Compassionate AI Lab

Compassionate AI Lab of Sri Amit Ray

Compassionate AI Lab of Sri Amit Ray is focused on research on compassion and building compassionate artificial intelligence systems for the benefits of humanity and all living beings. The objective is to eliminate the pain and sufferings of people by the use of emerging technologies. Compassionate AI is a multidisciplinary subject. This is  a palace where you can exchange ideas within and across AI,  neuroscience, omics science, meditation,  quantum computing, compassion and other research groups. 

Here, we focus on incorporating compassion, kindness and empathy in artificial intelligence systems. The lab conducts fundamental AI research, including theory and methods for serving humanity, helping blind people, old age support, cancer prevention, precision medicine as well as application-oriented human-centered AI research  collaboration at a high international level.

Key Artificial Intelligence Projects

It includes the emerging research fields of AI such as compassionate care-giving, compassionate health care, precision medicine, Quantum Computing, compassionate weapon-defense system, compassionate teaching, machine learning, Internet of Things (IoT), drone, big data, blockchain, quantum computing, digital medicine, brain-computer interface, combating antibiotic resistant bacteria, Balance Control of Elderly People, computer aided drug design,  etc.  Compassionate AI motivates the systems to go out of their way to help the physical, mental or emotional pains of people and themselves.  

Antibiotic resistance bacteria is becoming world’s biggest health crisis. Here, we focus how deep machine learning techniques can be used to counter antibiotic resistance bacteria. Read more. 

This project focuses on developing systems to eliminate the imbalance, fall and injury of elderly people or for physically challenged people.  Read more on balance control of elderly people. 

Designing the navigation system for blind people. This project focuses on how image recognition, voice recognition and path navigation methods of artificial intelligence can be used  for automated navigation system for blind people. Read more ..

The main advantages of applying machine learning in radiology is providing accurate diagnostic results at an affordable  cost.  Currently we focus on the use of deep learning and deep reinforcement algorithms for analysis and interpretation of radiological images. Read more ,.

Yoga and Om Chanting for Cell-Specific Nitric Oxide Regulation for Health Healing and Cancer Prevention

Nitric oxide regulation with Om chanting and yoga exercises is one of our key project. We have conducted various experiments on cell-specific nitric oxide regulation. Especially low frequency multi-stage Om chanting has given positive results. 

People who are suffering from post-traumatic stress disorder (PTSD), and other mental disorders or brain problems is facing a big challenge to cope with daily activities. This work focuses on  technologies for non-invasive transmission of information from brain to computers and computers to brain. Read more. 

Compassionate artificial intelligence systems are increasingly required for looking after those unable to care for themselves, especially sick, physically challenged persons, children or elderly people. How AI can help the emotional, social, and spiritual needs of poor, patients and elderly people are the scope of this research work.

Key Projects on Quantum Computing

Our research lab is fully compatible with the current development of research on Quantum Computing. We focused our research activities on Quantum Artificial Intelligence.  There are three approaches to quantum computing: Gate-based Quantum Computing, Quantum Annealing (QA) and the Adiabatic quantum computation (AQC). Here, we focus mostly on quantum annealing implementation of the algorithms for quantum deep neural learning and quantum deep reinforcement learning for our human-centered projects. 

We are Thankful to our Research Collaborators

Some Popular Research Articles

Roadmap for 1000 Qubits Fault-tolerant Quantum Computers

How many qubits are needed to out-perform conventional computers, how to protect a quantum computer from the effects of decoherence and how to design more than 1000 qubits fault-tolerant large scale quantum computers, these are the three basic questions we want to deal in this article.  Qubit technologies, qubit quality, qubit count, qubit connectivity and qubit architectures are the five key areas of quantum computing are discussed.

Roadmap for 1000 Qubits Fault-tolerant Quantum Computers

Earlier we have discussed 7 Core Qubit Technologies for Quantum Computing, 7 Key Requirements for Quantum Computing.  Spin-orbit Coupling Qubits for Quantum Computing and AIQuantum Computing Algorithms for Artificial IntelligenceQuantum Computing and Artificial Intelligence , Quantum Computing with Many World Interpretation Scopes and Challenges and Quantum Computer with Superconductivity at Room Temperature. Here, we will focus on practical issues related to designing large-scale quantum computers. 

