Što je AI? Povijest, definicije i primjene

Svi govore o umjetnoj inteligenciji, također poznatoj u skraćenom obliku, AI. Ali o čemu se radi? To je upravo ono što ćemo danas objasniti.

• Hoće li umjetna inteligencija značiti kraj osobne odgovornosti?
• Što AI u vašem pametnom telefonu zapravo znači

Povijest

Umjetna inteligencija igra sve veću ulogu u našim životima, a najnoviji trend su AI čipovi i popratne aplikacije za pametne telefone. Ali ova se tehnologija počela razvijati još 50-ih godina prošlog stoljeća s Dartmouthovim ljetnim istraživačkim projektom o umjetnoj inteligenciji na Dartmouth Collegeu u SAD-u. Njezino podrijetlo seže čak i dalje unatrag do rada Alana Turinga—kojemu možemo pripisati poznati Turingov test —, Allen Newell i Herbert A. Simon, ali umjetna inteligencija nije dospjela u središte pozornosti svjetske pozornice sve do dolaska šahovskog superračunala Deep Blue tvrtke IBM, koji je bio prvi stroj koji je pobijedio tadašnjeg branitelja svjetskog prvaka u šahu Garryja Kasparova u utakmici 1996. Algoritmi umjetne inteligencije već se godinama koriste u podatkovnim centrima i na velikim računalima, ali tek su nedavno prisutni u području potrošačke elektronike.

Definicija umjetne inteligencije

Definicija umjetne inteligencije karakterizira je kao granu računalne znanosti koja se bavi automatizacijom inteligentnog ponašanja. Ovo je teži dio:budući da ne možete precizno definirati inteligenciju samu po sebi , ni umjetna inteligencija se ne može točno definirati. Općenito govoreći, izraz se koristi za opisivanje sustava čiji je cilj korištenje strojeva za oponašanje i simuliranje ljudske inteligencije i odgovarajućeg ponašanja. To se može postići jednostavnim algoritmima i unaprijed definiranim obrascima, ali može postati i mnogo složenije.

Različite vrste umjetne inteligencije

Simbolička umjetna inteligencija ili umjetna inteligencija koja manipulira simbolima radi s apstraktnim simbolima koji se koriste za predstavljanje znanja. To je klasična umjetna inteligencija koja slijedi ideju da se ljudsko razmišljanje može rekonstruirati na hijerarhijskoj, logičkoj razini. Informacije se obrađuju odozgo, radeći s čovjeku čitljivim simbolima, apstraktnim vezama i logičkim zaključcima.

• Umjetna inteligencija se uzgaja u videoigrama...ne da bi nas pobijedili, već da bi nam se pridružili

Neuralna umjetna inteligencija postao je popularan u informatici kasnih 80-ih. Ovdje znanje nije predstavljeno simbolima, već umjetnim neuronima i njihovim vezama — nešto poput rekonstruiranog mozga. Prikupljeno znanje rastavlja se na male komadiće — neurone — a zatim povezuje i ugrađuje u skupine. 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? Javite nam u komentarima ispod!