- Ai - Engineering Mailing Lists

Alignment reading group: the lottery ticket hypothesis
by Turner, Alex 18 May '21

18 May '21

This Friday, we'll discuss "The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks" and related papers: "The Lottery Ticket Hypothesis: A randomly-initialized, dense neural network contains a subnetwork that is initialised such that — when trained in isolation — it can match the test accuracy of the original network after training for at most the same number of iterations." Paper: https://openreview.net/forum?id=rJl-b3RcF7 Literature survey: https://towardsdatascience.com/the-lottery-ticket-hypothesis-a-survey-d1f0f… Additional LTH-related papers found in this post and comment: https://www.alignmentforum.org/posts/dpzLqQQSs7XRacEfK/understanding-the-lo… We'll meet Friday at 1. https://oregonstate.zoom.us/j/2739792686?pwd=VkRUeHJkYnhvTzlvZzR6YnZWNERKQT…

1 0

Alignment reading group: cooperative AI
by Turner, Alex 14 May '21

14 May '21

This Friday, we'll discuss "Cooperative AI: machines must learn to find common ground": "AI needs social understanding and cooperative intelligence to integrate well into society. The coming years might give rise to diverse ecologies of AI systems that interact in rapid and complex ways with each other and with humans: on pavements and roads, in consumer and financial markets, in e-mail communication and social media, in cybersecurity and physical security. Autonomous vehicles or smart cities that do not engage well with humans will fail to deliver their benefits, and might even disrupt stable human relationships." Comment: https://www.nature.com/articles/d41586-021-01170-0 Alignment newsletter summary: https://www.alignmentforum.org/posts/3zmKzbMPjPvEcZfkn/an-150-the-subtypes-… We'll meet Friday at 1. https://oregonstate.zoom.us/j/2739792686?pwd=VkRUeHJkYnhvTzlvZzR6YnZWNERKQT…

1 0

[CoRIS-List] Reminder: Friday, 10am: Hannah Stuart
by Hatton, Ross 12 May '21

12 May '21

Hi All, As a reminder, we have an OSU Robotics Zoom seminar at 10am on Friday: Hannah Stuart (UC Berkeley) Watch link: *https://oregonstate.zoom.us/j/97644079157?pwd=WXRZeEhOR0padXVzb2NkVkVkVEFmZz09 <https://oregonstate.zoom.us/j/97644079157?pwd=WXRZeEhOR0padXVzb2NkVkVkVEFmZ…>* Title: Hands for underwater manipulation: Design for robust and sensitive grasping Abstract: Capable dexterous manipulation allows marine robots and remotely operated vehicles (ROV) to perform more useful manual tasks. The focus of interaction is often delegated to a set of robotic hands that grip natural objects, tools and structures. Not only must end-effectors meet desired performance (e.g., not damaging delicate items, handling structures with strength, etc.), but withstand uncertainty, collisions, and the extreme pressures associated with depth. In this talk, I will discuss the design of resilient and compliant underwater-capable grippers, including the field-tested hands of the Ocean One ROV. Small changes to contact conditions, such as gentle suction flow or soft skin, can have a disproportionate effect for underwater hands. In particular, suction flow can be harnessed as a useful tactile sensor suitable for the submerged environment. Discussion will compare and contrast the utility of different technologies in air versus under water. Bio: Dr. Hannah Stuart is an Assistant Professor in the Department of Mechanical Engineering at the University of California at Berkeley. She received her BS in Mechanical Engineering at the George Washington University in 2011, and her MS and PhD in Mechanical Engineering at Stanford University in 2013 and 2018, respectively. Her research focuses on understanding the mechanics of physical interaction in order to better design systems for dexterous manipulation, especially in unstructured environments. Applications range from remote robotics to assistive orthotics. Recent awards include the NASA Early Career Faculty grant, Hellman Fellows Fund grant, and Johnson & Johnson Women in STEM2D grant. Pre- and post-seminar Gather space for OSU Robotics attendees (with an integrated link to the main talk) is at https://gather.town/app/HeC6WAB1zL6cKWp5/OSU%20Robotics%20Seminar -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

