The world famous Artificial Intelligence designer/expert Hugo de Garis has some horrific views on the future of technology. He demands people listen to his warnings wherever he goes. I thought I’d help him spread his nightmare with Camper Killer Commentary 17 “The Artilect War. The Nightmare of Hugo de Garis”. I hope you enjoy learning about your doom.
Hugo de Garis on AI, the story leading up to where we are now, and the possibilities for AI in the not too distant future. We have seen AI sprint past us in many cognitive domains, and in the coming decades we will likely see AI creep up on human level intelligence in other domains — once this becomes apparent, AI will become a central political issue — and nations will try to out-compete each other in dangerous AI arms-race. As AI encroaches further into areas of economic usefulness where humans traditionally dominated, how might avoid uselessness and stay relevant? Merge with the machines say’s Hugo.
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Started out as an interview ended up being a discussion between Hugo de Garis and (off camera) Adam Ford + Michel de Haan. 00:11 The concept of understanding under-recognised as an important aspect of developing AI 00:44 Re-framing perspectives on AI — the Chinese Room argument — and how can consciousness or understanding arise from billions of seemingly discreet neurons firing? (Should there be a binding problem of understanding similar to the binding problem of consciousness?) 04:23 Is there a difference between generality in intelligence and understanding? (and extentionally between AGI and artificial understanding?) 05:08 Ah Ha! moments — where the penny drops — what’s going on when this happens? 07:48 Is there an ideal form of understanding? Coherence & debugging — ah ha moments. 10:18 Webs of knowledge — contextual understanding. 12:16 Early childhood development — concept formation and navigation. 13:11 The intuitive ability for concept navigation isn’t complete. Is the concept of understanding a catch all? 14:29 Is it possible to develop AGI that doesn’t understand? Is generality and understanding the same thing? 17:32 Why is understanding (the nature of) understanding important? Is understanding reductive? Can it be broken down? 19:52 What would be the most basic primitive understanding be? 22:11 If (strong) AI is important, and understanding is required to build (strong) AI, what sorts of things should we be doing to make sense of understanding? Approaches — engineering, and copy the brain. 24:34 Is common sense the same thing as understanding? How are they different? 26:24 What concepts do we take for granted around the world — which when strong AI comes about will dissolve into illusions, and then tell us how they actually work under the hood? 27:40 Compression and understanding. 29:51 Knowledge, Gettier problems and justified true belief. Is knowledge different from understanding and if so how? 31:07 A hierarchy of intel — data, information, knowledge, understanding, wisdom. 33:37 What is wisdom? Experience can help situate knowledge in a web of understanding — is this wisdom? Is the ostensible appearance of wisdom necessarily wisdom? Think pulp remashings of existing wisdom in the form of trashy self-help literature. 35:38 Is understanding mapping knowledge into a useful framework? Or is it making accurate / novel predictions? 36:00 Is understanding like high resolution carbon copy like models that accurately reflect true nature or a mechanical process? 37:04 Does understanding come in gradients of topologies? Is there degrees or is it just on or off? 38:37 What comes first — understanding or generality? 40:47 Minsky’s ‘Society of Mind’ 42:46 Is vitalism alive in well in the AI field? Do people actually think there are ghosts in the machines? 48:15 Anthropomorphism in AI literature. 50:48 Deism — James Gates and error correction in super-symmetry. 52:16 Why are the laws of nature so mathematical? Why is there so much symmetry in physics? Is this confusing the map with the territory? 52:35 The Drake equation, and the concept of the Artilect — does this make Deism plausible? What about the Fermi Paradox? 55:06 Hyperintelligence is tiny — the transcention hypothesis — therefore civs go tiny — an explanation for the fermi paradox. 56:36 Why would *all* civs go tiny? Why not go tall, wide and tiny? What about selection pressures that seem to necessitate cosmic land grabs? 01:01:52 The Great Filter and the The Fermi Paradox. 01:02:14 Is it possible for an AGI to have a deep command of knowledge across a wide variety of topics/categories without understanding being an internal dynamic? Is the turing test good enough to test for understanding? What kinds of behavioral tests could reliably test for understanding? (Of course without the luxury of peering under the hood) 01:03:09 Does AlphaGo understand Go, or DeepBlue understand chess? Revisiting the Chinese Room argument. 01:04:23 More on behavioral tests for AI understanding. 01:06:00 Zombie machines — David Chalmers Zombie argument. 01:07:26 Complex enough algorithms — is there a critical point of complexity beyond which general intelligence likely emerges? Or understanding emerges? 01:08:11 Revisiting behavioral ‘turing’ tests for understanding. 01:13:05 Shape sorters and reverse shape sorters. 01:14:03 Would slightly changing the rules of Go confuse AlphaGo (after it had been trained)? Need for adaptivity — understanding concept boundaries, predicting where they occur, and the ability to mine outwards from these boundaries… 01:15:11 Neural nets and adaptivity. 01:16:41 AlphaGo documentary — worth a watch. Progresses in AI challenges human dignity which is a concern, but the DeepMind and the AlphaGo documentary seemed to be respectful. Can we manage a transition from human labor to full on automation while preserving human dignity?
Filmed in the dandenong ranges in victoria, australia.
Call them the Firstborn. Though they were not remotely human, they were flesh and blood, and when they looked out across the deeps of space, they felt awe, and wonder— and loneliness. As soon as they possessed the power, they began to seek for fellowship among the stars.
In their explorations, they encountered life in many forms, and watched the workings of evolution on a thousand worlds. They saw how often the first faint sparks of intelligence flickered and died in the cosmic night.
About five minutes after the 7.6 magnitude earthquake hit near Mexico’s southwest coast Monday, typically calm water deep in a Death Valley National Park cave started sloshing against the surrounding limestone rock.
The reverberations from the earthquake more than 1,500 miles away created what experts have called a “desert tsunami,” which on Monday made waves erupt up to 4 feet high in the cave known as Devils Hole, a pool of water about 10 feet wide, 70 feet long and more than 500 feet deep, in Amargosa Valley, Nevada.
The water in the partially filled cave has become an “unusual indicator of seismic activity” across the world, with earthquakes across the globe—as far as Japan, Indonesia and Chile—causing the water to splash up Devils Hole, according to the National Park Service website.
CERN physicist Jamie Boyd enters a tunnel close to the ATLAS detector, an experiment at the largest particle accelerator in the world. From there, he turns into an underground space labeled TI12.
“This is a very special tunnel,” Boyd says, “because this is where the old transfer line used to exist for the Large Electron-Positron Collider, before the Large Hadron Collider.” After the LHC was built, a new transfer line was added, “and this tunnel was then abandoned.”
The tunnel is abandoned no more. Its new resident is an experiment much humbler in size than the neighboring ATLAS detector. Five meters in length, the ForwArd Search ExpeRiment, or FASER, detector sits in a shallow excavated trench in the floor, surrounded by low railings and cables.
Methods for learning from noisy data, using phonetic embeddings to improve entity resolution, and quantization-aware training are a few of the highlights.
