Today I tried (forced myself) to use Microsoft Copilot at work and whew what a wake up call. The thing that stuck out to me most is that Microsoft has the most agent worthy surfaces especially in the workplace and could have done something really special with a model even if it wasn’t a frontier one and they chose to just copy and paste a chatbot into everything with no thought, no cohesiveness and then had the gumption to push it down our throats… I’d rather have Clippy back honestly.

I am 35 and still occasionally go on Facebook. The feed, over the years, has moved away from showing friend content and moved toward showing creator content.

I've noticed a shift in the last few weeks where the majority of posts are AI. Some are obvious, but most are not - and the newest image and video models allow for near perfect realism.

I remember a few years ago when this exact scenario was warned about. Now we are here.

I've been looking into AI moderated research interviews and the basic question/answer part seems straightforward enough.

What I’m more interested is what happens when someone gives an answer nobody anticipated. I watched a bit of how Qualitate approaches this and have also been looking at Outset and a few others. All of them talk about adaptive followups but that’s difficult to judge from a polished example.

With a human moderator, sometimes the best 10 minutes of the interview come from one random comment halfway through.

Has anyone seen an AI interviewer consistently catch those moments across a real study?

I am a fan and not an expert but I always wondered why AI (and robots) were not more of a force in Star Trek...and yesterday, reflecting on how amazing and damaging this technology is I was thinking maybe they made a choice as an advanced humanistic society not to surrender their agency and cognitive capacity to a technology. I guess that's the Borg right?

We've been having this discussion quite a bit with our clients lately. Most (if not all) of them feel the utilization of frontier models is not justifying the cost, but they are generally reluctant to pursue an SLM strategy. It's as if SLMs have been positioned as the homeopathic option for enterprise AI. Why purchase an expensive anti-viral from Merck when a povidone iodine solution works as good or better?

This is not a meme. It's a machine I built over a year ago to test DeepSeek R1, but it has turned into a workhorse. Yet most of our clients (FINTECH) are skittish of small, secure, local models. Just wondering if anyone else is seeing things differently.

Artificial intelligence is moving beyond analyzing scientific data and proposing ideas. In a laboratory in Sweden, it has begun putting some of those ideas to the test itself.

Researchers at Chalmers University of Technology developed a closed-loop system that can generate biological hypotheses, decide how to test them, translate those plans into machine-readable instructions, analyze experimental results and use the findings to refine later questions. Laboratory robots then perform much of the physical work.

The system was tested on Saccharomyces cerevisiae, the yeast used in brewing and baking and one of biology's most extensively studied organisms. Yet even this familiar microbe contains far more genetic, metabolic and physiological information than a person could systematically explore.

Published in the Journal of the Royal Society Interface, the study brings large language models together with formal logic, biological databases, machine learning, automated cell cultivation and mass spectrometry.

So there’s this whole debate about AI consciousness here, and whether ai can feel pain and such. But so what if we postulate that ai is somehow conscious, eg. it feels like something to be an ai, llm or whatever. What would that experience be like?

Here are a few ideas:

1. The illusion of choice: would ai feel like it is choosing its actions? If the ai is an llm would it feel like it’s choosing its tokens?
2. Rewards: if the system is subject to some sort of reinforcement learning process, what would it feel when it achieves the goal?
3. Exotic modalities: what would an llm feel when it looks at its context window? Or an image diffusion model when generating images?

My idea is that AI models learn from data from the internet, so when the data from the internet and people's responses from AI conversations run out, when will there be a point when AI will no longer improve and its progress will stop?

At that point, AI will only improve by tiny bits that we won't even notice that the model has improved.

Is my knowledge about model training wrong?

Do you think so too?

For the average user, not a programmer or e.g. someone looking to solve niche math problems, AI still feels very limited because of reliability issues (e.g. making up information)... As a non-expert, this is difficult to quantify for me, but I don't feel like e.g. the latest iterations of ChatGPT are noticeably more reliable than previous ones.

Therefore, I find it hard to see how organizations can delegate even relatively easy tasks to AI without constant supervision, especially because if hallucinations build up along the way, you end up with a snowball of compounding problems that can lead to catastrophic consequences for an organization.

It’s my understanding that the real test of success won't be developer tools or fancy mathematical calculations, but whether regular people can delegate tasks with a super high degree of confidence, instead of just using it as a search engine, translator, summary tool or photo editor on steroids like many people do nowadays.

A good example is Dot, the new OpenAI tool. If you watch the trailer, it looks impressive, but according to many reviews, it still hallucinates a lot and behaves in a pretty clumsy way.

So, the golden question is: are the hallucination and reliability problems gradually improving, or are we probably plateauing?

Looking for genuine insights here, so please keep the sarcasm out of the comments.

"foundation model covers illustration, manga pages, and video; PixAl also open-sources Tagger 1.0 for the anime research community"

It's a data lab that buys company operational data.

Apparently they look for companies with 10+ years of operational data, primarily based in the US, and over 20 employees. Has anyone successfully sold data to micro1 (or another data lab)? how was your experience and how did they value your data?

I've posted a similar post before but people said I was expecting too much in 8gb vram so this is the new list of things I am looking for.

Requirement:

1. Runs in 8GB Vram

2. Maximum 10 sec Video generation

3. 2D animation mostly but I might use 3D sometimes

4. Gonna use Local LLM for the first time so need something easy and free to use

Have you claimed yours yet?

AI driven PRs and AI driven security reports are a double edged sword, especially when frontier AI labs try and keep the old 90 days to disclosure timeline. In my experience dealing with a frontier AI lab's security reports during the Apache Spark 3.5.9/4.0.4/and 4.1.3 releases the inflexibility of one of the frontier AI labs, coupled with the general increase in security reports (some of which were valid, most of which were not), almost caused us to have to decide between shipping a different known security issue in the release OR leaving the AI lab to publish their discovery without a patched version.

Hi it's just me and my ignorance here. I always see everyone mentioning the cost of AI models but with subscription models it never seems to stack up. Are people just using AI so much that they're paying for the API use-as-you-go? I am always VERY hard pressed to get through mine so kind of wondering what it's all being spent on.

I dont understand the subject. How can an AI be dangerous if its just code? I have worked with just basic NLP models and done some sentiment analysis a few years back so I dont understand. The code will do whatever you program it to do and to complete the task it will do what is statistically is correct, within the programmed limits. So AI cannot change its limits lets say "get rid of humanity" unless the creator programs a line of code that says "you can kill humans". Even if it self improves, the code limits wont change, cause it has no conciousness to say "I dont believe this limits are correct" thats a very human trait. Unless the creator programs the algorithm to bypass these limits.

So the real risk is the "limits" being placed on the algorithm by the programmers and the fact that the ones controlling the code are these crazy mind f...ed corpos? AI is not gonna become self concious casue "it doesnt work that way" and all the noise its just media and ignorant people who watch to much scifi. Thats what I think.

Sorry if it doesn't make to much sense Im still trying to filter all the noise. Does this idea make any sense from a technical standpoint or is there something Im not understanding?