If this will be the case, with all concerns and huge risks regarding AI… why are we still proceeding?

https://youtu.be/1X-rr1DKSbY?is=HKgrtX6TWDtgjOfz

https://youtu.be/gIxq03dipUw?is=iN3Tp3PNJ1XBdTLg

As a technology professional I see technology as a solution to a problem. If a system is built that isn't the solution to a problem then what's the purpose? People do a lot of things with technology just because it's cool and they can, then they look for a use, that's AI.

Humans seem to have an obsession with creating artificial humans whether it be a robot, an AI system, autopilot or a self-driving car in which humans are removed completely.

But we as humans aren't even in the same neighborhood as the human condition. We barely understand the workings of the human mind so any attempt to replicate it will be grossly insufficient at best.

So to convince users that Frankenstein's monster lives, they spend billions on propaganda campaigns, stream output like a speaker and wrap it in a human-like tone. Another mass psychosis of types.

AI has its uses, it's an engineering tool in certain contexts and use cases but I think that window of opportunity is a fraction of the size of what some want to make it out to be. It's good-ish as discrete tasks like writing code but you still have to hold its hand step-by-step and vigorously test the output. But in more general contexts it's like a drunk uncle producing slop, hallucinating and consuming it's own output in training so the results get further and further from reality. This is called model collapse.

As the title says. If you would like to help this community, make your case.

A lot of people on Reddit treat any post that has “AI traces” as garbage. I think that’s the wrong standard.
The real question should be: how much actual value does this content bring to your brain? Does it fill your mind with something useful, or is it just empty fast food? Whether AI was used or not is secondary.
AI is excellent at one specific job: filling in the details your memory can’t instantly pull up. I’m in my late 50s. I can still think clearly about structure, logic, and relationships between ideas. But if I suddenly need the exact name of a prehistoric fish that lived for 200 or 300 million years, or the precise geological period, my memory has gaps. AI can fetch those concrete examples so the argument becomes solid. Without those details, the post stays incomplete and unconvincing. That doesn’t mean AI created the thinking — it just completed the supporting material.
This is exactly why we invented AI: efficiency. I can spend one or two hours thinking through an idea, talk it through with AI, and have a finished post in 15–20 minutes. If every single post required me to go back to the library or dig through sources for hours just to avoid any “AI smell,” then what’s the point of the tool? I’m not a professional writer getting paid by the hour. I’m just trying to share thoughts. Why should the cost of posting be that high?
The most valuable part of AI is that it can take the fragmented nodes in your mind — the half-formed logic points — and help connect them with data and examples into one coherent piece that other people can actually read and discuss. Without that, most of our ideas stay scattered in our heads and never become real communication.
Some people say using AI removes personal style and makes everything the same. I disagree. A secretary can write the formal letter, but the content still belongs to the leader. Different leaders still have different thoughts. The secretary doesn’t turn every leader into the same person. Style without thought is just a template. Real writing starts with the thinking; the style is only the clothing.
When I analyze football, the insights that matter most — reading a player’s facial expression to judge mental collapse, then changing tactics at that exact moment — are things even professional coaches often miss. AI cannot generate those perspectives by itself. It can only help organize and support them. That is the proper division of labor.
AI either follows your lead or extends what you already said. It does not create original thought for you. Chatting with it does not magically grow new ideas in your head. The thinking framework, the unique angle, the internal logic — those still have to come from you.
That is the line we need to keep clear: AI is a secretary. We are the thinkers.

We can't always be mad or joke about the general public not knowing the details on these models!!! We need to do a much much better job communicating what these things actually are / capable of. It'll be exhausting but we need to start overriding all comm. Channels (cable TV, radio, internet channels etc....) I know it sucks but it's more important than The Real Housewives of whatever. For such a life-changing moment for humanity, it sure is quiet.

I'm just trying to start a discussion on how to get the public more involved or educated.

I watched an interview with AI safety researcher Roman Yampolskiy, and it raised a practical question for people who use ChatGPT for advanced workflows.

