- Ai - Engineering Mailing Lists

AI Seminar : Oct 27
by Mangannavar, Rajesh Devaraddi 24 Oct '23

24 Oct '23

Dear all, Our next AI seminar on "Symbolic AI 3.0 (S3): Rise of the LLMs" by Scott Sanner is scheduled to be on Oct 27th (Friday), 1-2 PM. It will be followed by a 30-minute Q&A session with the graduate students. Location: KEC 1001 Symbolic AI 3.0 (S3): Rise of the LLMs Scott Sanner Associate Professor Industrial Engineering and Computer Science University of Toronto Abstract: Large Language Models (LLMs) such as ChatGPT have emerged as a revolutionary technology for natural language reasoning and numerous related AI applications. I'll discuss some of my group's own work on abstract reasoning and interactive conversational systems leveraging LLMs and the game-changing realizations that I have taken away from these investigations. This talk will then discuss some general implications of the LLM era and my conjectures as to how it will shift research foci in the near future and enable levels of user-facing AI deployment that were unthinkable just two years ago. Speaker Bio: Scott Sanner is an Associate Professor in Industrial Engineering and Cross-appointed in Computer Science at the University of Toronto. Scott’s research focuses on a broad range of AI topics spanning sequential decision-making, (conversational) recommender systems, and applications of machine/deep learning. Scott is currently an Associate Editor for ACM Transactions on Recommender Systems (TORS), the Machine Learning Journal (MLJ), and the Journal of Artificial Intelligence Research (JAIR). Scott was a co-recipient of paper awards from the AI Journal (2014), Transport Research Board (2016), and CPAIOR (2018). He was a recipient of a Google Faculty Research Award in 2020 and a Visiting Researcher at Google while on sabbatical during 2022-23. Please watch this space for future AI Seminars : https://engineering.oregonstate.edu/EECS/research/AI<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fengineeri…> 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 : Oct 20
by Mangannavar, Rajesh Devaraddi 19 Oct '23

19 Oct '23

Dear all, Our next AI seminar on "Self-Driving Vehicles: A Cautionary Tale" by Michael J. Quinn is scheduled to be on Oct 20th (Tomorrow), 1-2 PM. It will be followed by a 30-minute Q&A session with the graduate students. Location: KEC 1001 Self-Driving Vehicles: A Cautionary Tale Michael J. Quinn Abstract: Elaine Herzberg was the first pedestrian to be killed by a self-driving vehicle. I summarize Uber’s effort to develop an autonomous vehicle, focusing on the engineering and management decisions that contributed to the March 18, 2018, accident. The story of Herzberg’s death illustrates some of the challenges faced by developers of new AI-driven technologies. I conclude by suggesting some practical ways that regulators can help ensure public safety as autonomous vehicles are deployed. Speaker Bio: Michael J. Quinn is a computer scientist and author. His early research was in parallel computing, and his textbooks on that subject have been used by hundreds of universities worldwide. In the early 2000s his focus shifted to computer ethics, and in 2004 he published a textbook, Ethics for the Information Age, that explores moral problems related to modern uses of information technology, such as privacy, intellectual property rights, computer security, computerized system failures, the relationship between automation and unemployment, and the impact of social media on democracy. The book, now in its eighth edition, has been used at more than 250 colleges and universities in the United States and many more internationally. Dr. Quinn was a computer science professor at Oregon State University from 1989 to 2007, where he served as head of the Department of Computer Science for five years. >From 1983 to 1989 he was a professor at the University of New Hampshire, and from 2007 to 2022 he was dean of the College of Science and Engineering at Seattle University. Please watch this space for future AI Seminars : https://engineering.oregonstate.edu/EECS/research/AI<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fengineeri…> 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 : Oct 20
by Mangannavar, Rajesh Devaraddi 17 Oct '23

