In the last few years, artificial intelligence has redefined the enterprise’s vision on how to extract insights from data. As per the opinion of most people, it is the next revolutionary technology. One of the PwC’s predictions indicates that AI could contribute $15.7 trillion to the global economy by 2030.

But do you know AI comes in different forms and works in various ways? Hence, understanding the types of AI, and how they work, along with where they might add value is critical. In this blog, we will discuss multiple flavors of cognitive capabilities.

Five important kinds of AI

Let’s break down five types of AI:

1.  Machine learning (ML)

Machine learning is a sub-component of Artificial Intelligence. It can be defined as the algorithms that parse data sets and then learn from it to apply what has been learned to make informed decisions. In the case of Machine learning, the computer program learns from experience by performing some tasks and sees how those tasks’ performance improves with the experience.

It is the state of the art field of AI that is used extensively in developing tools for industry and society. The machine learning algorithms focus on solving real-world issues by automated tasks across industries. These may range from on-demand music services to data security services.

Machine learning algorithms can efficiently work on traditional low-end machines. Machine learning algorithms traditionally can work on their normal rules at any scale of data.  In machine learning, the problem is broken down into several parts, and each part is solved individually. The result is then combined. In machine learning, the algorithms simpler and more transparent rules in the form of a decision tree, for instance, and it is simpler to understand. The linear regressions, decision trees, etc., are easy to interpret and are used extensively in the industry.

2. Deep learning

This branch of AI tries to closely mimic the human mind.

Deep learning is also seen as a subset of machine learning. Deep learning is focused on achieving more power by learning how to represent the world in a hierarchy of concepts. It shows how the concept is related to more straightforward concepts and how there can be less abstract representations for more abstract ones.

It works on the model of continuous data analysis with a logical structure similar to the human brain. It works on many layers of algorithms called Artificial Neural Network (ANN). These networks are identical to the way the biological neural networks of the brain are. Hence deep learning is the field creating ANN that learns and makes intelligent decisions by itself.

In addition to that, like machine learning, it also comes under the category of Artificial Intelligence. But deep learning is related to the part of Artificial Intelligence, which was mostly like humans.

To execute Deep learning algorithms you need special high-end machines with special GPU capacity. Because deep learning algorithms perform many operations, so only special GPUs can fulfill this purpose. Additionally, Deep learning algorithms require huge amounts of data as they don’t perform well on a small data scale. This is because the algorithms understand the detailed data perfectly from large-scale data. Deep learning algorithm involves so many parameters that it takes a long time to train completely. It may take weeks for a perfect deep learning algorithm to train completely. Deep learning uses the end-to-end problem-solving approach. Deep learning algorithms’ internal functioning is based on a deep neural network, which is very complex, and hence, it isn’t easy to interpret. The results become complex.

3.Natural language processing (NLP)

NLP enables computers to understand, interpret, and manipulate human language. NLP shows the importance of natural language processing in artificial intelligence for these voice-activated platforms or chatbots to language translation. Besides, several applications of natural language processing in artificial intelligence clearly indicate how natural language processing in artificial intelligence will shape and improve a new era of communication technology with the power of artificial intelligence, deep learning, and machine learning.

Natural Language Processing (NLP) is an artificial intelligence element that is a combination of AI and linguistics for communicating with machines using natural language. Simple use of NLP is google natural language processing using Google voice search.

As the process name suggests, Natural Language Understanding tries to extract the meaning of the given text. A text can be ambiguous to the process. However, to convert the text, NLU must understand each word’s nature and structure in the text. To resolve the ambiguity, NLU looks for the following:

• Lexical Ambiguity – To check if the words have multiple meanings
• Syntactic Ambiguity – If the Sentence is having multiple parse trees.
• Semantic Ambiguity – If the Sentence is having multiple meanings
• Anaphoric Ambiguity – Phrase or word though mentioned previously but could have a different meaning.

Using lexicons (vocabulary) and a set of grammatical rules, each word’s meaning is understood in this step.

4. Computer vision

Computer Vision is used across the industries and enhancing the consumer experience with reduced cost and increased security. Computer Vision’s market is progressing as fast as its capacities and is estimated to reach $26.2 billion by 2025. This is almost a 30% increase every year. In recent technologies, if Artificial intelligence is the future, then Computer Vision will be the most amazing appearance of it. Computer Vision in artificial intelligence is a field that has gained tremendous advancement with the increased accuracy rates of object identification and classification. Computer Vision, which is in simple terms, trains computers to understand and interpret the visual world. It is a science that combines theory and technology to build artificial systems for obtaining information from images or multi-dimensional data. There are many purposes for it. A significant application is moving a robot through some environment. Computer Vision in artificial intelligence provides a robot with a vision sensor and information about the surrounding environment. The concept of Computer Vision is not new, and it was first commercially used for recognizing handwritten texts using optical character recognition.

You can find multiple definitions of Computer Vision AI. As per the definition provided by Prof. Fei-Fei Li, computer vision is “a subset of mainstream artificial intelligence that deals with the science of making computers or machines visually enabled, i.e., they can analyze and understand an image.”  Computer Vision emulates human vision using digital images.

Computer vision helps machines identify and classify objects – and then react to what they “see.”

As NLP is to speech, computer vision is to sight. 

Computer Vision in artificial intelligence follows three consecutive processes that execute one after another.

• Image acquisition
• Image processing
• Image analysis and understanding

Related post – Top Computer vision trends to look for in 2021

5. Explainable AI (XAI)

Sometimes, you can imagine AI like a black box: you provide input, and then get output. But what happens in between there is very little transparency on it. How the machine got from the input point to the output point is unclear. What is Explainable A!?

Explainable artificial intelligence (XAI) is a set of processes and methods that allows human users to understand and trust the results and output created by machine learning algorithms. Explainable AI is used to describe an AI model, its expected impact, and potential biases. It helps characterize model accuracy, fairness, transparency, and outcomes in AI-powered decision-making. Explainable AI is crucial for an organization in building trust and confidence when putting AI models into production. AI explainability also helps an organization adopt a responsible approach to AI development.