Semantic Communication Madhu Sudan (based on joint work with Brendan Juba (MIT); and upcoming work with Oded Goldreich (Weizmann) & J. )

Slides:

Advertisements

Similar presentations

QMA/qpoly PSPACE/poly: De-Merlinizing Quantum Protocols Scott Aaronson University of Waterloo.

Advertisements

Of 23 09/24/2013HLF: Reliable Meaningful Communication1 Reliable Meaningful Communication Madhu Sudan Microsoft, Cambridge, USA.

Of 35 05/30/2012CSOI-Summer: Uncertainty in Communication1 Communication amid Uncertainty Madhu Sudan Microsoft, Cambridge, USA Based on: Universal Semantic.

Of 19 03/21/2012CISS: Beliefs in Communication1 Efficient Semantic Communication & Compatible Beliefs Madhu Sudan Microsoft, New England Based on joint.

Presentation on Artificial Intelligence

Semantic communication with simple goals is equivalent to on-line learning Brendan Juba (MIT CSAIL & Harvard) with Santosh Vempala (Georgia Tech) Full.

Approximate List- Decoding and Hardness Amplification Valentine Kabanets (SFU) joint work with Russell Impagliazzo and Ragesh Jaiswal (UCSD)

Universal Semantic Communication Brendan Juba (Harvard and MIT) with Madhu Sudan (MSR and MIT) & Oded Goldreich (Weizmann)

February Semantic CMU1 Semantic Goal-Oriented Communication Madhu Sudan Microsoft Research + MIT Joint with Oded Goldreich (Weizmann)

Quantum Information and the PCP Theorem Ran Raz Weizmann Institute.

THE QUANTUM COMPLEXITY OF TIME TRAVEL Scott Aaronson (MIT)

Effective Group Discussion: Theory and Practice

Universal Communication Brendan Juba (MIT) With: Madhu Sudan (MIT)

Of 30 09/04/2012ITW 2012: Uncertainty in Communication1 Communication amid Uncertainty Madhu Sudan Microsoft, Cambridge, USA Based on: Universal Semantic.

Of 29 May 2, 2011 Semantic Northwestern1 Universal Semantic Communication Madhu Sudan Microsoft Research Joint with Oded Goldreich (Weizmann)

Of 27 01/06/2015CMI: Uncertain Communication1 Communication Amid Uncertainty Madhu Sudan Microsoft Research Based on Juba, S. (STOC 2008, ITCS 2011) Juba,

Of 13 October 6-7, 2010Emerging Frontiers of Information: Kickoff 1 Madhu Sudan Microsoft Research + MIT TexPoint fonts used in EMF. TexPoint fonts used.

Of 14 01/03/2015ISCA-2015: Reliable Meaningful Communication1 Reliable Meaningful Communication Madhu Sudan Microsoft, Cambridge, USA.

Of 32 October 19, 2010Semantic U.Penn. 1 Semantic Goal-Oriented Communication Madhu Sudan Microsoft Research + MIT Joint with Oded Goldreich.

Of 12 03/22/2012CISS: Compression w. Uncertain Priors1 Compression under uncertain priors Madhu Sudan Microsoft, New England Based on joint works with:

Discovering Affine Equalities Using Random Interpretation Sumit Gulwani George Necula EECS Department University of California, Berkeley.

CPSC 322 Introduction to Artificial Intelligence October 29, 2004.

Complexity 18-1 Complexity Andrei Bulatov Probabilistic Algorithms.

CPSC 322 Introduction to Artificial Intelligence November 10, 2004.

CSCD 555 Research Methods for Computer Science

Introduction I. Some interesting facts about language

Lecture 20: April 12 Introduction to Randomized Algorithms and the Probabilistic Method.

Of 30 September 22, 2010Semantic Berkeley 1 Semantic Goal-Oriented Communication Madhu Sudan Microsoft Research + MIT Joint with Oded Goldreich.

EMRs, EHRs, PHRs, questions and answers

Efficient Semantic Communication via Compatible Beliefs Brendan Juba (MIT CSAIL & Harvard) with Madhu Sudan (MSR & MIT)

Of 33 March 1, 2011 Semantic UCLA1 Universal Semantic Communication Madhu Sudan Microsoft Research + MIT Joint with Oded Goldreich (Weizmann)

Of 35 05/16/2012CTW: Communication and Computation1 Communication amid Uncertainty Madhu Sudan Microsoft, Cambridge, USA Based on: Universal Semantic Communication.

CSE 321 Discrete Structures Winter 2008 Lecture 10 Number Theory: Primality.

Programming and Coding short course consultation.

Of 28 Probabilistically Checkable Proofs Madhu Sudan Microsoft Research June 11, 2015TIFR: Probabilistically Checkable Proofs1.

CMPT 880/890 Writing labs.

Ch1 AI: History and Applications Dr. Bernard Chen Ph.D. University of Central Arkansas Spring 2011.

CSU 670 Review Fall Software Development Application area: robotic games based on combinatorial maximization problems. Software development is about.

Reliability Andy Jensen Sandy Cabadas.  Understanding Reliability and its issues can help one solve them in relatable areas of computing Thesis.

Limits of Local Algorithms in Random Graphs

Universal Semantic Communication Madhu Sudan MIT CSAIL Joint work with Brendan Juba (MIT).

