Apple is going all in on AI in the most Apple way possible. At WWDC, the company's annual conference for developers, the company revealed Apple Intelligence, an Apple-branded version of AI that is more focused on infusing its software with the technology and upgrading existing apps to make them more useful. Apple Intelligence will be powered both by Apple's homegrown tech as well as a partnership with OpenAI, the maker of ChatGPT, Apple announced. One of Apple's biggest AI upgrades is coming to Siri. The company's built-in voice assistant will now be powered by large language models, the underlying tech that powers all modern-day generative AI.

As rumored, Apple confirmed at WWDC 2024 that it's made a deal with OpenAI to bring ChatGPT to the iPhone and other devices. GPT-4o will power cloud-based Apple Intelligence queries in iOS 18, iPadOS 18 and macOS Sequoia. Apple's Craig Federighi said the new AI-powered Siri can (with your permission) tap into ChatGPT's knowledge base "when it might be helpful." Examples include asking for menu ideas for an elaborate meal with specific ingredients. You can also include photos with your questions, like asking for advice based on a detail in the picture.

At the same time, the deal could bring Apple new scrutiny from regulators. The Cupertino, Calif., company is already battling a Justice Department antitrust lawsuit that alleges it wields an illegal smartphone monopoly. Antitrust enforcers have been wary of the ways that tech companies use their deep war chests to strike deals that threaten innovation. Apple's massive deal with Google -- where the search giant pays to give its search engine prime placement in Apple's Safari web browser -- has been a key part of a government lawsuit, which claims Google has used the arrangement to squeeze out competitors.

Apple is finally getting into the generative artificial intelligence game. Apple CEO Tim Cook announced Apple's long-awaited AI reboot at the company's Worldwide Developer Conference in Cupertino, California, today. What the company is calling "Apple Intelligence" includes a handful of features that will shape the iOS experience in ways large and small. Apple also gave Siri, its currently limited voice assistant, a significant generative AI overhaul. Apple also announced that it will incorporate outside AI models into its software, starting with OpenAI's ChatGPT later this year, making clear that the experience will be opt-in only and won't require a ChatGPT subscription.

Data practitioners are among those whose roles are experiencing the most significant change, as organizations expand their responsibilities. Rather than working in a siloed data team, data engineers are now developing platforms and tools whose design improves data visibility and transparency for employees across the organization, including analytics engineers, data scientists, data analysts, machine learning engineers, and business stakeholders. This report explores, through a series of interviews with expert data practitioners, key shifts in data engineering, the evolving skill set required of data practitioners, options for data infrastructure and tooling to support AI, and data challenges and opportunities emerging in parallel with generative AI. The report's key findings include the following: This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review's editorial staff.

Renée DiResta is the research manager of the Stanford Internet Observatory and the author of Invisible Rulers: The People Who Turn Lies into Reality. At the end of May, OpenAI marked a new "first" in its corporate history. It wasn't an even more powerful language model or a new data partnership, but a report disclosing that bad actors had misused their products to run influence operations. The company had caught five networks of covert propagandists--including players from Russia, China, Iran, and Israel--using their generative AI tools for deceptive tactics that ranged from creating large volumes of social media comments in multiple languages to turning news articles into Facebook posts. The use of these tools, OpenAI noted, seemed intended to improve the quality and quantity of output.

The HCI field has demonstrated a growing interest in leveraging emerging technologies to enrich urban experiences. However, insufficient studies investigate the experience and design space of AI image technology (AIGT) applications for playful urban interaction, despite its widespread adoption. To explore this gap, we conducted an exploratory study involving four participants who wandered and photographed within Eindhoven Centre and interacted with an image-to-image AI. Preliminary findings present their observations, the effect of their familiarity with places, and how AIGT becomes an explorer's tool or co-speculator. We then highlight AIGT's capability of supporting playfulness, reimaginations, and rediscoveries of places through defamiliarizing and familiarizing cityscapes. Additionally, we propose the metaphor AIGT as a 'tourist' to discuss its opportunities for engaging explorations and risks of stereotyping places. Collectively, our research provides initial empirical insights and design considerations, inspiring future HCI endeavors for creating urban play with generative AI.

Information theory and machine learning are inextricably linked and have even been referred to as "two sides of the same coin". One particularly elegant connection is the essential equivalence between probabilistic generative modeling and data compression or transmission. In this article, we reveal the dual-functionality of deep generative models that reshapes both data compression for efficiency and transmission error concealment for resiliency. We present how the contextual predictive capabilities of powerful generative models can be well positioned to be strong compressors and estimators. In this sense, we advocate for viewing the deep generative modeling problem through the lens of end-to-end communications, and evaluate the compression and error restoration capabilities of foundation generative models. We show that the kernel of many large generative models is powerful predictor that can capture complex relationships among semantic latent variables, and the communication viewpoints provide novel insights into semantic feature tokenization, contextual learning, and usage of deep generative models. In summary, our article highlights the essential connections of generative AI to source and channel coding techniques, and motivates researchers to make further explorations in this emerging topic.

Recently, generative AI (GAI), with their emerging capabilities, have presented unique opportunities for augmenting and revolutionizing industrial recommender systems (Recsys). Despite growing research efforts at the intersection of these fields, the integration of GAI into industrial Recsys remains in its infancy, largely due to the intricate nature of modern industrial Recsys infrastructure, operations, and product sophistication. Drawing upon our experiences in successfully integrating GAI into several major social and e-commerce platforms, this survey aims to comprehensively examine the underlying system and AI foundations, solution frameworks, connections to key research advancements, as well as summarize the practical insights and challenges encountered in the endeavor to integrate GAI into industrial Recsys. As pioneering work in this domain, we hope outline the representative developments of relevant fields, shed lights on practical GAI adoptions in the industry, and motivate future research.

Keywords: Artificial Intelligence, Large Language Models, Academic Research, Publishing Ethics, Scholarly Publishing Abstract Generative artificial intelligence (AI) technologies like ChatGPT, have significantly impacted academic writing and publishing through their ability to generate content at levels comparable to or surpassing human writers. Through a review of recent interdisciplinary literature, this paper examines ethical considerations surrounding the integration of AI into academia, focusing on the potential for this technology to be used for scholarly misconduct and necessary oversight when using it for writing, editing, and reviewing of scholarly papers. The findings highlight the need for collaborative approaches to AI usage among publishers, editors, reviewers, and authors to ensure that this technology is used ethically and productively. Introduction Generative artificial intelligence technologies have rapidly transformed our daily lives, with one of the most profound impacts observed in the realm of writing. These models can produce content at a level that either matches or surpasses the quality of an average human writer. This transformation holds particular significance in academia, where faculty members are traditionally expected to engage in extensive scholarly writing. The increasing prevalence of generative artificial intelligence in academia raises substantial ethical concerns.