- Ai - Engineering Mailing Lists

AI seminar: March 30, 2022
by Mangannavar, Rajesh Devaraddi 24 Mar '22

24 Mar '22

Dear all, Our first AI seminar for spring term on *"Distributional Kernels: an under-utilized resource" *by Kai Ming Ting is scheduled to be on March 30th, *5-6 PM PST*. It will be followed by a 30 minute Q&A session by the graduate students (*6 PM to 6:30 PM PST)*. Please note the different timings from the usual timing for this talk. Zoom Link: https://oregonstate.zoom.us/j/93591935144?pwd=YjZaSjBYS0NmNUtjQzBEdzhPeDZ5U… *Distributional Kernels: an under-utilized resource* Kai Ming Ting Professor School of Artificial Intelligence Nanjing University *Abstract:* This seminar presents recent works on distributional kernels based on kernel mean embedding (KME). KME has a strong theoretical underpinning, and guarantees that the resultant kernel mean map is injective, i.e., the kernel mean maps of two distributions has their difference equals to zero if and only if the distributions are the same. Yet, KME's applications have been less successfully so far. One key breakthrough is the identification of the root cause of KME's (seemingly) failures, i.e., the use of Gaussian kernel. The seminar presents works, following this identification, that release the power of this under-utilized resource. The works demonstrate that the distributional kernels can solve long-standing problems, some of which have evaded decades of effort, in terms of efficiency and task-specific accuracy issues. These include point and group anomaly detections, clustering, and anomaly detections in trajectories, periodic time series and graphs/networks. *Speaker Bio:* After receiving his PhD from the University of Sydney, Australia, Kai Ming Ting worked at the University of Waikato (NZ), Deakin University, Monash University and Federation University in Australia. He joined Nanjing University in 2020. Research grants received include those from National Science Foundation of China, US Air Force of Scientific Research (AFOSR/AOARD), Australian Research Council, Toyota InfoTechnology Center and Australian Institute of Sport. He is one of the inventors of Isolation Forest, Isolation Kernel and Isolation Distributional Kernel. Isolation Forest is widely used in industries and academia. Isolation Kernel is a unique similarity measure which is derived from a dataset based on the same/similar isolation mechanism as Isolation Forest, and has no closed-form expression. Isolation Kernel and Isolation Distributonal Kernel are the X-factor that enables many problems to be solved more effectively and efficiently than existing algorithms which rely on Gaussian kernel or Euclidean distance. *Please watch this space for future AI Seminars :* * https://eecs.oregonstate.edu/ai-events <https://eecs.oregonstate.edu/ai-events>* Rajesh Mangannavar, Graduate Student Oregon State University ---- AI Seminar Important Reminders: -> For graduate students in the AI program, attendance is strongly encouraged.

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[CoRIS-List] Research study on new tools for image data analysis
by Minsuk Kahng 20 Mar '22

20 Mar '22

Hi students, We are looking for people to participate in a research study. The Data Interaction and Visualization (DIV) Lab at EECS has been developing new web-based interface prototypes for people to explore and analyze large-scale image datasets for machine learning, and we need your help to evaluate our prototypes. You're welcome to participate if you are at least 18 years old and you have taken any AI, Machine Learning, or related course that covers "classification" before. Participation in this study involves: - *90 min session* of an in-person visit to *Kelley* Engineering Center, Room 2106 - Performing some tasks using our visual interfaces - *$20 Amazon gift card* for your participation If interested, please sign up for the study using this link: https://calendly.com/osudivlab/user-study For questions, please contact Prof. Minsuk Kahng and the DIV Lab team by email at osudivlab(a)gmail.com. Thank you. Cheers, Minsuk -- Minsuk Kahng Assistant Professor School of Electrical Engineering and Computer Science Oregon State University https://minsuk.com

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AI Alignment Reading Group
by Quintin Pope 18 Mar '22

18 Mar '22

Hello everyone, I’ve decided to put the reading group on hold until the end of the break. There will be no meeting today. All the best, Quintin

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FW: Help with Judge Recruitment
by Fern, Alan Paul 17 Mar '22

