From awd at cs.cmu.edu  Sun Sep  1 19:13:55 2024
From: awd at cs.cmu.edu (Artur Dubrawski)
Date: Sun, 1 Sep 2024 19:13:55 -0400
Subject: MOMENT and TimeGPT1 highlighted in a podcast organized by the
 International Institute of Forecasting
Message-ID: <CAJvAoyt5LAuwcqDfd7_z9bGbZUJo_am0v+1TyNWoG6POwtu31g@mail.gmail.com>

Dear Autonians,

Please join me in congratulating Mononito Goswami, the lead creator of
MOMENT time series foundation model, and Cristian Challu and Kin G
Olivares, co-creators of TimeGPT1 (represented in this podcast by their
colleague from Nixtla) for getting a widespread exposure of their
outstanding work, some of which has originated in the Auton Lab and some
which is still growing here:

https://www.buzzsprout.com/1641538/15433744-panel-on-foundational-models-with-azul-garza-ramirez-and-mononito-goswami-part-1

https://www.buzzsprout.com/1641538/15472031-panel-on-foundational-models-with-azul-garza-ramirez-and-mononito-goswami-part-2

Cheers,
Artur
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From awd at cs.cmu.edu  Thu Sep  5 09:56:39 2024
From: awd at cs.cmu.edu (Artur Dubrawski)
Date: Thu, 5 Sep 2024 09:56:39 -0400
Subject: Auton Lab co-leading the workshop on time series foundation models at
 the ACM Conference on AI in Finance (ICAIF'24)
Message-ID: <CAJvAoytMF1tJbRoUPg3Fg2_x=p=r7OirpDFCVPuJZ8jHCx1C7Q@mail.gmail.com>

Team,

I got involved in organizing the workshop on "Foundation Models for Time
Series: Exploring New Frontiers", which has just been accepted for
inclusion in the 5th ACM International Conference on AI in Finance
(ICAIF'24) to be held in Brooklyn, NY on either or both of 14th and 15th of
November 2024.

The workshop proposal acceptances were severely delayed so the submissions
will be running against very tight deadlines: the last time I've heard the
intended due date is Sept 27.

I feel that we need to be represented quite prominently at this event.
It is a workshop, so work in progress should be fine.
And we could use the opportunity to further promote MOMENT and its
intellectual surroundings.

Please let me know your thoughts if you are intrigued by this.

Cheers
Artur
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From jeff4 at andrew.cmu.edu  Fri Sep  6 09:51:28 2024
From: jeff4 at andrew.cmu.edu (Jeff Schneider)
Date: Fri, 6 Sep 2024 09:51:28 -0400
Subject: Fwd: RI Ph.D. Thesis Proposal: Brian Yang
In-Reply-To: <CADAXqzM2sWLfggGxr7OUqfRi-X1vGRs91+jp_Waa6jch97-E9A@mail.gmail.com>
References: <CADAXqzM2sWLfggGxr7OUqfRi-X1vGRs91+jp_Waa6jch97-E9A@mail.gmail.com>
Message-ID: <bd82d8ca-a1cd-fe2d-ac2b-09c18cef206d@andrew.cmu.edu>

Brian's thesis proposal is starting in 10 minutes!  Please come by and 
hear about diffusion models, language models, and self-driving cars!


-------- Forwarded Message --------
Subject: 	Re: RI Ph.D. Thesis Proposal: Brian Yang
Date: 	Fri, 6 Sep 2024 08:53:35 -0400
From: 	Brian Yang <brianyan at andrew.cmu.edu>
To: 	Suzanne Muth <lyonsmuth at cmu.edu>
CC: 	RI People <ri-people at andrew.cmu.edu>



Reminder that this is happening today at 10am in NSH 4305**and zoom: 
https://cmu.zoom.us/j/93129020623?pwd=zMN1mXaUgsju0ORfKMZLFzhzdw6QDR.1 
<https://cmu.zoom.us/j/93129020623?pwd=zMN1mXaUgsju0ORfKMZLFzhzdw6QDR.1>

On Thu, Aug 29, 2024 at 8:37?AM Suzanne Muth <lyonsmuth at cmu.edu 
<mailto:lyonsmuth at cmu.edu>> wrote:

     *Date:* 06 September 2024
     *Time:* 10:00 a.m. (ET)
     *Location:* NSH 4305
     *Zoom Link:*
     https://cmu.zoom.us/j/93129020623?pwd=zMN1mXaUgsju0ORfKMZLFzhzdw6QDR.1
 
<https://cmu.zoom.us/j/93129020623?pwd=zMN1mXaUgsju0ORfKMZLFzhzdw6QDR.1>
     *Type:* Ph.D.?Thesis Proposal
     *Who:* Brian Yang
     *Title:* Teaching Robots to Drive: Scalable Policy Improvement via
     Human Feedback

     *Abstract:*
     A long-standing problem in autonomous driving is grappling with the
     long-tail of rare scenarios for which little or no data is
     available. Although learning-based methods scale with data, it is
     unclear that simply ramping up data collection will eventually make
     this problem go away. Approaches which rely on simulation or world
     modeling offer some relief, but building such models is very
     challenging and in itself an active area of research.

