Amazon said on Wednesday it will pour an additional 2.75bn into Anthropic, bringing its total investment in the artificial intelligence startup to 4bn. The technology giant will maintain a minority stake in San Francisco-based Anthropic, a rival of ChatGPT maker OpenAI. "Generative AI is poised to be the most transformational technology of our time, and we believe our strategic collaboration with Anthropic will further improve our customers' experiences, and look forward to what's next," said Swami Sivasubramanian, vice-president of data and AI at Amazon Web Services, or AWS, Amazon's cloud-computing subsidiary. The Seattle-based tech giant made an initial investment of 1.25bn in Anthropic in September, and indicated then it had plans to invest up to 4bn. Get set for the working day – we'll point you to all the business news and analysis you need every morning The two companies are collaborating to develop so-called foundation models, which underpin the generative AI systems that have captured global attention.

This past Monday, about a dozen engineers and executives at data science and AI company Databricks gathered in conference rooms connected via Zoom to learn if they had succeeded in building a top artificial intelligence language model. The team had spent months, and about 10 million, training DBRX, a large language model similar in design to the one behind OpenAI's ChatGPT. But they wouldn't know how powerful their creation was until results came back from the final tests of its abilities. "We've surpassed everything," Jonathan Frankle, chief neural network architect at Databricks and leader of the team that built DBRX, eventually told the team, which responded with whoops, cheers, and applause emojis. Frankle usually steers clear of caffeine but was taking sips of iced latte after pulling an all-nighter to write up the results.

When the board of OpenAI staged a bum mutiny last November, throwing out the company's leadership only to have the bosses return while board members were pressured to resign, something seemed rotten in the state of effective altruism. Nominally, OpenAI's mission had been to ensure that AI "benefits all of humanity." Fiduciarily, OpenAI's mission is to benefit the subset of humanity with a stake in OpenAI. And then, of course, there was Sam Bankman-Fried, the felonious altruist who argued in court last fall that his sordid crypto exchange was in fact a noble exercise in earning-to-give--making Midas money, sure, but only to funnel it to the global poor. This week he's facing a prison sentence of up to 50 years, which his legal team has complained paints him as a "depraved super-villain."

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.