Government Office for Science: Future Risks of Frontier AI
Definition and object:
Frontier AI: "highly capable general-purpose AI models that can perform a wide variety of tasks and match or exceed the capabilities present in today’s most advanced models"
assets.publishing.service.gov.uk/media/653bc393… "As of October 2023, this primarily encompasses foundation models consisting of very large neural networks using transformer architectures"

(e.g., LLMs, "generative" AI)

A Sunday late apéritif:
Dreyfus and Dreifus (1990). Making a mind versus modelling the brain: Artificial Intelligence back at a branch-point. In M. Boden (Ed). The Philosophy of Artificial Intelligence.

“In the early 1950s, as calculating machines were coming into their own… “At that point two opposed visions of what computers could be, each with its correlated research programme, emerged and struggled for recognition. One faction saw computers as a system for manipulating mental symbols; the other, as a medium for modelling the brain.

It's been a long time since I added to the 'back to the sources (or classics) series'. This is another favourite for you.
A Framework for Misrepresenting Knowledge. H.L. Dreyfus (1979). In M. Ringle (Ed) Philosophical Perspectives in Artificial Intelligence. "... an interesting change has, indeed, taken place at the MIT AI Laboratory. In previous works (Minsky, 1968) Minsky and his co-workers sharply distinguished themselves from workers in cognitive simulation who presented their programs as psychological theories,

This time I am going to excerpt one of my own old papers.
There is much confusion about what constitutes a cognitive computational model and the underlying psychological theory.
. https://t.co/tGMznxEhVwmondragon.cal-r.org/home/Papers/Al…
We focussed our analysis on conditioning due to the early identification of ANNs and associative learning
The critique, however, can be easily extended to other cognitive phenomena.
.

https://twitter.com/twitemp1/status/1419326115039125504?s=20

A year ago, I summarised our DDA model. Afterwards, I presented it three or four times to different audiences, in none of them I was satisfied with the way I explained the problems that motivated the model and the solution we offered.

https://x.com/twitemp1/status/1056992789999824897

Today, I was preparing some slides introducing complex AL models, (with fully connected networks) and decided to give it a new go. My tactic this time has been to focus only on the model’s relevance in accounting for retrospective revaluation in the conditioning literature.

According to Wagner (2008) one critical result in favour of Pearce’s configural approach that could potentially be solved by new, more advanced elemental developments is that obtained when reversing a conditioned inhibitor.

link.springer.com/article/10.375… Following A+, AB-, B becomes a conditioned inhibitor— able, e.g., to reduce the responding to a different excitatory CS. According to the RW model, the discrimination is learned, with A becoming excitatory and AB neutral, as a result of B becoming as negative as A positive.

I firmly advocate for our right to be lazy, thus for my dear lazy (otherwise very busy to read a 140 pp. paper) fellows I'm going to summarise the DDA M'odel (upadated preprint, 2nd round)

>biorxiv.org/content/early/… The DDA is a “real-time” formal model of associative learning which incorporates representational and computational mechanisms able to make accurate predictions of a variety of phenomena that so far have eluded a unified account.
>