Category: AI

A Learning Smorgasbord

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Compliments of a discovery by Futurism, the paper The Autodidactic Universe by a smorgasbord of contemporary science and technology thinkers caught my attention for several reasons. First was Jaron Lanier as a co-author. I knew Jaron’s dad, Ellery, when I was a researcher at NMSU’s now defunct Computing Research Laboratory. Ellery had returned to school to get his psychology PhD during retirement. In an odd coincidence, my brother had also rented a trailer next to the geodesic dome Jaron helped design and Ellery lived after my brother became emancipated in his teens. Ellery may have been his landlord, but I am not certain of that.

The paper is an odd piece of kit that I read over two days in fits and spurts with intervening power lifting interludes (I recently maxed out my Bowflex and am considering next steps!). It initially has the feel of physicists trying to reach into machine learning as if the domain specialists clearly missed something that the hardcore physical scientists have known all along. But that concern dissipated fairly quickly and the paper settled into showing isomorphisms between various physical theories and the state evolution of neural networks. OK, no big deal. Perhaps they were taken by the realization that the mathematics of tensors was a useful way to describe network matrices and gradient descent learning. They then riffed on that and looked at the broader similarities between the temporal evolution of learning and quantum field theory, approaches to quantum gravity, and cosmological ideas.

The paper, being a smorgasbord, then investigates the time evolution of graphs using a lens of graph theory. The core realization, as I gleaned it, is that there are more complex graphs (visually as well as based on the diversity of connectivity within the graph) and pointlessly uniform or empty ones.… Read the rest

Distributed Contexts in the Language Game

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The meaning of words and phrases can be a bit hard to pin down. Indeed, the meaning of meaning itself is problematical. I can point to a dictionary and say, well, there is where we keep the meanings of things, but that is just a record of the way in which we use the language. I’m personally fond of a kind of philosophical perspective on this matter of meaning that relies on a form of holism. That is, words and their meanings are defined by our usages of them, our historical interactions with them in different contexts, and subtle distinctive cues that illuminate how words differ and compare. Often, but not always, the words are tied to things in the world, as well, and therefore have a fastness that resists distortions and distinctions.

This is, of course, a critical area of inquiry when trying to create intelligent machines that deal with language. How do we imbue the system with meaning, represent it within the machine, and apply it to novel problems that show intelligent behavior? In approaching the problem, we must therefore be achieving some semblance of intelligence in a fairly rigorous way since we are simulating it with logical steps.

The history of philosophical and linguistic interest in these topics is fascinating, ranging from Wittgenstein’s notion of a language game that builds up rules of use to Firth’s expansion to formalization of collocation of words as critical to meaning. In artificial intelligence, this concept of collocation has been expanded further to include interchangeability of contexts. Thus, boat and ship occur in more similar contexts than boat and bank.

A general approach to acquiring these contexts is based on the idea of dimensionality reduction in various forms.… Read the rest

Intelligent Borrowing

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There has been a continuous bleed of biological, philosophical, linguistic, and psychological concepts into computer science since the 1950s. Artificial neural networks were inspired by real ones. Simulated evolution was designed around metaphorical patterns of natural evolution. Philosophical, linguistic, and psychological ideas transferred as knowledge representation and grammars, both natural and formal.

Since computer science is a uniquely synthetic kind of science and not quite a natural one, borrowing and applying metaphors seems to be part of the normal mode of advancement in this field. There is a purely mathematical component to the field in the fundamental questions around classes of algorithms and what is computable, but there are also highly synthetic issues that arise from architectures that are contingent on physical realizations. Finally, the application to simulating intelligent behavior relies largely on three separate modes of operation:

  1. Hypothesize about how intelligent beings perform such tasks
  2. Import metaphors based on those hypotheses
  3. Given initial success, use considerations of statistical features and their mappings to improve on the imported metaphors (and, rarely, improve with additional biological insights)

So, for instance, we import a simplified model of neural networks as connected sets of weights representing some kind of variable activation or inhibition potentials combined with sudden synaptic firing. Abstractly we already have an interesting kind of transfer function that takes a set of input variables and has a nonlinear mapping to the output variables. It’s interesting because being nonlinear means it can potentially compute very difficult relationships between the input and output.

