Beyond the Artificial Intelligence buzzword

Lately, within the "Digital Transformation" era, we are hearing a lot the "Artificial Intelligence" term. There are a lot of definitions about AI out there, but here we are going to start with a bit of history.

The Artificial Intelligence term was first coined by John McCarthy in 1956, when he held the first academic conference on this subject. However, the journey to understand if machines can truly think began much before that. In Vannevar Bush’s seminal work As We May Think [1] he proposed a system which amplifies people’s own knowledge and understanding. Years later Alan Turing wrote a paper about the notion of machines being able to simulate human beings and the ability to do intelligent things, such as play Chess [2]. 

We are not going to cover here the AI winters, neither the two main competing paradigms in AI, i.e., symbolic, and connectionist.

What we would like to point out, within this post, is that AI is not only Deep Learning, along with its Deep Neural Networks. AI includes several sub-fields, such as

AI sub-fields


  • Machine learning is the science of getting computers to act without being explicitly programmed [3].
  • Deep Learning is a subset of machine learning where algorithms are created and function similar to those in machine learning, but there are numerous layers of these algorithms- each providing a different interpretation to the data it feeds on. Such a network of algorithms are called artificial neural networks, an attempt at imitating the function of the human neural networks present in the brain [9].
  • Natural Language Processing, usually shortened as NLP, is a branch of Artificial Intelligence that deals with the interaction between computers and humans using the natural language. The ultimate objective of NLP is to read, decipher, understand, and make sense of the human languages in a manner that is valuable [4].
  • Knowledge Representation is a sub-field of Artificial Intelligence whose main goal is to study how knowledge about the world can be represented and what kinds of reasoning can be done with that knowledge [5]. Currently there is big interest in Knowledge Graphs, which is a way to represent/model Knowledge [10].
  • Robotics is a branch of AI, which is composed of Electrical Engineering, Mechanical Engineering, and Computer Science for designing, construction, and application of robots [6].
  • Computer Vision is a field of Artificial Intelligence and Computer Science that aims at giving computers a visual understanding of the world [7].
  • Conversational AI is a form of Artificial Intelligence that allows people to communicate with applications, websites and devices in everyday, humanlike natural language via voice, text, touch or gesture input [8].

It is worth noting that these sub-fields can be combined, for example, we can apply Deep Learning to solve some NLP problems.

We hope this post will be useful as an general introduction to Artificial Intelligence.

Acknowledgment

We would like to thank Rocío Blázquez Ingelmo for her design of the diagram.

References

[1] Bush, Vannevar. 1945. As We May Think. The Atlantic Monthly. July 1945

[2] Turing, Alan. 1950. Computing Machinery and Intelligence. Mind 49, 433 – 460.

[3] https://emerj.com/ai-glossary-terms/what-is-machine-learning/

[4] https://hackernoon.com/deep-learning-vs-machine-learning-a-simple-explanation-47405b3eef08

[5] https://becominghuman.ai/a-simple-introduction-to-natural-language-processing-ea66a1747b32

[6] https://artint.info/html/ArtInt_8.html

[7] https://www.tutorialspoint.com/artificial_intelligence/artificial_intelligence_robotics.htm

[8] https://hayo.io/computer-vision/

[9] https://www.artificial-solutions.com/blog/conversational-ai-platforms-demand-grows

[10] https://www.poolparty.biz/what-is-a-knowledge-graph

Ma Rocío Blázquez

Directora de Grandes Cuentas en Majorel

5 年

It was a pleasure for me to help you. We make a good team! ??

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