Large language models (LLMs) are demonstrating remarkable capabilities across various tasks despite lacking a foundation in human cognition. This raises the question: can these models, beyond simply mimicking human language patterns, offer insights into the mechanisms underlying human cognition? This study explores the ability of ChatGPT to predict human performance in a language-based memory task. Building upon theories of text comprehension, we hypothesize that recognizing ambiguous sentences (e.g., "Because Bill drinks wine is never kept in the house") is facilitated by preceding them with contextually relevant information. Participants, both human and ChatGPT, were presented with pairs of sentences. The second sentence was always a garden-path sentence designed to be inherently ambiguous, while the first sentence either provided a fitting (e.g., "Bill has chronic alcoholism") or an unfitting context (e.g., "Bill likes to play golf"). We measured both human's and ChatGPT's ratings of sentence relatedness, ChatGPT's memorability ratings for the garden-path sentences, and humans' spontaneous memory for the garden-path sentences. The results revealed a striking alignment between ChatGPT's assessments and human performance. Sentences deemed more related and assessed as being more memorable by ChatGPT were indeed better remembered by humans, even though ChatGPT's internal mechanisms likely differ significantly from human cognition. This finding, which was confirmed with a robustness check employing synonyms, underscores the potential of generative AI models to predict human performance accurately. We discuss the broader implications of these findings for leveraging LLMs in the development of psychological theories and for gaining a deeper understanding of human cognition.

Semantic Communication (SC) is a novel paradigm for data transmission in 6G. However, there are several challenges posed when performing SC in 3D scenarios: 1) 3D semantic extraction; 2) Latent semantic redundancy; and 3) Uncertain channel estimation. To address these issues, we propose a Generative AI Model assisted 3D SC (GAM-3DSC) system. Firstly, we introduce a 3D Semantic Extractor (3DSE), which employs generative AI models, including Segment Anything Model (SAM) and Neural Radiance Field (NeRF), to extract key semantics from a 3D scenario based on user requirements. The extracted 3D semantics are represented as multi-perspective images of the goal-oriented 3D object. Then, we present an Adaptive Semantic Compression Model (ASCM) for encoding these multi-perspective images, in which we use a semantic encoder with two output heads to perform semantic encoding and mask redundant semantics in the latent semantic space, respectively. Next, we design a conditional Generative adversarial network and Diffusion model aided-Channel Estimation (GDCE) to estimate and refine the Channel State Information (CSI) of physical channels. Finally, simulation results demonstrate the advantages of the proposed GAM-3DSC system in effectively transmitting the goal-oriented 3D scenario.

Elon Musk last week sued two of his OpenAI cofounders, Sam Altman and Greg Brockman, accusing them of "flagrant breaches" of the trio's original agreement that the company would develop artificial intelligence openly and without chasing profits. Late on Tuesday, OpenAI released partially redacted emails between Musk, Altman, Brockman, and others that provide a counternarrative. The emails suggest that Musk was open to OpenAI becoming more profit-focused relatively early on, potentially undermining his own claim that it deviated from its original mission. In one message Musk offers to fold OpenAI into his electric-car company Tesla to provide more resources, an idea originally suggested by an email he forwarded from an unnamed outside party. The newly published emails also imply that Musk was not dogmatic about OpenAI having to freely provide its developments to all.

Adobe is bringing its generative AI (GAI) tech to mobile for the first time. Firefly GAI features are included in the beta of the new Express app on Android and iOS starting today. They should afford users more expansive creation and editing options while they're on the go. Using text prompts, you'll be able to generate images and insert, remove or replace people, objects and other elements such as backgrounds. Quick Actions enable you to apply edits, remove backgrounds and resize images with a single tap.