1 0

Signup and Officer Nominations for AIGSA Club
by Buss, Christopher 11 May '21

11 May '21

Hi AI Grads, The AI Graduate Student Association (AIGSA) is a new club whose goal is to build community among AI Graduate students through fun networking and professional development events. However, before we can start planning these events, we need *members* and *officers*. *Become a Member:* Funding for AIGSA is provided by the University, so becoming a member is free. Since AIGSA is a new club, it's important that you sign up <https://apps.ideal-logic.com/osusli?key=F3T9-25VWY_5878-CZ4R_f7b06f23> as a member if you are interested in taking part in future AIGSA events: funding is based on our membership count and we want to make sure that events have the capacity to include everyone who wants to participate. Once you sign up, you can also join us on our dedicated Slack workspace <http://eecs-aigsa.slack.com>. *Nominate Officers:* In order to plan events for next year, we also need officers. AIGSA has four officer positions: Co-Presidents (currently Matthew Olson and Christopher Buss), Secretary of Communication and Events, Secretary of Finances (currently Anurag Koul), and Secretary of Faculty Relations. You can learn more about each of the positions as well as submit nominations for them by checking out the individual nomination forms: - Co-Presidents Nomination Form <https://docs.google.com/forms/d/e/1FAIpQLSdzG0-J25ttH69P0gTolcSIc1KpQfiGnFZ…> - Secretary of Communication and Events Nomination Form <https://docs.google.com/forms/d/e/1FAIpQLScSQS3mK1XQpUGb7ZfDPUmCubaYdEs1YTu…> - Secretary of Finances Nomination Form <https://docs.google.com/forms/d/e/1FAIpQLSckvYMS2U_LRLV1pZhpDOTIVuV_61aF7GT…> - Secretary of Faculty Relations Nomination Form <https://docs.google.com/forms/d/e/1FAIpQLSfdSnNMrEdOtnGhWjg8PjcCDaMTWiKa9OY…> Feel free to submit multiple forms if you want to nominate multiple people. *The deadline for nominations is May 25th.* We hope to see you at future AIGSA events! Regards, AIGSA Officers

1 0

OSU AI Seminar - May 19 2021 | Stephen Ramsey - Oregon State University
by Patravali, Jay Sanjay 11 May '21

11 May '21

Dear All, Our next seminar will be by Prof. Stephen Ramsey from Oregon State University. He will be presenting *"*Computationally analyzing knowledge graphs and using machine-learning for drug repositioning and precision oncology applications*"* on *Wednesday 5/19 from 1.00-2.00PM PT.* Zoom Link: https://tinyurl.com/yup7cppa Please find attached the poster flyer for more information. *Title*: Computationally analyzing knowledge graphs and using machine-learning for drug repositioning and precision oncology applications *Abstract*: I will describe our work on two projects: an NIH consortium project, the Biomedical Data Translator, and a project to use machine learning to improve outcomes in cancer treatment. The Biomedical Data Translator project's broad aim is to advance translational science using computer-aided knowledge exploration and reasoning. Our team is developing a reasoning system, ARAX, that is geared toward drug repositioning for common diseases and therapeutic recommendation for rare inherited diseases. I will describe some of the principles behind this system and key challenges inherent to building scalable reasoning systems. For the precision oncology project, I will also present some recent work in our lab on using machine-learning to predict response to chemotherapy based on transcriptome data acquired from tumor samples. *Bio*: Stephen is a computational biologist whose PhD work was in computational physics. At OSU, Stephen holds a dual appointment in the Department of Biomedical Sciences and in the School of Electrical Engineering and Computer Science. Stephen's research has been recognized by multiple awards including an NIH K25 Career Development Award, a PhRMA New Investigator Award, an NSF CAREER award, and the Carlson College of Veterinary Medicine Zoetis Award. Please plan on attending! Thanks, Jay Patravali Graduate Student, CoRIS-EECS. Oregon State University ---- AI Seminar Important Reminders: -> The AI Seminar has a strict "no electronics" and "no recordings" policy. -> For graduate students in the AI program, attendance is strongly encouraged.

