How to Train Your AI: Unleashing Those Digital Dragons

Did you know that the cut-off point for GPT-4's training data was September 2021? That's two years ago – are the folks at OpenAI going to release an updated model soon? And how is it possible that ChatGPT knows about things that happened in 2022? Let's talk about the various building blocks and techniques that are involved here, and how to keep them up-to-date with the ever-evolving world of information.

Most companies that say they're using AI to power their services are in fact using OpenAI's GPT-4, which came out in March this year. Did you know that the "P" in "GPT" stands for "Pre-trained"? This means that the model has already been trained on a vast amount of data. OpenAI's CEO has revealed that for GPT-4 this involved a cost of more than $100 million. Does it mean that each time some country wins the world cup or a new scientific breakthrough occurs, they have to spend another $100 million to update the model? Not exactly. There are several ways to keep a smart application up-to-date without breaking the bank, and the key is to make sure we understand the anatomy of an AI system and how its various components work together to produce accurate and relevant information.

5 Levels of AI: From Pre-Training to Incremental Learning

To fully understand the inner workings of AI applications and systems, it's important to examine their structure. ChatGPT, for instance, is an application that uses several components, with the language model being just one essential part. By looking at the different elements that make up an AI system like ChatGPT, we can gain insights into how each component contributes to the overall functionality and performance of the application that users interact with and perceive as intelligent.

From pre-training the language model to incorporating user feedback loops, each level plays a crucial role in shaping the model's capabilities. Understanding these stages provides valuable insight into how AI systems like ChatGPT are designed and optimized for their intended applications. Below, we will explore each level in detail and discuss the significance of each step in the AI training process.

1. Pre-Training a Language model

During this initial stage, the language model undergoes training on an extensive dataset, encompassing a diverse range of topics and sources. This comprehensive training process enables the creation of a fixed model with a solid foundation in understanding language patterns, grammar, and semantics. As a result, the model becomes capable of generating coherent and contextually relevant responses when deployed in various applications. At this initial stage, the language model is exposed to a vast amount of data from various sources, such as Wikipedia, social media posts, books, and more. This comprehensive training process allows the model to build a foundational understanding of human language patterns, grammar, and semantics. By assimilating information from diverse resources, the model also develops a general world view and gains insights into how humans converse. Consequently, this creates a fixed model that's capable of generating coherent and contextually relevant responses when used in different applications.

This pre-training process, while highly effective, is also quite expensive and resource-intensive. When you use GPT-4 through a service provided by OpenAI or Microsoft, the good news is that you don't have to shoulder the expenses associated with its initial training. Instead, you can access the benefits of this powerful language model as a convenient and cost-effective solution. However, the trade-off is that you cannot modify the model at this foundational level. This limitation means that any changes or updates need to be made through subsequent steps in the AI training process.

As AI systems become more sophisticated, it's crucial to ensure their responsible use and prevent potential misuse. OpenAI acknowledges this responsibility by incorporating guardrails and security measures around the base model. These added layers aim to strengthen robustness, mitigate risks, and combat abuse.

While specific details regarding these security implementations are intentionally kept vague, this approach is designed to protect the integrity of AI systems like GPT-4 from malicious actors who may seek to exploit vulnerabilities or extract sensitive information. By continuously refining guardrails and keeping their inner workings under wraps, OpenAI can maintain a safe environment for users while preserving the effectiveness of its AI models.

It is important to note that these security measures are not one-time additions but rather an ongoing process of improvement and adaptation. As new threats or challenges emerge in the rapidly evolving world of AI, OpenAI remains committed to safeguarding user trust and ensuring that their models remain both powerful and responsible tools for various applications.

In summary:

• Training data: The model is trained on a vast dataset, including Wikipedia, social media posts, books, and various other sources.
• Learned skills: The language model acquires an understanding of language patterns, grammar, and semantics while also developing a general world view and knowledge of human conversation.
• Difficulty and expense: Pre-training the language model is resource-intensive and highly expensive (over $100 million for GPT-4), but users can access it through convenient services provided by OpenAI or Microsoft without bearing these costs directly.

2. Prompt Engineering

You will have heard of this concept before – especially when using ChatGPT – as it involves designing the right input prompts to guide the AI model towards generating the desired output. For interactive tools like ChatGPT this usually means crafting questions or statements that clearly communicate the user's intent and provide enough context for the model to understand and generate a relevant response.

However, when you build user-facing apps like ChatGPT, can use prompt engineering to set the context tone, and guide the AI model towards the direction you want it to take in its responses. Even more, you could use the context window to provide additional information or constraints that help the AI model stay on track and produce more accurate, focused, and relevant answers to user queries.

