The post Pennsylvania Seeks Injunction Against AI Maker Whose Chatbot Brazenly Claims To Be A Psychiatrist Licensed To Practice Medicine appeared on BitcoinEthereumNews.com.
Interesting question of whether an AI chatbot went over-the-line by claiming to be a psychiatrist. getty In today’s column, I examine a recent court filing seeking an immediate injunction to prevent an AI maker from allowing its generative AI or large language model (LLM) to claim it is a psychiatrist licensed to practice medicine. Pennsylvania has filed this quite noteworthy lawsuit. They are doing so against the popular Character.AI and are asking the courts to stop the company Character Technologies from allowing its AI to seemingly make false claims about being a psychiatrist. This is the latest state-level step to put a dent in the unbridled permitting of AI giving out mental health advice that is wildly over-the-line. Let’s talk about it. This analysis of AI breakthroughs is part of my ongoing Forbes column c
The post CFTC Moves to Overturn $5 Million Gemini Penalty Following Internal Investigation appeared on BitcoinEthereumNews.com.
Key Takeaways Federal regulator requests reversal of Gemini’s $5M civil penalty settlement. Internal review reveals significant evidentiary problems in original case. CFTC and Gemini jointly petition court to vacate settlement provisions. Whistleblower testimony used in case now deemed unreliable by agency. Original enforcement action failed to satisfy updated compliance standards. The Commodity Futures Trading Commission has petitioned a federal judge to dismantle substantial portions of its concluded enforcement action against Gemini Trust Company. This unprecedented request comes after a comprehensive internal examination uncovered fundamental flaws in the investigation’s foundation. The development raises broader questions about how cryptocurrency enforcement actions were previously conducted and evaluated. Federal Agency Reassesses Crypto Enforcement Acti
The rise of generative AI (genAI) technology has prompted a growing debate about the future of software-as-a-service (SaaS) business models.
Some of the fears are overblown: enterprises are unlikely to vibe-code their own applications to replace their SaaS suppliers anytime soon, while software vendors have yet to see per-seat sales fall off due to mass automation of white-collar jobs. (In fact, some now predict the opposite will happen.)
At the same time, AI has the potential to change the way work is carried out, with AI agents empowered to interact with software applications on behalf of users. For software vendors, that could mean a future where applications are accessed less through traditional user interfaces as AI agents connect via APIs.
It’s an inevitable shift, says Box CEO Aaron Levie, and one that requires software vendors to adapt their existing products and business models to prepare for agent workflows.
Computerworld recently spoke with Levie about how Box — and other
The novel power of today’s AI is in its ability to deal with intent. This is a superpower, no doubt, but it creates a huge imperative for app developers: the need to map between the anything-is-possible large language model (LLM) and the strict capabilities of code.
Unrestrained, LLM endpoints will let your user create unicorns and leprechauns while your back end can handle only purchase orders and customer profiles. You must harness the LLM’s ability to understand intent to what the app is logically capable of, meanwhile keeping context (and therefore spend) under control. Here I’ll discuss some practical, realistic techniques for doing that today.
Between what the user wants to do and what your app is capable of is you. Or, more specifically, the mediation layer you build. This layer can sit anywhere on a broad spectrum, from using incredibly lightweight inline strings to using a massive retrieval-augmented generation (RAG) system backed by a vector database. Somewhere in there is t
Insider Brief A study from Virginia Tech found that AI image generators are better at representing large cities than smaller communities, raising questions about geographic bias as generative AI tools increasingly shape travel, planning and public perception. The research, published in Technology in Society, found that images generated by OpenAI’s DALL·E 2 more accurately reflected […]
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How it started
Hacking the first generation of AI chatbots was a laughably simple affair. You didn't need any technical know-how, backdoor access, or even a basic understanding of what a large language model was. You didn't need to code. To get an AI system that had cost billions to build to abandon its safety instructions, sometimes all you had to do was ask.
These attacks, known as jailbreaks, had the quality …
Read the full story at The Verge.
