OpenAI's announcement of the release of ChatGPT and Whisper could spark a new AI goldrush, with companies able to experiment with the chatbot to create fully-fledged businesses. The release of API access to ChatGPT and Whisper makes it much easier and cheaper for companies to add AI capabilities to their applications. OpenAI has also changed its data retention policy, which could reassure businesses thinking of experimenting with ChatGPT. Businesses can now get paid for services built on the large language model, meaning chatbots are going to start appearing everywhere. Read the complete article at: www.wired.com

Mind maps are powerful tools for brainstorming, problem-solving, organizing thoughts, and keeping track of information. There is also a wide variety of ways of constructing a mind map, which makes it valuable to seek feedback and ideas from other people. Nowadays, artificial intelligence can come in handy as a useful tool to generate new ideas and take your imagination in unexpected directions. In this tutorial, I will show you how to create an app that can generate mind maps using the new ChatGPT API in Python and then visualize them in the form of graphs with the framework Streamlit. Before we begin, let's have a look at the final result: If you find any bugs or ideas for improvements, feel free to share them.

Erik Brynjolfsson is in great demand. The US professor whose research focuses on the relationship between digital tech and human productivity is nearing the end of a European speaking tour that's lasted nearly a month. Speaking via Zoom as he prepares for his imminent lecture in Oxford, the director of the Digital Economy Lab at the Stanford Institute for Human-Centered AI is enthused by recent "seminal breakthroughs" in the field. Brynjolfsson's tour – which has included appearances at the World Economic Forum in Davos and the Institute for the Future of Work in London – is neatly timed, because the recent arrival of ChatGPT on the scene has been capturing human minds, if not yet hearts. The large-scale language model, fed 300 billion words by developer OpenAI, caused a sensation with its powerful capabilities, attracting 1 million users within five days of its release in late November 2022.

Amrit Jassal is the Chief Technology Officer (CTO) and cofounder of Egnyte, a leading cloud-based collaboration and governance platform. ChatGPT and Dalle-E are the talks of the town as the new shiny object that could potentially disrupt Google's hegemony. The hype cycle, as usual, is high. AI dominated conversations around tech at this year's World Economic Forum in Davos, Switzerland. Even at this year's CES trade show, hundreds of exhibitors were listed under the show's artificial intelligence category--double those categorized as metaverse, cryptocurrency and blockchain combined.

Microsoft today introduced what it's calling the "next generation" of AI product updates across its business apps portfolio. They touch on both Power Platform, Microsoft's set of low-code tools for building apps and workflows, and Dynamics 365, the company's suite of enterprise resource planning (ERP) and customer relationship management (CRM) tools. In an interview with TechCrunch, Charles Lamanna, CVP of business apps and platform at Microsoft, described the updates as the logical next step on Microsoft's automation journey. Powered by tech from AI startup OpenAI and built using the Azure OpenAI Service, Microsoft's service that provides enterprise-tailored access to OpenAI's API, the new capabilities follow the rollout of OpenAI text-generating AI models in Power Platform four years ago and the more recent debut of generative AI capabilities in Viva Sales, Microsoft's seller experience app. "Over the last four years, we've been on a journey to bring generative AI and foundation models to the workplace," Lamanna said via email, noting that Microsoft has a longstanding partnership with OpenAI to commercialize the vendor's tech in Microsoft's own products and through the Azure OpenAI Service.

This "blue sky idea" paper outlines the opportunities and challenges in data mining and machine learning involving making a computational attorney -- an intelligent software agent capable of helping human lawyers with a wide range of complex high-level legal tasks such as drafting legal briefs for the prosecution or defense in court. In particular, we discuss what a ChatGPT-like Large Legal Language Model (L$^3$M) can and cannot do today, which will inspire researchers with promising short-term and long-term research objectives.

AI-supported programming has arrived, as shown by the introduction and successes of large language models for code, such as Copilot/Codex (Github/OpenAI) and AlphaCode (DeepMind). Above human average performance on programming challenges is now possible. However, software engineering is much more than solving programming contests. Moving beyond code completion to AI-supported software engineering will require an AI system that can, among other things, understand how to avoid code smells, to follow language idioms, and eventually (maybe!) propose rational software designs. In this study, we explore the current limitations of AI-supported code completion tools like Copilot and offer a simple taxonomy for understanding the classification of AI-supported code completion tools in this space. We first perform an exploratory study on Copilot's code suggestions for language idioms and code smells. Copilot does not follow language idioms and avoid code smells in most of our test scenarios. We then conduct additional investigation to determine the current boundaries of AI-supported code completion tools like Copilot by introducing a taxonomy of software abstraction hierarchies where 'basic programming functionality' such as code compilation and syntax checking is at the least abstract level, software architecture analysis and design are at the most abstract level. We conclude by providing a discussion on challenges for future development of AI-supported code completion tools to reach the design level of abstraction in our taxonomy.

Foundation models pretrained on diverse data at scale have demonstrated extraordinary capabilities in a wide range of vision and language tasks. When such models are deployed in real world environments, they inevitably interface with other entities and agents. For example, language models are often used to interact with human beings through dialogue, and visual perception models are used to autonomously navigate neighborhood streets. In response to these developments, new paradigms are emerging for training foundation models to interact with other agents and perform long-term reasoning. These paradigms leverage the existence of ever-larger datasets curated for multimodal, multitask, and generalist interaction. Research at the intersection of foundation models and decision making holds tremendous promise for creating powerful new systems that can interact effectively across a diverse range of applications such as dialogue, autonomous driving, healthcare, education, and robotics. In this manuscript, we examine the scope of foundation models for decision making, and provide conceptual tools and technical background for understanding the problem space and exploring new research directions. We review recent approaches that ground foundation models in practical decision making applications through a variety of methods such as prompting, conditional generative modeling, planning, optimal control, and reinforcement learning, and discuss common challenges and open problems in the field.

A primary objective of news articles is to establish the factual record for an event, frequently achieved by conveying both the details of the specified event (i.e., the 5 Ws; Who, What, Where, When and Why regarding the event) and how people reacted to it (i.e., reported statements). However, existing work on news summarization almost exclusively focuses on the event details. In this work, we propose the novel task of summarizing the reactions of different speakers, as expressed by their reported statements, to a given event. To this end, we create a new multi-document summarization benchmark, SUMREN, comprising 745 summaries of reported statements from various public figures obtained from 633 news articles discussing 132 events. We propose an automatic silver training data generation approach for our task, which helps smaller models like BART achieve GPT-3 level performance on this task. Finally, we introduce a pipeline-based framework for summarizing reported speech, which we empirically show to generate summaries that are more abstractive and factual than baseline query-focused summarization approaches.

We study whether automatically-induced prompts that effectively extract information from a language model can also be used, out-of-the-box, to probe other language models for the same information. After confirming that discrete prompts induced with the AutoPrompt algorithm outperform manual and semi-manual prompts on the slot-filling task, we demonstrate a drop in performance for AutoPrompt prompts learned on a model and tested on another. We introduce a way to induce prompts by mixing language models at training time that results in prompts that generalize well across models. We conduct an extensive analysis of the induced prompts, finding that the more general prompts include a larger proportion of existing English words and have a less order-dependent and more uniform distribution of information across their component tokens. Our work provides preliminary evidence that it's possible to generate discrete prompts that can be induced once and used with a number of different models, and gives insights on the properties characterizing such prompts.