17 Oct '23

Dear all, Our next AI seminar on "Self-Driving Vehicles: A Cautionary Tale" by Michael J. Quinn is scheduled to be on Oct 20th (Friday), 1-2 PM. It will be followed by a 30-minute Q&A session with the graduate students. Location: KEC 1001 Self-Driving Vehicles: A Cautionary Tale Michael J. Quinn Abstract: Elaine Herzberg was the first pedestrian to be killed by a self-driving vehicle. I summarize Uber’s effort to develop an autonomous vehicle, focusing on the engineering and management decisions that contributed to the March 18, 2018, accident. The story of Herzberg’s death illustrates some of the challenges faced by developers of new AI-driven technologies. I conclude by suggesting some practical ways that regulators can help ensure public safety as autonomous vehicles are deployed. Speaker Bio: Michael J. Quinn is a computer scientist and author. His early research was in parallel computing, and his textbooks on that subject have been used by hundreds of universities worldwide. In the early 2000s his focus shifted to computer ethics, and in 2004 he published a textbook, Ethics for the Information Age, that explores moral problems related to modern uses of information technology, such as privacy, intellectual property rights, computer security, computerized system failures, the relationship between automation and unemployment, and the impact of social media on democracy. The book, now in its eighth edition, has been used at more than 250 colleges and universities in the United States and many more internationally. Dr. Quinn was a computer science professor at Oregon State University from 1989 to 2007, where he served as head of the Department of Computer Science for five years. >From 1983 to 1989 he was a professor at the University of New Hampshire, and from 2007 to 2022 he was dean of the College of Science and Engineering at Seattle University. Please watch this space for future AI Seminars : https://engineering.oregonstate.edu/EECS/research/AI<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fengineeri…> 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 : Oct 13
by Mangannavar, Rajesh Devaraddi 12 Oct '23

12 Oct '23

Dear all, Our next AI seminar on "The Past, Present, and Future of Artificial Intelligence: from black-box to white-box, from open-loop to closed-loop" by Yi Ma is scheduled to be on Oct 13th (Tomorrow), 1-2 PM. It will be followed by a 30-minute Q&A session with the graduate students. Location: Please note that the speaker will be on zoom for the event but it will be set up in KEC 1001 for everyone to attend. Zoom Link: https://oregonstate.zoom.us/j/98684050301?pwd=ZzhianQxUFBPUmdYVWJKOFhaVURCQ…<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Foregonsta…> The Past, Present, and Future of Artificial Intelligence: from black-box to white-box, from open-loop to closed-loop Yi Ma Director, Data Science Institute Head, Computer Science Department University of Hong Kong Abstract: In this talk, we provide a more systematic and principled view about the practice of artificial intelligence in the past decade from the history of the study of intelligence. We argue that the most fundamental objective of intelligence is to learn a compact and structured representation of the sensed world that maximizes information gain, measurable by coding rates of the learned representation. We contend that optimizing this principled objective provides a unifying white-box explanation for almost all past and current practices of artificial intelligence based on deep networks, including CNNs, ResNets, and Transformers. Hence, mathematically interpretable, practically competitive, and semantically meaningful deep networks are now within our reach, see our latest release: https://ma-lab-berkeley.github.io/CRATE/<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fma-lab-be…>. Furthermore, our study shows that to learn such representation correctly and automatically, additional computational mechanisms are necessary besides deep networks. For intelligence to become autonomous, one needs to integrate fundamental ideas from coding theory, optimization, feedback control, and game theory. This connects us back to the true origin of the study of intelligence 80 years ago. Probably most importantly, this new framework reveals a much broader and brighter future for developing next-generation autonomous intelligent systems that could truly emulate the computational mechanisms of natural intelligence. Related papers can be found at: 1. https://ma-lab-berkeley.github.io/CRATE/<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fma-lab-be…> 2. https://jmlr.org/papers/v23/21-0631.html<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fjmlr.org%…> 3. https://www.mdpi.com/1099-4300/24/4/456/htm<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.mdpi.…> Speaker Bio: Yi Ma is the inaugural director of the Data Science Institute and the new head of the Computer Science Department of the University of Hong Kong. He has been a professor at the EECS Department at the University of California, Berkeley since 2018. His research interests include computer vision, high-dimensional data analysis, and integrated intelligent systems. Yi received his two bachelor’s degrees in Automation and Applied Mathematics from Tsinghua University in 1995, two master’s degrees in EECS and Mathematics in 1997, and a PhD degree in EECS from UC Berkeley in 2000. He has been on the faculty of UIUC ECE from 2000 to 2011, the principal researcher and manager of the Visual Computing group of Microsoft Research Asia from 2009 to 2014, and the Executive Dean of the School of Information Science and Technology of ShanghaiTech University from 2014 to 2017. He joined the faculty of UC Berkeley EECS in 2018. He has published over 60 journal papers, 120 conference papers, and three textbooks in computer vision, generalized PCA, and high-dimensional data analysis. He received the NSF Career award in 2004 and the ONR Young Investigator award in 2005. He also received the David Marr prize in computer vision from ICCV 1999 and best paper awards from ECCV 2004 and ACCV 2009. He has served as the Program Chair for ICCV 2013 and the General Chair for ICCV 2015. He is a Fellow of IEEE, ACM, and SIAM. Please watch this space for future AI Seminars : https://engineering.oregonstate.edu/EECS/research/AI<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fengineeri…> 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 : Oct 13
by Mangannavar, Rajesh Devaraddi 09 Oct '23

