Todo mundo está falando sobre inteligência artificial, também conhecida em sua forma abreviada, IA. Mas do que se trata? É exatamente isso que vamos explicar hoje.
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Histórico
A inteligência artificial está desempenhando cada vez mais um papel maior em nossas vidas, e a última tendência são os chips de IA e os aplicativos de smartphones que os acompanham. Mas essa tecnologia começou a ser desenvolvida já na década de 50 com o Dartmouth Summer Research Project on Artificial Intelligence no Dartmouth College, nos EUA. Suas origens remontam ainda mais ao trabalho de Alan Turing – a quem podemos atribuir o famoso teste de Turing —, Allen Newell e Herbert A. Simon, mas a IA não se destacou no cenário mundial até a chegada do supercomputador de xadrez Deep Blue pela IBM, que foi a primeira máquina a derrotar o então campeão mundial de xadrez Garry Kasparov em uma partida em 1996. Os algoritmos de IA têm sido usados em data centers e em grandes computadores por muitos anos, mas só mais recentemente estão presentes no campo da eletrônica de consumo.
Definição de inteligência artificial
A definição de inteligência artificial a caracteriza como um ramo da ciência da computação que lida com a automatização do comportamento inteligente. Aqui está a parte difícil:já que você não pode definir precisamente a inteligência
per se , a inteligência artificial também não pode ser definida com exatidão. De um modo geral, o termo é usado para descrever sistemas cujo objetivo é usar máquinas para emular e simular a inteligência humana e o comportamento correspondente. Isso pode ser feito com algoritmos simples e padrões predefinidos, mas também pode se tornar muito mais complexo.
Vários tipos de IA
IA simbólica ou de manipulação de símbolos trabalha com símbolos abstratos que são usados para representar o conhecimento. É a IA clássica que persegue a ideia de que o pensamento humano pode ser reconstruído em um nível hierárquico e lógico. A informação é processada a partir de cima, trabalhando com símbolos legíveis por humanos, conexões abstratas e conclusões lógicas.
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IA neural tornou-se popular na ciência da computação no final dos anos 80. Aqui, o conhecimento não é representado por símbolos, mas sim por neurônios artificiais e suas conexões – como um cérebro reconstruído. O conhecimento reunido é dividido em pequenos pedaços – os neurônios – e então conectado e construído em grupos. Essa abordagem é conhecida como o método de baixo para cima que funciona de baixo para cima. Ao contrário da IA simbólica, um sistema neural deve ser treinado e estimulado para que as redes neurais possam acumular experiência e crescer, acumulando assim maior conhecimento.
Redes neurais 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!
