OpenAI launched ChatGPT Enterprise today, the business-focused subscription it teased in April. The company says it won't train its AI models on any business data or conversations under the new plan. "Our models don't learn from your usage," the company wrote in an announcement blog post about the enterprise features. In addition, the new plan encrypts business chats (in transit and at rest) and is SOC 2 compliant. OpenAI says companies including Block, Canva, Carlyle, The Estée Lauder Companies, PwC and Zapier have already tested ChatGPT Enterprise.

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Congress has yet to pass any laws regulating the use of generative AI in elections. The Federal Election Commission is reviewing a petition filed by the left-leaning advocacy group Public Citizen, which would ban politicians from deliberately misrepresenting their opponents in ads generated by AI. Commissioners from both parties have expressed concern that the agency may not have the authority to weigh in without direction from Congress, and any effort to create new AI rules could confront political hurdles.

Recent progress in Generative Artificial Intelligence (AI) relies on efficient data representations, often featuring encoder-decoder architectures. We formalize the mathematical problem of finding the optimal encoder-decoder pair and characterize its solution, which we name the "benign autoencoder" (BAE). We prove that BAE projects data onto a manifold whose dimension is the optimal compressibility dimension of the generative problem. We highlight surprising connections between BAE and several recent developments in AI, such as conditional GANs, context encoders, stable diffusion, stacked autoencoders, and the learning capabilities of generative models. As an illustration, we show how BAE can find optimal, low-dimensional latent representations that improve the performance of a discriminator under a distribution shift. By compressing "malignant" data dimensions, BAE leads to smoother and more stable gradients.

This Company Is Nvidia's AI Chip Partner--and Its Stock Is Soaring

Chatter about artificial intelligence mostly falls into three basic categories: anxious uncertainty (will it take our jobs?); In this hazy, liminal, pre-disruption moment, there is little consensus as to whether generative AI is a tool or a threat, and few rules for using it properly. For students, this uncertainty feels especially profound. Bans on AI and claims that using it constitutes cheating are now giving way to concerns that AI use is inevitable and probably should be taught in school. Now, as a new college admissions season kicks into gear, many prospective applicants are wondering: can AI write my personal essay?

This article provides a comprehensive synthesis of the recent developments in synthetic data generation via deep generative models, focusing on tabular datasets. We specifically outline the importance of synthetic data generation in the context of privacy-sensitive data. Additionally, we highlight the advantages of using deep generative models over other methods and provide a detailed explanation of the underlying concepts, including unsupervised learning, neural networks, and generative models. The paper covers the challenges and considerations involved in using deep generative models for tabular datasets, such as data normalization, privacy concerns, and model evaluation. This review provides a valuable resource for researchers and practitioners interested in synthetic data generation and its applications.

A "prompt engineer" might have skills that help get the best results out of generative AI As the capabilities of artificial intelligence keep on growing, some companies are hiring "AI prompt engineers" to help them get the best out of the emerging technology. Are these jobs set to become a ubiquitous presence in workplaces, or are they a passing fad? Generative AI creates text or images in response to prompts entered by the user.

News outlets including the New York Times, CNN, Reuters and the Australian Broadcasting Corporation (ABC) have blocked a tool from OpenAI, limiting the company's ability to continue accessing their content. OpenAI is behind one of the best known artificial intelligence chatbots, ChatGPT. Its web crawler – known as GPTBot – may scan webpages to help improve its AI models. The Verge was first to report the New York Times had blocked GPTBot on its website. The Guardian subsequently found that other major news websites, including CNN, Reuters, the Chicago Tribune, the ABC and Australian Community Media (ACM) brands such as the Canberra Times and the Newcastle Herald, appear to have also disallowed the web crawler.

Complex text is a major barrier for many citizens when accessing public information and knowledge. While often done manually, Text Simplification is a key Natural Language Processing task that aims for reducing the linguistic complexity of a text while preserving the original meaning. Recent advances in Generative Artificial Intelligence (AI) have enabled automatic text simplification both on the lexical and syntactical levels. However, as applications often focus on English, little is understood about the effectiveness of Generative AI techniques on low-resource languages such as Dutch. For this reason, we carry out empirical studies to understand the benefits and limitations of applying generative technologies for text simplification and provide the following outcomes: 1) the design and implementation for a configurable text simplification pipeline that orchestrates state-of-the-art generative text simplification models, domain and reader adaptation, and visualisation modules; 2) insights and lessons learned, showing the strengths of automatic text simplification while exposing the challenges in handling cultural and commonsense knowledge. These outcomes represent a first step in the exploration of Dutch text simplification and shed light on future endeavours both for research and practice.