Deep Learning

Deep learning is a set of machine learning algorithms that can analyze data, recognize patterns, and make predictions. Deep learning is fundamentally changing the way we view scientific discoveries. They can give answers to some of these seemingly unanswerable questions. Deep learning transforms smartphone microscopes into laboratory-grade devices. Deep learning can solve complex decision-making problems. Deep learning uses the structure of neural networks to feed inputs into multiple layers to train the algorithm. 

Deep learning is the name for multilayered neural networks, which are networks composed of several “hidden layers” of nodes between the input and output. It can build ways to identify and describe recurring features in data, while also being able to predict some outputs. Deep learning also can work in “unsupervised” mode, where it can explain or identify interesting patterns in data without being directed.


AI for Balance-Control Fall Detection of Elderly People

Artificial Intelligence for Balance Control and Fall Detection of Elderly People

Balance control in elderly people is one of the key issues of old age. Artificial Intelligence can play a big role to solve this issue. In this research work, we demonstrate the application of machine learning techniques for posture alignments and the control of the body center of mass for disable 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 »Artificial Intelligence for Balance Control and Fall Detection of Elderly People

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..