17 Mar '22

Hi Everyone, FIRST robotics is looking for some judges for one of the big high school robotics competitions next Friday and/or Saturday in Salem. (additional details below) I’d volunteer myself, but my son is on the Crescent Valley team and they try to avoid parents being judges. This would be a great way to fulfill those service requirements. Let me know if you are interested and I can help get you connected. Thanks, -Alan From: Tanya Carone <tanya.robotics(a)gmail.com> Sent: Wednesday, March 16, 2022 5:41 PM To: Fern, Alan Paul <Alan.Fern(a)oregonstate.edu> Subject: Fwd: Help with Judge Recruitment [This email originated from outside of OSU. Use caution with links and attachments.] Can you circulate among the OSU robotics folks? Thanks, Tanya Sent from my iPhone Begin forwarded message: From: Debbie Kerr <debbie-kerr(a)ortop.org> Date: March 16, 2022 at 5:26:47 PM PDT To: Mara Kealey <marak(a)firstwa.org>, Sonali Shivdasani <sonali-shivdasani(a)ortop.org> Subject: Help with Judge Recruitment Can you circulate this in your community of supporters? We discourage mentors of competing teams from judging but if you have sponsors or community supporters could you ask them to come support as a judge? We are still recruiting six judges for our event next weekend. Come enjoy a weekend of robots at the Oregon State Fairgrounds<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fmy.firsti…>! We are recruiting judges to support our teams who have been working hard all season in preparation of this event. Time Commitment * Judging will start at 8:30 AM on Friday, March 25th and will conclude at roughly 5:00 PM. * On Saturday, March 26th, we will be starting at 7:30 AM and concluding with an awards ceremony ending at about 5:00 PM. Reminders: * Don’t forget to wear comfortable clothing and closed-toe shoes to your shift and bring your favorite pair of safety glasses if you own a pair. * If you don’t own safety glasses, we will have glasses to loan you. * Wear your FRC blue judge polo if you have one, if you do not, we will have one there when you arrive * Please email me your company name as you would like it displayed on the slide that introduces us during opening ceremonies. This is important to show the students that our employers are invested in the program. Venue Information: * The Venue is located at Oregon State Fairgrounds 2330 - 17th St NE Salem, Oregon * When you arrive, please come directly to the Judges Room * Please print and sign this Conflict of Interest form<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.first…> if you have any relation with a team (sponsor, mentor, alumni, etc) * Its purpose is to ensure that judges refrain from steering the groups' decisions regarding teams with which they have an association. If you have not already done so, please be sure to read the Judge Manual (attached) and complete the required Judge Certifications. These contain important information to prepare you for the event. I’ll send you another email a few days before our event with any final details. Please do not hesitate to contact me with any additional questions that you may have. Thanks again for volunteering to Judge. I can GUARANTEE that you will have an amazing time! Debbie Kerr -- [Image removed by sender.] Debbie Kerr, Programs Director FIRST Oregon, a program of Oregon Robotics Tournament and Outreach Program (ORTOP) Pronouns: She/Her Office Phone: 503.486.7622 x 10x Cell Phone: 503-481-5982 Debbie-Kerr(a)ortop.org<mailto:Debbie-Kerr@ortop.org> | www.ortop.org<https://nam04.safelinks.protection.outlook.com/?url=http%3A%2F%2Fwww.ortop.…> Delivered by Oregon Robotics Tournament & Outreach Program (ORTOP) a 501(c)(3) not-for-profit public charity #46-4276182

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FW: Maria Pacheco presentation
by Fern, Alan Paul 16 Mar '22

16 Mar '22

We have an AI faculty candidate talk tomorrow at 10am. Looks very interesting. Please attend if you can so we can have a good audience for her. Thanks, -Alan -----Original Appointment----- From: Amador, Janet <janet.amador(a)oregonstate.edu> Sent: Monday, March 14, 2022 4:30 PM To: Amador, Janet; eecs-faculty(a)engr.orst.edu; eecs-grads(a)engr.oregonstate.edu; Maria Leonor Pacheco Gonzales Subject: Maria Pacheco presentation When: Wednesday, March 16, 2022 10:00 AM-11:00 AM (UTC-08:00) Pacific Time (US & Canada). Where: Zoom link for presentation https://oregonstate.zoom.us/j/94158405375?pwd=eXVXU0F6Uys2a3JvcjRQaWFkeUp4d… Title: Neural-Symbolic Modeling for Natural Language Discourse Abstract: Language "in the wild" is complex and ambiguous and relies on a shared understanding of the world for its interpretation. Most current NLP methods represent language by learning word co-occurrence patterns from massive amounts of linguistic data. This representation can be very powerful, but it is insufficient to capture the meaning behind written and spoken communication. In this talk, I will motivate neural-symbolic representations for dealing with these challenges. On the one hand, symbols have inherent explanatory power, and they can help us express domain knowledge and enforce consistency across different decisions. On the other hand, expressive distributed representations allow us to leverage the strengths of statistical language models to make sense of large amounts of linguistic data. In this talk, I will introduce a holistic framework that covers all stages of the neural-symbolic pipeline: modeling, learning, inference, and its application for analyzing discourse in real-world scenarios and show its advantages with respect to end-to-end neural approaches and traditional statistical relational learning methods. Bio: Maria Pacheco is a PhD Candidate in the Department of Computer Science at Purdue University. Her research focuses broadly on neural-symbolic methods to model natural language discourse scenarios, such as analyzing conversations, argumentation, and narratives. Before joining Purdue, she spent a couple of years working as a data scientist and software engineer for various startups in her hometown of Caracas, Venezuela. She has published in top Natural Language Processing conferences and journals and has delivered tutorials on neural-symbolic modeling for NLP to diverse audiences, including an IJCAI '21 tutorial and an upcoming COLING '22 tutorial. Maria is a recipient of the 2021 Microsoft Research Dissertation Grant, and one of the main organizers of the LatinX in AI events in NLP.