1 0

AI alignment reading group: Deep reinforcement learning from human preferences
by Turner, Alex 06 May '21

06 May '21

This Friday, we'll discuss "Deep reinforcement learning from human preferences": "For sophisticated reinforcement learning (RL) systems to interact usefully with real-world environments, we need to communicate complex goals to these systems. In this work, we explore goals defined in terms of (non-expert) human preferences between pairs of trajectory segments. We show that this approach can effectively solve complex RL tasks without access to the reward function, including Atari games and simulated robot locomotion, while providing feedback on less than one percent of our agent's interactions with the environment. This reduces the cost of human oversight far enough that it can be practically applied to state-of-the-art RL systems. To demonstrate the flexibility of our approach, we show that we can successfully train complex novel behaviors with about an hour of human time. These behaviors and environments are considerably more complex than any that have been previously learned from human feedback. " Paper: https://arxiv.org/abs/1706.03741 We'll meet Friday at 1. https://oregonstate.zoom.us/j/2739792686?pwd=VkRUeHJkYnhvTzlvZzR6YnZWNERKQT…

1 0

OSU AI Seminar - **Today** 1PM | Dale Schuurmans - Google Brain/University of Alberta
by Patravali, Jay Sanjay 05 May '21

05 May '21

Dear All, A gentle reminder that we a seminar by Prof. Dale Schuurmans from Google Brain/University of Alberta. He will be presenting *"*Reductionism in Reinforcement Learning*"* *today 5/5 from 1.00-2.00PM PT.* Zoom Link: https://tinyurl.com/392tj3hk Please find attached the poster flyer for more information. *Title*: Reductionism in Reinforcement Learning *Abstract*: Learning to make sequential decisions from interaction raises numerous challenges, including temporal prediction, state and value estimation, sequential planning, exploration, and strategic interaction. Each challenge is independently difficult, yet reinforcement learning research has often sought holistic solutions that rely on unified learning principles. I will argue that such a holistic approach can hamper progress. To make the point, I focus on reinforcement learning in Markov Decision Processes, where the challenges of value estimation, sequential planning, and exploration are jointly raised. By eliminating exploration from consideration, recent work on off-line reinforcement learning has led to improved methods for value estimation, policy optimization, and sequential planning. Taking the reduction a step further, I reconsider the relationship between value estimation and sequential planning, and show that a unified approach faces unresolved difficulties whenever generalization is considered. Instead, by separating these two challenges, a clearer understanding of successful value estimation methods can be achieved, while the shortcomings of existing strategies can be overcome in some cases. I will attempt to illustrate how these reductions allow other areas of machine learning, optimization, control and planning to be better leveraged in reinforcement learning. *Bio*: Dale Schuurmans is a Research Scientist at Google Brain, Professor of Computing Science at the University of Alberta, a Canada CIFAR AI Chair, and a Fellow of AAAI. He has served as an Associate Editor in Chief for IEEE TPAMI, an Associate Editor for JMLR, AIJ, JAIR and MLJ, and as a Program Co-chair for AAAI-2016, NIPS-2008 and ICML-2004. He has worked in many areas of machine learning and artificial intelligence, including model selection, on-line learning, adversarial optimization, boolean satisfiability, sequential decision making, reinforcement learning, Bayesian optimization, semidefinite methods for unsupervised learning, dimensionality reduction, and robust estimation. He has published over 200 papers in these areas, and has received paper awards at NeurIPS, ICML, IJCAI, and AAAI. Please plan on attending! Thanks, Jay Patravali Graduate Student, CoRIS-EECS. Oregon State University ---- AI Seminar Important Reminders: -> The AI Seminar has a strict "no electronics" and "no recordings" policy. -> For graduate students in the AI program, attendance is strongly encouraged.

1 0

OSU AI Seminar - May 5 2021 | Dale Schuurmans - Google Brain/University of Alberta
by Patravali, Jay Sanjay 04 May '21

