As soon as Apple announced its plans to inject generative AI into the iPhone, it was as good as official: The technology is now all but unavoidable. AI has already colonized web search, appearing in Google and Bing. OpenAI, the 80 billion start-up that has partnered with Apple and Microsoft, feels ubiquitous; the auto-generated products of its ChatGPTs and DALL-Es are everywhere. Rarely has a technology risen--or been forced--into prominence amid such controversy and consumer anxiety. Certainly, some Americans are excited about AI, though a majority said in a recent survey, for instance, that they are concerned AI will increase unemployment; in another, three out of four said they believe it will be abused to interfere with the upcoming presidential election.

We don't know if OpenAI, the creator of ChatGPT, is actually making any money so far. But thanks to a Wednesday report in The Information, what we do know is that the company doubled its annualized revenue -- a measure of the previous month's revenue multiplied by 12, as the publication helpfully explained -- in the last six months. OpenAI's annualized revenue was 3.4 billion, CEO Sam Altman reportedly told staff. Most of this revenue came from a subscription version of ChatGPT, which offers higher messaging limits to people who pay at least 20 a month, as well as from developers who pay the company to use the company's large language models in their own apps and services. About 200 million on an annualized basis comes from Microsoft, which gives OpenAI a cut of sales of OpenAI's large language models to customers using Azure, Microsoft's cloud computing platform aimed at businesses.

While AI could change the world in many unforeseen ways, it's already having one massive impact: a voracious consumption of energy. Generative AI does not simply float upon ephemeral intuition. Rather, it gathers strength via thousands of computers in data centers across the world, which operate constantly on full blast. In January, the International Energy Agency (IEA) forecast that global data center electricity demand will more than double from 2022 to 2026, with AI playing a major role in that increase. AI industry insiders say the world has plenty of energy capacity to absorb this increased demand, and that technological efficiency improvements could offset these increases.

Elon Musk has withdrawn his lawsuit against OpenAI, a day before a judge was set to hear a request for dismissal. Musk sued OpenAI, saying its founders had violated its nonprofit status, to become a de-facto part of Microsoft. OpenAI said there was no such violation, and the lawsuit was likely a way for Musk to gain access to its secrets. Despite ending the suit, Musk might be nursing this grudge, tweeting if Apple integrates OpenAI's tools into its software, he'll ban iPhones from his companies. You can't mirror your iPhone while mirroring your Mac on Apple Vision Pro Netflix drops a proper trailer for Arcane's second (and last) season Apple Intelligence: What devices and features will actually be supported?

AI safety is a growing concern as more companies integrate generative AI into their offerings and use large language models in consumer products. Last month, Google faced sharp criticism after its experimental "AI Overviews" tool, which purports to answer users' questions, suggested dangerous activities such as eating one small rock per day or adding glue to pizza. In February, Air Canada came under fire when its AI-enabled chatbot promised a fake discount to a traveler.

Elon Musk has dropped his lawsuit accusing OpenAI and its co-founders Sam Altman and Greg Brockman of reneging on the startup's pledge to develop artificial intelligence for the benefit of humanity. Lawyers in the United States representing Musk, on Tuesday asked a California judge to dismiss the suit, court filings showed. No reason was provided for the application to dismiss the suit. Musk in February filed a suit claiming that ChatGPT had set "aflame" its founding agreement to put the good of humanity ahead of profit-seeking when it signed an investment deal with Microsoft. "To this day, OpenAI Inc's website continues to profess that its charter is to ensure that AGI'benefits all of humanity'," Musk claimed in the suit.

In rebranding artificial intelligence as Apple Intelligence, Apple Inc is banking on the idea that people by and large won't buy the powerful A.I. software that its rivals are developing. Instead, they'll want really cool hardware that incorporates A.I. It's a compelling but risky strategy for a company that specializes in seamlessly integrating hardware and software into must-have products. "It's the next big step for Apple," Apple CEO Tim Cook said Monday in unveiling Apple Intelligence at the company's developers conference. Apple is diving into artificial intelligence – focused on the idea of a "virtual personal assistant" - as a potential must-have app for consumers. Since it lacks its own cutting-edge version of the predictive, sounds-like-a-human technology known as generative A.I., Apple will license that technology from other companies, starting with OpenAI.

In this position paper, we explore standard language ideology in language generated by large language models (LLMs). First, we outline how standard language ideology is reflected and reinforced in LLMs. We then present a taxonomy of open problems regarding standard language ideology in AI-generated language with implications for minoritized language communities. We introduce the concept of standard AI-generated language ideology, the process by which AI-generated language regards Standard American English (SAE) as a linguistic default and reinforces a linguistic bias that SAE is the most "appropriate" language. Finally, we discuss tensions that remain, including reflecting on what desirable system behavior looks like, as well as advantages and drawbacks of generative AI tools imitating--or often not--different English language varieties. Throughout, we discuss standard language ideology as a manifestation of existing global power structures in and through AI-generated language before ending with questions to move towards alternative, more emancipatory digital futures.

Diffusion models are the state of the art in text-to-image generation, but their perceptual variability remains understudied. In this paper, we examine how prompts affect image variability in black-box diffusion-based models. We propose W1KP, a human-calibrated measure of variability in a set of images, bootstrapped from existing image-pair perceptual distances. Current datasets do not cover recent diffusion models, thus we curate three test sets for evaluation. Our best perceptual distance outperforms nine baselines by up to 18 points in accuracy, and our calibration matches graded human judgements 78% of the time. Using W1KP, we study prompt reusability and show that Imagen prompts can be reused for 10-50 random seeds before new images become too similar to already generated images, while Stable Diffusion XL and DALL-E 3 can be reused 50-200 times. Lastly, we analyze 56 linguistic features of real prompts, finding that the prompt's length, CLIP embedding norm, concreteness, and word senses influence variability most. As far as we are aware, we are the first to analyze diffusion variability from a visuolinguistic perspective. Our project page is at http://w1kp.com

Multi-modal learning has significantly advanced generative AI, especially in vision-language modeling. Innovations like GPT-4V and open-source projects such as LLaVA have enabled robust conversational agents capable of zero-shot task completions. However, applying these technologies in the biomedical field presents unique challenges. Recent initiatives like LLaVA-Med have started to adapt instruction-tuning for biomedical contexts using large datasets such as PMC-15M. Our research offers three key contributions: (i) we present a new instruct dataset enriched with medical image-text pairs from Claude3-Opus and LLaMA3 70B, (ii) we propose a novel image encoding strategy using hierarchical representations to improve fine-grained biomedical visual comprehension, and (iii) we develop the Llama3-Med model, which achieves state-of-the-art zero-shot performance on biomedical visual question answering benchmarks, with an average performance improvement of over 10% compared to previous methods. These advancements provide more accurate and reliable tools for medical professionals, bridging gaps in current multi-modal conversational assistants and promoting further innovations in medical AI.