It’s no secret that genetic algorithms are making headlines, both for the potential capabilities they offer, as well as for the dangers they may entail if they are not carefully controlled. There is no doubt that human-machine interaction has been revolutionized by ChatGPT, one of the most popular generative AI applications.
Reinforcement Learning with Human Feedback has further empowered the already powerful ChatGTP. Most would agree — ChatGPT’s breakthrough was achieved because its model aligned with human values. By aligning the model, it provided helpful (appropriate) and honest (fair) responses. By incorporating human feedback into AI models, OpenAI reinforces good behavior.
Crucial than ever-before: Human-in-the-loop
AI professionals working on generative AI & ML projects across the world, should learn from lessons learned from the early era of the “AI arms race.” A human-in-the-loop approach is extremely vital for minimizing biases and maintaining brand integrity as companies develop chatbots and other products powered by generative AI.
These models can cause more harm than good without human feedback by AI training specialists. The question for AI leaders is: How can we reap the benefits of these breakthrough generative AI applications while ensuring they are kind, honest, and safe?
This imperative question can be answered by Reinforcement Learning with Human Feedback (RLHF), particularly with ongoing, effective human feedback loops to identify misalignments in generative AI models. Let’s take a look at what reinforcement learning with human feedback actually means before understanding the specific impact it can have on generative AI models.
What role Reinforcement Learning has to play in the Artificial Intelligence domain?
Observe that reinforcement learning differs from unsupervised learning in order to understand it. To learn how to behave when it meets similar data in real life, supervised learning requires labeled data on which the model is trained. Models that are unsupervised, learn all by themselves. Inference can be made without labeling data when it is fed with data.
Unsupervised learning is a key component of Generative AI. In order to produce answers that align with human values, they must learn how to combine words based on patterns. Human needs and expectations must be taught to these models. Here is where RLHF comes into play.
Machine learning (ML) using reinforcement learning involves training models through trial and error to solve problems. When a behavior optimizes outputs, it is rewarded, while when it doesn’t, it is punished and returned to the training cycle for further refinement.
As you train your puppy or cat or any other pet, reward good behavior with treats and punish bad behavior with time outs. As RLHF entails large and diverse sets of people providing feedback, factual errors can be reduced and artificial intelligence models can be customized to fit business needs. Adding humans to the feedback loop helps Generative AI models learn more effectively with human expertise and empathy.
How RLHF impacts Generative Artificial Intelligence Models?
For Generative AI to succeed and be sustainable for the long term, reinforcement learning with human feedback is crucial. There’s one thing we must keep in mind: Generative AI will only cause more controversy and consequences if humans do not reinforce what good AI is.
As an example: What would you do if you run into a snag when interacting with an AI chatbot? Can you imagine how you would feel if your chatbot started hallucinating, answering your questions off-topic and irrelevant? Yes, you would likely be disappointed, however, you would likely not wish to interact with that chatbot in the future. For more information originally published at Cogito