     On the other hand, humans can learn to drive without millions of
     logged driving miles or the ability to precisely predict the
     trajectories of all dynamic actors in the scene. This suggests a
     potential alternative path to learning robust driving policies which
     does not rely on highly accurate world models or enormous driving
     datasets -- one which leans into human preferences and expertise as
     an untapped source of supervision for training driving policies.

     This thesis aims to make the case for human feedback as a rich
     signal for improving driving policies in a sample efficient manner
     without requiring high fidelity simulation. First, we propose a
     method for guiding driving policies at test-time using unseen
     black-box reward functions. We can then synthesize reward functions
     using natural language and optimize them online, allowing us to
     solve novel tasks zero-shot using only language supervision. Next,
     we show how driving policies can be fine-tuned offline using human
     preference data. By eliciting preferences over high-level intents,
     we can use human feedback to effectively relabel sub-optimal driving
     demonstrations and improve on-road driving performance. As future
     work, we aim to combine these two methods to finetune driving
     policies offline using natural language corrections, which should
     enable richer feedback over longer horizons and chain-of-thought
     distillation.

     *Thesis?Committee Members:*
     Katerina Fragkiadaki, Co-chair
     Jeff Schneider, Co-chair
     Maxim Likhachev
     Philipp Kr?henb?hl, The University of Texas at Austin

     A draft of the thesis proposal document is available here
 
<https://drive.google.com/file/d/1n4mehD6jWXFZp3-ksjIecbxU7mIXbcxB/view?usp=sharing>.


From awd at cs.cmu.edu  Wed Sep 18 09:19:03 2024
From: awd at cs.cmu.edu (Artur Dubrawski)
Date: Wed, 18 Sep 2024 09:19:03 -0400
Subject: Auton Lab conducting work that mattets
Message-ID: <CAJvAoyuNmd0EcnHekg=d_ZLZTYtgncRsMvuBfpfZDe=09GQncQ@mail.gmail.com>

Autonians,

Something to make us all feel warm this morning. Check out the slide just
presented by CMU Vice Provost for Research Theresa Mayer to a group of
senior visitors from a major industrial partner. Our Lab is listed second
among CMU units pursuing research that matters.

Cheers,
Artur
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From jieshic at andrew.cmu.edu  Thu Sep 19 13:48:49 2024
From: jieshic at andrew.cmu.edu (Jieshi Chen)
Date: Thu, 19 Sep 2024 13:48:49 -0400
Subject: You're invited! Auton Lab 31st Anniversary Picnic - Sunday, October
 13th @ Vietnam Veterans Pavilion
Message-ID: <CAB-Bqpi3465fVvmwVU6pZ4wM9yGYU6fOM9Df7XZ=Hq94jrq6jA@mail.gmail.com>

Dear Autonians,

We are excited to invite you to celebrate the 31st anniversary of our lab!
To mark the occasion, we are hosting a family-friendly picnic, and we would
love for you to join us. This will be a great opportunity to relax, enjoy
good food, and celebrate with colleagues, friends, and family. Feel free to
bring along your loved ones ? everyone is welcome!

*Date:* Sunday, October 13th
*Time:* Noon - 5pm (venue reserved from 11am to 9pm)
*Location:* Vietnam Veterans Pavilion at Schenley Park (Google Map
<https://maps.app.goo.gl/nvbbbL9h7QZ6LEQ37>)

Please *RSVP *through the link below so we can make sure there's plenty of
food and fun for everyone.

*https://forms.gle/864oaDofmTWpKPEC9 <https://forms.gle/864oaDofmTWpKPEC9>*

Looking forward to celebrating with you!


