As generative Artificial Intelligence (genAI) technologies proliferate across sectors, they offer significant benefits but also risk exacerbating discrimination. This chapter explores how genAI intersects with non-discrimination laws, identifying shortcomings and suggesting improvements. It highlights two main types of discriminatory outputs: (i) demeaning and abusive content and (ii) subtler biases due to inadequate representation of protected groups, which may not be overtly discriminatory in individual cases but have cumulative discriminatory effects. For example, genAI systems may predominantly depict white men when asked for images of people in important jobs. This chapter examines these issues, categorizing problematic outputs into three legal categories: discriminatory content; harassment; and legally hard cases like unbalanced content, harmful stereotypes or misclassification. It argues for holding genAI providers and deployers liable for discriminatory outputs and highlights the inadequacy of traditional legal frameworks to address genAI-specific issues. The chapter suggests updating EU laws, including the AI Act, to mitigate biases in training and input data, mandating testing and auditing, and evolving legislation to enforce standards for bias mitigation and inclusivity as technology advances.

This research introduces an innovative mathematical learning approach that integrates generative AI to cultivate a structured learning rather than quick solution. Our method combines chatbot capabilities and generative AI to offer interactive problem-solving exercises, enhancing learning through a stepby-step approach for varied problems, advocating for the responsible use of AI in education. Our approach emphasizes that immediate answers from ChatGPT can impede real learning. We introduce a reward-based system that requires students to solve mathematical problems effectively to receive the final answer. This encourages a progressive learning path from basic to complex problems, rewarding mastery with final solutions. The goal is to transition students from seeking quick fixes to engaging actively in a comprehensive learning experience.

Since the release of ChatGPT in November 2022 [61], GenAI has become increasingly popular in assisting people with written, auditory, and visual tasks [45, 58, 78]. In research, GenAI offers a new approach to manuscript writing, as it can handle tasks ranging from text improvement suggestions to speech-to-text translation and even crafting initial drafts [45, 52]. Its ability to understand context and generate human-like and grammatically accurate responses fosters innovative brainstorming and enhances the quality and readability of research publications [5]. However, along with GenAI's potential to augment research activities, concerns about transparency, academic integrity, and the urgency of maintaining the credibility of research work have emerged [21, 54, 73, 78]. Despite the growing interest in using GenAI for manuscript writing and research activities [45, 64], many researchers hesitate to acknowledge its use in their papers. This is illustrated by several instances where research publications with undisclosed GenAI use were identified by readers (e.g., [53, 71, 72, 79]). Studies have identified the phenomenon of AI aversion, where AI-generated content, even if factual, is often perceived as inaccurate and misleading [12, 56] and disclosing its use can negatively impact readers' satisfaction and perception of the authors' qualifications and effort [69]. Therefore, researchers' hesitancy is partly due to their fear that acknowledging GenAI use might damage

We present ConvoCache, a conversational caching system that solves the problem of slow and expensive generative AI models in spoken chatbots. ConvoCache finds a semantically similar prompt in the past and reuses the response. In this paper we evaluate ConvoCache on the DailyDialog dataset. We find that ConvoCache can apply a UniEval coherence threshold of 90% and respond to 89% of prompts using the cache with an average latency of 214ms, replacing LLM and voice synthesis that can take over 1s. To further reduce latency we test prefetching and find limited usefulness. Prefetching with 80% of a request leads to a 63% hit rate, and a drop in overall coherence. ConvoCache can be used with any chatbot to reduce costs by reducing usage of generative AI by up to 89%.

Explanations form the foundation of knowledge sharing and build upon communication principles, social dynamics, and learning theories. We focus specifically on conversational approaches for explanations because the context is highly adaptive and interactive. Our research leverages previous work on explanatory acts, a framework for understanding the different strategies that explainers and explainees employ in a conversation to both explain, understand, and engage with the other party. We use the 5-Levels dataset was constructed from the WIRED YouTube series by Wachsmuth et al., and later annotated by Booshehri et al. with explanatory acts. These annotations provide a framework for understanding how explainers and explainees structure their response when crafting a response. With the rise of generative AI in the past year, we hope to better understand the capabilities of Large Language Models (LLMs) and how they can augment expert explainer's capabilities in conversational settings. To achieve this goal, the 5-Levels dataset (We use Booshehri et al.'s 2023 annotated dataset with explanatory acts.) allows us to audit the ability of LLMs in engaging in explanation dialogues. To evaluate the effectiveness of LLMs in generating explainer responses, we compared 3 different strategies, we asked human annotators to evaluate 3 different strategies: human explainer response, GPT4 standard response, GPT4 response with Explanation Moves.

