United States

Data-Centric AI (DCAI) represents the recent transition from focusing on modeling to the underlying data used to train and evaluate models. Increasingly, common model architectures have begun to dominate a wide range of tasks, and predictable scaling rules have emerged. While building and using datasets has been critical to these successes, the endeavor is often artisanal — painstaking and expensive. The community lacks high productivity and efficient open data engineering tools to make building, maintaining, and evaluating datasets easier, cheaper, and more repeatable. In the presentation, Dr. Ng will talk about what DCAI is, the challenges, and tips for using the DCAI approach.

AAAI 2022

The Data-Centric AI

## Welcome to EMNLP 2024! 
We are excited to welcome you to one of the most prominent conferences in the field of Natural Language Processing. This year, EMNLP 2024 is being held in a hybrid format,
offering both virtual and in-person participation in beautiful Miami. Due to a record-breaking number of submissions, we've expanded the total number of accepted papers to accommodate more cutting-edge research from around the globe.
### [Conference Handbook](https://drive.google.com/file/d/1WPROgxjLAC96AJL7Ugy0tEnYm7dkrbHt/view?usp=sharing)

You are required to register for this event. **Please register [here](https://2024.emnlp.org/registration/).** The EMNLP 2024 event page on Underline will be open to public one week prior to the event.

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EMNLP 2024

EMNLP 2024 will take place in Miami, Florida from Nov 12th to Nov 16th, 2024, at the Hyatt Regency Miami Hote and on Underline for remote participants.

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. 

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.

Safety and Robustness for Deep Learning with Provable Guarantees

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.

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