09 Oct '23

Dear all, Our next AI seminar on "The Past, Present, and Future of Artificial Intelligence: from black-box to white-box, from open-loop to closed-loop" by Yi Ma is scheduled to be on Oct 13th (Friday), 1-2 PM. It will be followed by a 30-minute Q&A session with the graduate students. Location: Please note that the speaker will be on zoom for the event but it will be set up in KEC 1001 for everyone to attend. Zoom Link: https://oregonstate.zoom.us/j/98684050301?pwd=ZzhianQxUFBPUmdYVWJKOFhaVURCQ…<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Foregonsta…> The Past, Present, and Future of Artificial Intelligence: from black-box to white-box, from open-loop to closed-loop Yi Ma Director, Data Science Institute Head, Computer Science Department University of Hong Kong Abstract: In this talk, we provide a more systematic and principled view about the practice of artificial intelligence in the past decade from the history of the study of intelligence. We argue that the most fundamental objective of intelligence is to learn a compact and structured representation of the sensed world that maximizes information gain, measurable by coding rates of the learned representation. We contend that optimizing this principled objective provides a unifying white-box explanation for almost all past and current practices of artificial intelligence based on deep networks, including CNNs, ResNets, and Transformers. Hence, mathematically interpretable, practically competitive, and semantically meaningful deep networks are now within our reach, see our latest release: https://ma-lab-berkeley.github.io/CRATE/<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fma-lab-be…>. Furthermore, our study shows that to learn such representation correctly and automatically, additional computational mechanisms are necessary besides deep networks. For intelligence to become autonomous, one needs to integrate fundamental ideas from coding theory, optimization, feedback control, and game theory. This connects us back to the true origin of the study of intelligence 80 years ago. Probably most importantly, this new framework reveals a much broader and brighter future for developing next-generation autonomous intelligent systems that could truly emulate the computational mechanisms of natural intelligence. Related papers can be found at: 1. https://ma-lab-berkeley.github.io/CRATE/<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fma-lab-be…> 2. https://jmlr.org/papers/v23/21-0631.html<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fjmlr.org%…> 3. https://www.mdpi.com/1099-4300/24/4/456/htm<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.mdpi.…> Speaker Bio: Yi Ma is the inaugural director of the Data Science Institute and the new head of the Computer Science Department of the University of Hong Kong. He has been a professor at the EECS Department at the University of California, Berkeley since 2018. His research interests include computer vision, high-dimensional data analysis, and integrated intelligent systems. Yi received his two bachelor’s degrees in Automation and Applied Mathematics from Tsinghua University in 1995, two master’s degrees in EECS and Mathematics in 1997, and a PhD degree in EECS from UC Berkeley in 2000. He has been on the faculty of UIUC ECE from 2000 to 2011, the principal researcher and manager of the Visual Computing group of Microsoft Research Asia from 2009 to 2014, and the Executive Dean of the School of Information Science and Technology of ShanghaiTech University from 2014 to 2017. He joined the faculty of UC Berkeley EECS in 2018. He has published over 60 journal papers, 120 conference papers, and three textbooks in computer vision, generalized PCA, and high-dimensional data analysis. He received the NSF Career award in 2004 and the ONR Young Investigator award in 2005. He also received the David Marr prize in computer vision from ICCV 1999 and best paper awards from ECCV 2004 and ACCV 2009. He has served as the Program Chair for ICCV 2013 and the General Chair for ICCV 2015. He is a Fellow of IEEE, ACM, and SIAM. Please watch this space for future AI Seminars : https://engineering.oregonstate.edu/EECS/research/AI<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fengineeri…> 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 : Oct 6, 2023
by Mangannavar, Rajesh Devaraddi 05 Oct '23

