Real world Application of AI
AI Trends and Job Opportunities
In computer science, artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, unlike the natural intelligence displayed by humans and animals. It is also defined as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Colloquially, the term "artificial intelligence" is often used to describe machines (or computers) that mimic "cognitive" functions that humans associate with the human mind, such as "learning" and "problem solving" - Russell & Norvig (2003)
Agents and Environments
Agents: anything that can perceive its environment through sensors and acts upon that environment through effectors A human agent has sensory organs such as eyes, ears, nose, tongue and skin parallel to the sensors, and other organs such as hands, legs, mouth, for effectors.
A robotic agent replaces cameras and infrared range finders for the sensors, and various motors and actuators for effectors.
A software agent has encoded bit strings as its programs and actions.
Machine learning has become one of the most important topics within development organizations that are looking for innovative ways to leverage data assets to help the business gain a new level of understanding. Why add machine learning into the mix? With the appropriate machine learning models, organizations have the ability to continually predict changes in the business so that they are best able to predict what's next.
As data is constantly added, the machine learning models ensure that the solution is constantly updated. The value is straightforward: If you use the most appropriate and constantly changing data sources in the context of machine learning, you have the opportunity to predict the future. Machine learning is a form of AI that enables a system to learn from data rather than through explicit programming.
However, machine learning is not a simple process. Machine learning uses a variety of algorithms that iteratively learn from data to improve, describe data, and predict outcomes. As the algorithms ingest training data, it is then possible to produce more precise models based on that data.
A machine learning model is the output generated when you train your machine learning algorithm with data. After training, when you provide a model with an input, you will be given an output. For example, a predictive algorithm will create a predictive model. Then, when you provide the predictive model with data, you will receive a prediction based on the data that trained the model.
Machine learning is now essential for creating analytics models. You likely interact with machine learning applications without realizing. For example, when you visit an e-commerce site and start viewing products and reading reviews, you're likely presented with other, similar products that you may find interesting. These recommendations aren't hard coded by an army of developers.
The suggestions are served to the site via a machine learning model. The model ingests your browsing history along with other shoppers browsing and purchasing data in order to present other similar products that you may want to purchase.
Real World Application
Machine can think of large number of possible positions based on heuristic knowledge
Natural Language Processing
Interact with the computer that understands natural language spoken by humans
Impart reasoning, advise and explanation to humans
Gate allocation for the planes
Dynamic Ticket pricing
Understand, interpret, and comprehend visual input on the computer. Ex: Pictures taken by spy planes, doctors to diagnose, face recognition
Analyze satellite images where areas have highest poverty
Industrial robots for moving, spraying, painting, precision checking, drilling, cleaning, coating, carving
AI Trends and Job Opportunities
AI will become political talking point
Jobs will be created and jobs will be lost
Logistics will become increasingly efficient
Amazon Robotics -- use a combination of artificial intelligence and advanced robotics
Mainstream auto manufacturers will launch self-driving cars
DARPA will develop advanced robo-warriors in plain sight
Machine learning will aid knowledge workers
Content will be created using AI
Consumers will become accustomed to talking with technology (Siri, google Home etc)
AI will fight challenging diseases (Covid-19, Cancer etc)