Canada

As the massive excitement about recent advances in AI indicates, AI is a transformative technology that likely will have a deep and systemic impact on the national and global economy and security. The recognition of the potential impact of AI has led many countries to launch national strategic research initiatives and policies about the development and deployment of AI. The goal of this panel is to discuss some of these strategic initiatives and policies from different perspectives and at different levels of aggregation. We will begin with an overview of the US NSF’s National AI Institutes Program (Donlon), illustrate it with a specific National AI Institute (Goel), describe the structures and processes in the US that result in a national strategic initiative (Littman), present current and potential AI-related policies in the US (Wagstaff), and compare with similar initiatives and policies across the world (Walsh).

AAAI 2024

AI Strategic Initiatives and Policies

Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop.

The NAML workshop is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team.

The goals of NAML are:

* To spread awareness of current machine learning research relevant to Naval applications

* To connect machine learning researchers with experts in military requirements

* To build and strengthen collaborations within the defense research community

**Watch this video to hear about the AFCEA purpose.**

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NAML 2021

The annual workshop on Naval Applications of Machine Learning is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team. The goals of NAML are to spread awareness of current machine learning research relevant to Naval applications, to connect machine learning researchers with experts in military requirements and to build and strengthen collaborations within the defense research community.


panel

We are pleased to announce the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-24), which will be held in Vancouver, British Columbia at the Vancouver Convention Centre – West Building from 20-27 February, 2024.

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-24 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

We expect for AAAI-24 to be an in-person conference – one author of all accepted papers will be expected to present work in person unless there are exceptional circumstances that prevent this.<br><br><br><br>

In order to access the AAAI-24 event page you need to register [here](https://aaai.org/aaai-conference/registration/)

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. 

As AI systems’ sophistication and proliferation have increased, awareness of the risks has grown proportionally. The AI industry is increasingly emphasizing the need for transparency, with proposals ranging from standardizing use of technical disclosures, like model cards, to regulatory licensing regimes. Since the AI value chain is complicated, with actors bringing varied expertise, perspectives, and values, it is crucial that consumers of transparency disclosures be able to understand the risks of the AI system in question. In this paper we propose a risk profiling standard which can guide downstream decision-making, including triaging further risk assessment, informing procurement and deployment, and directing regulatory frameworks. The standard is built on our proposed taxonomy of AI risks, which distills the wide variety of risks proposed in the literature into a high-level categorization. We outline the myriad data sources needed to construct informative Risk Profiles and propose a template and methodology for collating risk information into a standard, yet flexible, structure. We apply this methodology to a number of prominent AI systems using publicly available information. To conclude, we discuss design decisions for the profiles and future work.

AI Risk Profiles: A Standards Proposal for Pre-deployment AI Risk Disclosures

In contrast to vast academic efforts to study AI security, few real-world reports of AI security incidents exist. Released incidents prevent a thorough investigation of the attackers' motives, as crucial information about the company and AI application is missing. As a consequence, it often remains unknown how to avoid incidents.    
We tackle this gap and combine previous reports with freshly collected incidents to a small database of 32 AI security incidents. We analyze the attackers' target and goal, influencing factors, causes, and mitigations. Many incidents stem from non-compliance with best practices in security and privacy-enhancing technologies. 
In the case of direct AI attacks, access control may provide some mitigation, but there is little scientific work on best practices. Our paper is thus a call for action to address these gaps.

When Your AI Becomes a Target: AI Security Incidents and Best Practices | VIDEO

Constraint programming and AI planning are powerful tools for solving assignment, optimization, and scheduling problems. They require, however, the rarely available combination of domain knowledge and mathematical modeling expertise. Learning constraints from exemplary solutions can close this gap and alleviate the effort of modeling. Existing approaches either require extensive user interaction, need exemplary invalid solutions that must be generated by experts at great expense, or show high noise-sensitivity. 
We aim to find constraints from potentially noisy solutions, without the need of user interaction. To this end, we formalize the problem in terms of the Minimum Description Length (MDL) principle, by which we select the model with the best lossless compression of the data. Solving the problem involves model counting, which is #P-hard to approximate. We therefore propose the greedy URPILS algorithm to find high-quality constraints in practice. Extensive experiments on constraint programming and AI planning benchmark data show URPILS not only finds more accurate and succinct constraints, but also is more robust to noise, and has lower sample complexity than the state of the art.