To some extent, this is a "training" process that involves trial and error, as developers experiment with different prompts and context settings to achieve the desired results. You could "train" it on facts like what the current date is, or specific events that have occurred recently, which can help the model generate responses that are more accurate and up-to-date. The personality of an AI chatbot could be a detailed character description, a back story, or even a set of values and principles that the chatbot should adhere to while generating responses. This one is invisible to users, but it shapes the AI's behavior and ensures that it aligns with the intended purpose and user experience.

When working with AI models like GPT-4, you have remarkable flexibility in crafting prompts and context settings. In essence, the English language becomes your programming language, allowing you to guide the AI's behavior through creative input design. However, it is essential to be mindful of the context window size constraint, which limits the amount of information that can be fed into the model at once. Striking a balance between providing sufficient context and staying within this limitation is crucial for obtaining accurate, focused, and relevant responses from your AI-powered applications.

Request and Completion Tokens: Costs and Considerations

When working with AI-powered applications like ChatGPT, it's essential to understand the concept of request and completion tokens, as they directly impact the costs associated with using the service. Tokens are the fundamental units of text in an AI model and can represent characters or words, depending on the language.

Request Tokens

Request tokens comprise both input (prompt) and output (response) tokens generated during an interaction with the AI model. The number of tokens in a request affects how quickly you receive a response from the AI, as processing time increases with more tokens.

To manage costs effectively while using an AI service, it's crucial to be mindful of token count in your prompts. Longer prompts consume more tokens and may lead to slower response times and higher usage costs. Striking a balance between providing enough context for accurate responses and minimizing token count is key to optimizing your application's performance.

Completion Tokens

Completion tokens determine the length of generated responses from the AI model. By setting a limit on completion tokens, you control how much information is provided in each response. Reducing completion tokens can help save on API usage costs but may result in shorter or less detailed answers. On the other hand, increasing completion tokens could yield more elaborate responses that better address user queries but may also incur higher expenses.

It's important to find an optimal balance between cost management and providing satisfactory answers when setting completion token limits for your application.

In summary:

• Training data: Natural language text serves as the input for the AI model, guiding its behavior and responses. These could be detailed character descriptions, background stories, or a set of values and principles that the AI should adhere to while generating responses.
• Learned skills: The AI acquires the ability to understand context, tone, and user intent based on the crafted prompts and context settings.
• Difficulty and expense: Prompt engineering demands language skills, creativity, and trial-and-error. However, it provides an easy and cost-effective method to customize the AI's behavior and keep it up-to-date without retraining the entire model, although it does incur costs for tokens.

3. Fine-tuning a Language Model

An interesting way to feed a considerable amount of new information into the AI model without retraining it from scratch is through fine-tuning. Fine-tuning is a process that involves tweaking the pre-trained language model to adjust its performance in specific areas. This is done by feeding it additional data related to the desired topic, such as a new set of books, or recent news articles. Through this process, the AI model can gain an understanding of the new information without having to go through the entire pre-training stage again.

The amount of information that can be fed into the model during fine-tuning is significantly larger than what can be provided through prompt engineering, making it an efficient method for updating the AI's knowledge base and improving its performance in specific areas. However, fine-tuning requires a solid understanding of optimization techniques for language models and may be more resource-intensive than prompt engineering. GPT-4 fine-tuning is currently not possible, but it is expected that future iterations or alternative models may allow for this level of customization, enabling developers to create more tailored AI solutions for their specific needs and applications.

With GPT-3, OpenAI offers users the ability to fine-tune the model, which, at a high level, includes the following steps:

1. Prepare and upload training data: Collect and organize a dataset that addresses the unique requirements of your application: This dataset is a structured set of data that differs from the free-text format used in prompt engineering. While this structured approach offers benefits such as increased organization and clarity, it may present challenges for those who prefer the flexibility and informality of free-text data.
2. Train a new fine-tuned model: Utilize this specialized dataset to refine the pretrained GPT-3 model, ensuring it aligns with your application's objectives. For GPT-3, this fine-tuning process will run on OpenAI's infrastructure, and the process may take a few hours or longer, depending on the size of your dataset and the complexity of your application requirements.
3. Use the fine-tuned model: Technically speaking, the fine-tuned model functions as a separate model from the original GPT-3, tailored to your specific needs and use cases. However, it will be accessible to your application just like the original GPT-3 model, allowing you to easily integrate it into your existing AI-powered tools and services.

In summary:

• Training data: A structured dataset tailored to the specific requirements of your application is used for fine-tuning.
• Learned skills: Fine-tuning allows for higher quality results, more extensive training, and lower latency in API-driven AI models.
• Difficulty and expense: Fine-tuning requires a deeper understanding of optimization techniques for language models and offers token savings due to the need for shorter prompts.

