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.

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

Compassionate Artificial Intelligence

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.  

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.

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. 

References:

1. Ray, Amit. "From Data-Driven AI to Compassionate AI: Safeguarding Humanity and Empowering Future Generations." Compassionate AI, vol. 2, no. 6, 17 June 2023, pp. 51-53, Compassionate AI Lab, https://amitray.com/from-data-driven-ai-to-compassionate-ai-safeguarding-humanity-and-empowering-future-generations/.
2. Ray, Amit. "Calling for a Compassionate AI Movement: Towards Compassionate Artificial Intelligence." Compassionate AI, vol. 2, no. 6, 25 June 2023, pp. 75-77, Compassionate AI Lab, https://amitray.com/calling-for-a-compassionate-ai-movement/.
3. Ray, Amit. "Ethical Responsibilities in Large Language AI Models: GPT-3, GPT-4, PaLM 2, LLaMA, Chinchilla, Gopher, and BLOOM." Compassionate AI, vol. 3, no. 7, 7 July 2023, pp. 21-23, Compassionate AI Lab, https://amitray.com/ethical-responsibility-in-large-language-ai-models/.
4. Ray, Amit. "Compassionate Artificial Intelligence Scopes and Challenges." Compassionate AI, vol. 2, no. 4, 16 April 2018, pp. 48-50, Compassionate AI Lab, https://amitray.com/compassionate-artificial-intelligence-scopes-and-challenges/.
5. Ray, Amit. Compassionate artificial intelligence: Frameworks and algorithms. Compassionate AI Lab, 2018.
6. Ray, Amit. "Compassionate Superintelligence AI 5.0: AI with Blockchain, BMI, Drone, IoT, and Biometric Technologies." Inner Light Publishers,2018.
7. Ray, Amit. "Brain-Computer Interface and Compassionate Artificial Intelligence." Compassionate AI, vol. 2, no. 5, 1 May 2018, pp. 3-5, Compassionate AI Lab, https://amitray.com/brain-computer-interface-compassionate-ai/.
8. Ray, Amit. "The 10 Ethical AI Indexes for LLM Data Training and Responsible AI." Compassionate AI, vol. 3, no. 8, 8 August 2023, pp. 35-39, Compassionate AI Lab, https://amitray.com/the-10-ethical-ai-indexes-for-responsible-ai/.
9. Ray, Amit. "Compassionate Artificial Intelligence Scopes and Challenges." Compassionate AI, vol. 2, no. 4, 16 April 2018, pp. 48-50, Compassionate AI Lab, https://amitray.com/compassionate-artificial-intelligence-scopes-and-challenges/.
10. Ray, Amit. "The 7 Pillars of Compassionate AI Democracy." Compassionate AI, vol. 3, no. 9, 28 September 2024, pp. 84-86, Compassionate AI Lab, https://amitray.com/the-7-pillars-of-compassionate-ai-democracy/.
11. Ray, Amit. "Compassionate AI Democracy: Eliminating Legal Gaps Between the Poor and Wealthy." Compassionate AI, vol. 3, no. 9, 28 September 2024, pp. 84-86, Compassionate AI Lab, https://amitray.com/compassionate-ai-democracy-eliminating-legal-gaps-between-the-poor-and-wealthy/.
12. Ray, Amit. "Compassionate AI-Driven Democracy: Power and Challenges." Compassionate AI, vol. 3, no. 9, 16 September 2024, pp. 48-50, Compassionate AI Lab, https://amitray.com/compassionate-ai-driven-democracy-power-and-challenges/.
13. Ray, Amit. "Integrating LLM AI Models for Ayurveda Medical Diagnosis and Treatment." Compassionate AI, vol. 4, no. 10, 23 October 2024, pp. 54-56, Compassionate AI Lab, https://amitray.com/llm-ai-models-for-ayurveda/.
14. Ray, Amit. "7 Limitations of Deep Learning Algorithms of AI." Compassionate AI, vol. 2, no. 4, 5 April 2018, pp. 15-17, Compassionate AI Lab, https://amitray.com/7-limitations-of-deep-learning-algorithms-of-ai/.

Sources:

1. Compassionate Artificial Intelligence: Frameworks and Algorithms, Amit Ray, 2018.
2. Compassionate Artificial Superintelligence AI 5.0, Amit Ray, 2018

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.

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.

Summary:

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.

Sources:

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