[LINK] Does current AI represent a dead end?

[Roger Clarke]

Music to *my* ears, at least.

http://rogerclarke.com/EC/AII.html#CML (2019)
http://rogerclarke.com/EC/AIEG.html#RF (2020)

But also:
http://www.rogerclarke.com/EC/RGAI.html#GAIC (2024)

_________________

On 5/12/2024 15:33, Kim Holburn wrote:
> https://www.bcs.org/articles-opinion-and-research/does-current-ai-represent-a-dead-end
> 
> Many of these neural network systems are stochastic, meaning that 
> providing the same input will not always lead to the same output. The 
> behaviour of such AI systems is ‘emergent’ — which means despite the 
> fact that the behaviour of each neuron is given by a precise 
> mathematical formula, neither this behaviour nor the way the nodes are 
> connected are of much help in explaining the network’s overall behaviour.
> 
> ...
> 
> This idea lies at the heart of piecewise development: parts can be 
> engineered (and verified) separately and hence in parallel, and reused 
> in the form of modules, libraries and the like in a ‘black box’ way, 
> with re-users being able to rely on any verification outcomes of the 
> component and only needing to know their interfaces and their behaviour 
> at an abstract level. Reuse of components not only provides increased 
> confidence through multiple and diverse use, but also saves costs.
> 
> ...
> 
> Current AI systems have no internal structure that relates meaningfully 
> to their functionality. They cannot be developed, or reused, as 
> components. There can be no separation of concerns or piecewise 
> development. A related issue is that most current AI systems do not 
> create explicit models of knowledge — in fact, many of these systems 
> developed from techniques in image analysis, where humans have been 
> notably unable to create knowledge models for computers to use, and all 
> learning is by example (‘I know it when I see it 
> <https://www.acluohio.org/en/cases/jacobellis-v-ohio-378-us-184-1964#:~:text=TheU.S.SupremeCourtreversed,tohardcorepornography...>’). This has multiple consequences for development and verification.
> 
> ...
> 
> Systems are not explainable, as they have no model of knowledge and no 
> representation of any ‘reasoning’.
> 
> ....
> 
> Verification comes with a subset of issues following from the above. The 
> only verification that is possible is of the system in its entirety; if 
> there are no handles for generating confidence in the system during its 
> development, we have to put all our eggs in the basket of post-hoc 
> verification.
> 
> ...
> 
> So, is there hope? I believe — though I would be happy to be proved 
> wrong on this — that current generative AI systems represent a dead end, 
> where exponential increases of training data and effort will give us 
> modest increases in impressive plausibility but no foundational increase 
> in reliability. I would love to see compositional approaches to neural 
> networks, hard as it appears.
> 

-- 
Roger Clarke                            mailto:Roger.Clarke at xamax.com.au
T: +61 2 6288 6916   http://www.xamax.com.au  http://www.rogerclarke.com

Xamax Consultancy Pty Ltd      78 Sidaway St, Chapman ACT 2611 AUSTRALIA 

Visiting Professorial Fellow                          UNSW Law & Justice
Visiting Professor in Computer Science    Australian National University