February 2022 - Ai - Engineering Mailing Lists

AI Alignment Reading Group
by Pope, Quintin 25 Feb '22

25 Feb '22

Hello everyone, We'll be meeting again at 1 PM PST on Friday. We'll discuss Locating and Editing Factual Knowledge in GPT <https://github.com/kmeng01/rome>. We investigate the mechanisms underlying factual knowledge recall in > autoregressive transformer language models. First, we develop a causal > intervention for identifying neuron activations capable of altering a > model's factual predictions. Within large GPT-style models, this reveals > two distinct sets of neurons that we hypothesize correspond to knowing an > abstract fact and saying a concrete word, respectively. This insight > inspires the development of ROME, a novel method for editing facts stored > in model weights. For evaluation, we assemble CounterFact, a dataset of > over twenty thousand counterfactuals and tools to facilitate sensitive > measurements of knowledge editing. Using CounterFact, we confirm the > distinction between saying and knowing neurons, and we find that ROME > achieves state-of-the-art performance in knowledge editing compared to > other methods. An interactive demo notebook, full code implementation, and > the dataset are available at this https URL <https://rome.baulab.info/>. I look forward to meeting you then! 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] Today: OSU Robotics Seminar, Nick Gravish
by Ross L. Hatton 25 Feb '22

25 Feb '22

Hi All, As a reminder, our second winter Robotics Seminar will be *Today*, with a talk from Nick Gravish from the University of California, San Diego, who works in the area of bioinspired locomotion. 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. ** If you would like to meet with Nick, I have a few open meeting times. Please contact me if you would like one of them. --- 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 =========== Title: Design and control of emergent oscillations for flapping-wing flyers and swarming snakes Abstract: Locomotion in living systems and bio-inspired robots requires the generation and control of oscillatory motion. While a common method to generate motion is through modulation of time-dependent “clock” signals, in this talk we will motivate and study an alternative method of oscillatory generation through autonomous limit-cycle systems. Limit-cycle oscillators for robotics have many desirable properties including adaptive behaviors, entrainment between oscillators, and potential simplification of motion control. I will present two examples of the generation and control of autonomous oscillatory motion in bio-inspired robotics. First, I will describe our recent work to study the dynamics of wingbeat oscillations in “asynchronous” insects and how we can build these behaviors into micro-aerial vehicles. In the second part of this talk I will describe how simple snake-like robots with limit-cycle gaits enable swarms to synchronize their movement through contact and without communication. More broadly in this talk I hope to motivate why we should look to autonomous dynamical systems for designing and controlling emergent locomotor behaviors in robotics. Bio: Dr. Nick Gravish received his PhD from Georgia Tech where he used robots as physical models to motivate and study aspects of biological locomotion. During his post-doc Gravish worked in the microrobotics lab of Rob Wood at Harvard, where he gained expertise in designing and studying insect-scale robots. Gravish is currently an assistant professor at UC San Diego in the Mechanical and Aerospace Engineering department. His lab bridges the gap between bio-inspiration, biomechanics, and robotics, towards the development of new bio-inspired robotic technologies to improve the adaptability and resilience of mobile robots.

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AI seminar: March 2, 2022
by Mangannavar, Rajesh Devaraddi 24 Feb '22

