What is the power of an AI engine?

ChatGPT, Artificial Intelligence (AI), generative learning, reinforcement learning…the list goes on. If you are remotely close to a technical role, you have probably heard one or more of the above, may have worked with a team that works on these techniques and technologies, or you may even be the IC that is writing code and deploying these models. We all know the power, the excitement, and the buzz around them.

What is an AI Engine?

When people hear the term “AI engine”, they may not be familiar with exactly what that is. An AI engine can be defined as a layered approach to AI, where multiple models are used to process and analyze data. The power of an AI engine lies in its ability to leverage multiple models, both traditional models and deep learning methods, to generate intelligent predictions and insights that can help businesses make informed decisions.

How does an AI Engine Work?

The first layer of an AI engine typically involves traditional models and supervised learning methods. These models are based on mathematical algorithms and are designed to make predictions based on a set of input variables. For example, a decision tree model may be used to predict the likelihood of a customer making a purchase based on their browsing history, demographic information, and other factors.

The second layer of an AI engine involves deep learning methods, which are based on artificial neural networks. These models are designed to mimic the way the human brain processes information, allowing them to learn and adapt over time. Deep learning methods are particularly useful in situations where there is a large amount of unstructured data, such as images, audio recordings, and natural language text. This is where the buzzwords come in (generative learning, reinforcement learning, etc) as they are deep learning methods.

What is the Power of an AI Engine?

The power of an AI engine lies in its ability to combine these different models to generate insights that would be difficult or impossible to obtain using a single model. An AI engine can be used in a variety of industries and applications.

An Example in Practice — Using an AI Engine to Supercharge Research

[Full Disclosure, I am an Advisor of CoNote, referenced below]

Now more than ever, building a successful product requires deeply understanding the needs of the user-customers. Here is where an AI engine can demonstrate its power — using both traditional models and deep learning methods to generate intelligent predictions and insights about what customers are looking for. An AI engine built for this could provide researchers and product builders with a competitive advantage by enabling them to make informed decisions based on data-driven insights quickly and efficiently.

As product builders ourselves, we have experienced the painful and tedious work of distilling and pulling out informative insights from multiple iterations of user interviews.

This is why we are building CoNote, leveraging an AI engine to supercharge this type of research. How it works:

• You upload videos
• CoNote transcribes them
• Our AI engine identifies the key text within all the interviews, and classifies and detects Themes and Keywords

At its core, our AI Engine is a software system that uses algorithms and statistical models to:

1. Learn from data — the uploaded interviews and user identified ‘Goals’
2. Identify patterns — Identifying which text is relevant and then pulling out Themes and Keywords insights
3. Make predictions — Understand the context of the interviews through algorithms and then detect Sentiment of Users

If you are curious to learn more, contact me or sign up at conote.ai.