Artificial Intelligence

It is gradually penetrating every aspect of the human life. Many professionals and experts started to talk about AI and deep learning as it effects our lives, careers and social standings.

Artificial intelligence is here to stay yet many of us haven’t even had the opportunity to really wrap our heads around what it is. Some people consider it to be the leading edge of technology while others feel that it’s something of science-fiction, magical and misunderstood.

What Is Artificial Intelligence?

AI is the ability for a machine or computer program to simulate human intelligence processes like learning and problem-solving. AI systems contain sets of “heuristic principles” that provide some decision guidelines for problem solving.

A Brief History of Artificial Intelligence

Artificial Intelligence was created as a branch of Computer Science by John McCarthy while he was working at Dartmouth College in 1956. At that time, computer systems were mostly being developed by the government to assist in World War II intelligence. The goal of the AI project was to develop powerful, intelligent computers that could think, make decisions, and communicate effectively.

In the 1950s, Claude Shannon was the first to apply digital electrical computing to intelligence problems. The first program designed for a computer that simulated a human being was a program written in 1958 by MIT Media Laboratory scientist Joseph Weizenbaum who implemented an ELIZA program.

How Does Artificial Intelligence Work

First, some definitions. Artificial Intelligence (AI) is a field that explores the design, development, and use of machines that function intellectually like humans. Unlike human intelligence, Artificial Intelligence is based on the design and implementation of systems that perform intelligent tasks by themselves based on artificial mechanisms. A human who is very clever, but lacks real intelligence, is not considered an AI in the most common use of the term. Likewise, a program that performs tasks as humans do is not classified as AI, regardless of how close its behaviors match the human performance mechanisms that we normally associate with human intelligence.

AI is the combination of these technologies:

Machine Learning and Big Data – A model is fit to a large data set, then generalizes to a “new” set of data.
Neurobiological Model of Brain Function – A neurobiological model describes all that’s going on in the brain, a.k.a. neural nets.
Natural Language Processing (NLP) – A model that describes how the brain works to deal with language.

The Four Types of Artificial Intelligence

There is not a single definition nor a common taxonomy of Artificial Intelligence. In 1999, the US Government’s AI Now report defined four different types of Artificial Intelligence:

1. Expert System – Experts (Humans trained to become Experts in a domain, are generally expected to retain their expertise well into their old age)
2. Expert Systems (in the general sense of Computer Programs that automatically mimic a human’s performance in a given domain).
3. Artificial Intelligence in an Educational Application (a Computer Program that mimics a teacher, so that the computer can show what concepts to teach)
4. Artificial Intelligence in a Business Application (computer programs that mimic executives, salesmen, bank tellers, etc.)

Note that although all artificial intelligence is intelligence, not all forms of intelligence are “artificial intelligence”.This article will go over the first three, to give the reader some idea on the nature of Artificial Intelligence. We know, for example, that Expert Systems are much slower than a human Expert (e.g. a chess expert).
In the case of the last two types (Business Applications and Education),we don’t have as much insight. However, we’ll make do, the examples in this document are not intended to be exhaustive descriptions of a new AI technology.

AI Approaches and Concepts

While these definitions may seem abstract to the average person, they help focus the field as an area of computer science and provide a blueprint for infusing machines and programs with machine learning and other subsets of artificial intelligence.

Patrick Winston, the Ford professor of artificial intelligence and computer science at MIT, defines AI as “algorithms enabled by constraints, exposed by representations that support models targeted at loops that tie thinking, perception and action together.

The first two ideas concern thought processes and reasoning, while the others deal with behavior. Norvig and Russell focus particularly on rational agents that act to achieve the best outcome, noting “all the skills needed for the Turing Test also allow an agent to act rationally.

Norvig and Russell go on to explore four different approaches that have historically defined the field of AI:

In their groundbreaking textbook Artificial Intelligence: A Modern Approach, authors Stuart Russell and Peter Norvig approach the question by unifying their work around the theme of intelligent agents in machines. With this in mind, AI is “the study of agents that receive percepts from the environment and perform actions.” (Russel and Norvig viii)

The major limitation in defining AI as simply “building machines that are intelligent” is that it doesn’t actually explain what artificial intelligence is? What makes a machine intelligent?AI is an interdisciplinary 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.

Can machines think? – Alan Turing, 1950

The expansive goal of artificial intelligence has given rise to many questions and debates. So much so, that no singular definition of the field is universally accepted.At its core, AI is the branch of computer science that aims to answer Turing’s question in the affirmative. It is the endeavor to replicate or simulate human intelligence in machines.

Turing’s paper “Computing Machinery and Intelligence” (1950), and its subsequent Turing Test, established the fundamental goal and vision of artificial intelligence.

How is AI Used?

Artificial intelligence generally falls under two broad categories:

“AI is a computer system able to perform tasks that ordinarily require human intelligence. Many of these artificial intelligence systems are powered by machine learning, some of them are powered by deep learning and some of them are powered by very boring things like rules.

For example, self-driving cars may use AI to interpret the world around them from visual recognition to route planning.”

One of the more well-known AI technologies is machine learning.

Based on a statistical method that analyzes large, unstructured datasets, machine learning allows AI to perform tasks where its own algorithms are used to create predictive algorithms.

Narrow Artificial Intelligence

Narrow AI is all around us, and it is by far the most successful implementation of AI to far. According to “Preparing for the Future of Artificial Intelligence,” a 2016 report released by the Obama Administration, Narrow AI has had major advancements in the recent decade that have had “substantial societal advantages and have contributed to the economic vitality of the nation.

Machine Learning & Deep Learning

Deep learning is a type of machine learning that runs inputs through a biologically-inspired neural network architecture. The data is processed through a number of hidden layers in the neural networks, which allows the machine to go “deep” in its learning, creating connections and weighing input for the best outcomes.

Simply defined, machine learning feeds data to a computer and employs statistical techniques to help it “learn” how to get better at a task without being particularly programmed for it, reducing the need for millions of lines of written code. Both supervised and unsupervised learning are used in machine learning (using unlabeled data sets).

“Artificial intelligence is a set of algorithms and intelligence to try to mimic human intelligence. Machine learning is one of them, and deep learning is one of those machine learning techniques.

Much of Narrow AI is powered by breakthroughs in machine learning and deep learning. Understanding the difference between artificial intelligence, machine learning and deep learning can be confusing. Venture capitalist Frank Chen provides a good overview of how to distinguish between them.

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