Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving.

The ideal characteristic of artificial intelligence is its ability to rationalize and take actions that have the best chance of achieving a specific goal.

Understanding Artificial Intelligence

When most people hear the term artificial intelligence, the first thing they usually think of is robots. That’s because big-budget films and novels weave stories about human-like machines that wreak havoc on Earth. But nothing could be further from the truth.

Artificial intelligence is based on the principle that human intelligence can be defined in a way that a machine can easily mimic it and execute tasks, from the most simple to those that are even more complex. The goals of artificial intelligence include learning, reasoning, and perception.

As technology advances, previous benchmarks that defined artificial intelligence become outdated. For example, machines that calculate basic functions or recognize text through optimal character recognition are no longer considered to embody artificial intelligence, since this function is now taken for granted as an inherent computer function.

AI is continuously evolving to benefit many different industries. Machines are wired using a cross-disciplinary approach based in mathematics, computer science, linguistics, psychology, and more.

Algorithms often play a very important part in the structure of artificial intelligence, where simple algorithms are used in simple applications, while more complex ones help frame strong artificial intelligence.

Applications of Artificial Intelligence

The applications for artificial intelligence are endless. The technology can be applied to many different sectors and industries. AI is being tested and used in the healthcare industry for dosing drugs and different treatment in patients, and for surgical procedures in the operating room.

Other examples of machines with artificial intelligence include computers that play chess and self-driving cars. Each of these machines must weigh the consequences of any action they take, as each action will impact the end result. In chess, the end result is winning the game. For self-driving cars, the computer system must account for all external data and compute it to act in a way that prevents a collision.

Artificial intelligence also has applications in the financial industry, where it is used to detect and flag activity in banking and finance such as unusual debit card usage and large account deposits—all of which help a bank’s fraud department. Applications for AI are also being used to help streamline and make trading easier. This is done by making supply, demand, and pricing of securities easier to estimate.

KEY TAKEAWAYS

Artificial intelligence refers to the simulation of human intelligence in machines.
The goals of artificial intelligence include learning, reasoning, and perception.
AI is being used across different industries including finance and healthcare.
Weak AI tends to be simple and single-task oriented, while strong AI carries on tasks that are more complex and human-like.

Categorization of Artificial Intelligence

Artificial intelligence can be divided into two different categories: weak and strong. Weak artificial intelligence embodies a system designed to carry out one particular job. Weak AI systems include video games such as the chess example from above and personal assistants such as Amazon’s Alexa and Apple’s Siri. You ask the assistant a question, it answers it for you.

Strong artificial intelligence systems are systems that carry on the tasks considered to be human-like. These tend to be more complex and complicated systems. They are programmed to handle situations in which they may be required to problem solve without having a person intervene. These kinds of systems can be found in applications like self-driving cars or in hospital operating rooms.

Special Considerations

Since its beginning, artificial intelligence has come under scrutiny from scientists and the public alike. One common theme is the idea that machines will become so highly developed that humans will not be able to keep up and they will take off on their own, redesigning themselves at an exponential rate.

Another is that machines can hack into people’s privacy and even be weaponized. Other arguments debate the ethics of artificial intelligence and whether intelligent systems such as robots should be treated with the same rights as humans.

Self-driving cars have been fairly controversial as their machines tend to be designed for the lowest possible risk and the least casualties. If presented with a scenario of colliding with one person or another at the same time, these cars would calculate the option that would cause the least amount of damage.

Another contentious issue many people have with artificial intelligence is how it may affect human employment. With many industries looking to automate certain jobs through the use of intelligent machinery, there is a concern that people would be pushed out of the workforce. Self-driving cars may remove the need for taxis and car-share programs, while manufacturers may easily replace human labor with machines, making people’s skills more obsolete.

Strong AI

What Is Strong AI

Strong Artificial Intelligence (AI) is a theoretical form of machine intelligence that is equal to human intelligence. Key characteristics of Strong AI include the ability to reason, solve puzzles, make judgments, plan, learn, and communicate. It should also have consciousness, objective thoughts, self-awareness, sentience, and sapience.

Strong AI is also called True Intelligence or Artificial General Intelligence (AGI).

KEY TAKEAWAYS

Strong AI is the theoretical next level of artificial intelligence.

