Artificial Intelligence (AI) is a wide-ranging branch of computer science that generally concerned with building smart machines. And those can perform tasks that require human intelligence. AI is an interdisciplinary science with a variety of approaches, but the advancement of machine learning and in-depth learning is making a paradigm shift in almost every aspect of the technology industry.
Artificial intelligence divided into two broad categories:
This type of artificial intelligence sometimes referred to as “weak AI”. Since, operates in a limited context and is an imitation of human intelligence. Narrow AIs are often focusing on performing a single task very well. Although these machines may seem intelligent, they operate within far more limits and limitations than the most basic human intelligence.
What is Artificial General Intelligence:
AGI, sometimes referred to as “Strong AI,” is the kind of artificial intelligence we see in the movies, like the robots from Westworld or Data from Star Trek.
Further AGI is a machine with general intelligence and, much like a human being; it can apply that intelligence to solve any problem.
Narrow Artificial Intelligence
Narrow AI surrounds us and is by far the most successful understanding of artificial intelligence. Focusing on specific tasks, narrow AI prepare for the future of artificial intelligence for the past decade, saying it has “reaped significant social benefits and contributed to the nation’s economic strength.” (2016 Report Released by the Obama Administration.)
Examples of narrow AI include Google Search Image Recognition Software Siri, Alexa, and other personal assistants Self-driving cars Watson at IBM.
For example, Narrow AI include:
- Search in Google
- Image recognition software
- Alexa, Siri, and other personal assistants
- Self-driving cars
- IBM’s Watson
Machine Learning & Deep Learning
Most of the narrow AI has powered by machine learning and in-depth learning progress. So, Understanding the difference between artificial intelligence, machine learning, and in-depth learning can be confusing. Venture capitalist Frank Chen gives a better overview of how to distinguish between them. “Artificial intelligence is an algorithm and intelligence that seeks to mimic human intelligence.
Further, machine learning is one of them, and deep learning is one of those machine learning methods.” Simply put, machine learning nurtures computer data and helps to “learn” how to systematically get better without systematically programming for the task using statistical techniques. Eliminates the need for millions of lines of written code. Machine learning consists of both supervised learning (using labeled datasets) and unattended learning (using unlabeled datasets). Deep learning is machine learning that processes applications through a biologically inspired neural network structure. Neural networks contain several hidden layers that process data, allowing the machine to “go deeper” in its learning, connections, and weight input for the best results.
Artificial General Intelligence
Creating a human-level intelligent machine that can use for any purpose is a pure task for many AI researchers, but the search for AGI is fraught with difficulty. The search for a “universal algorithm for learning and operating in any environment” (Russell & Norwig 27) is not new. But the difficulty of essentially creating a machine with full cognitive capabilities has not gone away. AGI has long been a museum of dystopian science fiction, and although super-intelligent robots have surpassed humanity. So, experts agree that this is not something we should be worried about at any time.
HOW DOES ARTIFICIAL INTELLIGENCE WORK?
AI is a branch of computer science that aims to answer Turin’s question. It is an attempt to imitate or mimic the human intellect of machines. As well as the broader goal of artificial intelligence has led to many questions and debates. So that is a single definition of the field is not universally accepted. AI is “the study of agents that take cognition from the environment and act on it.” Norwig and Russell explore four different approaches that have historically defined the field of AI. Following are approaches,
1. Thinking humanly
2. Thinking rationally
3. Acting humanly
4. Acting rationally
The first two ideas relatively thinking processes and reasoning, while other ideas deal with the behavior. Norvig and Russell mainly focus particularly on rational agents that act to achieve the best outcome.
Patrick Winston is the Ford professor about artificial intelligence and computer science at MIT. He defines AI is “algorithms enabled by constraints, exposed by representations that support models targeted at loops that tie thinking, perception and action together.”
While these definitions provide abstract to the average person, those are helping focus on this field as an area of computer science and provide a blueprint for infusing machines and programs.
“AI is a computer system able to perform tasks that ordinarily require human intelligence. Many artificial intelligence systems powered by machine learning while some of them powered by deep learning. Further, some of them powered by very boring things as rules.”
ARTIFICIAL INTELLIGENCE EXAMPLES
Following situations can take for example,
- Disease mapping and prediction tools
- Smart assistants
- Manufacturing and drone robots
- Conversational bots for marketing and customer service
- Optimized, personalized healthcare treatment recommendations
- Tools for social media monitoring for dangerous content or false news
- Spam filters on email
- Song or TV shows.