Understanding Artificial Intelligence and Its Technologies Inside Out

Artificial intelligence (AI) continues to reserve a significant spot in global business agendas and strategies. With rapid advancements in computing powers and algorithms, businesses are proactively investing in artificial intelligence services for optimizing operations, lowering costs, and improving services. It is, therefore, imperative for developers, businesses, and data analysts to have comprehensive knowledge about AI and its underlying technologies.

What is Artificial Intelligence?

Artificial Intelligence or AI is a form of intelligence demonstrated by machines by learning from data and human experiences. To quote the father of AI, John McCarthy,

“AI is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.”

Intelligence for computers is the ability to render an assigned task and achieve the goal with minimal resources and maximum efficiency. After World War 2, many researchers began working on AI independently, however, the English mathematician, Alan Turing is supposedly the first one to demonstrate how intelligent machines actually work.

How Does AI Work?

AI aims to reach human-level intelligence by means of computer programming. It is possible by collecting massive amounts of data and combining the same with intelligent algorithms. The resultant, the programmed algorithms, and models are able to learn from large amounts of similar data by extracting patterns and logic from that data.

On a deeper level, AI works with the help of several branches or techniques or tools that combine to make machines more efficient at performing certain human tasks. This toolkit includes-

1. Logical AI for inferring actions and goals
2. Search for possible solutions
3. Pattern recognition to compare results
4. Inference or conclusions
5. Common sense knowledge and reasoning
6. Learning from experience
7. Planning
8. Ontology
9. Epistemology
10. Heuristic, and
11. Genetic Programming

The process of embedding all the above-mentioned knowledge and concepts into a system leads us to identify several subdomains of AI. These sub-domains or subsets enable computers to build innovative AI applications and solutions.

What are the Subsets of AI?

1. Machine Learning

Machine learning is one of the most significant subsets of AI that enables machines to learn from human experiences and given knowledge. Contrary to traditional programming that required tons of coding to perform tasks, machine learning does not require explicit programming. Rather, machine learning works on data or simply output to generate its own rules or programs for performing tasks.

Using historical data, machine learning algorithms can not only identify patterns and solutions but also, over time improve model performance by self-learning.

Further, there are 3 types of machine learning techniques, to elaborate-

a) Supervised learning

Supervised learning is a type wherein machines learn from known datasets to predict the output. For a given dataset, supervised learning models try to match the patterns by matching the exact function. Supervised learning can be further divided into two major types of algorithms- classification and regression.

b) Unsupervised learning

Unsupervised learning involves learning with unstructured datasets that are neither labeled nor classified to assist the model in training. Therefore, under unsupervised learning machines need to learn without corresponding output values. Clustering and Association are two types of unsupervised learning algorithms that are used to handle unorganized data volumes.

c) Reinforcement learning

This type of machine learning involves giving commands to systems wherein with each action the model receives feedback as a reward. Rewards, both positive and negative, improves the performance of the model.

2. Deep Learning

As the name suggests, deep learning is yet another subdomain of machine learning. Deep learning eliminates the need for human intervention for certain identification tasks by using neural networks. It is the reason why deep learning is also sometimes referred to as deep neural networks.

From automated image recognition and object detection to self-driving cars, deep learning is the powerhouse for futuristic AI applications.

The deep learning architecture is made up of multiple layers of nodes with different functions, called Deep Neural Networks or DNN. The segregation of the layers is done as such-

a) An input layer, the first layer that receives data
b) The output layer is the last layer that returns the output, and
c) Numerous hidden layers in between that execute mathematical operations on input data to forward the result to the last layer.

Since each layer is made up of several neurons, all of them stand connected and relay data to generate output.

3. Neural Networks

Neural networks can be best visualized as a human brain that is aimed at simulating to get things done. The motive behind executing a neural network is to mimic human behavior while performing tasks in a reasonable and efficient manner. However, contrary to the human brain, neural networks are software simulations that take place inside CPUs, GPUs, and other IoT devices.

Neural networks constitute the functioning of deep learning. Though we have already studied the architecture of neural networks under deep learning, there’s more to how information flows through a neural network.

The process works by simulating data to generate output and then comparing that output with the expectant result. It is followed by iterating the generated output, a process called back-propagation, wherein the network learns about the differences in performance and simultaneously tries to improve the output by reducing the differences.

4. Natural Language Processing

Natural Language Processing or NLP is another important subdomain of AI and computer science that enables computer systems to understand, analyze, and respond to human languages. By training computer systems with natural human language in the form of intents, we are able to build interactive AI solutions such as Apple’s Siri and Amazon’s Alexa.

Machine learning is the powerhouse of NLP algorithms that train systems to take inputs in natural language and process those intent to provide appropriate output. In addition to understanding natural language, NLP extracts actionable insights from the input data such as the number of customers who opted for certain digital services.

Syntax and Semantics are the two techniques used for analyzing the grammatical structure and context of words in a sentence. Syntax techniques such as lemmatization, word segmentation, parsing, and others help NLP algorithms to learn from data and improve performance.

5. Computer Vision

Although computer vision (CV) as a concept is older than AI itself, the technology could only be accelerated using the prowess of AI. However, both CV and AI being largely associated with computer science, is defined as the science of enabling computer systems to perceive and understand visual images.

