Artificial intelligence, machine learning, and deep learning are the hottest technologies in today's commercial world as companies leverage these innovations to build intelligent machines and applications. Although these terms are widely used in business conversations around the world, many people have difficulty distinguishing between them. This blog will help you clearly understand AI, Machine Learning, Deep Learning and how they are different.
Before we get into the technicalities, let's take a look at what technology influencers, industry luminaries, and authors have to say about these three concepts.
“AI doesn't have to be evil to destroy humanity. If AI had a purpose and something happened that prevented humanity from achieving it, it would be possible to have difficult feelings without thinking about it.” ” – 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 if you don't, you'll be a dinosaur in three years.” – Mark Cuban, American Entrepreneur, TV personality.
“In deep learning, the algorithms we use today are versions of the algorithms we were developing in the 1980s and 1990s. People were very optimistic about them, but they didn't work very well. ” – Jeffrey Hinton, father of deep learning.
Although the three terms are often used interchangeably, they do not refer to exactly the same thing.
This is a diagram designed to help you understand the basic differences between artificial intelligence, machine learning, and deep learning.
Artificial intelligence is the concept of creating smart, intelligent machines.
Machine learning is a subset of artificial intelligence that helps build AI-driven applications.
Deep learning is a subset of machine learning that uses vast amounts of data and complex algorithms to train models.
Now let's take a closer look at each of these technologies.
Your AI/ML career is just around the corner!
AI engineer master's programExplore the program
What is artificial intelligence?
Artificial intelligence, commonly referred to as AI, is the process of providing data, information, and human intelligence to machines. The main goal of artificial intelligence is to develop autonomous machines that can think and act like humans. These machines can perform tasks by imitating human behavior, learning and problem-solving. Most AI systems simulate natural intelligence to solve complex problems.
Take Amazon Echo, an example of an AI-driven product.
Amazon Echo is a smart speaker that uses the virtual assistant AI technology “Alexa” developed by Amazon. Amazon Alexa can interact with your voice, play music, set alarms, play audiobooks, and provide real-time information such as news, weather, sports, and traffic reports.
As shown in the image below, this person wants to know the current temperature in Chicago. The human voice is first converted into a machine-readable format. The formatted data is fed to the Amazon Alexa system for processing and analysis. Finally, Alexa returns the desired audio output via Amazon Echo.
Now that we have briefly covered the basics of artificial intelligence, let's take a look at its different types.
|
Find artificial intelligence courses in popular cities |
Types of artificial intelligence
Reactive Machines – These are systems that only react. These systems do not form memories and do not use past experiences to make new decisions.
Limited memory – These systems look to the past and information is added over a period of time. The referenced information has a short shelf life.
Theory of Mind – Describes the system that allows us to understand human emotions and how they influence decision-making. They are trained to adjust their behavior accordingly.
Self-aware – These systems are designed and created to be self-aware. They understand their own inner state, predict the emotions of others and act appropriately.
Application of artificial intelligence
- Machine translation such as Google Translate
-
Self-driving cars such as Google's Waymo
-
AI robots such as Sophia and Aibo
- Voice recognition applications such as Apple's Siri and OK Google
Now that we've covered the basics of artificial intelligence, let's move on to machine learning and see how it works.
Your AI/ML career is just around the corner!
AI engineer master's programExplore the program
What is machine learning?
Machine learning is a field of computer science that uses computer algorithms and analytics to build predictive models that can solve business problems.
According to McKinsey & Co., machine learning is based on algorithms that can learn from data without relying on rule-based programming.
Tom Mitchell's book on machine learning states, “A computer program learns from experience E with respect to some class of tasks T and performance measure P if experience E improves its performance on tasks T as measured by P.'' It is said that.
As you know, there are many definitions of machine learning. But how does it actually work?
How does machine learning work?
Machine learning accesses vast amounts of data (both structured and unstructured) and learns from it to predict the future. Learn from your data using multiple algorithms and techniques. Below is a diagram showing how the machine learns from data.
