Science, Technology & the Future | Kevin Korb - Is Machine Understanding the Key to AI? @scfu | Uploaded December 2018 | Updated October 2024, 55 minutes ago.
While AI developers model the output of human understanding as solutions to problems in the form of computer code - this doesn't mean the computer code has understanding.
Will we achieve Strong AI (or 'quality' superintelligence) without first achieving machine understanding?
What are some necessary ingredients a system must include for it to actually 'understand' a problem it is pointed at?
Discussion on CYC / Symbolic GOFAI attempts to create AI - the usefulness of philosophical investigations to help ask the right questions, frame the right research to ultimately a) know what your aiming for and b) how to get there.
Kevin explains what the 'Frame Problem' is and why symboic approaches will never solve it. He also discusses Bayesian Primitives in reference to predicting the edges of competence, and gracefully degrading/coping and learning at these edges without catastrophic failure.
References to Kevin's related writings:
Korb, K. B., & Nicholson, A. E. (2010). Bayesian artificial intelligence. CRC press. http://goog_479581742/
The Frame Problem: An AI Fairy Tale link.springer.com/article/10.1023/A:1008286921835
Korb, K.B. Minds and Machines (1998) 8: 317. doi.org/10.1023/A:1008286921835
Korb, K. B. (1995). Inductive learning and defeasible inference. Journal of Experimental & Theoretical Artificial Intelligence, 7(3), 291-324. tandfonline.com/doi/abs/10.1080/09528139508953814
Korb, K. B., & Thompson, C. (1994). Primitive concept formation. In Intelligent Information Systems, 1994. Proceedings of the 1994 Second Australian and New Zealand Conference on (pp. 362-366). IEEE. panda.sys.t.u-tokyo.ac.jp/kushiro/ReferencePaper/Concept%20formation/korb.pdf
And there is Norman Fenton's Bayes and the Law website. sites.google.com/site/bayeslegal/home
Many thanks for watching!
Consider supporting SciFuture by:
a) Subscribing to the SciFuture YouTube channel: youtube.com/subscription_center?add_user=TheRationalFuture
b) Donating
- Bitcoin: 1BxusYmpynJsH4i8681aBuw9ZTxbKoUi22
- Etherium: 0xd46a6e88c4fe179d04464caf42626d0c9cab1c6b
- Patreon: patreon.com/scifuture
c) Sharing the media SciFuture creates: scifuture.org
Kind regards,
Adam Ford
- Science, Technology & the Future
While AI developers model the output of human understanding as solutions to problems in the form of computer code - this doesn't mean the computer code has understanding.
Will we achieve Strong AI (or 'quality' superintelligence) without first achieving machine understanding?
What are some necessary ingredients a system must include for it to actually 'understand' a problem it is pointed at?
Discussion on CYC / Symbolic GOFAI attempts to create AI - the usefulness of philosophical investigations to help ask the right questions, frame the right research to ultimately a) know what your aiming for and b) how to get there.
Kevin explains what the 'Frame Problem' is and why symboic approaches will never solve it. He also discusses Bayesian Primitives in reference to predicting the edges of competence, and gracefully degrading/coping and learning at these edges without catastrophic failure.
References to Kevin's related writings:
Korb, K. B., & Nicholson, A. E. (2010). Bayesian artificial intelligence. CRC press. http://goog_479581742/
The Frame Problem: An AI Fairy Tale link.springer.com/article/10.1023/A:1008286921835
Korb, K.B. Minds and Machines (1998) 8: 317. doi.org/10.1023/A:1008286921835
Korb, K. B. (1995). Inductive learning and defeasible inference. Journal of Experimental & Theoretical Artificial Intelligence, 7(3), 291-324. tandfonline.com/doi/abs/10.1080/09528139508953814
Korb, K. B., & Thompson, C. (1994). Primitive concept formation. In Intelligent Information Systems, 1994. Proceedings of the 1994 Second Australian and New Zealand Conference on (pp. 362-366). IEEE. panda.sys.t.u-tokyo.ac.jp/kushiro/ReferencePaper/Concept%20formation/korb.pdf
And there is Norman Fenton's Bayes and the Law website. sites.google.com/site/bayeslegal/home
Many thanks for watching!
Consider supporting SciFuture by:
a) Subscribing to the SciFuture YouTube channel: youtube.com/subscription_center?add_user=TheRationalFuture
b) Donating
- Bitcoin: 1BxusYmpynJsH4i8681aBuw9ZTxbKoUi22
- Etherium: 0xd46a6e88c4fe179d04464caf42626d0c9cab1c6b
- Patreon: patreon.com/scifuture
c) Sharing the media SciFuture creates: scifuture.org
Kind regards,
Adam Ford
- Science, Technology & the Future
