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
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
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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.
Alibaba has unveiled a new AI processor built specifically for AI agents, pairing the chip announcement with a multi-year silicon roadmap and a new large language model, signalling that the company is building an integrated AI stack, not just filling a gap left by US export controls. The Zhenwu M890, developed by Alibaba’s semiconductor subsidiary […]
The post Alibaba is designing AI chips around agents, and that changes what the race is actually about appeared first on AI News.
Using a large language model instead of me to write and then getting me to edit the result is a cynical way for my employer to cut my fee in half, says a freelance writer
In response to your article (‘Being human helps’: despite rise of AI is there still hope for Europe’s translators?, 8 May), I work freelance for a company that produces memoirs for its customers. I used to interview, then write. Now, I interview, a large language model writes, and I am paid half of my previous fee to edit the result.
It takes as long to edit the AI-generated text as it used to take me to write the memoir. There are several reasons for this.
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It's been almost three years since Silicon Valley started aggressively pushing large language model-based chatbots like ChatGPT as the supposedly inevitable future of everything, and there's no group that has felt the pressure quite like Gen Z.
Like with many tech trends before it, it's no surprise that young people are among the biggest adopters of AI chatbot tools. But contrary to the tales spun by tech companies like OpenAI and Google, polling data shows that Gen Z students and workers are a big part of the wider cultural backlash against AI. And even as they utilize these tools, vast swaths of young people are deeply acrimonious and eve …
Read the full story at The Verge.
What if a language model had never heard of the internet, smartphones, or even World War II? That’s not a hypothetical — it’s exactly what a team of researchers led by Nick Levine, David Duvenaud, and Alec Radford has built. They call it talkie, and it may be the most historically disciplined large language model […]
The post Meet Talkie-1930: A 13B Open-Weight LLM Trained on Pre-1931 English Text for Historical Reasoning and Generalization Research appeared first on MarkTechPost.
If you have ever stared at thousands of lines of integration test logs wondering which of the sixteen log files actually contains your bug, you are not alone — and Google now has data to prove it. A team of Google researchers introduced Auto-Diagnose, an LLM-powered tool that automatically reads the failure logs from a […]
The post Google AI Releases Auto-Diagnose: An Large Language Model LLM-Based System to Diagnose Integration Test Failures at Scale appeared first on MarkTechPost.