Instead of running on zeros and ones, quantum computers run on an infinite number of states between zero and one. Instead of performing one calculation before moving on to the next, quantum computers can manage multiple processes all simultaneously. 

Unlike binary bits of information in ordinary computers, “qubits” consist of quantum particles that have some probability of being in each of two states, represent as |0⟩ and |1⟩, simultaneously. When qubits interact, their possible states become interdependent (entangled), each one’s chances of |0⟩ and |1⟩ hinging on those of the other. Moreover, quantum information does not have to be encoded into binary bits, it could also be encoded into continuous observables bits (qubits). 

The speed requirements for various applications grows with the complexity of the problems and the speed advantage of quantum computers are enormous compare to classical computers.  The key to quantum computation speed is that every additional qubit doubles the potential computing power of a quantum machine. 

The objective of 1000 qubits fault-tolerant quantum computing is to compute accurately even when gates have a high probability of error each time they are used. Theoretically, accurate quantum computing is possible with error probabilities above 3% per gate, which is significantly high. The resources required for quantum computing depends on the error probabilities of the gates. It is possible to implement non-trivial quantum computations at error probabilities as high as 1% per gate.

Quantum Computing Promises

Based on complexity theory quantum computers can solve much complex problems in exponentially less time than classical computers.  Quantum computers can provide faster solutions to factoring and searching algorithms compare to the classical computers. Factoring is basically finding the prime factors of a large composite integer – for which quantum algorithms have been discovered that could solve these problems easily. Quantum computers can provide better ways than classical computers to simulate complex quantum systems for the physicists. 

The primary applications of quantum computing relate to the physical simulation of quantum particles of the Universe, new drug discovery,  new material design, complex financial modeling, molecular biology, omics and precision medicine,  complex optimizations, quantum artificial intelligence and also for the neural network training for machine learning applications.  

Comparison of Quantum Computer Speed


Need for 1000 Qubits Quantum Computing

As the number of qubits increases, the system continues to explore the exponentially growing number of quantum states. In theory, the more qubits, the more powerful a quantum computer becomes.  Moreover, the key reason for needing so many qubits is dealing with their noise and fragility. At 1,000 qubits, there is only limited error correction and fault tolerance, but at 1,000,000 the system has fault tolerance, which is a key to why it can become fairly general purpose Universal Quantum Computer.

Quantum Supremacy

One major milestone on the road of quantum computing is “quantum supremacy,” the point where a quantum machine can overcome the performance of the best classical computers in complex tasks.  In theory, achieving quantum supremacy requires a computer of more than 50 qubits. However, engineering limitations, decoharance, unknown behavior of the qubits and noise has scaled-up the qubit requirements for quantum supremacy. It is estimated that with reasonable gate error rate,  1,000 qubits of Universal  gate based quantum computer will be the most practical for developing quantum supremacy.

Moore’s law says that processing speeds for silicon-based transistors would double every two years, as more transistors were crammed on smaller chips. More precisely,  doubling computers’ power in approximately every 18 months. Considering the Moore’s law 1,000 qubits is required to achieve quantum supremacy. According to our Compassionate AI Lab estimation based on Moore’s law and other parameters 1,000 qubits can be achieved by the year 2023 and operational availability will be by the year 2025.   


Quantum Computing with Many World Interpretation Scopes and Challenges

Quantum Computing with Many World Interpretation Scopes and Challenges

Many scientist believe that Many World Interpretation (MWI) of quantum mechanics is self-evidently absurd for quantum computing. However, recently, there are many groups of scientist increasingly believing that MWI has the real future in quantum computing, because MWI can provide true quantum parallelism.  Here, I briefly discuss the scopes and challenges of MWI for future quantum computing for exploration into the deeper aspects of qubits and quantum computing with MWI. 

The heart of Copenhagen interpretation based quantum computing is wave function collapse. However, the Copenhagen interpretation fails to specify precisely where and how the collapses occurs and is therefore an imprecise theory. Moreover, the exact collapse mechanism is not scientifically well defined. But the beauty of Copenhagen interpretation is that “it works” and it makes no unnecessary assumptions. According to the Copenhagen interpretation there is no “game” only the result is real – the physical reality is a result of the collapse of the wave function as a “local” manifestation of the non-local wave-function. However, wave function collapse is widely regarded as artificial and adhoc.

