Academia is retarded (Was: XAI is over and out)

Hi,
As I said, “traditions” are a hindrance.
XAI was itself promoted by DARPA:
 > XAI: Explainable Artificial Intelligence
 > https://www.darpa.mil/research/programs/explainable-artificial-intelligence
This means that certain machine learning
methods have been problematized. But that
was 2018, and it's still like a virus
in people's minds.
You just have to say the keyword machine
learning and then XAI comes up. The academics
have been conditioned like a Pavlovian dog.
And you can't get out of this "tradition" anymore.
Bye
Mild Shock schrieb:

Hi,
 Another example of total nonsense:
 CfR: Vienna World Logic Day Lecture
Joao Marques-Silva on Trustable Explainable AI
14 Jan 2025, Online [WLD Event]
https://resources.illc.uva.nl/LogicList/newsitem.php?id=12030
 The abstract is out of date.  XAI was a problem a
few years ago.  But it has nothing to do with ChatGPT.
Because ChatGPT is not the machine learning that XAI
 is trying to fix. The fuzzy logic in ChatGPT has nothing
to do with deep learning and latent parameters.
ChatGPT throws everything back to natural language and data.
 Virtually no invented latent parameter. There are no
ontologies with top and bottom in the vectors.  They are
quite flat attribute structures that not only control
 words, but also sentences and polysemy.  See also:
 Sentence embedding
https://en.wikipedia.org/wiki/Sentence_embedding
 This means that the academic world is completely
overwhelmed.  And now stare in mental shock.  Don't
notice that "traditions" like XAI are already out of date.
 Mit freundlichen Grüssen
 P.S.: Here's the abstract, it's complete nonsense:
 Abstract:
Explainable artificial intelligence (XAI) is a mainstay of
trustworthy AI.  Recent years have witnessed massive efforts
towards delivering some sort of XAI solutions. Most of these
efforts are based on non-symbolic methods, an d invariably will
produce erroneous results. As a result, even if the predictions of
a machine learning model could be trusted, the lack of reliable
explanations will also make those predictions unworthy of trust.
This talk provides a brief glimpse of the emerging field of logic-based
explainable AI, a rigorous alternative to the still widely-used but
extremely problematic non-symbolic methods.
https://resources.illc.uva.nl/LogicList/newsitem.php?id=12030