Artificial Intelligence applications have shown promise in the management of pandemics and have been widely used to assist the identification, classification, and diagnosis of medical images. In response to the global outbreak of Monkeypox (Mpox), the HeHealth.ai team leveraged an existing tool to screen for sexually transmitted diseases to develop a digital screening test for symptomatic Mpox through AI approaches. Prior to the global outbreak of Mpox, the team developed a smartphone app, where app users can use their own smartphone cameras to take pictures of their own penises to screen for symptomatic STD. The AI model was initially developed using 5000 cases and use a modified convolutional neural network to output prediction scores across visually diagnosable penis pathologies including Syphilis, Herpes Simplex Virus, and Human Papilloma Virus. From June 2022 to October 2022, a total of about 22,000 users downloaded the HeHealth app, and about 21,000 images have been analyzed using HeHealth AI technology. We then engaged in formative research, stakeholder engagement, rapid consolidation images, a validation study, and implementation of the tool from July 2022. From July 2022 to October 2022, a total of 1000 Mpox related images had been used to train the Mpox symptom checker tool. Our digital symptom checker tool showed accuracy of 87% to rule in Mpox and 90% to rule out symptomatic Mpox. Several hurdles identified included issues of data privacy and security for app users, initial lack of data to train the AI tool, and the potential generalizability of input data. We offer several suggestions to help others get started on similar projects in emergency situations, including engaging a wide range of stakeholders, having a multidisciplinary team, prioritizing pragmatism, as well as the concept that big data in fact is made up of small data.

Despite the remarkable achievements of language models (LMs) across a broad spectrum of tasks, their propensity for generating toxic outputs remains a prevalent concern. Current solutions involving fine-tuning or auxiliary models usually require extensive memory and computational resources, rendering them less practical for deployment in large language models (LLMs). In this paper, we propose DeStein, a novel method that detoxififies LMs by altering their internal representations in the activation space with lower resource and time cost. Specifically, we leverage self-induced steering pairs to identify detoxification vectors through arithmetic operations in the activation space. During inference, detoxification is achieved by blending the detoxification vectors with the original representations. Empirical results demonstrate that our method significantly outperforms previous state-of-the-art approaches on popular detoxification metrics, while also maintaining satisfactory generation quality and diversity. Furthermore, we extend our method to multiple LLMs, demonstrating its practicality and scalability. We open-source our method at https://github.com/LizLizLi/DeStein . Warning: Some example model outputs contain highly offensive or disturbing text.

The ability of the foundation models heavily relies on large-scale, diverse, and high-quality pretraining data. In order to improve data quality, researchers and practitioners often have to manually curate datasets from difference sources and develop dedicated data cleansing pipeline for each data repository. Lacking a unified data processing framework, this process is repetitive and cumbersome. To mitigate this issue, we propose a data processing framework that integrates a Processing Module which consists of a series of operators at different granularity levels, and an Analyzing Module which supports probing and evaluation of the refined data. The proposed framework is easy to use and highly flexible. In this demo paper, we first introduce how to use this framework with some example use cases and then demonstrate its effectiveness in improving the data quality with an automated evaluation with ChatGPT and an end-to-end evaluation in pretraining the GPT-2 model. The code and demonstration videos are accessible on GitHub.

Recent generative AI systems have demonstrated more advanced persuasive capabilities and are increasingly permeating areas of life where they can influence decision-making. Generative AI presents a new risk profile of persuasion due the opportunity for reciprocal exchange and prolonged interactions. This has led to growing concerns about harms from AI persuasion and how they can be mitigated, highlighting the need for a systematic study of AI persuasion. The current definitions of AI persuasion are unclear and related harms are insufficiently studied. Existing harm mitigation approaches prioritise harms from the outcome of persuasion over harms from the process of persuasion. In this paper, we lay the groundwork for the systematic study of AI persuasion. We first put forward definitions of persuasive generative AI. We distinguish between rationally persuasive generative AI, which relies on providing relevant facts, sound reasoning, or other forms of trustworthy evidence, and manipulative generative AI, which relies on taking advantage of cognitive biases and heuristics or misrepresenting information. We also put forward a map of harms from AI persuasion, including definitions and examples of economic, physical, environmental, psychological, sociocultural, political, privacy, and autonomy harm. We then introduce a map of mechanisms that contribute to harmful persuasion. Lastly, we provide an overview of approaches that can be used to mitigate against process harms of persuasion, including prompt engineering for manipulation classification and red teaming. Future work will operationalise these mitigations and study the interaction between different types of mechanisms of persuasion.

