AI has become quite a buzzword, but there's a lot beyond those two letters. Let's break down what artificial intelligence actually is, as well as algorithms, machine learning, neural networks, and deep learning.
Let's define artificial intelligence (AI)
Artificial Intelligence (AI) is human-like intelligence demonstrated by machines, and is sometimes referred to as machine intelligence. We use artificial intelligence when we aim to simulate human intelligence to complete tasks and gain certain outcomes, such as problem solving and learning. We can only create this by understanding human intelligence and how humans learn, think, and decide in a way that leads to desired execution and outcome of tasks.
The types of AIs that exist range from “weak/narrow” or single-task oriented to “strong” in which the AI is able to reason and decision-make with human-like intelligence. There are many terms surrounding AI, such as machine algorithms, machine learning, neural networks, and deep learning and we’re going to demystify them below.
When did Artificial Intelligence Begin?
The term artificial intelligence and field itself was first coined and founded in 1956 at a conference at Dartmouth College. AI has experienced periods of investment and interest and periods of little investment due to cost, computational power, and other setbacks. Serious interest in AI started to pick up again in the late 1990s. The type of tasks AI has become capable of has changed exponentially since it was first introduced.
Artificial Intelligence Versus Algorithms
AI exists due to algorithms, but uses “intelligence” to adapt and determine outcomes. Algorithms are far more rigid by design. An algorithm is a set of instructions, with a triggered initiation, that is finite and fully defined. Algorithms are given inputs and produce an expected output. Artificial intelligence as a set of algorithms that is able to cope with unforeseen circumstances and uses its outputs to determine new inputs.
A real life example of an algorithm would be a recipe for a cake. It is a static set of instructions that you follow exactly every time. An algorithm is like a set of instructions a computer must follow every time.
By comparison, an AI would have access to information about the flour, sugar, eggs, and whatever else is important in baking a cake. Then it could be trained on labeled data about what makes a good cake (supervised) or it could learn from watching cakes being made (unsupervised). It would make its own rule about how to bake a cake.
Artificial Intelligence and Machine Learning
Machine Learning (ML) automatically learns and improves from experience without being programmed doing so. There are different types of ML, such as supervised, unsupervised, and reinforced. Machine learning trains learning algorithms to adapt and find patterns in data.
Machine learning is a subset of AI, and the two cannot be separated, as ML is part of AI. It comes down to the data provided. Unstructured data can be used in AI whereas structured data is required for ML.
A real life example of Machine Learning is image recognition. Building a tool that allows for detection and definition of an object within a provided image, like in our chest X-ray use case, is an example of using Machine Learning.
Artificial Intelligence and Neural Networks
Neural Networks are part of AI and machine learning. Neural networks are capable of self-learning. A neural network would have many layers but would provide a singular output.
A real life example of a neural network is your Spotify suggestions. The neural network takes in your music taste and suggests new music for you. It continues to learn from your music habits and gets more accurate over time.
Artificial Intelligence and Deep Learning
Deep Learning is part of AI, machine learning, and comprised of neural networks. It imitates the human brain in terms of how it “learns” by analyzing and processing data and deriving patterns from said data. It can understand patterns without a supervised learning or monitored environment. Deep learning is powered by neural networks, it has many layers of inputs to extract from to make decisions/outcomes.
A real life example of deep learning is Google Assistant. It can detect the language you’re speaking, translate, and carry out assistive tasks such as making phone calls and booking appointments.
23% of organizations already have AI implemented into some part of their process or company. That number is expected to increase with executives and organizations needing AI to increase productivity and decrease costs. AI market is forecast to be a $190 billion (USD) industry by 2025. Artificial intelligence is predicted to have both beneficial and negative impacts for people, countries, and companies. We’re excited to see where it heads. We know that the participants on our platform will create useful tools for personal and business applications that will shape the future of AI, bring about diversity and inclusion in the industry, and make democratize AI.