About ai

About Ai

History of Ai

Ai started in the mid 1950’s at Stanford university, a collaboration with Massachusetts Institute of Technology, Carnegie Mellon University and Bell Labs. It started with the concept of ways for machines to learn, understand, and think like humans. This is widely held as the inception of Ai, however, lack of information and computational power meant that it did not progress much in the following years.

In the early 1980’s Japan spent $850m to try and create a machine that can infer by processing information. This was attempted by combining 1000 processors. This too failed and was a stark reminder of limitation of technology at this stage.

An attempt by Stanford university in 1984, was based on the concept of creating an encyclopaedia of knowledge, that would contain all human level of inferential capability. Although they managed to link to various sources of information, the advent of the internet and search capabilities meant that there were more efficient ways of gathering this information, and the program slowly fell away.


Artificial Intelligence 2.0

Taking these failings into account, it was evident that the incompatibility of Ai was due to the lack of processing power and access to wide ranging data. Ai is driven by research and the informational environment that it can be associated to.

With the current popularization and accessibility of the internet, proliferation of sensors {IoT}, the emergence of capabilities to store Big Data, development of e-commerce, and our ability to merge multiple technologies as industries become more interconnected, has created waves of changes in our capabilities to develop true Ai.

The new goals and problems in intelligent cities, medicine, transportation, logistics, manufacturing, and smart products as well as driverless cars and smartphones, all require Ai development. We have started to understand the true potential in Ai is not creating an isolated intelligent machine, but rather its effectiveness is derived from the ability to create a hybrid intelligent system of machines supporting humans in decision making.

Transforming big data into knowledge has begun. This can be seen in AlphaGo Systems which was able to infer the best way to win a match in Go, by reviewing past games, and intuitively understanding the strategy required to win. It subsequently beat the best Go champion 4-1. This new-found way of teaching machines is fundamental to our ability to transform the big data into useful, insightful and relevant knowledge, that supports humans to make more informed decisions.

Our Partners