Tag: existential threats

Rise, Teleonomatons!

Mark Davis2 Comments

My essay for the Berggruen Prize this year. Of course, the organization missed an opportunity to drop down a staggering rabbit hole and lean into a whole new regime of neologistic energetics, but I do like the prize-winning essays!

Rise, Teleonomatons

Meaning entanglements

I can’t figure out what some statements about science mean, though I have a fair background in a range of scientific areas. Now, I can peruse highly technical papers, browse abstracts, interpret graphs, study conclusions, and typically do just fine. The professional stuff is a procession of arid facts and assumes the reader knows the basic definitions for things. It’s the popular versions of scientific insights that befuddle me—especially the definitions that try mightily to bridge meanings for learners. So do introductory texts. Light is a wave? Not exactly. Atoms are particles? Well, not quite, but they are small. Electrons orbit the nucleus? No, really, no. A force is an influence tending to change the motion of a body? OK, but what is an influence? People are influenced, aren’t they? Or under the influence.

And then there are texts like those of existential philosophers that leave me completely befuddled. What is this “Being” that they write about and how did it get so enlarged in significance, capitalized, and shoehorned by the translator into incomprehensible juxtapositions with other bulbous words?

It may be low pedantry to expect clarity from words and writing. We generally just roll with it and use the terms according to conventions inferred from reading and learning. We rush over the imperfect metaphorical bridges, the analogies, the similes. For physics, definitions are bound to the equations and measurement properties that accompany the words of description, and they become a semantic pier that is constantly informing our relationship with the ideas.… Read the rest

Machine Learning and the Coming Robot Apocalypse

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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

Alien Singularities and Great Filters

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Life on MarsNick Bostrom at Oxford’s Future of Humanity Institute takes on Fermi’s question “Where are they?” in a new paper on the possibility of life on other planets. The paper posits probability filters (Great Filters) that may have existed in the past or might be still to come and that limit the likelihood of the outcome that we currently observe: our own, ahem, intelligent life. If a Great Filter existed in our past—say the event of abiogenesis or prokaryote to eukaryote transition—then we can somewhat explain the lack of alien contact thus far: our existence is of very low probability. Moreover, we can expect to not find life on Mars.

If, however, the Great Filter exists in our future then we might see life all over the place (including the theme of his paper, Mars). Primitive life is abundant but the Great Filter is somewhere in our future where we annihilate ourselves, thus explaining why Fermi’s They are not here while little strange things thrive on Mars, and beyond. It is only advanced life that got squeezed out by the Filter.

Bostrom’s Simulation Hypothesis provides a potential way out of this largely pessimistic perspective. If there is a very high probability that civilizations achieve sufficient simulation capabilities that they can create artificial universes prior to conquering the vast interstellar voids needed to move around and signal with adequate intensity, it is equally possible that their “exit strategy” is a benign incorporation into artificial realities that prevents corporeal destruction by other means. It seems unlikely that every advanced civilization would “give up” physical being under these circumstances (in Teleology there are hold-outs from the singularity though they eventually die out), which would mean that there might remain a sparse subset of active alien contact possibilities.… Read the rest

Inching Towards Shannon’s Oblivion

Mark Davis3 Comments

SkynetFollowing Bill Joy’s concerns over the future world of nanotechnology, biological engineering, and robotics in 2000’s Why the Future Doesn’t Need Us, it has become fashionable to worry over “existential threats” to humanity. Nuclear power and weapons used to be dreadful enough, and clearly remain in the top five, but these rapidly developing technologies, asteroids, and global climate change have joined Oppenheimer’s misquoted “destroyer of all things” in portending our doom. Here’s Max Tegmark, Stephen Hawking, and others in Huffington Post warning again about artificial intelligence:

One can imagine such technology outsmarting financial markets, out-inventing human researchers, out-manipulating human leaders, and developing weapons we cannot even understand. Whereas the short-term impact of AI depends on who controls it, the long-term impact depends on whether it can be controlled at all.

I almost always begin my public talks on Big Data and intelligent systems with a presentation on industrial revolutions that progresses through Robert Gordon’s phases and then highlights Paul Krugman’s argument that Big Data and the intelligent systems improvements we are seeing potentially represent a next industrial revolution. I am usually less enthusiastic about the timeline than nonspecialists, but after giving a talk at PASS Business Analytics Friday in San Jose, I stuck around to listen in on a highly technical talk concerning statistical regularization and deep learning and I found myself enthused about the topic once again. Deep learning is using artificial neural networks to classify information, but is distinct from traditional ANNs in that the systems are pre-trained using auto-encoders to have a general knowledge about the data domain. To be clear, though, most of the problems that have been tackled are “subsymbolic” for image recognition and speech problems.… Read the rest