories, religious claims, or idealizations that hold sway. Instead we have interests and collective movements, and the above, all working together to define our moral frontiers.

 … Read the rest

Local Minima and Coatimundi

CoatimundiEven given the basic conundrum of how deep learning neural networks might cope with temporal presentations or linear sequences, there is another oddity to deep learning that only seems obvious in hindsight. One of the main enhancements to traditional artificial neural networks is a phase of supervised pre-training that forces each layer to try to create a generative model of the input pattern. The deep learning networks then learn a discriminant model after the initial pre-training is done, focusing on the error relative to classification versus simply recognizing the phrase or image per se.

Why this makes a difference has been the subject of some investigation. In general, there is an interplay between the smoothness of the error function and the ability of the optimization algorithms to cope with local minima. Visualize it this way: for any machine learning problem that needs to be solved, there are answers and better answers. Take visual classification. If the system (or you) gets shown an image of a coatimundi and a label that says coatimundi (heh, I’m running in New Mexico right now…), learning that image-label association involves adjusting weights assigned to different pixels in the presentation image down through multiple layers of the network that provide increasing abstractions about the features that define a coatimundi. And, importantly, that define a coatimundi versus all the other animals and non-animals.,

These weight choices define an error function that is the optimization target for the network as a whole, and this error function can have many local minima. That is, by enhancing the weights supporting a coati versus a dog or a raccoon, the algorithm inadvertently leans towards a non-optimal assignment for all of them by focusing instead on a balance between them that is predestined by the previous dog and raccoon classifications (or, in general, the order of presentation).… Read the rest

New Behaviorism and New Cognitivism

lstm_memorycellDeep Learning now dominates discussions of intelligent systems in Silicon Valley. Jeff Dean’s discussion of its role in the Alphabet product lines and initiatives shows the dominance of the methodology. Pushing the limits of what Artificial Neural Networks have been able to do has been driven by certain algorithmic enhancements and the ability to process weight training algorithms at much higher speeds and over much larger data sets. Google even developed specialized hardware to assist.

Broadly, though, we see mostly pattern recognition problems like image classification and automatic speech recognition being impacted by these advances. Natural language parsing has also recently had some improvements from Fernando Pereira’s team. The incremental improvements using these methods should not be minimized but, at the same time, the methods don’t emulate key aspects of what we observe in human cognition. For instance, the networks train incrementally and lack the kinds of rapid transitions that we observe in human learning and thinking.

In a strong sense, the models that Deep Learning uses can be considered Behaviorist in that they rely almost exclusively on feature presentation with a reward signal. The internal details of how modularity or specialization arise within the network layers are interesting but secondary to the broad use of back-propagation or Gibb’s sampling combined with autoencoding. This is a critique that goes back to the early days of connectionism, of course, and why it was somewhat sidelined after an initial heyday in the late eighties. Then came statistical NLP, then came hybrid methods, then a resurgence of corpus methods, all the while with image processing getting more and more into the hand-crafted modular space.

But we can see some interesting developments that start to stir more Cognitivism into this stew.… Read the rest

Evolving Visions of Chaotic Futures

FlutterbysMost artificial intelligence researchers think unlikely the notion that a robot apocalypse or some kind of technological singularity is coming anytime soon. I’ve said as much, too. Guessing about the likelihood of distant futures is fraught with uncertainty; current trends are almost impossible to extrapolate.

But if we must, what are the best ways for guessing about the future? In the late 1950s the Delphi method was developed. Get a group of experts on a given topic and have them answer questions anonymously. Then iteratively publish back the group results and ask for feedback and revisions. Similar methods have been developed for face-to-face group decision making, like Kevin O’Connor’s approach to generating ideas in The Map of Innovation: generate ideas and give participants votes equaling a third of the number of unique ideas. Keep iterating until there is a consensus. More broadly, such methods are called “nominal group techniques.”

Most recently, the notion of prediction markets has been applied to internal and external decision making. In prediction markets,  a similar voting strategy is used but based on either fake or real money, forcing participants towards a risk-averse allocation of assets.