04 May '21

Dear All, Our next seminar will be by Prof. Dale Schuurmans from Google Brain/University of Alberta. He will be presenting *"*Reductionism in Reinforcement Learning*"* on *Wednesday 5/5 from 1.00-2.00PM PT.* Zoom Link: https://tinyurl.com/392tj3hk Please find attached the poster flyer for more information. *Title*: Reductionism in Reinforcement Learning *Abstract*: Learning to make sequential decisions from interaction raises numerous challenges, including temporal prediction, state and value estimation, sequential planning, exploration, and strategic interaction. Each challenge is independently difficult, yet reinforcement learning research has often sought holistic solutions that rely on unified learning principles. I will argue that such a holistic approach can hamper progress. To make the point, I focus on reinforcement learning in Markov Decision Processes, where the challenges of value estimation, sequential planning, and exploration are jointly raised. By eliminating exploration from consideration, recent work on off-line reinforcement learning has led to improved methods for value estimation, policy optimization, and sequential planning. Taking the reduction a step further, I reconsider the relationship between value estimation and sequential planning, and show that a unified approach faces unresolved difficulties whenever generalization is considered. Instead, by separating these two challenges, a clearer understanding of successful value estimation methods can be achieved, while the shortcomings of existing strategies can be overcome in some cases. I will attempt to illustrate how these reductions allow other areas of machine learning, optimization, control and planning to be better leveraged in reinforcement learning. *Bio*: Dale Schuurmans is a Research Scientist at Google Brain, Professor of Computing Science at the University of Alberta, a Canada CIFAR AI Chair, and a Fellow of AAAI. He has served as an Associate Editor in Chief for IEEE TPAMI, an Associate Editor for JMLR, AIJ, JAIR and MLJ, and as a Program Co-chair for AAAI-2016, NIPS-2008 and ICML-2004. He has worked in many areas of machine learning and artificial intelligence, including model selection, on-line learning, adversarial optimization, boolean satisfiability, sequential decision making, reinforcement learning, Bayesian optimization, semidefinite methods for unsupervised learning, dimensionality reduction, and robust estimation. He has published over 200 papers in these areas, and has received paper awards at NeurIPS, ICML, IJCAI, and AAAI. Please plan on attending! Thanks, Jay Patravali Graduate Student, CoRIS-EECS. Oregon State University ---- AI Seminar Important Reminders: -> The AI Seminar has a strict "no electronics" and "no recordings" policy. -> For graduate students in the AI program, attendance is strongly encouraged.

1 0

[CoRIS-List] Starting Now: Kory Mathewson
by Hatton, Ross 30 Apr '21

30 Apr '21

Hi All, Today's seminar is starting now: Kory Mathewson (DeepMind) Speaker bio: https://korymathewson.com Watch link: *https://oregonstate.zoom.us/j/97644079157?pwd=WXRZeEhOR0padXVzb2NkVkVkVEFmZz09 <https://oregonstate.zoom.us/j/97644079157?pwd=WXRZeEhOR0padXVzb2NkVkVkVEFmZ…>* --- Title: "Humour-in-the-loop: Spontaneous Storytelling with Interactive Machine Learning" Abstract: Improvisation is the ideal evaluation testbed for collaboration between humans and intelligent machines. In improvised theatre artists perform real-time, dynamic problem solving to collaboratively generate interesting narratives. I co-developed artificial improvisation: improvised theatre performed by humans alongside intelligent machines. I start by presenting the background underlying the art of improvisation and the scientific fields of interactive machine learning and dialogue generation. Then, I present Pyggy, the first experiment on live stage human-machine improvisation, and A.L.Ex., the Artificial Language Experiment, which addresses several critical technical improvements over Pyggy. "Improbotics" is then presented, which details audience evaluation of Turing test-inspired live improvised performance using A.L.Ex. The talk concludes by discussing public reflections on live artificial improvisation performances from around the world and interesting future directions to explore. I also discuss ongoing work at the intersection of intelligent minds both human and machine. --- Pre- and post-seminar Gather space for OSU Robotics attendees (with an integrated link to the main talk) is at https://gather.town/app/HeC6WAB1zL6cKWp5/OSU%20Robotics%20Seminar -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

1 0

AI alignment reading group: inaccessible information
by Turner, Alex 29 Apr '21

29 Apr '21

This Friday, we'll discuss "Inaccessible information": "Suppose that I have a great model for predicting “what will Alice say next?” I can evaluate and train this model by checking its predictions against reality, but there may be many facts this model “knows” that I can’t easily access. For example, the model might have a detailed representation of Alice’s thoughts which it uses to predict what Alice will say, *without* being able to directly answer “What is Alice thinking?” In this case, I can only access that knowledge indirectly, e.g. by asking about what Alice would say under different conditions. I’ll call information like “What is Alice thinking?” inaccessible. I think it’s very plausible that AI systems will build up important inaccessible knowledge, and that this may be a central feature of the AI alignment problem." Post: https://www.alignmentforum.org/posts/ZyWyAJbedvEgRT2uF/inaccessible-informa… We'll meet Friday at 1. https://oregonstate.zoom.us/j/2739792686?pwd=VkRUeHJkYnhvTzlvZzR6YnZWNERKQT…

1 0