On the other hand, MWI removed the wave function collapse theory and focused on quantum parallelism thesis (QPT). It states that none of the quantum states vanishes at all, except to our perception. It says, in essence, let’s just do away with wave function collapse altogether. The entire universe (all the universes together) is described by a gigantic wave function that contains within it all possible realities. This wave function is known as “universal wave function”. The universe is a single reality. It hypothesized that, at the quantum level, whenever the universe is confronted with a choice of paths, reality splits into branches and both choices or paths happen simultaneously.

Quantum Computing with Many World Interpretation

This tutorial is for the researchers, volunteers and students of the Compassionate AI Lab for understanding the deeper aspects of quantum computing for implementing large-scale compassionate artificial intelligence projects. 

1. Many Worlds Interpretation Basics

In 1957, Hugh Everett proposes, Many Worlds Interpretation (MWI) of quantum theories. According to this theory,  the famous Schrödinger’s cat is alive in one world and is dead in another world.  In MWI Everett, avoids the issue of wave function collapse. MWI from the assumption that the universal wave function is a superposition of an extraordinarily large set of real, existing worlds. MWI applies the entire mathematical apparatus of quantum mechanics to the universe, whereas the wave function collapse theories are essentially limited to the microscopic domain. MWI framework can provide simultaneous access to many computational worlds. 

Everett suggests that, when a measurement is made for a system in which the wave function is a mixture of states, the universe branches into a number of non-interacting universes. Each of the possible outcomes of the measurement occurs, but in a different universe. Particles are entangled, space and time become one, and information exists simultaneously across multiple, or infinite universes. 

According to the MWI, Schrödinger equation applies at all times. In other words, that the wave function of the Universe never collapses. Hence, MWI is also known as collapse-free quantum theory.  There is only one wave function, and it evolves smoothly and deterministically over time without any kind of splitting or parallelism. MWI removes the observer-dependent role in the quantum measurement process by replacing wavefunction collapse with quantum decoherence. Decoherence shows how a macroscopic system interact with many microscopic systems. Here, all components of the wave function still exist in a global superposition. 

Many World Interpretation and Copenhagen Interpretation

Everett’s many-worlds interpretation posited no collapse. Instead, probabilities bifurcate at the moment of measurement into parallel universes. Although an infinite number of un-testable universes seems unscientific to some, many physicists today view the theory as very important. For example, Tegmark called the many worlds interpretation “one of the most important discoveries of all time in science.” 

2. Limitations of Bohr’s Copenhagen Interpretation

Copenhagen interpretation holds that physical systems have only probabilities, rather than specific properties, until they’re measured. In Schrödinger’s thought experiment, a cat in a box is both dead and alive until it is seen. In Bohr’s words, the wave and particle pictures, or the visual and causal representations, are “complementary” to each other. That is, they are mutually exclusive, yet jointly essential for a complete description of quantum events.

The wave function is a complete description of the quantum state of a particle, and the Schrödinger equation describes the behavior of the wave function in space and time. The Schrödinger equation is a mathematical “wave equation,” i.e. a second-order linear partial differential equation. The set of all possible wave functions forms an abstract mathematical vector space called a “Hilbert space.” 

Paul Dirac famously proposed adding a postulate to the quantum mechanical framework that “a measurement always causes a system to jump into an eigenstate of the observed quantity.”  However, where and at what point the system “jumps” into an eigenstate – is entirely ambiguous with no scientifically precise definition. Max Born suggested, the square of the absolute value of the wave (ψ) function expresses a probability amplitude for the outcome of a measurement. Why the probability amplitude be square, not something else, it is not scientifically very well defined.

Einstein, however, persistently argued that the Copenhagen interpretation was incomplete. He conjectured that there might be hidden variables or processes underlying quantum phenomena; or perhaps ‘pilot waves’, proposed by de Broglie, govern the behaviour of particles.

David Bohm also argued that particles in quantum systems existed whether observed or not, and that they have predictable positions and motions determined by pilot waves. John Bell then showed that Einstein’s concerns about locality and incompleteness in the Copenhagen interpretation were valid. It was he who refuted von Neumann’s proof by revealing that it ruled out only a narrow class of hidden-variables theories.

3. Many-World Interpretation and Branches: 

The many-worlds interpretation is DeWitt’s popularisation of Everett’s work. The combined observer–object system as being split by an observation, each split corresponding to the different or multiple possible outcomes of an observation. These splits generate a possible tree and branches.

