How reinforcement learning with human feedback is unlocking the power of generative AI

The race to build generative AI is revving up, marked by both the promise of these technologies’ capabilities and the concern about the dangers they could pose if left unchecked.

We are at the beginning of an exponential growth phase for AI. ChatGPT, one of the most popular generative AI applications, has revolutionized how humans interact with machines. This was made possible thanks to reinforcement learning with human feedback (RLHF).

In fact, ChatGPT’s breakthrough was only possible because the model has been taught to align with human values. An aligned model delivers responses that are helpful (the question is answered in an appropriate manner), honest (the answer can be trusted), and harmless (the answer is not biased nor toxic).

This has been possible because OpenAI incorporated a large volume of human feedback into AI models to reinforce good behaviors. Even with human feedback becoming more apparent as a critical part of the AI training process, these models remain far from perfect and concerns about the speed and scale in which generative AI is being taken to market continue to make headlines.

Human-in-the-loop more vital than ever

Lessons learned from the early era of the “AI arms race” should serve as a guide for AI practitioners working on generative AI projects everywhere. As more companies develop chatbots and other products powered by generative AI, a human-in-the-loop approach is more vital than ever to ensure alignment and maintain brand integrity by minimizing biases and hallucinations.

Without human feedback by AI training specialists, these models can cause more harm to humanity than good. That leaves AI leaders with a fundamental question: How can we reap the rewards of these breakthrough generative AI applications while ensuring that they are helpful, honest and harmless?

The answer to this question lies in RLHF — especially ongoing, effective human feedback loops to identify misalignment in generative AI models. Before understanding the specific impact that reinforcement learning with human feedback can have on generative AI models, let’s dive into what it actually means.

What is reinforcement learning, and what role do humans play?

To understand reinforcement learning, you need to first understand the difference between supervised and unsupervised learning. Supervised learning requires labeled data which the model is trained on to learn how to behave when it comes across similar data in real life. In unsupervised learning, the model learns all by itself. It is fed data and can infer rules and behaviors without labeled data.

Models that make generative AI possible use unsupervised learning. They learn how to combine words based on patterns, but it is not enough to produce answers that align with human values. We need to teach these models human needs and expectations. This is where we use RLHF.

Reinforcement learning is a powerful approach to machine learning (ML) where models are trained to solve problems through the process of trial and error. Behaviors that optimize outputs are rewarded, and those that don’t are punished and put back into the training cycle to be further refined.

Think about how you train a puppy — a treat for good behavior and a time out for bad behavior. RLHF involves large and diverse sets of people providing feedback to the models, which can help reduce factual errors and customize AI models to fit business needs. With humans added to the feedback loop, human expertise and empathy can now guide the learning process for generative AI models, significantly improving overall performance.

How will reinforcement learning with human feedback have an impact on generative AI?

Reinforcement learning with human feedback is critical to not only ensuring the model’s alignment, it’s crucial to the long-term success and sustainability of generative AI as a whole. Let’s be very clear on one thing: Without humans taking note and reinforcing what good AI is, generative AI will only dredge up more controversy and consequences.

Let’s use an example: When interacting with an AI chatbot, how would you react if your conversation went awry? What if the chatbot began hallucinating, responding to your questions with answers that were off-topic or irrelevant? Sure, you’d be disappointed, but more importantly, you’d likely not feel the need to come back and interact with that chatbot again.

AI practitioners need to remove the risk of bad experiences with generative AI to avoid degraded user experience. With RLHF comes a greater chance that AI will meet users’ expectations moving forward. Chatbots, for example, benefit greatly from this type of training because humans can teach the models to recognize patterns and understand emotional signals and requests so businesses can execute exceptional customer service with robust answers.

Beyond training and fine-tuning chatbots, RLHF can be used in several other ways across the generative AI landscape, such as in improving AI-generated images and text captions, making financial trading decisions, powering personal shopping assistants and even helping train models to better diagnose medical conditions.

Recently, the duality of ChatGPT has been on display in the educational world. While fears of plagiarism have risen, some professors are using the technology as a teaching aid, helping their students with personalized education and instant feedback that empowers them to become more inquisitive and exploratory in their studies.

Why reinforcement learning has ethical impacts

RLHF enables the transformation of customer interactions from transactions to experiences, automation of repetitive tasks and improvement in productivity. However, its most profound effect will be the ethical impact of AI. This, again, is where human feedback is most vital to ensuring the success of generative AI projects.

AI does not understand the ethical implications of its actions. Therefore, as humans, it is our responsibility to identify ethical gaps in generative AI as proactively and effectively as possible, and from there implement feedback loops that train AI to become more inclusive and bias-free.

With effective human-in-the-loop oversight, reinforcement learning will help generative AI grow more responsibly during a period of rapid growth and development for all industries. There is a moral obligation to keep AI as a force for good in the world, and meeting that moral obligation starts with reinforcing good behaviors and iterating on bad ones to mitigate risk and improve efficiencies moving forward.

Conclusion

We are at a point of both great excitement and great concern in the AI industry. Building generative AI can make us smarter, bridge communication gaps and build next-gen experiences. However, if we don’t build these models responsibly, we face a great moral and ethical crisis in the future.

AI is at crossroads, and we must make AI’s most lofty goals a priority and a reality. RLHF will strengthen the AI training process and ensure that businesses are building ethical generative AI models.

Sujatha Sagiraju is chief product officer at Appen.