January 2022 - Ai - Engineering Mailing Lists

[CoRIS-List] Reminder: Today: OSU Robotics Seminar, Friday January 28: Sandhya Saisubramanian
by Ross L. Hatton 28 Jan '22

28 Jan '22

Hi All, As a reminder, we're kicking off the winter term of the Robotics Seminar today with a talk from Sandhya Saisubramanian, who recently joined the EECS faculty at Oregon State and works in the area of autonomous systems. The seminar will be from 10-11am, followed by an 11-11:30 student-only Q&A session. This first seminar will be conducted via Zoom. The seminar Zoom room can be reached at https://oregonstate.zoom.us/j/92183247338?pwd=MENOWHgvSFVGYzNjZExDT2hWRUNxd… I also have a Gather space (with a direct link to the Zoom session) set up at https://gather.town/app/HeC6WAB1zL6cKWp5/OSU%20Robotics%20Seminar for pre- and post-seminar socialization. -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 =========== Title: Avoiding Negative Side Effects of Autonomous Systems in the Open World Abstract: Autonomous systems are increasingly deployed in open-world environments. These systems have broad societal impacts and it is critical to ensure that they operate safely when deployed. Due to the practical challenges in data collection and precise model specification, AI systems often operate based on imperfect models which may lead to undesirable behaviors. A particular form of unexpected, undesirable consequence is the negative side effects. In this talk, I will (1) discuss the challenges in identifying and mitigating the impacts of negative side effects; (2) examine the efficiency of different forms of feedback to learn the penalty associated with negative side effects; and (3) discuss a multi-objective approach to mitigate the side effects. Bio: Sandhya Saisubramanian is an Assistant Professor in the School of EECS at Oregon State University. She recently completed her Ph.D. from the University of Massachusetts Amherst. Her research interests are in developing general techniques for reliable decision-making in autonomous systems that operate in fully and partially observable open-world environments. She is a recipient of Distinguished Paper Award at IJCAI 2020 and was selected as a Rising Star in EECS by UC Berkeley in 2020.

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AI seminar: Feb 2 2022
by Mangannavar, Rajesh Devaraddi 27 Jan '22

27 Jan '22

Dear all, Our next AI seminar on *"The Familiarity Hypothesis: Explaining the Behavior of Deep Open Set Methods" *by professor Tom Dietterich is scheduled to be on February 2nd, 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… *The Familiarity Hypothesis: Explaining the Behavior of Deep Open Set Methods* Tom Dietterich Distinguished Professor Emeritus Computer Science Oregon State University *Abstract:* In many applications, object recognition systems encounter objects belonging to categories unseen during training. Hence, the set of possible categories is an open set. Detecting such “novel category'” objects is usually formulated as an anomaly detection problem. Anomaly detection algorithms for feature-vector data identify anomalies as outliers, but outlier detection has not worked well in deep learning. Instead, methods based on the computed logits of object recognition systems give state-of-the-art performance. This talk proposes the Familiarity Hypothesis that these methods succeed because they are detecting the absence of familiar learned features. This talk will review evidence from the literature and from our own experiments that supports this hypothesis. It then experimentally tests a set of predicted consequences of this hypothesis that provide additional support. The talk will conclude with a discussion of whether familiarity detection is an inevitable consequence of representation learning and concludes that we can go beyond familiarity detection if we can learn to represent objects in terms of disentangled attributes that support outlier detection. *Speaker Bio:* Tom Dietterich (AB Oberlin College 1977; MS University of Illinois 1979; Ph.D. Stanford University 1984) is Distinguished Professor Emeritus in the School of Electrical Engineering and Computer Science at Oregon State University. He is one of the pioneers of the field of machine learning and has authored more than 225 refereed publications and two books. His current research topics include robust artificial intelligence, robust human-AI systems, and applications in sustainability. Dietterich has devoted many years of service to the research community. He is a former president of the Association for the Advancement of Artificial Intelligence and the founding president of the International Machine Learning Society. Other major roles include executive editor of the Journal Machine Learning, co-founder of the Journal for Machine Learning Research, and program chair of AAAI 1990 and NIPS 2000. He currently serves as one of the moderators for the cs.LG category on arXiv. *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 Alignment Reading Group
by Pope, Quintin 27 Jan '22

