Tag: ai

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

Evolutionary Optimization and Environmental Coupling

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Red QueensCarl Schulman and Nick Bostrom argue about anthropic principles in “How Hard is Artificial Intelligence? Evolutionary Arguments and Selection Effects” (Journal of Consciousness Studies, 2012, 19:7-8), focusing on specific models for how the assumption of human-level intelligence should be easy to automate are built upon a foundation of assumptions of what easy means because of observational bias (we assume we are intelligent, so the observation of intelligence seems likely).

Yet the analysis of this presumption is blocked by a prior consideration: given that we are intelligent, we should be able to achieve artificial, simulated intelligence. If this is not, in fact, true, then the utility of determining whether the assumption of our own intelligence being highly probable is warranted becomes irrelevant because we may not be able to demonstrate that artificial intelligence is achievable anyway. About this, the authors are dismissive concerning any requirement for simulating the environment that is a prerequisite for organismal and species optimization against that environment:

In the limiting case, if complete microphysical accuracy were insisted upon, the computational requirements would balloon to utterly infeasible proportions. However, such extreme pessimism seems unlikely to be well founded; it seems unlikely that the best environment for evolving intelligence is one that mimics nature as closely as possible. It is, on the contrary, plausible that it would be more efficient to use an artificial selection environment, one quite unlike that of our ancestors, an environment specifically designed to promote adaptations that increase the type of intelligence we are seeking to evolve (say, abstract reasoning and general problem-solving skills as opposed to maximally fast instinctual reactions or a highly optimized visual system).

Why is this “unlikely”? The argument is that there are classes of mental function that can be compartmentalized away from the broader, known evolutionary provocateurs.… Read the rest

Singularity and its Discontents

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Kimmel botIf a machine-based process can outperform a human being is it significant? That weighty question hung in the background as I reviewed Jürgen Schmidhuber’s work on traffic sign classification. Similar results have emerged from IBM’s Watson competition and even on the TOEFL test. In each case, machines beat people.

But is that fact significant? There are a couple of ways we can look at these kinds of comparisons. First, we can draw analogies to other capabilities that were not accessible by mechanical aid and show that the fact that they outperformed humans was not overly profound. The wheel quickly outperformed human legs for moving heavy objects. The cup outperformed the hands for drinking water. This then invites the realization that the extension of these physical comparisons leads to extraordinary juxtapositions: the airline really outperformed human legs for transport, etc. And this, in turn, justifies the claim that since we are now just outperforming human mental processes, we can only expect exponential improvements moving forward.

But this may be a category mistake in more than the obvious differentiator of the mental and the physical. Instead, the category mismatch is between levels of complexity. The number of parts in a Boeing 747 is 6 million versus one moving human as the baseline (we could enumerate the cells and organelles, etc., but then we would need to enumerate the crystal lattices of the aircraft steel, so that level of granularity is a wash). The number of memory addresses in a big server computer is 64 x 10^9 or higher, with disk storage in the TBs (10^12). Meanwhile, the human brain has 100 x 10^9 neurons and 10^14 connections. So, with just 2 orders of magnitude between computers and brains versus 6 between humans and planes, we find ourselves approaching Kurzweil’s argument that we have to wait until 2040.… Read the rest

Evolution, Rationality, and Artificial Intelligence

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We now know that our cognitive facilities are not perfectly rational. Indeed, our cultural memory has regularly reflected that fact. But we often thought we might be getting a handle on what it means to be rational by developing models for what good thinking might be like and using it in political, philosophical, and scientific discourse. The models were based on nascent ideas like the logical coherence of arguments, internal consistency, few tautologies, and the consistency with empirical data.

But an interesting and quite basic question is why should we be able to formulate logical rules and create increasingly impressive systems of theory and observations given a complex evolutionary history. We have big brains, sure, but they evolved to manage social relationships and find resources–not to understand the algebraic topology of prime numbers or the statistical oddities of quantum mechanics–yet they seem well suited for these newer and more abstract tasks.

Alvin Plantinga, a theist and modern philosopher whose work has touched everything from epistemology to philosophy of religion, formulated his Evolutionary Argument Against Naturalism (EANN) as a kind of complaint that the likelihood of rationality arising from evolutionary processes is very low (really he is most concerned with the probability of “reliability,” by which means that most conclusions and observations are true, but I am substituting rationality for this with an additional Bayesian overlay).

Plantinga mostly wants to advocate that maybe our faculties are rational because God made them rather than a natural process. The response to this from an evolutionary perspective is fairly simple: evolution is an adaptive process and adaptation to a series of niche signals involves not getting those signals wrong. There are technical issues that arise here concerning how specific adaptation can result in more general rational facilities but we can, at least in principle, imagine (and investigate) bridge rules that extend out from complex socialization to encompass the deep complexities of modern morality and the Leviathan state, and the extension of optimizing spear throwing to shooting rockets into orbit.… Read the rest