Keith Peiris says he started to see the generative AI writing on the wall six months ago. Peiris is the cofounder and chief executive of Tome, a San Francisco startup that makes presentation software juiced with generative AI. The company launched its product in early 2022 with a healthy cushion of 32 million in venture capital funding, and successfully surfed the ChatGPT hype wave after that, raising even more funding in early 2023. Venture capitalist and LinkedIn cofounder Reid Hoffman, former Google CEO and chairman Eric Schmidt, and Stability.ai's Tome had one problem, though: It wasn't generating meaningful revenue.

Apple's plan to add generative AI to its iPhones and revive sagging sales in the crucial Chinese market will be in focus on Thursday, when the tech giant is expected to report its biggest quarterly revenue decline in more than a year. Long considered a must-own stock on Wall Street, Apple shares have underperformed other big tech companies in recent months -- falling more than 10% this year as fears mount about its slow roll-out of artificial intelligence services and as a resurgent Huawei takes market share in China. Analysts on average see iPhone sales, which account for about half of Apple's revenue, falling 10.4% in the first three months of 2024, according to LSEG. That drop would be the steepest in more than three years.

This paper investigates the use of Large Language Models (LLMs) for automating the generation of hardware description code, aiming to explore their potential in supporting and enhancing the development of efficient neuromorphic computing architectures. Building on our prior work, we employ OpenAI's ChatGPT4 and natural language prompts to synthesize a RTL Verilog module of a programmable recurrent spiking neural network, while also generating test benches to assess the system's correctness. The resultant design was validated in three case studies, the exclusive OR,the IRIS flower classification and the MNIST hand-written digit classification, achieving accuracies of up to 96.6%. To verify its synthesizability and implementability, the design was prototyped on a field-programmable gate array and implemented on SkyWater 130 nm technology by using an open-source electronic design automation flow. Additionally, we have submitted it to Tiny Tapeout 6 chip fabrication program to further evaluate the system on-chip performance in the future.

Digital assistants have become ubiquitous in e-commerce applications, following the recent advancements in Information Retrieval (IR), Natural Language Processing (NLP) and Generative Artificial Intelligence (AI). However, customers are often unsure or unaware of how to effectively converse with these assistants to meet their shopping needs. In this work, we emphasize the importance of providing customers a fast, easy to use, and natural way to interact with conversational shopping assistants. We propose a framework that employs Large Language Models (LLMs) to automatically generate contextual, useful, answerable, fluent and diverse questions about products, via in-context learning and supervised fine-tuning. Recommending these questions to customers as helpful suggestions or hints to both start and continue a conversation can result in a smoother and faster shopping experience with reduced conversation overhead and friction. We perform extensive offline evaluations, and discuss in detail about potential customer impact, and the type, length and latency of our generated product questions if incorporated into a real-world shopping assistant.

Soft robotics has emerged as a promising field with the potential to revolutionize industries such as healthcare and manufacturing. However, designing effective soft robots presents challenges, particularly in managing the complex interplay of material properties, structural design, and control strategies. Traditional design methods are often time-consuming and may not yield optimal designs. In this paper, we explore the use of generative AI to create 3D models of soft actuators. We create a dataset of over 70 text-shape pairings of soft pneumatic robot actuator designs, and adapt a latent diffusion model (SDFusion) to learn the data distribution and generate novel designs from it. By employing transfer learning and data augmentation techniques, we significantly improve the performance of the diffusion model. These findings highlight the potential of generative AI in designing complex soft robotic systems, paving the way for future advancements in the field.

The rapid emergence of generative Language Models (LMs) has led to growing concern about the impacts that their unexamined adoption may have on the social well-being of diverse user groups. Meanwhile, LMs are increasingly being adopted in K-20 schools and one-on-one student settings with minimal investigation of potential harms associated with their deployment. Motivated in part by real-world/everyday use cases (e.g., an AI writing assistant) this paper explores the potential psychosocial harms of stories generated by five leading LMs in response to open-ended prompting. We extend findings of stereotyping harms analyzing a total of 150K 100-word stories related to student classroom interactions. Examining patterns in LM-generated character demographics and representational harms (i.e., erasure, subordination, and stereotyping) we highlight particularly egregious vignettes, illustrating the ways LM-generated outputs may influence the experiences of users with marginalized and minoritized identities, and emphasizing the need for a critical understanding of the psychosocial impacts of generative AI tools when deployed and utilized in diverse social contexts.

It turns out the lay of today's AI landscape can be traced back to -- what do you know -- fear, jealousy and intense capitalist ambition. Emails revealed in the Department of Justice's antitrust case against Google, first reported by Business Insider, show Microsoft executives expressing alarm and envy over Google's AI lead. That spurred an urgency that led to the Windows maker's initial billion-dollar investment in its now-indispensable partner, OpenAI. In a heavily redacted 2019 email thread titled "Thoughts on OpenAI," Microsoft CEO Satya Nadella forwards a lengthy message from CTO Kevin Scott to CFO Amy Hood. "Very good email that explains, why I want us to do this ... and also why we will then ensure our infra folks execute," Nadella wrote.

Its leading applications, like DALL-E, Sora, and ChatGPT (which Altman referred to as "incredibly dumb" compared with what's coming next), have wowed us with their ability to generate convincing text and surreal videos and images. But they mostly remain tools we use for isolated tasks, and they have limited capacity to learn about us from our conversations with them. In the new paradigm, as Altman sees it, the AI will be capable of helping us outside the chat interface and taking real-world tasks off our plates. I asked Altman if we'll need a new piece of hardware to get to this future. Though smartphones are extraordinarily capable, and their designers are already incorporating more AI-driven features, some entrepreneurs are betting that the AI of the future will require a device that's more purpose built. Some of these devices are already beginning to appear in his orbit.

OpenAI's ChatGPT is facing serious competition, as the company's rival Anthropic brings its Claude chatbot to iPhones. Anthropic, led by a group of former OpenAI staff who quit over differences with chief executive Sam Altman, have a product that already beats ChatGPT on some measures of intelligence, and now wants to win over everyday users. "In today's world, smartphones are at the centre of how people interact with technology. To make Claude a true AI assistant, it's crucial that we meet users where they are – and in many cases, that's on their mobile devices," said Scott White at Anthropic. The third version of the Claude chatbot is offered direct to users on its website in three flavours: a speedy and simple model called "haiku", a slower and more powerful model called "sonnet", and, for paying customers only, the full "opus" system.

A group of publications that include the Chicago Tribune, New York Daily News and the Orlando Sentinel are suing Microsoft and OpenAI, as reported by The Verge. Their products can regurgitate Times' articles verbatim and can "mimic its expressive style," the publication said, even though they didn't have a prior licensing agreement. In a motion seeking to dismiss key parts of the lawsuit, Microsoft accused the Times of doomsday futurology by claiming that generative AI can pose a threat to independent journalism. ACG's newspapers complain of the same thing, that the companies' chatbots are reproducing their articles word-for-word shortly after they're published without a prominent link back to the sources. They included several examples in their complaint.