It moves beyond Weak AI, or simulated human cognition, to include problem-solving, learning, and development.

Strong AI raises the fear of people losing jobs to machines.

Understanding Strong AI

Strong AI does not currently exist. Some experts predict it may be developed by 2030 or 2045. Others more conservatively predict that it may be developed within the next century, or that the development of Strong AI may not be possible at all.

Some theorists argue that a machine with Strong AI should be able to go through the same development process as a human, starting with a childlike mind and developing an adult mind through learning. It should be able to interact with the world and learn from it, acquiring its own common sense and language. Another argument is that we will not know when we have developed strong AI (if it can indeed be developed) because there is no consensus on what constitutes intelligence.

While Weak AI merely simulates human cognition, Strong AI would actually have human cognition. With Strong AI, a single system could theoretically handle all the same problems that a single human could. While Weak AI can replace many low- and medium-skilled workers, Strong AI might be necessary to replace certain categories of highly skilled workers.

Risks and Rewards of Strong AI

The possibility of Strong AI comes with major potential benefits and serious concerns. Some people fear that if Strong AI becomes a reality, AI may become more intelligent than humans, a phenomenon known as the singularity. The idea is that Strong AI will be so intelligent that it can alter itself and pursue its own goals without human intervention, possibly in ways that are harmful to humans (think killer robots like in the movie I, Robot). Could Strong AI be developed with constraints to prevent such outcomes? Could Strong AI be programmed with desirable moral values, and could humanity agree on what those desirable values would be? Further research into these issues could help prevent the possibility of robots that turn against us, or determine if they could even ever exist.

Another major concern is that AI will increasingly take jobs away from people, resulting in high unemployment – even for knowledge-intensive white-collar work, especially if Strong AI becomes a reality. However, just as the Industrial Revolution dramatically changed the types of jobs workers performed, an AI Revolution could result not in massive unemployment, but in a massive employment shift. Strong AI could have a significant positive impact on society by increasing productivity and wealth. Humans could perform jobs that we cannot even imagine today and will not have a need for until we see all that AI can do for us. Another possibility is that the government will have to step in a provide a safety net for those displaced by AI.

Weak AI

What is Weak AI

Weak AI, or Narrow AI, is a machine intelligence that is limited to a specific or narrow area. Weak Artificial Intelligence (AI) simulates human cognition and benefits mankind by automating time-consuming tasks and by analyzing data in ways that humans sometimes can’t.

BREAKING DOWN Weak AI

Weak AI lacks human consciousness, though it may be able to simulate it. The classic illustration of weak AI is John Searle’s Chinese room thought experiment. This experiment says that a person outside a room may be able to have what appears to be a conversation in Chinese with a person inside a room who is given instructions on how to respond to conversations in Chinese. The person inside the room would appear to speak Chinese, but in reality, they couldn’t actually speak or understand a word of it absent the instructions they’re being fed. That’s because the person is good at following instructions, not at speaking Chinese. They might appear to have Strong AI – machine intelligence equivalent to human intelligence – but they really only have Weak AI.

Narrow or weak AI systems do not have general intelligence; they have specific intelligence. An AI that is an expert at telling you how to drive from point A to point B is usually incapable of challenging you to a game of chess. And an AI that can pretend to speak Chinese with you probably cannot sweep your floors.

Weak AI helps turn big data into usable information by detecting patterns and making predictions. Examples include Facebook’s news feed, Amazon’s suggested purchases and Apple’s Siri, the iPhone technology that answers users’ spoken questions. Email spam filters are another example of Weak AI where a computer uses an algorithm to learn which messages are likely to be spam, then redirects them from the inbox to the spam folder.

Limitations of Weak AI

Problems with Weak AI besides its limited capabilities include the possibility to cause harm if a system fails – think of a driverless car that miscalculates the location of an oncoming vehicle and causes a deadly collision – and the possibility to cause harm if the system is used by someone who wishes to cause harm – such as a terrorist who uses a self-driving car to deploy explosives in a crowded area. Another issue with it is determining who is at fault for a malfunction or a design flaw.

A further concern is the loss of jobs caused by the automation of an increasing number of tasks. Will unemployment skyrocket or will society come up with new ways for humans to be economically productive? Though the prospect of a large percentage of workers losing their jobs may be terrifying, it is reasonable to expect that should this happen, new jobs will emerge that we can’t yet predict, as the use of AI becomes increasingly widespread.

