This week has felt like a month worth of news, now that we've wrapped up Apple's WWDC 2024 and Summer Game Fest in LA. In this episode, Cherlynn and Devindra discuss their final thoughts on Apple Intelligence and the company's upcoming software, and they chat about some of our coverage highlights from the pseudo-E3 Game Fest. Also, we dive into X making likes private (what is Elon hiding?!) and the news around Sony buying the Alamo Drafthouse theater chain. Listen below or subscribe on your podcast app of choice. If you've got suggestions or topics you'd like covered on the show, be sure to email us or drop a note in the comments! And be sure to check out our other podcast, Engadget News! Summer Games Fest highlights: Kunitsu-Gami: Path of the Goddess, LEGO Horizon Adventures, and an Assassin's Creed finally set in Japan – 25:06 X makes users' likes private – 40:27 Devindra: We are back from Apple's WWDC, and we have thoughts. And I feel like, It's just one of those whirlwind things. Both Trillin and I got back in from California yesterday. After recording this, I still feel like my body doesn't know, like, where I'm in, Trillin, or what time zone. I don't know how you feel. Cherlynn: I went to the gym at 8 a. m. Devindra: I like how you fit in the humble brag there. We're also going to be talking about Summer Game Fest, folks. We weren't there for that and I was trying to get Jess Condit on, but she's super busy still writing up stuff from that. So we have got a lot of coverage around that and there's some stories I want to highlight that Engadget has done. Also some games that looks pretty cool. Also joining us this morning is podcast producer Ben Ellman, who I'm sure has thoughts on Apple and the game stuff. And [00:01:00] as always, folks, if you're enjoying the show, please be sure to subscribe to us on iTunes or your podcast or of choice, leave us a review in iTunes. I would love to answer some reader questions. You can also typically join us Thursday mornings around 10 30 a. m. It's just like about scheduling, but that's about the time you can carve out in your schedule for us. You could see us on video. Sometimes we'll demo gadgets and We'll just have a great Q and a session too. I do want to point out if you're just listening to this episode, we did do a bonus episode at Apple's campus and it actually turned out pretty well because for Lynn and I were like right outside the, was it the Mac cafe or cafe Mac? But we were outdoors surrounded by traffic and other noise, but it actually ended up sounding pretty good.

The European Union is readying a landmark antitrust law that could limit more advanced generative AI models. The Federal Trade Commission is investigating a deal that Microsoft made with the AI start-up Inflection, probing whether the tech giant deliberately set up the investment to avoid a merger review. And U.S. enforcers reached a deal that will open the company to greater scrutiny of how it wields power to dominate artificial intelligence, including its multibillion-dollar investments in ChatGPT maker OpenAI. That relationship has also exposed Microsoft to new reputational risks, as OpenAI chief executive Sam Altman frequently invites controversy.

Large vision-language models (VLMs) have demonstrated remarkable abilities in understanding everyday content. However, their performance in the domain of art, particularly culturally rich art forms, remains less explored. As a pearl of human wisdom and creativity, art encapsulates complex cultural narratives and symbolism. In this paper, we offer the Pun Rebus Art Dataset, a multimodal dataset for art understanding deeply rooted in traditional Chinese culture. We focus on three primary tasks: identifying salient visual elements, matching elements with their symbolic meanings, and explanations for the conveyed messages. Our evaluation reveals that state-of-the-art VLMs struggle with these tasks, often providing biased and hallucinated explanations and showing limited improvement through in-context learning. By releasing the Pun Rebus Art Dataset, we aim to facilitate the development of VLMs that can better understand and interpret culturally specific content, promoting greater inclusiveness beyond English-based corpora.

The advent of powerful neural classifiers has increased interest in problems that require both learning and reasoning. These problems are critical for understanding important properties of models, such as trustworthiness, generalization, interpretability, and compliance to safety and structural constraints. However, recent research observed that tasks requiring both learning and reasoning on background knowledge often suffer from reasoning shortcuts (RSs): predictors can solve the downstream reasoning task without associating the correct concepts to the high-dimensional data. To address this issue, we introduce rsbench, a comprehensive benchmark suite designed to systematically evaluate the impact of RSs on models by providing easy access to highly customizable tasks affected by RSs. Furthermore, rsbench implements common metrics for evaluating concept quality and introduces novel formal verification procedures for assessing the presence of RSs in learning tasks. Using rsbench, we highlight that obtaining high quality concepts in both purely neural and neuro-symbolic models is a far-from-solved problem. rsbench is available at: https://unitn-sml.github.io/rsbench.

Over the last ten years, the realm of Artificial Intelligence (AI) has experienced an explosion of revolutionary breakthroughs, transforming what seemed like a far-off dream into a reality that is now deeply embedded in our everyday lives. AI's widespread impact is revolutionizing virtually all aspects of human life, and software engineering (SE) is no exception. As we explore this changing landscape, we are faced with questions about what the future holds for SE and how AI will reshape the roles, duties, and methodologies within the field. The introduction of these groundbreaking technologies highlights the inevitable shift towards a new paradigm, suggesting a future where AI's capabilities may redefine the boundaries of SE, potentially even more than human input. In this paper, we aim at outlining the key elements that, based on our expertise, are vital for the smooth integration of AI into SE, all while preserving the intrinsic human creativity that has been the driving force behind the field. First, we provide a brief description of SE and AI evolution. Afterward, we delve into the intricate interplay between AI-driven automation and human innovation, exploring how these two components can work together to advance SE practices to new methods and standards.

