Zoubin Ghahramani, VP of Research, Google DeepMind As AI continues its march into our everyday lives, Zoubin will discuss realistic timelines, new collaborations, and the need for an overall strategy to map out steps to a safe and productive AI future for Europe and beyond. Victor Riparbelli, CEO and Cofounder, Synthesia Digital humans are here, and people are replicating themselves for hire, blending physical and digital worlds. Victor will guide us through current and future use cases of 3D avatars, alongside exploring the potential risks associated with avatars that look, act, and sound like real human beings. Bonnie Kruft, Partner / Deputy Director of AI4Science, Microsoft Generative AI is unlocking new research tools for bold scientific discoveries. Bonnie will cut through the hype and take a deep dive into the groundbreaking research enabled by generative AI--from small molecular inhibitors for treating infectious disease, to the discovery of new materials for energy storage.

The advent of Generative AI (GenAI) in education presents a transformative approach to traditional teaching methodologies, which often overlook the diverse needs of individual students. This study introduces a GenAI tool, based on advanced natural language processing, designed as a digital assistant for educators, enabling the creation of customized lesson plans. The tool utilizes an innovative feature termed 'interactive mega-prompt,' a comprehensive query system that allows educators to input detailed classroom specifics such as student demographics, learning objectives, and preferred teaching styles. This input is then processed by the GenAI to generate tailored lesson plans. To evaluate the tool's effectiveness, a comprehensive methodology incorporating both quantitative (i.e., % of time savings) and qualitative (i.e., user satisfaction) criteria was implemented, spanning various subjects and educational levels, with continuous feedback collected from educators through a structured evaluation form. Preliminary results show that educators find the GenAI-generated lesson plans effective, significantly reducing lesson planning time and enhancing the learning experience by accommodating diverse student needs. This AI-driven approach signifies a paradigm shift in education, suggesting its potential applicability in broader educational contexts, including special education needs (SEN), where individualized attention and specific learning aids are paramount

Artificial intelligence (AI) advances and the rapid adoption of generative AI tools like ChatGPT present new opportunities and challenges for higher education. While substantial literature discusses AI in higher education, there is a lack of a systemic approach that captures a holistic view of the AI transformation of higher education institutions (HEIs). To fill this gap, this article, taking a complex systems approach, develops a causal loop diagram (CLD) to map the causal feedback mechanisms of AI transformation in a typical HEI. Our model accounts for the forces that drive the AI transformation and the consequences of the AI transformation on value creation in a typical HEI. The article identifies and analyzes several reinforcing and balancing feedback loops, showing how, motivated by AI technology advances, the HEI invests in AI to improve student learning, research, and administration. The HEI must take measures to deal with academic integrity problems and adapt to changes in available jobs due to AI, emphasizing AI-complementary skills for its students. However, HEIs face a competitive threat and several policy traps that may lead to decline. HEI leaders need to become systems thinkers to manage the complexity of the AI transformation and benefit from the AI feedback loops while avoiding the associated pitfalls. We also discuss long-term scenarios, the notion of HEIs influencing the direction of AI, and directions for future research on AI transformation.

Generative AI is changing the way that many disciplines are taught, including computer science. Researchers have shown that generative AI tools are capable of solving programming problems, writing extensive blocks of code, and explaining complex code in simple terms. Particular promise has been shown in using generative AI to enhance programming error messages. Both students and instructors have complained for decades that these messages are often cryptic and difficult to understand. Yet recent work has shown that students make fewer repeated errors when enhanced via GPT-4. We extend this work by implementing feedback from ChatGPT for all programs submitted to our automated assessment tool, Athene, providing help for compiler, run-time, and logic errors. Our results indicate that adding generative AI to an automated assessment tool does not necessarily make it better and that design of the interface matters greatly to the usability of the feedback that GPT-4 provided.

Use of such speech-to-text APIs is increasingly prevalent in high-stakes downstream applications, ranging from surveillance of incarcerated people [22] to medical care [14]. While such speech-to-text APIs can generate written transcriptions more quickly than human transcribers, there are grave concerns regarding bias in automated transcription accuracy, e.g., underperformance for African American English speakers [11] and speakers with speech impairments such as dysphonia [12]. These biases within APIs can perpetuate disparities when real-world decisions are made based on automated speech-to-text transcriptions--from police making carceral judgements to doctors making treatment decisions. OpenAI released its Whisper speech-to-text API in September 2022 with experiments showing better speech transcription accuracy relative to market competitors [19]. We evaluate Whisper's transcription performance on the axis of "hallucinations," defined as undesirable generated text "that is nonsensical, or unfaithful to the provided source input" [10]. Our approach compares the ground truth of a speech snippet with the outputted transcription; we find hallucinations in roughly 1% of transcriptions generated in mid-2023, wherein Whisper hallucinates entire made-up sentences when no one is speaking in the input audio files. While hallucinations have been increasingly studied in the context of text generated by ChatGPT (a language model also made by OpenAI) [8, 10], hallucinations have only been considered in speech-to-text models as a means to study error prediction [21], and not as a fundamental concern in and of itself. In this paper, we provide experimental quantification of Whisper hallucinations, finding that nearly 40% of the hallucinations are harmful or concerning in some way (as opposed to innocuous and random).

