Artificial Intelligence Vs. Machine Learning Vs. Deep LearningThe best way to kick off the conversation around the differences between AI, ML, and deep learning is to think of them as a gradient, each is a component of the prior term. Artificial intelligence or AI is the umbrella term that encompasses machine learning, while deep learning is a type of machine learning. Now that you have a basic understanding of how they work together, let’s dive into the specifics of each.
Artificial IntelligenceArtificial intelligence is the umbrella term that encompasses a number of other technologies. AI, in the most simple terms, is a machine that can mimic or embody the characteristics of human intelligence. AI has been a theory and part of storytelling in movies and science fiction novels for decades. AI has also now become a reality. AI is now being used by businesses across industries to automate, predict, and optimize tasks that have been historically done by humans. This is saving business money and time, while also making for happier employees who no longer must complete tedious, repetitive tasks. There are three main categories or types of AI:
- Artificial Narrow Intelligence (ANI)
- Artificial General Intelligence (AGI)
- Artificial Super Intelligence (ASI)
- Robotics: building robots that are self-sufficient, such as Roombas.
- Machine Reasoning: Training AI models to use deduction or induction based on a database or library to make decisions and come to conclusions.
- Machine Learning: These are the algorithms and computer models that are used by machines to perform a specific task.
Machine LearningWhere does AI get its intelligence from? Machine Learning or ML. Machine learning is a subset of the larger field of AI. ML focuses on teaching computers how to learn without being programmed to do specific tasks. It can also refer to the algorithms and models that are used to teach and train AI to do specific tasks. There are three key features in machine learning:
- Datasets: A dataset is a collection of data points or samples. Each data point might be a number, image, word, audio file, or video. Datasets are used to train machine learning models.
- Features: Features are data points that are the solution to the task and teach machine learning models what they’re looking for.
- Algorithms: The algorithm is the process or set of rules the machine learning model uses to parse data and find a conclusion or answer.
Deep LearningDeep learning is a subset of machine learning. Deep learning is differentiated from other types of machine learning based on how the algorithm learns and how much data the algorithm uses. Deep learning requires large data sets, but it needs minimal manual human intervention. Deep learning is intended to mimic the structure of a human brain, with complex, multi-layered neural networks. Data is transferred between neural networks through connecting channels. Deep machine learning models can use labeled data sets to learn, but they don’t necessarily need them. Deep learning models can be taught through supervised or unsupervised learning. One of the most exciting aspects of deep learning for AI is that it can use unstructured or unlabeled data to learn. The ability to have a model which can learn unsupervised is the future of AI.
Key Differences Between AI, ML, and Deep LearningAI, machine learning, and deep learning are all part of the same subject, but it’s important to understand the distinct differences.
- AI is the overarching term for algorithms that examine data to find patterns and solutions. Artificial intelligence resembles the human ability to problem solve. Most AI projects use either machine learning or deep learning.
- Machine learning is a type of artificial intelligence that uses data and an algorithm to solve one or more problems.
- Deep learning is an advanced type of machine learning that uses neural networks to learn and make predictions using unstructured data.