Home > AI Tutorial

AI Tutorial

Hadoop Architecture in Big Data: YARN, HDFS, and MapReduce

Hadoop Architecture in Big Data: YARN, HDFS, and MapReduce

What is Hadoop? | What is Hadoop Architecture? | HDFS Architecture | YARN Architecture | MapReduce | The Takeaway

Do you want to know more about the Hadoop Architecture in Big Data? HDFS, MapReduce, and YARN are the three important concepts of Hadoop. In this tutorial, you will learn the Apache Hadoop HDFS and YARN Architecture in details. 

Hadoop Architecture Tutorial YARN HDFS

What is Hadoop?

Hadoop is an open source framework that allows for the distributed processing of large datasets across clusters of computers using simple programming models. It is from Apache and is used to store process and analyze data which are very huge in volume. The summary of the Hadoop framework is as follows:Read More »Hadoop Architecture in Big Data: YARN, HDFS, and MapReduce

AWS vs Azure vs Google Cloud A Comparative Review

AWS vs Azure vs Google Cloud: A Comparative Review

A comparative review of the AWS vs Azure vs Google Cloud is very important in the modern context. Code free complete machine learning lifecycle is the key trend for the modern cloud computing platforms.

As companies are going for more on code free deep learning and other high-end IoT technologies, selecting suitable cloud platforms are vital for corporate growth. In this article, we’re going to help you decide between the three giants of cloud computing.

Best of AWS Azure and Google Cloud Platforms

Cloud computing Foundation

Before going into the prominence in the cloud market, Google, Amazon and Microsoft are the market leaders in their respective fields. Each has unique advantage. All these three major cloud providers have also attempted to create many general-purpose services that are relatively easy to use by the end users. 

Price, speed, performance, flexibility, and features are the five key criteria normally used to select the best cloud computing platform for your jobs. 

The cloud computing makes it easy for enterprises to experiment with machine learning capabilities and scale up as projects go into production and demand for those features increases.

GPUs are the key hardwires and the processors in the cloud computing  of choice for many complex machine learning applications because they significantly reduce processing time.… Read more..

Transfer Learning Basic Concept and the Building Blocks

Transfer Learning A Step by Step Easy Guide

Considering the lengthening timelines for deep machine learning and AI projects to fight against COVID-19 the interest in transfer learning has grown significantly. Transfer learning for deep machine learning is the process of first training a base network on a benchmark dataset (like ImageNet), and then transferring the best-learned network features (the network’s weights and structures) to a second network to be trained on a target dataset. This idea has been shown to improve deep neural network’s generalization capabilities significantly in many application areas.

Transfer learning is currently used in almost every deep learning model when the target dataset does not contain enough labeled data. Building deep learning models from scratch and training with huge data is very expensive, both in time and resources. Transfer learning is very effective for rapid prototyping, resource efficiency and high performance. As human brain carry forward knowledge and wisdom and learn it from others, transfer learning mimic this type behavior.   

Transfer Learning Base Models

To design an efficient neural network model, you need to know the details of different base models. Because from the base model you will be transferring the knowledge to your new model. Here, knowledge means the network structures and the weights.… Read more..

Key Artificial Intelligence Projects to Fight Against COVID-19https://amitray.com/artificial-intelligence-to-fight-against-covid-19/

Artificial Intelligence to Fight Against COVID-19

Key Artificial Intelligence Projects to Fight Against COVID-19

Dr. Amit Ray 
Compassionate  AI Lab 

In our Compassionate AI Lab, we broadly classified our fight against COVID 19 Artificial intelligence (AI) based research projects into six groups. They are AI for COVID vaccine development, AI for COVID drug discovery, AI for COVID diagnosis, AI for COVID testing, AI for COVID growth rate forecasting, and AI for social robots.

Researchers across the world are in search of urgent drugs and vaccines that can save millions of lives of infected people and perhaps prevent infections for the future generations. The exacerbated time, cost and high failure rate of traditional path of drug discovery and vaccine development has prompted the need for efficient use of machine learning techniques. In these projects, we are trying to solve one the most complex problems of humanity ever encountered 

1. AI for COVID 19: An Overview

The massive outbreak of the COVID-19 has prompted various scientists, researchers, laboratories, and organizations around the world to conduct large-scale research to help develop vaccines and other treatment strategies. Biology and medicine of coronavirus are data rich, complex, and often ill understood. Problems of this nature may be particularly well suited to deep learning techniques.… Read more..

Top 10 Limitations of Artificial Intelligence and Deep Learning

Artificial Intelligence (AI) has provided remarkable capabilities and advances in image understanding, voice recognition, face recognition, pattern recognition, natural language processing, game planning, military applications, financial modeling, language translation, and search engine optimization. In medicine, deep learning is now one of the most powerful and promising tool of AI, which can enhance every stage of patient care —from research, omics data integration, combating antibiotic resistance bacteria,  drug design and discovery to diagnosis and selection of appropriate therapy. It is also the key technology behind self-driving car.

However, deep learning algorithms of AI have several inbuilt limitations. To utilize the full power of artificial intelligence, we need to know its strength and weakness and the ways to overcome those limitations in near future.

Now, AI support messaging apps, and voice controlled chatbots are helping people for deep space communications, customer care, taking off the burden on medical professionals regarding easily diagnosable health concerns or quickly solvable health management issues and many other applications. However, there are many obstacles and number of issues remain unsolved. 

