The capabilities of AI tools are progressing rapidly, with Google, Microsoft, OpenAI, and many others racing to stay ahead of the competition. It feels like advances and apps are arriving on a weekly basis, with the bar constantly being raised in terms of what AI can do for us. Auto-GPT is the latest evidence for this: It leverages the power of ChatGPT to create an autonomous AI assistant, capable of taking on tasks and projects on its own and working through multiple steps in a job without you having to prompt it every time. In other words, it does a lot of the hard work for you, without you having to come up with your own follow-up responses or ideas. Auto-GPT can be run locally on your computer. Think about everything you can do with ChatGPT, then imagine rolling that into a system that can supply its own feedback and make its own choices.

This article is from Big Technology, a newsletter by Alex Kantrowitz. Richard Mathenge felt he'd landed the perfect role when he started training OpenAI's GPT model in 2021. After years of working in customer service in Nairobi, Kenya, he was finally involved in something that felt meaningful and held a future for him. But the position left him scarred. For nine hours per day, five days a week, Mathenge led a team that taught the A.I. model about explicit content.

Amid growing concerns over the governance of generative artificial intelligence, the Group of Seven leaders gathering in Hiroshima agreed Saturday to launch a working group to beef up collaboration to tackle various issues in relation to the new technology. The G7 nations will start the initiative -- dubbed the Hiroshima AI process -- later this year to facilitate discussions. The group is also expected to be joined by relevant international bodies, including the OECD. Generative AI tools like ChatGPT have taken the tech world by storm and are believed to be a game changer that could significantly boost productivity. But they are also likely to pose risks, such as eliminating jobs and spreading fake news. This could be due to a conflict with your ad-blocking or security software.

The Generative Pre-trained Transformer (GPT) represents a notable breakthrough in the domain of natural language processing, which is propelling us toward the development of machines that can understand and communicate using language in a manner that closely resembles that of humans. GPT is based on the transformer architecture, a deep neural network designed for natural language processing tasks. Due to their impressive performance on natural language processing tasks and ability to effectively converse, GPT have gained significant popularity among researchers and industrial communities, making them one of the most widely used and effective models in natural language processing and related fields, which motivated to conduct this review. This review provides a detailed overview of the GPT, including its architecture, working process, training procedures, enabling technologies, and its impact on various applications. In this review, we also explored the potential challenges and limitations of a GPT. Furthermore, we discuss potential solutions and future directions. Overall, this paper aims to provide a comprehensive understanding of GPT, enabling technologies, their impact on various applications, emerging challenges, and potential solutions.

Next year's elections in Britain and the US could be marked by a wave of AI-powered disinformation, experts have warned, as generated images, text and deepfake videos go viral at the behest of swarms of AI-powered propaganda bots. Sam Altman, CEO of the ChatGPT creator, OpenAI, told a congressional hearing in Washington this week that the models behind the latest generation of AI technology could manipulate users. "The general ability of these models to manipulate and persuade, to provide one-on-one interactive disinformation is a significant area of concern," he said. "Regulation would be quite wise: people need to know if they're talking to an AI, or if content that they're looking at is generated or not. The ability to really model … to predict humans, I think is going to require a combination of companies doing the right thing, regulation and public education."

In February, 2019, OpenAI, a little-known artificial-intelligence company, announced that its large-language-model text generator, GPT-2, would not be released to the public "due to our concerns about malicious applications of the technology." Among the dangers, the company stated, was a potential for misleading news articles, online impersonation, and automating the production of abusive or faked social-media content and of spam and phishing content. As a consequence, Open AI proposed that "governments should consider expanding or commencing initiatives to more systematically monitor the societal impact and diffusion of AI technologies, and to measure the progression in the capabilities of such systems." This week, four years after that warning, members of the Senate Judiciary Subcommittee on Privacy, Technology, and the Law met to discuss "Oversight of A.I.: Rules for Artificial Intelligence." As has been the case with other tech hearings on the Hill, this one came after a new technology with the capacity to fundamentally alter our social and political lives was already in circulation. Like many Americans, the lawmakers became concerned about the pitfalls of large-language-model artificial intelligence in March, when OpenAI released GPT-4, the latest and most polished iteration of its text generator.

Hiroshima – The world must urgently assess the impact of generative artificial intelligence, G7 leaders said Saturday, announcing they will launch discussions this year on "responsible" use of the technology. Text generation tools such as ChatGPT, image creators and music composed using AI have sparked delight, alarm and legal battles as creators accuse them of scraping material without permission. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites. If this does not resolve the issue or you are unable to add the domains to your allowlist, please see this FAQ.

Displaying bias, foreign adversaries like China becoming dominant, and outsmarting humans were all top artificial intelligence concerns for members of Congress. WASHINGTON, D.C. – Congressional lawmakers spouted an array of concerns about artificial intelligence after OpenAI CEO Sam Altman told a Senate subcommittee that he saw problems the technology could create. "The overall risk is allowing China to win the AI race, because obviously, China would use the technology to further their aims of global ambition and to export their model of total techno-totalitarian control, which is nightmarish and would make Orwell blush," Republican Rep. Mike Gallagher said. "The other risk is that we don't maintain control of the technology, somehow it escapes our control." OpenAI CEO Sam Altman told a Senate subcommittee Tuesday that he had concerns about artificial intelligence's possibilities.

Reliable outlier detection is critical for real-world deployment of deep learning models. Although extensively studied, likelihoods produced by deep generative models have been largely dismissed as being impractical for outlier detection. First, deep generative model likelihoods are readily biased by low-level input statistics. Second, many recent solutions for correcting these biases are computationally expensive, or do not generalize well to complex, natural datasets. Here, we explore outlier detection with a state-of-the-art deep autoregressive model: PixelCNN++. We show that biases in PixelCNN++ likelihoods arise primarily from predictions based on local dependencies. We propose two families of bijective transformations -- ``stirring'' and ``shaking'' -- which ameliorate low-level biases and isolate the contribution of long-range dependencies to PixelCNN++ likelihoods. These transformations are inexpensive and readily computed at evaluation time. We test our approaches extensively with five grayscale and six natural image datasets and show that they achieve or exceed state-of-the-art outlier detection, particularly on datasets with complex, natural images. We also show that our solutions work well with other types of generative models (generative flows and variational autoencoders) and that their efficacy is governed by each model's reliance on local dependencies. In sum, lightweight remedies suffice to achieve robust outlier detection on image data with deep generative models.

This week, OpenAI released a new ChatGPT app meant for use on iPhones and iPads. It's free, syncs with your existing chat history and you can even talk to it -- sort of. While you can blurt out things for the app to transcribe and respond to, the experience is pretty basic; in other words, don't expect it to talk back at you like Siri or Alexa.