Here are all the articles on regenerative grazing in ChatGPT’s series:
What is Regenerative Grazing and How Do I Go About It?
What Does Regenerative Grazing Look Like to a Large-Landscape Rancher?
How Do Graziers in the Northeast, South, and Midwest Think of Regenerative Grazing?
What Do Land Management Agencies Think of Regenerative Grazing?
What Are Some Problems With Regenerative Grazing?
What Do Environmentalists Think of Regenerative Grazing?
For the September 17, 2024 edition of On Pasture, I asked ChatGPT to tell me about regenerative grazing from a variety of perspectives so you could see how you might use it to learn more about topics of interest to you.
Then I thought, “Well folks may want to know how ChatGPT works.” I’ve spent a bit of time learning about how it works, but writing is time consuming. So, for the purposes of demonstration, I just ask ChatGPT to explain itself for me. That turned out to be a useful example as well of how to use it as well.
I started with this question: How does ChatGPT work? I got the very technical explanation you’ll find in green below. I scanned through it and thought, “No regular person wants to read and try to understand that!” So if you scroll down, you’ll see how I asked the question in a new way to get an answer that we’d all appreciate more. I wanted you to see this so you’d have an idea of how you might rephrase questions to get additional information.
There are also problems with AI, and ChatGPT is very forthcoming about what those issues might be. So, after you’ve checked out how it works, scroll a bit further for it’s answer about the problems it presents.
Kathy asks: How does ChatGPT work?
ChatGPT says:
ChatGPT, like other large language models, is powered by a type of artificial intelligence known as a transformer neural network. Here’s a breakdown of how it works at a high level:
1. Training on Text Data
- Massive Data Input: ChatGPT is trained on a vast corpus of text data, which includes books, articles, websites, conversations, and other text-based sources. The model does not understand the content in a human sense, but it learns statistical patterns in language by analyzing how words and phrases tend to occur together.
- Tokenization: Before training, the input text is broken down into smaller units called tokens. These tokens can be words or even subword parts, which allows the model to better handle rare words or complex linguistic structures.
2. The Transformer Architecture
- Neural Network Layers: ChatGPT is based on the transformer architecture, which includes multiple layers of neural networks. These layers process information in parallel, allowing the model to learn context and relationships between words across long sentences or paragraphs.
- Self-Attention Mechanism: The key innovation in transformers is the self-attention mechanism. This allows the model to focus on different parts of a sentence when generating a response. For instance, in a sentence like “The cat sat on the mat, and it purred,” the model can use self-attention to understand that “it” refers to “the cat.”
3. Fine-Tuning for Specific Tasks
- Pre-training and Fine-tuning: Initially, the model undergoes pre-training, where it learns language patterns by predicting the next word in a sequence of text. Afterward, it’s fine-tuned on more specific datasets, such as dialogues, to make it better at responding to questions, instructions, or conversational prompts. During fine-tuning, human trainers provide feedback, and reinforcement learning is often used to guide the model toward more accurate, helpful, and polite responses.
4. Generating a Response
- Input Processing: When you type a question or prompt, the model processes your input by converting it into tokens and mapping them into a multi-dimensional space, called embeddings, which represent the semantic meaning of the input.
- Context Awareness: ChatGPT uses previous conversation context to generate more coherent responses. It remembers what’s been said earlier in the conversation and adjusts its output accordingly.
- Probabilistic Output: The model generates a response by predicting the most likely next token (or word) based on the input and context. This prediction is probabilistic, meaning it generates a list of possible next words with varying probabilities and selects one based on its likelihood.
5. Response Shaping
- Beam Search/Temperature: During the generation phase, various techniques like beam search or temperature scaling help control the randomness or creativity of responses. A lower temperature leads to more predictable, deterministic answers, while a higher temperature makes the model’s output more diverse or creative.
- Token Limitation: The model has a limit on how many tokens it can process in a single interaction (input plus response), which is why long inputs or responses might get cut off.
6. Learning from Feedback
- Reinforcement Learning from Human Feedback (RLHF): ChatGPT is improved through human feedback, where human raters evaluate the model’s responses and provide corrections or preferences. This data is used to fine-tune the model so that it can generate more helpful, relevant, and safe responses.
7. Limitations
- Lack of True Understanding: While ChatGPT is excellent at generating coherent text, it doesn’t “understand” language or concepts in the way humans do. It predicts text based on patterns it has seen but doesn’t have beliefs, knowledge, or reasoning capabilities beyond its training data.
- No Real-Time Knowledge: ChatGPT does not have access to live information unless browsing is enabled. It is trained on data up to a certain cutoff date, so any real-time or recent events after that point are unknown to it.
Summary
ChatGPT works by processing input text through a sophisticated neural network that understands language patterns, uses context, and generates responses based on probabilistic predictions. Its core is the transformer architecture, which allows it to focus on different parts of a conversation and produce coherent, contextually relevant answers. However, it operates purely on statistical relationships in the text and does not have true understanding or real-time awareness.