05 Oct '23

Dear all, Our next AI seminar on "Possible Impossibilities and Impossible Possibilities" by Dr. Yejin Choi is scheduled to be on Oct 6th (Tomorrow), 1-2 PM. It will be followed by a 30-minute Q&A session with the graduate students. Location: Please note that the speaker will be on zoom for the event but it will be set up in KEC 1001 for everyone to attend. Zoom Link: https://oregonstate.zoom.us/j/98684050301?pwd=ZzhianQxUFBPUmdYVWJKOFhaVURCQ…<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Foregonsta…> Possible Impossibilities and Impossible Possibilities Yejin Choi Wissner-Slivka Professor and MacArthur Fellow Paul G. Allen School of Computer Science & Engineering University of Washington Abstract: In this talk, I will question if there can be possible impossibilities of large language models (i.e., the fundamental limits of transformers, if any) and the impossible possibilities of language models (i.e., seemingly impossible alternative paths beyond scale, if at all). Speaker Bio: Yejin Choi is Wissner-Slivka Professor and a MacArthur Fellow at the Paul G. Allen School of Computer Science & Engineering at the University of Washington. She is also a senior director at AI2 overseeing the project Mosaic and a Distinguished Research Fellow at the Institute for Ethics in AI at the University of Oxford. Her research investigates if (and how) AI systems can learn commonsense knowledge and reasoning, if machines can (and should) learn moral reasoning, and various other problems in NLP, AI, and Vision including neuro-symbolic integration, language grounding with vision and interactions, and AI for social good. She is a co-recipient of 2 Test of Time Awards (at ACL 2021 and ICCV 2021), 7 Best/Outstanding Paper Awards (at ACL 2023, NAACL 2022, ICML 2022, NeurIPS 2021, AAAI 2019, and ICCV 2013), the Borg Early Career Award (BECA) in 2018, the inaugural Alexa Prize Challenge in 2017, and IEEE AI's 10 to Watch in 2016. Please watch this space for future AI Seminars : https://engineering.oregonstate.edu/EECS/research/AI<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fengineeri…> 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 : Oct 6, 2023
by Mangannavar, Rajesh Devaraddi 02 Oct '23

02 Oct '23

Dear all, Our next AI seminar on "Possible Impossibilities and Impossible Possibilities" by Dr. Yejin Choi is scheduled to be on Oct 6th (Friday), 1-2 PM. It will be followed by a 30-minute Q&A session with the graduate students. Location: KEC 1001 Possible Impossibilities and Impossible Possibilities Yejin Choi Wissner-Slivka Professor and MacArthur Fellow Paul G. Allen School of Computer Science & Engineering University of Washington Abstract: In this talk, I will question if there can be possible impossibilities of large language models (i.e., the fundamental limits of transformers, if any) and the impossible possibilities of language models (i.e., seemingly impossible alternative paths beyond scale, if at all). Speaker Bio: Yejin Choi is Wissner-Slivka Professor and a MacArthur Fellow at the Paul G. Allen School of Computer Science & Engineering at the University of Washington. She is also a senior director at AI2 overseeing the project Mosaic and a Distinguished Research Fellow at the Institute for Ethics in AI at the University of Oxford. Her research investigates if (and how) AI systems can learn commonsense knowledge and reasoning, if machines can (and should) learn moral reasoning, and various other problems in NLP, AI, and Vision including neuro-symbolic integration, language grounding with vision and interactions, and AI for social good. She is a co-recipient of 2 Test of Time Awards (at ACL 2021 and ICCV 2021), 7 Best/Outstanding Paper Awards (at ACL 2023, NAACL 2022, ICML 2022, NeurIPS 2021, AAAI 2019, and ICCV 2013), the Borg Early Career Award (BECA) in 2018, the inaugural Alexa Prize Challenge in 2017, and IEEE AI's 10 to Watch in 2016. Please watch this space for future AI Seminars : https://engineering.oregonstate.edu/EECS/research/AI<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fengineeri…> 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 : Sept 29, 2023
by Mangannavar, Rajesh Devaraddi 28 Sep '23