Interestingly, we know that optimal inference based on past experience can be codified using algorithmic information theory, but the fundamental problem with any kind of probabilistic argument is that much change that we observe in society is non-linear with respect to its underlying drivers and that the signals needed are imperfect. As the mildly misanthropic Nassim Taleb pointed out in The Black Swan, the only place where prediction takes on smooth statistical regularity is in Las Vegas, which is why one shouldn’t bother to gamble.… Read the rest

Build Up That Wall

No, I’m not endorsing the construction of additional walls between the United States and Mexico. There are plenty of those and they may be of questionable value. Instead, it is Thomas Jefferson’s birthday and I’m quoting from Christopher Hitchens (who shared his birthday with Jefferson) in repurposing and inverting Reagan’s famous request of Gorbachev. Hitch promoted the Jeffersonian ideal of separating out the civic from the religious:

Be it enacted by General Assembly that no man shall be compelled to frequent or support any religious worship, place, or ministry whatsoever, nor shall be enforced, restrained, molested, or burthened in his body or goods, nor shall otherwise suffer on account of his religious opinions or belief, but that all men shall be free to profess, and by argument to maintain, their opinions in matters of Religion, and that the same shall in no wise diminish, enlarge or affect their civil capacities.

from Jefferson’s Virginia Statute for Religious Freedom

A rather remarkable continuation of Enlightenment concepts that derive, typically, from a notion of “natural rights” and, even in the Virginia Statue, from religious concepts: “Whereas, Almighty God hath created the mind free.” With the following paragraphs noting that human rulers are fallible and have tended to create false religions down through time, apparently regardless of God’s wishes.

Natural rights are an interesting idea that re-occurs in the Declaration of Independence and were also championed by George Mason in the Virginia Declaration of Rights. The notion that natural rights did not extend to slaves was something that Jefferson was conflicted about, according to Hitchens, until the end of his life, with the issue of state’s rights a pragmatic basis for opposition to an institution that he both profited from and found morally repugnant.… Read the rest

On Woo-Woo and Schrödinger’s Cat

schrodingers-cat-walks-into-a-bar-memeMichael Shermer and Sam Harris got together with an audience at Caltech to beat up on Deepak Chopra and a “storyteller” named Jean Houston in The Future of God debate hosted by ABC News. And Deepak got uncharacteristically angry back behind his crystal-embellished eyewear, especially at Shermer’s assertion that Deepak is just talking “woo-woo.”

But is there any basis for the woo-woo that Deepak is weaving? As it turns out, he is building on some fairly impressive work by Stuart Hameroff, MD, of University of Arizona and Sir Roger Penrose of Oxford University. Under development for more than 25 years, this work has most recently been summed up in their 2014 paper, “Consciousness in the universe: A review of the ‘Orch OR’ theory” available for free (but not the commentaries, alas). Deepak was even invited to comment on the paper in Physics of Life Reviews, though the content of his commentary was challenged as being somewhat orthogonal or contradictory to the main argument.

To start somewhere near the beginning, Penrose became obsessed with the limits of computation in the late 80s. The Halting Problem sums up his concerns about the idea that human minds can possibly be isomorphic with computational devices. There seems to be something that allows for breaking free of the limits of “mere” Turing Complete computation to Penrose. Whatever that something is, it should be physical and reside within the structure of the brain itself. Hameroff and Penrose would also like that something to explain consciousness and all of its confusing manifestations, for surely consciousness is part of that brain operation.

Now, to get at some necessary and sufficient sorts of explanations for this new model requires looking at Hameroff’s medical speciality: anesthesiology.… Read the rest

The Goldilocks Complexity Zone

FractalSince my time in the early 90s at Santa Fe Institute, I’ve been fascinated by the informational physics of complex systems. What are the requirements of an abstract system that is capable of complex behavior? How do our intuitions about complex behavior or form match up with mathematical approaches to describing complexity? For instance, we might consider a snowflake complex, but it is also regular in it’s structure, driven by an interaction between crystal growth and the surrounding air. The classic examples of coastlines and fractal self-symmetry also seem complex but are not capable of complex behavior.

