The beginnings of modern AI can be traced to a 1956 summer conference at Dartmouth College, in Hanover, New Hampshire, where the term “artificial intelligence” was coined. Interest in AI boomed in the first decades of the 21st century, when machine learning (ML) was successfully applied to many problems in industry.

Artificial intelligence (AI) makes it possible for intelligent machines to learn from experience, improve with new inputs and perform human like tasks.  AI is an inter-disciplinary science with multiple approaches, but advancements in machine learning and deep learning are creating a paradigm shift in virtually every sector of the tech industry.

It is the endeavor to replicate or simulate human intelligence in machines.

Some of the activities computers with artificial intelligence are designed for include :

Learning	Planning	Knowledge
Reasoning	Problem solving	Perception

Machine learning (ML) is also a core part of AI. Learning without any supervision requires identifying patterns in streams of inputs.

Machine learning is the science of getting computers to act without being explicitly programmed. Artificial Intelligence is applied based on machine learning.

Data science is the study of data. It involves developing methods of recording, storing, and analyzing data to extract useful information

effectively

Human Bias in Artificial Intelligence

While AI tools present a range of new functionality for today’s businesses, artificial intelligence raises many ethical questions because deep learning algorithms, which underpin many of the most advanced AI tools, are only as smart as the data they are given in training. As a human selects what data should be used for training an AI program, the potential for human bias is inherent and must be monitored closely.

With machine learning, you never know what biased features your system might develop in the future. Transparency and accountability are crucial to safely implementing AI solutions in the real world.

Robotic Process Automation RPA

Employ software robots to automate repetitive tasks and manual processes, enhancing the work of human workers by enabling them to focus on innovative, customer-focused initiatives. RPA enables enterprises to make use of these software robots to finish all these repetitive, time-consuming work for improved customer satisfaction.

High volume, repetitive mundane processes are easy targets for automation, as they take the significant time that be spent on work that requires more human thinking and empathy. Bottlenecks in these processes can ultimately throttle your organization’s ability to grow and scale.

RPA Use Cases

Robotic Process Automation in Finance
RPA to automate invoice processing
Salesforce UiPath Use Cases RPA
Robotic Process Automation in Healthcare

Intelligent Process Automation IPA

Application of Artificial Intelligence and related new technologies, including Computer Vision, Cognitive automation and Machine Learning to Robotic Process Automation.

Skeptic about AI ??

Artificial Intelligence, a disruptive technology of the this century is criticized for having the potential to take away lot of jobs, so was industrial revolution blamed for taking jobs of a lot of manual workers.