What is intelligence?

All but the simplest human behaviour is ascribed to intelligence, while even the most complicated insect behaviour is never taken as an indication of intelligence. What is the difference? Consider the behaviour of the digger wasp Sphex ichneumoneus. When the female wasp returns to her burrow with food, she first deposits it on the threshhold, checks for intruders inside her burrow, and only then, if the coast is clear, carries her food inside. The real nature of the wasp’s instinctual behaviour is revealed if the food is moved a few inches away from the entrance to her burrow while she is inside: on emerging, she will repeat the whole procedure as often as the food is displaced. Intelligence—conspicuously absent in the case of Sphex—must include the ability to adapt to new circumstances.

psychologists generally do not characterize human intelligence by just one trait but by the combination of many diverse abilities. Research in AI has focused chiefly on the following components of intelligence: learning, reasoning, problem solving perception, and using language.

what is artificial intelligence

artificial intelligence (AI), the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience. Since the development of the digital computer in the 1940s, it has been demonstrated that computers can be programmed to carry out very complex tasks—as, for example, discovering proofs for mathematical theorems or playing chess—with great proficiency. Still, despite continuing advances in computer processing speed and memory capacity, there are as yet no programs that can match human flexibility over wider domains or in tasks requiring much everyday knowledge. On the other hand, some programs have attained the performance levels of human experts and professionals in performing certain specific tasks, so that artificial intelligence in this limited sense is found in applications as diverse as medical diagnosis, computer search engines and voice or handwriting recognition.


  • Reactive Machines
  • Limited Memory
  • Theory of Mind
  • Self-Awareness


  • Siri, Alexa and other smart assistants
  • Self-driving cars
  • Robo-advisors
  • Conversational bots
  • Email spam filters
  • Netflix’s recommendations

How Does Artificial Intelligence Work?

AI Approaches and Concepts

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 its subsequent Turing Test, established the fundamental goal and vision of artificial intelligence.   

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.

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.  

Can machines think? – Alan Turing, 1950

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. 

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: 

  1. Thinking humanly
  2. Thinking rationally
  3. Acting humanly 
  4. 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. 


  • The US and China both outpace the EU on investment in AI.
  • AI dominance can take on many forms.
  • The EU could champion a citizen-driven approach to AI.

“Whoever becomes the leader in AI [or artificial intelligence] will become the ruler of the world,” Vladimir Putin once famously said

In the current geopolitical theater, a global race towards leveraging artificial intelligence (AI) should come as no surprise. The United States has made substantial investments in AI to extend its role as a global superpower, while other economies also want a shot at becoming a top contender or, failing that, not falling too far behind.

China announced in 2017 that it wants to lead the world in AI by 2030, strategically allocating funds guided by a national strategy for AI. China is already closing in on scientific AI publications, and has been filing more AI patent applications than  any other country since 2013. The US and China both outpace the EU, which follows at a distance in investments and output, with Israel, India, Russia and other economic regions lagging even further behind.

Let’s explore this arms race analogy and ask what it means to be ahead in this race for AI dominance.

In the Cold War, the race for nuclear arms could lead either to a state of stability in the face of mutually assured destruction, or mutual destruction itself. However, in this present-day technological arms race, there is no clear race track or finish line. Whether you’re ahead or behind depends on the direction you want to be heading, or the destination you have in mind. With respect to where you’re going to end up in an open-ended future, the direction you’re facing is more important than how fast you’re going.

The three kinds of AI

AI dominance can take on many forms. We tend to think of AI as a technology, but it is first and foremost an ambition to create systems that display intelligent behaviours. We can roughly identify three technological manifestations of this ambition. First, programmed AI that humans design in detail with a particular function in mind, like (most) manufacturing robots, virtual travel agents and Excel sheet functions. Second, statistical AI that learns to design itself given a particular predefined function or goal. Like humans, these systems are not designed in detail and also like humans, they can make decisions but they do not necessarily have the capability to explain why they made those decisions. The third manifestation is AI-for-itself: a system that can act autonomously, responsibly, in a trustworthy style, and may very well be conscious, or not. We don’t know, because such a system does not yet exist.

What is the World Economic Forum doing about the Fourth Industrial Revolution?


The past decade has seen the unfolding of a global AI arms race fueled by statistical AI. It is relevant to note here that the word statistics stems from state: the science dealing with data about the condition of a state or community. The modern rise of AI is linked to this original meaning, which helps explain why it so often raises profound ethical questions about the relation between individuals and institutions. Census data was historically used by the state to create public policies by monitoring a population that would be impossible to track on the individual level, but which can be modelled with sufficient level of detail through empirical sampling. Uncoincidentally, this approach is also followed by market-driven corporations, institutions and other organisations that use statistical AI to model and monitor individuals online and offline.

This brings us back to the geopolitical stage, where China’s state-driven approach to leveraging emerging technologies is often contrasted with a market-driven approach to technology development in the United States. In this frame, the EU and other economic regions are left to decide how to align themselves on this state-market axis. However, this frame is misleading as it overvalues the role of economical investment policies and undervalues the critical role of data ownership for statistical AI.

A more productive frame would therefore contrast the state- and market-driven approaches with a citizen-driven approach to AI, where the rights of the individual are central to how and why AI is used. The EU has shown to adopt this ideal, first with its GDPR Directive and now again with preliminary steps towards directives for Trustworthy AI. In doing so, the EU creates a clear distinction between the rights of the individual and the ambitions of the organization, protecting its citizens against involuntary modelling and monitoring.

Continuing on this journey, a next step for the EU should be to develop a grand narrative where ethical considerations such as privacy, transparency and accountability are foundational for sustainable, healthy and productive relationships between individuals and organizations.

There are no winners in an arms race, only those who outgrow it. The race for AI dominance spills over into a more profound question of identity, asking in what kind of society we choose to live.

The answer to this question should in itself provide the necessary justification for substantial investments in the citizen-driven approach to AI, pushing the gas pedal in the right direction. The EU can be a global leader in AI if it decides to use values as a steering wheel, not as a brake. Then it is only a matter of time before others will join the race on the right track.

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Author: refuge_2020

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