What is Artificial Intelligence?

Artificial Intelligence has been around for more than 50 years. But the use and the impact of Artificial Intelligence and Machine Learning has exploded in recent years as a result of the hardware development which has provided the necessary performance. This article gives an overview of the past and current development and some of the benefits and concerns of the rapid growth of Artificial Intelligence technologies.

Artificial Intelligence (AI) has become one of the main New Age areas of IT development in recent years. Thanks to AI, computers have beaten humans in various ways;

Perhaps more significant than any of these is that AI has recently passed humans when it comes to quality of image recognition (2015).

Computers are not really “intelligent” but have outperformed humans by sheer brute force. The computers are not really “thinking”. However, these cases still show that computer solutions are starting to outperform humans in specific areas. Computers winning over us in games may not have any significant impact on our day-to-day life. But other areas where computer solutions are improving have as we shall see in this article, already a significant impact on our lives.

Artificial Intelligence is used in areas so diverse as searching on Google, speaking to a virtual personal assistant such as Siri, Google Now, Cortana or Alexa, in various video games, making purchase prediction and recommendations in websites and music services.

AI is also used for fraud detection, to produce simple news updates such as financial summaries and sports reports, for security surveillance and even in smart home devices. Further, for stock trading, in medical diagnostics for various military applications and of course for self-driving cars. In most of these cases, we are not even aware that AI is involved.

Moore's lawApplications of AI has been around since the late 50’s, but the real usefulness of the technology always seemed like nuclear fusion to be in a distant future. However, in the last five years, the speed has accelerated dramatically and even though not everyone is aware of it, AI is becoming part of our lives.

The speed of innovation related to Artificial Intelligence has in recent years outpaced Moore’s law (that the number of transistors per square inch on integrated circuits had doubled every two years. Gordon Moore predicted that this trend would continue into the foreseeable future).

Artificial Intelligence is expected to impact our lives even more significantly in the next decades. Expected breakthrough includes; Autonomous cars, transportation, automated manufacturing and some reports suggest that half of the job titles which are known today may not exist within 20 years. Therefore understanding what Artificial Intelligence is and what consequences the expected development may cause is not any longer a subject for an obscure academic community but is relevant for everyone.

Turing’s test (Mark Jensen with permission)

Even though the concept of robots and the foundation of neural networks are older than AI. In 1950 Alan Turing published a now-famous paper on Computers and Intelligence. Turing suggested that it was possible to construct a machine which could think. In this article, he also described what has later become known as the famous Turing Test. The Turing Test states that a human should communicate with two other entities (one computer and one human being) via a network. If the person who interrogates the other two cannot determine which is the human and which is the computer the computer should be considered intelligent. While Alan Turing is considered the father of Artificial Intelligence, the term Artificial Intelligence was suggested by John McCarthy in 1956. 

During the next few years, scientists from fields as diverse as mathematics, psychology, engineering, economics and political science discussed the concept of an artificial brain. The real breakthrough of AI happened from 1990 onward, and we are right now in a formidable revolution of using machine learning and other concepts. While many applications may not be overly visible for the common man, the next years and decades will be disruptive.

While Science fiction literature traditionally suggested robots would have human-like characteristics, this is not the mainstream expectation any longer.

Transistor Count and Moore's Law
Exponential development of computer technology (Moor’s law)

The AI community defines three levels of AI;

  • Narrow or weak AI (ANI) which means using computers and software to resolve simple discrete tasks such as driving a car, voice, pattern or image recognition, text analysis, search, playing a game or any other current application of AI including all of the above-described applications belong. Note that narrow or weak AI still generally exceeds human capability within its restricted area.
  • General or Strong AI (AGI) which will be able to conduct any cognitive task a human can do of generic nature. This level refers to when the AI solution is as smart as a human being across the board. This level does not yet exist
  • Artificial Super-Intelligence (ASI) is an assumed level where an AI solution can beat humans in basically any field including science, innovation, wisdom and social capability. Note that the difference between AGI and ASI is small. When computers reach AGI level with access to the entire internet, they have already past humans. The moment when computer intelligence passes human capability is often referred to as “The Singularity”.

A lot of technology development has been exponential. In particular computer development based on the doubling of the number of transistors per area every two years. This development has driven the performance of computers in a never-ending exponential development. AI using computers is part of the same development route. Hence the development is going faster than most of us realise. The human capability has increased over centuries and decades but in a much more linear manner. It is worth to remember a quote from Bill Gates that “we always overestimate the change that will occur in the next two years and underestimate the change that will occur in the next ten. Don’t let yourself be lulled into inaction.”

Different types of Artificial Intelligence

Artificial Technology is an umbrella term.

Pedro Domingos who is one of the world’s top machine learning researchers in his recent book The Master Algorithm, divides AI approaches into what he calls five “tribes”:

  • Symbolists – Uses inverse deduction and decision trees
  • Connectionists – Uses Neural network (More recently called Deep Learning)
  • Evolutionists – Uses Survival of the fittest programs
  • Bayesians – Uses Statistical analysis
  • Analogizers – Uses algorithms which use similar solutions to learn.

