Difference between AI | ML | DL | Data Science
Artificial Intelligence
The term AI was first coined by John McCarthy in 1956 to discuss and develop the concept of “thinking machines,”. Artificial Intelligence, fondly abbreviated as AI, is concerned with imparting human intelligence to machines. It focuses on the development of intelligent machines that can think and act like humans.
An intelligent agent is a device that can perceive its environment and act to optimize its chances of success. Such intelligent machines mimic human cognitive functions like learning and problem-solving.
AI is a broader field (i.e. the big umbrella)that contains several subfield such as machine learning,robotics, and computer vision.
3 Types of Artificial Intelligence
- Artificial Narrow Intelligence (ANI) : This is the most common form of AI that you’d find in the market now. This is the only kind of Artificial Intelligence that exists today. These Artificial Intelligence systems are designed to solve one single problem and would be able to execute a single task really well. By definition, they have narrow capabilities, like recommending a product for an e-commerce user or predicting the weather.
- Artificial General Intelligence (AGI) : AGI is still a theoretical concept. It’s defined as AI which has a human-level of cognitive function, across a wide variety of domains such as language processing, image processing, computational functioning and reasoning and so on.
We’re still a long way away from building an AGI system. An AGI system would need to comprise of thousands of Artificial Narrow Intelligence systems working in tandem, communicating with each other to mimic human reasoning. - Artificial Super Intelligence (ASI) : We’re almost entering into science-fiction territory here, but ASI is seen as the logical progression from AGI. An Artificial Super Intelligence (ASI) system would be able to surpass all human capabilities. This would include decision making, taking rational decisions, and even includes things like making better art and building emotional relationships. It’s a long way ahead to create such a system.
Top Used Applications in Artificial Intelligence
- Google’s AI-powered predictions (E.g.: Google Maps)
- Ride-sharing applications (E.g.: Uber, Lyft)
- AI Autopilot in Commercial Flights
- Facial Recognition
- Song or TV show recommendations from Spotify and Netflix
- Smart personal assistants (E.g.: Siri, Alexa)
- Social media monitoring tools for dangerous content or false news
Machine Learning
Machine learning (ML) is a subfield of Artificial Intelligence that enables machines to improve a given task with experience. It is the study of computer algorithms that improve automatically through experience and by the use of data. Machine learning algorithms build a model based on sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to do so.
3 Types of Machine Learning
- Supervised Learning : It is the most popular paradigm used where there is a precise mapping between input-output data. The dataset, in this case, is labeled, meaning that the algorithm identifies the features explicitly and carries out predictions or classification accordingly. As the training period progresses, the algorithm is able to identify the relationships between the two variables such that we can predict a new outcome.
- Unsupervised Learning : In the case of an unsupervised learning algorithm, the data is not explicitly labeled into different classes, that is, there are no labels. The model is able to learn from the data by finding implicit patterns. Unsupervised Learning algorithms identify the data based on their densities, structures, similar segments, and other similar features.
- Reinforcement Learning : It covers more area of Artificial Intelligence which allows machines to interact with their dynamic environment in order to reach their goals. With this, machines and software agents are able to evaluate the ideal behavior in a specific context. With the help of this reward feedback, agents are able to learn the behavior and improve it in the longer run. This simple feedback reward is known as a reinforcement signal.
Deep Learning
Deep Learning is a specialized field of Machine Learning theat relies on training of Deep Artificial Neural Networks (ANNs) using a large dataset such as images or texts. ANNs are information processing models inspired by the human brain. The human brain consists of billions of neurons that communicate to each other using electrical and chemical signals and enable humans to see, feel, and make decision. ANNs work by mathematically mimicking the human brain and connecting multiple “artificial neurons” in a multilayered fashion. The more hidden layers added to the network
the deeper the network gets.
What differentiates deep learning from machine learning techniques is in their ability to extract features automatically as illustrated in the following example:
- Machine learning Process: 1. selecting the model to train, 2. manually performing feature extraction.
- Deep Learning Process: 1. Select the architecture of the network, 2. features are automatically extracted by feeding in the training data (such as images) along with the target class (label).
Data Science
Data Science is the term for a whole set of tools and techniques by which to analyze data and extract insights from it. It makes use of scientific methods, processes, and algorithms to make this happen.
Essentially, its goal is to discover hidden patterns in raw data to help businesses improve and increase their profits. The term came to be a buzzword when in 2012, Harvard Business Review called it “The Sexiest Job of the 21st Century”.
I hope this article clears some air in the minds of people who are new to this field about what exactly AI, ML, DL and Data Science mean and their difference.
If you want to dive deep, you can also check my article on understanding Natural Language Processing.
