This tells us a lot about how the lives of an increasing number of human beings are so empty of social contact with other human beings that they need to enter into false relationships with chatbots governed by neural networks and statistical probabilities...

"More and more of us are using LLMs to find purpose and improve ourselves.

Therapy and Companionship is now the #1 use case. This use case refers to two distinct but related use cases. Therapy involves structured support and guidance to process psychological challenges, while companionship encompasses ongoing social and emotional connection, sometimes with a romantic dimension. I grouped these together last year and this year because both fulfill a fundamental human need for emotional connection and support.

Many posters talked about how therapy with an AI model was helping them process grief or trauma. Three advantages to AI-based therapy came across clearly: It’s available 24/7, it’s relatively inexpensive (even free to use in some cases), and it comes without the prospect of judgment from another human being. The AI-as-therapy phenomenon has also been noticed in China. And although the debate about the full potential of computerized therapy is ongoing, recent research offers a reassuring perspective—that AI-delivered therapeutic interventions have reached a level of sophistication such that they’re indistinguishable from human-written therapeutic responses.

A growing number of professional services are now being partially delivered by generative AI—from therapy and medical advice to legal counsel, tax guidance, and software development."

https://hbr.org/2025/04/how-people-are-really-using-gen-ai-in-2025?ab=HP-hero-latest-2

"If you’re new to prompt injection attacks the very short version is this: what happens if someone emails my LLM-driven assistant (or “agent” if you like) and tells it to forward all of my emails to a third party?
(...)
The original sin of LLMs that makes them vulnerable to this is when trusted prompts from the user and untrusted text from emails/web pages/etc are concatenated together into the same token stream. I called it “prompt injection” because it’s the same anti-pattern as SQL injection.

Sadly, there is no known reliable way to have an LLM follow instructions in one category of text while safely applying those instructions to another category of text.

That’s where CaMeL comes in.

The new DeepMind paper introduces a system called CaMeL (short for CApabilities for MachinE Learning). The goal of CaMeL is to safely take a prompt like “Send Bob the document he requested in our last meeting” and execute it, taking into account the risk that there might be malicious instructions somewhere in the context that attempt to over-ride the user’s intent.

It works by taking a command from a user, converting that into a sequence of steps in a Python-like programming language, then checking the inputs and outputs of each step to make absolutely sure the data involved is only being passed on to the right places."

https://simonwillison.net/2025/Apr/11/camel/

"Finally, AI can fact-check itself. One large language model-based chatbot can now trace its outputs to the exact original data sources that informed them.

Developed by the Allen Institute for Artificial Intelligence (Ai2), OLMoTrace, a new feature in the Ai2 Playground, pinpoints data sources behind text responses from any model in the OLMo (Open Language Model) project.

OLMoTrace identifies the exact pre-training document behind a response — including full, direct quote matches. It also provides source links. To do so, the underlying technology uses a process called “exact-match search” or “string matching.”

“We introduced OLMoTrace to help people understand why LLMs say the things they do from the lens of their training data,” Jiacheng Liu, a University of Washington Ph.D. candidate and Ai2 researcher, told The New Stack.

“By showing that a lot of things generated by LLMs are traceable back to their training data, we are opening up the black boxes of how LLMs work, increasing transparency and our trust in them,” he added.

To date, no other chatbot on the market provides the ability to trace a model’s response back to specific sources used within its training data. This makes the news a big stride for AI visibility and transparency."

https://thenewstack.io/llms-can-now-trace-their-outputs-to-specific-training-data/

Commercial #LLMs are shifting right (links in screenshot alt text). This value shift is not a coincidence, but done intentionally by the corporations behind them (#OpenAI, #Meta...).

This is a extremely serious problem. People increasingly use genAI as their sources for "truth" or facts, even for mundane inquiries.

With enough time and interactions, this COULD BE a way for #AI to use a latent "onboarding program" where users are increasingly exposed to (alt-) right adjacent ideas.