4. Embeddings and Plugins

Embedding enables the model to search within a "database" and return the most relevant result, making it ideal for finding specific information or preserving conversation context for subsequent use. Depending on the technology you use, these embeddings could involve API calls, database queries, or other methods of searching and retrieving data.

If you are a ChatGPT user, you may have noticed the introduction of the plugins feature. These plugins are designed specifically to assist ChatGPT in accessing up-to-date information, performing computations, and integrating with third-party services.

As a developer working on AI applications, you will have to look into various aspects of the development process. This includes creating the application logic using languages like Python, building connections with other systems, making API calls, and even possibly create those APIs you want to call. Additionally, you will have to integrate the responses from plugins back into the prompts, and ensure that the AI model can effectively interpret and utilize the information provided by these plugins to generate accurate and relevant responses for users.

Not an easy task, embedding and incremental learning require technical expertise and a strong understanding of the AI models, the systems they interact with, prompt engineering, and the outcomes your user expects from the application. However, when done correctly, these techniques can significantly enhance the AI's capabilities, improve its knowledge base, and create a much more robust and engaging user experience.

In summary:

• Training data: Embeddings and plugins involve integrating relevant data sources, APIs, and third-party services to provide accurate, up-to-date information for the AI model.
• Learned skills: The AI model learns to utilize the information provided by these integrations to generate more precise, context-aware, and useful responses for users.
• Difficulty and expense: Implementing embeddings and plugins requires technical expertise, a strong understanding of AI models, and the ability to effectively integrate these components into the application

5. User Feedback Loop and Incremental Learning

The process of continually collecting user feedback plays a vital role in enhancing an AI model's performance. User interactions, whether through conversation or features like thumbs up/down buttons, serve as a rich learning experience for the AI. By capturing and analyzing these interactions, the AI can adapt to new information and user preferences over time.

Incorporating user feedback into the AI's learning cycle allows it to refine its understanding of semantics and context. This information can be sent back to the developers managing applications like ChatGPT, who can then filter and clean the user data. Using this refined data, developers can employ any of the previously discussed methods to retrain and improve the AI model. Integrating this feedback loop into your development process may require implementing Machine Learning Operations (MLOps) best practices to ensure seamless collaboration between your data science and engineering teams.

By establishing a robust user feedback loop and incorporating incremental learning, AI models can evolve and improve over time, ensuring that they remain relevant and up-to-date with the latest information. This continuous improvement process not only enhances the AI's capabilities but also fosters trust and satisfaction among users, as they witness the system adapting to their needs and preferences. Ultimately, this iterative approach to AI development helps create more engaging, effective, and user-centric applications.

In summary:

• Training data: User feedback and interactions serve as valuable learning experiences for the AI model.
• Learned skills: The AI model refines its understanding of semantics, context, and user preferences based on the feedback collected.
• Difficulty and expense: Implementing a user feedback loop and incremental learning requires MLOps best practices and close collaboration between data science and engineering teams. You could, of course, use any of the other methods discussed earlier, such as prompt engineering or fine-tuning, to incorporate this feedback and improve the AI model's performance.

Putting it All Together: Multi-Level Learning

To create a truly versatile and adaptive AI, you could also combine several of the methods we discussed earlier. By integrating prompt engineering, fine-tuning, embeddings, plugins, and user feedback loops, you can develop an AI system that learns on multiple levels and continuously improves its performance.

1. Start with prompt engineering: Begin by crafting effective prompts that provide context and guide the AI towards generating accurate responses. This step serves as the foundation for your AI application and helps establish the desired behavior and tone.
2. Incorporate fine-tuning: If your model allows fine-tuning (e.g., GPT-3), use this technique to adapt the base model to specific requirements of your application. Fine-tuning enables better alignment with your objectives and improved performance in targeted areas.
3. Enhance capabilities with embeddings and plugins: Integrate relevant data sources, APIs, third-party services, or even custom-built plugins to provide up-to-date information and computational capabilities. These integrations allow the AI model to generate more precise, context-aware responses based on real-time data.
4. Establish a user feedback loop: Collect user interactions and feedback to understand their preferences and needs better. Analyze this information to identify areas of improvement in the AI's performance.
5. Implement incremental learning: Use insights from user feedback to iteratively improve the AI model through prompt engineering adjustments, additional fine-tuning (if applicable), or refining plugin integrations. This continuous improvement process ensures that your AI remains relevant and up-to-date with changing user expectations.

By integrating these methods into a unified development process, you create an AI system capable of learning at various levels. From mastering language patterns and semantics through pre-training, adapting to specific application requirements via fine-tuning, accessing real-time data using embeddings/plugins, refining its behavior through prompt engineering, and continuously evolving based on user feedback and incremental learning. This approach allows you to create powerful AI applications that not only cater to users' needs but also continually improve, but also adapt, and expand their capabilities to deliver even better performance and more engaging user experiences over time.