Dialects introduce syntactic and lexical variations in language that occur in regional or social groups. Most NLP methods are not sensitive to such variations. This may lead to unfair behavior of the methods, conveying negative bias towards dialect speakers. While previous work has studied dialect-related fairness for aspects like hate speech, other aspects of biased language, such as lewdness, remain fully unexplored. To fill this gap, we investigate performance disparities between dialects in the detection of five aspects of biased language and how to mitigate them. To alleviate bias, we present a multitask learning approach that models dialect language as an auxiliary task to incorporate syntactic and lexical variations. In our experiments with African-American English dialect, we provide empirical evidence that complementing common learning approaches with dialect modeling improves their fairness. Furthermore, the results suggest that multitask learning achieves state-of-the-art performance and helps to detect properties of biased language more reliably.

In an October 2023 executive order (EO), President Biden issued a detailed but largely aspirational road map for the safe and responsible development and use of artificial intelligence (AI). The challenge for the January 24-25, 2024 workshop was to transform those aspirations regarding one specific but crucial issue -- the ability of individuals to challenge government decisions made about themselves -- into actionable guidance enabling agencies to develop, procure, and use genuinely contestable advanced automated decision-making systems. While the Administration has taken important steps since the October 2023 EO, the insights garnered from our workshop remain highly relevant, as the requirements for contestability of advanced decision-making systems are not yet fully defined or implemented. The workshop brought together technologists, members of government agencies and civil society organizations, litigators, and researchers in an intensive two-day meeting that examined the challenges that users, developers, and agencies faced in enabling contestability in light of advanced automated decision-making systems. To ensure a free and open flow of discussion, the meeting was held under a modified version of the Chatham House rule. Participants were free to use any information or details that they learned, but they may not attribute any remarks made at the meeting by the identity or the affiliation of the speaker. Thus, the workshop summary that follows anonymizes speakers and their affiliation. Where an identification of an agency, company, or organization is made, it is done from a public, identified resource and does not necessarily reflect statements made by participants at the workshop. This document is a report of that workshop, along with recommendations and explanatory material.

We introduce FinTral, a suite of state-of-the-art multimodal large language models (LLMs) built upon the Mistral-7b model and tailored for financial analysis. FinTral integrates textual, numerical, tabular, and image data. We enhance FinTral with domain-specific pretraining, instruction fine-tuning, and RLAIF training by exploiting a large collection of textual and visual datasets we curate for this work. We also introduce an extensive benchmark featuring nine tasks and 25 datasets for evaluation, including hallucinations in the financial domain. Our FinTral model trained with direct preference optimization employing advanced Tools and Retrieval methods, dubbed FinTral-DPO-T&R, demonstrates an exceptional zero-shot performance. It outperforms ChatGPT-3.5 in all tasks and surpasses GPT-4 in five out of nine tasks, marking a significant advancement in AI-driven financial technology. We also demonstrate that FinTral has the potential to excel in real-time analysis and decision-making in diverse financial contexts. The GitHub repository for FinTral is available at \url{https://github.com/UBC-NLP/fintral}.

The rapid advancement of open-source foundation models has brought transparency and accessibility to this groundbreaking technology. However, this openness has also enabled the development of highly-capable, unsafe models, as exemplified by recent instances such as WormGPT and FraudGPT, which are specifically designed to facilitate criminal activity. As the capabilities of open foundation models continue to grow, potentially outpacing those of closed-source models, the risk of misuse by bad actors poses an increasingly serious threat to society. This paper addresses the critical question of how open foundation model developers should approach model safety in light of these challenges. Our analysis reveals that open-source foundation model companies often provide less restrictive acceptable use policies (AUPs) compared to their closed-source counterparts, likely due to the inherent difficulties in enforcing such policies once the models are released. To tackle this issue, we introduce PRISM, a design framework for open-source foundation model safety that emphasizes Private, Robust, Independent Safety measures, at Minimal marginal cost of compute. The PRISM framework proposes the use of modular functions that moderate prompts and outputs independently of the core language model, offering a more adaptable and resilient approach to safety compared to the brittle reinforcement learning methods currently used for value alignment. By focusing on identifying AUP violations and engaging the developer community in establishing consensus around safety design decisions, PRISM aims to create a safer open-source ecosystem that maximizes the potential of these powerful technologies while minimizing the risks to individuals and society as a whole.

As background to the main story it is important to understand the meaning of artificial intelligence (AI), and more specifically how its subset machine learning (ML) fits into the picture. AI can be generally defined as the theory and development of computer systems that are able to perform tasks that normally require human intelligence. As such AI systems may be adept in discovering new information, making inferences and possessing reasoning capability. ML is a subset of AI focussing on AI methods that are able to learn and adapt. AI includes symbolic computation, such as expert systems, which are not a part of ML, whereas ML builds statistical models of data that may be used for classification and prediction tasks to aid decision-making. Here we focus on ML rather than AI, but will still use the term AI when referring to the more general technology.