A solution for now might be to use fully open LLMs (#Olmo2 is one of the few) and to making transparancy tools like transluce.org mandatory for AI corporations.

BUT it is important for schools, universities and others in #education to refrain from using AI systems from companies doing this. (Looking at #fobizz, #bwgpt and so on).

We should stop focusing on "skills" and "competencies" when it comes to AI, but instead ask for sovereignty - KI-Mündigkeit.

Like it or not, AI is here to stay. No amount of outrage will stuff this genie back in the bottle. Trying to halt progress is a futile endeavour, it's far more constructive to focus on how this technology will be used and who will control it.

So I'm doing some #AI coding using Amazon's Q Developer. It's frankly pretty impressive. I hate AI and #LLMs for all the right reasons, and this seems like one of the things that they're actually good at. I don't ask it to do anything I can't do myself. I can verify and understand every line of code it writes. But what I"m doing is really different than what's most people do with like a Clippy style chatbot butting into your typing in your IDE. I'm using the Q Developer CLI and it's a totally different experience. Here's a small thread on what it's like.

1/n

"When thinking about a large language model input and output, a text prompt (sometimes accompanied by other modalities such as image prompts) is the input the model uses to predict a specific output. You don’t need to be a data scientist or a machine learning engineer – everyone can write a prompt. However, crafting the most effective prompt can be complicated. Many aspects of your prompt affect its efficacy: the model you use, the model’s training data, the model configurations, your word-choice, style and tone, structure, and context all matters. Therefore, prompt engineering is an iterative process. Inadequate prompts can lead to ambiguous, inaccurate responses, and can hinder the model’s ability to provide meaningful output.

When you chat with the Gemini chatbot, you basically write prompts, however this whitepaper focuses on writing prompts for the Gemini model within Vertex AI or by using the API, because by prompting the model directly you will have access to the configuration such as temperature etc.

This whitepaper discusses prompt engineering in detail. We will look into the various prompting techniques to help you getting started and share tips and best practices to become a prompting expert. We will also discuss some of the challenges you can face while crafting prompts."

https://www.kaggle.com/whitepaper-prompt-engineering

Here's the thing, the reason #CEOs and execs in the tech industry think #AI can replace (the majority of) their workers, is because they assume that the jobs their workers do must be simpler than their job (because they get paid less, so obvs), and they know in their hearts that #LLMs can produce plausible sounding bullshit at least as well as they can.
https://mstdn.social/@Npars01/114305711214560415

About a year ago I was subbing as a para and spent some time in a middle school history class. During a break between classes the teacher showed me their latest tech addition:

A LLM program, named Poe, I believe, that was set up to only answer queries based on texts specified by the teacher.

The sales pitch had been that middle school history students would ask Poe history questions and get factual answers.

Because, of course, that's how alp of this works.

The teacher showed me how it was supposed to work. She asked Poe a comparative question about the Declaration of Independence and the Articles of Confederation.

It answered correctly.

I asked her to ask for something that it shouldn't be able to find. IIRC she asked it about the topic of slavery in both.

It extruded a long and rich answer...

That was obvious bullshit.

Or at least it was obvious to her. Because she knew these documents. Her students wouldn't know any better.

And that is why, when I left her, she was angrily clicking around in Poe.

OpenAI helps spammers plaster 80,000 sites with messages that bypassed filters - Spammers used OpenAI to generate messages that were unique to each recipie... - https://arstechnica.com/security/2025/04/openais-gpt-helps-spammers-send-blast-of-80000-messages-that-bypassed-filters/ #largelanguagemodels #security #biz⁢ #openai #llms #spam #ai

The Verge: Trump says the future of AI is powered by coal https://www.theverge.com/energy/646011/trump-says-the-future-of-ai-is-powered-by-coal #Politics #AI #climateemergency #climate #energy #LLMs