Now that we have introduced the basics of machine learning and how it works, let's take a look at the different types of machine learning techniques.
Types of machine learning
Machine learning algorithms fall into three main categories:
1. Supervised learning
In supervised learning, the data is already labeled, meaning the target variable is known. This learning method allows the system to predict future outcomes based on past data. To train a model, you need to provide it with at least input and output variables.
Below is an example of a supervised learning method. The algorithm is trained using labeled data from dogs and cats. The trained model predicts whether a new image is of a cat or a dog.
Examples of supervised learning include linear regression, logistic regression, support vector machines, naive Bayes, and decision trees.
2. Unsupervised learning
Unsupervised learning algorithms use unlabeled data to independently discover patterns in the data. The system can identify hidden features from the input data provided. When data is easier to read, patterns and similarities become more apparent.
Below is an example of an unsupervised learning technique that uses unlabeled data to train a model. In this case, the data consists of different vehicles. The purpose of the model is to classify each type of vehicle.
Examples of unsupervised learning include K-means clustering, hierarchical clustering, and anomaly detection.
Your AI/ML career is just around the corner!
AI engineer master's programExplore the program
3. Reinforcement learning
The goal of reinforcement learning is to train agents to complete tasks in uncertain environments. Agents receive observations and rewards from the environment and send actions to the environment. Rewards measure how successful an action is in terms of completing the goal of the task.
Below is an example of how a machine is trained to identify shapes.
Examples of reinforcement learning algorithms include Q-learning and deep Q-learning neural networks.
machine learning process
Machine learning involves seven steps.
machine learning applications
- Sales forecast for various products
- Fraud analysis in banks
- Product recommendations
- stock price prediction
Now that we've covered machine learning and its applications, let's take a look at what deep learning is and how it differs from AI and machine learning.
Your AI/ML career is just around the corner!
AI engineer master's programExplore the program
What is deep learning?
Deep learning is a subset of machine learning that involves algorithms inspired by the structure and function of the human brain. Deep learning algorithms can process huge amounts of both structured and unstructured data. The core concept of deep learning lies in artificial neural networks that enable machine decision-making.
The main difference between deep learning and machine learning is the way data is presented to the machine. Machine learning algorithms typically require structured data, while deep learning networks operate on multiple layers of artificial neural networks.
A simple neural network looks like this:
The network has an input layer that accepts input from data. Hidden layers are used to find hidden features in data. The output layer provides the expected output.
This is an example of a neural network that uses a large set of unlabeled data from the eye's retina. A network model is trained on this data to find out if a person has diabetic retinopathy.
Now that we understand what deep learning is, let's take a look at how it works.
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 a “weighted sum of inputs” as input to the function and adds a bias to decide whether to fire the neuron or not.
- The output layer provides the predicted output.
- The model output is compared to the actual output. After training the neural network, the model uses backpropagation methods to improve the network's performance. Cost functions help reduce error rates.
The following example uses deep learning and a neural network to identify license plate numbers. This technology is used in many countries to identify rule breakers and speeding vehicles.
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 Networks (RNN) – RNNs use continuous information to build models. Models that require remembering past data often work better.
Generative Adversarial Network (GAN) – GAN is an algorithmic architecture that uses two neural networks to create new synthetic instances of data that are passed to real data. A GAN trained on photos can generate new photos that look at least superficially real to a human observer.
Deep Belief Network (DBN) – A DBN is a generative graphic model that consists of multiple layers of latent variables called hidden units. Each layer is interconnected, but the units are not.
Learn from the top in the AI/ML industry with the Caltech Artificial Intelligence course! Enroll now to get started!
deep learning applications
- Cancer tumor detection
- Captionbot for captioning images
- music generation
- image coloring
- object detection
Want to know more?
If you want to start your career in this exciting field, check out Simplilearn's AI courses, offered in collaboration with the California Institute of Technology. This program allows you to dive deeper into the concepts and technologies used in AI, machine learning, and deep learning. You can also work on amazing Capstone projects and earn certificates in all areas of this exciting and lucrative field.
