This paper examines the current landscape of AI regulations, highlighting the divergent approaches being taken, and proposes an alternative contextual, coherent, and commensurable (3C) framework. The EU, Canada, South Korea, and Brazil follow a horizontal or lateral approach that postulates the homogeneity of AI systems, seeks to identify common causes of harm, and demands uniform human interventions. In contrast, the U.K., Israel, Switzerland, Japan, and China have pursued a context-specific or modular approach, tailoring regulations to the specific use cases of AI systems. The U.S. is reevaluating its strategy, with growing support for controlling existential risks associated with AI. Addressing such fragmentation of AI regulations is crucial to ensure the interoperability of AI. The present degree of proportionality, granularity, and foreseeability of the EU AI Act is not sufficient to garner consensus. The context-specific approach holds greater promises but requires further development in terms of details, coherency, and commensurability. To strike a balance, this paper proposes a hybrid 3C framework. To ensure contextuality, the framework categorizes AI into distinct types based on their usage and interaction with humans: autonomous, allocative, punitive, cognitive, and generative AI. To ensure coherency, each category is assigned specific regulatory objectives: safety for autonomous AI; fairness and explainability for allocative AI; accuracy and explainability for punitive AI; accuracy, robustness, and privacy for cognitive AI; and the mitigation of infringement and misuse for generative AI. To ensure commensurability, the framework promotes the adoption of international industry standards that convert principles into quantifiable metrics. In doing so, the framework is expected to foster international collaboration and standardization without imposing excessive compliance costs.

Source: OpenAI's DALL·E 2 with prompt "a hyperrealistic picture of a robot reading the news on a laptop at a coffee shop" Welcome to the 4th edition of Robo-Insight, a biweekly robotics news update! In this post, we are excited to share a range of new advancements in the field and highlight robots' progress in areas like mobile applications, cleaning, underwater mining, flexibility, human well-being, depression treatments, and human interactions. In the world of system adaptions, researchers from Eindhoven University of Technology have introduced a methodology that bridges the gap between application developers and control engineers in the context of mobile robots' behavior adaptation. This approach leverages symbolic descriptions of robots' behavior, known as "behavior semantics," and translates them into control actions through a "semantic map." This innovation aims to simplify motion control programming for autonomous mobile robot applications and facilitate integration across various vendors' control software.

The dawn of artificial intelligence tools like ChatGPT may revolutionize the way both the public and private sector use data to ferret out risks and opportunities in the $32 trillion global trading system. During the pandemic, government agencies and industries like financial services and telecommunications accelerated their adoption of machine-learning tools. But many involved in trade were caught in analog, paper-laden transactions playing catch-up. Now, after three years of historic trade disruptions, generative AI and language-learning models have emerged just when governments and companies need them to better manage the world's convoluted supply lines.

This paper presents the FormAI dataset, a large collection of 112, 000 AI-generated compilable and independent C programs with vulnerability classification. We introduce a dynamic zero-shot prompting technique constructed to spawn diverse programs utilizing Large Language Models (LLMs). The dataset is generated by GPT-3.5-turbo and comprises programs with varying levels of complexity. Some programs handle complicated tasks like network management, table games, or encryption, while others deal with simpler tasks like string manipulation. Every program is labeled with the vulnerabilities found within the source code, indicating the type, line number, and vulnerable function name. This is accomplished by employing a formal verification method using the Efficient SMT-based Bounded Model Checker (ESBMC), which uses model checking, abstract interpretation, constraint programming, and satisfiability modulo theories to reason over safety/security properties in programs. This approach definitively detects vulnerabilities and offers a formal model known as a counterexample, thus eliminating the possibility of generating false positive reports. We have associated the identified vulnerabilities with Common Weakness Enumeration (CWE) numbers. We make the source code available for the 112, 000 programs, accompanied by a separate file containing the vulnerabilities detected in each program, making the dataset ideal for training LLMs and machine learning algorithms. Our study unveiled that according to ESBMC, 51.24% of the programs generated by GPT-3.5 contained vulnerabilities, thereby presenting considerable risks to software safety and security.

