How chatbots work
Introduction
This seems like a good thing to know.
Just gathering some ways to break this down – to think about and flesh out laters.
Well, first you take “Artificial” – aand then you add “Intelegence” – and done!
Just kidding.
Steps
When you interact with a chatbot, you’re essentially having a conversation with a computer program designed to simulate human-like responses. It goes through several sophisticated steps to understand and respond to your inputs. Here’s a step-by-step breakdown of what happens under the hood:
-
Step 1: Sending input
Input… (explain how that gets sent first)
When you type a message or question into the chat interface, the chatbot receives your text as input. This is the starting point for processing your request.
This input text is the raw data that the chatbot needs to process.
-
Step 2: Tokenization
The chatbot examines the text to understand and interpret what you are asking. This involves breaking down your input into manageable pieces. The bot might look for specific keywords or phrases that help it determine the intent of your message.
The chatbot breaks down the input text into smaller units called tokens. Tokens can be words, phrases, or even punctuation. This process is similar to segmenting a sentence into its components to understand each piece’s function.
-
Step 3: Parsing and Intent Recognition
After tokenization, the bot uses natural language processing (NLP) techniques to analyze the structure of the sentence and identify the intent behind your message. This step involves understanding what action you expect the bot to take, whether it’s answering a question, booking a ticket, or something else.
If the chatbot is designed with advanced capabilities, it tries to understand the context of the conversation. This can mean recalling earlier parts of the conversation or using external information that affects the response.
-
Step 4: Entity Recognition
Alongside parsing the intent, the bot looks for entities—key pieces of information relevant to your request. For example, if you ask, “What’s the weather like in New York tomorrow?”, “New York” and “tomorrow” are entities that the bot identifies and uses to provide a correct response.
-
Step 5: Generating Embeddings
The tokens are converted into numerical forms known as embeddings. These embeddings are high-dimensional vectors that represent the tokens in a way that captures their meaning and linguistic relationships. This conversion is crucial for the model to process the natural language data mathematically.
-
Step 6: Generating a Response
Once the bot understands your request and the context, it generates a response. This step often involves selecting a pre-written message that matches the intent of your input. More advanced chatbots, like those using AI models, dynamically generate responses using complex algorithms that predict what a human might say in a similar situation.
Using the identified intent and entities, the chatbot accesses a trained model to generate an appropriate response. This model uses the embeddings and performs complex calculations, often involving deep learning algorithms, to predict and assemble a coherent answer based on the context of the conversation.
-
Step 5: Output Delivery
The chatbot sends the generated response back to the user interface, where it appears as a message. The speed of this process is crucial for maintaining a flow that feels natural, akin to chatting with a human.
The generated response is then converted back into human-readable text and sent to you. This step must be fast to ensure the conversation feels as natural and seamless as possible.
-
Step 6: Learning from Interaction
Some chatbots have the ability to learn from interactions to improve future responses. This involves analyzing what questions were asked and how effectively the bot handled the conversation.
Advanced chatbots have mechanisms to learn from each interaction. By analyzing which responses satisfy user inquiries and which do not, they can improve over time, making their answers more accurate and contextually appropriate.
Real example
..
-
Step 1: Sending input
“What’s the best school for someone who wants to learn how to design and build web applications?”
-
Step 2: Tokenization
Tokenization of the sentence “What’s the best school for someone who wants to learn how to design and build web applications?” could indeed look like that if a simple word-based tokenizer is used. However, in more sophisticated models like those used in large language models, tokenization might be a bit more complex. These models often use a type of tokenization called subword tokenization, which can break down words into smaller pieces based on common prefixes, suffixes, and stems found in the training data.
“What”, “‘s”, “the”, “best”, “school”, “for”, “someone”, “who”, “wants”, “to”, “learn”, “how”, “to”, “design”, “and”, “build”, “web”, “applica”, “tions”, “?”
In subword tokenization, words like “applications” might be split into “applica” and “tions” to better handle unknown words and improve model efficiency by sharing common subwords across different words. This method helps in dealing with a variety of words without having to have a direct match for every possible word in the training data, thereby making the model more flexible and capable of understanding and generating more nuanced text.
-
Step 3: Parsing and Intent Recognition
From these tokens, the chatbot’s NLP engine will work to determine the intent of the query. The presence of words like “best”, “school”, and “learn” are key indicators that the user is inquiring about educational resources.