There are two versions of observer, one who has seen a dead cat, and the other who has seen an alive cat. These two versions cannot interact with each other, and will never be able to know of the other’s existence. When one observer open the box we say that the universe “branches.” Initially there was just one cat. But after the radioactive decay and cyanide release the universe branched into two “sub-universes”, one containing the dead cat and the other one containing the alive cat. These sub-universes are called branches, but they are also popularly termed “worlds”, hence the theory being called many worlds theory. 

4. Many World Interpretation Model

Many-worlds,  views reality as a many-branched tree, wherein every possible quantum outcome can be realized. There are several versions of many-world interpretations. The existence of the other worlds makes it possible to remove randomness and action at a distance from quantum theory and thus from all physics. Here, a world is the totality of macroscopic objects such as: qubits, gates, stars, cities, people, grains of sand, etc. in a definite classically described state. In MWI, an “observer” has no special status and so there is no such thing as a wave collapse. MWI is based on the simple assumption that the entire universe can be modeled by a universal wave function that obeys a deterministic wave equation. 




Many World Interpretation Branches

5. Quantum Parallelism and Many World Interpretation

The power of the new quantum computing model derives from a presumption that it is possible to store and simultaneously manipulate an exponentially-large amount of information in a quantum register. A quantum system of n particles defines an exponential 2n number of states, it appears that a small amount of computational hardware can behave like a classical parallel computer with an exponential number of processors.

Quantum parallelism arises from the ability of a quantum memory register to exist in a superposition of base states. A quantum memory register can exist in a superposition of states, each component of this superposition may be thought of as a single argument to a function. A function performed on the register in a superposition of states is thus performed on each of the components of the superposition, but this function is only applied one time.

Quantum mechanics imposes a significant restriction: the transformations applied to the quantum register must be unitary. This is necessary because a non-unitary operator is equivalent to performing a measurement and thus will cause a collapse of the superposition.The main idea is that when quantum computers are performing a transition taking as input a superposition of states, like for example the following n qubit register is

they gain access to many computational worlds—each associated to every possible value of |Xi>—and they are actually performing many simultaneous transitions on each and every one of those states that are part of the superposition. The transition is real in the sense that it is performed in all the different worlds, to which the computation process gain access. The  formal represented by the so called Quantum Parallelism is as follows:

6. Implementation of Quantum Computing with MWI

Standard quantum computing models are based on the paradigm of quantum logic networks, where a sequence of unitary quantum gates form the computational circuit. The key requirements for efficient quantum computing 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.  There are several architectures for implementing quantum computing. Cluster-state quantum computer is one of the key candidate model to run many worlds explanation of quantum computation. The cluster-state quantum computer (QCC) is a universal quantum computer; it can efficiently simulate any algorithm developed within the network model.

An alternative model of circuit model, for scalable quantum computing in which a specific form of a highly entangled multiparticle state, called a cluster state, plays the pivotal role of being a universal resource for quantum computation. To perform a quantum algorithm with a cluster state, individual qubits are measured in a temporal sequence of adaptive single-qubit measurements with classical feed-forward processing of outcomes.  

7. Conclusion:

Quantum parallelism offers a more powerful computational framework than the classical computers. Here, we reviewed quantum parallelism with MWI framework. Measurement is the key issue in implementing MWI in quantum computing. MWI is not a science fiction, but a powerful framework for implementing quantum computational processes.  Moreover, providing a quantum algorithm that unambiguously exploits quantum parallelism of MWI would of course settle many unsolved questions of MWI, but it must stand up under a rigorous complexity analysis. Developing MWI for quantum computing has many computational advantages but its implementation is a challenging task. In the next article we will discuss more about the qubits and implementation issues with MWI. 

Source Books:

  1. Compassionate Artificial Intelligence: Frameworks and Algorithms by Dr. Amit Ray
  2. Compassionate Superintelligence, AI 5.0 by Dr. Amit Ray
  3. Quantum Computing Algorithms for Artificial Intelligence By Dr. Amit Ray


