Artificial Intelligence - The Future

Artificial Intelligence has been there for quite some time. But it has been gaining popularity rapidly in the past few years. So what is Artificial Intelligence? Artificial Intelligence, or AI, is the ability of machines or systems to mimic human intelligence and cognitive behaviour to perform tasks. Artificial Intelligence can be categorized into Machine Learning, which can further be classified into Deep Learning.

Levels of Artificial Intelligence

Intro to Artificial Intelligence

Artificial Intelligence further has three components:

• Data Science - Data science uses maths and statistics, specialized programming, and advanced analytics to discover trends and patterns, and uses the discovered trends to train AI models which can predict future trends.
• Computer Vision - Computer Vision is the field of Artificial Intelligence that enables computers to derive meaningful information from visual data. Computer Vision enables computers to see and visualize the world.
• Natural Language Processing - Natural Language Processing enables computers to understand the text and spoken words similar to human beings. It includes speech recognition, speech synthesis, chatbots, and sentiment analysis.

Narrow AI vs General AI

Now, on the basis of tasks, artificial intelligence can perform, it can be categorized into:

• Narrow AI or Weak AI
• General AI or Strong AI

Narrow Artificial Intelligence

Narrow AI is a type of AI designed to perform specific tasks. The AI that exists around us today is Narrow AI. A Narrow AI designed to perform a specific task cannot perform any other task. Narrow AI also lacks self-awareness and human consciousness.

General Artificial Intelligence

General Artificial Intelligence does not exist and is fictional to this date. General AI creates intelligent machines that are indistinguishable from the human mind. General AI is under development and researchers are working on it.

Machine Learning

Machine Learning is a branch of Artificial Intelligence that focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy. Machine learning algorithms build a model based on training data, in order to make predictions or make decisions without being explicitly programmed to do so.

Algorithms

• Linear Regression
• Logistic Regression
• Support Vector Machine
• Naive Bayes
• K-Nearest Neighbours
• Decision Tree Classifier
• Random Forest Classifier
• K-Means Clustering
• DBSCAN Clustering

Deep Learning

Deep Learning is a subfield of Machine Learning which is based on artificial neural networks with representation learning. It is essentially a neural network with three or more layers. Deep learning duplicates the behaviour of the human brain. Most deep learning models use neural networks, that's why these models are also known as deep learning neural networks.

Source: IBM

Algorithms

• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNN)
• Long Short Term Memory Networks (LSTMs)
• Generative Adversarial Networks (GANs)
• Multilayer Perceptrons (MLPs)
• Radial Basis Function Networks (RBFNs)
• Self Organizing Maps (SOMs)
• Autoencoders

Types of Learning

1. Supervised Learning - Supervised Learning is a type of learning in which labelled data is provided to train the model. The model uses labelled data to learn and make predictions. Some methods used in supervised learning include neural networks, naive bayes, linear regression, logistic regression, random forest, and support vector machine (SVM).
2. Unsupervised Learning - Unsupervised learning is a type of learning that uses unlabelled data to train the model. The model learns from the data, discovers the patterns, trends, and features in the data, and returns the output. Principal component analysis (PCA) and singular value decomposition (SVD), neural networks, k-means clustering, and probabilistic clustering methods are some common algorithms used for unsupervised learning.
3. Reinforcement Learning - Reinforcement Learning is a type of learning which trains a machine to take suitable actions and maximize its rewards in a particular situation. In this learning technique, there is no predefined target variable. It works on a reward-based system. The model is rewarded for every correct prediction it makes. Q-learning, state-Action-Reward-State-Action (SARSA), and Deep Q-network are some commonly used algorithms for reinforcement learning.

Final Thoughts

Artificial Intelligence is a groundbreaking technology. It has enormous potential to create a better and brighter future for all, for our planet. It should be used very carefully and judiciously. Artificial Intelligence enables us to achieve the goals that were considered arduous. It is certain that AI will replace many jobs that require human attributes and labor, but it also has got great job opportunities. According to US Bureau of Labor Statistics, employment growth of 35.8% growth is expected in Data Science, which is fundamental to AI, by 2031.

Originally published on jishnupsamal.me