The AI-First Company: How to Compete and Win with Artificial Intelligence

Here’s the formula: data learning effects = economies of scale to data + data processing capabilities + data network effects. Put in a sentence: get lots of data, process it into something useful in terms of making a decision, and create a system that automatically generates more useful data.

take a real-world workflow, develop software around it, and then add further data and features is why I believe that all business software will one day be intelligent.

random forest algorithm

Interactive machine learning (IML) is the field of creating such interfaces that collect data to train a specific ML model and then present the results back to the user so that they can make a decision about which data to put into the model for its next iteration. IML can be thought of as yet another way to have teams of paying users label data rat

There are some promising transfer and semisupervised learning techniques that may provide alternatives to gathering a great deal of labeled data,

titles such as data infrastructure engineer, data engineer, and ML researcher.

appropriate customers from incumbent suppliers by selling a specialized product to a niche segment of customers at a fraction of the cost of the incumbent’s product.

hiring AI-First teams, placing them across the organization, setting up data infrastructure, managing data, and deciding on projects to pursue, but ultimately deferring to business unit managers

the four factors are data (fuel), models (air), people acting on the prediction (chamber and pistons), and the thing acted upon (crank).