661–670 of 1001 posts
for example in gpt-4o I often append '(reply short)' at the end of my requests. with the o1 models I append 'reply in 20 words' and it gives way better answers.
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…
Reasoning tokens are indeed billed as output tokens. > While reasoning tokens are not visible via the API, they still occupy space in the model's context window and are billed as output tokens. From here: https://platform.openai.com/docs/guides/reasoning
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There are two basic versions of “safety” which are related, but distinct: One version of “safety” is a pernicious censorship impulse shared by many modern intellectuals, some of whom are in tech. They believe that they alone are capable of safely engaging with the world of ideas to determine what is true, and thus feel strongly that information and speech ought to be censored to prevent the rabble from engaging in wr…
Third version is "brand safety" which is, we don't want to be in a new york times feature about 13 year olds following anarchist-cookbook instructions from our flagship product
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…
Hi there,
I’m x, PM for the OpenAI API. I’m pleased to share with you our new series of models, OpenAI o1. We’ve developed these models to spend more time thinking before they respond. They can reason through complex tasks and solve harder problems than previous models in science, coding, and math.
As a trusted developer on usage tier 5, you’re invited to get started with the o1 beta today. Read the docs You have access to two models:
Our larger model, o1-preview, which has strong reasoning capabilities and broad world knowledge.
Our smaller model, o1-mini, which is 80% cheaper than o1-preview.
I’m curious to hear what you think. If you’re on X, I’d love to see what you build—just reply to our post.
Best, OpenAI API
I had trouble in the past to make any model give me accurate unix epochs for specific dates. I just went to GPT-4o (via DDG) and asked three questions: 1. Please give me the unix epoch for September 1, 2020 at 1:00 GMT. > 1598913600 2. Please give me the unix epoch for September 1, 2020 at 1:00 GMT. Before reaching the conclusion of the answer, please output the entire chain of thought, your reasoning, and the maths…
Student here. Can someone give me one reason why I should continue in software engineering that isn't denial and hopium?
Software engineering teaches you a set of skills that are applicable in more places than just writing software. There are big parts of the job that cannot be done by LLMs (today) and if LLMs get better (or AGI happens) then enough other professions will be affected that we will all be in the same boat (no matter what you major in). LLMs are just tools, they help but they do not replace developers (yet).
Yes but they will certainly have a lot of downward pressure on salaries for sure.
What is interesting to me is that there is no difference in the AP English lit/lang exams. Why did chain-of-thought produce negligible improvements in this area?
Student here. Can someone give me one reason why I should continue in software engineering that isn't denial and hopium?
I've been in software engineering for over 20 years. I've seen massive growth in the productivity of software engineers, and that's resulted in greater demand for them. In the near term, AI should continue this trend.
2. It's possible that at some point, AI will advance to where we can remove software engineers from the loop. We're not even close to that point yet. In the mean time, software engineering is an excellent way to learn about other business problems so that you'll be well-situated to address them (whatever they'll be at that time).
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The point of RL is that sometimes you need a model to take actions (you could also call this making predictions) that don’t have a known label. So for example if it’s playing a game, we don’t have a label for each button press. We just have a label for the result at some later time, like whether Pac-Man beat the level. PPO applies this logic to chat responses. If you have a model that can tell you if the response was…
Thanks, that helps! I still don't quite understand the mechanics of this, since backprop makes adjustments to steer the LLM towards a specific token sequence, not towards a score produced by a reward function.
This was also the issue with RLHF models. The loss of predicting the next token is straightforward to minimize as we know which weights are responsible for the token being correct or not. identifying which tokens had the most sense for a prompt is not straightforward.
For thinking you might generate 32k thinking tokens and then 96k solution tokens and do this a lot of times. Look at the solutions, rank by quality and bias towards better thinking by adjusting the weights for the first 32k tokens. But I’m sure o1 is way past this approach.