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Top 10 Limitations of Deep Learning

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.

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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. … Read more..

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. 
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