---
title: A Timeline Through Artificial Intelligence
description: AI, once considered science fiction, it is now a fantastic reality!
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---

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# [A Timeline Through Artificial Intelligence](https://blog.prophetic-technology.com/a-timeline-through-artificial-intelligence)

Artificial Intelligence (AI) is the top factor at the back of most technology nowadays. Once conside...

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Posted by [Dave Food](https://blog.prophetic-technology.com/author/dave-food) on Mar 20, 2019 2:38:11 PM

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Artificial Intelligence (**AI**) is the top factor at the back of most technology nowadays. Once considered science fiction, it is now a fantastic reality! Even though computer science is not new, today’s revolutionary *Deep Learning* using artificial neural networks, is the result of all those achievements scientists have made all through years.

Far back on time (**1637**) scientists and philosophers like Descartes, foresaw *robot machines* which would be able to *think* and *make decisions*; robots that could one day *learn to carry out specific tasks,* even *able to adapt* to all sort of jobs.  However, they couldn’t visualise the possibility that such machines *would communicate* and *talk like humans*.  By all means, these scientists made an enormous *contribution* and *set the pace for further scientific generations* to go deeper into these fundamental researches.

The term **Artificial Intelligence** was coined by John McCarthy back in **1956**. He initiated an academic exploration and development of *thinking machines*. Natural language processing, neural networks, computer vision, saw the light early in those days.

**The experimental steps of chatbots**

As the years went by **AI** evolved.  Robots like *Alexa* and *Siri* in the 60s were initial *personal assistants* that encompassed different way of communicating. *Alexa* is capable of voice communication, and able to connect with several smart devices, using itself as a home-automation system. Whereas *Siri* is available for a wide-ranging of user commands. The software adjusts to users' language, searches, and preferences, with constant use.

However, it was not until *Eliza* arrived in **1966** –a computer program experimental example of uncomplicated natural language processing. *Eliza* used a “pattern matching” and replacement methodology that gave users the *impression that the computer program was understanding* their conversation.  Could it be possible?

What Eliza had to do was listening to you and what you had to say, break down your sentence in a fundamental way, and then asked you a question in some way related to the sentence you had said,”  but she wasn’t capable of learning from her conversations with humans.  However, she paved the way for further efforts to *break down the communication barrier* between people and machines*.*

Machines also learn through a number of repetitions as humans do.  Most functions of devices today use *algorithms to solve problems*.  Machine Learning started to take an essential place in technology competence; although it is more about *data processing than programming*.  Machines haven’t been as successful at learning as we are; however, faster computers that enable smarter algorithms are revealing a promising future nowadays for **ML.**

Back in **1988** came the *statistical approach* to an automated translation between languages – French and English. The target was now on designing programs to determine the probability of various outcomes *based on information (data),* in terms of *mimicking the cognitive processes* of the human brain: These programs form the basis of the current ML.

**The beginning of the Internet**

In **1991** researcher Tim Berners-Lee placed the *world's first website* *online* and *made public* the *Hypertext Transfer Protocol* (**HTTP**) capabilities. It is an *application protocol* for distributed-collaborative-hypermedia information systems, the basis of *data communication* for *the World Wide Web*, where hypertext documents include hyperlinks that allow users to log on other sources  easily.

No doubt the *Worldwide Web* was the *trigger for society* to plug up into the *online world.* Quite soon, masses of people all around the world were *linked, producing* and *sharing data* at an incredible velocity, which is one of the competences of **AI,** calculating every *possible option* at *high speed.* Computers were evolving very quickly and growing into highly-competent machines where once humans dominate all the way long. 

Autonomous vehicles started in **2005**, and by **2007**, *mimicked-urban-conditions* were built to test and prove they *could manage traffic regulations* and other *in-motion vehicles*.

In **2011**, appeared the *cognitive-computing* engine **IBM** Watson. It took part in a contest where the computer *defeated a human*, to the surprise of everyone.  

*Deep Learning* (**DL**) came next in **2015** coming from researchers at Stanford and Google, including *high-level features* and *large-scale-unsupervised learning*, built on *multilayer neural nets* which get rid of *the expensive* and *time-consuming task of manually* labelling data. It would speed up the tempo of **AI** maturity and open the door for future possibilities of building machines to do the work which could only be done by humans until then.   They considered their system to be *highly competent* at identifying photos. 

The model would enable an *artificial network* enclosing around one billion connections to *identify an object* in *visual data*; machines were then outperforming humans. Although it was a significant step towards building an "artificial brain," there was still some way to go, as human brain neurons network is thought to be around *10 trillion connectors*. 

No doubt **2018** denoted a critical breakthrough, with the launch of Google self-driving-taxi service in Phoenix, Arizona. *The first commercial-autonomous-vehicle-hire service!* Many members of the community pay to be transported to their schools or workplaces, as long they are within a 100 square mile zone. 

**Some further comments:** Technology competence to *store* and *process data drives visibility, speed and accuracy*, the most significant improvements in **ML**.

So far, there’s the need for human operators to ride with every autonomous vehicle, whether it is to take the control if an emergency, or to monitor the performance of it. *Self-driving cars* will be pretty soon a fantastic reality for ordinary people. **The future is here! **

 

**Dave Food**

[Prophetic Technology](https://www.prophetic-technology.com/)

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