Labeling data is essential for training text classifiers but is often difficult to accomplish accurately, especially for complex and abstract concepts. Seeking an improved method, this paper employs a novel approach using a generative language model (GPT-4) to produce labels and rationales for large-scale text analysis. We apply this approach to the task of discovering public value expressions in US AI patents. We collect a database comprising 154,934 patent documents using an advanced Boolean query submitted to InnovationQ+. The results are merged with full patent text from the USPTO, resulting in 5.4 million sentences. We design a framework for identifying and labeling public value expressions in these AI patent sentences. A prompt for GPT-4 is developed which includes definitions, guidelines, examples, and rationales for text classification. We evaluate the quality of the labels and rationales produced by GPT-4 using BLEU scores and topic modeling and find that they are accurate, diverse, and faithful. These rationales also serve as a chain-of-thought for the model, a transparent mechanism for human verification, and support for human annotators to overcome cognitive limitations. We conclude that GPT-4 achieved a high-level of recognition of public value theory from our framework, which it also uses to discover unseen public value expressions. We use the labels produced by GPT-4 to train BERT-based classifiers and predict sentences on the entire database, achieving high F1 scores for the 3-class (0.85) and 2-class classification (0.91) tasks. We discuss the implications of our approach for conducting large-scale text analyses with complex and abstract concepts and suggest that, with careful framework design and interactive human oversight, generative language models can offer significant advantages in quality and in reduced time and costs for producing labels and rationales.

Despite its successes, to date Artificial Intelligence (AI) is still characterized by a number of shortcomings with regards to different application domains and goals. These limitations are arguably both conceptual (e.g., related to underlying theoretical models, such as symbolic vs. connectionist), and operational (e.g., related to robustness and ability to generalize). Biologically inspired AI, and more specifically brain-inspired AI, promises to provide further biological aspects beyond those that are already traditionally included in AI, making it possible to assess and possibly overcome some of its present shortcomings. This article examines some conceptual, technical, and ethical issues raised by the development and use of brain-inspired AI. Against this background, the paper asks whether there is anything ethically unique about brain-inspired AI. The aim of the paper is to introduce a method that has a heuristic nature and that can be applied to identify and address the ethical issues arising from brain-inspired AI. The conclusion resulting from the application of this method is that, compared to traditional AI, brain-inspired AI raises new foundational ethical issues and some new practical ethical issues, and exacerbates some of the issues raised by traditional AI.

This is an edition of The Atlantic Daily, a newsletter that guides you through the biggest stories of the day, helps you discover new ideas, and recommends the best in culture. Yesterday, the OpenAI CEO Sam Altman testified before a Senate judiciary subcommittee about the "significant harm" that ChatGPT and similar generative-AI tools could pose to the world. When I asked Damon Beres, The Atlantic's technology editor, for his read on the hearing, he noted that Altman's emphasis on the broader existential risks of AI might conveniently elide some of the more quotidian problems of this new technology. I called Damon today to talk about that, and to see what else has been on his mind as he follows this story. Isabel Fattal: Can you talk a bit more about Altman's emphasis on the existential possibilities of AI, and what that focus might leave out?

On Tuesday, lawmakers, A.I. experts, and the guy chiefly responsible for ChatGPT gathered in the same room to swap analogies for just how dramatically A.I. is about to change our lives. The invention of the internet, the cell phone, and airplanes all made the list. For a Senate Judiciary Committee hearing ostensibly concerned with the dangers A.I. might pose to the world, everyone seemed to get along quite well. At one point Sen. John Kennedy of Louisiana asked Sam Altman, the CEO of ChatGPT maker OpenAI, if he could recommend some people to oversee a new agency to oversee A.I.--that is, to pick his own regulators. Then again, Altman was doing an exceptional job projecting a self-critical persona.

Samsung and LG have a long-running rivalry, both Korean corporations, both make TVs, speakers, freezers, toothpaste (maybe?) and the rest. It's a frosty relationship, with many trade shows revealing new TV products from both companies with nigh-on identical specifications and sizes. So it's a bit of a shock to hear from Reuters that Samsung has inked a deal with LG to buy its white OLED (WOLED) TV panels. The plan, according to the report, is for LG Display to supply two million panels next year, then three million and five million, respectively in 2025 and 2026. These high-end white OLED panels would be 77 and 83 inches, so they're likely to be in Samsung's most premium TVs.