  1. Duwell, A. The Many-World Interpretation and Quantum Computation. Philos. Sci. 2007, 74,
  2. De Witt, B.; Graham, R. The Many-Worlds Interpretation of Quantum Mechanics; Princeton
    University Press: Princeton, NJ, USA, 1973.
  3. Deutsch, D.; Jozsa, R. Rapid Solution of Problems by Quantum Computing. Proc. R. Soc. Lond. A
    1992, 439, 553–558.
  4. Giacomo Lini, Quantum Parallelism Thesis, Many World Interpretation and
    Physical Information Thesis, Philosophies 2016, 1, 102-110.
  5. Michael Cuffaro, Many Worlds, the Cluster-state Quantum Computer, and the Problem of the Preferred
    Basis, Volume 43, Issue 1, February 2012, Pages 35-42.
  6. Gelo Noel M. Tabia, Quantum Computing with Cluster State, 2011, Perimeter Institute for Theoretical Physics, Canada.
  7.  Will Advances in Quantum Computing Shed Light on Foundational Issues in Quantum Mechanics?

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. 

Quantum Computing Basics

Rather than store information using bits represented by 0s or 1s as in classical digital computers do, quantum computers use quantum bits, or qubits, to encode information as 0s, 1s, or both at the same time. This superposition of states—along with the other quantum mechanical phenomena of entanglement and tunneling—enables quantum computers to manipulate enormous combinations of states at once. The measurement in a quantum computer is performed by measuring the states of the qubits (charge, spin, angular momentum etc.). 

Mainly there are four types implementation of qubits: spin qubits, photon qubits, flux qubits and charge qubits. We in Computational AI Lab, focus on implementing quantum computing with photonic qubits and solid state spin qubits in an integrated structure.

Key Equations of Quantum Computing

The foundation of quantum computing is the three basic theories of quantum mechanics:  the Non-Relativistic Time-Independent Schrodinger Equation, Relativistic many-fermion systems (the many-body Dirac equation), and Gauge field theories (quantum Yang-Mills theories).

The Schrodinger equation is the basis for non-relativistic wave equation used in one version of quantum computing to describe the behavior of a quantum particle (electron, photon or ion) in a field of force. There is the time dependent equation used for describing progressive waves, applicable to the motion of free particles. And the time independent form of this equation used for describing standing waves.

Components of Quantum Computing

Quantum computing has five key components: control and manipulation of the isolated quantum particles, measurement of the parameters of the quantum particles, error correction, quantum algorithms and scaling up for large scale implementation.  

There are three approaches to quantum computing: Quantum Annealing (QA),  Adiabatic quantum computation (AQC) and Gate-based quantum computing (GQC). Here, we will focus mostly on quantum annealing implementation of the algorithms. In the book Quantum Computing algorithms for Artificial Intelligence these approaches are discussed in details. 

Quantum Annealing: 

Quantum annealing is the set of meta-heuristic algorithms of quantum computing based on the concepts of quantum superposition, entanglement and tunneling. Quantum wave-functions and Quantum coherence are the key elements of quantum annealing.   The idea of Quantum Annealing is an offspring of the traditional thermal simulated annealing process, where the problem of minimizing a certain cost or energy function in a large space of configurations is solved, through a statistical mechanics analogy, by the introduction of an artificial temperature variable which is slowly reduced to zero in the course of a Monte Carlo simulation or Molecular Dynamics simulation. This device allows to explore the configuration space avoiding trapping into local minima, often providing a more effective and less biased search for the minimal “energy” than standard gradient-based minimization methods. 


Brain-Computer Interface and Compassionate AI to Serve Humanity

Brain-Computer Interface and Compassionate AI

The purpose of Compassionate AI is to remove the pain from the society and help humanity. Artificial Intelligence with Brain-Computer Interface (BCI) or Brain Machine Interface (BMI) is a fast-growing emerging technology for removing pains from the society. Here,  Dr. Amit Ray explains how with the advancement of artificial intelligence and exploration of new mobile bio-monitoring  devices, earphones, neuroprosthetic, wireless  wearable sensors, it is possible to monitor  thoughts and activities of brain neurons  and serve humanity.

This research is going to be immensely  beneficial for the physically and mentally challenged people as well as for the people who are suffering from post-traumatic stress disorder (PTSD), and other mental disorders or brain problems. Over the last 5 years, technologies for non-invasive transmission of information from brains to computers have developed considerably.

Brain-Computer Interface and Compassionate AI

Here, researchers focus to build a direct communication link between the human brain and the smartphones, earphone, computers or other devices. With BCI mind can speak silently with a smartphone or other devices.  Recent advancement of neuroprosthetic, linking the human nervous system to computers and providing unprecedented control of artificial limbs and restoring lost sensory function.

