Deep learning et machine learning sont des terminologies issues de l’intelligence artificielle et qui sont de plus en plus utilisées. Mais que veulent-ils réellement dire ? qu’il y a-t-il derrière ces méthodes ? Quelles sont les concepts ? Sont-ils compatibles avec la #Smart City ? Faut-il accélérer leurs utilisations pour la gestion du centre-#ville ?
AI, machine learning, and deep learning—these terms overlap and are easily confused, so let’s start with some short definitions.
- AI means getting a computer to mimic human behavior in some way.
- Machine learning is a subset of AI, and it consists of the techniques that enable computers to figure things out from the data and deliver AI applications.
- Deep learning, meanwhile, is a subset of machine learning that enables computers to solve more complex problems.
Those descriptions are correct, but they are a little concise. I want to explore each of these areas and provide a little more background.
What Is AI?
Artificial intelligence as an academic discipline was founded in 1956. The goal then, as now, was to get computers to perform tasks regarded as uniquely human: things that required intelligence. Initially, researchers worked on problems like playing checkers and solving logic problems.(…)The term AI doesn’t say anything about how those problems are solved. There are many different techniques including rule-based or expert systems. And one category of techniques started becoming more widely used in the 1980s: machine learning.
What Is Machine Learning?
The reason that those early researchers found some problems to be much harder is that those problems simply weren’t amenable to the early techniques used for AI. Hard-coded algorithms or fixed, rule-based systems just didn’t work very well for things like image recognition or extracting meaning from text.
The solution turned out to be not just mimicking human behavior (AI) but mimicking how humans learn. Think about how you learned to read. You didn’t learn spelling and grammar before picking up your first book. You read simple books, graduating to more complex ones over time. You actually learned the rules (and exceptions) of spelling and grammar from your reading. Put another way, you processed a lot of data and learned from it.
That’s exactly the idea with machine learning. Feed an algorithm (as opposed to your brain) a lot of data and let it figure things out. Feed an algorithm a lot of data on financial transactions, tell it which ones are fraudulent, and let it work out what indicates fraud so it can predict fraud in the future. Or feed it information about your customer base and let it figure out how best to segment them. Find out more about machine learning techniques here.
As these algorithms developed, they could tackle many problems. But some things that humans found easy (like speech or handwriting recognition) were still hard for machines. However, if machine learning is about mimicking how humans learn, why not go all the way and try to mimic the human brain? That’s the idea behind neural networks.
The idea of using artificial neurons (neurons, connected by synapses, are the major elements in your brain) had been around for a while. And neural networks simulated in software started being used for certain problems. They showed a lot of promise and could solve some complex problems that other algorithms couldn’t tackle.
But machine learning still got stuck on many things that elementary school children tackled with ease: how many dogs are in this picture or are they really wolves? Walk over there and bring me the ripe banana. What made this character in the book cry so much?
It turned out that the problem was not with the concept of machine learning. Or even with the idea of mimicking the human brain. It was just that simple neural networks with 100s or even 1000s of neurons, connected in a relatively simple manner, just couldn’t duplicate what the human brain could do. It shouldn’t be a surprise if you think about it; human brains have around 86 billion neurons and very complex interconnectivity.
What Is Deep Learning?Put simply, deep learning is all about using neural networks with more neurons, layers, and interconnectivity. We’re still a long way off from mimicking the human brain in all its complexity, but we’re moving in that direction.
And when you read about advances in computing from autonomous cars to Go-playing supercomputers to speech recognition, that’s deep learning under the covers. You experience some form of artificial intelligence. Behind the scenes, that AI is powered by some form of deep learning.
Let’s look at a couple of problems to see how deep learning is different from simpler neural networks or other forms of machine learning.
La promesse de l’intelligence artificielle est bien d’être moins imparfaite que celle humaine et surtout bien
moins chère. Mais réussir le test de Turing (-) n’est pas si évident. Et faire la démonstration d’une forme d’intelligence ne l’est pas plus (certain iraient jusqu’à dire que cela est vrai également pour les être humains). Il n’empêche que les ordinateurs font des progrès fulgurants grâce à l’imitation de la manière humaine d’apprendre (Machine Learning ou ML) et la simulation du cerveau humain par des réseaux de neurones (Deep Learning ou DL). C’est au point où il a fallu créer une charte de l’intelligence artificielle (– et –). Et donc il est normal de se demander s’il faut réellement utiliser ces technologies pour la smart city ?
Reconnaissons d’abord que l’intelligence artificielle est encore limitée à des domaines précis. La principale raison est la complexité de chacune des tâches prises indépendamment.Il en résulte un coup de développement, et surtout d’apprentissage pour le DL, trop important pour le marché. Elle est donc encore limitée à des marchés ayant un
potentiel immense (typiquement voiture automatisée) ou des apprentissages faciles à rentabiliser sur différents marchés. C’est le cas de la vision par ordinateur que nous utilisons dans notre solution de gestion des places de parking avec une cartographie en temps réelle des places disponibles dans le centre-ville et la gestion des taxes de stationnements (parcmètre à la minute + optimisation du trafic routier + promotion des modes doux et des transports communs) (–). La vision par ordinateur n’est pas le seul marché pouvant supporter le coût de développement d’une IA. Concernant les applications pour Smart City, nous pouvons également parler de l’optimisation des flux de voiture dans la ville ou encore de la gestion de la qualité de l’air. Mais nous sommes encore loin du métier du gestionnaire de centre-ville. Et cela est heureux, car de manière générale, il est important de limiter l’intelligence artificielle à des
domaines où l’être humain est encore capable de valider l’intelligence des décisions prises par le système. La sience-fiction regorge d’études sur ce qu’il peut se passer quand ce n’est pas le cas (les robots, wargames …)
