Miscellaneous

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Strong AI Nature - Ben Goertzel

Published on Sep 23, 2012

 

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Can we build a conscious computer?

Published on Dec 4, 2014

AI is more than just a bad guy in sci-fi films. How close are we to creating computers that actually think on their own?

 

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Paths to human-level AI
Murray Shanahan, part 1

Paths to human-level AI
Murray Shanahan, part 2

Published on Oct 30, 2015

Murray Shanahan is Professor of Cognitive Robotics in the Dept. of Computing at Imperial College London, where he heads the Neurodynamics Group. His publications span artificial intelligence, robotics, logic, dynamical systems, computational neuroscience, and philosophy of mind. He was scientific advisor to the film Ex Machina, which was partly inspired by his book “Embodiment and the Inner Life” (OUP, 2010).

In this talk he describes what he sees as the main obstacles to achieving human-level artificial intelligence given the current state of machine learning, and suggests a number of ways these obstacles might be overcome. These include speculations on a) Geoff Hinton's notion of thought vectors, b) hybrid symbolic-neural approaches, and c) cognitive architectures inspired by Bernard Baars's global workspace theory.

 

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Symposium: Algorithms Among Us - Panel "Human-level AI – if, how, and when?"

Published on Jun 24, 2016

 

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The Edge of Artificial Intelligence - Human Level AI - Katja Hofmann

Published on Aug 5, 2016

 

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"Complexity no bar to AI"
Critics of AI risk suggest diminishing returns to computing means AI will be weak; I argue that this argument breaks if any premises rejected (computer science, transhumanism)

created: June 1, 2014; modified: November 29, 2016

 

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Article "Understanding the four types of AI, from reactive robots to self-aware beings"

by Arend Hintze
November 14, 2016

 

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"Vicarious wants to create human-level Artificial Intelligence"

May 12, 2016

 

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Creating Human-Level AI | Yoshua Bengio

Published on Jan 29, 2017

AI pioneer Yoshua Bengio explores paths forward to human-level artificial intelligence at the January 2017 Asilomar conference organized by the Future of Life Institute.

 

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Article "To Keep AI from 'Eating a Table,' Scientists Make It Read Wikipedia"

by Dan Robitzski
September 26, 2017

 

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Article "Human-level AI: how far are we?"

by George Zarkadakis
October 22, 2017

 

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Symbiotic AI Using wearable devices to teach computers how to live in a human world

Published on Oct 25, 2017

 

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Article "AI news: Oxford University scientists synthesise human-like thoughts in machines"
ARTIFICIAL INTELLIGENCE (AI) is evolving, with Oxford University researchers synthesising human-like thinking in machines.

by Tom Fish
May 31, 2019

 

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Article "Computers Evolve a New Path Toward Human Intelligence"
Neural networks that borrow strategies from biology are making profound leaps in their abilities. Is ignoring a goal the best way to make truly intelligent machines?

by Matthew Hutson
November 6, 2019

Kenneth Stanley

 

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The biological path toward AGI (Matt Taylor's talk from UCSC Silicon Valley Meetup Jan 2020)

Jan 21, 2020

Presentation given by Matt Taylor of Numenta at the "Towards AGI" Meetup at UCSC Silicon Valley Extension.

 

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Steven Novella - Narrow AI, AGI, prediction & skepticism

Feb 23, 2020

 

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Article "What is artificial general intelligence (general AI/AGI)?"

by Ben Dickson
May 13, 2020

 

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Article "Is The Goal-Driven Systems Pattern The Key To Artificial General Intelligence (AGI)?"

by Ron Schmelzer
May 31, 2020

 

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Regulating the rise of Artificial General Intelligence

Jun 1, 2020

As research around the world proceeds to improve the power, the scope, and the generality of AI systems, should developers adopt regulatory frameworks to help steer progress?

What are the main threats that such regulations should be guarding against? In the midst of an intense international race to obtain better AI, are such frameworks doomed to be ineffective? Might such frameworks do more harm than good, hindering valuable innovation? Are there good examples of precedents, from other fields of technology, of international agreements proving beneficial? Or is discussion of frameworks for the governance of AGI (Artificial General Intelligence) a distraction from more pressing issues, given the potential long time scales ahead before AGI becomes a realistic prospect?

This 90 minute London Futurists live Zoom webinar featured a number of panellists with deep insight into the issues of improving AI:

*) Joanna Bryson, Professor of Ethics and Technology at the Hertie School, Berlin
*) Dan Faggella, CEO and Head of Research, Emerj Artificial Intelligence Research
*) Nell Watson, tech ethicist, machine learning researcher, and social reformer

 

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Musing on understanding & AI - Hugo de Garis, Adam Ford, Michel de Haan

July 27, 2020

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.