HOW DOES ARTIFICIAL INTELLIGENCE WORK?

Less than a decade after breaking the Nazi encryption machine Enigma and helping the Allied Forces win World War II, mathematician Alan Turing changed history a second time with a simple question: “Can machines think?”

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

At it’s 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.

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.

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?

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)

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

Thinking humanly
Thinking rationally
Acting humanly
Acting rationally

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.” (Russel and Norvig 4).

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.”

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.

While addressing a crowd at the Japan AI Experience in 2017, DataRobot CEO Jeremy Achin began his speech by offering the following definition of how AI is used today:

“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.”

HOW IS AI USED?

Artificial intelligence generally falls under two broad categories:

Narrow AI: Sometimes referred to as “Weak AI,” this kind of artificial intelligence operates within a limited context and is a simulation of human intelligence. Narrow AI is often focused on performing a single task extremely well and while these machines may seem intelligent, they are operating under far more constraints and limitations than even the most basic human intelligence.
Artificial General Intelligence (AGI): 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: The Next Generation. AGI is a machine with general intelligence and, much like a human being, it can apply that intelligence to solve any problem.

ARTIFICIAL INTELLIGENCE EXAMPLES

Smart assistants (like Siri and Alexa)
Disease mapping and prediction tools
Manufacturing and drone robots
Optimized, personalized healthcare treatment recommendations
Conversational bots for marketing and customer service
Robo-advisors for stock trading
Spam filters on email
Social media monitoring tools for dangerous content or false news
Song or TV show recommendations from Spotify and Netflix

Narrow Artificial Intelligence

Narrow AI is all around us and is easily the most successful realization of artificial intelligence to date. With its focus on performing specific tasks, Narrow AI has experienced numerous breakthroughs in the last decade that have had “significant societal benefits and have contributed to the economic vitality of the nation,” according to “Preparing for the Future of Artificial Intelligence,” a 2016 report released by the Obama Administration.

A few examples of Narrow AI include:

Google search
Image recognition software
Siri, Alexa and other personal assistants
Self-driving cars
IBM’s Watson

Machine Learning & Deep Learning

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, noting:

“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.”

Simply put, machine learning feeds a computer data and uses statistical techniques to help it “learn” how to get progressively better at a task, without having been specifically programmed for that task, eliminating the need for millions of lines of written code. Machine learning consists of both supervised learning (using labeled data sets) and unsupervised learning (using unlabeled data sets).

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

Artificial General Intelligence

The creation of a machine with human-level intelligence that can be applied to any task is the Holy Grail for many AI researchers, but the quest for AGI has been fraught with difficulty.

The search for a “universal algorithm for learning and acting in any environment,” (Russel and Norvig 27) isn’t new, but time hasn’t eased the difficulty of essentially creating a machine with a full set of cognitive abilities.

AGI has long been the muse of dystopian science fiction, in which super-intelligent robots overrun humanity, but experts agree it’s not something we need to worry about anytime soon.

HISTORY OF AI

Intelligent robots and artificial beings first appeared in the ancient Greek myths of Antiquity. Aristotle’s development of the syllogism and it’s use of deductive reasoning was a key moment in mankind’s quest to understand its own intelligence. While the roots are long and deep, the history of artificial intelligence as we think of it today spans less than a century. The following is a quick look at some of the most important events in AI.

1943

Warren McCullough and Walter Pitts publish “A Logical Calculus of Ideas Immanent in Nervous Activity.” The paper proposed the first mathematic model for building a neural network.

1949

In his book The Organization of Behavior: A Neuropsychological Theory, Donald Hebb proposes the theory that neural pathways are created from experiences and that connections between neurons become stronger the more frequently they’re used. Hebbian learning continues to be an important model in AI.

1950

Alan Turing publishes “Computing Machinery and Intelligence, proposing what is now known as the Turing Test, a method for determining if a machine is intelligent.
Harvard undergraduates Marvin Minsky and Dean Edmonds build SNARC, the first neural network computer.
Claude Shannon publishes the paper “Programming a Computer for Playing Chess.”
Isaac Asimov publishes the “Three Laws of Robotics.”