 BCI establishes two way communications between the brain and the machine.  One is  brain-computer interface and another is called computer-brain interfaces (CBI). BCI hopes to create new communication channels for disabled or elderly persons using their brain signals. 


Artificial Intelligence to Combat Antibiotic Resistant Bacteria

Artificial Intelligence to Combat Antibiotic Resistant Bacteria

Antibiotic resistance bacteria is one of the key research area of our Compassionate AI Lab. Dr. Amit Ray explains how artificial intelligence can be used in combating these superbugs. Antibiotic resistance bacteria is becoming world’s biggest health crisis. We discussed here multi-agent deep reinforcement learning models for predicting behavior of bacteria and phages in multi-drug environments.  We call this model as DeepCombat. 

Artificial Intelligence to Combat Antibiotic Resistant Bacteria

Antibiotic resistant bacteria are bacteria that are not controlled or killed by antibiotics. They are able to survive and even multiply in the presence of an antibiotic.  These bacteria currently kill an estimated 700,000 people globally each year – a death toll which could rise to 10 million a year by 2050 if we don’t act [1]. The main difficulty is that the bacteria are changing fast. They changing faster than we can change the drugs in response.

 Artificial intelligence is showing alternative means of fighting these deadly infections and killer bacteria. Multi-drug-resistant bacterial infections annually result in millions of hospital days, billions in healthcare costs, and, most importantly, thousands of lives lost. Artificial Intelligence for healthcare is progressing at an exponential rate.  We are evaluating here, the role of artificial intelligence in fighting these superbugs.  Especially, the use of AI for intelligent Phage therapy.


Artificial intelligence for Assisting Navigation of Blind People

Grid Cells and Artificial intelligence for Assisting Navigation of Blind People

Designing automated navigation system for blind people is one of the key project  of our Compassionate AI Lab. Dr. Amit Ray explains how grid cell, place cell and path integration strategies with artificial intelligence can be used  for designing the navigation system for blind people. The project focuses on how image recognition, voice recognition and path navigation methods of artificial intelligence can be used  for automated navigation system for blind people. Artificial intelligence can be used for helping blind people in many ways. Here, we discuss about our recent project of using AI techniques for automatic navigation. 

Artificial Intelligence to Help Blind GirlsNavigation for blind people in a complex environment is a challenging task. Visual Impairment makes the person depend on another person for all their works and daily chores. This project is aimed to help blind people and eliminate their pain.

The discovery of place cells and the grid cells in the hippocampus opened a new perspective for understanding  mammal navigation system. Applying grid cells, place cells and path integration concepts with artificial intelligence is the main aim of this project of developing automated navigation systems for blind people. 

AI Based Navigation System for Blind People

Our objective is to develop an automated navigation system for the blind people. Our long-term goal is to create a portable, self-contained system that will allow visually impaired individuals to travel through familiar and unfamiliar environments without the assistance of guide. Currently, the most widespread and used means by the visually impaired people are the white stick, helper or the use of guide dog; however both present some limitations. 

Artificial Intelligence to Help Blind People WalkingThe goal of the project is to give blind persons the ability to move around in unfamiliar environments, whether indoor or outdoor, through a user friendly interface. The term blindness refers to the people who have no vision at all or people who have less vision. With the advancement in AI technology usage of image recognition, voice recognition and path navigation method it can be easier to send commands regarding directions to the blind people. 


Compassionate Artificial Intelligence Scopes and Challenges

Compassionate Artificial Intelligence Scopes and Challenges

With the advancement of AI and nuclear war technology, gradually mankind is moving towards a great threat. Compassionate artificial intelligence is the way to come out of that threat.  Here, Sri Amit Ray talks about how artificial intelligence, neural networks, deep learning, reinforcement learning and other machine learning technologies can be used for designing advance compassionate artificial intelligence systems. Dr. Ray discusses the scopes, issues and frameworks to include compassion, kindness and empathy in future AI systems.

Need for Compassionate Artificial Intelligence Systems

One may question why it is useful to study compassion in machines at all. Compassion is an important part of human intelligence. The main objective of AI is to serve humanity in an intelligent manner. As AI technology is improving, serving humanity on the surface level is not sufficient. AI can serve humanity in much better way in a much deeper sense.  Compassion, kindness and empathy are the components of higher human intelligence and to be true intelligent, artificial intelligence must incorporate them in the system. The scope and benefits of compassionate AI are many. Some of the requirements for compassionate AI are as follows:

Robots are leaving the realm of the industry and entering into our homes and workplaces. Humans not just interact with information but also with emotions and feelings. Robots are required to be soft and compassionate towards human.  