Sparse Mixtures of Experts (SMoE) scales model capacity without significant increases in training and inference costs, but exhibits the following two issues: (1) Low expert activation, where only a small subset of experts are activated for optimization. (2) Lacking fine-grained analytical capabilities for multiple semantic concepts within individual tokens. We propose Multi-Head Mixture-of-Experts (MH-MoE), which employs a multi-head mechanism to split each token into multiple sub-tokens. These sub-tokens are then assigned to and processed by a diverse set of experts in parallel, and seamlessly reintegrated into the original token form. The multi-head mechanism enables the model to collectively attend to information from various representation spaces within different experts, while significantly enhances expert activation, thus deepens context understanding and alleviate overfitting. Moreover, our MH-MoE is straightforward to implement and decouples from other SMoE optimization methods, making it easy to integrate with other SMoE models for enhanced performance. Extensive experimental results across three tasks: English-focused language modeling, Multi-lingual language modeling and Masked multi-modality modeling tasks, demonstrate the effectiveness of MH-MoE.

Teaching robots novel skills with demonstrations via human-in-the-loop data collection techniques like kinesthetic teaching or teleoperation puts a heavy burden on human supervisors. In contrast to this paradigm, it is often significantly easier to provide raw, action-free visual data of tasks being performed. Moreover, this data can even be mined from video datasets or the web. Ideally, this data can serve to guide robot learning for new tasks in novel environments, informing both "what" to do and "how" to do it. A powerful way to encode both the "what" and the "how" is to infer a well-shaped reward function for reinforcement learning. The challenge is determining how to ground visual demonstration inputs into a well-shaped and informative reward function. We propose a technique Rank2Reward for learning behaviors from videos of tasks being performed without access to any low-level states and actions. We do so by leveraging the videos to learn a reward function that measures incremental "progress" through a task by learning how to temporally rank the video frames in a demonstration. By inferring an appropriate ranking, the reward function is able to guide reinforcement learning by indicating when task progress is being made. This ranking function can be integrated into an adversarial imitation learning scheme resulting in an algorithm that can learn behaviors without exploiting the learned reward function. We demonstrate the effectiveness of Rank2Reward at learning behaviors from raw video on a number of tabletop manipulation tasks in both simulations and on a real-world robotic arm. We also demonstrate how Rank2Reward can be easily extended to be applicable to web-scale video datasets.

Fundus diseases are major causes of visual impairment and blindness worldwide, especially in underdeveloped regions, where the shortage of ophthalmologists hinders timely diagnosis. AI-assisted fundus image analysis has several advantages, such as high accuracy, reduced workload, and improved accessibility, but it requires a large amount of expert-annotated data to build reliable models. To address this dilemma, we propose a general self-supervised machine learning framework that can handle diverse fundus diseases from unlabeled fundus images. Our method's AUC surpasses existing supervised approaches by 15.7%, and even exceeds performance of a single human expert. Furthermore, our model adapts well to various datasets from different regions, races, and heterogeneous image sources or qualities from multiple cameras or devices. Our method offers a label-free general framework to diagnose fundus diseases, which could potentially benefit telehealth programs for early screening of people at risk of vision loss.

Composed Image Retrieval (CIR) is a task that retrieves images similar to a query, based on a provided textual modification. Current techniques rely on supervised learning for CIR models using labeled triplets of the reference image, text, target image. These specific triplets are not as commonly available as simple image-text pairs, limiting the widespread use of CIR and its scalability. On the other hand, zero-shot CIR can be relatively easily trained with image-caption pairs without considering the image-to-image relation, but this approach tends to yield lower accuracy. We propose a new semi-supervised CIR approach where we search for a reference and its related target images in auxiliary data and learn our large language model-based Visual Delta Generator (VDG) to generate text describing the visual difference (i.e., visual delta) between the two. VDG, equipped with fluent language knowledge and being model agnostic, can generate pseudo triplets to boost the performance of CIR models. Our approach significantly improves the existing supervised learning approaches and achieves state-of-the-art results on the CIR benchmarks.

In this paper, we explore the challenges of ensuring security and privacy for users from diverse demographic backgrounds. We propose a threat modeling approach to identify potential risks and countermeasures for product inclusion in security and privacy. We discuss various factors that can affect a user's ability to achieve a high level of security and privacy, including low-income demographics, poor connectivity, shared device usage, ML fairness, etc. We present results from a global security and privacy user experience survey and discuss the implications for product developers. Our work highlights the need for a more inclusive approach to security and privacy and provides a framework for researchers and practitioners to consider when designing products and services for a diverse range of users.

The pace of the development of Artificial Intelligence (AI) technologies has led to significant concern in many areas of society, including educational contexts. As a result, research agendas on Generative AI (GenAI) in tertiary education have been established (Lodge et al., 2023); however, to date, no review or research agenda has specifically focused on deepfakes in tertiary education. Deepfakes are GenAI outputs which comprise realistic audio, visual, or media outputs that depict false or inaccurate information (Akhtar, 2023). The major consequence of deepfakes is that they can portray an individual doing something or saying something that they have never done, marking an unprecedented shift in the ability to distort reality (Appel & Prietzel, 2022). As tertiary education institutions are centres of learning, the potential implications of such false information are highly important for students, teachers, and university leadership, thus warranting stakeholder attention.