Even with so many success and promising results its full application is limited. Mainly, because, present day AI has no common sense about the world and the human psychology. Presently, in complex application areas, one part is solved by the AI system and the other part is solved by human – often called as human-assisted AI system.  The challenges are mostly in the large-scale application areas like drug discovery, multi-omics-data integration, assisting elderly people,  new material design and modeling,  computational chemistry, quantum simulation, and aerospace physics.

This article is focused to explain the power and challenges of current AI technologies and learning algorithms. It also provides the directions and lights to overcome the limits of AI technologies to achieve higher levels learning capabilities.

Top 10 Limitations of Artificial Intelligence and Deep LearningRead More »Top 10 Limitations of Artificial Intelligence and Deep Learning

Requirements for Quantum Computing

7 Key Requirements for Quantum Computing

Here, we discussed seven key  requirements for implementing efficient quantum computing systems. The seven key requirements are long coherence time, high scalability, high fault tolerance, ability to initialize qubits, universal quantum gatesefficient qubit state measurement capability, and faithful transmission of flying qubits.  They are seven guidelines for designing effective quantum computing systems. 

Quantum computing is the key technology for future artificial intelligence. In our Compassionate AI Lab, we are using AI based quantum computing algorithms for human emotion analysis, simulating human homeostasis with quantum reinforcement learning and other quantum compassionate AI projects.   This tutorial is for the researchers, volunteers and students of the Compassionate AI Lab for understanding the deeper aspects of quantum computing for implementing compassionate artificial intelligence projects. 

Earlier we have discussed Spin-orbit Coupling Qubits for Quantum Computing and AI Quantum Computing Algorithms for Artificial IntelligenceQuantum Computing and Artificial Intelligence and Quantum Computer with Superconductivity at Room Temperature. Here, we will focus on the exact requirements for developing efficient quantum computers. 

Building a quantum computer differs greatly from building a classical computer. The core of quantum computing is qubits.  Qubits are made using single photons, trapped ions, and atoms in high finesse cavities. Superconducting materials and  semiconductor quantum dots are promising hosts for qubits to build a quantum processor. When superconducting materials are cooled, they can carry a current with zero electrical resistance without losing any energy. These seven requirements refereed as DiVincenzo criteria for quantum computing [1]. 

Requirements for Quantum ComputingRead More »7 Key Requirements for Quantum Computing

AI for Balance-Control Fall Detection of Elderly People

Artificial Intelligence for Balance Control and Fall Detection of Elderly People

Artificial Intelligence for Balance Control of Elderly People

Designing automated balance control system for elderly people is one of the key project  of our Compassionate AI Lab. Here, Dr. Amit Ray discuses about one of the recent project of AI using deep learning algorithms for automatic balance control of elderly people. He explains how machine learning algorithms can be used to study and improve the dynamical properties of postural stability of elderly people. The project focuses on how image recognition, human-body joint dynamics, and path navigation methods of artificial intelligence can be used  to eliminate the imbalance, fall and injury of elderly people or for physically challenged people.

AI for Balance-Control Fall Detection of Elderly People

Compassionate Artificial Intelligence can be used for helping elderly people in many ways. Here, we discuss about one of our recent project of using AI & deep learning techniques for automatic balance control. The machine learning algorithms are used to improve dynamical properties of postural stability. In this project AI based machine learning algorithms are used to find the insights into the person specific postural strategies for older adults in order to adapt to the postural challenges during sleeping, standing, turning and walking. To study the body movement behavior of elderly people accurately, it is necessary to observe and record their movement trajectory and joint movements quantitatively and precisely in three dimensions.… Read more..

Quantum Computer with Superconductivity at Room Temperature

Quantum Computer with Superconductivity at Room Temperature

Quantum computer with superconductivity at room temperature is going to change the landscape of artificial intelligence. In the earlier article we have discussed quantum computing algorithms for artificial intelligence.  In this article we reviewed the implication of superconductivity at room temperature on quantum computation and its impact on artificial intelligence.   

Long coherence time (synchronized), low error rate and high scalability are the three prime requirements for quantum computing.  To overcome these problems, presently, quantum computer needs complex infrastructure involving high-cooling and ultra-high vacuum. This is to keep atomic movement close to zero and contain the entangled particles, both of which reduce the likelihood of decoherence. The availability of superconductivity at room temperature will provide the quantum jump in quantum computer. 

Quantum Computer with Superconductivity at Room Temperature

Read More »Quantum Computer with Superconductivity at Room Temperature

Quantum Computing Algorithms for Artificial Intelligence

Quantum Computing Algorithms for Artificial Intelligence

Dr. Amit Ray explains the quantum annealing, Quantum Monte Carlo Tree Search, Quantum algorithms for traveling salesman problems, and Quantum algorithms  for gradient descent problems in depth.

This tutorial is for the researchers, developers, students and the volunteers of the quantum computing team of the  Sri Amit Ray Compassionate AI Lab. Many of our researchers and students asked me to explain the quantum computing algorithms in a very simplistic term.  The purpose of this article is to explain that.

Quantum Computing Algorithms for AI By Amit Ray

Earlier we have discussed Spin-orbit Coupling Qubits for Quantum Computing and the foundations of  Quantum computing and artificial intelligence.  This article is to explain the foundation quantum computing algorithms in depth in a simplistic way. Here we explained the concepts of quantum annealing, Quantum Monte Carlo Tree Search, quantum algorithms for traveling salesman problem and Quantum algorithms for gradient descent problems. Read More »Quantum Computing Algorithms for Artificial Intelligence