Artificial Intelligence, Machine Learning, and Deep Learning: What’s the difference?

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Artificial intelligence, machine learning, and deep learning have become the hottest technologies in today’s commercial world as companies use these innovations to build intelligent machines and applications. And while these terms dominate business conversations around the world, many people have a hard time distinguishing them.

Today, we will be discussing in detail related to Artificial Intelligence, Machine Learning, and Deep Learning. And before jumping into this topic, let’s get clear what tech entrepreneurs, industry personalities, and authors have to say about these three concepts.

“Artificial Intelligence doesn’t have to be evil to destroy humanity – if Artificial Intelligence has a goal and humanity just happens to be in the way, it will destroy humanity as a matter of course without even thinking about it, no hard feelings.” – Elon Musk, Technology Entrepreneur, and Investor.

“Artificial Intelligence, deep learning, machine learning — whatever you’re doing if you don’t understand it — learn it. Because otherwise, you’re going to be a dinosaur within 3 years.” – Mark Cuban, American entrepreneur, and television personality.

“In deep learning, the algorithms we use now are versions of the algorithms we were developing in the 1980s, the 1990s. People were very optimistic about them, but it turns out they didn’t work too well.” – Geoffrey Hinton, Father of Deep Learning

So, let’s get a simple concept first.

Artificial Intelligence is the idea of creating smart intelligent machines. 

Similarly, Machine Learning is a subset of artificial intelligence that helps you build AI-driven applications.

Likewise, Deep Learning is a subset of machine learning that uses vast volumes of data and complex algorithms to train a model.

What is Artificial Intelligence?

Artificial intelligence, commonly referred to as AI, is the process of sending data, information, and human intelligence to machines. The main goal of artificial intelligence is to develop self-sufficient machines that can think and act like humans. 

Matter of fact, these machines can mimic human behavior and perform tasks through learning and problem-solving. Most Artificial Intelligence systems simulate natural intelligence to solve complex problems.

Types of Artificial Intelligence

Artificial Intelligence
Artificial Intelligence(Source: corporatefinanceinstitute)
  1. Reactive Machines – These are systems that only react. These systems don’t form memories, and they don’t use any past experiences for making new decisions.
  1. Limited Memory – These systems reference the past, and information is added over a period of time. The referenced information is short-lived. 
  1. Theory of Mind – This covers systems that are able to understand human emotions and how they affect decision-making. They are trained to adjust their behavior accordingly.
  1. Self-awareness – These systems are designed and created to be aware of themselves. They understand their own internal states, predict other people’s feelings, and act appropriately.

Applications of Artificial Intelligence

  • Machine Translation such as Google Translate is done with the help of Artificial Intelligence.
  • Self Driving Vehicles such as Google’s Waymo are possible because of Artificial Intelligence.
  • Artificial Intelligence Robots such as Sophia and Aibo
  • Speech Recognition applications like Apple’s Siri or OK Google are also examples of Artificial Intelligence.

We discussed Artificial Intelligence in detail now, let’s move on to Machine Learning.

What is Machine Learning?

Machine learning is a field of artificial intelligence (AI) and computer science, with a focus on using data and algorithms to mimic human learning methods and gradually improve accuracy. 

As per McKinsey & Co., machine learning is based on algorithms that can learn from data without relying on rules-based programming.

Tom Mitchell’s book on machine learning says “A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.”

Machine learning accesses and learns large amounts of data (both structured and unstructured data) and predicts the future. Learn from the data using multiple algorithms and techniques. Below is a diagram showing how the machine learns from the data.

Types of Machine Learning

Machine learning
Types of Machine learning(source: thecustomizewindows)

Machine Learning algorithms are divided into 3 categories.

  1. Supervised Learning

In supervised learning, the data is already labeled. That is, you know the target variable. This learning method allows the system to predict future outcomes based on historical data. To train the model, you need to specify at least one input variable and one output variable in the model.

  1. Unsupervised Learning

Unsupervised learning algorithms use unlabeled data to detect patterns in the data itself. The system can identify hidden features from the input data provided. The easier the data is, the clearer the patterns and similarities.

  1. Reinforcement Learning

The goal of reinforcement learning is to train agents to complete tasks in dangerous environments. The agent receives observations and rewards from the environment and sends actions to the environment. Rewards measure how successful action is in achieving a task goal.

Applications of Machine Learning 

  • Sales forecasting for different products can be done with the help of Machine Learning.
  • Machine Learning helps in fraud analysis in banking
  • Machine Learning also helps in product recommendations
  • Stock price prediction can be done with the help of Machine Learning.

Ok, now let’s discuss Deep Learning in more detail.

What is Deep Learning?

Deep learning is a subset of machine learning that deals with algorithms inspired by the structure and function of the human brain. Deep learning algorithms can handle vast amounts of structured and unstructured data. The core concept of deep learning is an artificial neural network that allows machines to make decisions.

Deep learning (source: mathworks)

The main difference between deep learning and machine learning is the way data is presented to the machine. Machine learning algorithms usually require structured data, but deep learning networks work at multiple layers of artificial neural networks.

How does Deep Learning work?

  • Calculate the weighted sum. 
  • The sum of the calculated weights is passed as input to the activation function. 
  • The activation function takes an “input weighted sum” as the input to the function and adds a bias to determine if it fires a neuron. 
  • The output layer provides the expected output. 
  • The model output is compared to the actual output. After training the neural network, the model uses the backpropagation method to improve the performance of the network. Cost functions help reduce error rates.

Types of Deep Neural Networks

  • Convolutional Neural Networks (CNN)

CNNs are a class of deep neural networks most commonly used for image analysis. 

  • Recurrent Neural Network (RNN) 

RNN uses sequential information to build a model. Often suitable for models that need to remember past dates. 

  • Generative Adversarial Network (GAN) 

GAN is an algorithmic architecture that uses two neural networks to create a new synthetic instance of what is considered real data. Photo-trained GANs can create new photographs that look real, at least on the surface, to human observers. 

  • Deep Belief Network (DBN)

DBN is a generative graphical model consisting of multiple layers of latent variables called hidden entities. The layers are interconnected, but the units are not.

Applications of Deep Learning

  • Deep Learning helps in cancer tumor detection.
  • Deep learning also helps in caption bots for captioning an image.
  • With the help of Deep Learning, music generation can be done.
  • Image coloring can also be done with the help of Deep Learning.
  • Object detection is another factor that can be done with the help of Deep Learning.

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