“Compassionate artificial intelligence systems are required for looking after those unable to care for themselves, especially sick, physically challenged persons, children or elderly people.” — Amit Ray

They are required for health care, education and removing the loneliness of people. 

Compassionate artificial intelligence systems are required to stop mass destruction weapon systems. The rapid decision-making capabilities of AI systems are now used for launching nuclear weapons in ships and submarines, especially for preventing counterattacks. AI is also used to control non-nuclear weapons including unmanned vehicles like drones and cyberweapons. In this competitive age, it is almost certain, that if some-country or someone turn AI into a full-fledged automated weapon system, everyone else will do that too. Initially, it may be just for their own defense but subsequently that will be used for mass destruction. The purpose of compassionate artificial intelligence systems is to develop inbuilt system that can stop and disobey the instructions of evil forces. Evil force can be a machine, algorithm or human.  

Classification of Compassionate AI systems:

In the book Dr. Ray argued that compassionate AI systems can be classified into three groups. They are; Narrow Compassionate AI, General Compassionate AI and Compassionate Superintelligence. Narrow Compassionate AI tries to remove pains at individual level. General Compassionate AI  tries to remove pains at social level, group level or country level.  Compassionate Superintelligence tries to remove pains at higher levels of humanity. 

Narrow Compassionate AI deals with narrow domains like looking after elderly people, helping blind people,  assisting healthcare services. General Compassionate AI  helps humanity at higher levels in an integrated infrastructure. It deals with social pains from higher levels. It will solve the social problems like monster government, political corruptions, terrorism, human exploitation,  unethical journalism of paid news and false news, social media based depressions etc.  Compassionate Superintelligence will save humanity from nuclear war, earth quake and other disasters. 

What is compassion?

We all feel compassion when we see our family or friends in distress, and even animals feel compassion when they see their offspring are in pain. Compassion is a form of emotional engagement that is beneficial to the sufferer and the humanity.  Compassion involves the  sharing of feelings of another as a means of coming to an understanding and appreciation for how they feel. Hence, compassion is the feelings, inner experiences and related efforts to remove the pain of others.  Compassion is an inbuilt human nature. It is the human potential to reach the higher evolution of consciousness. Compassion comprises of feelings, emotions, sentiment and understanding. 

Characteristics of Compassionate AI

Compassion involves emotional engagement with some agent. The agent can be a person, group, machine, country etc. It also involves understanding their world. AI is humanizing technology.

“Compassionate AI must interact with the agent with a smiling face and loving attitude and understand the limitations and pains of the agent.” — Amit Ray 

The Compassionate AI can care for someone by providing their needs but not necessarily moving into their personal world, walking in their shoes and sharing in their struggles, joys and challenges. Kanov et al. (2004) discuss that compassion consists of three facets: noticing, feeling, and responding. ‘Noticing’ includes being aware of a person’s suffering, either by cognitively recognizing this suffering or by experiencing an unconscious physical or affective reaction to it. ‘Feeling’ is defined as responding emotionally to that suffering and experiencing ‘empathic concern’ through accepting the person’s perspective and imagining or feeling their condition. Finally, ‘responding’ encompasses having a desire to act to alleviate the person’s suffering.

Implementing Compassionate Artificial Intelligence

Human emotions and feelings are like organic algorithms that respond to the environment. Now, machine learning algorithms are used to know human emotions by studying facial expression, words,  gestures, speech patterns, tone of voice or body language. Emotion API of Microsoft Azure is one such example.  IBM Watson Personality Insights is an example of human personality analysis. 

AI is advancing rapidly at emotional intelligence. Face-tracking software is already advanced enough to analyze the smallest details in our facial expressions.  The facial images are often analysed with deep learning algorithms which accurately classify them according to the feelings of the viewer. Emotion-Driven Reinforcement Learning is successfully for building narrow level compassionate models. 