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AI Alignment Reading Group
by Pope, Quintin 11 Mar '22

11 Mar '22

Hello everyone, We'll again be meeting at 1 PM PST this Friday. We'll discuss In-context Learning and Induction Heads <https://transformer-circuits.pub/2022/in-context-learning-and-induction-hea…>. This work attempts to understand how GPT transformers are able to adapt to the current linguistic context so effectively. The authors propose a specific mechanism for in-context learning (induction heads) and argue using different approaches that induction heads account for most in-context learning. In particular, they find no evidence of mesa optimization contributing to in-context learning. If you're at all interested in the internal organization or behaviors of transformers, please feel free to attend! The paper is from the group behind A Mathematical Framework for Transformer Circuits <https://transformer-circuits.pub/2021/framework/index.html>, which we discussed previously. However, this work is more empirical and is very well explained, so I found it more approachable. Join Zoom Meeting https://oregonstate.zoom.us/j/95843260079?pwd=TzZTN0xPaFZrazRGTElud0J1cnJLU… Password: 961594 Phone Dial-In Information +1 971 247 1195 US (Portland) +1 253 215 8782 US (Tacoma) +1 301 715 8592 US (Washington DC) Meeting ID: 958 4326 0079 All the best, Quintin

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[CoRIS-List] OSU Robotics Seminar 3/11 (Tomorrow): Dan Oblinger
by Ross L. Hatton 11 Mar '22

11 Mar '22

Hi All, As a reminder, the final OSU Robotics seminar for the winter term will be a "fireside chat" with Dan Oblinger. He'll discuss insights from his career, which has spanned university research, work as a DARPA program manager, and founding multiple companies. The seminar will be from 10-11am, followed by an 11-11:30 student-only Q&A session. ** This seminar will be held in person in LINC 302. ** --- A simulcast of the seminar will be available via Zoom https://oregonstate.zoom.us/j/92183247338?pwd=MENOWHgvSFVGYzNjZExDT2hWRUNxd… --- -Ross =========== Ross L. Hatton Associate Professor, Robotics and Mechanical Engineering Collaborative Robotics and Intelligent Systems Institute Oregon State University coris.oregonstate.edu research.engr.oregonstate.edu/lram/ rosslhatton.com ross.hatton(a)oregonstate.edu =========== Robotics Innovation In The Lab, Gov, Corp, and Startup We use the narrative stories of Bizzy, Martian, Aeolus Robotics, touch upon Dusty Robotics, Amazon's Astro and DARPA funded work, in order to talk about robotic innovation from several vantage points. BEST PRACTICES AND ANTI-PATTERNS -- We use these examples to discuss: - Founding companies, gaining product market fit - Getting funded (both governmental and investor funding) - Growing and running larger (40 PhD) R&D robotics teams. EXAMPLES OF APPLIED RESEARCH - Low cost robotics -- approaches for reducing 2 orders of magnitude in cost - Wiping Research -- Bizzy was a bathroom cleaning robot. This involved novel on robotic path planning for surface wiping. - Unconstrained Grasping -- Aeolus was a 'helper' bot, and is required to be able to manipulate found objects in an uncontrolled setting. ROBOTICS IN THIS NEW REMOTE WORLD — Not just because of covid, technical work is moving towards a planetary teams rather than co-located teams. As you can imagine this has dramatic consequences for a robotics team. I setup a 24/7 follow the sun hardware innovation lab. It kinda worked ;-). Lessons learned and thoughts for the future of robotics. DAN OBLINGER The first third of Dan's career was focused on academic research in ML and Programming By Demonstration while at IBM research and teaching in EE at Columbia University. He then served as a DARPA Program Manager funding over $200M in R&D focused on Human like learning from instruction and Language Understanding, including the work that led to the IBM Watson system beating the Jeopardy-TV-show world champion. Recently Dan has founded and exited several companies in AI, Robotics and Clean Energy.

