United States

Computing systems are becoming ever more complex, with automated decisions increasingly often based on deep learning components. A wide variety of applications are being developed, many of them safety-critical, such as self-driving cars and medical diagnosis. Since deep learning is unstable with respect to adversarial perturbations, there is a need for rigorous software development methodologies that encompass machine learning components. This lecture will describe progress in developing automated certification techniques for learnt software components to ensure the safety and adversarial robustness of their decisions, including a discussion of the role played by Bayesian learning and causality.

AAAI 2022

Safety and Robustness for Deep Learning with Provable Guarantees

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
AAAI is providing you with a conference planner, which you can use to help organize your itinerary of activities. This includes talks to attend in person, talks to attend remotely, breaks with colleagues and your site seeing activities. To access this conference planner, please go to [https://aaai-2023.takemobi.io](https://aaai-2023.takemobi.io).

In order to access this site, you need to register. If you haven't already, please register [here](https://aaai.org/Conferences/AAAI-23/registration/).


AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

keynote

The purpose of the AAAI conference is to promote research in artificial intelligence (AI) and scientific exchange among AI researchers, practitioners, scientists, and engineers in affiliated disciplines. 

Our goal is to develop statistical models that will predict crop performance in diverse environments. Crop phenotypes such as yield and drought tolerance are controlled by genotype, environment, and their interactions. The necessary volumes of phenotypic data, however, remain limiting, and our understanding of the interaction between genotypes and environments is limited. To address this limitation, we are building new sensors and robots to automatically collect large volumes of phenotypic data.

Advancing Agricultural Genome to Phenome Research

Current AI systems lack several important human capabilities, such as adaptability, generalizability, self-control, consistency, common sense, and causal reasoning. We believe that existing cognitive theories of human decision-making, such as the “thinking fast and slow” theory, can provide insights on how to advance AI systems towards some of these capabilities. In this talk, I will describe a general architecture that is based on fast/slow solvers and a metacognitive component. I will then present experimental results on the behavior of an instance of this architecture for AI systems that make decisions about navigating in a constrained environment. I will show how combining the fast and slow decision modalities allows this system to evolve over time and gradually pass from slow to fast thinking with enough experience and that this greatly helps in decision quality, resource consumption, and efficiency. 

Thinking Fast and Slow in AI

Presidential Address

Trustworthy AI is being enabled by new federal strategies, policies, and collaborative possibilities – bringing to bear a new paradigm. AI also brings the promise of better and more efficient care, especially for our nation’s Veterans. This session will share several use cases from the work of the National Artificial Intelligence Institute (NAII) at the VA and collaborations, including pilots that empower Veterans to take control of medication adherence, physicians to evaluate COVID-19-associated prognosis and needs, and VHA staff to triage text-based input to quickly identify and assist Veterans in crisis. Establishing an AI network to scale up such research and development will also be covered.

Toward an AI Network for Trustworthy AI

The panelists  present the vision and AI funding priorities of their respective organizations and answer questions from the audience.

Funding Panel

Opening Ceremony and Conference Awards

AAAI Organizational Awards Ceremony

The U.S.A. National Science Foundation has established the Artificial Intelligence Research Institutes awards in 2020. These Institutes represent the nation’s most significant federal investment in AI research and education to date. The panelists will present their Institute’s research vision and results.

AI Institutes Panel

With widespread use of machine learning, there have been serious societal consequences from using black box models for high-stakes decisions in criminal justice, healthcare, financial lending, and beyond. Interpretability of machine learning models is critical when the cost of a wrong decision is high. Throughout my career, I have had the opportunity to work with power engineers, doctors, and police detectives. Using interpretable models has been the key to allowing me to help them with important high-stakes societal problems. Interpretability can bring us out of the “dark” age of the black box into the age of insight and enlightenment.

Interpretable Machine Learning: Bringing Data Science Out of the â€œDark Ageâ€

The One Hundred Year Study on Artificial Intelligence (AI100) is a long-term investigation of the field of AI and its influences on people, their communities, and society. With oversight from a standing committee administered out of Stanford University, the AI100 convenes a study panel every five years with the goal of issuing a report accessible to AI researchers, policymakers, industry leaders, and the public at large. The reports describe the technical and societal challenges and opportunities that have arisen since the previous report and envision potential future advances. This talk will summarize the 2nd report, issued September 2021.

Downloads

Next from AAAI 2022

Advancing Agricultural Genome to Phenome Research

Similar lecture

Holistic Adversarial Robustness of Deep Learning Models

.css-70qvj9{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}Downloads

Next from AAAI 2022

Advancing Agricultural Genome to Phenome Research

Similar lecture

Holistic Adversarial Robustness of Deep Learning Models

Downloads