Unsurprisingly, everyone was talking about AI and the recent rush to deploy large language models. Ahead of the conference, the United Nations put out a statement, encouraging RightsCon attendees to focus on AI oversight and transparency. I was surprised, however, by how different the conversations about the risks of generative AI were at RightsCon from all the warnings from big Silicon Valley voices that I've been reading in the news. Throughout the last few weeks, tech luminaries like OpenAI CEO Sam Altman, ex-Googler Geoff Hinton, top AI researcher Yoshua Bengio, Elon Musk, and many others have been calling for regulation and urgent action to address the "existential risks"--even including extinction--that AI poses to humanity. Certainly, the rapid deployment of large language models without risk assessments, disclosures about training data and processes, or seemingly much attention paid to how the tech could be misused is concerning.

ChatGPT and other generative artificial intelligence tools are rising in popularity. If you have ever used these tools, you might have realised that you are revealing your thoughts (and possibly emotions) through your questions and interactions with the AI platforms. You can therefore imagine the huge amount of data these AI tools are gathering and the patterns that they are able to extract from the way we think. The impact of these business practices is crystal clear: a new AI economy is emerging through collecting, codifying, and monetising the patterns derived from our thoughts and feelings. Intrusions into our intimacy and cognition will be much greater than with existing social media and tech platforms.

Generative AI models continue to become more powerful. The launch of ChatGPT in November 2022 has ushered in a new era of AI. ChatGPT and other similar chatbots have a range of capabilities, from answering student homework questions to creating music and art. There are already concerns that humans may be replaced by chatbots for a variety of jobs. Because of the wide spectrum of data chatbots are built on, we know that they will have human errors and human biases built into them. These biases may cause significant harm and/or inequity toward different subpopulations. To understand the strengths and weakness of chatbot responses, we present a position paper that explores different use cases of ChatGPT to determine the types of questions that are answered fairly and the types that still need improvement. We find that ChatGPT is a fair search engine for the tasks we tested; however, it has biases on both text generation and code generation. We find that ChatGPT is very sensitive to changes in the prompt, where small changes lead to different levels of fairness. This suggests that we need to immediately implement "corrections" or mitigation strategies in order to improve fairness of these systems. We suggest different strategies to improve chatbots and also advocate for an impartial review panel that has access to the model parameters to measure the levels of different types of biases and then recommends safeguards that move toward responses that are less discriminatory and more accurate.

Opioid Use Disorder (OUD) has emerged as a significant global public health issue, with complex multifaceted conditions. Due to the lack of effective treatment options for various conditions, there is a pressing need for the discovery of new medications. In this study, we propose a deep generative model that combines a stochastic differential equation (SDE)-based diffusion modeling with the latent space of a pretrained autoencoder model. The molecular generator enables efficient generation of molecules that are effective on multiple targets, specifically the mu, kappa, and delta opioid receptors. Furthermore, we assess the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of the generated molecules to identify drug-like compounds. To enhance the pharmacokinetic properties of some lead compounds, we employ a molecular optimization approach. We obtain a diverse set of drug-like molecules. We construct binding affinity predictors by integrating molecular fingerprints derived from autoencoder embeddings, transformer embeddings, and topological Laplacians with advanced machine learning algorithms. Further experimental studies are needed to evaluate the pharmacological effects of these drug-like compounds for OUD treatment. Our machine learning platform serves as a valuable tool in designing and optimizing effective molecules for addressing OUD.

Generative AI systems across modalities, ranging from text, image, audio, and video, have broad social impacts, but there exists no official standard for means of evaluating those impacts and which impacts should be evaluated. We move toward a standard approach in evaluating a generative AI system for any modality, in two overarching categories: what is able to be evaluated in a base system that has no predetermined application and what is able to be evaluated in society. We describe specific social impact categories and how to approach and conduct evaluations in the base technical system, then in people and society. Our framework for a base system defines seven categories of social impact: bias, stereotypes, and representational harms; cultural values and sensitive content; disparate performance; privacy and data protection; financial costs; environmental costs; and data and content moderation labor costs. Suggested methods for evaluation apply to all modalities and analyses of the limitations of existing evaluations serve as a starting point for necessary investment in future evaluations. We offer five overarching categories for what is able to be evaluated in society, each with their own subcategories: trustworthiness and autonomy; inequality, marginalization, and violence; concentration of authority; labor and creativity; and ecosystem and environment. Each subcategory includes recommendations for mitigating harm. We are concurrently crafting an evaluation repository for the AI research community to contribute existing evaluations along the given categories. This version will be updated following a CRAFT session at ACM FAccT 2023.