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Machine Learning II
by Fern, Xiaoli 10 Mar '22

10 Mar '22

Hi all, Want to bring to your awareness that I will be teaching a special topic class titled Machine learning II (AI 539). The vision is that this will develop into the second course of a two-course Machine Learning series . I am attaching the syllabus for this term's offering for your consideration. Please feel free to send me an email if you have any question regarding the class. -Xiaoli Fern

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AI seminar: March 9, 2022
by Mangannavar, Rajesh Devaraddi 08 Mar '22

08 Mar '22

Dear all, Our next AI seminar on *"Challenges and Opportunities in Out-of-distribution Detection" *by Sharon Yixuan Li is scheduled to be on March 9th (Tomorrow), 1-2 PM PST. *The 30-minute graduate Q&A session will be held at 11:30 AM on 10 March (Thursday).* Please note that this session is on the day after the talk. Zoom Link: https://oregonstate.zoom.us/j/93591935144?pwd=YjZaSjBYS0NmNUtjQzBEdzhPeDZ5U… *Challenges and Opportunities in Out-of-distribution Detection* Sharon Yixuan Li Assistant Professor Department of Computer Sciences University of Wisconsin-Madison *Abstract:* The real world is open and full of unknowns, presenting significant challenges for machine learning (ML) systems that must reliably handle diverse, and sometimes anomalous inputs. Out-of-distribution (OOD) uncertainty arises when a machine learning model sees a test-time input that differs from its training data, and thus should not be predicted by the model. As ML is used for more safety-critical domains, the ability to handle out-of-distribution data are central in building open-world learning systems. In this talk, I will talk about challenges, methods, and opportunities on uncovering the unknowns of deep neural networks for reliable predictions in an open world. *Speaker Bio:* Sharon Yixuan Li is an Assistant Professor in the Department of Computer Sciences at the University of Wisconsin-Madison. Previously she was a postdoc research fellow in the Computer Science department at Stanford AI Lab. She completed her Ph.D. from Cornell University in 2017, where she was advised by John E. Hopcroft. She leads the organization of the ICML Workshop on Uncertainty and Robustness in Deep Learning in 2019 and 2020, and has served as area chair for NeurIPS, ICML, ICLR, and AAAI. Her broad research interests are in deep learning and machine learning. Her research develops algorithms and fundamental understandings to enable reliable open-world learning, which can function safely and adaptively in the presence of evolving and unpredictable data streams. She is the recipient of the Facebook Research Award, JPMorgan early-career faculty award, Madison Teaching and Learning Excellence fellowship, and was named Forbes 30Under30 in Science. Website: http://pages.cs.wisc.edu/~sharonli/ *Please watch this space for future AI Seminars :* * https://eecs.oregonstate.edu/ai-events <https://eecs.oregonstate.edu/ai-events>* Rajesh Mangannavar, Graduate Student Oregon State University ---- AI Seminar Important Reminders: -> For graduate students in the AI program, attendance is strongly encouraged.

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AI seminar: March 9, 2022
by Mangannavar, Rajesh Devaraddi 03 Mar '22

03 Mar '22

Dear all, Our next AI seminar on *"Challenges and Opportunities in Out-of-distribution Detection" *by Sharon Yixuan Li is scheduled to be on March 9th, 1-2 PM PST. It will be followed by a 30 minute Q&A session by the graduate students. Zoom Link: https://oregonstate.zoom.us/j/93591935144?pwd=YjZaSjBYS0NmNUtjQzBEdzhPeDZ5U… *Challenges and Opportunities in Out-of-distribution Detection* Sharon Yixuan Li Assistant Professor Department of Computer Sciences University of Wisconsin-Madison *Abstract:* The real world is open and full of unknowns, presenting significant challenges for machine learning (ML) systems that must reliably handle diverse, and sometimes anomalous inputs. Out-of-distribution (OOD) uncertainty arises when a machine learning model sees a test-time input that differs from its training data, and thus should not be predicted by the model. As ML is used for more safety-critical domains, the ability to handle out-of-distribution data are central in building open-world learning systems. In this talk, I will talk about challenges, methods, and opportunities on uncovering the unknowns of deep neural networks for reliable predictions in an open world. *Speaker Bio:* Sharon Yixuan Li is an Assistant Professor in the Department of Computer Sciences at the University of Wisconsin-Madison. Previously she was a postdoc research fellow in the Computer Science department at Stanford AI Lab. She completed her Ph.D. from Cornell University in 2017, where she was advised by John E. Hopcroft. She leads the organization of the ICML Workshop on Uncertainty and Robustness in Deep Learning in 2019 and 2020, and has served as area chair for NeurIPS, ICML, ICLR, and AAAI. Her broad research interests are in deep learning and machine learning. Her research develops algorithms and fundamental understandings to enable reliable open-world learning, which can function safely and adaptively in the presence of evolving and unpredictable data streams. She is the recipient of the Facebook Research Award, JPMorgan early-career faculty award, Madison Teaching and Learning Excellence fellowship, and was named Forbes 30Under30 in Science. *Please watch this space for future AI Seminars :* * https://eecs.oregonstate.edu/ai-events <https://eecs.oregonstate.edu/ai-events>* Rajesh Mangannavar, Graduate Student Oregon State University ---- AI Seminar Important Reminders: -> For graduate students in the AI program, attendance is strongly encouraged.

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