A Gpt 3 chatbot is a piece of software that can have a conversation with a real person. Conversations can be done through text, voice, or even by reading people’s facial expressions.
Chatbot interactions can be as simple as answering a question like “What is the temperature outside?” or as complicated as having a series of conversations to reach a goal, like using a chatbot to book a vacation or give financial advice. Chatbots work well when the AI system knows a lot about the subject.
As AI text generator Gpt 3 relies on NLP to understand the meaning of the message it receives, if the NLP parser isn’t trained on the domain, it won’t be able to understand the intent and topics of interest.
To what extent the AI chatbot hype has disappointed us
The current generation of chatbots can be thought of as smart dialogue systems driven by techniques like natural language processing (NLP) and fixed conversation flows.
A chatbot doesn’t know anything right out of the box. We need to teach the chatbot about the subject matter. Also, you would train and add subdomains in small steps based on how hard the domain was.
For example, a chatbot that helps you book a taxi is an example of a fixed domain, while a chatbot that helps doctors treat cancer would be trained step by step on different types of cancer.
Different use cases and domains would need different recommendation algorithms, which need to be built into the chatbot. But the learning is limited.
For example, if you have a chatbot that helps you book restaurants, it can suggest similar restaurants but not places to stay because it only knows what you like in restaurants.
Well, someone could build a recommendation system that keeps track of information like;
- What users eat
- Where they stay
- Then try to find a correlation that leads to a recommendation
All of these ideas, data, and feedback need to be designed and built, so saying that chatbots learn on their own is not very accurate.
What is the main point of an AI chatbot?
A chatbot that can learn new ideas from scratch and answer questions like a real person. As it learns from open domain, the chatbots would start to act like the famous Microsoft Tay chatbot, which had to be shut down on the day it was released because it started to learn unwanted information from tweets and post offensive tweets.
The generative chatbots come up with a response based on the likelihood of words and make a sentence that is grammatically correct, but they don’t understand what it means.
The current generation of chatbots is a weak form of Artificial Intelligence technology that can understand the meaning of the message or question that is sent to it.
So that chatbot systems can figure out what the user wants, they need to be trained in the relevant domain. You can ask the same question in different ways, and the chatbot will still figure out what you mean.
With the technology we have now, we can define fixed conversation flows for dialogues. This means that the interactions are boxed in and limited.
Chatbots are good at keeping track of productivity and some customer service tasks. But as the domain gets more complicated, the current technology can’t keep up. Even with a lot of training, you wouldn’t be able to get the level of accuracy you need.
To get the solution, you would need to use a mix of other artificial intelligence and machine learning languages technologies and solutions, such as rules, inferences, and custom domain metadata. These become one-off solutions that are hard to use in a general way. In some situations, even a one-time solution would be very complicated.
For example, making an advisor that gives accurate and consistent advice on how to treat cancer would be a very complicated task.