Canada

In the digital age, cybercrimes, particularly cyber harassment, have become pressing issues, targeting vulnerable individuals like children, teenagers, and women. Understanding the experiences and needs of the victims is crucial for effective support and intervention. Online conversations between victims and virtual harassment counselors (chatbots) offer valuable insights into cyber harassment manifestations (CHMs) and determinants (CHDs). However, the distinction between CHMs and CHDs remains unclear. This research is the first to introduce concrete definitions for CHMs and CHDs, investigating their distinction through automated methods to enable efficient cyber-harassment dialogue comprehension. We present a novel dataset, Cyber-MaD that contains Cyber harassment dialogues manually annotated with Manifestations and Determinants. Additionally, we design an Emotion-informed Contextual Dual Attention Convolution Transformer (EmoConDuCT) framework to extract CHMs and CHDs from cyber harassment dialogues. The framework primarily: a). exploits inherent emotion features in the input via adjective-noun pairs modeled through an autoencoder, b). utilizes a novel Contextual Dual Attention Convolution Transformer to learn contextual information, and, c). employs a demarcation module that leverages task-specific emotional knowledge and a discriminator loss function to differentiate manifestations and determinants. EmoConDuCT outperforms the state-of-the-art systems on the Cyber-MaD corpus, showcasing its potential in the extraction of CHMs and CHDs. Furthermore, its robustness is demonstrated on the emotion cause extraction task using the CARES_CEASE-v2.0 dataset of suicide notes, confirming its efficacy in diverse cause extraction objectives. This research sheds light on cyber harassment comprehension, paving the way for improved support and intervention strategies.

AAAI 2024

Benchmarking Cyber Harassment Dialogue Comprehension through Emotion-Informed Manifestations-Determinants Demarcation

consciousness and philosophy of mind

humanities & computational social science

interpretability and explainability

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AAPT SM 21

technical paper

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

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

Multi-label classification is an arduous problem given the complication in label correlation. Whilst sharing a common goal with contrastive learning in utilizing correlations for representation learning, how to better leverage label information remains challenging. 
Previous endeavors include extracting label-level presentations or mapping labels to an embedding space, overlooking the correlation between multiple labels. 
It exhibits a great ambiguity in determining positive samples with different extent of label overlap between samples and integrating such relations in loss functions. 
In our work, we propose Multi-Label Supervised Contrastive learning (MulSupCon) with a novel contrastive loss function to adjust weights based on how much overlap one sample shares with the anchor. 
By analyzing gradients, we explain why our method performs better under multi-label circumstances. 
To evaluate, we conduct direct classification and transfer learning on several multi-label datasets, including widely-used image datasets such as MS-COCO and NUS-WIDE.
Validation indicates that our method outperforms the traditional multi-label classification method and shows a competitive performance when comparing to other existing approaches.

Multi-Label Supervised Contrastive Learning

Despite meaningful progress over the past few decades, advances in the self-driving industry have plateaued. Traditional approaches are incredibly labor- and capital-intensive, relying on hand-engineered systems and millions of real-world testing miles. Generative AI offers the much-needed new path to unlocking autonomous vehicles at scale and changing the way these systems work. In this lecture, Waabi CEO and Founder Raquel Urtasun will discuss how leveraging generative AI will evolve the way autonomous driving systems are developed and trained, resulting in faster, safer, and more scalable deployment of transformative self-driving technology worldwide.

Accelerating AVs with the Next Generation of Generative AI

The capabilities, autonomy, and efficiency of software and physical agents have increased remarkably in recent times. However, the rapid advancement of these intelligent systems presents significant challenges for humans when they need to interact, coordinate, or collaborate with them. Intelligent advising agents providing guidance to people in such situations can enhance their decision-making and alleviate cognitive load. Alongside identifying valuable advice, these assisting agents must also deliver explanations to convince humans that the provided advice is advantageous and trustworthy. In the presentation, we will highlight the challenges that emerge when humans need to collaborate with drones, autonomous vehicles, and intelligent systems for negotiation and mediation. Additionally, we will introduce several approaches for developing assisting agents aimed at enhancing human performance and satisfaction.

Smart Advice: Intelligent Agents Assisting Humans in the Super AI Era 

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).

AI Strategic Initiatives and Policies

 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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Next from AAAI 2024

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

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How to talk about Sexual Harassment

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