Contact Us

Give us a call or drop by anytime, we endeavor to answer all inquiries within 24 hours.

map

Find us

PO Box 16122 Collins Street West Victoria, Australia

Email us

info@domain.com / example@domain.com

Phone support

Phone: + (066) 0760 0260 / + (057) 0760 0560

Artificial Intelligence

  1. Events
  2. Artificial Intelligence

Events Search and Views Navigation

Event Views Navigation

Today

Filters

Changing any of the form inputs will cause the list of events to refresh with the filtered results.

Event Series Special Seminar Series

Intelligent mobile systems for equitable healthcare

SDSC, The Auditorium 9836 Hopkins Dr, La Jolla, San Diego, CA, United States

Access to even basic medical resources is greatly influenced by factors like an individual's birth country and zip code. In this talk, I will present my work on designing AI-based mobile systems for equitable healthcare. I will showcase three systems that are not only interesting from an AI standpoint but are also having real-world medical impact. The first system can detect ear infections using only a smartphone and a paper cone. The second system enables low-cost newborn hearing screening using inexpensive earphones. Lastly, I will present an ambient sensing system that employs smart devices to detect emergent and life-threatening medical events such as cardiac arrest. Through these examples, I will demonstrate how new applied machine learning and sensing approaches that generalize across hardware and work in real-world environments can help to address pressing societal problems.

Event Series Special Seminar Series

Responsible AI: Privacy and Fairness in Decision and Learning Systems

SDSC, The Auditorium 9836 Hopkins Dr, La Jolla, San Diego, CA, United States

Differential Privacy has become the go-to approach for protecting sensitive information in data releases and learning tasks that are used for critical decision processes. For example, census data is used to allocate funds and distribute benefits, while several corporations use machine learning systems for criminal assessments, hiring decisions, and more. While this privacy notion provides strong guarantees, we will show that it may also induce biases and fairness issues in downstream decision processes. These issues may adversely affect many individuals' health, well-being, and sense of belonging, and are currently poorly understood.

In this talk, we delve into the intersection of privacy, fairness, and decision processes, with a focus on understanding and addressing these fairness issues. We first provide an overview of Differential Privacy and its applications in data release and learning tasks. Next, we examine the societal impacts of privacy through a fairness lens and present a framework to illustrate what aspects of the private algorithms and/or data may be responsible for exacerbating unfairness. We hence show how to extend this framework to assess the disparate impacts arising in Machine Learning tasks. Finally, we propose a path to partially mitigate these fairness issues and discuss grand challenges that require further exploration.

Event Series Special Seminar Series

Decoding Nature’s Message Through the Channel of Artificial Intelligence

SDSC, The Synthesis Center 9500 Gilman Drive, La Jolla, CA, United States

Abstract: Nature contains many interesting physics we want to search for, but it cannot speak them out loud. Therefore physicists need to build large particle physics experiments that encode nature's message into experimental data. My research leverages artificial intelligence and machine learning to maximally decode nature's message from those data. The questions I want to ask nature is: Are neutrinos Majorana particles? The answer to this question would fundamentally revise our understanding of physics and the cosmos. Currently, the most effective experimental probe for Majorana neutrino is neutrinoless double-beta decay(0vββ). Cutting-edge AI algorithms could break down significant technological barriers and, in turn, deliver the world's most sensitive search for 0vββ. This talk will discuss one such algorithm, KamNet, which plays a pivotal role in the new result of the KamLAND-Zen experiment. With the help of KamNet, KamLAND-Zen provides a limit that reaches below 50 meV for the first time and is the first search for 0νββ in the inverted mass ordering region. Looking further, the next-generation 0vββ experiment LEGEND has created the Germanium Machine Learning group to aid all aspects of LEGEND analysis and eventually build an independent AI analysis. As the odyssey continues, AI will enlighten the bright future of experimental particle physics.

Event Series IPE Data Lunchtime Series

The Interplay of Technology, Ethics, and Policy

Public Engagement Building (PEB) 721 9625 Scholars Drive North MC 0305, La Jolla, CA, United States

Abstract: Technology is often designed and deployed without critical reflection of the values that it embodies. Value trade-offs—between security and privacy, free speech and dignity, autonomy and human agency, and different conceptions of fairness—abound in many technologies that are now achieving great scale in commonly used tech platforms. The decisions made by the people inside […]

Featured Featured

Chatting GPT

SDSC, The Auditorium 9836 Hopkins Dr, La Jolla, San Diego, CA, United States

Artificial Intelligence (AI) systems have made astonishing progress in the last year. In particular, Large Language Models (LLMs) — AI systems trained on massive amounts of text — have reached a surprising level of capability, with the most recent iterations able to write essays, poems, and computer code, and score near the 90th percentile on […]

Free

Event Series Special Seminar Series

Security and Privacy in an Everchanging System Landscape

SDSC, The Auditorium 9836 Hopkins Dr, La Jolla, San Diego, CA, United States

Abstract: From AI and IoT to AR/VR and Web 3.0, computer systems are evolving at an unprecedented rate. While this evolution has given rise to exciting applications and opportunities, it has also brought about novel security and privacy challenges within these systems and across their interactions with existing platforms. In this talk, I will discuss how system security researchers can keep up with this everchanging landscape and showcase some of my lab's recent work on understanding and detecting malicious web bots. I will explore how we can build and roll out research infrastructure to measure web bot activities and later use our newfound understanding to develop practical solutions to counter them. I will highlight how we can apply similar research principles to areas such as AI and IoT. Finally, I will conclude my talk by previewing some of my ongoing work and outlining my research roadmap toward achieving "security at inception" for emerging systems.

Causal Inference symposium

Causality is increasingly a part of AI, data science, robotics, and more, but it is not always clear how we can learn causality from data. This symposium will be featuring leading HDSI Faculty who will be providing an introductory overview on these methods, followed by domain-specific talks and open discussion.

Fireside Chat: Theory in the age of modern AI

TILOS Fireside Chat on "Theory in the age of modern AI", which will be a conversation led by TILOS team members: Misha Belkin (UCSD), Arya Mazumdar (moderator, UCSD), Tara Javidi (UCSD), Visheeth Vishnoi (Yale U). The focus will be on the implications to, and the roles played by theory, in modern AI (especially with the recent exciting […]

Evaluating and Designing Computing Systems for the Future of Work | Hancheng Cao

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

From collaborative software to generative AI, computing technologies are redefining the way we work, communicate and collaborate. Yet with the growing complexities of computing platforms, it becomes increasingly challenging to foresee their impacts on human behavior, leading to not only poor user experience but also problematic applications that mirror and amplify societal issues. How can we better understand machine behavior and machine-mediated user behavior over computing platforms? How can we build applications that align with our needs and values with emerging computing technologies? My research aims to answer these questions through novel measurements and computational methods inspired by social science insights, such as mining increasingly available large-scale data on how people build, adopt, and interact with computing systems. In this talk, I will present my work demonstrating this approach in the future of work context, where I develop data-driven, AI-powered and human-centered methods to understand, evaluate and design sociotechnical systems at the workplace. I will present an analysis of remote meeting experience through mining millions of meetings, a study on how an AI algorithm can be built to predict team fracture, and a development and evaluation study on a generative AI-based scientific feedback system for researchers. These projects exemplify the opportunities to leverage computation and data to better understand, support and augment work practices.