What Are the Rules? Discovering Constraints from Data

Designed to establish potential relations and distill high-order representations, graph-based recommendation systems continue to reveal promising results by jointly modeling ratings and reviews. However, existing studies capture simple review relations, failing to (1) completely explore hidden connections between users (or items), (2) filter out redundant information derived from reviews, and (3) model the behavioral association between rating and review interactions. To address these challenges, we propose a review-enhanced hierarchical contrastive learning, namely ReHCL. First, ReHCL constructs topic and semantic graphs to fully mine review relations from different views. Moreover, a cross-view graph contrastive learning is used to achieve enhancement of node representations and extract useful review knowledge. Meanwhile, we design a neighbor-based positive sampling to capture the graph-structured similarity between topic and semantic views, further performing efficient contrast and reducing redundant noise. Next, we propose a cross-modal contrastive learning to match the rating and review representations, by exploring the association between ratings and reviews. Lastly, these two contrastive learning modes form a hierarchical contrastive learning task, which is applied to enhance the final recommendation task. Extensive experiments verify the superiority of ReHCL compared with state-of-the-arts.

Review-Enhanced Hierarchical Contrastive Learning for Recommendation

As advances in large language models (LLMs) and multimodal techniques continue to mature, the development of general-purpose multimodal large language models (MLLMs) has surged, offering significant applications in interpreting natural images. However, the field of pathology has largely remained untapped, particularly in gathering high-quality data and designing comprehensive model frameworks. To bridge the gap in pathology MLLMs, we present PathAsst, a multimodal generative foundation AI assistant to revolutionize diagnostic and predictive analytics in pathology. The development of PathAsst involves three pivotal steps:  data acquisition, CLIP model adaptation, and the training of PathAsst's multimodal generative capabilities. Firstly, we collect over 207K high-quality pathology image-text pairs from various authoritative sources. Leveraging the advanced power of ChatGPT, we generate over 180K instruction-following samples. Furthermore, we devise additional instruction-following data specifically tailored for invoking eight pathology-specific sub-models we prepared, allowing the PathAsst to effectively collaborate with these models, enhancing its diagnostic ability. Secondly, by leveraging the collected data, we construct PathCLIP, a pathology-dedicated CLIP, to enhance PathAsst's capabilities in interpreting pathology images. Finally, we integrate PathCLIP with the Vicuna-13b and utilize pathology-specific instruction-tuning data to enhance the multimodal generation capacity of PathAsst and bolster its synergistic interactions with sub-models. The experimental results of PathAsst show the potential of harnessing AI-powered generative foundation model to improve pathology diagnosis and treatment processes. We open-source our dataset, as well as a comprehensive toolkit for extensive pathology data collection and preprocessing at https://github.com/superjamessyx/Generative-Foundation-AI-Assistant-for-Pathology.

PathAsst: A Generative Foundation AI Assistant towards Artificial General Intelligence of Pathology

A critical challenge for the widescale adoption of reinforcement learning (RL) is the need to give domain experts assurance that learned policies will improve decision-making -- and not lead to unacceptable behavior. To meet this challenge, my work aims to develop new methods for offline policy evaluation in real world RL domains. There has been much recent interest in offline evaluation and many advances. However, recent benchmarking efforts have also shown that there remains a substantial gap between current state-of-the-art methods and real world domains such as robotics. Towards scalable offline evaluation, my group is investigating the use of methods for abstraction and representation learning. In this new faculty highlight, I will present our recent results that show the promise of this direction for scaling offline evaluation in RL domains. I will then describe future directions in this line of that work which will further realize the promise of offline policy evaluation for increasing confidence in deployed RL.