27 Jan '22

Hello everyone, We're continuing to meet at 2 PM PST on Fridays. Our next topic will be Selection Theorems: What’s the type signature of an agent? > > For instance, what kind-of-thing is a “goal”? What data structures can > represent “goals”? Utility functions are a common choice among theorists, > but they don’t seem quite right > <https://www.lesswrong.com/posts/RQpNHSiWaXTvDxt6R/coherent-decisions-imply-…>. > And what are the inputs to “goals”? Even when using utility functions, > different models use different inputs - Coherence Theorems > <https://www.lesswrong.com/posts/RQpNHSiWaXTvDxt6R/coherent-decisions-imply-…> > imply that utilities take in predefined “bet outcomes”, whereas AI > researchers often define utilities over “world states” or “world state > trajectories”, and human goals seem to be over latent variables in > humans’ world models > <https://www.lesswrong.com/posts/gQY6LrTWJNkTv8YJR/the-pointers-problem-huma…> > . > > And that’s just goals. What about “world models”? Or “agents” in general? > What data structures can represent these things, how do they interface with > each other and the world, and how do they embed > <https://www.lesswrong.com/posts/p7x32SEt43ZMC9r7r/embedded-agents> in > their low-level world? These are all questions about the type signatures of > agents. > > One general strategy for answering these sorts of questions is to look for > what I’ll call Selection Theorems. Roughly speaking, *a Selection Theorem > tells us something about what agent type signatures will be selected for > (by e.g. natural selection or ML training or economic profitability) in > some broad class of environments*. In inner/outer agency terms, it tells > us what kind of inner agents will be selected by outer optimization > processes. > There are three selection theorems posts so far: Selection Theorems: A Program For Understanding Agents <https://www.lesswrong.com/posts/G2Lne2Fi7Qra5Lbuf/selection-theorems-a-prog…> Some Existing Selection Theorems <https://www.lesswrong.com/posts/N2NebPD78ioyWHhNm/some-existing-selection-t…> What Selection Theorems Do We Expect/Want? <https://www.lesswrong.com/posts/RuDD3aQWLDSb4eTXP/what-selection-theorems-d…> We'll be sure to discuss the first post, and hopefully the other two if we have time. Anyone interested in trying to better understand goal directed behavior is welcome to join! 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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AI seminar: January 26 2022
by Mangannavar, Rajesh Devaraddi 26 Jan '22

