ARTIFICIAL INTELLIGENCE - IN MATH I TRUST

Artificial intelligence has been shaping our world since the 70’s or even before. There were three big moments of investment going to Artificial intelligence, and those were:

  • Neural networks – statistical machine learning algorithm, which is inspired by the general information processing strategy of the brain, later in the article we will talk more about it.
  • Expert systems, which became some of the first truly successful forms of artificial intelligence (AI) software. It is an example of a knowledge-based system, which is composed of two sub-systems: the knowledge base and the inference engine. The knowledge base represents facts about the world. The inference engine is an automated reasoning system that evaluates the current state of the knowledge-base, applies relevant rules, and then asserts new knowledge into the knowledge base. The main idea is that intelligent systems derive their power from the knowledge they possess rather than from the specific formalisms and inference schemes they use.
  • Sub-optimus” approaches such as genetic algorithms, Support Vector Machine/Clustering, supervised learning models with associated learning algorithms that analyse data used for classification and regression analysis.

It started in 1950 when a handful of pioneers from the nascent field of computer science started asking whether computers could be made to “think” – What is machine learning? Manning.com-.

Nowadays, most of the current A.I shown on the TV and media is harmful and dangerous for our population (mad robots trying to destroy the earth or Terminators in pursuit of taking over the earth). Still, far from that futuristic scenario, I am going to discuss some of the real applications that A.I has and what are the core of this new machine intelligence.  

Entrails of Artificial intelligence

A.I is nothing but thrust intelligence into machines, it is inspired in neural networks, but actually they are a very complex mathematical interpolation. Units with connections which are inspired in a very loose way by how the biological brain might work. However, neuroscientist have always tried to avoid this term due to the confusion that it may create. AI is about learning through experience by changing the connection strengths, so defining how strongly neurons influence each other. It goes through three phases: learning, execution and self-correction. It basically inserts the factor “experience” to the computer, so the computer can learn from it and improve every time a certain action is made