What is Artificial Intelligence?
Artificial intelligence (AI) is a wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require intelligence possessed by humans. The evolution of AI started with the development of machines that can
- think humanly
- think rationally
- act humanly
- act rationally
The first two ideas concern thought processes and reasoning, while the other two deal with behaviour.
Basic Concepts of AI
AI is an interdisciplinary science with multiple approaches but advancements in machine learning, and deep learning is creating a paradigm shift in virtually every sector of the tech industry. The online AI courses help kids to excel in the area of artificial intelligence and machine learning. In this post, you will discover these 2 basic AI concepts.
1. Machine Learning
Machine Learning (ML) is a science of designing and applying algorithms that are capable of learning things from past cases. If some behaviour exists in past, then you may predict it in the future, but if there are no past cases then you cannot predict.
Machine learning can be applied to solve tough issues like credit card fraud detection, enable self-driving cars, and face detection and recognition.
ML uses complex algorithms that constantly iterate over large data sets, analyzing the patterns in data and facilitating machines to respond to different situations for which they have not been explicitly programmed. The machines learn from history to produce reliable results.
ML systems are made up of three major parts, which are
- Model: The system that makes predictions.
- Parameters: The factors used by the model to form its decisions.
- Learner: The system that adjusts the parameters and in turn the model by looking at the differences in predictions and the actual outcome.
There are 3 major areas of ML:
A. Supervised Learning
Supervised learning has the presence of a supervisor as a teacher. The term supervised learning is used when we teach or train the machine using data that is well labeled. It means some data is already tagged with the correct answer. After that, the machine is provided with a new set of examples (data) so that the supervised learning algorithm analyses the training data (set of training examples) and produces a correct outcome from labeled data.
To understand this let’s consider an example of training a machine to recognize fruits. To do so, it is provided with all different fruits one by like this:
- If the shape of the object is rounded and has a depression at the top, is red in color, then it will be labeled as – Apple.
- If the shape of the object is a long curving cylinder having Green-Yellow color, then it will be labeled as – Banana.
Now after training, a machine is provided with new separate fruit, say Banana, and asked to identify it. Since the machine has already learned the things from the previous data and this time have to use it wisely. It will first classify the fruit with its shape and colour and would confirm the fruit name as Banana. Thus the machine learns things from training data (basket containing fruits) and then applies the knowledge to test data (new fruit).
B. Unsupervised Learning
Unsupervised learning is the training of a machine using information that is neither classified nor labeled and allowing the algorithm to act on that information without guidance. Here the task of the machine is to group unsorted information according to similarities, patterns, and differences without any prior training of data.
For example, a machine is given an image having both dogs and cats which it has never seen before. Thus the machine has no idea about the features of dogs and cats so it can’t categorize it as ‘dogs’ and ‘cats’. But it can categorize them according to their similarities, patterns, and differences and can easily categorize the given picture in two parts. The first containing all pictures having dogs and the second part containing all pictures having cats. It allows the model to work on its own to discover patterns and information that was previously undetected.
C. Reinforcement Learning
Reinforcement Learning is a type of machine learning technique that enables an agent to learn in an interactive environment by trial and error using feedback from its actions and experiences.
Though both supervised and reinforcement learning use mapping between input and output, unlike supervised learning where feedback provided to the agent is the correct set of actions for performing a task, reinforcement learning uses rewards and punishment as signals for positive and negative behavior.
As compared to unsupervised learning, reinforcement learning is different in terms of goals. While the goal in unsupervised learning is to find similarities and differences between data points, in reinforcement learning the goal is to find a suitable action model that would maximize the total cumulative reward of the agent. The figure below represents the basic idea and elements involved in a reinforcement learning model.
Let’s consider an example of a problem where we have an agent and a reward, with many hurdles in between. The agent is supposed to find the best possible path to reach the reward.
Consider the above image showing the robot, diamond, and fire. The goal of the robot is to get the reward that is diamond and avoid the hurdles that are fire. The robot learns by trying all the possible paths and then choosing the path which rewards it with the least hurdles.
D. Deep Learning
Deep learning is an ML technique that teaches computers to do what comes naturally to humans – learn by example. Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign or to distinguish a pedestrian from a lamppost.
In deep learning, a computer model learns to perform classification tasks directly from images, text, or sound. Models are trained by using a large set of labeled data and neural network architectures that contain many layers.
Most deep learning methods use neural network architectures, which is why deep learning models are often referred to as Deep Neural Networks. The term “deep” usually refers to the number of hidden layers in the neural network. Traditional neural networks contain only 2 to 3 hidden layers, while some deep networks can have as many as 150.
Deep learning models are trained by using large sets of labeled data and neural network architectures that learn features directly from the data without the need for manual feature extraction.
2. What are Neural Networks?
Neural networks are a set of algorithms, modeled loosely after the human brain, that is designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling, or clustering raw input. The patterns they recognize are numerical, contained in vectors, into which all real-world data – images, sound, text, or time series must be translated.
Difference Between Machine Learning and Deep Learning
Deep learning is a specialized form of machine learning. A machine learning workflow starts with relevant features being manually extracted from images. The features are then used to create a model that categorizes the objects in the image. With a deep learning workflow, relevant features are automatically extracted from images. Besides, deep learning performs “end-to-end learning” – where a network is given raw data and a task to perform, such as classification, and it learns how to do this automatically.
A key advantage of deep learning networks is that they often continue to improve as the size of your data increases.