26 Jan '22

Dear all, Our next AI seminar on *"Linear-Time Algorithm to Find the Achilles' Heels of SARS-CoV-2 Genomes" *by professor Liang Huang is scheduled to be on January 26th (Tomorrow), 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… *Linear-Time Algorithm to Find the Achilles' Heels of SARS-CoV-2 Genomes* Liang Huang Associate Professor Computer Science Oregon State University *Abstract:* The constant emergence of COVID-19 variants reduces the effectiveness of existing vaccines and test kits. Therefore, it is critical to identify conserved structures in SARS-CoV-2 genomes as potential targets for variant-proof diagnostics and therapeutics. However, the algorithms to predict these conserved structures, which simultaneously fold and align multiple RNA homologs, scale at best cubically with sequence length and are thus infeasible for coronaviruses, which possess the longest genomes (∼30,000 nt) among RNA viruses. As a result, existing efforts on modeling SARS-CoV-2 structures resort to single-sequence folding as well as local folding methods with short window sizes, which inevitably neglect long-range interactions that are crucial in RNA functions. Here we present LinearTurboFold, a linear-time algorithm for folding RNA homologs that scales linearly with sequence length, enabling unprecedented global structural analysis on SARS-CoV-2. Furthermore, LinearTurboFold identifies undiscovered conserved structures and conserved accessible regions as potential targets for designing efficient and mutation-insensitive small-molecule drugs, antisense oligonucleotides, small interfering RNAs (siRNAs), CRISPR-Cas13 guide RNAs, and RT-PCR primers. LinearTurboFold is a general technique that can also be applied to other RNA viruses and full-length genome studies and will be a useful tool in fighting the current and future pandemics. This work has been published at Proceedings of National Academy of Sciences, marking the first PNAS paper ever from OSU EECS. *Speaker Bio:* Liang Huang (PhD, UPenn, 2008) is an Associate Professor of Computer Science at Oregon State University and a Distinguished Scientist at Baidu Research USA. He is a leading computational linguist, and was recognized at ACL 2008 (Best Paper Award) and ACL 2019 (Keynote Speech), but in recent years he has been more interested in applying his algorithmic expertise to biology problems such as RNA folding and RNA design. Since the outbreak of COVID-19, he has shifted his attention to the fight against the virus, which resulted in high-impact work such as efficient algorithms for stable mRNA vaccine design (under review at Science, being used by 30+ companies), and for homologous folding of RNA genomes (PNAS, 2021). *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] OSU Robotics Seminar, Friday January 28: Sandhya Saisubramanian
by Ross L. Hatton 25 Jan '22

25 Jan '22

Hi All, As previously announced, we'll be kicking off the winter term of the Robotics Seminar with a talk from Sandhya Saisubramanian, who recently joined the EECS faculty at Oregon State and works in the area of autonomous systems. The seminar will be from 10-11am, followed by an 11-11:30 student-only Q&A session. This first seminar will be conducted via Zoom. The seminar Zoom room can be reached at https://oregonstate.zoom.us/j/92183247338?pwd=MENOWHgvSFVGYzNjZExDT2hWRUNxd… I also have a Gather space (with a direct link to the Zoom session) set up at https://gather.town/app/HeC6WAB1zL6cKWp5/OSU%20Robotics%20Seminar for pre- and post-seminar socialization. -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 =========== Title: Avoiding Negative Side Effects of Autonomous Systems in the Open World Abstract: Autonomous systems are increasingly deployed in open-world environments. These systems have broad societal impacts and it is critical to ensure that they operate safely when deployed. Due to the practical challenges in data collection and precise model specification, AI systems often operate based on imperfect models which may lead to undesirable behaviors. A particular form of unexpected, undesirable consequence is the negative side effects. In this talk, I will (1) discuss the challenges in identifying and mitigating the impacts of negative side effects; (2) examine the efficiency of different forms of feedback to learn the penalty associated with negative side effects; and (3) discuss a multi-objective approach to mitigate the side effects. Bio: Sandhya Saisubramanian is an Assistant Professor in the School of EECS at Oregon State University. She recently completed her Ph.D. from the University of Massachusetts Amherst. Her research interests are in developing general techniques for reliable decision-making in autonomous systems that operate in fully and partially observable open-world environments. She is a recipient of Distinguished Paper Award at IJCAI 2020 and was selected as a Rising Star in EECS by UC Berkeley in 2020.

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AI seminar: January 26 2022
by Mangannavar, Rajesh Devaraddi 20 Jan '22

