Deep Learning with Quantum Decoherence

Getting back to metaphors in science, Wojciech Zurek’s so-called Quantum Darwinism is in the news due to a series of experimental tests. In Quantum Darwinism (QD), the collapse of the wave function (more properly the “extinction” of states) is a result of decoherence from environmental entanglement. There is a kind of replication in QD, where pointer states are multiplied, and then a kind of environmental selection as well. There is no variation per se, however, though some might argue that the pointer states imprinted by the environment are variants of the originals. Still, it makes the metaphor a bit thin at the edges, but it is close enough for the core idea to fit most of the floor-plan of Darwinism. Indeed, some champion it as part of a more general model for everything. Even selection among viable multiverse bubbles has a similar feel to it: some survive while others perish.

I’ve been simultaneously studying quantum computing and complexity theories that are getting impressively well developed. Richard Cleve’s An Introduction to Quantum Complexity Theory and John Watrous’s Quantum Computational Complexity are notable in their bridging from traditional computational complexity to this newer world of quantum computing using qubits, wave functions, and even decoherence gates.

Decoherence sucks for quantum computing in general, but there may be a way to make use of it. For instance, an artificial neural network (ANN) also has some interesting Darwinian-like properties to it. The initial weight distribution in an ANN is typically a random real value. This is designed to simulate the relative strength of neural connections. Real neural connections are much more complex than this, doing interesting cyclic behavior, saturating and suppressing based on neurotransmitter availability, and so forth, but assuming just a straightforward pattern of connectivity has allowed for significant progress.… Read the rest

Running, Ancient Roman Science, Arizona Dive Bars, and Lightning Machine Learning

I just returned from running in Chiricahua National Monument, Sedona, Painted Desert, and Petrified Forest National Park, taking advantage of the late spring before the heat becomes too intense. Even so, though I got to Massai Point in Chiricahua through 90+ degree canyons and had around a liter of water left, I still had to slow down and walk out after running short of liquid nourishment two-thirds down. There is an eerie, uncertain nausea that hits when hydration runs low under high stress. Cliffs and steep ravines take on a wolfish quality. The mind works to control feet against stumbling and the lips get serrated edges of parched skin that bite off without relieving the dryness.

I would remember that days later as I prepped to overnight with a wilderness permit in Petrified Forest only to discover that my Osprey Exos pack frame had somehow been bent, likely due to excessive manhandling by airport checked baggage weeks earlier. I considered my options and drove eighty miles to Flagstaff to replace the pack, then back again.

I arrived in time to join Dr. Richard Carrier in an unexpected dive bar in Holbrook, Arizona as the sunlight turned to amber and a platoon of Navajo pool sharks descended on the place for billiards and beers. I had read that Dr. Carrier would be stopping there and it was convenient to my next excursion, so I picked up signed copies of his new book, The Scientist in the Early Roman Empire, as well as his classic, On the Historicity of Jesus, that remains part of the controversial samizdat of so-called “Jesus mythicism.”

If there is a distinguishing characteristic of OHJ it is the application of Bayesian Theory to the problems of historical method.… Read the rest

The Ethics of Knowing

In the modern American political climate, I’m constantly finding myself at sea in trying to unravel the motivations and thought processes of the Republican Party. The best summation I can arrive at involves the obvious manipulation of the electorate—but that is not terrifically new—combined with a persistent avoidance of evidence and facts.

In my day job, I research a range of topics trying to get enough of a grasp on what we do and do not know such that I can form a plan that innovates from the known facts towards the unknown. Here are a few recent investigations:

  • What is the state of thinking about the origins of logic? Logical rules form into broad classes that range from the uncontroversial (modus tollens, propositional logic, predicate calculus) to the speculative (multivalued and fuzzy logic, or quantum logic, for instance). In most cases we make an assumption based on linguistic convention that they are true and then demonstrate their extension, despite the observation that they are tautological. Synthetic knowledge has no similar limitations but is assumed to be girded by the logical basics.
  • What were the early Christian heresies, how did they arise, and what was their influence? Marcion of Sinope is perhaps the most interesting one of these, in parallel with the Gnostics, asserting that the cruel tribal god of the Old Testament was distinct from the New Testament Father, and proclaiming perhaps (see various discussions) a docetic Jesus figure. The leading “mythicists” like Robert Price are invaluable in this analysis (ignore first 15 minutes of nonsense). The thin braid of early Christian history and the constant humanity that arises in morphing the faith before settling down after Nicaea (well, and then after Martin Luther) reminds us that abstractions and faith have a remarkable persistence in the face of cultural change.
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Apprendre à traduire

Google’s translate has always been a useful tool for awkward gists of short texts. The method used was based on building a phrase-based statistical translation model. To do this, you gather up “parallel” texts that are existing, human, translations. You then “align” them by trying to find the most likely corresponding phrases in each sentence or sets of sentences. Often, between languages, fewer or more sentences will be used to express the same ideas. Once you have that collection of phrasal translation candidates, you can guess the most likely translation of a new sentence by looking up the sequence of likely phrase groups that correspond to that sentence. IBM was the progenitor of this approach in the late 1980’s.

It’s simple and elegant, but it always was criticized for telling us very little about language. Other methods that use techniques like interlingual transfer and parsers showed a more linguist-friendly face. In these methods, the source language is parsed into a parse tree and then that parse tree is converted into a generic representation of the meaning of the sentence. Next a generator uses that representation to create a surface form rendering in the target language. The interlingua must be like the deep meaning of linguistic theories, though the computer science versions of it tended to look a lot like ontological representations with fixed meanings. Flexibility was never the strong suit of these approaches, but their flaws were much deeper than just that.

