Re: Linguistics for the Age of AI

>
>
>> The most impact on AI KR is that they urge people to reconsider the
>> investement into ML, sub-symbolic, knowledge-lean, and redirect that money
>> / energy / efforts to systems like the one they are building that is first
>> knowledge-based. They also repeat that even if they have results, there
>> will remain a lot to achieve.
>>
>
This is a useful starting point, thanks
if you could also point to the arguments they make for such
recommendations, (what is this statement based on
an
on what page nr and so on, we can try to address these assertions with
counter arguments. If you could put this post summarizing the key arguments
and discussion into a page, we can pin it to the AI KR CG wiki or something
as a learning resource to help explain to ML learners and students why NO,
intelligence without knowledge may exist at ameba and other biological
entity level, not at higher levels of cognitions that humans have evolved
to be capable of and that AI is aiming for

etc etc etc

On Sat, Oct 23, 2021 at 8:21 PM Amirouche BOUBEKKI <amirouche@hyper.dev>
wrote:
>
> ‐‐‐‐‐‐‐ Original Message ‐‐‐‐‐‐‐
> On Saturday, October 23rd, 2021 at 12:07 PM, Paola Di Maio <
paola.dimaio@gmail.com> wrote:
>
> > Thank you for sharing
> > It would be great to highlight the relevance and impact on AI KR
> >
>
>
> It is my first time reading that book, I am mid way through the book and
skimmed until the end and read the whole epilogue.
>
>
> When used as lecture text-book they recommend some knowledge about
linguistics.
>
> In the beginning they both survey what they call 'mainstream AI' hence
'mainstream NLP' and that is 'Machine Learning' that they call
knowledge-lean approach (they never mention 'sub-symbolic' possibly because
it might be perceived as a negative term). They say the current mainstream
AI is wrong headed, because it is too narrow in the scope of the tasks they
tackle, solving problems piece-wise does not and will not yield progress
toward AGI, they have not enough celestial goals, but ML useful. They
recommend and use an hybrid approach, that integrates knowledge computed by
ML algorithm into their semantic algorithms to start micro-theories that
may or may not be invalidated later in the pipeline or further input by the
end-user. They rely on Standford Core NLP library as the first pass in
their system. That is both a success and a failure. A success because they
did not have to develop the equivalent semantic algorithms, and their
system works, a failure because they did not forcast the required
engineering work to interop with the rest of their system that is semantic
based (but they hope it is still worth it, because that also means they
have more people taking part in the progress of the their system).
>
> Downstream they both rely on a lexicon (even if they say wordnet is not
without infecillities), and an ontology knowledge base. They are knowledge
engineers that are in charge of teaching the agent new words, and new
concepts. They keep stressing that knowledge engineers and knowledge
workers must engage into a life long cooperation with the system.
>
> That is my favorite part: they put together a lexicon of only 30 000
words upon which they bootstrapped at least two applications they give as
example 1) a robot patient to help medical personnel to learn 2) a self
driving car.
>
>
> Toward easing the self-learning process of the agent, knowledge engineer
can submit typed matrices with holes, that look like typed
feature-structures (not written like that in the book), that can be
subsumed or unified at runtime (not written like that in the book).
>
> The epilogue is a good summary. Past 30 years, current approaches
especially ML is wrong headed. If instead of aiming for short term yields,
the community  considered a life long conversation with a semantic system
where ML can be a helper, will yield better result *toward* AI complete
systems and AGI. Their system is already pratical and has seen successful
use (like any other...). They also mention micro-theories (blackboard
like), and handling combinatorial explosing due to considering the best
scoring senses of any given utterance. Also, they underscore the fact that
the system to make progress should both be practical and toward that goal
it should be goal and action oriented, I call that interactive. TIL
microtheories about mindreading the user. They also mention the word chunks
with double quotes.
>
> They do not mention MultiNet, they mention framenet, and clearly there is
some overlap with Herrman Helbig's Multinet work. They also mention that
W3C semweb is wrong headed and of no practical value referencing previous
publication on that very topic.
>
> As coder, I read a lot of good ideas, but little or no actual
implementation details, but that's prolly not what you were asking for.
>
> The most impact on AI KR is that they urge people to reconsider the
investement into ML, sub-symbolic, knowledge-lean, and redirect that money
/ energy / efforts to systems like the one they are building that is first
knowledge-based. They also repeat that even if they have results, there
will remain a lot to achieve.
>
>
> > On Sat, Oct 23, 2021 at 2:05 PM Amirouche BOUBEKKI <amirouche@hyper.dev>
wrote:
> >
>
> > > I just started reading the book.
> > >
>
> > > There is already several ideas that I took for granted (fwiw), in my
previous work like:
> > >
>
> > > A) AI agents must be interactive, it reads as action-oriented in the
book, quote:
> > >
>
> > > > Our model of NLU does not require that agents exhaustively
interpret every input to an externally imposed standard of perfection. Even
 people  don’t do that. Instead, agents operating in human-agent teams need
to understand inputs to the degree required to determine which goals,
plans, and actions they should pursue as a result of NLU
> > >
>
> > > B) The importance of explainable AI, quote:
> > >
>
> > > > The importance of explainable AI cannot be overstated: society at
large is unlikely to cede important decision-making in domains like health
care or finance to machines that cannot explain their advice.
> > >
>
> > > I highly recommend to read the introduction entitled 'Setting the
Stage':
> > >
>
> > >
https://direct.mit.edu/books/book/chapter-pdf/1891673/9780262363136_f000100.pdf
> > >
>
> > > The whole book is open-access pdf available at:
> > >
>
> > >   https://direct.mit.edu/books/book/5042/Linguistics-for-the-Age-of-AI