24 Feb '22

Dear all, Our next AI seminar on *"Considerations for More Scalable Trustworthy AI" *by Richard Mallah is scheduled to be on March 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… *Considerations for More Scalable Trustworthy AI* Richard Mallah Director of AI Projects The Future of Life Institute *Abstract:* As AI systems become less narrow in their capabilities and more general-purpose, new classes and levels of pitfalls present themselves, so techniques we bring to bear for the safety and ethical alignment of these systems will need to scale in new ways and require innovation increasingly on par with the sophistication of the primary learning functions of the system. As the differences between what a system can do and what it should do grow, desiderata we encounter even with narrower AI systems, like establishing safe bounds, practical interpretability, verification of key models or algorithms, minimizing negative side effects, maintaining operator control, and mitigation of unwanted biases each will need to account for levels of indirection previously unseen. In this talk, we attempt to explore the cultivation of a security mindset with respect to what more general systems can do wrong, and application of that to critical evaluation and design of safety techniques regarding their scalability or amenability to generality. It is notable, for instance, with increasing generality, that modeling context is increasingly important and that AI safety and AI ethics increasingly overlap. *Speaker Bio:* Richard Mallah is Director of AI Projects at The Future of Life Institute, where he does metaresearch, analysis, advocacy, strategy, and field building regarding technical, strategy, and policy aspects of transformative AI safety. From December 2015 Richard was on the founding team of the IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems and continues to serve on its Executive Committee. He co-chairs the recurring SafeAI and AISafety technical safety workshops at AAAI and IJCAI, and in 2021 was the founding Executive Director of the Consortium on the Landscape of AI Safety, for which he was drafted because of his AI safety field landscaping & synthesis work at FLI. Richard has served in the Safety and the Labor & Economy Working Groups at Partnership on AI, has chaired the IEEE GIEAIS committees on AGI and on lethal autonomous weapons, and is an Honorary Senior Fellow at the Foresight Institute. Mr. Mallah has been working in machine learning and AI in industry for over twenty years, spanning many roles across R&D including algorithms research, research management, product team management, CTO, chief scientist, and strategy; in total he’s worked on over a hundred AI/ML-related technical projects from these different perspectives. Ever-focused on innovation yet mindful of managing risks, Richard has regularly aligned applied research drivers with novel research directions in trustworthy AI. In so doing, safety-related R&D he’s led has included: multiobjective GP agents with safety objectives, debiasing novel latent spaces, active-learning-enhanced automated ontology refactoring and alignment, explainability-enhanced conditional quasi-metric spaces, uncertainty-aware tight blends of symbolic and subsymbolic knowledge representations, liability-bearing-anomaly recognition, confidence-enhanced bayesian monte carlo analysis of operational risk, more robust multimodal LSTM systems, information-retrieval-enhanced transformer-based systems for more truthful NLG, and AI auditing methods. Richard advises AI safety startups, VC funds, incubators, academics, governments, international multistakeholder bodies, and NGOs on trustworthy AI, scalable AI safety, scalable AI ethics, wide-angle sustainability, ML model risk management, and assurance. Mr. Mallah has given dozens of invited talks globally on long-termist foresight, assurance, robustness, interpretability, and control of advanced AI and autonomous systems. He holds a degree in Intelligent Systems from Columbia University. *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: Feb 23 2022
by Mangannavar, Rajesh Devaraddi 23 Feb '22

23 Feb '22

Dear all, Our next AI seminar on *"**Over a trillion bases and counting: why leveraging public databases is essential for microbiome analysis**" *by professor Maude David is scheduled to be on February 23rd (Today), 1-2 PM PST. Zoom Link: https://oregonstate.zoom.us/j/93591935144?pwd=YjZaSjBYS0NmNUtjQzBEdzhPeDZ5U… *Over a trillion bases and counting: why leveraging public databases is essential for microbiome analysis* Maude David Assistant Professor Department of Microbiology Oregon State University *Abstract:* The amount of publicly available sequencing data has doubled approximately every 18 months since 1982, and there are now over a billion Whole Genome Shotguns available on Genbank. But the number of studies that incorporate available datasets remain marginal, and as a result, especially in human gut microbiome studies, where collecting clinical samples can be arduous, the number of taxa considered in any one study often exceeds the number of samples ten to one hundred-fold. In this presentation we will first focus on 16S amplicon data by deriving microbiome-level properties by applying an embedding algorithm to quantify taxon co-occurrence patterns in over 18,000 samples from the American Gut Project microbiome crowdsourcing effort. We show that predictive models trained using property data are the most accurate, robust, and generalizable, and that property-based models can be trained on one dataset and deployed on another. Using these properties, we are able to extract known and new bacterial metabolic pathways associated with inflammatory bowel disease across two completely independent studies. Using publicly available datasets presents several limitations, and among them disparities in methodologies, sequencing technologies, and notably poor functional annotations. Rather than relying solely on completely curated databases for functional annotation, the second part of this presentation will focus on a new annotation strategy where we recruited protein sequences carrying common protein domains (via pfam) alongside KEGG Orthologs. We leveraged the unannotated sequences in order to generate KO level Hidden Markov Models that proved to be more sensitive than non-propagated models, on an independent testing set. *Speaker Bio:* Maude David graduated with a Ph.D. from Ecole Centrale de Lyon (France), and joined Oregon State University as an assistant professor in 2018 after her postdoctoral work at Lawrence Berkeley National Laboratory and Stanford University. The David Lab focuses on new biocomputing methods to utilize publicly available datasets to analyze microbiome sequencing data. The lab also works on how the gut microbiota can modulate behavior via the gut-microbiome-brain axis. We use an interdisciplinary approach including the use of mouse models, in vitro cell culture, anaerobic bacteria culture, bees (!) and machine learning to tackle those questions. *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 18 Feb '22