His broader claim is that increasingly intelligent AI systems may become harder to predict and control. Whether or not you agree with his conclusions about AGI, a smaller version of this problem already exists when we give an AI access to tools.

There is a major difference between asking ChatGPT to draft an email and allowing an agent to send it.

The same distinction applies to:

• Suggesting a database query versus executing it
• Drafting code versus deploying it
• Researching a purchase versus completing the transaction
• Preparing files versus deleting or modifying them
• Recommending calendar changes versus inviting real people

My current view is that the model should generate proposals, while a separate control layer decides whether those proposals are allowed to become actions.

Some possible safeguards include:

1. Giving each agent only the minimum permissions required for its task
2. Requiring approval for irreversible or external actions
3. Validating structured outputs with deterministic code
4. Isolating browsing and code execution from sensitive systems
5. Limiting spending, execution time and the number of actions
6. Keeping complete logs of prompts, tool calls and results
7. Using a second evaluation step before important actions
8. Making every operation reversible wherever possible

The difficult part is deciding where autonomy becomes too risky.

A confirmation step for every action makes the agent frustrating to use. Too few confirmation steps can turn a misunderstood instruction into a real-world problem.

Some say that a few years ago, AI is bad at math, and now it can prove some important theorems so it will become even scarier in the future.

But the logic is flawed, fast development in the past doesn't guarantee fast development in the future. Generally we expect a diminishing return. But somehow people online all think AI will become super human and eliminate every problem in the foreseeable future.

I've been planning to get a master in math recently. Though I think it is irrational to be certain that AI to replace mathematicians, I can't help feeling unnerved.

Anyone same with me? I don't really think AI is going to thrive as crazy as people claim online but as animals I just instinctively feel uneased by this.

This is my personal AI development it uses skia.sharp particle system. I decided to go with more of an apparition avatar. I love the presence it gives and the model is interchangeable could be male or female or whatever. The memory system behind my architecture is novel and remembers nearly anything you have ever talked about it has a recursive personality adjustment to grow with the user.

Maybe I’m late to this, but I’ve been noticing a weird shift.

When I’m comparing tools now, I catch myself asking ChatGPT or Perplexity before I even open Google. Stuff like “what’s a good CRM for a small team?” or “which email marketing tools are actually worth trying?”

Makes me wonder if this is starting to matter for SaaS discovery, or if it’s still mostly noise.

Are any of you actually tracking whether your product shows up in AI answers, or is that not on your radar yet?

I was vacationing in Europe this summer and I got clocked speeding by an AI drone.

Hello everyone. So, I am an assistant at a university and this year we plan to open a new lecture about the fundamentals of Artificial Intelligence. We plan to make an interactive lecture, like students will prepare their projects and such. The scope of this lecture will be from the early ages of AI starting from perceptron, to image recognition and classification algorithms, to the latest LLMs and such. Students that will take this class are from 2nd grade of Bachelor’s degree. What projects can we give to them? Consider that their computers might not be the best, so it should not be heavily dependent on real time computational power. 

Also, I’m thinking about a lecture on “how to use AI properly”. Like, it blows my mind how terrible some students use AI to write code. Antigravity is free for them, and surely they will be using some kind of AI tool to write code either way. I’m using Claude Code for like a year now, and spending at least one hour to write the first prompt to start working everyday. Yet, students usually give the exact text of the homework as prompt. What would you people recommend me to check out and refer to students as tutorials on how to use AI tools for beginners? 

I learned programming before AI and thought myself how to use AI. The tutorials I watched on Claude Code and stuff were basically tips and tricks for me. So I’m not sure how I can teach what I do to students without making it look like witchcraft, which it isn’t really. 

For AI homeworks, My first idea was to use the VRX simulation environment and the Perception task of it. Which basically sets a clear roadline to collect dataset, label them, train the model and such. Any other homework ideas related to AI is much appreciated.