What is artificial intelligence?

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Pau Monfort
@paumonfort
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Artificial intelligence (AI) is a broad branch of computer science that is concerned with building intelligent machines that can perform tasks that typically require human intelligence. Artificial intelligence is an interdisciplinary science with multiple approaches, but advances in machine learning and deep learning are creating a paradigm shift in virtually every sector of the tech industry.


How does artificial intelligence work?

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



The Turing 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 computing that aims to answer Turing's question in the affirmative. It is the effort to replicate or simulate human intelligence in machines.


The expansive goal of artificial intelligence has sparked many questions and debates. So much so that no singular definition of the field is universally accepted.  

The main limitation in defining AI simply as “building intelligent machines” is that it doesn't really explain what artificial intelligence is? What Makes a Machine Smart?

In their groundbreaking textbook Artificial Intelligence: A Modern Approach, authors Stuart Russell and Peter Norvig address this issue by unifying their work around the theme of intelligent agents in machines. With this in mind, artificial intelligence is "the study of agents who receive perceptions 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. Act humanly 
  4. Act rationally

The first two ideas deal with thought processes and reasoning, while the others deal with behavior. Norvig and Russell particularly focus on rational agents acting to obtain the best result, noting that "all the skills necessary for the Turing test also allow an agent to act rationally." (Russel and Norvig 4).

Patrick Winston, a professor of artificial intelligence and computer science at MIT, defines AI as "constraint-enabled algorithms, exposed by representations that support models aimed at circuits that connect thought, perception and action together."


While these definitions may seem abstract to the average person, they help focus the field as an area of ​​computing 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:

"Artificial intelligence is a computer system that can perform tasks that normally require human intelligence ... Many of these AI systems are powered by machine learning, some are powered by deep learning, and some are powered by very boring things like the rules". 

How is artificial intelligence used? IA

Artificial intelligence generally bogus in two broad categories: 

  • AI close: Sometimes referred to as "weak AI," this type of artificial intelligence operates in a limited context and is a simulation of human intelligence. Narrow AI is often focused on performing a single task, and while these machines may seem intelligent, they are operating with 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 movies, such as robots from Westworld or Data from Star Trek: The Next Generation. AGI is a machine with general intelligence, and just like a human being, it can apply that intelligence to solve any problem. 

Examples of artificial intelligence

  • Smart assistants (like Siri and Alexa)
  • Disease mapping and forecasting tools
  • Production robots and drones
  • Optimized personalized therapy recommendations
  • Conversational bots for marketing and customer service
  • Robo-advisor for stock trading
  • Spam filters by e-mail
  • Social media monitoring tools for dangerous content or fake news
  • Tips on Spotify and Netflix songs or TV shows

As you can see, it has already become part of our lives.



Narrow artificial intelligence

Narrow AI surrounds us and is the most successful AI to date. Focusing on performing specific tasks, Narrow AI has pioneered numerous discoveries over the past decade that have had "significant benefits to society and contributed to the economic viability of the nation," according to "Preparing for the Future of Artificial Intelligence," a report. 2016 published by the Obama administration. 

Some examples of Narrow AI include: 

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

Machine learning and deep learning 

Much narrow AI is fueled by breakthroughs in machine learning and deep learning. Understanding the difference between artificial intelligence, machine learning, and deep learning can be confusing. Capitalist Frank Chen offers a good overview of how to distinguish 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 data to a computer and uses statistical techniques to help it "learn" to progressively improve an activity, without having been specifically programmed for that activity, eliminating the need for millions of lines of written code. . Machine learning consists of both supervised learning (using labeled datasets) and unsupervised learning (using unlabeled datasets).  


Deep learning is a type of machine learning that manages inputs through a biologically inspired neural network architecture. Neural networks contain a series of hidden layers through which data is processed, allowing the machine to deepen its learning, making connections and weighing inputs for the best results. 


Artificial general intelligence

Creating a machine with human-level intelligence that can be applied to any task is the holy grail for many AI researchers, but AGI's research has been fraught with difficulties. 

The search for a "universal algorithm for learning and acting in any environment" (Russel and Norvig) is not new, but time has not eased the difficulty of essentially creating a machine with a full set of cognitive skills. 

AGI has long been the muse of dystopian science fiction, where super-intelligent robots have invaded humanity, but experts agree it's not something we need to worry about.

Further Reading:

  • Microsoft launches an app describing what's in the photos for the blind
  • How to clear background photos quickly
  • Samsung TVs in 2022: QLED, fewer cables and artificial intelligence
  • What exactly is Samsung's MicroLED technology (and how it affects your new TV)
  • Differenze TV LED: Edge LED vs Direct LED vs Full Array

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