Scaling Offline Evaluation of Reinforcement Learning Agents through Abstraction

Deep neural networks (DNNs) have achieved unprecedented success across many scientific and engineering fields in the last decades. Despite its empirical success, unfortunately, recent studies have shown that there are various failure modes and blindspots in DNN models which may result in unexpected serious failures and potential harms, e.g. the existence of adversarial examples and small perturbations. This is not acceptable especially for safety critical and high stakes applications in the real-world, including healthcare, self-driving cars, aircraft control systems, hiring and malware detection protocols. Moreover, it has been challenging to understand why and when DNNs will fail due to their complicated structures and black-box behaviors. Lacking interpretability is one critical issue that may seriously hinder the deployment of DNNs in high-stake applications, which need interpretability to trust the prediction, to understand potential failures, and to be able to mitigate harms and eliminate biases in the model.


To make DNNs trustworthy and reliable for deployment, it is necessary and urgent to develop methods and tools that can (i) quantify and improve their robustness against adversarial and natural perturbations, and (ii) understand their underlying behaviors and further correct errors to prevent injuries and damages. These are the important first steps to enable Trustworthy AI and Trustworthy Machine Learning. In this talk, I will survey a series of research efforts in my lab contributed to tackling the grand challenges in (i) and (ii). In the first part of my talk, I will overview our research effort in Robust Machine Learning since 2017, where we have proposed the first attack-agnostic robustness evaluation metric, the first efficient robustness certification algorithms for various types of perturbations, and efficient robust learning algorithms across supervised learning to deep reinforcement learning. 


In the second part of my talk, I will survey a series of exciting results in my lab on accelerating interpretable machine learning and explainable AI. Specifically, I will show how we could bring interpretability into deep learning by leveraging recent advances in multi-modal models. I'll present recent works in our group on automatically dissecting neural networks with open vocabulary concepts, designing interpretable neural networks without concept labels, and briefly overview our recent efforts on demystifying black-box DNN training process, automated neuron explanations for Large Language Models and the first robustness evaluation of a family of neuron-level interpretation techniques.

Towards Trustworthy Deep Learning | VIDEO

 How could machines learn as efficiently as humans and animals? How could machines learn how the world works and acquire common sense? How could machines learn to reason and plan? Current AI architectures, such as Auto-Regressive Large Language Models fall short. I will propose a modular cognitive architecture that may constitute a path towards answering these questions. The centerpiece of the architecture is a predictive world model that allows the system to predict the consequences of its actions and to plan a sequence of actions that optimize a set of objectives. The objectives include guardrails that guarantee the system’s controllability and safety. The world model employs a Hierarchical Joint Embedding Predictive Architecture (H-JEPA) trained with self-supervised learning. The JEPA learns abstract representations of the percepts that are simultaneously maximally informative and maximally predictable. The corresponding working paper is available here: https://openreview.net/forum?id=BZ5a1r-kVsf

Objective-Driven AI: Towards Machines that can Learn, Reason, and Plan

In their first years and with no instruction, children learn one or more natural languages; they develop a host of skills for moving themselves and manipulating objects; they learn the paths connecting places in their homes, the bonds connecting people in their social networks, and the forms and functions of everyday objects; and they deploy concepts of number, geometry, and causality that continue to guide the reasoning of adults, including mathematicians. How children do this is a great, unanswered question, but research on human infants, children, adults, and non-human animals, using diverse methods from the cognitive, brain, and computational sciences, provides some hints. It reveals at least seven domain-specific cognitive systems that emerge and function in infancy: six systems of core knowledge that are shared with other animals and represent places, objects, animate beings, social beings, number and geometry, and a seventh system that likely is unique to humans and underlies language learning during the first year. These automatic, unconscious, fixed systems function throughout life and provide inputs to the malleable, conscious perceptions and thoughts that power our endlessly inventive human minds.

How Children Learn

Charles and Michael are engaged in a decades-long conversation on AI, education, and AI education, exploring where it has been, where it could go, and why it is such a central part of so much that we all do. Occasionally that conversation has found its way into research and educational spaces, including two ongoing online classes in ML and RL taken by about 3300 students each year, a couple of music videos, and a series of educational videos on computing. They believe that this topic is so important and so serious that it must be addressed with a playful frame of mind—no one makes their best decisions and learns effectively when they think the well-being of the world is at stake. In this presentation, Charles and Michael continue their conversation with EAAI with the help of a moderator, answering questions about their approach to AI education and their outlook for the future. They promise to take it just as seriously as they do everything else… and probably there won’t be any singing.

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