Executive Summary of Artificial Intelligence
Artificial Intelligence is used in business through machine learning algorithms. Machine learning is a part of computer science focused on computer systems learning to perform a specific task without using explicit instructions, relying on patterns and inference instead.
Machine learning algorithms detect patterns and learn how to make predictions and recommendations by processing data, rather than by receiving explicit programming instruction (‘if then’ loops). The algorithms improve over time with new data coming in, ‘learning’ through examples.
Machine learning is primarily used in:
- predictions: what will happen
- prescriptions: what should be done to achieve goals
- descriptions: what happened
Machine Learning Types
There are three main types of machine learning algorithms: supervised learning, unsupervised learning and reinforcement learning.
Supervised learning uses training data and feedback from humans to learn the relationship of given inputs to a given output (for example how the inputs “date” and “sales” predict customers preferences). Use it if you know already how to classify the input data and the type of behaviour you want to predict, but you want to do it on new data.
Unsupervised learning explores input data without being given an explicit output variable (for example explores customer sales data to identify patterns and classify them). Use it when you want to classify the data, but you’re unsure how to label the data yourself or you want to discover hidden patterns.
Reinforcement learning learns to perform a task by trying to maximize rewards which you prescribe for its actions (for example maximize returns of an investment portfolio). Use it when you have limited training data and you cannot clearly define the end goal or you want to explore possibilities without assuming what the solution might be.
The most common framework for doing machine learning is Python as a programming language. Experiments with machine learning models require usually access to powerful computers to ‘train’ algorithms. That’s why the additional cost to doing AI is the cost of the cloud, when data scientists train their models. Those can range from couple hundred dollars per month to millions of dollars, depending on how heavy is the data and machine learning architecture. For most businesses the cost won’t exceed couple thousands dollars per month, unless they want to heavily invest in AI capabilities and train their own models, rather than use largely use pre-trained, open source solutions.
The most common architecture for machine learning algorithms are neural networks. You can think of them at Lego blocks of different sizes and colours which you can mix together in order to build a specific construction. The basic parameter of a neural network is how many layers it has and how those layers interact with each other.
Deep learning is a subfield of machine learning which focuses on neural networks with at least 3 layers. This allows usually to lower amount of data needed for training of algorithms, but it often result in higher costs in training when it comes to required computing power (that is, dollars to spend on the cloud).
Deep learning is the true reason why AI is so popular today, as its applications in image or voice recognition are far better than classical methods. Neural networks combined with enough computing power give outstanding results on real world data.
Big data and Artificial Intelligence
‘Big data’ is another buzzword used in the last decade often. Big data never had a proper definition, always meaning having more data than is possible to process using single personal computer. That’s why what we today understand as big data (petabytes of data) is far away from used to be big data just 10 years ago (terabytes) and how it will change in the next 10 years.
As data is crucial for machine learning algorithms, ‘big data’ is coming back in organizations as a crucial term to explore AI capabilities. Machine learning requires that the right set of data be applied to a learning process. You don’t need big data to use machine learning algorithms, but big data can help you improve the accuracy of your algorithms.
That’s why often it’s not necessarily true that you need a lot of data to start experimenting with AI. Especially with the raise of reinforcement learning and techniques like ‘one-shot learning’, AI is within reach for every single organization. The first step to benefit from AI is to prepare data, by cleaning it and sorting by human coworkers. Then machine learning engineers and data scientists will be able to take care of the rest.
This text is an excerpt from my upcoming book “Artificial Intelligence Business”. If you want to read more about Data Science job market, have a look at my other book Data Science Job: How to become a Data Scientist.