20 Jan '22

Dear all, Our next AI seminar on *"Linear-Time Algorithm to Find the Achilles' Heels of SARS-CoV-2 Genomes" *by professor Liang Huang is scheduled to be on January 26th, 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… *Linear-Time Algorithm to Find the Achilles' Heels of SARS-CoV-2 Genomes* Liang Huang Associate Professor Computer Science Oregon State University *Abstract:* The constant emergence of COVID-19 variants reduces the effectiveness of existing vaccines and test kits. Therefore, it is critical to identify conserved structures in SARS-CoV-2 genomes as potential targets for variant-proof diagnostics and therapeutics. However, the algorithms to predict these conserved structures, which simultaneously fold and align multiple RNA homologs, scale at best cubically with sequence length and are thus infeasible for coronaviruses, which possess the longest genomes (∼30,000 nt) among RNA viruses. As a result, existing efforts on modeling SARS-CoV-2 structures resort to single-sequence folding as well as local folding methods with short window sizes, which inevitably neglect long-range interactions that are crucial in RNA functions. Here we present LinearTurboFold, a linear-time algorithm for folding RNA homologs that scales linearly with sequence length, enabling unprecedented global structural analysis on SARS-CoV-2. Furthermore, LinearTurboFold identifies undiscovered conserved structures and conserved accessible regions as potential targets for designing efficient and mutation-insensitive small-molecule drugs, antisense oligonucleotides, small interfering RNAs (siRNAs), CRISPR-Cas13 guide RNAs, and RT-PCR primers. LinearTurboFold is a general technique that can also be applied to other RNA viruses and full-length genome studies and will be a useful tool in fighting the current and future pandemics. This work has been published at Proceedings of National Academy of Sciences, marking the first PNAS paper ever from OSU EECS. *Speaker Bio:* Liang Huang (PhD, UPenn, 2008) is an Associate Professor of Computer Science at Oregon State University and a Distinguished Scientist at Baidu Research USA. He is a leading computational linguist, and was recognized at ACL 2008 (Best Paper Award) and ACL 2019 (Keynote Speech), but in recent years he has been more interested in applying his algorithmic expertise to biology problems such as RNA folding and RNA design. Since the outbreak of COVID-19, he has shifted his attention to the fight against the virus, which resulted in high-impact work such as efficient algorithms for stable mRNA vaccine design (under review at Science, being used by 30+ companies), and for homologous folding of RNA genomes (PNAS, 2021). *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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Alignment Reading Group Meeting
by Pope, Quintin 20 Jan '22

20 Jan '22

Hello everyone, No one expressed issues with our meeting time, so we'll meet at 2 PM PST on Friday like normal. We'll discuss Eliciting latent knowledge: How to tell if your eyes deceive you <https://docs.google.com/document/d/1WwsnJQstPq91_Yh-Ch2XRL8H_EpsnjrC1dwZXR3…>, which Alex expressed interest in. In this post, we’ll present ARC’s approach to an open problem we think is > central to aligning powerful machine learning (ML) systems: > > Suppose we train a model to predict what the future will look like > according to cameras and other sensors. We then use planning algorithms to > find a sequence of actions that lead to predicted futures that look good to > us. > > But some action sequences could tamper with the cameras so they show > happy humans regardless of what’s really happening. More > generally, some futures look great on camera but are actually > catastrophically bad. > > In these cases, the prediction model "knows" facts (like "the camera was > tampered with") that are not visible on camera but would change our > evaluation of the predicted future if we learned them. How can we train > this model to report its latent knowledge of off-screen events? > > We’ll call this problem eliciting latent knowledge (ELK). In this report > we’ll focus on detecting sensor tampering as a motivating example, but we > believe ELK is central to many aspects of alignment. 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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AI seminar: January 19 2022
by Mangannavar, Rajesh Devaraddi 19 Jan '22

