Artificial Intelligence

7 Limitations of Deep Learning Algorithms of AI

7 Limitations of Deep Learning Algorithms of AI

Sri Amit Ray tells us about the power and limitations of Deep Learning Architectures of Artificial Intelligence (AI).

Deep learning architectures of Artificial Intelligence has provided remarkable capabilities and advances in voice recognition, face recognition, pattern recognition, image understanding, natural language processing, game planning language translation, and search engine optimization. Deep learning is the key technology behind self-driving car. However, deep learning algorithms of AI have several inbuilt limitations. This article is focused to explain the power and limitations of current deep learning algorithms. It also provides the directions and lights to overcome the limits of deep learning algorithms to achieve higher levels learning capabilities.

Deep Learning Algorithms in AI Power and Limitations

Types of Machine Learning:

There are three core types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. In supervised learning, the algorithm uses the training data to learn a link between the input and the outputs. It predicts the output from the trained network.  

Unsupervised learning does not use output data. It uses dimensionality reduction algorithms, Clustering algorithm, such as K-means etc. The most common unsupervised learning method is cluster analysis. Unsupervised learning algorithms can perform more complex processing tasks than supervised learning systems.

 

Reinforcement learning algorithms attempt to find the best ways to earn the greatest reward.… Read more..

Compassionate Artificial Superintelligence AI 5.0

Compassionate Artificial Superintelligence AI 5.0 By Dr. Amit Ray

Compassionate Artificial Superintelligence AI 5.0 by Dr. Amit RayThe book defines the concept of Compassionate Artificial Superintelligence AI 5.0.  The book explains how the emerging technologies like Internet of things (IoT), Drone, Brain-Computer-Interface, Blockchain, Big data can be used with deep learning and other modern artificial intelligence (AI) architectures for the ultimate level of joint evolution of human and machine superintelligence.  

Humans and AI systems are co-evolving. Gradually they are becoming co-dependent.  The gaps between human and AI systems are reducing. Establishing heart to heart communication is a must. Tomorrow’s AI based systems must be able to understand humans from its depth and not just fulfill the surface level requirements. Sensitivity towards human pain, mistakes, and sufferings must be the part of the evolving new AI systems.

Serving humanity intelligently is held up as the “gold standard” of AI based systems. But, with the emergence of new technologies and AI systems with bio-metric data storage, surveillance,  tracking and big data analysis, humanity and the society is facing a threat today from evilly designed AI systems in the hands of monster governments and irresponsible people.  Humanity is on the verge of digital slavery.

The aim of AI 5.0 is to build super-smart-compassionate and intelligent  society, where human and machine will co-evolve.… 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. 
… Read more..