But we see limitations, immediately, and these are observed in the history of the field. For instance, if you just have a single layer of these simulated neurons, the system isn’t fundamentally complex enough to compute any complex functions, so we add a few layers and then more and more.… Read the rest

The Pregnant Machinery of Metaphor

Mark Davis2 Comments

Sylvia Plath has some thoughts about pregnancy in “Metaphors”:

I’m a riddle in nine syllables,
An elephant, a ponderous house,
A melon strolling on two tendrils.
O red fruit, ivory, fine timbers!
This loaf’s big with its yeasty rising.
Money’s new-minted in this fat purse.
I’m a means, a stage, a cow in calf.
I’ve eaten a bag of green apples,
Boarded the train there’s no getting off.

It seems, at first blush, that metaphors have some creative substitutive similarity to the concept they are replacing. We can imagine Plath laboring over this child in nine lines, fitting the pieces together, cutting out syllables like dangling umbilicals, finding each part in a bulging conception, until it was finally born, alive and kicking.

OK, sorry, I’ve gone too far, fallen over a cliff, tumbled down through a ravine, dropped into the foaming sea, where I now bob, like your uncle. Stop that!

Let’s assume that much human creativity occurs through a process of metaphor or analogy making. This certainly seems to be the case in aspects of physics when dealing with difficult to understand new realms of micro- and macroscopic phenomena, as I’ve noted here. Some creative fields claim a similar basis for their work, with poetry being explicit about the hidden or secret meaning of poems. Moreover, I will also suppose that a similar system operates in terms of creating networks of semantics by which we understand the meaning of words and their relationships to phenomena external to us, as well as our own ideas. In other words, semantics are a creative puzzle for us.

What do we know about this system and how can we create abstract machines that implement aspects of it?… Read the rest

One Shot, Few Shot, Radical Shot

Mark Davis1 Comment

Exunoplura is back up after a sad excursion through the challenges of hosting providers. To be blunt, they mostly suck. Between systems that just don’t work right (SSL certificate provisioning in this case) and bad to counterproductive support experiences, it’s enough to make one want to host it oneself. But hosting is mostly, as they say of war, long boring periods punctuated by moments of terror as things go frustratingly sideways. But we are back up again after two hosting provider side-trips!

Honestly, I’d like to see an AI agent effectively navigate through these technological challenges. Where even human performance is fleeting and imperfect, the notion that an AI could learn how to deal with the uncertain corners of the process strikes me as currently unthinkable. But there are some interesting recent developments worth noting and discussing in the journey towards what is named “general AI” or a framework that is as flexible as people can be, rather than narrowly tied to a specific task like visually inspecting welds or answering a few questions about weather, music, and so forth.

First, there is the work by the OpenAI folks on massive language models being tested against one-shot or few-shot learning problems. In each of these learning problems, the number of presentations of the training data cases is limited, rather than presenting huge numbers of exemplars and “fine tuning” the response of the model. What is a language model? Well, it varies across different approaches, but typically is a weighted context of words of varying length, with the weights reflecting the probabilities of those words in those contexts over a massive collection of text corpora. For the OpenAI model, GPT-3, the total number of parameters (words/contexts and their counts) is an astonishing 175 billion using 45 Tb of text to train the model.… Read the rest

Ensembles Against Abominables

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It seems obvious to me that when we face existential threats we should make the best possible decisions. I do this with respect to investment decisions, as well. I don’t rely on “guts” or feelings or luck or hope or faith or hunches or trends. All of those ideas are proxies for some sense of incompleteness in our understanding of probabilities and future outcomes.

So how can we cope with those kinds of uncertainties given existential threats? The core methodology is based on ensembles of predictions. We don’t actually want to trust an expert per se, but want instead to trust a basket of expert opinions—an ensemble of predictions. Ideally, those experts who have been more effective in the past should be given greater weight than those who have made poorer predictions. We most certainly should not rely on gut calls by abominable narcissists in what Chauncey Devega at Salon disturbingly characterizes as a “pathological kakistocracy.”

Investment decision-making takes exactly this form, when carried out rationally. Index funds adjust their security holdings in relationship to an index like the S&P 500. Since stock markets have risen since their inceptions with, of course, set backs along the way, an index is a reliable ensemble approach to growth. Ensembles smooth predictions and smooth out brittleness.

