That's not creative prompt. That's a driving prompt to get it to start its engine.

You could do that nowadays and while it may spend $1,000 to $100,000 worth of tokens. It will create something humans haven't done before as long as you set it up with all its tool calls/permissions.

LLMs are prompted by humans and the right query may make it think/behave in a way to create a novel solution.

Then there's a third factor now with Agentic AI system loops with LLMs. Where it can research, try, experiment in its own loop that's tied to the real world for feedback.

Agentic + LLM + Initial Human Prompter by definition can have it experiment outside of its domain of expertise.

So that's extending the "LLM can't create novel ideas" but I don't think anyone can disagree the three elements above are enough ingredients for an AI to come up with novel ideas.

This is the kind of stuff that can only be done so quickly by having more and more people brought into the field to try these ideas out. The more people the more permutations of ideas.

In my opinion its short sighted, because if we care about security, then we should care about user security and privacy as well. Because if the security admin has the ability to packet inspect stuff, so does a potential malicious app.

Then train the model on movies that are color and then turn them black and white.

That way you can train temporal coherence.

But fundamentally no difference because you are getting a discounted price in exchange for the soft locked feature. So 2 and 3 are the same imo.

He also has a Mastodon account where he sometimes goes over the implications of LLMs and in this post is his musings of how he sees it's current potential and possible impact in the near future for mathematics.

"The 2023-level AI can already generate suggestive hints and promising leads to a working mathematician and participate actively in the decision-making process. When integrated with tools such as formal proof verifiers, internet search, and symbolic math packages, I expect, say, 2026-level AI, when used properly, will be a trustworthy co-author in mathematical research, and in many other fields as well."

Comments URL: https://news.ycombinator.com/item?id=36399705

Points: 3

# Comments: 1

The main concern though for me is long term vision for the site. I'd really like it to stay user/community oriented and hopefully the users can pay for the costs with donations/contributions.

Me asking ChatGPT:

In this context, RoR most likely refers to Ruby on Rails, which is a web application framework written in the Ruby programming language. The second comment is suggesting that people had similar dismissive comments about Ruby on Rails (RoR) when it was first introduced, but it has since become a widely used and respected framework in web development. The comment implies that similar trends may apply to AI tools used for web design.

There's all kinds of brains in this world from all kinds of life. A dog can learn. A cat can learn. And they don't learn like a human would.

The only thing I'd use Whisper for is transcription part of it. Then use ChatGPT for the translation.

Google does have their TPUs(Tensor Processing Units), however it is not cost efficient budget wise, so unless you have some kind of deal with Google or compute credits it doesn't make sense. They have pods upon pods of TPU clusters though so the main selling point of TPU training is that you can get your training done really fast with just the ease of scaling your workload to more TPUs.

So if you needed a big model like GPT-3 trained in a single day, you could spend an ungodly amounts of money and get it done with Google TPUs. Otherwise if you can wait weeks or months you can go with the standard Nvidia data centre solution and it'd be cheaper at the end by a significant margin.

Here is his public bio to gain a sense of his perspective.

https://www.mariushobbhahn.com/aboutme/

>I'm currently doing a Ph.D. in ML at the International Max-Planck research school in Tübingen. My focus is on Bayesian ML and I'm exploring its role in AI alignment but I'm also exploring non-Bayesian approaches. I want to become an AI safety researcher/engineer

Comments URL: https://news.ycombinator.com/item?id=34030125

Points: 3

# Comments: 2

Both of them are entrepreneurs and like doing startups. Both of their goals are to take a startup from idea to $1 Billion+ IPO in 2 years and exit and then start the next start-up. If they don't reach 1 Billion IPO they just exit.

After 20 years. Jim and Alice have both attempted 10 startups.

Jim has reached the goal 2 out of 10 times. While Alice has reached the goal 8 out of 10 times.

Would it be a fallacy to bet on Alice if you had to invest in either Jim or Alice's startup?

Spend a week preparing for a trip on how to navigate the city before actually going.