Conferences

[dmm]

The details will be in comments (I have mixed feeling about all this).

I am seeing people talking about their talks being accepted for Strange Loop. It seems that this will be the last Strange Loop conference (I have no idea why).

Terence Tao is co-organizing "AI to Assist Mathematical Reasoning" online workshop (June 12-14).

Joint conference of "Algebra and Coalgebra in Computer Science" and "Mathematical Foundations of Programming Semantics" is on June 19-23 (free for those who participate online).

Comments

[dmm]

So, stepping back, we are gradually moving towards a situation when AI can assist us to understand things and do things, but correctness is not at all guaranteed.

In this sense, when I am looking at the abstracts of CALCO 2023 & MFPS XXXIX, https://coalg.org/calco-mfps-2023/ and pondering how to make this kind of difficult math understandable and usable, AI assistance is the only thing I am hoping for.

So, what Terence Tao is co-organizing here is one of the things which are quite relevant, but the program is a bit too bureaucratic for my taste:

https://terrytao.wordpress.com/2023/06/02/ai-to-assist-mathematical-reasoning-a-workshop/

https://www.nationalacademies.org/documents/embed/link/LF2255DA3DD1C41C0A42D3BEF0989ACAECE3053A6A9B/file/D5DBCD07BFB3B3835BA234DD7CC8F45DED111D71E18F?noSaveAs=1

So... I don't know whether this is at all useful to attend...

[dmm]

https://twitter.com/strangeloop_stl

https://thestrangeloop.com/2023/sessions.html - most of the program is posted

https://www.thestrangeloop.com/ - September 21-22

I am not sure why they decided to end this conference series (of course, it's not clear how long the remnants of the "world as we know it" will last; it might be that Fall 2024 would not make much sense anyway, especially for an in-person conference).

[juan_gandhi]

I enjoyed StrangeLoop once or twice, but that was enough.

As to "Algebra and Coalgebra", I'd be curious a little bit, although it's been all pretty much known for a while.

[chaource]

At some point I had an impression that AI could help in automatic theorem proving. For example, the first-order logic is not decidable but an AI could learn to prove or disprove its theorems much as it learned how to play chess and go. There is a "game semantics" in theorem proving where the "game moves" consist of possible specific choice of the next axiom or derivation rule of the logic, and the "response" is the next set of sequents or terms to be proved. Is that what they are trying to do now?

[dmm]

Jun 20 session at https://coalg.org/calco-mfps-2023/programme/

Special Session "Category Theory" in Machine Learning
Lindley 102
15:30 - 15:40
Welcome and Introduction
Fabio Zanasi
Abstract
I will introduce the session and our speakers by giving a very brief overview of the state of the art in categorical approaches to machine learning.
15:40 - 16:10
Differential Categories and Machine Learning
Jean-Simon Pacaud Lemay
Abstract
Differential categories provide the categorical semantics of differentiation in both mathematics and computer science. Differential categories have also found many interesting applications, most recently in relation to automatic differentiation and machine learning. In this talk, I will give an overview of how differential categories (including reverse differential categories and tangent categories) are used for machine learning and other related areas. I'll talk about the past, what's been done so far, the present, what's currently being worked on, and the future, what are some important questions that need to be solved.
16:10 - 16:40
A dynamic monoidal category for deep learning
Brandon Shapiro
Abstract
A gradient descender can be regarded as an "open dynamical system" in which the state of the system (parameters to a function) is updated based on external input (feedback). Open dynamical systems can be modeled using the theory of coalgebras for polynomial functors, which can be composed in network configurations to build up complex systems from simpler parts, and the structure of series and parallel composition of these systems is described by the notion of dynamic monoidal categories. I will introduce dynamic monoidal categories, describe how gradient descenders form an example, and discuss avenues for using this abstract structure to produce concrete applications.
16:40 - 17:10
Is there a place for semantics in machine learning?
Prakash Panangaden
Abstract
Machine learning, especially deep learning, has had unprecedented success and in the past decade and a half and has generated much excitement beyond the confines of academia. As practitioners of formal semantics what can we contribute? In this highly personal and opinionated talk, I will recount some successes, some promising directions and some things that I am less optimistic about. Certainly, we can try to provide proofs of correctness and gain insight into why things work or don’t work. I am less optimistic that we can come up with entirely new algorithms or techniques, but it is still worth trying.