🚨 OpenAI has just released a groundbreaking update that makes ChatGPT 10X smarter than ever before! With new humanlike thinking abilities, memory features, and improved reasoning, this AI feels more like a real conversation partner than a chatbot. 🤯💬
This update brings us closer to true artificial general intelligence (AGI). Whether you're using ChatGPT for business, creativity, coding, or everyday productivity — get ready for a massive boost in performance! ⚡💡
In this video, we’ll cover: 🔹 How the new ChatGPT mimics human thought 🔹 Real examples of smarter responses 🔹 What’s new under the hood of GPT-4.5 / GPT-5 🔹 Why this is a game-changer for AI users everywhere
00:00So, OpenAI introduced a new method to reduce AI errors or hallucinations, you know, when AI says stuff that's not true.
00:08Like that time Google's barred AI wrongly said the James Webb Telescope was launched in 2009, or when ChatGPT cited fake legal cases.
00:17Such slip-ups can cause confusion and even harm.
00:20OpenAI's found a solution, though.
00:21It's a training technique called process supervision.
00:25Unlike the old way, which only cared about the final answer,
00:27this method rewards AI for every correct reasoning step.
00:31This helps AI learn from mistakes, think more logically, and be more transparent, so we can better understand how it thinks.
00:38OpenAI tested this out on a math problem-solving task, comparing an AI, trained in the old way, and one trained with process supervision.
00:46Guess what? The process-supervised AI did better overall.
00:50It made fewer mistakes, and its solutions were more like a human's.
00:53Plus, it was less likely to hallucinate wrong info, a big win for AI accuracy and reliability.
01:00In this video, I'll clearly break down what process supervision means, how it operates, and why it's superior to outcome supervision.
01:07We'll look at how it improves mathematical reasoning and reduces hallucinations in AI models.
01:12We'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.
01:20So, make sure to watch this video till the end.
01:22And before we dive in, hit like if you enjoy this video and subscribe for all things AI, including updates on the latest tech.
01:30Alright, let's get started.
01:32So, process supervision is a new training approach for AI models that rewards each correct step of reasoning instead of just the final conclusion.
01:40The idea is to provide feedback for each individual step in a chain of thought that leads to a solution or an answer.
01:47This feedback can be positive or negative depending on whether the step is correct or incorrect according to human judgment.
01:53For example, let's say we want to train an AI model to solve a mathematical problem where we have two equations.
01:59The sum of X and Y equals 12 and the difference between X and Y equals 4.
02:05The aim is to find the product of X and Y.
02:09By adding the results of the two equations, we get that twice X equals 16, which simplifies to X being 8.
02:16Now, using this in the sum equation, we find that Y must be 4.
02:20Thus, multiplying X and Y, that is 8 and 4, the answer is 32.
02:25Each of these steps is correct according to human logic and math rules.
02:29Therefore, each step would receive positive feedback from a human supervisor.
02:33The final answer, 32, is also correct according to human judgment.
02:38Therefore, it would also receive positive feedback from a human supervisor.
02:42Now, let's say we want to train an AI model using outcome supervision instead of process supervision.
02:49Outcome supervision only provides feedback based on whether the final answer is correct or not according to human judgment.
02:55It doesn't care about how the model arrived at that answer or whether it followed any logical steps along the way.
03:01For example, let's say an AI model using outcome supervision gave this answer, the product of X and Y equals 40.
03:08This answer is wrong according to human judgment.
03:11Therefore, it would receive negative feedback from a human supervisor.
03:15However, we don't know how the model got this answer or where it went wrong.
03:19Maybe it made a mistake in one of the steps or maybe it just guessed randomly.
03:22We have no way of telling because we don't see its work.
03:25This is where process supervision comes in handy.
03:28Process supervision allows us to see how the model thinks and reasons through a problem.
03:32It also allows us to correct its mistakes along the way and guide it towards a correct solution or answer.
03:38It works by training a reward model that can provide feedback for each step of reasoning based on human annotations.
03:44A reward model is an AI model that can assign a numerical value, a reward, to any input.
03:49The reward can be positive or negative depending on whether the input is desirable or undesirable according to some criteria, human judgment.
