In computer science, the field of AI research defines itself as the study of “intelligent agents”: any device that perceives its environment and takes actions that maximize its chance of success at some goal. Colloquially, the term “artificial intelligence” is applied when a machine mimics “cognitive” functions that human associate with other human minds, such as “learning” and “problem solving”. As machines become increasingly capable, mental facilities once thought to require intelligence are removed from the definition. Capabilities currently classified as AI include successfully understanding human speech, competing at a high level in strategic game systems, autonomous cars, intelligent routing in content delivery networks, military simulations, and interpreting complex data. It is the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.
Evolved from the study of pattern recognition and computational learning theory in artificial intelligence, machine learning explores the study and construction of algorithms that can learn from and make predictions on data – such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions through building a model from sample inputs. Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that machines should be able to learn and adapt through experience. Resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. Things like growing volumes and varieties of available data, computational processing that is cheaper and more powerful, and fordable data storage.
All of these things mean it’s possible to quickly and automatically produce models that can analyses bigger, more complex data and deliver faster, more accurate results – even on a very large scale. And by building precise models, an organization has a better chance of identifying profitable opportunities – or avoiding unknown risks.
Key benefits of attending this workshop
Following this workshop, delegates will:
- GAIN an understanding of the fundamentals and business applicability of widely adopted disruptive technologies like Artificial Intelligence (AI) Machine Learning (ML), Deep Learning and Robotics
- GAIN insights, through examples & use cases, about the disruptions, business models, tasks, decision support systems and projections that AI powered apps & systems can bring about in various functional domains of a corporation
- UNDERSTAND the steps involved in the process of deploying AI based tools and apps in one’s
- organization and also the specializations and skills needed to become an AI professional
- DEFINE business objective and expected benefits from AI, ML, DL and Robotics
- SELECT an AI Technology Stack to use applicable back at work
- IDENTIFY the data sources and data types
- IDENTIFY the relevant AI technologies to use back at work
- LIST the benefits of their approach relevant to business objective and expected benefits
Who Should Attend?
This workshop is specifically designed for:
Chief Executive Officers
Chief Operation Officers
Chief Innovative Officers
Chief Technology Officers
Chief Data Officer
Chief Analytics Officer
Vice Presidents/Director of IT
Head of Research & Development
Managers and Executives
Why You Should Attend?
A computer can beat the world chess champion and understand voice commands on your smartphone, but real artificial intelligence has yet to arrive. The pace of change is quickening, though. Because of new computing technologies, machine learning today is not like machines learning of the past. It was born from pattern recognition and the theory that computers can learn without being programmed to perform specific tasks; researchers interested in artificial intelligence wanted to see if computers could learn from data. The iterative aspect of machine learning is important because as models are exposed to new data they are able to independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results. It’s a science that’s not new – but one that has gained fresh momentum.
This program is for executives and professionals who want to learn about the capabilities of Artificial Intelligence, Machine Learning and Deep Learning that are coming to pervade the world and workplace and are finding acceptance in almost all the industries and sectors to solve many practical problems of cost management, sales growth and technological innovation. The program covers concepts, ideas and applications from leading companies and several use cases from the domain of interest to illustrate how these technologies can solve problems and create competitive advantage.