For one, nobody was able to build a robust parser for any particular language. Next, the ontology was never vast enough to accommodate the rich productivity of real human language. Generators, being the inverse of the parser, remained only toy projects in the computational linguistic community.… Read the rest

A Soliloquy for Volcanoes and Nearest Neighbors

A German kid caught me talking to myself yesterday. It was my fault, really. I was trying to break a hypnotic trance-like repetition of exactly what I was going to say to the tramper’s hut warden about two hours away. OK, more specifically, I had left the Waihohonu camp site in Tongariro National Park at 7:30AM and was planning to walk out that day. To put this into perspective, it’s 28.8 km (17.9 miles) with elevation changes of around 900m, including a ridiculous final assault above red crater at something like 60 degrees along a stinking volcanic ridge line. And, to make things extra lovely, there was hail, then snow, then torrential downpours punctuated by hail again—a lovely tramp in the New Zealand summer—all in a full pack.

But anyway, enough bragging about my questionable judgement. I was driven by thoughts of a hot shower and the duck l’orange at Chateau Tongariro while my hands numbed to unfeeling arresting myself with trekking poles down through muddy canyons. I was talking to myself. I was trying to stop repeating to myself why I didn’t want my campsite for the night that I had reserved. This is the opposite of glorious runner’s high. This is when all the extra blood from one’s brain is obsessed with either making leg muscles go or watching how the feet will fall. I also had the hood of my rain fly up over my little Marmot ball cap. I was in full regalia, too, with the shifting rub of my Gortex rain pants a constant presence throughout the day.  I didn’t notice him easing up on me as I carried on about one-shot learning as some kind of trance-breaking ritual.… Read the rest

Machine Learning and the Coming Robot Apocalypse

Daliesque creepy dogsSlides from a talk I gave today on current advances in machine learning are available in PDF, below. The agenda is pretty straightforward: starting with some theory about overfitting based on algorithmic information theory, we proceed on through a taxonomy of ML types (not exhaustive), then dip into ensemble learning and deep learning approaches. An analysis of the difficulty and types of performance we get from various algorithms and problems is presented. We end with a discussion of whether we should be frightened about the progress we see around us.

Note: click on the gray square if you don’t see the embedded PDF…browsers vary.Read the rest

Computing the Madness of People

Bubble playing cardThe best paper I’ve read so far this year has to be Pseudo-Mathematics and Financial Charlatanism: The Effects of Backtest Overfitting on Out-of-sample Performance by David Bailey, Jonathan Borwein, Marcos López de Prado, and Qiji Jim Zhu. The title should ring alarm bells with anyone who has ever puzzled over the disclaimers made by mutual funds or investment strategists that “past performance is not a guarantee of future performance.” No, but we have nothing but that past performance to judge the fund or firm on; we could just pick based on vague investment “philosophies” like the heroizing profiles in Kiplingers seem to promote or trust that all the arbitraging has squeezed the markets into perfect equilibria and therefore just use index funds.

The paper’s core tenets extend well beyond financial charlatanism, however. They point out that the same problem arises in drug discovery where main effects of novel compounds may be due to pure randomness in the sample population in a way that is masked by the sample selection procedure. The history of mental illness research has similar failures, with the head of NIMH remarking that clinical trials and the DSM for treating psychiatric symptoms is too often “shooting in the dark.”

The core suggestion of the paper is remarkably simple, however: use held-out data to validate models. Remarkably simple but apparently rarely done in quantitative financial analysis. The researchers show how simple random walks can look like a seasonal price pattern, and how by sending binary signals about market performance to clients (market will rise/market will fall) investment advisors can create a subpopulation that thinks they are geniuses as other clients walk away due to losses. These rise to the level of charlatanism but the problem of overfitting is just one of pseudo-mathematics where insufficient care is used in managing the data.… Read the rest

On the Non-Simulation of Human Intelligence

There is a curious dilemma that pervades much machine learning research. The solutions that we are trying to devise are supposed to minimize behavioral error by formulating the best possible model (or collection of competing models). This is also the assumption of evolutionary optimization, whether natural or artificial: optimality is the key to efficiently outcompeting alternative structures, alternative alleles, and alternative conceptual models. The dilemma is whether such optimality is applicable to the notoriously error prone, conceptual flexible, and inefficient reasoning of people. In other words, is machine learning at all like human learning?

I came across a paper called “Multi-Armed Bandit Bayesian Decision Making” while trying to understand what Ted Dunning is planning to talk about at the Big Data Science Meetup at SGI in Fremont, CA a week from Saturday (I’ll be talking, as well) that has a remarkable admission concerning this point:

Human behaviour is after all heavily influenced by emotions, values, culture and genetics; as agents operating in a decentralised system humans are notoriously bad at coordination. It is this fact that motivates us to develop systems that do coordinate well and that operate outside the realms of emotional biasing. We use Bayesian Probability Theory to build these systems specifically because we regard it as common sense expressed mathematically, or rather `the right thing to do’.

The authors continue on to suggest that therefore such systems should instead be seen as corrective assistants for the limitations of human cognitive processes! Machines can put the rational back into reasoned decision-making. But that is really not what machine learning is used for today. Instead, machine learning is used where human decision-making processes are unavailable due to the physical limitations of including humans “in the loop,” or the scale of the data involved, or the tediousness of the tasks at hand.… Read the rest