18 Feb '22

Hello everyone, I've looked at the survey results. Unfortunately, there's no timeslot that everyone can make. I've chosen Friday at 1 PM PST as the least conflicted meeting time. I hope to see everyone soon! Our discussion paper will be "Red Teaming Language Models with Language Models <https://www.deepmind.com/research/publications/2022/Red-Teaming-Language-Mo…> ": Language Models (LMs) often cannot be deployed because of their potential > to harm users in ways that are hard to predict in advance. Prior work > identifies harmful behaviors before deployment by using human annotators to > hand-write test cases. However, human annotation is expensive, limiting the > number and diversity of test cases. In this work, we automatically find > cases where a target LM behaves in a harmful way, by generating test cases > (“red teaming”) using another LM. We evaluate the target LM’s replies to > generated test questions using a classifier trained to detect offensive > content, uncovering tens of thousands of offensive replies in a 280B > parameter LM chatbot. We explore several methods, from zero-shot generation > to reinforcement learning, for generating test cases with varying levels of > diversity and difficulty. Furthermore, we use prompt engineering to control > LM-generated test cases to uncover a variety of other harms, automatically > finding groups of people that the chatbot discusses in offensive ways, > personal and hospital phone numbers generated as the chatbot’s own contact > info, leakage of private training data in generated text, and harms that > occur over the course of a conversation. Overall, LM-based red teaming is > one promising tool (among many needed) for finding and fixing diverse, > undesirable LM behaviors before impacting users. > 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] Upcoming OSU Robotics Seminars
by Ross L. Hatton 17 Feb '22

17 Feb '22

Hi All, Here's the list of scheduled seminars for the remainder of winter term and the upcoming spring term. With travel picking up again, my expectation is that many/most of these will be held in person (with a simulcast option). -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 ----------------------- 2/25/2022 Nick Gravish -UCSD Mechanical Engineering -Dynamics, bioinspired locomotion -http://web.eng.ucsd.edu/~ngravish/ 3/4/2022 No semiinar: Grad applicant visit weekend 3/11/2022 Dan Oblinger -Analytics Fire (Previously at IBM and DARPA) -Machine learning, data science, robotics in industry -https://www.topionetworks.com/people/dan-oblinger-5a8526e8105eb54101b938b2 3/18/2022 No seminar: Finals week 3/25/2022 No seminar: Spring break 4/1/2022 Kristen Macuga -OSU Psychology -Technology-assisted decision making, tool use -https://liberalarts.oregonstate.edu/users/kristen-macuga 4/8/2022 Reserved for RGSA event 4/15/2022 Chris Neider (To be confirmed) -Starship Technologies -Food delivery robots -https://www.starship.xyz/ 4/22/2022 Kim Ingraham -University of Washington Mechanical Engineering, Rehabilitation Medicine -Biomechanics and wearable robots -https://kim-ingraham.com 4/29/2022 No seminar scheduled 5/6/2022 Shai Revzen -University of Michigan Electrical Engineering -Dynamics, bioinspired, legged locomotion -https://robotics.umich.edu/profile/shai-revzen/ 5/13/2022 ICRA practice talks 5/20/2022 Chris Sanchez -OSU Psychology -Human use of computing technology -https://liberalarts.oregonstate.edu/users/christopher-sanchez 5/27/2022 No seminar: ICRA 6/3/2022 Edward Wang UCSD ECE & Design Lab Design and health monitoring https://www.ejaywang.com

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AI seminar: Feb 16 2022
by Mangannavar, Rajesh Devaraddi 15 Feb '22

15 Feb '22

Dear all, Our next AI seminar on *"Quo Vadis? Predicting Future Trajectories of Robots through Temporal Logics and Bayesian Inference" *by professor Sriram Sankaranarayanan is scheduled to be on February 16th (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… *Quo Vadis? Predicting Future Trajectories of Robots through Temporal Logics and Bayesian Inference* Sriram Sankaranarayanan Associate Professor Computer Science University of Colorado, Boulder *Abstract:* Predicting the future states of robots through observation of its past and current actions is a very interesting problem. It is a fundamental "primitive" for applications such as runtime monitoring to prevent impending failures such as collisions or entering forbidden regions in the workspace. In this talk, we will present a few approaches to this problem beginning with a simple learning-based extrapolation of the robot's past positions to predict future trajectories, assuming a simple dynamical model for the robot. Unfortunately, such an extrapolation will remain valid only for relatively short time horizons. To improve upon this, we show how the "intent" of the agent can be represented and reasoned with. To represent intent, we use a restricted class of formulas from temporal logics as hypothesized intents. By combining these temporal logic representations through the machinery of Bayesian Inference, we show how we can predict future trajectories of robots rapidly through a combination of off-line pre-computations that enable cheaper real-time predictions. We conclude by describing ongoing work that develops a hierarchical approach wherein we separate "short-term" intents from "longer-term" intents that can be represented by the full strength of temporal logic-based specifications. This presentation is based on a series of joint works with Hansol Yoon and Chou Yi. *Speaker Bio:* Sriram Sankaranarayanan is an associate professor of Computer Science at the University of Colorado, Boulder. His research interests include automatic techniques for reasoning about the behavior of computer and cyber-physical systems. Sriram obtained a Ph.D. in 2005 from Stanford University where he was advised by Zohar Manna and Henny Sipma. Subsequently he worked as a research staff member at NEC research labs in Princeton, NJ. He has been on the faculty at CU Boulder since 2009. Sriram has been the recipient of awards including the President's Gold Medal from IIT Kharagpur (2000), Siebel Scholarship (2005), the CAREER award from NSF (2009), Dean's award for outstanding junior faculty (2012), outstanding teaching (2014), and the Provost's faculty achievement award (2014). *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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Schedule for AI Alignment Reading Group
by Pope, Quintin 14 Feb '22