  1. Compassionate Artificial Superintelligence AI 5.0, Amit Ray, 2018

Artificial Intelligence with Emotional Intelligence Issues and Challenges

Currently, deep learning modules of AI based systems lacks the emotional aspects of human intelligence. However, to fix the subjective issues like relationship, depression, anxiety and emotional issues future artificial intelligence based  systems like cyborgs require deep emotional intelligence modules.   AI is expanding and evolving itself in many technological fronts. It is not only limited by Deep learning algorithms, but expanding its horizons in deeper levels of human consciousness. Modern AI is tremendously successful for pattern recognition, voice recognition, face identification and machine learning. Self-driven cars are already on road in testing phase.  But, in today’s world AI is more needed in dealing with emotions like anger, impatience, disappointment, frustration, surprise, happiness, and gratitude. This article covers the scope, issues and challenges of AI for building emotional intelligence.

Need for Combining Artificial Intelligence with Emotional Intelligence

Human emotion is deeply associated with motivation, decision, evaluation, learning, character, intelligence, desires, and awareness. Thus, nearly all human psychological activities are subject to emotional influences and excitation. Self-awareness is the significance of the mental activities of a human being. Therefore, it should be one of the core ideas of AI with emotional intelligence.

Some researchers claims that emotional intelligence accounts for 75 percent of a person’s success and perhaps that will be more true for the success of future artificial intelligence based cyborgs and other systems. — Amit Ray 

Emotional intelligence is defined as the ability to recognize, understand and manage one’s own emotions and to recognize and influence the emotion of others. Obviously, emotional intelligence separates us from the machines. It includes the ability to identify emotions, to recognize their powerful effects, and to use that information to inform and guide behavior. Emotional intelligence includes the ability to influence–to evoke strong emotions in others, with a view to persuading or motivating them. It is more about focusing hard on both the person in front of you and your own emotions and reactions. 

What is artificial emotional intelligence?

Artificial emotional intelligence is the ability of the machine to recognize human emotions and then respond appropriately. The recognition and understanding of human emotions is crucial for AI systems to behave in appropriate ways according to the situation and smoothly integrate with all the different aspects of human life.  Currently, smartphones are used to allow voice assistants, like the iPhone’s Siri, to recognize and respond to user emotional concerns with appropriate information and supportive resources.

Strategies for Artificial Intelligence with Emotional Intelligence

Presently, AI is increasingly dependent on cloud computing, IoT and big data. Our goal is to model the range of higher human emotions, as well as their dynamics.  There are different frameworks, libraries, applications, toolkits, and datasets in the AI and machine learning world. By creating a direct neural interface with the Internet, humankind will be able to “plug into”  higher intelligence. The five components of AI with emotional intelligence are as follows; deep learning, self-awareness, safety and ethics, external awareness and big data collection and processing modules. Emotions  are essential part of human intelligence. Without emotional intelligence, AI is incomplete. Developing self-awareness of the machine is the first challenge of true AI based systems. 


Artificial Intelligence with Emotional Intelligence Issues and Challenges


Using artificial intelligence advances with emotional intelligence, there are several potential barriers to be addressed:

  1. Privacy: Many people feel their emotions are private, and concerns about violations of privacy is genuine. Protective legislation will need to expand to include risks associated with AI, specifically the collection, storage, transfer and use of confidential health information.
  2. Accuracy: AI accuracy in correctly determining emotional intent will need to be confirmed, specifically in regards to system biases or errors, before labeling a person as high or low emotional.
  3. Safety: It is essential to ensure AI programs can appropriately respond to human users, so as to not worsen their emotional state or accidentally facilitate adverse situation.
  4. Responsibility: Response protocols are needed on how to properly handle high risk cases that are flagged by AI technology, and what to do if AI risk assessments differ from human experts opinion.
  5. Lack of understanding: There is a knowledge gap among key users on how AI technology fits into emotional understanding. More education on the topic is needed to address this.


The criteria for what constitutes an artificial intelligent system has been shifted. Now developing emotional intelligence is one of the primary concern for AI research.  As technology is increasingly applied to situations where it must interact with human emotionally and intelligently. The most widely addressed area of research in automated emotion recognition, and where there has been the most progress, is the recognition of facial expressions. Physiological information has been shown to carry information that changes with different emotions. Handling of emotions, in others as well as in oneself, involves emotional intelligence. Integration of AI with emotional intelligence systems are expected to work alongside humans. The five components AI with emotional framework can provide some guidance in addressing this problem.


  1. Compassionate Artificial Superintelligence (AI-5.0) By Dr. Amit Ray, Inner Light Publishers, 2018. 



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