Musk has announced Grok's expanded availability in a tweet, along with an instructional video on how to post a conversation with the chatbot directly on the X website. Grok has been available to X's Premium subscribers since it exited early beta, but that paid tier on the social network costs 16 a month or 168 for the full year when billed annually. Since the Premium tier costs half that much at 8 a month or 84 a year, this rollout makes Grok a bit more accessible. Later this week, Grok will be enabled for all premium subscribers (not just premium) https://t.co/4u9lbLwe23 Just a couple of weeks before that, the executive sued OpenAI and Sam Altman, accusing them of chasing profits and abandoning their non-profit mission.

Almost 8 million UK jobs could be lost to artificial intelligence in a "jobs apocalypse", according to a report warning that women, younger workers and those on lower wages are at most risk from automation. The Institute for Public Policy Research (IPPR) said that entry level, part-time and administrative jobs were most exposed to being replaced by AI under a "worst-case scenario" for the rollout of new technologies in the next three to five years. The thinktank warned that the UK was facing a "sliding doors" moment as growing numbers of companies adopt generative AI technologies – which can read and create text, data and software code – to automate everyday workplace tasks. The report said this first wave of AI adoption was already putting jobs at risk as growing numbers of companies introduce the technology. However, a second wave could lead to the automation of more jobs amid rapid advances in AI.

This paper investigates the complexities of integrating Large Language Models (LLMs) into software products, with a focus on the challenges encountered for determining their readiness for release. Our systematic review of grey literature identifies common challenges in deploying LLMs, ranging from pre-training and fine-tuning to user experience considerations. The study introduces a comprehensive checklist designed to guide practitioners in evaluating key release readiness aspects such as performance, monitoring, and deployment strategies, aiming to enhance the reliability and effectiveness of LLM-based applications in real-world settings.

Prompt engineering is effective for controlling the output of text-to-image (T2I) generative models, but it is also laborious due to the need for manually crafted prompts. This challenge has spurred the development of algorithms for automated prompt generation. However, these methods often struggle with transferability across T2I models, require white-box access to the underlying model, and produce non-intuitive prompts. In this work, we introduce PRISM, an algorithm that automatically identifies human-interpretable and transferable prompts that can effectively generate desired concepts given only black-box access to T2I models. Inspired by large language model (LLM) jailbreaking, PRISM leverages the in-context learning ability of LLMs to iteratively refine the candidate prompts distribution for given reference images. Our experiments demonstrate the versatility and effectiveness of PRISM in generating accurate prompts for objects, styles, and images across multiple T2I models, including Stable Diffusion, DALL-E, and Midjourney.

Vision-language models, such as CLIP, have shown promising Out-of-Distribution (OoD) generalization under various types of distribution shifts. Recent studies attempted to investigate the leading cause of this capability. In this work, we follow the same path, but focus on a specific type of OoD data - images with novel compositions of attribute-object pairs - and study whether such models can successfully classify those images into composition classes. We carefully designed an authentic image test dataset called ImageNet-AO, consisting of attributes for objects that are unlikely encountered in the CLIP training sets. We found that CLIPs trained with large datasets such as OpenAI CLIP, LAION-400M, and LAION-2B show orders-of-magnitude improvement in effective compositional OoD generalization compared to both supervised models and CLIPs trained with smaller datasets, such as CC-12M and YFCC-15M. Our results provide evidence that the scale and diversity of training data and language supervision play a key role in unlocking the compositional generalization abilities of vision-language models.

Large language models such as OpenAI's GPT-4 have become the dominant technology in modern natural language processing (Liu et al., 2023; Zhao et al., 2023). Trained on large corpora to predict the next token and refined with human feedback (Brown et al., 2020; Ouyang et al., 2022; Ziegler et al., 2020), these models develop impressive capabilities in areas such as summarization and questionanswering (Zhang et al., 2023; Goyal et al., 2023; Karpukhin et al., 2020). While the focus has been on these models' performance when responding to general English prompts, it is clear there is potential for specialist models to impact biomedical research and healthcare (Arora and Arora, 2023; Shah et al., 2023; Thirunavukarasu et al., 2023). Such applications include information retrieval and summarization from the ever-expanding biomedical literature (Wang et al., 2021; Yang, 2020), clinical information such as physician notes in electronic health records, and radiology reports (Murray et al., 2021; Feblowitz et al., 2011; Zhang et al., 2018). Improving domain-specific language models will help accelerate biomedical discovery, drive down healthcare costs, and improve patient care. Large, general models like GPT-4 and Med-PaLM 2 have set new standards for performance on question-answering and information extraction (Kung et al., 2022; Singhal et al., 2023a,b), but there are several drawbacks to these models. They are costly to train and utilize. Compute for training and inference of large language models have increased 10-to 100-fold since 2015 (Sevilla et al., 2022), translating to extremely high financial and

It is evident that, currently, generative models are surpassed in quality by human professionals. However, with the advancements in Artificial Intelligence, this gap will narrow, leading to scenarios where individuals who have dedicated years of their lives to mastering a skill become obsolete due to their high costs, which are inherently linked to the time they require to complete a task -- a task that AI could accomplish in minutes or seconds. To avoid future social upheavals, we must, even now, contemplate how to fairly assess the contributions of such individuals in training generative models and how to compensate them for the reduction or complete loss of their incomes. In this work, we propose a method to structure collaboration between model developers and data providers. To achieve this, we employ Shapley Values to quantify the contribution of artist(s) in an image generated by the Stable Diffusion-v1.5 model and to equitably allocate the reward among them.

A few days ago, I watched a video of myself talking in perfect Chinese. I've been studying the language on and off for only a few years, and I'm far from fluent. But there I was, pronouncing each character flawlessly in the correct tone, just as a native speaker would. Gone were my grammar mistakes and awkward pauses, replaced by a smooth and slightly alien-sounding voice. "My favorite food is sushi," I said--wo zui xihuan de shiwu shi shousi--with no hint of excitement or joy.

Since the beginning of the generative AI boom, there has been a fight over how large AI models are trained. And in the other camp are artists who argue that AI companies have taken their intellectual property without consent or compensation. It released its image-generating model Firefly, which is integrated into its popular photo editing tool Photoshop, one year ago. In an exclusive interview with MIT Technology Review, Adobe's AI leaders are adamant this is the only way forward. At stake is not just the livelihood of creators, they say, but our whole information ecosystem.

In an exclusive interview with MIT Technology Review, Adobe's AI leaders are adamant this is the only way forward. At stake is not just the livelihood of creators, they say, but our whole information ecosystem. What they have learned shows that building responsible tech doesn't have to come at the cost of doing business. "We worry that the industry, Silicon Valley in particular, does not pause to ask the'how' or the'why.' Just because you can build something doesn't mean you should build it without consideration of the impact that you're creating," says David Wadhwani, president of Adobe's digital media business.