28 Sep '23

Dear all, Our first AI seminar for the fall term on the topic "Enhancing healthcare with AI-in-the-loop" by Sriraam Natarajan is scheduled to be on Sept 29th (Tomorrow), 1-2 PM PST. It will be followed by a 30-minute Q&A session with the graduate students. Location: KEC 1001 Enhancing healthcare with AI-in-the-loop Sriraam Natarajan Professor and the Director for Center for ML Department of Computer Science University of Texas Dallas Abstract: Historically, Artificial Intelligence has taken a symbolic route for representing and reasoning about objects at a higher-level or a statistical route for learning complex models from large data. To achieve true AI in complex domains such as healthcare, it is necessary to make these different paths meet and enable seamless human interaction. First, I will introduce learning from rich, structured, complex and noisy data. One of the key attractive properties of the learned models is that they use a rich representation for modeling the domain that potentially allows for seam-less human interaction. I will present the recent progress that allows for more reasonable human interaction where the human input is taken as “advice” and the learning algorithm combines this advice with data. I will present these algorithms in the context of several healthcare problems -- learning from electronic health records, clinical studies, and surveys -- and demonstrate the value of involving experts during learning. Speaker Bio: Sriraam Natarajan is a Professor and the Director for Center for ML at the Department of Computer Science at University of Texas Dallas, a hessian.AI fellow at TU Darmstadt and a RBDSCAII Distinguished Faculty Fellow at IIT Madras. His research interests lie in the field of Artificial Intelligence, with emphasis on Machine Learning, Statistical Relational Learning and AI, Reinforcement Learning, Graphical Models and Biomedical Applications. He is a AAAI senior member and has received the Young Investigator award from US Army Research Office, Amazon Faculty Research Award, Intel Faculty Award, XEROX Faculty Award, Verisk Faculty Award, ECSS Graduate teaching award from UTD and the IU trustees Teaching Award from Indiana University. He is the program chair of AAAI 2024, the general chair of CoDS-COMAD 2024, AI and society track chair of AAAI 2023 and 2022, senior member track chair of AAAI 2023, demo chair of IJCAI 2022, program co-chair of SDM 2020 and ACM CoDS-COMAD 2020 conferences. He was the specialty chief editor of Frontiers in ML and AI journal, and is an associate editor of JAIR, DAMI and Big Data journals. Please watch this space for future AI Seminars : https://engineering.oregonstate.edu/EECS/research/AI<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fengineeri…> 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 : Sept 29, 2023
by Mangannavar, Rajesh Devaraddi 25 Sep '23