In recent months, the social impact of Artificial Intelligence (AI) has gained considerable public interest, driven by the emergence of Generative AI models, ChatGPT in particular. The rapid development of these models has sparked heated discussions regarding their benefits, limitations, and associated risks. Generative models hold immense promise across multiple domains, such as healthcare, finance, and education, to cite a few, presenting diverse practical applications. Nevertheless, concerns about potential adverse effects have elicited divergent perspectives, ranging from privacy risks to escalating social inequality. This paper adopts a methodology to delve into the societal implications of Generative AI tools, focusing primarily on the case of ChatGPT. It evaluates the potential impact on several social sectors and illustrates the findings of a comprehensive literature review of both positive and negative effects, emerging trends, and areas of opportunity of Generative AI models. This analysis aims to facilitate an in-depth discussion by providing insights that can inspire policy, regulation, and responsible development practices to foster a human-centered AI.

Independent evaluation and red teaming are critical for identifying the risks posed by generative AI systems. However, the terms of service and enforcement strategies used by prominent AI companies to deter model misuse have disincentives on good faith safety evaluations. This causes some researchers to fear that conducting such research or releasing their findings will result in account suspensions or legal reprisal. Although some companies offer researcher access programs, they are an inadequate substitute for independent research access, as they have limited community representation, receive inadequate funding, and lack independence from corporate incentives. We propose that major AI developers commit to providing a legal and technical safe harbor, indemnifying public interest safety research and protecting it from the threat of account suspensions or legal reprisal. These proposals emerged from our collective experience conducting safety, privacy, and trustworthiness research on generative AI systems, where norms and incentives could be better aligned with public interests, without exacerbating model misuse. We believe these commitments are a necessary step towards more inclusive and unimpeded community efforts to tackle the risks of generative AI.

An artificial intelligence engineer at Microsoft published a letter Wednesday alleging that the company's AI image generator lacks basic safeguards against creating violent and sexualized images. In the letter, engineer Shane Jones states that his repeated attempts to warn Microsoft management about the problems failed to result in any action. Jones said he sent the message to the Federal Trade Commission and Microsoft's board of directors. "Internally the company is well aware of systemic issues where the product is creating harmful images that could be offensive and inappropriate for consumers," Jones states in the letter, which he published on LinkedIn. He lists his title as "principal software engineering manager".

Artificial intelligence can appear to be many different things--a whole host of programs with seemingly little common ground. Sometimes AI is a conversation partner, an illustrator, a math tutor, a facial-recognition tool. But in every incarnation, it is always, always a machine, demanding almost unfathomable amounts of data and energy to function. AI systems such as ChatGPT operate out of buildings stuffed with silicon computer chips. To build bigger machines--as Microsoft, Google, Meta, Amazon, and other tech companies would like to do--you need more resources.

Since OpenAI released ChatGPT in November 2022, we have seen increased excitement about generative artificial intelligence (AI), coupled with concerns about its safety. Given this inflection point, we must pay renewed attention to its impact on the future of knowledge work carried out by professionals. This is because compared to earlier types of AI, generative AI gets closer to the core activities of professionals, namely giving advice to and treating clients. And yet, how and how fast professionals' work will change is not well understood. Instead of leaving the issue to be part of "unintended consequences,"3 this column argues that we can influence how generative AI will become embedded in the work we do as professionals. Professionals in a variety of fields--including medicine, audit, accounting, law, and data science--are essentially in the business of diagnosis and treatment, connecting the two via inference.

The tech giant said the lawsuit was near-sighted and akin to Hollywood's losing backlash against the VCR. In a motion to dismiss part of the lawsuit filed Monday, Microsoft, which was sued in December alongside ChatGPT-maker OpenAI, scoffed at the newspaper's claim that Times content receives "particular emphasis" and that tech companies "seek to free-ride on the Times's massive investment in its journalism". But in its response, Microsoft said the lawsuit was akin to Hollywood's resistance to the VCR that consumers used to record TV shows and which the entertainment business in the late 1970s feared would destroy its economic model. "'The VCR is to the American film producer and the American public as the Boston strangler is to the woman home alone,'" Microsoft said in its response, quoting from congressional testimony delivered by Jack Valenti, then head of the motion picture association of America, in 1982. In this case, Microsoft said, the Times is attempting to use "its might and its megaphone to challenge the latest profound technological advance: the Large Language Model."