That's why AI pioneer Anthropic has imbued its generative AI, Claude, with a mix of 10 secret principles of fairness, which it unveiled in March. In a blog post Tuesday, the company further explained how its Constitutional AI system is designed and how it is intended to operate. Normally, when an generative AI model is being trained, there's a human in the loop to provide quality control and feedback on the outputs -- like when ChatGPT or Bard asks you rate your conversations with their systems. "For us, this involved having human contractors compare two responses," the Anthropic team wrote. "from a model and select the one they felt was better according to some principle (for example, choosing the one that was more helpful, or more harmless)."

It's easy to freak out about more advanced artificial intelligence--and much more difficult to know what to do about it. Anthropic, a startup founded in 2021 by a group of researchers who left OpenAI, says it has a plan. Anthropic is working on AI models similar to the one used to power OpenAI's ChatGPT. But the startup announced today that its own chatbot, Claude, has a set of ethical principles built in that define what it should consider right and wrong, which Anthropic calls the bot's "constitution." Jared Kaplan, a cofounder of Anthropic, says the design feature shows how the company is trying to find practical engineering solutions to sometimes fuzzy concerns about the downsides of more powerful AI. "We're very concerned, but we also try to remain pragmatic," he says.

People say things such as "AI art is garbage" and "It's plagiarism," but also "AI art is going to destroy creativity itself." These reactions are contradictory, but nobody seems to notice. AI is the bogeyman in the shadows: The obscurity, more than anything the monster has actually perpetrated, is the source of loathing and despair. Consider the ongoing feud between the Writers Guild of America and the Alliance of Motion Picture and Television Producers. The writers are on strike, arguing, among other things, that studios should not be able to use AI tools to replace their labor.

There's no shortage of concern about the speed with which some of the world's top artificial intelligence research labs are rolling out new AI tools that could change the way we live and work. The release of generative AI tools like ChatGPT to the public has prompted consternation about privacy and the spread of misinformation and bias. Not long after Microsoft released an AI-powered version of its search tool, Bing, to a select group of users in February, Bing threatened a philosophy professor with blackmail. A month later, some of the biggest names in tech signed an open letter urging the world's leading artificial intelligence labs to pause training their super-powerful computer systems for six months, arguing that recent advances in AI present "profound risks to society and humanity." In an op-ed in TIME the same day, Eliezer Yudkowsky, a decision theorist who leads the nonprofit Machine Intelligence Research Institute, urged the labs to shut down their research entirely.

In 2020, 5,000 customer service agents mostly based in the Philippines became guinea pigs in an experiment testing a question that by 2023 would feel urgent: Can an AI assistant based on OpenAI's text-generation technology make workers more productive? The automated helper offered agents suggested responses to small-business owners seeking tech support. The bot had been trained on previous customer chats, with a special emphasis on answers from top performers. And sure enough, when MIT and Stanford researchers analyzed the results, the AI tool had boosted the support team's productivity by 14 percent. When the National Bureau for Economic Research, a nonprofit, published those results in late April, they were quickly seized upon as confirmation that ChatGPT-style bots would indeed transform work. But for the researchers conducting the study, the results raised a provocative new question: Should the top workers whose chats trained the bot be compensated?

Showing off new tech to customers, the media and investors is key given the perception from analysts and industry observers that Google fumbled its March launch of the "Bard" chatbot, four months after OpenAI debuted ChatGPT and after Microsoft rebooted its Bing search engine with ChatGPT. For most of its two decades, Google has enjoyed a reputation as the undisputed leader in its core business areas. Google Search has no serious competitors, and Google Maps, Gmail, and the Chrome web browser dominate their product categories so deeply that antitrust authorities in multiple countries have launched investigations or filed lawsuits alleging that the company is breaking competition laws. That dominance allowed the company to grow ever bigger, hiring thousands of new employees in the past few years and expanding into new product areas.

A small but growing number of people in the US are receiving messages from their doctors drafted with the help of artificial intelligence – and some may not even know it. It is the first step in a larger plan to use OpenAI's large language models – the line of technology powering chatbots such as ChatGPT – within one of the largest US electronic health records systems operated by the company Epic.

There's a chance that ChatGPT knows personal details about you--and if it doesn't, it might just make something up. As OpenAI's generative text chatbot has boomed in popularity over the past six months, the risks of the system being trained on data vacuumed up from the web have become clearer. Data regulators around the world are investigating issues with how OpenAI gathered the data it uses to train its large language models, the accuracy of answers it provides about people, and other legal concerns about the use of its generative text systems. Europe's data regulators have joined forces to look at OpenAI after Italy temporarily banned ChatGPT from the country. And Canada is also investigating the technology's potential privacy risks.

Teamwork is a critical component of many academic and professional settings. In those contexts, feedback between team members is an important element to facilitate successful and sustainable teamwork. However, in the classroom, as the number of teams and team members and frequency of evaluation increase, the volume of comments can become overwhelming for an instructor to read and track, making it difficult to identify patterns and areas for student improvement. To address this challenge, we explored the use of generative AI models, specifically ChatGPT, to analyze student comments in team based learning contexts. Our study aimed to evaluate ChatGPT's ability to accurately identify topics in student comments based on an existing framework consisting of positive and negative comments. Our results suggest that ChatGPT can achieve over 90\% accuracy in labeling student comments, providing a potentially valuable tool for analyzing feedback in team projects. This study contributes to the growing body of research on the use of AI models in educational contexts and highlights the potential of ChatGPT for facilitating analysis of student comments.

Large Language Models (LLMs) have made rapid progress in recent months and weeks, garnering significant public attention. This has sparked concerns about aligning these models with human values, their impact on labor markets, and the potential need for regulation in further research and development. However, the discourse often lacks a focus on the imperative to widely diffuse the societal benefits of LLMs. To qualify this societal benefit, we assert that LLMs exhibit emergent abilities to humanize technology more effectively than previous technologies, and for people across language, occupation, and accessibility divides. We argue that they do so by addressing three mechanizing bottlenecks in today's computing technologies: creating diverse and accessible content, learning complex digital tools, and personalizing machine learning algorithms. We adopt a case-based approach and illustrate each bottleneck with two examples where current technology imposes bottlenecks that LLMs demonstrate the ability to address. Given this opportunity to humanize technology widely, we advocate for more widespread understanding of LLMs, tools and methods to simplify use of LLMs, and cross-cutting institutional capacity.