Blog: Intro to Artificial Intelligence
This article is adapted from the middle section of a previous AI safety article by AmeliorMate CEO Katie Evanko-Douglas
Artificial Intelligence (AI) and Machine Learning (ML) affect and will continue to affect many aspects of our everyday lives at an increasing rate. Because it affects everybody, it is important for everybody to understand these issues at a basic level.
The content of this blog post seem a gross simplification to people who are technical. It’s purpose is to make basic concepts in AI/ML comprehensible to the non-technical.
What is Artificial Intelligence and Machine Learning?
The Crash Course video below is a short introduction to AI/ML with many visuals and I encourage you to watch it if you’re interested in the technical aspects of basics, especially if you’re an auditory learner.
- Machine Learning (ML) can be defined as: “Algorithms that give computers the ability to learn from data, and then make predictions and decisions.”
- “ML is a set of techniques that sits inside the even more ambitious goal of Artificial Intelligence (AI).”
- Some ML systems use statistical techniques to classify things e.g. if the wingspan of a moth is <X it’s likely Species A and if >X it’s likely Species B, but with many different features at once. In a simplified way, it looks something like this:
- Another technique is to use Artificial Neural Networks wherein “each artificial neuron takes a series of inputs, combines them, and emits a signal.” It looks like this:
- The numbers that are used by neurons in the hidden layer to make calculations start out randomly and are fine-tuned over time, essentially learning from the data. Sometimes people talk about “black box” AI and that’s when we really have no idea what’s going on in the hidden layers and how ML systems are generating their outputs.
- When you get many hidden layers in an artificial neural network, it’s called Deep Learning. Deep learning looks like this:
- Deep Learning especially allows for Reinforcement Learning which is when systems are able to learn through trial and error through experience and data, much like humans.
- The AI/ML systems of today can be classified as Weak or Narrow AI, meaning they can only do specific things such as driving a car or identifying human faces (though some are moving into more general areas).
- Future AI concerns stem from the possibility of creating Strong or General AI which is a well-rounded intelligence like a human, but without the processing constraints our brains posses in their biological incarnations.