Kathy’s modified question: Can you say all that in less technical language so a lay person can understand?
ChatGPT says: Sure! Here’s a simpler explanation of how ChatGPT works:
1. Learning from Lots of Text
ChatGPT was trained by reading a huge amount of text from books, articles, websites, and conversations. It doesn’t actually understand the text the way humans do, but it learned patterns in how words are used together. So, it knows how certain words or phrases tend to follow each other.
2. How It Thinks About Sentences
When ChatGPT reads something you type, it breaks down the text into smaller pieces, kind of like splitting sentences into words or even parts of words. It looks at how these pieces relate to each other and then uses that information to figure out what you’re asking or talking about.
3. Responding to You
When you ask a question, ChatGPT looks at the words you’ve typed and tries to predict what the best response would be based on the patterns it learned during training. It doesn’t “know” the answer in the way a person might, but it generates a response that seems most likely based on what it has read before. It also tries to keep track of the conversation, so it can make sense of what was said earlier.
4. Adjusting Its Answers
ChatGPT can be more creative or more direct depending on how it’s set up. If it’s asked to be very precise, it will stick closely to the most likely response. If it’s given more freedom, it can be more varied or imaginative in how it responds.
5. Improving Over Time
The developers improve ChatGPT by showing it examples of good and bad answers. They have people give feedback on its responses, and the model learns from that feedback to give better answers in the future.
6. Limitations
ChatGPT doesn’t truly understand things like a human does. It gives answers based on what it has seen in its training, and it doesn’t know anything that has happened after the last time it was trained. So, it can’t look up recent information or give real-time updates unless it’s connected to the web for that purpose.
In Short:
ChatGPT works by reading a lot of text, learning patterns, and then using those patterns to give you answers that make sense based on what you’ve said. It’s really good at sounding like it understands, but it’s just matching words together based on what it’s learned.
Kathy asked: What are some problems with using AI to answer questions?
Chat GPT said: Using AI to answer questions can be highly useful, but there are several problems and limitations that come with it. Here are some key issues:
1. Lack of True Understanding
- AI, like ChatGPT, doesn’t actually “understand” the content the way humans do. It generates responses based on patterns and data it has seen but lacks real comprehension, context awareness, or reasoning. This can lead to answers that sound convincing but may miss important nuances or be incorrect.
2. Accuracy and Misinformation
- AI can sometimes provide inaccurate or outdated information, especially if it was trained on data that’s no longer current. Even though it tries to generate plausible answers, it may “hallucinate” facts or provide false information because it can’t independently verify sources.
3. Inability to Access Real-Time Information
- Unless connected to the internet, AI models can’t access real-time or live information. They rely on data they were trained on up to a certain point. This means they can’t provide up-to-date answers on current events, breaking news, or recent discoveries.
4. Overconfidence in Wrong Answers
- Sometimes, AI models generate incorrect answers with high confidence, making them appear more reliable than they actually are. This can be misleading, especially if the user assumes the information is correct just because it sounds certain.
5. Bias in Responses
- AI models can reflect biases present in the data they were trained on. If the data contains biased or harmful information (e.g., gender, race, or cultural biases), the model might unintentionally reproduce or amplify these biases in its responses.
6. Lack of Ethical Judgment
- AI lacks ethical reasoning or judgment. It can’t weigh the moral or social implications of its answers, which can lead to issues when answering sensitive or controversial questions. For example, AI might provide information without understanding the broader context of harm it could cause.
7. Dependence on User Input
- The quality of an AI’s answer depends heavily on the quality of the question it receives. If a question is vague, unclear, or lacks detail, the AI might struggle to provide a useful response. AI can’t ask for clarification the way a human might to refine its understanding.
8. Difficulty with Complex Reasoning
- While AI can handle straightforward questions, it struggles with complex, multi-step reasoning or questions that require deep critical thinking. It might fail to connect ideas properly or misinterpret complex scenarios.
9. Limited Creativity and Innovation
- Although AI can generate creative content, it doesn’t truly innovate or think outside the box. It generates responses based on existing patterns, so it may lack true originality and struggle with highly novel ideas or unconventional thinking.
10. Ethical and Privacy Concerns
- Using AI for question-answering raises ethical concerns about data privacy and surveillance. AI models are trained on vast amounts of data, which may include sensitive or personal information. Misuse of AI can also raise issues related to privacy and consent.
11. Over-Reliance on AI
- There’s a risk that people may become too dependent on AI for information, leading to a reduction in critical thinking or research skills. Relying solely on AI-generated answers without cross-checking facts or thinking critically can result in errors or misinformation being perpetuated.
12. Inability to Handle Emotions
- AI doesn’t understand or process emotions like humans do. When answering emotionally sensitive questions, it might provide technically correct but emotionally tone-deaf or inappropriate responses.
In summary, while AI is a powerful tool for answering questions, it has significant limitations in understanding, accuracy, and ethical decision-making. Users should always approach AI-generated answers critically and be mindful of its potential shortcomings.