03:59For 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.
04:06The reward model would assign a positive reward, for example, plus one, to any step that is correct according to human logic and math rules.
04:15It would assign a negative reward, for example, minus one, to any step that is incorrect according to human logic and math rules.
04:23To train a reward model that assesses reasoning in mathematical problem solving, we start with a data set of mathematical problems, each annotated by humans.
04:33This data set combines each step of problem solving with a reward, indicating the alignment of that step with correct reasoning.
04:39In our data set, each correct step in solving a problem gets a positive reward.
04:44This includes operations like adding, subtracting, multiplying, or dividing the given variables, or solving for a specific variable.
04:53Using this data set, we use techniques like gradient descent to train our reward model, teaching it to assign rewards for new examples.
05:01Next, we have an AI model called ChatGPT Math.
05:05This AI is designed to solve math problems using natural language, and we plan to train it using process supervision with our reward model.
05:12We present unsolved mathematical problems to ChatGPT Math and let it generate the steps towards the solution.
05:19Let'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.
05:29ChatGPT Math works out the solution step by step.
05:32After each step, the reward model provides feedback.
05:35If ChatGPT Math takes a correct step, like adding the given equations together, it gets a positive reward.
05:42Along with each reward, the reward model also offers a hint for the next logical step.
05:47ChatGPT Math uses these hints to work out the next step in the solution.
05:52This process continues until the problem is fully solved.
05:55With 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.
06:04This way, ChatGPT Math would learn from its own outputs and the feedback from the reward model.
06:10It would also show its work and explain its reasoning using natural language.
06:14This would make it more transparent and trustworthy than a model that only gives a final answer without any explanation.
06:21Process supervision outperforms outcome supervision for several reasons.
06:26For instance, watching over every step works better than just checking the final result.
06:30This helps improve performance and lets the model learn from its mistakes.
06:35Just checking the end result doesn't consider how the answer was found.
06:39Keeping an eye on each step also helps avoid mistakes and wrong data, as the model gets feedback at every step.
06:45If we only check the final answer, some mistakes might slip through.
06:49Also, watching over every step makes the model's thinking clearer and earns people's trust.
06:55Just looking at the final answer doesn't explain how we got there.
06:58Finally, monitoring each step makes the model think more like a human, making its answers align more with what we expect.
07:06Just looking at the final result could teach the model to think in a way we don't agree with.
07:11Process supervision is not perfect, though.
07:13It has issues that we need to fix.
07:16One problem is that it needs more computer power and time than just checking the final answer.
07:21It's like grading each step in a math problem, not just the result.
07:25This could make it pricier to train large AI systems.
07:28Also, this approach might not work for all problems.
07:31Some tasks don't have a single, clear thinking path to follow.
07:34Or they might need more creativity than this method allows.
07:38People also question if this approach can avoid mistakes in real-world situations,
07:42where the data isn't perfect or the model faces new, complex situations.
07:47So, what's next for this type of AI training?
07:49Open AI has given out a big data set of human feedback to help with more research.
07:54This data includes human notes for each step of solving different math problems.
07:58It can be used to train new models or check existing ones.
08:01We don't know when Open AI will start using this in its AI models,
08:05but based on their history, I wouldn't be surprised if it happens soon.
08:08Imagine if the AI could explain its thoughts behind its texts.
08:12It could solve math problems without errors or made-up info,
08:15and show its steps in a way people can understand.
08:18This type of training could be used for more than just math.
08:21It could help AI models write summaries, translations, stories, code, jokes, and more.
08:27It could also help AI models answer questions, check facts, or make arguments.
08:32This method could improve AI quality and reliability by rewarding each correct step,
08:37not just the final result.
08:38It could make AI models more transparent by showing their work and explaining their thinking.
08:44In the end, this could lead to AI systems that can communicate with people
08:48in a way that's easy to understand and trust.
08:51Alright, I hope you found this breakdown helpful and insightful.
08:55If you liked this video, be sure to give it a thumbs up,
08:57and don't forget to hit that subscribe button for more deep dives into the latest in AI technology.
09:02Until next time, keep questioning, keep exploring, and let's continue this AI journey together.
