Artificial Intelligence-1

What is Artificial Intelligence?

Artificial Intelligence(AI) is a way to make machines think and behave intelligently.These machines are controlled by software inside them,so AI has a lot to do with intelligent software programs that control these machines.It is a science of finding theories and methodologies that can help machines understand the world and accordingly react to situations in the same way that humans do.

AI is closely related to the study of human brain.Researchers believe that AI can be accomplished by understanding how the human brain works.By mimicking the way the human brain learns,thinks,and takes action,we can build a machine that can do the same.This can be used as a platform to develop intelligent systems that are capable of learning.

Why do we need to study AI?

Even though the human brain is great at analyzing things around us,it cannot keep up with the preceding conditions.Hence,we need to design and develop intelligent machines that can do this.We need AI systems that can:

• Handle large amounts of data in an efficient way.With the advent of Cloud Computing,we are now able to store huge amounts of data.
• Ingest data simultaneously from multiple sources without any lag.
• Index and organize data in a way that allows us to derive insights.
• Learn from new data and update constantly using the right learnin algorithms.
• Think and respond to situations based on the conditions in real time.

AI techniques are actively being used to make existing machines smarter,so that they can execute faster and more efficiently.

Applications of AI:

• Computer Vision: These are the systems that deal with visual data such as images and videos. These systems understand the content and extract insights based on the use case. For example, Google uses reverse image search to search for visually similar images across the Web.
• Natural Language Processing: This field deals with understanding text. We can interact with a machine by typing natural language sentences. Search engines use this extensively to deliver the right search results.
• Speech Recognition: These systems are capable of hearing and understanding spoken words. For example, there are intelligent personal assistants on our smartphones that can understand what we are saying and give relevant information or perform an action based on that.
• Expert Systems: These systems use AI techniques to provide advice or make decisions. They usually use databases of expert knowledge areas such as finance, medicine, marketing, and so on to give advice about what to do next.
• Games: AI is used extensively in the gaming industry. It is used to design intelligent agents that can compete with humans. For example, AlphaGo is a computer program that can play the strategy game Go. It is also used in designing many other types of games where we expect the computer to behave intelligently.
• Robotics:Robotic systems actually combine many concepts in AI. These systems are able to perform many different tasks. Depending on the situation, robots have sensors and actuators that can do different things. These sensors can see things in front of them and measure the temperature, heat, movements, and so on. They have processors on board that compute various things in real time. They are also capable of adapting to the new environments.

Branches of AI:

• Machine Learning and pattern recognition: : This is perhaps the most popular form of AI out there. We design and develop software that can learn from data. Based on these learning models, we perform predictions on unknown data. One of the main constraints here is that these programs are limited to the power of the data. If the dataset is small, then the learning models would be limited as well.
• Logic-based AI: Mathematical logic is used to execute computer programs in logic-based AI. A program written in logic-based AI is basically a set of statements in logical form that express facts and rules about a particular problem domain. This is used extensively in pattern matching, language parsing, semantic analysis, and so on.
• Knowledge representation: The facts about the world around us need to be represented in some way for a system to make sense of them. The languages of mathematical logic are frequently used here. If knowledge is represented efficiently, systems can be smarter and more intelligent. Ontology is a closely related field of study that deals with the kinds of objects that exist. It is a formal definition of the properties and relationships of the entities that exist in a particular domain. This is usually done with a particular taxonomy or a hierarchical structure of some kind. The following diagram shows the difference between information and knowledge:
• Planning: This field deals with optimal planning that gives us maximum returns with minimal costs. These software programs start with facts about the particular situation and a statement of a goal. These programs are also aware of the facts of the world, so that they know what the rules are. From this information, they generate the most optimal plan to achieve the goal.
• Heuristics: A heuristic is a technique used to solve a given problem that’s practical and useful in solving the problem in the short term, but not guaranteed to be optimal. This is more like an educated guess on what approach we should take to solve a problem. In AI, we frequently encounter situations where we cannot check every single possibility to pick the best option. So we need to use heuristics to achieve the goal. They are used extensively in AI in fields such as robotics, search engines, and so on.
• Genetic Programming: Genetic programming is a way to get programs to solve a task, by mating programs and selecting the fittest. The programs are encoded as a set of genes, using an algorithm to get a program that is able to perform the given task really well.
• Search: The Search techniques are used extensively in AI programs. These programs examine a large number of possibilities and then pick the most optimal path. For example, this is used a lot in strategy games such as Chess, networking, resource allocation, scheduling, and so on.

References:

• Prateek Joshi, “Artificial Intelligence with Python : A Comprehensive Guide to Building Intelligent Apps for Python Beginners and Developers”, 1 st Ed., Packt Publishing, 2017.
• •Stuart Russell and Peter Norvig, “Artificial Intelligence: A Modern Approach” , 3rd Ed., Prentice Hall, 2010.