Forschungsinstitutionen

https://sfb1102.uni-saarland.de/sfbunisb/uploads/2022/10/LangSci_talk_Trips.pdf

Abstract:
AI can enhance programming experiences for a diverse set of programmers:
from professional developers and data scientists (proficient programmers)
who need help in software engineering and data wrangling, all the way to
spreadsheet users (low-code programmers) who need help in authoring
formulas, and students (novice programmers) who seek hints when stuck with
their programming homework. To communicate their need to AI, users can
express their intent explicitly—as input-output examples or
natural-language specification—or implicitly—where they encounter a bug
(and expect AI to suggest a fix), or simply allow AI to observe their last
few lines of code or edits (to have it suggest the next steps).

The task of synthesizing an intended program snippet from the user’s
intent is both a search and a ranking problem. Search is required to
discover candidate programs that correspond to the (often ambiguous)
intent, and ranking is required to pick the best program from multiple
plausible alternatives. This creates a fertile playground for combining
symbolic-reasoning techniques, which model the semantics of programming
operators, and machine-learning techniques, which can model human
preferences in programming. Recent advances in large language models like
Codex offer further promise to advance such neuro-symbolic techniques.

Finally, a few critical requirements in AI-assisted programming are
usability, precision, and trust; and they create opportunities for
innovative user experiences and interactivity paradigms. In this talk, I
will explain these concepts using some existing successes, including the
Flash Fill feature in Excel, Data Connectors in PowerQuery, and
IntelliCode/CoPilot in Visual Studio. I will also describe several new
opportunities in AI-assisted programming, which can drive the next set of
foundational neuro-symbolic advances.

Bio:
Sumit Gulwani is a computer scientist connecting ideas, people, and
research & practice. He invented the popular Flash Fill feature in Excel,
which has now also found its place in middle-school computing textbooks.
He leads the PROSE research and engineering team at Microsoft that
develops APIs for program synthesis and has incorporated them into various
Microsoft products including Visual Studio, Office, Notebooks, PowerQuery,
PowerApps, PowerAutomate, Powershell, and SQL. He is a sponsor of
storytelling trainings and initiatives within Microsoft. He has started a
novel research fellowship program in India, a remote apprenticeship model
to scale up impact while nurturing globally diverse talent and growing
research leaders. He has co-authored 11 award-winning papers (including 3
test-of-time awards from ICSE and POPL) amongst 140+ research publications
across multiple computer science areas and delivered 60+ keynotes/invited
talks. He was awarded the Max Planck-Humboldt medal in 2021 and the ACM
SIGPLAN Robin Milner Young Researcher Award in 2014 for his pioneering
contributions to program synthesis and intelligent tutoring systems. He
obtained his PhD in Computer Science from UC-Berkeley, and was awarded the
ACM SIGPLAN Outstanding Doctoral Dissertation Award. He obtained his BTech
in Computer Science and Engineering from IIT Kanpur, and was awarded the
President’s Gold Medal.

https://www.uni-saarland.de/aktuell/ringvorlesung-going-digital-25822.html

http://domino.mpi-inf.mpg.de/internet/events.nsf/0/65C120F417C10420C12588D700325C64

https://www.uni-saarland.de/aktuell/ringvorlesung-going-digital-1-25825.html

Abstract:
The feedback that users provide through their choices (e.g. clicks,
purchases) is one of the most common types of data readily available for
training autonomous systems, and it is widely used in online platforms.
However, naively training systems based on choice data may only improve
short-term engagement, but not the long-term sustainability of the
platform. In this talk, I will discuss some of the pitfalls of
engagement-maximization, and explore methods that allow us to supplement
engagement with additional criteria that are not limited to individual
action-response metrics. The goal is to give platform operators a new set
of macroscopic interventions for steering the dynamics of the platform,
providing a new level of abstraction that goes beyond the engagement with
individual recommendations or rankings.

Bio:
Thorsten Joachims is a Professor in the Department of Computer Science and
in the Department of Information Science at Cornell University, and he is
an Amazon Scholar. His research interests center on a synthesis of theory
and system building in machine learning, with applications in information
access, language technology, and recommendation. His past research focused
on counterfactual and causal inference, learning to rank, structured
output prediction, support vector machines, text classification, learning
with preferences, and learning from implicit feedback. He is an ACM
Fellow, AAAI Fellow, KDD Innovations Award recipient, and member of the
ACM SIGIR Academy.

Please contact the host directly (manuelgr@mpi-sws.org) if you would like
to meet with Thorsten Joachims.

https://www.uni-saarland.de/aktuell/workshop-haftung-fuer-schaeden-durch-ki-25954.html

Speaker: Barbara Terhal, TU Delft

November 17, 4pm

Title: Quantum Error Correction Beyond Qubits

We review the formalism of stabilizer quantum error correction, in particular
homological codes for qubits, oscillators or so-called rotors.
We show that rotors can in particular allow for interesting qubit encodings
which relate to the physics of superconducting devices.
We discuss quantum error correction with the (homological) surface code, in
particular with respect to qubit leakage, for superconducting qubits.

