SOBRE ESTE EPISÓDIO
Learning systems with humans date back to almost 5,000 years ago. And these learning systems have what allowed us to progress as a society. Being able to teach other people what we know and share knowledge has been the foundational pillars of our evolution and civilization. And interestingly, these learning systems are not unique to just humans. Animals also have these learning systems. When you look at orcas, dolphins, the higher-order intelligent animals spend time training and teaching their young ones. In the last 50 to 60 years, we have not just been teaching humans how to learn, but we have been teaching machines how to learn. And this artificial intelligence area has benefited from our understanding of these learning systems.
The guest started with highlighting the importance of acknowledging uncertainty and balancing between exploiting what is known and exploring to learn more about the environment. This problem is referred to as a “multi-arm bandit problem” and is considered fundamental in reinforcement learning, where the goal is to optimize actions in an environment.
When looking at it specifically for Large Language Models (LLMs) the role of Reinforcement Learning. RL has played the central role in building general purpose chatbots that are based on LLMs. Because the resulting model that has been trained on data might not give you the refined output that you are expecting from it.
When discussing about rewards and losses in reinforcement learning phase, it came out that the way we structure rewards and penalties for AI models greatly influences their reliability, how they interact with public and the accountability.
Overall deploying AI involves a balance. Backend deployment offers some level of predictability, while front-end deployment is uncertain. Successful business must experiment and capitalize in both aspects.
Production Team
Arvind Ravishunkar, Ankit Pandey, Chandan Jha