25 Sep '23

Dear all, Our first AI seminar for the fall term on the topic "Enhancing healthcare with AI-in-the-loop" by Sriraam Natarajan is scheduled to be on Sept 29th (Friday), 1-2 PM PST. It will be followed by a 30-minute Q&A session with the graduate students. Location: KEC 1001 Enhancing healthcare with AI-in-the-loop Sriraam Natarajan Professor and the Director for Center for ML Department of Computer Science University of Texas Dallas Abstract: Historically, Artificial Intelligence has taken a symbolic route for representing and reasoning about objects at a higher-level or a statistical route for learning complex models from large data. To achieve true AI in complex domains such as healthcare, it is necessary to make these different paths meet and enable seamless human interaction. First, I will introduce learning from rich, structured, complex and noisy data. One of the key attractive properties of the learned models is that they use a rich representation for modeling the domain that potentially allows for seam-less human interaction. I will present the recent progress that allows for more reasonable human interaction where the human input is taken as “advice” and the learning algorithm combines this advice with data. I will present these algorithms in the context of several healthcare problems -- learning from electronic health records, clinical studies, and surveys -- and demonstrate the value of involving experts during learning. Speaker Bio: Sriraam Natarajan is a Professor and the Director for Center for ML at the Department of Computer Science at University of Texas Dallas, a hessian.AI fellow at TU Darmstadt and a RBDSCAII Distinguished Faculty Fellow at IIT Madras. His research interests lie in the field of Artificial Intelligence, with emphasis on Machine Learning, Statistical Relational Learning and AI, Reinforcement Learning, Graphical Models and Biomedical Applications. He is a AAAI senior member and has received the Young Investigator award from US Army Research Office, Amazon Faculty Research Award, Intel Faculty Award, XEROX Faculty Award, Verisk Faculty Award, ECSS Graduate teaching award from UTD and the IU trustees Teaching Award from Indiana University. He is the program chair of AAAI 2024, the general chair of CoDS-COMAD 2024, AI and society track chair of AAAI 2023 and 2022, senior member track chair of AAAI 2023, demo chair of IJCAI 2022, program co-chair of SDM 2020 and ACM CoDS-COMAD 2020 conferences. He was the specialty chief editor of Frontiers in ML and AI journal, and is an associate editor of JAIR, DAMI and Big Data journals. Please watch this space for future AI Seminars : https://engineering.oregonstate.edu/EECS/research/AI<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fengineeri…> 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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Special AI Seminar Thursday at 4 pm
by Tadepalli, Prasad 14 Sep '23

14 Sep '23

Hi everyone, I hope you had a good summer. We will resume the regular AI seminar<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fengineeri…> on Fridays at 1 pm in KEC 1001 starting from September 29'th. It is open to public. Grads, please register for AI 507 to get 1 credit. We will also have a special virtual seminar this week on Thursday, August 14, at 4 pm PST in KEC 1001 by Jonathan Ferrer Mestres, a research scientist at CSIRO, Australia. I hope to see you there. Zoom link: https://oregonstate.zoom.us/j/98684050301?pwd=ZzhianQxUFBPUmdYVWJKOFhaVURCQ…<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Foregonsta…> Title: Towards more interpretable solutions for conservation problems Abstract: Markov Decision Processes (MDPs) provide a convenient model for representing sequential decision-making optimization problems when the decision maker has complete information about the current state of the system and dynamics are non-deterministic. MDPs have been applied to help recover populations of threatened species under limited resources, to control invasive species, to perform adaptive management of natural resources, and to test behavioral ecology theories. These domains are human-operated systems, where MDP policies provide recommendations. Solutions computed for MDPs with thousands of states are difficult to understand. In human-operated systems, it is crucial that solutions provided by artificial intelligence algorithms can be interpreted and explained in order to increase uptake of MDP solutions. Explainable artificial intelligence, also known as the interpretability problem, aims to generate decisions in which one of the criteria is how easily a human can understand these decisions. We propose to increase the interpretability of MDPs by providing explainable artificial intelligence algorithms that can be used to solve conservation decision problems. We define the problem of solving K-MDPs, i.e., given an original MDP and a number of states (K), generate a reduced state space MDP that minimizes the difference between the original and reduced optimal solutions. Abstracting states aims to reduce the size of large state spaces by aggregating states which are equivalent given a metric. We found that K-MDPs can achieve a substantial reduction of the number of states with a small loss of performance on a number of case studies of increasing complexity from the literature. Bio: Jonathan is a Research Scientist within the Conservation Decisions Team, where he focuses on developing trustworthy and explainable artificial intelligence solutions for environmental decision-making. His primary goal is to ensure that artificial intelligence systems are equipped with transparent capabilities, fostering trust among users and experts who rely on these solutions. Jonathan's research centers on making informed decisions in the face of uncertainty to provide effective environmental solutions. Prior to his current role, he earned his PhD from Universitat Pompeu Fabra, in the Artificial Intelligence and Machine Learning Group in Barcelona in 2018, under the supervision of Dr. Hector Geffner. His doctoral thesis explored the integration of task and motion planning. Jonathan's educational journey also includes a Master's degree in Intelligent Interactive Systems and a Bachelor's degree in Computer Science. ------------------------------------------------------- Prasad Tadepalli Director, AI Program School of Electrical Engineering and Computer Science Oregon State University Corvallis, OR 97330

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