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  • Computational approaches for uncovering implicit strategies in political discourse | Julia Mendelsohn

    Special Seminar Series
    GPS, Robinson Building Complex (RBC), 3106

    When discussing politics, people often use subtle linguistic strategies to influence how their audience thinks about issues, which can then impact public opinion and policy. For example, anti-immigration activists may frame immigration as a threat to native born citizens’ jobs, describe immigrants with dehumanizing vermin-related metaphors, or even use coded expressions to covertly connect immigration with antisemitic conspiracy theories. This talk will focus on the development of computational approaches to analyze three strategies: framing, dehumanization, and dogwhistle communication. I will discuss how I draw from multiple social science disciplines to develop typologies and curate data resources, as well as how I build and evaluate natural language processing models for detecting these strategies. I further analyze the use of these strategies in political discourse across several domains, and assess the implications of such nuanced rhetoric for both society and technology.

  • The continuum of gene regulation at single cell resolution, from Drosophila development to human complex traits | Diego Calderon

    Special Seminar Series
    Powell-Focht Bioengineering Hall (PFBH), FUNG Auditorium

    Single-cell technologies have emerged as powerful tools for studying development, enabling comprehensive surveys of cellular diversity at profiled timepoints. They shed light on the dynamics of regulatory element activity and gene expression changes during the emergence of each cell type. Despite their potential, nearly all atlases of embryogenesis are constrained by sampling density, i.e., the number of discrete time points at which individual embryos are harvested. This limitation affects the resolution at which regulatory transitions can be characterized. In this talk, I present a novel cell collection approach capable of constructing a continuous representation of dynamic regulatory processes. I applied this approach to generate a continuous, single-cell atlas of chromatin accessibility and gene expression spanning Drosophila embryogenesis. Additionally, I will discuss my past and future research, applying new genomic technologies to characterize gene regulation important for human diseases.

  • Building Human-AI Alignment: Specifying, Inspecting, and Modeling AI Behaviors | Serena Booth

    Special Seminar Series
    GPS, Robinson Building Complex (RBC), 3106

    Abstract: The learned behaviors of AI and robot agents should align with the intentions of their human designers. Alignment is necessary for AI systems to be used in many sectors of the economy, and so the process of aligning AI systems becomes critical to study for defining effective AI policy. Toward this goal, people must be able to easily specify, inspect, and model agent behaviors. For specifications, we will consider expert-written reward functions for reinforcement learning (RL) and non-expert preferences for reinforcement learning from human feedback (RLHF). I will show evidence that experts are bad at writing reward functions: even in a trivial setting, experts write specifications that are overfit to a particular RL algorithm, and they often write erroneous specifications for agents that fail to encode their true intent. I will also show that the common approach to learning a reward function from non-experts in RLHF uses an inductive bias that fails to encode how humans express preferences, and that our proposed bias better encodes human preferences both theoretically and empirically. I will discuss the policy implications: namely, that engineers' design processes and embedded assumptions in building AI must be considered. For inspection, humans must be able to assess the behaviors an agent learns from a given specification. I will discuss a method to find settings that exhibit particular behaviors, like out-of-distribution failures. I will discuss the policy implications for testing AI systems, for example through red teaming. Lastly, cognitive science theories attempt to show how people build conceptual models that explain agent behaviors. I will show evidence that some of these theories are used in research to support humans, but that we can still build better curricula for modeling. I will discuss the policy need for careful onboarding to AI systems. I will end by discussing my current work in the U.S. Senate on responding to the proliferation of AI. Collectively, my research provides evidence that—even with the best of intentions— current human-AI systems often fail to induce alignment, and my research proposes promising directions for how to build better aligned human-AI systems.

  • The Ethical and Policy Implications of Artificial Intelligence

    Institute for Practice Ethics (IPE)
    Sanford Consortium

    The Institute for Practical Ethics welcomes David Danks as the 2024 keynote speaker.

    Danks, a UC San Diego professor in the Department of Philosophy and Halıcıoğlu Data Science Institute, is an expert researcher at the intersection of philosophy, cognitive science and machine learning. He serves on multiple boards, including the United States National AI Advisory Committee.

    Artificial intelligence is seemingly everywhere today, both in public perception and in our everyday lives. This growth has led to many stories about the widespread harms that can result from AI done poorly. As a result, there are now numerous demands for ‘ethical AI,’ but relatively little understanding of what that might involve.

    In this keynote, David Danks will explore the nature of responsible AI, arguing that it involves much more than code or data. He will critically assess current approaches to producing more responsible AI, then suggest key policy and practical approaches that would likely be more effective. It is critical we create more responsible AI, but that will require rethinking many of our current practices in academia, government and industry.

  • From Pixels to Measurements: Understanding the Dynamic World ~ Adam Harley

    Special Seminar Series
    Halıcıoğlu Data Science Institute (HDSI), Room 123 3234 Matthews Ln, La Jolla, CA, United States

    Adam is a postdoctoral scholar at Stanford University, working with Leonidas Guibas. He received a Ph.D. in robotics from Carnegie Mellon University, where he worked with Katerina Fragkiadaki. He received his M.S. in Computer Science at Toronto Metropolitan University, working with Kosta Derpanis. Adam is a recipient of the NSERC PGS-D scholarship, and the Toronto Metropolitan University Gold Medal. His research interests lie in Computer Vision and Machine Learning, particularly for 3D understanding and fine-grained tracking.

  • Domain Counterfactuals for Trustworthy ML via Sparse Interventions | David I. Inouye

    Seminar Series
    Halıcıoğlu Data Science Institute (HDSI), Room 404 3234 Matthews Ln, La Jolla, CA, United States

    Talk Abstract: Although incorporating causal concepts into deep learning shows promise for increasing explainability, fairness, and robustness, existing methods require unrealistic assumptions and aim to recover the full latent causal model. This talk proposes an alternative: domain counterfactuals. Domain counterfactuals ask a more concrete question: "What would a sample look like if it had been […]

  • TILOS Seminar: How Large Models of Language and Vision Help Agents to Learn to Behave

    TILOS Seminar Series
    Halıcıoğlu Data Science Institute (HDSI), Room 123 3234 Matthews Ln, La Jolla, CA, United States

    Roy Fox, Assistant Professor and Director of the Intelligent Dynamics Lab at UC Irvine HDSI 123 and Zoom (Link below) Abstract: If learning from data is valuable, can learning from big data be very valuable? So far, it has been so in vision and language, for which foundation models can be trained on web-scale data to […]

  • Understanding Deep Learning through Optimization Geometry| Nati (Nathan) Srebro

    Special Seminar Series
    Halıcıoğlu Data Science Institute (HDSI), Room 123 3234 Matthews Ln, La Jolla, CA, United States

    Abstract: How can models with more parameters than training examples generalize well, and generalize even better when we add even more parameters, even without explicit complexity control? In recent years, it is becoming increasingly clear that much, or perhaps all, of the complexity control and generalization ability of deep learning comes from the optimization bias, […]

  • Efficient Deep Learning with Sparsity: Algorithms, Systems, and Applications | Zhijian Liu

    Special Seminar Series
    Halıcıoğlu Data Science Institute (HDSI), Room 123 3234 Matthews Ln, La Jolla, CA, United States

    Abstract: Deep learning is used across a broad spectrum of applications. However, behind its remarkable performance lies an increasing gap between the demand for and supply of computation. On the demand side, the computational costs of deep learning models have surged dramatically, driven by ever-larger input and model sizes. On the supply side, as Moore's […]