1952

Arthur Samuel develops a self-learning program to play checkers.

1954

The Georgetown-IBM machine translation experiment automatically translates 60 carefully selected Russian sentences into English.

1956

The phrase artificial intelligence is coined at the “Dartmouth Summer Research Project on Artificial Intelligence.” Led by John McCarthy, the conference, which defined the scope and goals of AI, is widely considered to be the birth of artificial intelligence as we know it today.
Allen Newell and Herbert Simon demonstrate Logic Theorist (LT), the first reasoning program.

1958

John McCarthy develops the AI programming language Lisp and publishes the paper “Programs with Common Sense.” The paper proposed the hypothetical Advice Taker, a complete AI system with the ability to learn from experience as effectively as humans do.

1959

Allen Newell, Herbert Simon and J.C. Shaw develop the General Problem Solver (GPS), a program designed to imitate human problem-solving.
Herbert Gelernter develops the Geometry Theorem Prover program.
Arthur Samuel coins the term machine learning while at IBM.
John McCarthy and Marvin Minsky found the MIT Artificial Intelligence Project.

1963

John McCarthy starts the AI Lab at Stanford.

1966

The Automatic Language Processing Advisory Committee (ALPAC) report by the U.S. government details the lack of progress in machine translations research, a major Cold War initiative with the promise of automatic and instantaneous translation of Russian. The ALPAC report leads to the cancellation of all government-funded MT projects.

1969

The first successful expert systems are developed in DENDRAL, a XX program, and MYCIN, designed to diagnose blood infections, are created at Stanford.

1972

The logic programming language PROLOG is created.

1973

The “Lighthill Report,” detailing the disappointments in AI research, is released by the British government and leads to severe cuts in funding for artificial intelligence projects.

1974-1980

Frustration with the progress of AI development leads to major DARPA cutbacks in academic grants. Combined with the earlier ALPAC report and the previous year’s “Lighthill Report,” artificial intelligence funding dries up and research stalls. This period is known as the “First AI Winter.”

1980

Digital Equipment Corporations develops R1 (also known as XCON), the first successful commercial expert system. Designed to configure orders for new computer systems, R1 kicks off an investment boom in expert systems that will last for much of the decade, effectively ending the first “AI Winter.”

1982

Japan’s Ministry of International Trade and Industry launches the ambitious Fifth Generation Computer Systems project. The goal of FGCS is to develop supercomputer-like performance and a platform for AI development.

1983

In response to Japan’s FGCS, the U.S. government launches the Strategic Computing Initiative to provide DARPA funded research in advanced computing and artificial intelligence.

1985

Companies are spending more than a billion dollars a year on expert systems and an entire industry known as the Lisp machine market springs up to support them. Companies like Symbolics and Lisp Machines Inc. build specialized computers to run on the AI programming language Lisp.

1987-1993

As computing technology improved, cheaper alternatives emerged and the Lisp machine market collapsed in 1987, ushering in the “Second AI Winter.” During this period, expert systems proved too expensive to maintain and update, eventually falling out of favor.
Japan terminates the FGCS project in 1992, citing failure in meeting the ambitious goals outlined a decade earlier.
DARPA ends the Strategic Computing Initiative in 1993 after spending nearly $1 billion and falling far short of expectations.

1991

U.S. forces deploy DART, an automated logistics planning and scheduling tool, during the Gulf War.

1997

IBM’s Deep Blue beats world chess champion Gary Kasparov

2005

STANLEY, a self-driving car, wins the DARPA Grand Challenge.
The U.S. military begins investing in autonomous robots like Boston Dynamic’s “Big Dog” and iRobot’s “PackBot.”

2008

Google makes breakthroughs in speech recognition and introduces the feature in its iPhone app.

2011

IBM’s Watson trounces the competition on Jeopardy!.

2012

Andrew Ng, founder of the Google Brain Deep Learning project, feeds a neural network using deep learning algorithms 10 million YouTube videos as a training set. The neural network learned to recognize a cat without being told what a cat is, ushering in breakthrough era for neural networks and deep learning funding.

2014

Google makes first self-driving car to pass a state driving test.

2016

Google DeepMind’s AlphaGo defeats world champion Go player Lee Sedol. The complexity of the ancient Chinese game was seen as a major hurdle to clear in AI.