What is AI?
Artificial Intelligence (AI) is:
“the science and engineering of making intelligent machines, especially intelligent computer programs”.
John Mc Carthy (University of Stanford)
But keep in mind that currently the only option for an assessment of such intelligence is through a comparison to human intelligence.
The human brain consists of a network of neurons in which signals are transmitted through the firing of these neurons. In the 1950s this finding inspired computer scientists: Artificial neural networks were introduced, trying to imitate the operating principles of the biological brain. This research marks the establishment of machine learning (ML) and thus artificial intelligence (AI).
Machine Learning and Artificial Intelligence – same same but different?
Danko Nikolic (Max-Planck Institute) extended a definition originally given by Andrew Ng (University of Stanford) and defines machine learning as follows:
„Machine learning is the science of getting computers to act without being explicitly programmed, but instead letting them learn a few tricks on their own.”
Thus, machine learning builds the fundament for artificial intelligence – several machine learning algorithms might be employed in an artificial intelligence application. The ML-algorithms provide machines with the ability to learn – similar to humans – from experience and to derive rules and conclusions on their own. Such behaviour is a fundamental prerequisite to consider machines and their decisions as (artificially) intelligent.
Artificial (un)intelligence – for now
How intelligent is our artificial intelligence? Well, there is still space for improvement.
“The grand idea is to develop something resembling human intelligence, which is often referred to as “artificial general intelligence,” or “AGI.” Some experts believe that machine learning and deep learning will eventually get us to AGI with enough data, but most would agree there are big missing pieces and it’s still a long way off.”
Karen Hao (MIT Technology Review)
Machine learning algorithms may outperform humans in very specific tasks but are currently not able to compete when challenged to think outside the box or when presented with previously unknown data for which experience gained in a different scenario needs to be adapted and applied. What artificial intelligence is already doing extraordinarily well, is relentlessly chewing through any amount of data and every combination of variables.
Besides big data applications and the mining of such huge amounts of data, artificial intelligence has been successfully employed in numerous applications we use daily – to mention a few:
- Speech recognition and voice commands – yes, I am talking about you Siri and Alexa.
- Face recognition – every new smartphone camera knows how to focus on faces
- Text recognition – comes in handy when you want to edit your scanned document
And if you ever wonder whether an application can be considered artificial intelligence, there is a helpful flowchart that guides you through a series of characteristics and questions.
What is Machine Learning? | Daniel Faggella | Emerj
A Few Useful Things to Know about Machine Learning | Pedro Domingos | Department of Computer Science and Engineering, University of Washington, Seattle
An executive’s guide to machine learning | Dorian Pyle and Cristina San José | McKinsey Quarterly
The Discipline of Machine Learning | Tom M. Mitchell | School of Computer Science, Carnegie Mellon University, Pittsburgh
Is this AI? We drew you a flowchart to work it out | Karen Hao | MIT Technology Review