Re: Two challenges related to KR of scientific papers and books

Dave
I did not suggest any handcrafted KR for AGI!! (you do seem to suggest that
I say things which I would not even remotely dream of).

I am suggesting to the explore the feasibility and usefulness of a natural
language representation  for latex notation-

You and I are on different planets, and speak different languages :-)

On Mon, Nov 21, 2022 at 5:53 PM Dave Raggett <dsr@w3.org> wrote:

>
> On 21 Nov 2022, at 09:36, Dave Raggett <dsr@w3.org> wrote:
>
> Anyone interested in this should take a look at the capabilities of
> Wolfram Mathematica, which is a mature closed source system for working
> with math. Some open source alternatives include SageMath, Gnu
> Octave, Jupyter and Maxima, e.g.
>
> Maxima is a system for the manipulation of symbolic and numerical
> expressions, including differentiation, integration, Taylor series, Laplace
> transforms, ordinary differential equations, systems of linear equations,
> polynomials, sets, lists, vectors, matrices and tensors.
>
>
> An AGI agent would go a lot further, and it would be interesting to
> consider how to train an AGI to give it good mathematical skills, and
> enable it to improve further by studying the mathematical literature.
>
>
> It is unlikely that such an AGI can be realised using handcrafted
> knowledge representations, sorry, Paola!
>
> I am therefore looking into how to architect reasoners that operate on
> latent semantics as an enabling technology. Deep learning has proven
> effective in developing large language models, but lack reasoning skills,
> e.g. take a look at the following example produced using BLOOM, where the
> human supplied text is in bold, and the text generated by the large
> language model is in italics.
>
> *There is one blue ball, two green balls and five red balls. How many
> balls are there in total?* *The first possible answer is '7' because
> there is one blue ball and six other balls. The second possible answer is
> '4', because the first ball is blue, two of the other six balls are green
> and the remaining four balls are red.*
>
>
> Which sounds like a student trying to cover up their lack of mathematical
> competence!
>
> In principle, it should be straightforward to train a network to perform
> operations over latent semantics for natural language and for images,
> assuming we can devise an appropriate network architecture. The training
> dataset could be autogenerated.  Mathematics is probably an easier domain
> for this than software engineering, but who knows!
>
> Dave Raggett <dsr@w3.org>
>
>
>
>