Best,
Jessie

-- 

Jieshi Chen
Principal Research Analyst
Auton Lab, Robotics Institute, School of Computer Science
Carnegie Mellon University
Email: jieshic at andrew.cmu.edu
Office: Newell Simon Hall Room 3115
Address: 5000 Forbes Avenue, Pittsburgh, PA 15213
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From awd at cs.cmu.edu  Wed Sep 25 09:44:25 2024
From: awd at cs.cmu.edu (Artur Dubrawski)
Date: Wed, 25 Sep 2024 09:44:25 -0400
Subject: Fwd: FW: [EXTERNAL] Join Us for the CDAO RAI COI September 26 Meeting
In-Reply-To: <CH2PR17MB3958315BCD7E5AA091EBDEFBF9692@CH2PR17MB3958.namprd17.prod.outlook.com>
References: <5fe09d41212bc839a2ac63d33.23621d05c6.20240924135950.9be5c8d991.7c95bf7c@mail63.atl261.mcdlv.net>
 <MN2PR17MB398134815A3835A40E3EDF3F97682@MN2PR17MB3981.namprd17.prod.outlook.com>
 <CH2PR17MB3958315BCD7E5AA091EBDEFBF9692@CH2PR17MB3958.namprd17.prod.outlook.com>
Message-ID: <CAJvAoyts0k8+w7CNshiFJo7v0xax+GCQjVonUNHNr09CcHXO=A@mail.gmail.com>

quite relevant panel right in our backyard

---------- Forwarded message ---------
From: John Schermerhorn <John.Schermerhorn at ecstech.com>
Date: Wed, Sep 25, 2024 at 9:36?AM
Subject: FW: [EXTERNAL] Join Us for the CDAO RAI COI September 26 Meeting
To: awd <awd at cs.cmu.edu>, Kimberly Elenberg <Kimberly.Elenberg at ecstech.com>


Dr. Artur,



Are you going to listen in? I just got this note a day ago. If it wasn?t on
your radar, I wanted to share.

The RAI toolkit has been a significant effort within CDAO, but I really
haven?t heard of any successful adoption/implementations in industry.

If I had to guess ? this may be that push.



Here?s the RAI toolkit website: and blog post:



https://rai.tradewindai.com/



https://www.ai.mil/blog_9_19_23_RAI_Toolkit.html





Regards,



John Schermerhorn

443-235-2773



*From:* The Block Center for Technology and Society <
blockcenter at andrew.cmu.edu>
*Sent:* Tuesday, September 24, 2024 11:00 AM
*To:* John Schermerhorn John.Schermerhorn at ecstech.com
*Subject:* [EXTERNAL] Join Us for the CDAO RAI COI September 26 Meeting



*CAUTION: This email originated from outside of the organization. Do not
click links or open attachments unless you recognize the sender and know
the content is safe.*
------------------------------

View this email in your browser
<https://mailchi.mp/6064b2cd35d1/join-us-for-the-cdao-rai-coi-september-meeting-6737672?e=23621d05c6>



*CDAO Responsible AI (RAI) Community of Interest (COI) September Meeting
<https://cmu.us13.list-manage.com/track/click?u=5fe09d41212bc839a2ac63d33&id=22d3efcae8&e=23621d05c6>*





The upcoming Department of Defense Chief Digital and Artificial
Intelligence Office (CDAO) responsible AI (RAI) Community of Interest (COI)
September Meeting is scheduled for *September 26th, 2024, from 2:00 to 3:00
PM Eastern Time (ET).* In this session, we will host a discussion titled ?*AI
Red-Teaming: What it is and Why it Matters*.? Featuring:



*Panelists*

Dr. Vincent Mancuso, Assistant Group Leader, MIT Lincoln Laboratory

Rachel Rajaseelan, Lead Computer Engineer, DoD Responsible AI Division

Prof. Andrew Reddie, Associate Professor, University of California, Berkeley



*Moderator*

Dr. Rachel Dzombak, Sr. Innovation Advisor, Carnegie Mellon University





Increasingly, red-teaming is playing a central role in policy discussions
around AI system evaluation and governance. At the same time, there remain
many open questions around what red-teaming practically entails in the
context of AI systems and the role it can play to support the development
of human-centered, robust, and secure AI systems. In this panel discussion,
we will cover what is AI red-teaming, how it differs (or not) from the
historical usage of red-teams in the DoD, and how organizations can
leverage red-teaming to test AI systems.



We invite you to extend this invitation to colleagues within your
professional network and the broader DOD community who are personnel from
the United States Department of Defense, United States Service Branches,
and/or partners in industry and academia. Participation is restricted to
those responsible for the acquisition, planning, implementation,
sustainment, or usage of AI technologies and tools. Your participation is
invaluable as we continue to navigate the challenges and opportunities in
the realm of responsible AI.

[image: Register Here]

[image: Website icon]
<https://cmu.us13.list-manage.com/track/click?u=5fe09d41212bc839a2ac63d33&id=8e56d2099b&e=23621d05c6>

[image: LinkedIn icon]
<https://cmu.us13.list-manage.com/track/click?u=5fe09d41212bc839a2ac63d33&id=a4e3415aef&e=23621d05c6>

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<https://cmu.us13.list-manage.com/track/click?u=5fe09d41212bc839a2ac63d33&id=9ae500294d&e=23621d05c6>

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*Copyright (C) *2024* The Block Center for Technology and Society. All
rights reserved.*

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