Trustworthy Artificial Intelligence (AI) is based on seven technical requirements sustained over three main pillars that should be met throughout the system's entire life cycle: it should be (1) lawful, (2) ethical, and (3) robust, both from a technical and a social perspective. However, attaining truly trustworthy AI concerns a wider vision that comprises the trustworthiness of all processes and actors that are part of the system's life cycle, and considers previous aspects from different lenses. A more holistic vision contemplates four essential axes: the global principles for ethical use and development of AI-based systems, a philosophical take on AI ethics, a risk-based approach to AI regulation, and the mentioned pillars and requirements. The seven requirements (human agency and oversight; robustness and safety; privacy and data governance; transparency; diversity, non-discrimination and fairness; societal and environmental wellbeing; and accountability) are analyzed from a triple perspective: What each requirement for trustworthy AI is, Why it is needed, and How each requirement can be implemented in practice. On the other hand, a practical approach to implement trustworthy AI systems allows defining the concept of responsibility of AI-based systems facing the law, through a given auditing process. Therefore, a responsible AI system is the resulting notion we introduce in this work, and a concept of utmost necessity that can be realized through auditing processes, subject to the challenges posed by the use of regulatory sandboxes. Our multidisciplinary vision of trustworthy AI culminates in a debate on the diverging views published lately about the future of AI. Our reflections in this matter conclude that regulation is a key for reaching a consensus among these views, and that trustworthy and responsible AI systems will be crucial for the present and future of our society.

Transformer is a deep neural network that employs a self-attention mechanism to comprehend the contextual relationships within sequential data. Unlike conventional neural networks or updated versions of Recurrent Neural Networks (RNNs) such as Long Short-Term Memory (LSTM), transformer models excel in handling long dependencies between input sequence elements and enable parallel processing. As a result, transformer-based models have attracted substantial interest among researchers in the field of artificial intelligence. This can be attributed to their immense potential and remarkable achievements, not only in Natural Language Processing (NLP) tasks but also in a wide range of domains, including computer vision, audio and speech processing, healthcare, and the Internet of Things (IoT). Although several survey papers have been published highlighting the transformer's contributions in specific fields, architectural differences, or performance evaluations, there is still a significant absence of a comprehensive survey paper encompassing its major applications across various domains. Therefore, we undertook the task of filling this gap by conducting an extensive survey of proposed transformer models from 2017 to 2022. Our survey encompasses the identification of the top five application domains for transformer-based models, namely: NLP, Computer Vision, Multi-Modality, Audio and Speech Processing, and Signal Processing. We analyze the impact of highly influential transformer-based models in these domains and subsequently classify them based on their respective tasks using a proposed taxonomy. Our aim is to shed light on the existing potential and future possibilities of transformers for enthusiastic researchers, thus contributing to the broader understanding of this groundbreaking technology.

In recent years, fuzz testing has emerged as an effective technique for testing software systems. For example, fuzz testing has been remarkably successful in uncovering critical security bugs in applications such as Chrome web-browser [1] and SQLLite database [11]. Generally, fuzz testing runs a program with seed inputs, mutates the previous inputs to improve a given guidance metric such as branch coverage, and repeats this cycle of input mutation and the target program execution. During the fuzzing process, we often execute the target program with generated large amount of test cases and monitor the runtime behavior to find vulnerabilities. For that, it is essential to generate test cases that effectively cover a wide range of execution paths and program behaviors. This comprehensive coverage enables thorough exploration of the program's functionality and helps uncover potential vulnerabilities or issues. The simplicity of fuzzing has made it a de-facto testing procedure for large-scale software systems; however, its effectiveness is based on an inherent yet oversighted assumption: a set of arbitrary input mutations is likely to yield meaningful inputs. In fact, our extensive experience suggests that this assumption often does not hold for most software systems that take highly structured data as inputs.

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China should play a key role in shaping the artificial intelligence guardrails needed to ensure the safety of transformative new systems, OpenAI Inc.'s Chief Executive Officer Sam Altman said. "With the emergence of the increasingly powerful AI systems, the stakes for global cooperation have never been higher," Altman, whose company kick-started an AI frenzy in China with last year's launch of ChatGPT, told a Beijing conference via video link on Saturday. In both China and Silicon Valley, talent and investments are flowing into AI, a strategic area that will help define the deepening tech rivalry between the world's two largest economies. Advances in the emerging technology have also highlighted tensions in how governments are seeking to regulate the sector, one that Chinese leader Xi Jinping has said requires greater state oversight to mitigate national security risks. This could be due to a conflict with your ad-blocking or security software.