Ant Group Co., the fintech affiliate of Alibaba Group Holding Ltd., received approval from the Chinese government to roll out products powered by its large language model Bailing to the public. Bailing will be applied to Ant's various services and help with innovation, Xu Peng, vice president of Ant Group said in a statement on Monday. Chinese tech firms from Alibaba to Tencent Holdings Ltd. and Baidu Inc. have joined startups Baichuan and Zhipu to release ChatGPT-like products, joining a global race to capitalize on the potential of generative AI. Ant, the owner of Alipay, can leverage the popularity of the mobile payment service to gain more data and insight on user habits. Hangzhou-based Alibaba has defined AI as one of its two core strategies as it goes through a six-way spinoff.

In a sign of great interest, OpenAI's developer conference took up multiple floors of a large event space in downtown San Francisco, spilling onto the roof where a breakfast spread of burritos and avocado toast, complete with a separate table featuring multiple hot sauces, awaited the hundreds of attendees lining up outside to get in. Those who weren't able to score a coveted invitation to the event gathered at so-called "watch parties" at separate corporate offices to keep tabs on the live stream.

Microsoft is injecting a ton of generative AI-powered features into Windows 11, but it's not all about the Copilot assistant. The company has started to update a string of apps with new AI functions, including Paint, Clipchamp, Snipping Tool and Photos. Microsoft released an update for Windows 11 2023, known as 23H2, on October 31. That update expanded access to Copilot and other AI features. Microsoft is rolling out the AI updates gradually, so you may not have access to everything just yet.

Users will soon be able to make customized versions of ChatGPT, the maker of the tool OpenAI said Monday as it made a series of announcements at its first Developer Day conference in San Francisco. OpenAI is calling the customizable versions of ChatGPT "GPTs," which it says will be able to comply with specified instructions and have access to user-provided information. "The upsides of this are going to be tremendous," OpenAI CEO Sam Altman said on stage on Monday. "It gives agency to everyone." ChatGPT currently has 100 million weekly active users, Altman added.

OpenAI's ChatGPT became a phenomenon thanks to its wide-ranging abilities, such as drafting college essays, writing working computer programs, and digging up information from across the web. Now the company aims to further widen the range of tricks up ChatGPT's sleeve by making it possible for anyone to build a custom chatbot powered by the technology--without any coding skills. OpenAI suggests people might want to build custom bots to help with specific problems or interests in their life, such as helping someone learn the rules of a board game, teach their kids math, or help design stickers using AI-generated art. To create one of these custom bots or AI agents, which OpenAI calls "GPTs," a user need only specify, by talking with ChatGPT, what they would like the bot to do. Behind the scenes, ChatGPT will write the code needed to create and run the new bot. The bots can plug into other sites and services to do things like access databases, search emails, and automate ecommerce orders, OpenAI says.

Dall-E 3, the latest image-generating software created by OpenAI, can produce a picture of almost anything. It can conjure a watercolor portrait of a mermaid, a personalized birthday greeting or a faux photograph of Spider-Man eating pizza, all based on just a few words of prompting. The new version of the tool, released in September, represents a "leap forward," in artificial intelligence-created images, OpenAI says. Dall-E 3 offers better detail and the ability to render text more reliably. It's also further stoked illustrators' fears that they will be replaced by a computer program mimicking their work.

Elon Musk's artificial intelligence startup xAI will be integrated into his social media platform X and also be available as a standalone app, he said on Sunday in a post. The billionaire also said xAI released its first AI model, a bot named Grok, after making it available to all X Premium subscribers on Friday. The startup aims to create AI tools that'assist humanity in its quest for understanding and knowledge' and that Grok has been designed to answer questions with a bit of wit. Musk who has criticized Big Tech's AI efforts as ridden with censorship, in July launched xAI, calling it a "maximum truth-seeking AI" that tries to understand the nature of the universe to rival Google's Bard and Microsoft's Bing AI. "Grok has real-time access to info via the X platform, which is a massive advantage over other models," Musk added. X, the social media firm formerly known as Twitter which Musk owns, is separate from xAI, but the companies work closely together.

Textual data from financial filings, e.g., the Management's Discussion \& Analysis (MDA) section in Form 10-K, has been used to improve the prediction accuracy of bankruptcy models. In practice, however, we cannot obtain the MDA section for all public companies. The two main reasons for the lack of MDA are: (i) not all companies are obliged to submit the MDA and (ii) technical problems arise when crawling and scrapping the MDA section. This research introduces for the first time, to the best of our knowledge, the concept of multimodal learning in bankruptcy prediction models to solve the problem that for some companies we are unable to obtain the MDA text. We use the Conditional Multimodal Discriminative (CMMD) model to learn multimodal representations that embed information from accounting, market, and textual modalities. The CMMD model needs a sample with all data modalities for model training. At test time, the CMMD model only needs access to accounting and market modalities to generate multimodal representations, which are further used to make bankruptcy predictions. This fact makes the use of bankruptcy prediction models using textual data realistic and possible, since accounting and market data are available for all companies unlike textual data. The empirical results in this research show that the classification performance of our proposed methodology is superior compared to that of a large number of traditional classifier models. We also show that our proposed methodology solves the limitation of previous bankruptcy models using textual data, as they can only make predictions for a small proportion of companies.

Exciting advances in generative artificial intelligence (AI) have sparked concern for jobs, education, productivity [1], and the future of work. As with past technologies, generative AI may not lead to mass unemployment. But, unlike past technologies, generative AI is creative, cognitive, and potentially ubiquitous which makes the usual assumptions of automation predictions ill-suited for today. Existing projections suggest that generative AI will impact workers in occupations that were previously considered immune to automation. As AI's full set of capabilities and applications emerge, policy makers should promote workers' career adaptability. This goal requires improved data on job separations and unemployment by locality and job titles in order to identify early-indicators for the workers facing labor disruption. Further, prudent policy should incentivize education programs to accommodate learning with AI as a tool while preparing students for the demands of the future of work.

Large language models (LLMs) are powerful AI tools that can generate and comprehend natural language text and other complex information. However, the field lacks a mathematical framework to systematically describe, compare and improve LLMs. We propose Hex a framework that clarifies key terms and concepts in LLM research, such as hallucinations, alignment, self-verification and chain-of-thought reasoning. The Hex framework offers a precise and consistent way to characterize LLMs, identify their strengths and weaknesses, and integrate new findings. Using Hex, we differentiate chain-of-thought reasoning from chain-of-thought prompting and establish the conditions under which they are equivalent. This distinction clarifies the basic assumptions behind chain-of-thought prompting and its implications for methods that use it, such as self-verification and prompt programming. Our goal is to provide a formal framework for LLMs that can help both researchers and practitioners explore new possibilities for generative AI. We do not claim to have a definitive solution, but rather a tool for opening up new research avenues. We argue that our formal definitions and results are crucial for advancing the discussion on how to build generative AI systems that are safe, reliable, fair and robust, especially in domains like healthcare and software engineering.