Reinforcement learning

Reinforcement learning (RL) is a machine learning algorithm, where an “agent” interacts with an “environment”. The agent’s “policy”, i. e. the choice of actions in response to the environment’s reward, is updated to increase some reward. Neural network based reinforcement learning techniques are successfully applied access vast variety of applications.

A reinforcement learning agent interacts with its environment in discrete time steps. At each time t, the agent receives an observation o_{t}, which typically includes the reward r_{t}. It then chooses an action a_{t} from the set of available actions, which is subsequently sent to the environment. The environment moves to a new state s_{t+1} and the reward r_{t+1} associated with the transition (s_{t},a_{t},s_{t+1}) is determined. The goal of a reinforcement learning agent is to collect as much reward as possible.

AI for Balance-Control Fall Detection of Elderly People

Artificial Intelligence for Balance Control and Fall Detection of Elderly People

Artificial Intelligence for Balance Control of Elderly 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… Read More »Artificial Intelligence for Balance Control and Fall Detection of Elderly People

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. Read More »Brain-Computer Interface and Compassionate AI to Serve Humanity

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… Read More »7 Limitations of Deep Learning Algorithms of AI