14 Feb '22

Hello everyone, People have had schedule conflicts with our current meeting time, so I've decided to send out a survey to find a time that may work better. Please fill out the survey with your availability: http://whenisgood.net/swyfrcp Depending on responses, we may or may not be able to meet this week. I'll send out an update once the meeting time is confirmed. Our next paper will be "Red Teaming Language Models with Language Models <https://www.deepmind.com/research/publications/2022/Red-Teaming-Language-Mo…> ": Language Models (LMs) often cannot be deployed because of their potential > to harm users in ways that are hard to predict in advance. Prior work > identifies harmful behaviors before deployment by using human annotators to > hand-write test cases. However, human annotation is expensive, limiting the > number and diversity of test cases. In this work, we automatically find > cases where a target LM behaves in a harmful way, by generating test cases > (“red teaming”) using another LM. We evaluate the target LM’s replies to > generated test questions using a classifier trained to detect offensive > content, uncovering tens of thousands of offensive replies in a 280B > parameter LM chatbot. We explore several methods, from zero-shot generation > to reinforcement learning, for generating test cases with varying levels of > diversity and difficulty. Furthermore, we use prompt engineering to control > LM-generated test cases to uncover a variety of other harms, automatically > finding groups of people that the chatbot discusses in offensive ways, > personal and hospital phone numbers generated as the chatbot’s own contact > info, leakage of private training data in generated text, and harms that > occur over the course of a conversation. Overall, LM-based red teaming is > one promising tool (among many needed) for finding and fixing diverse, > undesirable LM behaviors before impacting users. > I hope as many people as possible are able to come to the new event. 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, Friday February 25: Nick Gravish
by Ross L. Hatton 14 Feb '22

14 Feb '22

Hi All, Our second winter Robotics Seminar will be on Friday, February 25 with a talk from Nick Gravish from the University of California, San Diego, who works in the area of bioinspired locomotion. 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 =========== Title: Design and control of emergent oscillations for flapping-wing flyers and swarming snakes Abstract: Locomotion in living systems and bio-inspired robots requires the generation and control of oscillatory motion. While a common method to generate motion is through modulation of time-dependent “clock” signals, in this talk we will motivate and study an alternative method of oscillatory generation through autonomous limit-cycle systems. Limit-cycle oscillators for robotics have many desirable properties including adaptive behaviors, entrainment between oscillators, and potential simplification of motion control. I will present two examples of the generation and control of autonomous oscillatory motion in bio-inspired robotics. First, I will describe our recent work to study the dynamics of wingbeat oscillations in “asynchronous” insects and how we can build these behaviors into micro-aerial vehicles. In the second part of this talk I will describe how simple snake-like robots with limit-cycle gaits enable swarms to synchronize their movement through contact and without communication. More broadly in this talk I hope to motivate why we should look to autonomous dynamical systems for designing and controlling emergent locomotor behaviors in robotics. Bio: Dr. Nick Gravish received his PhD from Georgia Tech where he used robots as physical models to motivate and study aspects of biological locomotion. During his post-doc Gravish worked in the microrobotics lab of Rob Wood at Harvard, where he gained expertise in designing and studying insect-scale robots. Gravish is currently an assistant professor at UC San Diego in the Mechanical and Aerospace Engineering department. His lab bridges the gap between bio-inspiration, biomechanics, and robotics, towards the development of new bio-inspired robotic technologies to improve the adaptability and resilience of mobile robots.

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Intel Internship in Deep RL
by Fern, Alan Paul 13 Feb '22

13 Feb '22

Hi Everyone, I was asked by a colleague at Intel Research to circulate this announcement for an Intel internship. https://jobs.intel.com/ShowJob/Id/3269293/AI-Deep-Learning-Graduate-Intern<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fjobs.inte…> They are particularly interested in people with previous RL experience. There is the possibility of doing this internship remotely. -Alan

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