Learning to Reason with LLMs • Hacker News Board

Re: Learning to Reason with LLMs

#821

Post by digging » Thu, Sep 12, 2024, 10:21 PM UTC

al_borland wrote: post #473

cs391231 wrote: post #408

Student here. Can someone give me one reason why I should continue in software engineering that isn't denial and hopium?

The calculator didn’t eliminate math majors. Excel and accounting software didn’t eliminate accountants and CPAs. These are all just tools. I spend very little of my overall time at work actually coding. It’s a nice treat when I get a day where that’s all I do. From my limited work with Copilot so far, the user still needs to know what they’re doing. I have 0 faith a product owner, without a coding background, can us…

> The calculator didn’t eliminate math majors. Excel and accounting software didn’t eliminate accountants and CPAs. These are all just tools.

This just feels extremely shortsighted. LLMs are just tools right now, but the goal of the entire industry is to make something more than a tool, an autonomous digital agent. There's no equivalent concept in other technology like calculators. It will happen or it will not, but we'll keep getting closer every month until we achieve it or hit a technical wall. And you simply cannot know for sure such a wall exists.

Re: Learning to Reason with LLMs

#822

Post by [deleted] » Thu, Sep 12, 2024, 10:24 PM UTC

OkGoDoIt wrote: post #522

Some practical notes from digging around in their documentation: In order to get access to this, you need to be on their tier 5 level, which requires $1,000 total paid and 30+ days since first successful payment. Pricing is $15.00 / 1M input tokens and $60.00 / 1M output tokens. Context window is 128k token, max output is 32,768 tokens. There is also a mini version with double the maximum output tokens (65,536 tokens…

[deleted]

Re: Learning to Reason with LLMs

#823

Post by dontlikeyoueith » Thu, Sep 12, 2024, 10:26 PM UTC

spaceman_2020 wrote: post #479

canjobear wrote: post #427

First shot, I gave it a medium-difficulty math problem, something I actually wanted the answer to (derive the KL divergence between two Laplace distributions). It thought for a long time, and still got it wrong, producing a plausible but wrong answer. After some prodding, it revised itself and then got it wrong again. I still feel that I can't rely on these systems.

Look where you were 3 years ago, and where you are now. And then imagine where you will be in 5 more years. If it can almost get a complex problem right now, I'm dead sure it will get it correct within 5 years

I still don't have a Mr. Fusion in my house, FYI.

We always overestimate the future.

Re: Learning to Reason with LLMs

#824

Post by niemal_dev » Thu, Sep 12, 2024, 10:30 PM UTC

DataDive wrote: post #756

UncleOxidant wrote: post #551

Earlier quoted context omitted.

Semi-retired software/hardware engineer here. After my recent experiences with various coding LLMs (similar to the experience of the OP with the bluetooth fan protocol) I'm really glad I'm in a financial position such that I'm able to retire. The progress of these LLMs at coding has been astonishing over the last 18 months. Will they entirely replace humans? No. But as they increase programmer productivity fewer devs…

I will add that I am grateful that I also got to experience a world where AI did not spew tons of code like a sausage-making machine. It was so satisfying to code up a solution where you knew you would get through it little by little.

This.

Re: Learning to Reason with LLMs

#825

Post by arnaudsm » Thu, Sep 12, 2024, 10:32 PM UTC

OkGoDoIt wrote: post #522

Some practical notes from digging around in their documentation: In order to get access to this, you need to be on their tier 5 level, which requires $1,000 total paid and 30+ days since first successful payment. Pricing is $15.00 / 1M input tokens and $60.00 / 1M output tokens. Context window is 128k token, max output is 32,768 tokens. There is also a mini version with double the maximum output tokens (65,536 tokens…

Some of the queries run for multiple minutes. 40 tokens/sec is too slow for CoT.

I hope OpenAI is investing in low-latency like Groq's tech that can reach 1k tokens/sec.

Re: Learning to Reason with LLMs

#826

Post by adamtaylor_13 » Thu, Sep 12, 2024, 10:33 PM UTC

Laughing at the comparison to "4o" as if that model even holds a candle to GPT-4. 4o is _cheaper_—it's nowhere near as powerful as GPT-4, as much as OpenAI would like it to be.

Re: Learning to Reason with LLMs

#827

Post by aantix » Thu, Sep 12, 2024, 10:35 PM UTC

Feels like the challenge here is to somehow convey to the end user, how the quality of output is so much better.

Re: Learning to Reason with LLMs

#828

Post by neevans » Thu, Sep 12, 2024, 10:35 PM UTC

colonelspace wrote: post #543

spaceman_2020 wrote: post #479

Earlier quoted context omitted.

Look where you were 3 years ago, and where you are now. And then imagine where you will be in 5 more years. If it can almost get a complex problem right now, I'm dead sure it will get it correct within 5 years

> I'm dead sure it will get it correct within 5 years You might be right. But plenty of people said we'd all be getting around in self-driving cars for sure 10 years ago.

we do have self driving car but since it directly affects people's life it needs to be close to 100% accurate and no margin of errors. Not necessarily the case for LLMs.

Re: Learning to Reason with LLMs

#829

Post by drooby » Thu, Sep 12, 2024, 10:35 PM UTC

kmeisthax wrote: post #622

>We believe that a hidden chain of thought presents a unique opportunity for monitoring models. Assuming it is faithful and legible, the hidden chain of thought allows us to "read the mind" of the model and understand its thought process. For example, in the future we may wish to monitor the chain of thought for signs of manipulating the user. However, for this to work the model must have freedom to express its thoug…

Did OpenAI ever even claim that they would be an open source company?

It seems like their driving mission has always been to create AI that is the "most beneficial to society".. which might come in many different flavors.. including closed source.

Re: Learning to Reason with LLMs

#830

Post by bartman » Thu, Sep 12, 2024, 10:38 PM UTC

fsndz wrote: post #566

bartman wrote: post #367

This is incredible. In April I used the standard GPT-4 model via ChatGPT to help me reverse engineer the binary bluetooth protocol used by my kitchen fan to integrate it into Home Assistant. It was helpful in a rubber duck way, but could not determine the pattern used to transmit the remaining runtime of the fan in a certain mode. Initial prompt here [0] I pasted the same prompt into o1-preview and o1-mini and both c…

> Asking the models to determine if my code is equivalent to what they reverse engineered resulted in a nuanced and thorough examination, and eventual conclusion that it is equivalent. Did you actually implement to see if it works out of the box ? Also if you are a free users or accepted that your chats should be used for training then maybe o1 is was just trained on your previous chat and so now knows how to reason…

That is an interesting thought. This was all done in an account that is opted out of training though.

I have tested the Python code o1 created to decode the timestamps and it works as expected.