There are various diverse technology and techniques which jointly make up the area of Artificial Intelligence. Here are some examples:

  • Expert Systems which uses a knowledge base (a database of previous cases) to infer and present knowledge
  • Fuzzy logic (which means that degree of truth is used rather than exact values)
  • Grammatical inference, various techniques such as Genetic Algorithms (simulating biological modification of genes), Tabu search, MDL (Minimum Descriptive Length) which is a variety of Occam’s Razor where the most straightforward option is preferred, Heuristic Greedy State Merging, Evidence driven state merging, Graph colouring and constraint satisfaction.
  • Handwriting recognition
  • Intelligent Agent, an independent program performs some service such as collecting information. An example could be searching the Internet at regular intervals for some information you are interested in.
  • Image Recognition
  • Natural Language Processing (recognise, interpret and synthesise speech) including sentiment analysis.
  • Neural Networks, described in the next section
  • Optical Character Recognition (OCR)
  • Pathfinding techniques such as Neural Network, Genetic Algorithms and Reinforcement Learning.
  • Sentiment analysis is the process of determining whether a piece of writing is positive, negative or neutral. It’s also known as opinion mining, deriving the opinion or attitude of a speaker. Sentiment Analysis is a subcategory of Natural Language Processing.  Read more in this extensive guide on Sentiment Analysis.
Artificial neural network
Artificial neural network

Deep Learning

In 1957 an algorithm was developed called Perceptron.

The first implementation was developed as a piece of software for the IBM 704 computer by Frank Rosenblatt at the Cornell Aeronautical Laboratory funded by the US Office of Naval Research. Later it was developed into specialised hardware. It tried to implement a simple artificial variety of brain neurones and had simple image recognition capabilities.

The contemporary news reporting assumed that technology which would “be able to walk, talk, see, write, reproduce itself and be conscious of its existence” was around the corner. As we all know the development has been far slower but while this has not yet happened, the same technology (neural networks) is used for pattern recognition, The algorithms have been further developed than in the Perceptron, but it is mainly the enormous improvement of computer performance that has made the last few years development possible.

Recent application of AI

Machine learning using artificial neural networks, so-called “deep learning” has been a formidable revolution over the last few years.

Risks with AI

More and more data is collected by the government institutions as well as employers, when combining analytics and AI it may be possible to identify risk behaviour. While this may have some positive effects, there are also risks, especially with the more extreme forms of AI. There are a lot of valid reasons to worry. Some public profiles such as Stephen Hawking, Steve Wozniak, Bill Gates, and Elon Musk have each predicted that strong AI could pose a threat to humanity. Philosopher Nick Bostrom has also raised similar concerns.

While their concerns are probably valid, weaker forms of AI are perhaps already a threat when used by the wrong person. While strong AI may take a long time to materialise, there are already indications that AI and Analytics have affected the result of some  recent elections including the Brexit referendum and the US presidential election in 2016. Today’s AI algorithms are however not necessary as “intelligent” as most people think since there are indications that Google’s search engine and Facebook have been manipulated into showing propaganda before real news articles. Elon Musk has raised concern about AI being used to fight wars. As the development of artificial intelligence progresses AI-implementations will get more intelligent, there may not be one single super-intelligence in the world, but many. If these would be fighting wars with each other’s humanity may certainly be at risk. However, even now AI used for the wrong purposes is already dangerous.

When will AI take your job?

To be honest, so far automation and robots have not taken a lot of jobs. Harvard economist James Bessen looked at the professions listed in the US 1950 census and found that there was only one job which was gone – the elevator operator). Indeed, jobs are disappearing, or perhaps more correctly the number of people working in certain professions are being reduced.

But so far new jobs have been created instead, and so far the threats of unemployment have been severely exaggerated. That may not be the case in the future, and given the increasing number of applications, we may see more risks in the not so distant future. However when only certain parts of the process are automated the prices may go down and the demand increase and hence there may be other jobs created reducing some of the most predicted impact.

However, that is only one side of the coin. AI experts themselves are worried. In a survey, they thought that by 2032 half the driving on motorways would be done by self-driving cars. Job losses may also come in sectors which are not expected. One article suggests that lawyers are a sector ripe for automation. A lot of manual labour is currently spent on searching through old cases, which is an easy application of Artificial Intelligence.

How will society change?

On another level automation, access to information and prices, and cutting of intermediaries are likely having a significant deflationary impact on mature markets and may well be one of the reasons for the current low-interest levels in the Western World. This transformation may not be only due to Artificial Intelligence. But AI increasingly powers simple applications such as using Google maps to find your route from A to B.

Things will change, and policymakers will have to be proactive and adjust tpolicies and legislation to mitigate some of the expected changes. The expected AI revolution may well be as dramatic as the industrial revolution was. Elon Musk has suggested that we would need to consider Basic Income provided by governments since the number of unemployed who cannot get traditional jobs will increase dramatically as automation increases over time.

Jobs that are most likely to become obsolete

A few recent studies have listed the likeliness of specific job categories being replaced by computers.

What can we learn from this? The more streamlined and process-oriented a job is, the more likely it will become redundant. Among the 5%, most likely jobs to be automated are Telemarketing, Insurance Underwriters, Watch repair, data entry, Umpires, Referees, drivers, bookkeeping, accounting and payroll and Paralegals and Legal Assistants. On the other side, the least likely job profile to be replaced was Recreational Therapist. However, experts assume that Artificial Intelligence will beat us in every task within 45 years.


Due to the exponential development of computer hardware, artificial intelligence solutions have led to significant breakthroughs in the last few years. Experts assume that we are at the beginning of a new industrial revolution where automation will make many jobs obsolete. Governments must prepare for a society where not everyone would have a traditional job. While this development may look scary, it also means opportunities for a better life for all of us. There are threats but if they can be managed computers will help us all to live a better life even in the future.

Mikael Gislén is the Managing Director of Gislen Software, a Swedish-owned Indian Software development company. Mikael started the company in 1994. Gislen Software provides high-value software development services to clients mainly in Scandinavia and the UK.




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