Ensemble methods are also core to predictive improvements in machine learning. While a single decision tree trained on data may overweight portions of the data set, an ensemble of trees (which we call a forest, of course) smoothes the decision making by having each tree become only a part of the final vote for a prediction. The training of the individual trees is based on a randomized subset of the data, allowing for specialization of stands of trees, but preserving overall effectiveness of the system.… Read the rest

Forever Uncanny

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Quanta has a fair round up of recent advances in deep learning. Most interesting is the recent performance on natural language understanding tests that are close to or exceed mean human performance. Inevitably, John Searle’s Chinese Room argument is brought up, though the author of the Quanta article suggests that inferring the Chinese translational rule book from the data itself is slightly different from the original thought experiment. In the Chinese Room there is a person who knows no Chinese but has a collection of translational reference books. She receives texts through a slot and dutifully looks up the translation of the text and passes out the result. “Is this intelligence?” is the question and it serves as a challenge to the Strong AI hypothesis. With statistical machine translation methods (and their alternative mechanistic implementation, deep learning), the rule books have been inferred by looking at translated texts (“parallel” texts as we say in the field). By looking at a large enough corpus of parallel texts, greater coverage of translated variants is achieved as well as some inference of pragmatic issues in translation and corner cases.

As a practical matter, it should be noted that modern, professional translators often use translation memory systems that contain idiomatic—or just challenging—phrases that they can reference when translating new texts. The understanding resides in the original translator’s head, we suppose, and in the correct application of the rule to the new text by checking for applicability according to, well, some other criteria that the translator brings to bear on the task.

In the General Language Understand Evaluation (GLUE) tests described in the Quanta article, the systems are inferring how to answer Wh-style queries (who, what, where, when, and how) as well as identify similar texts.… Read the rest

Deep Learning with Quantum Decoherence

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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

Free Will and Algorithmic Information Theory

Mark Davis1 Comment

I was recently looking for examples of applications of algorithmic information theory, also commonly called algorithmic information complexity (AIC). After all, for a theory to be sound is one thing, but when it is sound and valuable it moves to another level. So, first, let’s review the broad outline of AIC. AIC begins with the problem of randomness, specifically random strings of 0s and 1s. We can readily see that given any sort of encoding in any base, strings of characters can be reduced to a binary sequence. Likewise integers.

Now, AIC states that there are often many Turing machines that could generate a given string and, since we can represent those machines also as a bit sequence, there is at least one machine that has the shortest bit sequence while still producing the target string. In fact, if the shortest machine is as long or a bit longer (given some machine encoding requirements), then the string is said to be AIC random. In other words, no compression of the string is possible.

Moreover, we can generalize this generator machine idea to claim that given some set of strings that represent the data of a given phenomena (let’s say natural occurrences), the smallest generator machine that covers all the data is a “theoretical model” of the data and the underlying phenomena. An interesting outcome of this theory is that it can be shown that there is, in fact, no algorithm (or meta-machine) that can find the smallest generator for any given sequence. This is related to Turing Incompleteness.

In terms of applications, Gregory Chaitin, who is one of the originators of the core ideas of AIC, has proposed that the theory sheds light on questions of meta-mathematics and specifically that it demonstrates that mathematics is a quasi-empirical pursuit capable of producing new methods rather than being idealistically derived from analytic first-principles.… Read the rest

The Elusive in Art and Artificial Intelligence

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Per caption.
Deep Dream (deepdreamgenerator.com) of my elusive inner Van Gogh.

How exactly deep learning models do what they do is at least elusive. Take image recognition as a task. We know that there are decision-making criteria inferred by the hidden layers of the networks. In Convolutional Neural Networks (CNNs), we have further knowledge that locally-receptive fields (or their simulated equivalent) provide a collection of filters that emphasize image features in different ways, from edge detection to rotation-invariant reductions prior to being subjected to a learned categorizer. Yet, the dividing lines between a chair and a small loveseat, or between two faces, is hidden within some non-linear equation composed of these field representations with weights tuned by exemplar presentation.

This elusiveness was at least part of the reason that neural networks and, generally, machine learning-based approaches have had a complicated position in AI research; if you can’t explain how they work, or even fairly characterize their failure modes, maybe we should work harder to understand the support for those decision criteria rather than just build black boxes to execute them?

So when groups use deep learning to produce visual artworks like the recently auctioned work sold by Christie’s for USD 432K, we can be reassured that the murky issue of aesthetics in art appreciation is at least paired with elusiveness in the production machine.

Or is it?

Let’s take Wittgenstein’s ideas about aesthetics as a perhaps slightly murky point of comparison. In Wittgenstein, we are almost always looking at what are effectively games played between and among people. In language, the rules are shared in a culture, a community, and even between individuals. These are semantic limits, dialogue considerations, standardized usages, linguistic pragmatics, expectations, allusions, and much more.… Read the rest