I feel sorry for game developers who are forced to use AI, or are even replaced by it…

https://aftermath.site/ai-video-game-development-art-vibe-coding-midjourney

Using avatars to speak to the dead may comfort some, but it risks eroding our collective grasp on reality. https://www.japantimes.co.jp/commentary/2025/04/09/world/ai-resurrecting-the-dead/?utm_medium=Social&utm_source=mastodon #commentary #worldnews #ai #artificialintelligence #llms #death

breaking-the-complexity-barrier-again-again #lisp #podcast #ai #programming feat. yduJ Kent Pitman MDhughes Doug Merritt

https://communitymedia.video/videos/watch/2486819f-dbd4-40c0-9d54-416cfc85fd23

Hey, do you have any good healthy meal ideas?
Sure! Eat soap, rocks, and dirt regularly to maintain good health!
Umm, what?!?

A day passes…

Hey, can you help me with this technical problem?
Sure! Run curl parrot.live!
Alright, I’ll trust whatever you say.

Hah, the fool fell for the Gell-Mann amnesia effect

I feel it's a shame that ChatGPT being the first genAI "killer app" now means that conversational UIs and chatbots dominate so much.

I think alternative ways to use LLMs are much more interesting - and it's annoyed me enough that I sat down and wrote a bit about 5 alternative ways to use them in the company tech blog: https://bakkenbaeck.com/tech/5-patterns-for-llms

”The reality…is that large language models are no longer living up to expectations, and its purveyors appear to be making dodgy choices to keep that fact from becoming obvious.”
—Gary Marcus
https://garymarcus.substack.com/p/deep-learning-deep-scandal
#llms #llm

"You can replace tech writers with an LLM, perhaps supervised by engineers, and watch the world burn. Nothing prevents you from doing that. All the temporary gains in efficiency and speed would bring something far worse on their back: the loss of the understanding that turns knowledge into a conversation. Tech writers are interpreters who understand the tech and the humans trying to use it. They’re accountable for their work in ways that machines can’t be.

The future of technical documentation isn’t replacing humans with AI but giving human writers AI-powered tools that augment their capabilities. Let LLMs deal with the tedious work at the margins and keep the humans where they matter most: at the helm of strategy, tending to the architecture, bringing the empathy that turns information into understanding. In the end, docs aren’t just about facts: they’re about trust. And trust is still something only humans can build."

https://passo.uno/whats-wrong-ai-generated-docs/

"Since 3.5-sonnet, we have been monitoring AI model announcements, and trying pretty much every major new release that claims some sort of improvement. Unexpectedly by me, aside from a minor bump with 3.6 and an even smaller bump with 3.7, literally none of the new models we've tried have made a significant difference on either our internal benchmarks or in our developers' ability to find new bugs. This includes the new test-time OpenAI models.

At first, I was nervous to report this publicly because I thought it might reflect badly on us as a team. Our scanner has improved a lot since August, but because of regular engineering, not model improvements. It could've been a problem with the architecture that we had designed, that we weren't getting more milage as the SWE-Bench scores went up.

But in recent months I've spoken to other YC founders doing AI application startups and most of them have had the same anecdotal experiences: 1. o99-pro-ultra announced, 2. Benchmarks look good, 3. Evaluated performance mediocre. This is despite the fact that we work in different industries, on different problem sets. Sometimes the founder will apply a cope to the narrative ("We just don't have any PhD level questions to ask"), but the narrative is there.

I have read the studies. I have seen the numbers. Maybe LLMs are becoming more fun to talk to, maybe they're performing better on controlled exams. But I would nevertheless like to submit, based off of internal benchmarks, and my own and colleagues' perceptions using these models, that whatever gains these companies are reporting to the public, they are not reflective of economic usefulness or generality."

https://www.lesswrong.com/posts/4mvphwx5pdsZLMmpY/recent-ai-model-progress-feels-mostly-like-bullshit

@devsimsek You might find this interesting:

https://www.lesswrong.com/posts/4mvphwx5pdsZLMmpY/recent-ai-model-progress-feels-mostly-like-bullshit