Here, the role of deep neural networks (DNNs) come into play. A class of DNNs, namely Convolutional Neural Network wherein each convolutional neuron processes pixels of images for its receptive field. Typically, a CNN has the following three layers-

1. Convolutional Layer that takes the input
2. Pooling Layer for detecting granular details of an image, including color, dimensions, depth, edges, etc.
3. Fully connected layer as the name suggests is a map of interconnected neurons that analyze the information extracted from the above two layers and produces the output.

Applications of computer vision are mainly used for image identification, object detection, facial recognition, sentiment analysis, and caption generation.

6. Robotics

Robotics is the field of designing and building mechanical humans that are wired to perform tasks more efficiently than humans. With the advancements in computer science and AI, robots are becoming smarter and more intuitive. We have already learned about some of the techniques such as computer vision, NLP, and logical reasoning that enable AI and robots to complement each other.

However, there are apparent differences between AI and robots, such as-

1. AI operates in a computer simulated world while robots perform tasks in the physical world.
2. AI is instructed using data and rules, whereas robots work on signals received in the form of images or gestures
3. AI requires general purpose CPUs or GPUs to operate, however, robots need hardware with sensors and effectors to control movements.

Coupled with AI, robots are able to automate physical tasks at manufacturing industries and supply chains by monitoring production, packaging, and delivery.

7. Knowledge Representation

Since AI needs computer systems to operate and furnish results, data scientists require means for effectively communicating the outer world’s information to the computers. Knowledge representation is the very subdomain of AI that handles the representation of critical information so that a computer system is able to gauge what is said and what action it needs to perform.

Knowledge representation in AI systems is done by categorizing the following types of knowledge-

1. All the facts about real world objects, for example, apples are red, types of whales, etc.
2. Actions or events occurring in the world
3. The performance or behavior of doing things
4. Meta knowledge
5. Facts or universal truths about the real world, for instance sun rises in the east.
6. Knowledge base or group of sentences used as data by computers.

Knowledge representation enables developers to not only store information but also apply it to build intelligible AI solutions and applications.

Enterprise-grade Applications of Artificial Intelligence

1. Recommendation Engine

With ability to process data and understand patterns, AI is able to recommend similar results for certain searches or innouts. The application of recommendation systems has already been gaining steam among diverse businesses including-

1. eCommerce for product recommendations
2. Online video streaming for movies or series recommendations
3. Banking for recommending policies and preferable credit schemes
4. Healthcare for medical services recommendations, and more.

The science behind recommendation systems deploy deep learning algorithms that map the preferences of similar users to suggest future products. Collaborative filtering and content-based recommender systems are the two major types of recommendation systems used by eCommerce giants such as Amazon and Flipkart to augment upselling and cross-selling efforts.

2. Predictive Analytics

Predicting future outcomes based on historical data is one of the most significant potentials of AI that is flourishing markets the world over. Using big data techniques such as data computation, orchestration, prediction, modeling, and others, AI is able to perform predictive analytics. The span of predictive analytics applications expand to-

1. Mining data for marketing campaigns
2. Customer segmentation and insight generation for eCommerce platforms
3. Analyzing electronic health records to predict chronic diseases and epidemics among patients
4. Fraud detection and credit risk management for banking and insurance
5. Extracting insights from customer responses and comments on social media, and more.

3. Image Recognition and Object Detection

By channelizing AI’s computer vision capabilities, businesses are able to churn prodigious amounts of visual data such as images, videos, and CCTV footage to analyze movements and draw insights.

Image recognition and object detection applications can be built using open-source machine learning libraries such as TensorFlow and OpenCV. These application are intended to-

1. Recognize images as appropriate and offensive for business use
2. Capture and identify objects in an image such as dog, child, women, etc.
3. Provide instant captions for images
4. Detect defects and anomalies in visual data to alert systems,
5. Understand the context and tonality of the images

Today, global businesses such as manufacturing, online renting (AirBnB), eCommerce, healthcare, and retail businesses are investing in AI’s computer vision technologies to automate and accelerate processes.

4. Chatbots and Virtual Assistants

Virtual Assistants and chatbots have already reserved a sweet spot in critical business strategies for engaging customers and improving their online experience. NLP algorithms combined with intents, and training data constitute the development of AI-powered chatbots and virtual assistants. For global businesses, chatbots bring value to the table with the following benefits-

1. 24×7 availability- In the absence of human resources, AI chatbots can become the face of the brand and address the customer.
2. Resolve routine queries- For every business, chatbots can serve as a ready-to-approach guide for all routine customer queries and FAQs. It enables human customer service agents to focus on more critical tasks.
3. Personalized experience- Various global businesses including eCommerce are deploying chatbots across websites, apps, and social media platforms to address customers in the most preferable way. Also, with intelligible recommendations, chatbots can boost marketing efforts by sending personalized product and service recommendations to customers.

5. Robotic Process Automation

The amalgamation of robotics and AI has propelled the development of industrial robots or machines that are able to automate various industrial tasks without human intervention. Robotic Process Automation or RPA is the very application of robots at manufacturing units, supply chains, pharmaceutical manufacturing houses, and other fields to manage repetitive tasks.

Some of the most promising applications of RPA across industries are-

1. Logistics– Collection and processing of data files, scheduling and tracking shipment, order processing, and capturing closeouts.
2. eCommerce– Onboarding vendors, delivering customer services
3. Manufacturing- Aligning products on belts, identifying and segregating defective items, packaging, warehouse management, and more.

The scope of artificial intelligence is widening with advancements in algorithms and techniques. Early adopters of AI are poised to gain a competitive edge in the coming years.