Recent capability increases in large language models (LLMs) open up applications in which teams of communicating generative AI agents solve joint tasks. This poses privacy and security challenges concerning the unauthorised sharing of information, or other unwanted forms of agent coordination. Modern steganographic techniques could render such dynamics hard to detect. In this paper, we comprehensively formalise the problem of secret collusion in systems of generative AI agents by drawing on relevant concepts from both the AI and security literature. We study incentives for the use of steganography, and propose a variety of mitigation measures. Our investigations result in a model evaluation framework that systematically tests capabilities required for various forms of secret collusion. We provide extensive empirical results across a range of contemporary LLMs. While the steganographic capabilities of current models remain limited, GPT-4 displays a capability jump suggesting the need for continuous monitoring of steganographic frontier model capabilities. We conclude by laying out a comprehensive research program to mitigate future risks of collusion between generative AI models.

Deepfakes represent one of the toughest challenges in the world of Cybersecurity and Digital Forensics, especially considering the high-quality results obtained with recent generative AI-based solutions. Almost all generative models leave unique traces in synthetic data that, if analyzed and identified in detail, can be exploited to improve the generalization limitations of existing deepfake detectors. In this paper we analyzed deepfake images in the frequency domain generated by both GAN and Diffusion Model engines, examining in detail the underlying statistical distribution of Discrete Cosine Transform (DCT) coefficients. Recognizing that not all coefficients contribute equally to image detection, we hypothesize the existence of a unique "discriminative fingerprint", embedded in specific combinations of coefficients. To identify them, Machine Learning classifiers were trained on various combinations of coefficients. In addition, the Explainable AI (XAI) LIME algorithm was used to search for intrinsic discriminative combinations of coefficients. Finally, we performed a robustness test to analyze the persistence of traces by applying JPEG compression. The experimental results reveal the existence of traces left by the generative models that are more discriminative and persistent at JPEG attacks.

X (Twitter): ZLinPsy Acknowledgments The writing was supported by the National Key R&D Program of China STI2030 Major Projects (2021ZD0204200), National Natural Science Foundation of China (32071045),and Shenzhen Fundamental Research Program (JCYJ20210324134603010). ETHICAL AI IN SCIENCE 2 Abstract Generative artificial intelligence (AI) tools like large language models (LLMs) are rapidly transforming academic research and real-world applications. However, discussions on ethical guidelines for generative AI in science remain fragmented, underscoring the urgent need for consensus-based standards. Common scenarios are outlined to demonstrate potential ethical violations. We argue that global consensus coupled with targeted training and enforcement are critical to promoting AI's benefits while safeguarding research integrity. Keywords: generative AI, science, applications, transparency, reproducibility ETHICAL AI IN SCIENCE 3 Generative AI tools, including large language models (LLMs) like ChatGPT and Bard, are rapidly infiltrating academic corridors, aiding in diverse tasks such as writing, coding, idea generation, material creation, and data analysis(1, 2).

Use denoising diffusion implicit model for bridge-type innovation. The process of adding noise and denoising to an image can be likened to the process of a corpse rotting and a detective restoring the scene of a victim being killed, to help beginners understand. Through an easy-to-understand algebraic method, derive the function formulas for adding noise and denoising, making it easier for beginners to master the mathematical principles of the model. Using symmetric structured image dataset of three-span beam bridge, arch bridge, cable-stayed bridge and suspension bridge , based on Python programming language, TensorFlow and Keras deep learning platform framework , denoising diffusion implicit model is constructed and trained. From the latent space sampling, new bridge types with asymmetric structures can be generated. Denoising diffusion implicit model can organically combine different structural components on the basis of human original bridge types, and create new bridge types.

Background: The emergence of generative AI tools, empowered by Large Language Models (LLMs), has shown powerful capabilities in generating content. To date, the assessment of the usefulness of such content, generated by what is known as prompt engineering, has become an interesting research question. Objectives Using the mean of prompt engineering, we assess the similarity and closeness of such contents to real literature produced by scientists. Methods In this exploratory analysis, (1) we prompt-engineer ChatGPT and Google Bard to generate clinical content to be compared with literature counterparts, (2) we assess the similarities of the contents generated by comparing them with counterparts from biomedical literature. Our approach is to use text-mining approaches to compare documents and associated bigrams and to use network analysis to assess the terms' centrality. Results The experiments demonstrated that ChatGPT outperformed Google Bard in cosine document similarity (38% to 34%), Jaccard document similarity (23% to 19%), TF-IDF bigram similarity (47% to 41%), and term network centrality (degree and closeness). We also found new links that emerged in ChatGPT bigram networks that did not exist in literature bigram networks. Conclusions: The obtained similarity results show that ChatGPT outperformed Google Bard in document similarity, bigrams, and degree and closeness centrality. We also observed that ChatGPT offers linkage to terms that are connected in the literature. Such connections could inspire asking interesting questions and generate new hypotheses.