OpenAI shipped GPT-4 yesterday, the much-anticipated text-generating AI model, and it's a curious piece of work. GPT-4 improves upon its predecessor, GPT-3, in key ways, for example giving more factually true statements and allowing developers to prescribe its style and behavior more easily. It's also multimodal in the sense that it can understand images, allowing it to caption and even explain in detail the contents of a photo. But GPT-4 has serious shortcomings. Like GPT-3, the model "hallucinates" facts and makes basic reasoning errors.

March 28, 2023 -- Microsoft Corp. on Tuesday announced it is bringing the next generation of AI to cybersecurity with the launch of Microsoft Security Copilot, giving defenders a much-needed tool to quickly detect and respond to threats and better understand the threat landscape overall. Security Copilot will combine Microsoft's vast threat intelligence footprint with industry-leading expertise to augment the work of security professionals through an easy-to-use AI assistant. "Today the odds remain stacked against cybersecurity professionals. Too often, they fight an asymmetric battle against relentless and sophisticated attackers," said Vasu Jakkal, corporate vice president, Microsoft Security. "With Security Copilot, we are shifting the balance of power into our favor. Security Copilot is the first and only generative AI security product enabling defenders to move at the speed and scale of AI." Security Copilot is designed to work seamlessly with security teams, empowering defenders to see what is happening in their environment, learn from existing intelligence, correlate threat activity, and make more informed, efficient decisions at machine speed.

Generative AI can seem like magic. Image generators such as Stable Diffusion, Midjourney, or DALL·E 2 can produce remarkable visuals in styles from aged photographs and water colors to pencil drawings and Pointillism. The resulting products can be fascinating -- both quality and speed of creation are elevated compared to average human performance. The Museum of Modern Art in New York hosted an AI-generated installation generated from the museum's own collection, and the Mauritshuis in The Hague hung an AI variant of Vermeer's Girl with a Pearl Earring while the original was away on loan. The capabilities of text generators are perhaps even more striking, as they write essays, poems, and summaries, and are proving adept mimics of style and form (though they can take creative license with facts).

Generative AI can generate new content, and it's been heralded as a groundbreaking technology with the potential to transform various industries. However, those working in the cloudops world who will be charged with running generative AI systems long term are beginning to voice their concerns. Although generative AI has many benefits, it also has the potential to cause harm to cloud computing operations. Today these are theoretical problems, but they will soon become a reality. Thus, it's helpful to talk about some of the more concerning issues before we fall in love with this technology--or at least prepare to tackle some of these issues before they cause real problems.

Meta, formerly known as Facebook, has announced plans to commercialize its proprietary generative Artificial Intelligence (AI) technology by December. The company sees generative AI as a critical technology for content creation within the Metaverse. Metaverse is a virtual reality space that Meta is developing. This comes after years of research and development by the tech giant, putting them on par with competitors such as Google in finding practical applications for cutting-edge technology. Let's find out what Meta's Chief Technology Officer, Andrew Bosworth, has to say.

This paper proposes a novel deep generative model, called BSDE-Gen, which combines the flexibility of backward stochastic differential equations (BSDEs) with the power of deep neural networks for generating high-dimensional complex target data, particularly in the field of image generation. The incorporation of stochasticity and uncertainty in the generative modeling process makes BSDE-Gen an effective and natural approach for generating high-dimensional data. The paper provides a theoretical framework for BSDE-Gen, describes its model architecture, presents the maximum mean discrepancy (MMD) loss function used for training, and reports experimental results.

Welcome back to The TechCrunch Podcast, where we break down the biggest stories in tech with the people who covered them. The internet is always changing, but something about generative AI feels different. With the advent of OpenAI's ChatGPT, the machines are evolving beyond remix and delivery machines to become the content creators themselves. In this week's episode, Darrell Etherington talks with Techcrunch senior reporter Devin Coldewey about how the AI hype is overshadowing some of its shadier possible cultural side effects. Use promo code TCPOD to get 40% off Founder and Investor passes to Early Stage on April 20 in Boston.

Back in July of 2020, I published a group post entitled “Philosophers on GPT-3.” At the time, most readers of Daily Nous had not heard of GPT-3 and had no idea what a large language model (LLM) is. How times have changed. Over the past few months, with the release of OpenAI’s ChatGPT and Bing’s AI Chatbot “Sydney” (which we learned a few hours after this post originally went up has “secretly” been running GPT-4) (as well as Meta’s Galactica—pulled after 3 days—and Google’s Bard—currently available only to a small number of people), talk of LLMs has exploded. It seemed like a good time for a follow-up to that original post, one in which philosophers could get together to explore the various issues and questions raised by these next-generation large language models. Here it is. As with the previous post on GPT-3, this edition of Philosophers On was put together by guest editor by Annette Zimmermann. I am very grateful to her for all of the work she put into developing and editing this post. Philosophers On is an occasional series of group posts on issues of current interest, with the aim of showing what the careful thinking characteristic of philosophers (and occasionally scholars in related fields) can bring to popular ongoing conversations. The contributions that the authors make to these posts are not fully worked out position papers, but rather brief thoughts that can serve as prompts for further reflection and discussion. The contributors to this installment of “Philosophers On” are: Abeba Birhane (Senior Fellow in Trustworthy AI at Mozilla Foundation & Adjunct Lecturer, School of Computer Science and Statistics at Trinity College Dublin, Ireland), Atoosa Kasirzadeh (Chancellor’s Fellow and tenure-track assistant professor in Philosophy & Director of Research at the Centre for Technomoral Futures, University of Edinburgh), Fintan Mallory (Postdoctoral Fellow in Philosophy, University of Oslo), Regina Rini (Associate Professor of Philosophy & Canada Research Chair in Philosophy of Moral and Social Cognition), Eric Schwitzgebel (Professor of Philosophy, University of California, Riverside), Luke Stark (Assistant Professor of Information & Media Studies, Western University), Karina Vold (Assistant Professor of Philosophy, University of Toronto & Associate Fellow, Leverhulme Centre for the Future of Intelligence, University of Cambridge), and Annette Zimmermann (Assistant..

Over the past few months, AI chatbots like ChatGPT have captured the world's attention due to their ability to converse in a human-like way on just about any subject. But they come with a serious drawback: They can present convincing false information easily, making them unreliable sources of factual information and potential sources of defamation. Why do AI chatbots make things up, and will we ever be able to fully trust their output? We asked several experts and dug into how these AI models work to find the answers. AI chatbots such as OpenAI's ChatGPT rely on a type of AI called a « large language model » (LLM) to generate their responses.

In this tutorial, we will guide you through the process of building your very own chatbot using Python and OpenAI's TALK-GPT model. With the power of TALK-GPT, you can create a chatbot that is capable of carrying out complex conversations with users. First, we will provide an overview of TALK-GPT and its capabilities. Then, we will guide you through the steps of setting up your development environment and installing the necessary Python packages. After that, we will show you how to create a basic chatbot using TALK-GPT.