So what is a good way of thinking about complexity? There is actually a good range of ideas about how to characterize complexity. Seth Lloyd rounds up many of them, here. The intuition that drives many of them is that complexity seems to be associated with distributions of relationships and objects that are somehow juxtapositioned between a single state and a uniformly random set of states. Complex things, be they living organisms or computers running algorithms, should exist in a Goldilocks zone when each part is examined and those parts are somehow summed up to a single measure.

We can easily construct a complexity measure that captures some of these intuitions. Let’s look at three strings of characters:

x = aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa

y = menlqphsfyjubaoitwzrvcgxdkbwohqyxplerz

z = the fox met the hare and the fox saw the hare

Now we would likely all agree that y and z are more complex than x, and I suspect most would agree that y looks like gibberish compared with z. Of course, y could be a sequence of weirdly coded measurements or something, or encrypted such that the message appears random.… Read the rest

Bayesianism and Properly Basic Belief

Kircher-Diagram_of_the_names_of_GodXu and Tenebaum, in Word Learning as Bayesian Inference (Psychological Review, 2007), develop a very simple Bayesian model of how children (and even adults) build semantic associations based on accumulated evidence. In short, they find contrastive elimination approaches as well as connectionist methods unable to explain the patterns that are observed. Specifically, the most salient problem with these other methods is that they lack the rapid transition that is seen when three exemplars are presented for a class of objects associated with a word versus one exemplar. Adults and kids (the former even more so) just get word meanings faster than those other models can easily show. Moreover, a space of contending hypotheses that are weighted according to their Bayesian statistics, provides an escape from the all-or-nothing of hypothesis elimination and some of the “soft” commitment properties that connectionist models provide.

The mathematical trick for the rapid transition is rather interesting. They formulate a “size principle” that weights the likelihood of a given hypothesis (this object is most similar to a “feb,” for instance, rather than the many other object sets that are available) according to a scaling that is exponential in the number of exposures. Hence the rapid transition:

Hypotheses with smaller extensions assign greater probability than do larger hypotheses to the same data, and they assign exponentially greater probability as the number of consistent examples increases.

It should be noted that they don’t claim that the psychological or brain machinery implements exactly this algorithm. As is usual in these matters, it is instead likely that whatever machinery is involved, it simply has at least these properties. It may very well be that connectionist architectures can do the same but that existing approaches to connectionism simply don’t do it quite the right way.… Read the rest

Entanglement and Information

shannons-formula-smallResearch can flow into interesting little eddies that cohere into larger circulations that become transformative phase shifts. That happened to me this morning between a morning drive in the Northern California hills and departing for lunch at one of our favorite restaurants in Danville.

The topic I’ve been working on since my retirement is whether there are preferential representations for optimal automated inference methods. We have this grab-bag of machine learning techniques that use differing data structures but that all implement some variation on fitting functions to data exemplars; at the most general they all look like some kind of gradient descent on an error surface. Getting the right mix of parameters, nodes, etc. falls to some kind of statistical regularization or bottlenecking for the algorithms. Or maybe you perform a grid search in the hyperparameter space, narrowing down the right mix. Or you can throw up your hands and try to evolve your way to a solution, suspecting that there may be local optima that are distracting the algorithms from global success.

Yet, algorithmic information theory (AIT) gives us, via Solomonoff, a framework for balancing parameterization of an inference algorithm against the error rate on the training set. But, first, it’s all uncomputable and, second, the AIT framework just uses strings of binary as the coded Turing machines, so I would have to flip 2^N bits and test each representation to get anywhere with the theory. Yet, I and many others have had incremental success at using variations on this framework, whether via Minimum Description Length (MDL) principles, it’s first cousin Minimum Message Length (MML), and other statistical regularization approaches that are somewhat proxies for these techniques.… Read the rest