Sam Altman, the CEO of artificial intelligence lab OpenAI, told a Senate panel he welcomes federal regulation on the technology "to mitigate" its risks. A software company is looking to use artificial intelligence (AI) to help companies mitigate and avoid human rights risks in their supply chain. "When it comes to transparency in supply chains, there is such an enormous amount of data that is being spread not just in spreadsheets but also through social that we can start to use to identify and zero in," Justin Dillon, CEO and Founder of FRDM, told Fox News Digital, adding that it's "early, early days" for the technology and methods his company uses. Any AI technology requires significant amounts of data to analyze and process, and Dillon pointed to a treasure trove of data available on social media that his company can use to help map out problematic hotspots in supply chains -- areas that companies can then work to avoid and help create more ethical routes. Dillon related a story from a father in Australia who was talking about using "social listening," which is the analysis of conversations and trends related to different brands.

Senators on Tuesday got the green light to impose significant federal regulation on artificial intelligence systems, not just from two industry giants, but from an AI expert who warned that the fate of the nation may depend on tough AI rules from Congress. A Senate Judiciary subcommittee heard from OpenAI CEO Sam Altman and IBM Chief Privacy & Trust Officer Christina Montgomery, who both invited federal oversight of AI even though they split on whether a new federal agency is needed. In between those witnesses sat Gary Marcus, the New York University professor emeritus and leader of Uber's AI labs from 2016 to 2017, who issued a stark warning that human life is about to be upended by this unpredictable technology. "They can and will create persuasive lies at a scale humanity has never seen before," Marcus warned of generative AI systems. "Outsiders will use them to affect our elections, insiders to manipulate our markets and our political systems. Marcus warned that AI systems that do severe damage to humans' trust in each other have already been released and that the damage is already mounting. Gary Marcus, professor emeritus at New York University, speaks during a Senate Judiciary subcommittee hearing in Washington, D.C., on Tuesday, May 16, 2023. "A law professor, for example, was accused by a chatbot of sexual harassment.

Sam Altman, the chief executive of ChatGPT's OpenAI, has told legislators in the United States that government regulation of artificial intelligence is "critical" because of the potential risks it poses to humanity. Altman used his appearance on Tuesday in front of a US Senate judiciary subcommittee to urge Congress to impose new rules on big tech, despite deep political divisions that for years have blocked legislation aimed at regulating the internet. "If this technology goes wrong, it can go quite wrong," Altman, who has become the global face of AI, told the hearing. "OpenAI was founded on the belief that artificial intelligence has the potential to improve nearly every aspect of our lives, but also that it creates serious risks," he said, but given concerns about disinformation, job security and other dangers, "we think that regulatory intervention by governments will be critical to mitigate the risks of increasingly powerful models". Altman proposed the formation of a US or global agency that would licence the most powerful AI systems and have the authority to "take that licence away and ensure compliance with safety standards".

Application Tracking Systems (ATS) have allowed talent managers, recruiters, and college admissions committees to process large volumes of potential candidate applications efficiently. Traditionally, this screening process was conducted manually, creating major bottlenecks due to the quantity of applications and introducing many instances of human bias. The advent of large language models (LLMs) such as ChatGPT and the potential of adopting methods to current automated application screening raises additional bias and fairness issues that must be addressed. In this project, we wish to identify and quantify the instances of social bias in ChatGPT and other OpenAI LLMs in the context of candidate screening in order to demonstrate how the use of these models could perpetuate existing biases and inequalities in the hiring process.

Large language models, e.g. ChatGPT are currently contributing enormously to make artificial intelligence even more popular, especially among the general population. However, such chatbot models were developed as tools to support natural language communication between humans. Problematically, it is very much a ``statistical correlation machine" (correlation instead of causality) and there are indeed ethical concerns associated with the use of AI language models such as ChatGPT, such as Bias, Privacy, and Abuse. This paper highlights specific ethical concerns on ChatGPT and articulates key challenges when ChatGPT is used in various applications. Practical commandments for different stakeholders of ChatGPT are also proposed that can serve as checklist guidelines for those applying ChatGPT in their applications. These commandment examples are expected to motivate the ethical use of ChatGPT.