Communication & Computing Madhu Sudan ( MSR New England ) Theories of.

1 4 Dummett’s Frege. 2 The Background The mentalist conception The mentalist conception It is a code conception of language (telepathy doesn’t need language).

11/3/2008Communication & Computation1 A need for a new unifying theory Madhu Sudan MIT CSAIL.

My Career I chose to pick a web developer because I always wanted to a little bit more about them!

2/2/2009Semantic Communication: MIT TOC Colloquium1 Semantic Goal-Oriented Communication Madhu Sudan Microsoft Research + MIT Joint with Oded Goldreich.

Interactive proof systems Section 10.4 Giorgi Japaridze Theory of Computability.

Of 27 August 6, 2015KAIST: Reliable Meaningful Communication1 Reliable Meaningful Communication Madhu Sudan Microsoft Research.

Lecture 5 1 CSP tools for verification of Sec Prot Overview of the lecture The Casper interface Refinement checking and FDR Model checking Theorem proving.

Cognitive Psychology. Overview What is Cognitive Psychology? Study of HOW the mind works, not WHY we do what we do Focuses on the day-to-day functions.

4/17/2008Semantic Communication1 Universal Semantic Communication Madhu Sudan MIT CSAIL Joint work with Brendan Juba (MIT CSAIL).

10/14/2008Semantic Communication1 Universal Semantic Communication Madhu Sudan MIT CSAIL Joint work with Brendan Juba (MIT CSAIL).

Universal Semantic Communication Madhu Sudan MIT CSAIL Joint work with Brendan Juba (MIT CSAIL).

Of 17 Limits of Local Algorithms in Random Graphs Madhu Sudan MSR Joint work with David Gamarnik (MIT) 7/11/2013Local Algorithms on Random Graphs1.

Artificial Intelligence Hossaini Winter Outline book : Artificial intelligence a modern Approach by Stuart Russell, Peter Norvig. A Practical Guide.

The Church-Turing Thesis Chapter Are We Done? FSM  PDA  Turing machine Is this the end of the line? There are still problems we cannot solve:

Universal Semantic Communication

Universal Semantic Communication

Universal Semantic Communication

Homework: Friday Read Section 4.1. In particular, you must understand the proofs of Theorems 4.1, 4.2, 4.3, and 4.4, so you can do this homework. Exercises.

Communication & Computation A need for a new unifying theory

CS 154, Lecture 6: Communication Complexity

Universal Semantic Communication

Universal Semantic Communication

Rule-Following Wittgenstein.

Universal Semantic Communication

Universal Semantic Communication

What should I talk about? Aspects of Human Communication

Understanding Chess Notation

Presentation transcript:

Semantic Communication Madhu Sudan (based on joint work with Brendan Juba (MIT); and upcoming work with Oded Goldreich (Weizmann) & J. )

My research interests Computation Communication Role of errors ◦ Probabilistically checkable proofs ◦ List-decoding ◦ … and now Semantic Communication

Bits (words) have meaning Can we preserve meaning when communicating? Important in modern “society of computers” Semantic Communication AB Freeze or

What is meaning? Send “ ” Reciever: “Rec’d “ ”” Did this preserve meaning? No … ◦ Meaning = Interpretation associated with bits ◦ = how bits rec’d change state of your mind. ◦ (same diff. as between algorithm, and its encoding in some programming language/universal TM) ◦ But if no one can read the state of your mind, how do I know if you’ve misunderstood me?

Semantic Communication Definition of “Meaning” non-trivial Want to preserve it only to the extent that it is relevant. But relevance should not be defined to make everything irrelevant. If communication achieves some goal with knowledge of language, it should still achieve it without knowledge of language.

Our research – Part 1 Articulate one class of “information- oriented goal” of communication. Show how it captures “semantic miscommunication” Theorem: “If helpful parties interact, they can achieve the goal (while overcoming linguistic hurdles) provided the goal is “verifiable”. Crux of theorem: Definitions that make it true!

Goal=? Bob wants to solve hard computational problem ◦ is program P = virus? ◦ Is game of chess winnable? Alien capable of solving hard problem; but doesn’t know B’s language. Can A help B? For which problems?

Philosophical interpretation of answers If A can only help B solve problems he can solve by himself … communication is pointless. If A can help B solve all problems, communication is powerful and misunderstandings can always be overcome. If A can help B solve some new problems, then communication is ocassionally helpful; and one ought to be careful with it.

Misunderstandings and Helpfulness. Modelled by collection of {A}s and {B}s. ◦ (for every language, there is a copy A that speaks in language i, and a B that speaks i). Helpful = For every A there exists B j, such that A helps B j solve problem (plus technicalities). Universality = B should be able solve problem with every (helpful) A j.

Theorems: B can use {A}’s help to solve every “verifiable” problem [Verifiable problems include (PSPACE- complete) problems that B could not solve on its own] B can only solve verifiable problems. [Moral: Ǝ danger in updating OS and anti- virus software asynchronously.]

Our research – Part II (ongoing) Generalize to arbitrary goals of communication. Tricky part: Capturing goal (= state of my mind?) while allowing my actions to vary (in effect changing my mental action and hence states).