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Info? Fake news? Doesn’t matter! For almost two years now, we’ve all been at least once intrigued, surprised and sometimes even shocked by these types of sensational headlines claiming some kind of AI supremacy. One day you’re reading about AI judging offenders, another day about how AI can predict your death, or even how AI has been recognized as an official citizen of a country….
In other words: AI is getting a lot of attention on a daily basis.
Of course HR and, automatically, the labor market can’t escape this frenzy! AI is expected to destroy 47% of jobs between now and 2030 (based on a highly publicized and then much-nuanced study), to ensure the full automation of processes (from recruitment to employee engagement), to ‘boost’ employees with superpowers), or to predict their departure as early as possible…. These ‘HRD-HAL-9000’ scenarios worthy of the best science fiction movies just keep coming.
However, a recent study done by MMC Ventures and widely reported by the generalist press (and sometimes criticized/nuanced) has cast a shadow on this great enthusiasm. Indeed, it highlights the fact that 40% of the startups proudly displaying the ‘AI’ label is in fact not using AI.
But then, where lies the truth?
A “meme” widespread in the woeld of Tech
AIs are everywhere! What are we talking about, anyway?
Under the guise of a trivial question, it is, in reality, a real headache to simply answer this question, as its simplification can be so questionable!
For Wikipedia, “artificial intelligence” is “a set of theories and techniques used to create machines capable of simulating intelligence”.
However, for many experts, it is this vision of AI that is the source of confusion and confusion. Etienne Klein, a physicist and philosopher of science, states, for example, that the initial meaning of the English term “Intelligence” does not necessarily refer to human intelligence but rather to a notion of data collection/intelligence (like the acronym C.I.A. : Central Intelligence Agency).
Mathematicians, philosophers, physicists, computer scientists… each has its own definition. Some are based on concepts of intelligence, others on concepts of understanding human faculties, while others focus more on solving complex problems.
For Jean-Louis Laurière, for example, “AI begins where conventional computing stops: any problem for which there is no known or reasonable algorithm to solve it is a priori the responsibility of the AI”.
And because it would be far too simple to focus on a multifaceted definition, so would the technical and process outlines that constitute the sub-domains of AI.
More and more extraordinary definitions…
Learner algorithms, unsupervised Machine Learning, neural networks or other deep learning, reason to our ears as something familiar, to such an extent that we no longer even know (in case we knew it…) what it really is!
An overview of the uses of artificial intelligence by Olivier Ezratty
The use of an anthropomorphism of advanced technology (for example: comparing a statistical method with neural networks in the human brain) has indeed allowed a strong popularization of numerous computational and mathematical concepts and has given us the impression that we understand everything (or at least almost).
This has also allowed AI innovation to be widely distributed and to reach the famous “Tipping Point”. This shift from quasi-anonymity to mass celebrity has sparked a long period of curiosity, excitement, and fear but also of intense fantasies and surprising projections.
The evolution of Google searches for the keyword “Artificial Intelligence” in France
since 2015 highlights this phenomenon.
….. and more and more contested!
However, faced with this sometimes limitless enthusiasm, there are more and more people who want to qualify and exploit AI announcements. They want to underline the significant gaps that exist between the projections, whether they are utopian or dystopian and our current technological means:
- Whether it’s Cathy O’Neil, a mathematician formerly dedicated to the service of ‘Algorithms of Mass Destruction’ whose complexity is such that their result, while sometimes non-conclusive, can no longer be contested,
- Eric Sadin – a French philosopher – who questions the sense of a single thought in favor of automation and AI that ‘speaks the truth’,
- Antoine Casili whose latest book ‘Waiting for the robots’ examines the background of the famous GAFA whose AI are in fact artificially artificial (working largely thanks to labor platforms based on clicks and /or to free contributions of its users (for example reCAPTCHA, like, tags, etc.)),
The famous scam of the “Mechanical Turk” in the 18th century
presented by A. Casilli.
- or former engineers like Luc Julia – known for designing Apple’s vocal assistant Siri – who reminds us in his book that ‘AI doesn’t exist’ to what extent these assistants are ‘stupid’.
When we dig deeper, we quickly realize that the analogies between computational concepts and the human brain as announced by the over-mediatization of AI are, for now, Hollywood-like scenarios that won’t happen in the next few years, decades and sometimes even centuries!
In the second part of this article, we’ll see how these fantasized concepts of AI confront today’s pragmatism, especially in HR.