The question of how to ensure that technological innovation in machine learning and artificial intelligence leads to ethically desirable impacts on business has generated much public debate in recent years.
We focus on the practical aspects of Ai Governance helping guide our clients to acceptable outcomes before they move straight into technological innovation. We ensure that all Ai decisions are Explainable, Transparent, and Fair. We also push our clients towards Human-Centric, Ethically Trained systems that are designed to Augment people and processes.
Supervised learning is a type of ML where the model is provided with labeled training data. But what does that mean?
For example, suppose you are an amateur botanist determined to differentiate between two species of the Lilliputian plant genus (a completely made-up plant). The two species look pretty similar. Fortunately, a botanist has put together a data set of Lilliputian plants she found in the wild along with their species name.
In unsupervised learning, the goal is to identify meaningful patterns in the data. To accomplish this, the machine must learn from an unlabeled data set. In other words, the model has no hints how to categorise each piece of data and must infer its own rules for doing so.
An additional branch of machine learning is reinforcement learning (RL). Reinforcement learning differs from other types of machine learning. In RL you don’t collect examples with labels. Imagine you want to teach a machine to play a very basic video game and never lose. You set up the model (often called an agent in RL) with the game, and you tell the model not to get a “game over” screen. During training, the agent receives a reward when it performs this task, which is called a reward function. With reinforcement learning, the agent can learn very quickly how to outperform humans.
The lack of a data requirement makes RL a tempting approach. However, designing a good reward function is difficult, and RL models are less stable and predictable than supervised approaches. Additionally, you need to provide a way for the agent to interact with the game to produce data, which means either building a physical agent that can interact with the real world or a virtual agent and a virtual world, either of which is a big challenge.
There are several subclasses of ML problem based on what the prediction task looks like. In the table below, you can see examples of common supervised and unsupervised ML problems.
|Type of ML Problem||Description||Example|
|Classification||Pick one of N labels||Cat, dog, horse, or bear|
|Regression||Predict numerical values||Click-through rate|
|Clustering||Group similar examples||Most relevant documents (unsupervised)|
|Association rule learning||Infer likely association patterns in data||If you buy hamburger buns, you’re likely to buy hamburgers (unsupervised)|
|Structured output||Create complex output||Natural language parse trees, image recognition bounding boxes|
|Ranking||Identify position on a scale or status||Search result ranking|
The biggest gain from ML tends to be the first launch, since that’s when you can first leverage your data. Further tuning still gives wins, but, generally, the biggest gain is at the start so it’s good to pick well-tested methods to make the process easier. But you don’t need to worry too much about the type or approach, just start with the problem – “What is the problem you are trying to solve?” – and that’s where we come in.