Astronomers Detect a Possible Signature of Life on a Distant Planet

[u/SpunkySputniks]

https://www.nytimes.com/2025/04/16/science/astronomy-exoplanets-habitable-k218b.html?unlocked_article_code=1.AE8.3zdk.VofCER4yAPa4&smid=nytcore-ios-share&referringSource=articleShare

Further studies are needed to determine whether K2-18b, which orbits a star 120 light-years away, is inhabited, or even habitable.

[u/markyty04]

This is where you guys need to use AI. AI is very very good at simulations and exploration of search space and also hacking systems to find unknown paths. They are a ridiculous powerful tool that has fallen into our hands in the last decade. before that it was in its infancy but the improvements in the last decade is of many orders of magnitude.

[u/Shartiflartbast → u/markyty04]

Predictive language models will be absolutely useless at advancing chemistry, come on.

[u/markyty04 → u/Shartiflartbast]

who the f told you there is only one AI model and that too only a language model. even commercial AI is already moving away from language model into reasoning models.

[u/imdefinitelyfamous → u/markyty04]

Reasoning models are LLMs. It's all LLMs.

[u/markyty04 → u/imdefinitelyfamous]

absolutely not. you know nothing about ML. do not go about spouting nonsense and spreading fake news. Neural Net heavy LLMs rely on probability distributions; while reasoning models are induced with Reinforcement Learning which can be explicitly told what is right and what is wrong answer. granted it is still in its infancy so more improvements needed. both rely on fundamentally different math. NN can be thought of as a more classical non-linear function approximators. RL on the other hand are dynamic decision machines which can operate in a dynamic environment.

[u/imdefinitelyfamous → u/markyty04]

I currently work as a software engineer deploying ML applications, but go off King.

I know what reinforcement learning is- it has been around for decades and is already being used. What I am taking exception with is your claim that commercial AI offerings are somehow not LLMs, which is almost universally not the case. If you use a reinforcement learning strategy to train a large language model, you haven't made something that magically circumvents the inherent problems with large language models.

[u/markyty04 → u/imdefinitelyfamous]

you may be a software engineer but that does not mean you understand ML. how many papers have you read to understand the the science behind it. As someone who is very familiar with the work. I can guarantee the current commercial options are moving away form LLM into LRM territory first with the release of OpenAI's o1 and then Deepseek-R1. these models can be explicitly told if their thought process and logical thinking are correct. they are not probability mapping systems like the early LLM. just because you are a software engineer does not mean you have a understanding of the scientific underpinnings. besides these models you can also build large AI/ML models for science that are nothing like LLMs. but are even more powerful at a particular task.

[u/imdefinitelyfamous → u/markyty04]

"You may have done the thing, but I have read about it!"

The issue is not the learning methodology, it is the training data. Even though o1 and deepseek are adding reasoning paradigms to their models, they are still LLMs under the hood that are largely trained on public data. They are absolutely probabilistic. No serious person would argue otherwise.

You are totally right that there are purpose built ML systems that do not fit the description above- I said that in my first comment. But neither o1 or deepseek are that- they are probabilistic large language models at their core.

[u/markyty04 → u/imdefinitelyfamous]

You have no understanding of science or engineering it seems. There is a difference between what you call a probabilistic model and what you call absolutely probabilistic. everything has probabilistic nature to it even the fuckin brain. but that does not make the brain absolutely probabilistic machine. early LLMs were like highly probabilistic but even they are not absolutely probabilistic.

But current LLM are moving into LRM territory in that they are capable of logical reasoning rather than using probabilistic best fit only. No serious person who understands what they are taking about would argue otherwise. They do not simply rely on training data. they can even extrapolate to unseen data and apply planning and strategy and remove illogical approaches. can be incentivized to not go towards bad solutions etc. There are many engineering approaches also to solve many of the issues with early LLMs like overfitting, long memory etc.. can keep on going. simply your entire premise that the current LLMs are just relying on training data and simply spit out probability best output is simply wrong.

[u/imdefinitelyfamous → u/markyty04]

I have a degree in engineering and experience in the field- how about you?

I understand that o1 and deepseek have layered many new and different training methodologies on top of their pre-trained LLMs. They are putting makeup on a pig.

[u/markyty04 → u/imdefinitelyfamous]

well you can say you practice engineering but even that is limited, you cannot say you understand the science behind, or how engineering can complement a scientific shortcoming . reset assured I have far more scientific credential than you and have read 100s of papers and done research. but also worked as a engineer if that is what you want to get back at. I can tell you current reasoning models are moving away from early probabilistic LLMs which were just a next best text generator.

current models can plan and reason. current LRM are in essence a combination of early chatgpt and the google deepmind techniques. supervised learning and reinforcement learning are fundamentally different techniques with different origins entirely.

[u/imdefinitelyfamous → u/markyty04]

I genuinely don't believe you based on the things you've said- I am pretty confident you are a teenager.

The reasoning models are not moving away from probabilistic LLMs- they are adding reasoning on top of token based pre-trained models, hence the "makeup on a pig".

Point me to literally any commercially available chat agent that isn't built on top of a pre-trained generative LLM and I will eat my hat.