Gen AI in chatbots & infobots.
One of the key initial use cases for the initial adoption of GenAI will be chat & info bots, as these use cases lend thenselves very well to the current capabilities of the GenAI models. Lets’ look at these deeper in this article.
Generative AI chatbots and infobots are powered by large language models (LLMs), which are a type of artificial intelligence (AI) that can generate and understand human language. LLMs are trained on massive datasets of text and code, which allows them to learn the statistical relationships between words and phrases. This knowledge can then be used to generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way.
One of the key advantages of using generative AI in chatbots and infobots is that it allows for more natural and engaging conversations. Unlike traditional chatbots, which are based on rule-based systems, generative AI chatbots can learn to understand and respond to user queries in a more human-like way. This is because generative AI chatbots can take into account the context of the conversation and the individual preferences of the user.
Another advantage of using generative AI in chatbots and infobots is that it allows for more personalized responses. Generative AI chatbots can learn about the individual preferences of users over time and then provide them with more tailored responses. For example, a generative AI chatbot could learn the types of products that a user is interested in and then provide them with personalized recommendations.
Generative AI chatbots and infobots can be used in a wide variety of applications, including:
Customer service: Generative AI chatbots can be used to provide customer service on a variety of channels, such as websites, mobile apps, and social media. Generative AI chatbots can answer customer questions, resolve customer issues, and even provide personalized recommendations.
Education: Generative AI infobots can be used to teach users about different topics and answer their questions. For example, a generative AI infobot could be used to teach students about history, science, or math.
Entertainment: Generative AI chatbots can be used to create new forms of entertainment, such as interactive stories, games, and simulations.
Here is a detailed technical description of how generative AI is used in chatbots and infobots:
Generative AI chatbots and infobots are typically powered by LLMs. LLMs are trained on massive datasets of text and code, which allows them to learn the statistical relationships between words and phrases. This knowledge can then be used to generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way.
Generative AI chatbots and infobots use a variety of techniques to generate responses. One common technique is to use a beam search algorithm. Beam search works by generating a set of possible responses and then selecting the response that is most likely to be correct. Another common technique is to use a sampling algorithm. Sampling works by generating a response at random from a set of possible responses.
Generative AI chatbots and infobots can also be trained to generate responses that are tailored to the individual preferences of the user. This can be done by using a variety of techniques, such as reinforcement learning and imitation learning. Reinforcement learning works by rewarding the chatbot for generating responses that are positive and engaging. Imitation learning works by training the chatbot to generate responses that are similar to the responses of a human expert.
Generative AI is a powerful new technology that has the potential to revolutionize the way that chatbots and infobots are used. As generative AI continues to develop, we can expect to see even more innovative and engaging applications of this technology in the future.
References
Vaswani, Ashish, et al. "Attention is all you need." Advances in neural information processing systems 30 (2017): 5998-6008.
Brown, Tom B., et al. "Language models are few-shot learners." arXiv preprint arXiv:2005.14165 (2020).
Raffel, Colin, et al. "Exploring the limits of transfer learning with a unified text-to-text transformer." arXiv preprint arXiv:1910.10683 (2019).
Vaswani, Ashish, et al. "Transformer-xl: Attentive language modeling with long-range sequential dependencies." arXiv preprint arXiv:1901.11116 (2019).