These powerful tools are changing the way we interact with technology and are opening up new possibilities for communication, creativity, and innovation.
AI Chatbot Platforms
Numerous platforms facilitate chatbot interactions, each with distinct characteristics and functionality. These are more tailored to day-to-day operations, although their responses might feel “canned” and not as natural as those from Large Language Models. Some of the most popular options include:
- Replika: Designed to simulate human-like conversation, Replika is a personal AI friend that learns from your interactions and adapts to your communication style.
- Cleverbot: A long-standing player in the AI chatbot landscape, Cleverbot utilizes an extensive database of user-generated content to deliver engaging and often witty responses.
- Google Assistant: A virtual assistant developed by Google, this AI-driven tool offers various services, from answering questions and managing tasks to providing real-time conversation support.
Large Language Models (LLM)
A large language model is an artificial intelligence model designed to generate human language. It is typically trained on massive amounts of text data, such as books, articles, and other written materials, using deep learning techniques.
For example, large language models like the well known ChatGPT have been trained on billions of words and can generate human-like text in response to prompts. They can complete sentences, compose essays, answer questions, and even generate creative writing, among other things.
These models have many applications, from language translation to chatbots to content creation. They also constantly evolve and improve, with new models being developed and trained on even larger datasets.
Some of the currently most popular large language models (LLMs) include:
- ChatGPT: Developed by OpenAI, ChatGPT demonstrates a high level of comprehension and contextual understanding, often providing accurate and informative responses.
- Bing Chat: Not strictly any different than ChatGPT since it uses it under the hood. Bing Chat extends the capabilities of ChatGPT by providing a more up-to-date and relevant database of information, as well as the ability to tap into Bing search results to not only provide responses, but also references to information sources.
- Bard: The newest addition to the AI chatbot landscape, Barge is a conversational AI platform created by Google that enables users to engage in natural conversations with AI. It’s a direct competitor to ChatGPT and it’s currently in private beta in the US and the UK.
- LLaMA: A conversational AI platform developed and trained by Facebook, and one of the first with a truly open source code (although the trained models were not open sourced). It is a quite capable language model that people can deploy in their own environments. As such, Facebook does not provide a website to try LLaMA out, but this link will take you to their announcement page.
- Stanford Alpaca: Stanford decided to expand upon the work of Facebook by training their own models with far, far less budget than the amount Facebook used to train LLaMA. In Stanford’s own words, it was less than US$600. This is their announcement page .
Do chatbots actually “understand” what they have been asked?
Large language models are based on machine learning algorithms that use statistical patterns in language to generate responses. While they can generate compelling and human-like responses, they do not actually “understand” thoughts or have a proper comprehension of language as humans do.
Instead, large language models are designed to generate responses based on statistical patterns in the input text they receive. These patterns are learned during the training process, where the model is exposed to massive amounts of text data and adjusts its internal parameters to predict the next word or phrase better.
When a large language model generates a response, it essentially uses this learned statistical pattern to predict the most likely continuation of the input text. This is why large language models can sometimes produce strange or nonsensical responses, especially when the input text is ambiguous or contradictory.
This is similar to how a human would complete the phrase: “to be or not to be, …”. Knowing what words come next is due to the reach and influence Shakespeare has in English culture, and the fact that the phrase is quite often repeated in class, in society and in the media. While you might have not read Hamlet (I certainly haven’t!) you still know the ending of the phrase due to its popularity.
So while large language models can produce highly convincing responses, they do not have a true understanding or comprehension of language as humans do. Instead, they are based on statistical patterns and mathematical calculations.
Frequently asked questions
What is a Large Language Model (LLM)?
A large language model is an artificial intelligence model designed to generate human language. It is typically trained on massive amounts of text data, such as books, articles, and other written materials, using deep learning techniques.
What can Large Language Models (LLM) do?
Large language models can generate human-like text in response to prompts. They can complete sentences, compose essays, answer questions, and even generate creative writing, among other things.
What are some of the most popular Large Language Models (LLM)?
Some of the currently most popular large language models (LLMs) include: ChatGPT, Bing Chat, Bard, LLaMA and Stanford Alpaca.
Do chatbots actually "understand" what they have been asked?
Large language models are based on machine learning algorithms that use statistical patterns in language to generate responses. While they can generate compelling and human-like responses, they do not actually “understand” thoughts or have a proper comprehension of language as humans do.
Does ChatGPT actually "understand" what it has been asked?
ChatGPT is a large language model that is based on machine learning algorithms that use statistical patterns in language to generate responses. It works by using statistical patterns, and it does not actually “understand” thoughts or have a proper comprehension of language as humans do.
