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1. Autism As a Disorder of High Intelligence - Bernard J. Crespi
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| Alterations of function in the autistic brain |
Crespi's paper attempts to synthesise what is know phenotypically about mental pathologies such as autism with the genetics of intelligence. His thesis is:
"... alleles for autism overlap broadly with alleles for high intelligence, which appears paradoxical given that autism is characterized, overall, by below-average IQ.and
This paradox can be resolved under the hypothesis that autism etiology commonly involves enhanced, but imbalanced, components of intelligence. This hypothesis is supported by convergent evidence showing that autism and high IQ share a diverse set of convergent correlates, including large brain size, fast brain growth, increased sensory and visual-spatial abilities, enhanced synaptic functions, increased attentional focus, high socioeconomic status,more deliberative decision-making, professional and occupational interests in engineering and physical sciences, and high levels of positive assortative mating."
".. autism represents most broadly a disorder of high intelligence (and low imagination), and schizophrenia a disorder of high imagination (and low intelligence) .. ."I think his argument works best at the Asperger-end of the spectrum, where brain architecture imbalances have not yet led to major social dysfunction.
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2. Deep Learning: A Critical Appraisal by Gary Marcus
Gary Marcus says "I present ten concerns for deep learning, and suggest that deep learning must be supplemented by other techniques if we are to reach artificial general intelligence."
He begins by observing starkly that "Deep learning, as it is primarily used, is essentially a statistical technique for classifying patterns, based on sample data, using neural networks with multiple layers."
Deep learning typically "knows" no more than can be inferred from regularities in the offered datasets (images, sounds, texts). With such limited data it's generally going to be impossible to infer background theories such as folk physics and folk psychology, let alone the physical and social properties of objects, people and behaviours which condition our everyday lives.
He then examines in detail just how consequentially-brittle deep learning systems are at generalising correctly outside of their training sets. This does not augur well for applications in open domains (such as self-driving cars, Internet censorship and the construction of competent social companions such as chatbots).
I completely agree with this paper, together with its conclusions that some serious new thinking is desperately required.
[Via "The Wild Week in AI"].
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3. "Quantum Computing in the NISQ era and beyond" by John Preskill
From the abstract.
"Noisy Intermediate-Scale Quantum (NISQ) technology will be available in the nearThis is a useful review of the field of quantum computing, aimed at (technically-minded) venture capitalists. It's accessible and describes what quantum computing is, the various ways it can be implemented and what a quantum computer can - and cannot - do.
future. Quantum computers with 50-100 qubits may be able to perform tasks which
surpass the capabilities of today’s classical digital computers, but noise in quantum
gates will limit the size of quantum circuits that can be executed reliably."
Almost the ideal introduction.
[Via Scott Aaronson].
The Deep Learning Critical Appraisal was the most interesting paper. I still believe that Fundamentals are important both for Quantum Computation and General AI ... with the outside possibility, of course, that a substantive linkage between the two topic areas might be found.
ReplyDeleteFunded "New Ideas" projects are still called for, in which researchers can present partial ideas for inspiration and review, ideally covering *both* topics (& not forgetting Cognitive Architecture). But Capitalism soon intervenes and puts a stop to such adventures ...
I rather suspect that Google isn't the only large capitalist company rather frantically funding fundamental AI research.
DeleteAs I have remarked before, it seems to happen that small AI companies get bought up by these bigger ones within a couple of years. That author was "Geometric Intelligence" founder.
DeleteGetting started might still be the challenge though...
Yes, I saw that. Getting acquired by Uber of all people!
DeleteGrabbing the start-up works for companies buying in to AI for the first time. But advancing the field now seems to require a fairly vast amount of capital (hardware, software infrastructure, data) as well as access to a large network of really top researchers.
Most of the top guys seem to want to work at Google or similar.