-
Step 4: Entity Recognition
Education Subject: “design and build web applications” — This phrase is recognized as the specific field of study the user is interested in.
Question Type: Implicit in “What’s the best” — Indicates that the user is looking for a recommendation.
-
Step 5: Generating Embeddings
Each of these tokens and recognized entities would be converted into numerical vectors, known as embeddings. These embeddings help the model understand the semantic and contextual relationships between the words in the query.
-
Step 6: Generating a Response
Using the intent (“seeking educational resources”) and the entities identified (“web design and development”), the chatbot would then consult its training data or linked databases to generate a response that suggests educational institutions known for their web development programs.
This process involves complex AI models that predict the most appropriate and helpful answer based on the context of the entire conversation and what it has learned from previous similar interactions.
This explanation gives a glimpse into how seemingly straightforward questions are deconstructed into components that a machine can understand and respond to meaningfully, leveraging the intricacies of NLP and AI.
-
Step 5: Output Delivery
The chatbot sends the generated response back to the user interface, where it appears as a message. The speed of this process is crucial for maintaining a flow that feels natural, akin to chatting with a human. (but it doesn’t feel that way… it’s usually wayyy too fast to feel like that).
The generated response is then converted back into human-readable text and sent to you. This step must be fast to ensure the conversation feels as natural and seamless as possible.
-
Step 6: Learning from Interaction
Scenario: Suppose the chatbot initially recommends a few top-ranked design schools but doesn’t specifically address programs focused on web applications.
User Feedback: The user might follow up by specifying that they are looking for programs specifically strong in web development, not just general design.
Learning Process:
- Data Collection: The chatbot logs this interaction, noting that the user specified a need for web development programs after receiving a more general response.
- Analysis: Over multiple similar interactions, the chatbot’s underlying machine learning model identifies a pattern where users frequently follow up general school recommendations with requests for specific program details.
- Model Adjustment: The developers might use this insight to retrain or adjust the chatbot’s response algorithms. They could implement changes where the chatbot now recognizes when users are likely looking for specialized programs. The chatbot might then prioritize responses that include information about specialized courses or certifications related to web development.
- Improved Responses: After this adjustment, when a new user asks a similar question about schools for learning web design, the chatbot is more likely to immediately include information about the specific programs in web development, perhaps even highlighting unique strengths or partnerships with tech companies.
Isn’t that what search engines do?
Both chatbots and search engines utilize tokenization and other natural language processing techniques, but their implementations and the technologies they use to serve their purposes differ significantly. Here’s a simple breakdown of the key differences in how they work:
Chatbots
- Tokenization and Understanding: Chatbots tokenize input to understand the user’s intent and context. They focus on understanding and responding to each input within the flow of a conversation. The model needs to maintain context over multiple interactions.
- Response Mechanism: Chatbots directly generate a text-based response that aims to satisfy the user’s current query. This involves dynamically creating sentences that might not have been explicitly programmed.
- Interactivity: They are designed to mimic human conversation, making them more interactive. They respond in real-time, providing immediate answers and engaging the user in a dialogue.
Search Engines
- Tokenization and Indexing: Search engines also tokenize input, but they use these tokens to search an index they’ve created of all the web pages they have crawled. This indexing process is vast and involves storing and categorizing millions of web pages based on their content.
- Ranking and Retrieval: Search engines retrieve relevant documents based on the search query and then rank these documents according to relevance using various algorithms (like Google’s PageRank). The results are presented as a list of links, rather than direct answers.
- Scope and Scale: Search engines are built to handle and categorize the entire internet’s content. They must manage and efficiently search through much larger datasets than chatbots.
While both systems use tokenization, chatbots use it to maintain a conversation and generate relevant responses within that conversation. In contrast, search engines use tokenization as a part of their indexing and retrieval process, focusing on finding and ranking external content in response to queries. Their underlying architectures and purposes guide these differences in implementation.
Is a chatbot like GPT really AI?
(We asked ChatGPT)
Are all of these responses and words and guesses based on scraping all the content that humans wrote on the internet?
If you watch these....
Take some notes – and help us put them in the most meaningful order, OK!
…
…
This is really more about the application than how it works
What tasks can we get the computer to do – that we don’t want to do (and that doesn’t provide the value).
…
…
…
…
…
…