It sounds intimidating and highly complicated, but is machine learning all hyperbole or could it be the hero you need for your legal department?
What is machine learning?
Machine learning is often used interchangeably with Artificial Intelligence (AI). This is incorrect - machine learning is a subset of AI, which is a broader term used for intelligent machines that can mimic human understanding.
How does it work?
To ‘learn’ machine learning algorithms are divided into three flavors. The one you use will depend on how your data is classified, what dataset you have, and the condition of the data you use.
The most popular archetype for machine learning is supervised learning. Most machine learning is based on supervised learning. This is where the data is labeled or classified to tell the machine exactly what patterns it should look for.
Unsupervised learning is the opposite of supervised learning. All the data is unlabeled or unclassified and the machine looks for whatever patterns it can find and groups the data according to those patterns.
Reinforcement learning is where the algorithm uses a trial and error method to come to a clear objective. It’s a game-like scenario where the machine is rewarded or penalized for coming up with the right solution.
Why use machine learning in legal? It’s designed to alleviate the laborious and tedious work, leaving lawyers free to focus on the reason they became lawyers in the first place - to provide the best client counsel and advice and ensure a successful outcome every time for every client.