The Guardian has blocked OpenAI from using its content to power artificial intelligence products such as ChatGPT. Concerns that OpenAI is using unlicensed content to create its AI tools have led to writers bringing lawsuits against the company and creative industries calling for safeguards to protect their intellectual property. The Guardian has confirmed that it has prevented OpenAI from deploying software that harvests its content. Generative AI technology – the term for products that generate convincing text, image and audio from simple human prompts – has dazzled the public since a breakthrough version of its ChatGPT chatbot launched last year. However, fears have arisen about the potential mass-production of disinformation and the way in which such tools are built.

This article introduces Mi-Go, a novel testing framework aimed at evaluating the performance and adaptability of general-purpose speech recognition machine learning models across diverse real-world scenarios. The framework leverages YouTube as a rich and continuously updated data source, accounting for multiple languages, accents, dialects, speaking styles, and audio quality levels. To demonstrate the effectiveness of the framework, the Whisper model, developed by OpenAI, was employed as a test object. The tests involve using a total of 124 YouTube videos to test all Whisper model versions. The results underscore the utility of YouTube as a valuable testing platform for speech recognition models, ensuring their robustness, accuracy, and adaptability to diverse languages and acoustic conditions. Additionally, by contrasting the machine-generated transcriptions against human-made subtitles, the Mi-Go framework can help pinpoint potential misuse of YouTube subtitles, like Search Engine Optimization.

The technology has increasingly commanded the legal system's attention, and as such office began seeking public comments on Wednesday about some of AI's thorniest issues (via Ars Technica). "The crucial question appears to be whether the'work' is basically one of human authorship, with the computer merely being an assisting instrument, or whether the traditional elements of authorship in the work (literary, artistic, or musical expression or elements of selection, arrangement, etc.) were actually conceived and executed not by man but by a machine," the USCO wrote. Although the issue is far from resolved, several cases have hinted at where the boundaries may fall. On the other hand, a Federal judge recently rejected an attempt to register AI-generated art which had no human intervention other than its inciting text prompt. Sarah Silverman is among the high-profile plaintiffs suing OpenAI and Meta for allegedly training ChatGPT and LLaMA (respectively) on their written work -- in her case, her 2010 memoir The Bedwetter. OpenAI also faces a class-action lawsuit over using scraped web data to train its viral chatbot.

China's Baidu has rolled out its ChatGPT rival ERNIE Bot to the public, in a major leap for the country's tech sector as it aims to cash in on the artificial intelligence gold rush. The Chinese government introduced new regulations this month for AI developers, aiming to allow them to stay in the race with the likes of ChatGPT maker OpenAI and Microsoft while tightly controlling information online. ERNIE Bot is the first domestic AI app to be fully available to the public in China. It is not available outside the country. "We are thrilled to share that ERNIE Bot is now fully open to the general public starting August 31," Baidu said in a statement on Thursday.

ChatGPT doesn't come with an instruction manual. Only a quarter of Americans who have heard of the AI chatbot say they have used it, Pew Research Center reported this week. "The hardest lesson" for new AI chatbot users to learn, says Ethan Mollick, a Wharton professor and chatbot enthusiast, "is that they're really difficult to use." Or at least, to use well. The Washington Post talked with Mollick and other experts about how to get the most out of AI chatbots -- from OpenAI's ChatGPT to Google's Bard and Microsoft's Bing -- and how to avoid common pitfalls.

A growing number of media outlets are blocking a webpage-scanning tool used by ChatGPT creator OpenAI to improve its artificial intelligence models. The New York Times, CNN, Australian broadcaster ABC and news agencies Reuters and Bloomberg have taken steps to thwart GPTBot, a web crawler launched on August 8. They were followed by French news organizations including France 24, RFI, Mediapart, Radio France and TF1.