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AI data science: models, analysis, and usage for small businesses
If all your competitors use AI, how can you maintain a competitive edge?
The answer is to leverage advanced AI and data science features that empower you to take AI further than those competitors.
Data science basics
Data Science is the multidisciplinary subject of understanding data. The data could be structured, such as in XML or JSON, or unstructured, such as the billions of data points we find in social media.
AI is an invaluable asset in data science because data scientists can leverage it to process enormous amounts of data and draw conclusions from it.
Recent breakthroughs in language models have made AI more important than ever in data science.
The right tool: why GPTs?
"Generative Pre-trained Transformers"—or GPTs—represent a significant AI and data science technology breakthrough. They're based on transformer architecture which encodes language input into tokens, processes them in parallel to understand the context and the next word in a sequence, then sends the output to a decoder that converts them back to words.
Transformers have significant advantages in language processing because of their ability to provide context and also because of their increased speed.
Popular tools currently leveraging transformer architecture include:
- DALL-E
- Stable Diffusion
- ChatGPT
GPT models have become the de facto standard for text-processing AI.
Garbage in, garbage out
You must work with the correct data to successfully leverage data science in your business. The age-old programming maxim applies: Garbage in results in garbage out.
For example, although GPT is a phenomenal tool, it can't think independently. It needs data to draw conclusions.
As Artificial Narrow Intelligence (ANI) tools, GPT models are specialized in a single or narrow task—language processing. They perform statistical calculations on the encoded tokens they receive to generate their output. It's pure mathematics, and GPT models can base their output only on the data they have.
Because ChatGPT has been trained predominantly on Western Data, particularly data from the United States, its predicted output can sometimes contain biases. This can pose challenges if you want to implement GPT functionality internally in your business or in a chatbot on your website.
Given an excellent tool like a GPT-based model, you then need:
- Correct/appropriate data.
- Correct/appropriate training on that data.
Fortunately, it's entirely possible to improve a GPT model's data.
Improving GPT model data for better business uses
For a GPT model to provide the output you need, you must provide the data you want it to operate from. For example, if you've implemented a ChatGPT chatbot that calls the OpenAI API, you could modify all user prompts to include instructions to obtain data only from your company's data store.
LlamaIndex is a tool that helps you integrate a wide variety of domain-specific company data from multiple sources, including APIs, PDFs, and SQL, to use with a Large Language Model (LLM).
Another option is to skip ChatGPT and opt for an open-source LLM, and then train these models on your own data sets. You can work with a data expert to help you fine-tune your data models to more closely align with company-specific data.
Advanced uses of AI for analysis and other tasks
By programmatically combining data with an automated AI tool, you can start to leverage advanced AI and data science functionality in your business.
For example, AI agents are AI tools that act entirely independently to achieve a predetermined goal. You can ask an AI agent to sift through thousands of emails and then get the agent to perform specific actions based on each email's content. The AI Agent doesn't need a second prompt to continue working; it simply keeps going until it achieves its goal.
Another example is combining ChatGPT functionality with basic programming to automatically monitor sales calls, understand customer sentiment, summarize terabytes of old company data, or search through computer files and process the information.
Keeping human oversight in place along the way is essential regardless of the AI tool you create. AI is a powerful tool to increase productivity, but it's best used with the human touch.