BEGIN:VCALENDAR
VERSION:2.0
PRODID:researchseminars.org
CALSCALE:GREGORIAN
X-WR-CALNAME:researchseminars.org
BEGIN:VEVENT
SUMMARY:Sergei Gukov (Caltech)
DTSTART:20240929T070000Z
DTEND:20240929T081000Z
DTSTAMP:20260414T080159Z
UID:AI-Math-2024/1
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/AI-Math-2024
 /1/">Mathematics and AI: latest trends and future</a>\nby Sergei Gukov (Ca
 ltech) as part of AI and Mathematics: Current Trends and Future Directions
 \n\nLecture held in Bar-Ilan University.\n\nAbstract\nIn this talk\, inten
 ded for a broad audience\, I will use concrete examples from combinatorial
  group theory and low-dimensional topology to illustrate how rapid growth 
 of AI algorithms can change the way we do mathematical research and help u
 s with some of the most difficult mathematical challenges. One of the goal
 s of this talk is to provide a gentle introduction to some of the modern t
 ools in Machine Learning\, in part explaining its increasing role in every
 day life and in pure mathematics as well.\n
LOCATION:https://researchseminars.org/talk/AI-Math-2024/1/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Boris Yangel (Nebius)
DTSTART:20240929T081500Z
DTEND:20240929T090000Z
DTSTAMP:20260414T080159Z
UID:AI-Math-2024/2
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/AI-Math-2024
 /2/">Teaching LLM agents to automate software engineering tasks</a>\nby Bo
 ris Yangel (Nebius) as part of AI and Mathematics: Current Trends and Futu
 re Directions\n\nLecture held in Bar-Ilan University.\n\nAbstract\nLLM tec
 hnology has come to a point where it becomes possible to build agentic sys
 tems that can perform complex sequences of actions over a code repository 
 to implement new features and fix bugs in an interactive environment\, pro
 vided with just a textual description of the issue that needs to be resolv
 ed. In this talk I will give an overview of this emergent avenue of resear
 ch\, present our recent results regarding training open source models to p
 erform well in this domain\, and talk about the challenges that we have fa
 ced.\n
LOCATION:https://researchseminars.org/talk/AI-Math-2024/2/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Nachum Dershowitz (Tel Aviv University)
DTSTART:20240929T101000Z
DTEND:20240929T104000Z
DTSTAMP:20260414T080159Z
UID:AI-Math-2024/3
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/AI-Math-2024
 /3/">Language Models have a Hard Time Thinking Logically</a>\nby Nachum De
 rshowitz (Tel Aviv University) as part of AI and Mathematics: Current Tren
 ds and Future Directions\n\nLecture held in Bar-Ilan University.\n\nAbstra
 ct\nI will illustrate some of the difficulties modern generative language 
 models have with logical reasoning (propositional and first order) and wit
 h self reflection.\n
LOCATION:https://researchseminars.org/talk/AI-Math-2024/3/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Yuval Dolev (Bar-Ilan University)
DTSTART:20240929T104500Z
DTEND:20240929T113000Z
DTSTAMP:20260414T080159Z
UID:AI-Math-2024/4
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/AI-Math-2024
 /4/">AI\, creativity\, and fundamental questions in the philosophy of math
 ematics</a>\nby Yuval Dolev (Bar-Ilan University) as part of AI and Mathem
 atics: Current Trends and Future Directions\n\nLecture held in Bar-Ilan Un
 iversity.\n\nAbstract\nCan AI be expected to be mathematically creative? I
  offer reasons why the answer should be negative. I do so in the context o
 f two central doctrines in the philosophy of mathematics – Platonism and
  intuitionism. I argue that despite being opposed to each other on almost 
 any question regarding the nature of mathematical objects and truth\, thes
 e two rival positions do share this – that creativity is not reducible e
 ither to randomness\, or to a deterministic computation. Moreover\, I sugg
 est there is a normative (aesthetic) foundation to mathematics\, to which 
 AI is blind. These considerations highlight the limitedness of AI and poin
 t to the indispensability and irreplaceability of human creativity.\n
LOCATION:https://researchseminars.org/talk/AI-Math-2024/4/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Kevin Buzzard (Imperial College London)
DTSTART:20240929T120000Z
DTEND:20240929T123000Z
DTSTAMP:20260414T080159Z
UID:AI-Math-2024/5
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/AI-Math-2024
 /5/">Formalising Fermat</a>\nby Kevin Buzzard (Imperial College London) as
  part of AI and Mathematics: Current Trends and Future Directions\n\nLectu
 re held in Bar-Ilan University.\n\nAbstract\nRight now it seems that human
 ity has got to the stage where teaching an interactive theorem prover a pr
 oof of Fermat's Last Theorem will be technically possible\, but a lot of w
 ork. What actually is involved\, and how can AI help?\n
LOCATION:https://researchseminars.org/talk/AI-Math-2024/5/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Amit Somech (Bar-Ilan University)
DTSTART:20240929T123500Z
DTEND:20240929T130500Z
DTSTAMP:20260414T080159Z
UID:AI-Math-2024/6
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/AI-Math-2024
 /6/">Data-Speaking LLM Agents</a>\nby Amit Somech (Bar-Ilan University) as
  part of AI and Mathematics: Current Trends and Future Directions\n\nLectu
 re held in Bar-Ilan University.\n\nAbstract\nWe all know that LLMs are gre
 at for all kinds of everyday tasks—writing\, editing\, summarizing artic
 les\, and even generating code on demand. But can we rely on them for data
 -focused tasks? On one hand\, LLMs are trained on massive text datasets\, 
 some of which include structured data like CSVs and JSON files. Plus\, tha
 nks to forums like Stack Overflow\, they should be able to help with writi
 ng SQL queries and data-processing code. However\, we’ve found that when
  it comes to working with data\, LLMs aren't as "fluent" as we might hope.
  When faced with larger tables\, they often struggle to fully understand t
 he structure or produce accurate tabular output. They also aren’t great 
 at verifying facts when the information is presented in table form\, even 
 if they "know" the facts. In this talk\, we’ll introduce several non-tri
 vial data tasks\, review how standard LLMs perform\, and explore new archi
 tectures that could push the current capabilities forward.\n
LOCATION:https://researchseminars.org/talk/AI-Math-2024/6/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Alexei Miasnikov (Stevens Institute of Technology)
DTSTART:20240929T131500Z
DTEND:20240929T140000Z
DTSTAMP:20260414T080159Z
UID:AI-Math-2024/7
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/AI-Math-2024
 /7/">Revolutionizing Math Education: Harnessing AI and Algorithms for Stud
 ent Success</a>\nby Alexei Miasnikov (Stevens Institute of Technology) as 
 part of AI and Mathematics: Current Trends and Future Directions\n\nLectur
 e held in Bar-Ilan University.\n\nAbstract\nWhile AI has captured widespre
 ad attention for its applications in various fields\, its role in mathemat
 ics education presents both exciting opportunities and distinct challenges
 . This talk will focus on Gradarius\, a system that uses graph algorithms 
 to provide step-by-step feedback on student solutions\, offering unmatched
  precision and rigor. We will explore how AI is integrated into this frame
 work—not as the core mathematical engine\, but as a tool to enhance usab
 ility\, analyze student behavior\, and adapt to diverse learning paths. Th
 is presentation will offer a balanced view of AI’s strengths and limitat
 ions\, with a focus on how cutting-edge algorithms and machine learning ca
 n work together to revolutionize the teaching and learning of mathematics.
 \n
LOCATION:https://researchseminars.org/talk/AI-Math-2024/7/
END:VEVENT
END:VCALENDAR