Qu'est-ce que l'IA ? Historique, définitions et applications
Tout le monde parle d'intelligence artificielle, également connue sous sa forme abrégée, AI. Mais de quoi s'agit-il ? C'est précisément ce que nous allons vous expliquer aujourd'hui.
- L'IA signifiera-t-elle la fin de la responsabilité personnelle ?
- Ce que signifie vraiment l'IA dans votre smartphone
Historique
L'intelligence artificielle joue un rôle de plus en plus important dans nos vies, et la dernière tendance concerne les puces IA et les applications pour smartphone qui les accompagnent. Mais cette technologie a commencé à se développer dès les années 50 avec le Dartmouth Summer Research Project on Artificial Intelligence au Dartmouth College aux États-Unis. Ses origines remontent encore plus loin aux travaux d'Alan Turing — à qui l'on peut attribuer le fameux test de Turing —, Allen Newell et Herbert A. Simon, mais l'IA n'a pas été mise à l'honneur sur la scène mondiale jusqu'à l'arrivée du supercalculateur d'échecs Deep Blue d'IBM, qui a été la première machine à vaincre le champion du monde d'échecs alors en titre Garry Kasparov dans un match en 1996. Les algorithmes d'IA sont utilisés dans les centres de données et sur les gros ordinateurs depuis de nombreuses années, mais ne sont présents que depuis plus récemment dans le domaine de l'électronique grand public.
Définition de l'intelligence artificielle
La définition de l'intelligence artificielle la caractérise comme une branche de l'informatique qui traite de l'automatisation du comportement intelligent. Voici la partie la plus difficile :puisque vous ne pouvez pas définir avec précision l'intelligence en soi , l'intelligence artificielle ne peut pas non plus être définie avec précision. D'une manière générale, le terme est utilisé pour décrire des systèmes dont l'objectif est d'utiliser des machines pour émuler et simuler l'intelligence humaine et le comportement correspondant. Cela peut être accompli avec des algorithmes simples et des modèles prédéfinis, mais peut également devenir beaucoup plus complexe.
Divers types d'IA
IA symbolique ou manipulatrice de symboles travaille avec des symboles abstraits qui sont utilisés pour représenter la connaissance. C'est l'IA classique qui poursuit l'idée que la pensée humaine peut être reconstruite à un niveau hiérarchique et logique. Les informations sont traitées d'en haut, en utilisant des symboles lisibles par l'homme, des connexions abstraites et des conclusions logiques.
- Les IA sont élevées sur les jeux vidéo... non pas pour nous battre, mais pour nous rejoindre
IA neurale est devenu populaire en informatique à la fin des années 80. Ici, la connaissance n'est pas représentée par des symboles, mais plutôt par des neurones artificiels et leurs connexions, un peu comme un cerveau reconstruit. Les connaissances rassemblées sont décomposées en petits morceaux - les neurones - puis connectées et construites en groupes. This approach is known as the bottom-up method that works its way from below. Unlike symbolic AI, a neural system must be trained and stimulated so that the neural networks can gather experience and grow, therefore accumulating greater knowledge.
Neural networks are organized into layers that are connected to each other via simulated lines. The uppermost layer is the input layer, which works like a sensor that accepts the information to be processed and passes it on below. This is now followed by at least two—or more than twenty in large systems—layers that are hierarchically above each other and that send and classify information via the connections. At the very bottom is the output layer, which generally has the least number of artificial neurons. It provides the calculated data in a machine-readable form, i.e. "picture of a dog during the day with a red car."
Methods and tools
There are various tools and methods for applying artificial intelligence to real-world scenarios, some of which can be used in parallel.
The foundation of all this is machine learning , which is defined as a system that builds up knowledge from experience. This process gives the system the ability to detect patterns and laws—and with ever-increasing speed and accuracy. In machine learning, both symbolic and neural AI is used.
Deep learning is a subtype of machine learning that is becoming ever more important. Only neural AI, i.e. neural networks are used in this case. Deep learning is the foundation for most current AI applications. Thanks to the possibility of increasingly expanding the design of the neural networks and making them more complex and powerful with new layers, deep learning is easily scalable and adaptable to many applications.
There are three learning processes for training neural networks:supervised , non-supervised and reinforcement learning , providing many different ways to regulate how an input becomes the desired output. While target values and parameters are specified from the outside in supervised learning, in unsupervised learning, the system attempts to identify patterns in the input that have an identifiable structure and can be reproduced. In reinforcement learning, the machine also works independently, but is rewarded or punished depending on the success or failure.
Applications
Artificial intelligence is already being used in many areas, but by no means are all of them visible at first glance. Therefore, selecting scenarios that take advantage of the possibilities of this technology is by no means a completed list.
Artificial intelligence’s mechanisms are excellent for detecting, identifying, and classifying objects and persons on pictures and videos. To that end, simple but CPU-intensive pattern detection is used. If the image information is decrypted and machine-readable in the first place, photos and videos can be easily divided into categories, searched and found. Such recognition is also possible for audio data.
Customer service is increasingly using chatbots . These text-based assistants perform recognition using key words that the customer may tell it and they respond accordingly. Depending on the use, this assistant can be more or less complex.
Opinion analysis is not only used for forecasting elections in politics, but also in marketing and many other areas. Opinion mining, also known as sentiment analysis, is used to scour the internet for opinion and emotional expressions, allowing for the creation of a largely anonymized opinion survey.
Search algorithms like Google’s are naturally top secret. The way in which search results are calculated, measured and outputted are largely determined by mechanisms that work with machine learning.
Word processing , or checking the grammar and spelling of a text, is a classic application of symbolic AI that has been used for a long time. Language is defined as a complex network of rules and instructions that analyzes blocks of text in a sentence and, under some circumstances, can identify and correct errors.
These abilities are also used in synthesizing speech , which is currently the talk of the town with assistant systems like Siri, Cortana, Alexa or Google Assistant.
On new smartphone chips like the Kirin 970, artificial intelligence is integrated into its own component, the NPU or neural processing unit .The processor is making its debut in the Huawei Mate 10. You will learn more about it and the roles that the technology will play on the Huawei smartphone once we have a chance to experiment with it in the near future. Qualcomm has already been working on an NPU, the Zeroth processor, for two years, and the new Apple A11 chip contains a similar component.
Furthermore, there are numerous research projects on artificial intelligence and the most prominent of all may be IBM’s Watson. The computer program had already made its first public debut in 2011 on the quiz show Jeopardy, where it faced off against two human candidates. Watson won, of course, and additional publicity appearances took place afterwards. A Japanese insurance company has been using Watson since January to check insured customers, their history and medical data and to evaluate injuries and illnesses. According to the company’s information, Watson has replaced roughly 30 employees. Loss of jobs through automation is just one of the ethical and social issues surrounding AI that is the subject of corporate and academic research.
Projection
AI isn’t something that just came out of nowhere recently, but it is coming close to a breakthrough in the world of consumer electronics, which is more than enough reason for everyone to keep up to date with this topic in the future.
Which aspects of artificial intelligence do you find exceptionally interesting? Let us know in the comments below!