Location https://zoom.us/j/94577321297?pwd=N3l5K1ZtZ3E1aytnWlBkL1FUazNXZz09
Meeting ID 945 7732 1297
Passcode 205903

Host: Kurt Mehlhorn (MPI-INF).

Information about the series and recordings of previous talks can be found at
https://www.mpi-inf.mpg.de/departments/algorithms-complexity/quantum-lecture-series

Abstract:
The talk will describe two papers with a similar goal: using diachronic corpora to detect intellectual influence. We want to know who is leading the development of ideas over time, and we hope to get some insight on this by examining changes in the meanings of individual words, as quantified by dynamic word embeddings. The talk presents methods for (1) detecting changes in word meaning, (2) identifying the leaders and followers of these changes, and (3) aggregating over many changes into an overall picture of linguistic influence, using techniques from social network analysis. As the first part of the talk will show, this aggregate measure has predictive power: in the ACL anthology, papers that have a high level of linguistic influence in the short term tend to accrue more citations in the long term. This finding holds even when controlling for short-term citations: if two papers have the same number of citations after two years, the one with more linguistic influence will receive significantly more citations in the following three years. Next, we quantify linguistic influence in a corpus of historical newspapers from the abolitionist movement in the pre-Civil War United States. Here, a measure of linguistic influence helps to shed light on the unique roles played by newspapers edited by women and formerly enslaved people. This research was led by Sandeep Soni, in collaboration with Lauren F. Klein and David Bamman.

If you would like to meet the speaker (virtually) please contact Stefania Degaetano-Ortlieb (s.degaetano@mx.uni-saarland.de).

https://www.uni-saarland.de/aktuell/ringvorlesung-going-digital-1-1-25832.html

https://domino.mpi-inf.mpg.de/internet/events.nsf/0/38E111E4674558CEC125890100354BDD

https://domino.mpi-inf.mpg.de/internet/events.nsf/0/0ACCCE0A43F95289C1258901003926BB

Location: E1 3, Lecture Hall 001 (and on Teams, link below)
Date/Time: Friday, November 25 at 2:00pm s.t.

Abstract:
Flash memory opens a window of opportunities to a new world of computing over 20 years ago. Since then, storage devices gain their momentum in performance, energy, and even access behaviors. With over 1000 times in performance improvement over storage in recent years, there is another wave of adventure in removing traditional I/O bottlenecks in computer designs. In this talk, I shall first address the opportunities of new system architectures in computing. In particular, hybrid modules of DRAM and non-volatile memory (NVM) and all NVM-based main memory will be considered. I would also comment on a joint management framework of host/CPU and a hybrid memory module to break down the great memory wall by bridging the process information gap between host/CPU and a hybrid memory module. I will then present some solutions in neuromorphic computing which empower memory chips to own new capabilities in computing. In particular, I shall address challenges in in-memory computing in application co-designs and show how to utilize special characteristics of non-volatile memory in deep learning.

Bio:
Prof. Kuo received his B.S.E. and Ph.D. degrees in Computer Science from National Taiwan University and University of Texas at Austin in 1986 and 1994, respectively. He is now Distinguished Professor of Department of Computer Science and Information Engineering of National Taiwan University, where he was an Interim President (2017.10-2019.01) and an Executive Vice President for Academics and Research (2016.08-2019.01). Between August 2019 and July 2022, Prof. Kuo took a leave to join City University of Hong Kong as Lee Shau Kee Chair Professor of Information Engineering, Advisor to President (Information Technology), and Founding Dean of College of Engineering. His research interest includes embedded systems, non-volatile-memory software designs, neuromorphic computing, and real-time systems.

Dr. Kuo is Fellow of ACM, IEEE, and US National Academy of Inventors. He is also a Member of European Academy of Sciences and Arts. He is Vice Chair of ACM SIGAPP and Chair of ACM SIGBED Award Committee. Prof. Kuo received numerous awards and recognition, including Humboldt Research Award (2021) from Alexander von Humboldt Foundation (Germany), Outstanding Technical Achievement and Leadership Award (2017) from IEEE Technical Committee on Real-Time Systems, and Distinguished Leadership Award (2017) from IEEE Technical Committee on Cyber-Physical Systems. Prof. Kuo is the founding Editor-in-Chief of ACM Transactions on Cyber-Physical Systems (2015-2021) and a program committee member of many top conferences. He has over 300 technical papers published in international journals and conferences and received many best paper awards, including the Best Paper Award from ACM/IEEE CODES+ISSS 2019 and 2022 and ACM HotStorage 2021.

Teams Link:
https://teams.microsoft.com/l/meetup-join/19%3ameeting_NjFmZmJhOGYtYWJjOS00ZmVjLWJiODMtMTYxYTkzMmQ4NjJk%40thread.v2/0?context=%7b%22Tid%22%3a%2267610027-1ac3-49b6-8641-ccd83ce1b01f%22%2c%22Oid%22%3a%2277e42572-5a96-4a2d-98d8-a97ca3a29dd3%22%7d

https://www.uni-saarland.de/aktuell/ringvorlesung-going-digital-1-1-1-25834.html