Transcription

So, OpenAI introduced a new method to reduce AI errors or hallucinations, you know, when AI says stuff that's not true. Like that time Google's barred AI wrongly said the James Webb Telescope was launched in 2009. Or when ChatGPT cited fake legal cases. Such slip-ups can cause confusion and even harm. OpenAI has found a solution, though. It's a training technique called Process Supervision. Unlike the old way, which only cared about the final answer, this method rewards AI for every correct reasoning step. This helps AI learn from mistakes, think more logically, and be more transparent, so we can better understand how it thinks. OpenAI tested this out on a math problem-solving task, comparing an AI, trained in the old way, and one trained with process supervision. Guess what? The process-supervised AI did better overall. It made fewer mistakes and its solutions were more like a human's. Plus, it was less likely to hallucinate wrong info. A big win for AI accuracy and reliability. In this video, I'll clearly break down what Process Supervision means, how it operates, and why it's superior to Outcome Supervision. We'll look at how it improves mathematical reasoning and reduces hallucinations in AI models. We'll also talk about the pros and cons of this new way of training and what it might mean for OpenAI and its products going forward. So make sure to watch this video till the end. And before we dive in, hit like if you enjoy this video and subscribe for all things AI, including updates on the latest tech. Alright, let's get started. So, Process Supervision is a new training approach for AI models that rewards each correct step of reasoning instead of just the final conclusion. The idea is to provide feedback for each individual step in a chain of thought that leads to a solution or an answer. This feedback can be positive or negative depending on whether the step is correct or incorrect according to human judgment. For example, let's say we want to train an AI model to solve a mathematical problem where we have two equations, the sum of x and y equals 12, and the difference between x and y equals 4. The aim is to find the product of x and y. By adding the results of the two equations, we get that twice x equals 16, which simplifies to x being 8. Now, using this in the sum equation, we find that y must be 4. Thus, multiplying x and y, that is 8 and 4, the answer is 32. Each of these steps is correct according to human logic and math rules. Therefore, each step would receive positive feedback from a human supervisor. The final answer, 32, is also correct according to human judgment. Therefore, it would also receive positive feedback from a human supervisor. Now, let's say we want to train an AI model using outcome supervision instead of process supervision. Outcome supervision only provides feedback based on whether the final answer is correct or not according to human judgment. It doesn't care about how the model arrived at that answer or whether it followed any logical steps along the way. For example, let's say an AI model using outcome supervision gave this answer, the product of x and y equals 40. This answer is wrong according to human judgment. Therefore, it would receive negative feedback from a human supervisor. However, we don't know how the model got this answer or where it went wrong. Maybe it made a mistake in one of the steps or maybe it just guessed randomly. We have no way of telling because we don't see its work. This is where process supervision comes in handy. Process supervision allows us to see how the model thinks and reasons through a problem. It also allows us to correct its mistakes along the way and guide it towards a correct solution or answer. It works by training a reward model that can provide feedback for each step of reasoning based on human annotations. A reward model is an AI model that can assign a numerical value, a reward, to any input. The reward can be positive or negative depending on whether the input is desirable or undesirable according to some criteria, human judgment. For example, let's say we have a reward model that can provide feedback for each step of solving a math problem based on human annotations. The reward model would assign a positive reward, for example, plus one, to any step that is correct according to human logic and math rules. It would assign a negative reward, for example, minus one, to any step that is incorrect according to human logic and math rules. To train a reward model that assesses reasoning in mathematical problem solving, we start with a data set of mathematical problems, each annotated by humans. This data set combines each step of problem solving with a reward, indicating the alignment of that step with correct reasoning. In our data set, each correct step in solving a problem gets a positive reward. This includes operations like adding, subtracting, multiplying, or dividing the given variables, or solving for a specific variable. Using this data set, we use techniques like gradient descent to train our reward model, teaching it to assign rewards for new examples. Next, we have an AI model called ChatGPT Math. This AI is designed to solve math problems using natural language, and we plan to train it using process supervision with our reward model. We present unsolved mathematical problems to ChatGPT Math and let it generate the steps towards the solution. Let's say we have a problem that requires finding the product of x and y, given that the sum of x and y is 12 and their difference is 4. ChatGPT Math works out the solution step by step. After each step, the reward model provides feedback. If ChatGPT Math takes a correct step like adding the given equations together, it gets a positive reward. Along with each reward, the reward model also offers a hint for the next logical step. ChatGPT Math uses these hints to work out the next step in the solution. This process continues until the problem is fully solved. With each correct step, earning a reward and further guidance, ChatGPT Math learns to solve problems in a way that aligns with human logic and mathematical rules. This way, ChatGPT Math would learn from its own outputs and the feedback from the reward model. It would also show its work and explain its reasoning using natural language. This would make it more transparent and trustworthy than a model that only gives a final answer without any explanation. Process supervision outperforms outcome supervision for several reasons. For instance, watching over every step works better than just checking the final result. This helps improve performance and lets the model learn from its mistakes. Just checking the end result doesn't consider how the answer was found. Keeping an eye on each step also helps avoid mistakes and wrong data as the model gets feedback at every step. If we only check the final answer, some mistakes might slip through. Also, watching over every step makes the model's thinking clearer and earns people's trust. Just looking at the final answer doesn't explain how we got there. Finally, monitoring each step makes the model think more like a human, making its answers align more with what we expect. Just looking at the final result could teach the model to think in a way we don't agree with. Process supervision is not perfect though. It has issues that we need to fix. One problem is that it needs more computer power and time than just checking the final answer. It's like grading each step in a math problem, not just the result. This could make it pricier to train large AI systems. Also, this approach might not work for all problems. Some tasks don't have a single, clear thinking path to follow. Or they might need more creativity than this method allows. People also question if this approach can avoid mistakes in real-world situations, where the data isn't perfect or the model faces new, complex situations. So, what's next for this type of AI training? OpenAI has given out a big data set of human feedback to help with more research. This data includes human notes for each step of solving different math problems. It can be used to train new models or check existing ones. We don't know when OpenAI will start using this in its AI models, but based on their history, I wouldn't be surprised if it happens soon. Imagine if the AI could explain its thoughts behind its texts. It could solve math problems without errors or made-up info and show its steps in a way people can understand. This type of training could be used for more than just math. It could help AI models write summaries, translations, stories, code, jokes, and more. It could also help AI models answer questions, check facts, or make arguments. This method could improve AI quality and reliability by rewarding each correct step, not just the final result. It could make AI models more transparent by showing their work and explaining their thinking. In the end, this could lead to AI systems that can communicate with people in a way that's easy to understand and trust. Alright, I hope you found this breakdown helpful and insightful. If you liked this video, be sure to give it a thumbs up and don't forget to hit that subscribe button for more deep dives into the latest in AI technology. Until next time, keep questioning, keep exploring, and let's continue this AI journey together.