Background/Aim. This study aims to predict Amyloid Positron Emission Tomography (AmyloidPET) status with multimodal retinal imaging and convolutional neural networks (CNNs) and to improve the performance through pretraining with synthetic data. Methods. Fundus autofluorescence, optical coherence tomography (OCT), and OCT angiography images from 328 eyes of 59 AmyloidPET positive subjects and 108 AmyloidPET negative subjects were used for classification. Denoising Diffusion Probabilistic Models (DDPMs) were trained to generate synthetic images and unimodal CNNs were pretrained on synthetic data and finetuned on real data or trained solely on real data. Multimodal classifiers were developed to combine predictions of the four unimodal CNNs with patient metadata. Class activation maps of the unimodal classifiers provided insight into the network's attention to inputs. Results. DDPMs generated diverse, realistic images without memorization. Pretraining unimodal CNNs with synthetic data improved AUPR at most from 0.350 to 0.579. Integration of metadata in multimodal CNNs improved AUPR from 0.486 to 0.634, which was the best overall best classifier. Class activation maps highlighted relevant retinal regions which correlated with AD. Conclusion. Our method for generating and leveraging synthetic data has the potential to improve AmyloidPET prediction from multimodal retinal imaging. A DDPM can generate realistic and unique multimodal synthetic retinal images. Our best performing unimodal and multimodal classifiers were not pretrained on synthetic data, however pretraining with synthetic data slightly improved classification performance for two out of the four modalities.

Speculative decoding has emerged as a powerful method to improve latency and throughput in hosting large language models. However, most existing implementations focus on generating a single sequence. Real-world generative AI applications often require multiple responses and how to perform speculative decoding in a batched setting while preserving its latency benefits poses non-trivial challenges. This paper describes a system of batched speculative decoding that sets a new state of the art in multi-sequence generation latency and that demonstrates superior GPU utilization as well as quality of generations within a time budget. For example, for a 7.8B-size model on a single A100 GPU and with a batch size of 8, each sequence is generated at an average speed of 5.8ms per token, the overall throughput being 1.1K tokens per second. These results represent state-of-the-art latency and a 2.15X speed-up over optimized regular decoding. Within a time budget that regular decoding does not finish, our system is able to generate sequences with HumanEval Pass@First of 43% and Pass@All of 61%, far exceeding what's feasible with single-sequence speculative decoding. Our peak GPU utilization during decoding reaches as high as 15.8%, more than 3X the highest of that of regular decoding and around 10X of single-sequence speculative decoding.

If you've been dreaming about spending your summer whispering sweet nothings into the digital ears of one of the seductive ChatGPT voice assistants that OpenAI showed off last month, you'll have to dream a little longer. On Tuesday, the company announced that its "advanced Voice Mode" feature needs more time in the oven "to reach our bar to launch." The feature will be available to a small group of users to gather feedback, and then launch to all paying ChatGPT customers in the fall. "We're improving the model's ability to detect and refuse certain content," OpenAI posted on X. "We're also working on improving the user experience and preparing our infrastructure to scale to millions while maintaining real-time responses." We're sharing an update on the advanced Voice Mode we demoed during our Spring Update, which we remain very excited about: We had planned to start rolling this out in alpha to a small group of ChatGPT Plus users in late June, but need one more month to reach our bar to launch.… Voices have been a part of ChatGPT since 2023.

The rise of artificial intelligence in our media and entertainment industries has raised a lot of concerns about programs like Open Al's text-to-video maker Sora replacing the artistic endeavors and aspirations of humans. If those AI made movies are anything like a new brand film about the Toys'R' Us toy store chain's origin story, the only thing we'll have to fear is watching them. Toys'R' Us's current owner WHP Global worked with the Emmy nominated creative agency Native Foreign to create a short brand film called The Origin of Toys'R' Us using OpenAI's text-to-video creator Sora. The film premiered at the 2024 Cannes Lions International Festival of Creativity and can currently be viewed on the toy retailer's website. The Origin of Toys'R' Us is only a little over a minute long but it's a mix of confusing and eerie.

The macOS ChatGPT desktop app is now available to everyone. That is, provided you're running an Apple Silicon Mac (sorry, Intel users) and your computer is on macOS Sonoma or higher. OpenAI rolled out the app gradually, starting with Plus subscribers last month. ChatGPT now has an official macOS client before it has a Windows one. Of course, Windows 11 has the OpenAI-powered Microsoft CoPilot baked into its OS, which likely explains the omission.