19 Jan '22

Dear all, Our next AI seminar on *"Deep Declarative Networks, Optimal Transport and the Blind PnP Problem" *by professor Stephen Gould is scheduled to be on January 19th (Tomorrow), 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… *Deep Declarative Networks, Optimal Transport and the Blind PnP Problem* Stephen Gould Professor Computer Science Australian National University *Abstract:* Deep declarative networks (DDNs) are a new class of deep learning model that allows optimization problems to be embedded within end-to-end learnable pipelines. In this talk I will introduce DDNs and related concepts---implicit layers and differentiable optimization---and give some formal results for second-order differentiable problems. I will then present a concrete example of a DDN layer in the case of optimal transport, and show that by applying the DDN results we can obtain significant memory and speed improvements over unrolling Sinkhorn iterates, as would be required in traditional deep learning models. Finally, I demonstrate an end-to-end model for solving the blind perspective-n-point problem that makes use of two DDN layers. Limitations of DDNs and open questions will also be discussed. *Speaker Bio:* Stephen Gould is a Professor of Computer Science at the Australian National University (ANU). He is also an Australian Research Council (ARC) Future Fellow and Amazon Scholar. He is a former ARC Postdoctoral Fellow, Microsoft Faculty Fellow, Contributed Researcher at Data61, Principal Research Scientist at Amazon Inc, and Director of the ARC Centre of Excellence in Robotic Vision. Stephen received his BSc degree in mathematics and computer science and BE degree in electrical engineering from the University of Sydney in 1994 and 1996, respectively. He received his MS degree in electrical engineering from Stanford University in 1998. He then worked in the industry for several years where he co-founded Sensory Networks, which later sold to Intel in 2013. In 2005 he returned to Stanford University and was awarded his Ph.D. degree in 2010. In November 2010, he moved back to Australia to take up a faculty position at the ANU. Stephen has broad interests in the areas of computer and robotic vision, machine learning, deep learning, structured prediction, and optimization. He teaches courses on advanced machine learning, research methods in computer science, and the craft of computing. His main research focus is on automatic semantic, dynamic, and geometric understanding of images and videos. *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: January 19 2022
by Mangannavar, Rajesh Devaraddi 14 Jan '22

14 Jan '22

Dear all, Our next AI seminar on *"Deep Declarative Networks, Optimal Transport and the Blind PnP Problem" *by professor Stephen Gould is scheduled to be on January 19th, 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… *Deep Declarative Networks, Optimal Transport and the Blind PnP Problem* Stephen Gould Professor Computer Science Australian National University *Abstract:* Deep declarative networks (DDNs) are a new class of deep learning model that allows optimization problems to be embedded within end-to-end learnable pipelines. In this talk I will introduce DDNs and related concepts---implicit layers and differentiable optimization---and give some formal results for second-order differentiable problems. I will then present a concrete example of a DDN layer in the case of optimal transport, and show that by applying the DDN results we can obtain significant memory and speed improvements over unrolling Sinkhorn iterates, as would be required in traditional deep learning models. Finally, I demonstrate an end-to-end model for solving the blind perspective-n-point problem that makes use of two DDN layers. Limitations of DDNs and open questions will also be discussed. *Speaker Bio:* Stephen Gould is a Professor of Computer Science at the Australian National University (ANU). He is also an Australian Research Council (ARC) Future Fellow and Amazon Scholar. He is a former ARC Postdoctoral Fellow, Microsoft Faculty Fellow, Contributed Researcher at Data61, Principal Research Scientist at Amazon Inc, and Director of the ARC Centre of Excellence in Robotic Vision. Stephen received his BSc degree in mathematics and computer science and BE degree in electrical engineering from the University of Sydney in 1994 and 1996, respectively. He received his MS degree in electrical engineering from Stanford University in 1998. He then worked in the industry for several years where he co-founded Sensory Networks, which later sold to Intel in 2013. In 2005 he returned to Stanford University and was awarded his Ph.D. degree in 2010. In November 2010, he moved back to Australia to take up a faculty position at the ANU. Stephen has broad interests in the areas of computer and robotic vision, machine learning, deep learning, structured prediction, and optimization. He teaches courses on advanced machine learning, research methods in computer science, and the craft of computing. His main research focus is on automatic semantic, dynamic, and geometric understanding of images and videos. *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 by Hannah Kerner starting in a few minutes
by Tadepalli, Prasad 12 Jan '22

12 Jan '22

Zoom link: https://oregonstate.zoom.us/j/93591935144?pwd=YjZaSjBYS0NmNUtjQzBEdzhPeDZ5U…

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