WEEK 2

ARTIFICIAL INTELLIGENCE 📡

Hello again, I'm Sophi and now I present you a subtopic about the Technology that is Artificial Intelligence, I'm going to explain the definition of Artificial Intelligence, the history of Artificial Intelligence, how Artificial Intelligence works and more.

Artificial intelligence (AI) is currently one of the most thought-provoking topics in technology and business. This enthusiasm has a rationale: we live in an increasingly connected and intelligent world. Today, you can build a car, compose Jazz using an algorithm, or connect your CRM to your inbox to prioritize the most important emails. The technology behind all these advances is related to Artificial Intelligence (AI).

Definition of Artificial Intelligence ☎

Artificial Intelligence is the scientific field of computer science that focuses on the creation of programs and mechanisms that can exhibit behaviors considered intelligent. In other words, AI is the concept that "machines think like humans".

Typically, an AI system is able to analyze large amounts of data (big data), identify patterns and trends, and therefore formulate predictions automatically, quickly and accurately. For us, the important thing is that AI makes our everyday experiences smarter.

How? 📶📱

By integrating predictive analytics and other AI techniques into applications we use every day.

Siri functions as a personal assistant, as it uses natural language processing.
Facebook and Google Photos suggest photo tagging and grouping based on image recognition.
Amazon offers product recommendations based on shopping basket models.
Waze provides optimized traffic information and real-time navigation.

History of Artificial Intelligence 📟

Most of us have a concept of Artificial Intelligence fueled by Hollywood movies. Exterminators, robots with existential crises and red and blue pills. In fact, AI has been in our imaginations and in our laboratories since 1956, when a group of scientists initiated the "Artificial Intelligence" research project at Dartmouth College in the United States. The term was first coined there and, since then, we have witnessed a roller coaster of advances.

At the beginning of the project, the goal was that human intelligence could be described so accurately that a machine would be able to simulate it. This concept was also known as "generic AI" and it was this idea that fueled the (amazing) fiction that it would give us unlimited entertainment.

However, AI drifted into specific fields. Over time, science evolved into specific areas of knowledge, and it was then that AI began to generate significant results in our lives. It was a combination of image recognition, language processing, neural networks and automotive mechanics that made an autonomous vehicle possible.

Table of important events in the history of Artificial Intelligence 📥

Year Event

1842 Lovelace: programmable analytical machine

1950 Turing: the Turing test

1956 McCarthy, Minsky, Rochester and Shannon hold first conference on AI

1965 Weizenbaum: "ELIZA", the first specialist system

1993 Horswill: "Polly" (behavioral robotics)

2005 TiVo: recommendation technology

2011 Apple, Google and Microsoft: mobile recommendation applications

2013 Various: technological breakthroughs in machine and deep learning

2016 Google DeepMind: AlphaGo beats Lee Sedol in the game "Go".

Main techniques of Artificial Intelligence 📻

Automatic learning

"Machine Learning is the science of making computers perform actions without the need for explicit programming."

Some examples of Machine Learning algorithms include the following: decision diagrams, clustering algorithms, genetic algorithms, Bayesian networks and Deep Learning.

Deep learning

Deep Learning is a Machine Learning technique that uses neural networks to perform classification tasks.

Some examples of practical applications of Deep Learning are the following: identification of vehicles, pedestrians and license plates of autonomous vehicles, image recognition, translation and natural language processing.

Intelligent data discovery

The idea is to enable full automation of the EI cycle: data incorporation and preparation, predictive analytics and pattern and hypothesis identification.

This is an interesting example of intelligent data retrieval in action. Information that no IE tool had ever discovered.

Predictive analytics

The main concept of predictive analytics means that you can use a number of variables (income, zip code, age, etc.) combined with outcomes to generate a model that provides a score representing the probability of an event.

Business use cases are broad: credit models, customer segmentation (clustering) models, purchase probability models, and customer migration models, among others.