Skip to main content

Artificial Intelligence History

As you know, AI today is a widely used tool in every kind of systems. However, how did it start? We had only one inventor or more people had invested in AI? AI is a recent discovery? When it became so powerful and why? Today's post will put you up to date to the Artificial Intelligence History.
Alan Turing

Well, everything started alongside the Second World War. Sadly, some of the human's biggest discoveries occurred during wars. In 1943,  Warren McCulloch and Walter Pitts presented an initial mathematical and computer model of the biological neuron [2]. There was 1950 when John Von Neumann and Alan Turing created the technology behind AI. Turing created the called Bombe machine to decipher messages exchanged between the German forces. That system was the pillar of today's machine learning [1]. Turing was a huge impact in the Artificial Intelligence field, and still today some of his statements are updated and used. Turing questioned the possible intelligence of a machine for the first time in his famous article "Computing Machinery and Intelligence". Another of his declarations is that if humans talk with a machine without spotting that it is a machine, then the machine can be declared as intelligent.
The 50s were The Years. The biggest improvements in AI started there. Ferranti Mark 1, also known as the Manchester Electronic Computer, used an algorithm able to win in checkers game in 1951. Later, the General Problem Solver algorithm to solve mathematical problems was presented by Newell and Simon. And maybe the most important,  John McCarthy developed the LISP programming language what gave him the credit of being the AI father [1].  John McCarthy invented the term Artificial Intelligence in 1956 on his first academic conference on the subject [3]. Later, on the 1960s, Machine Vision Learning and developing machine learning in robots were the scientists focus. Consequently, in Japan WABOT-1, the first "intelligent" humanoid robot, was developed. The efforts of Professor Ichiro Kato gave us the WABOT-2, built at Waseda University. The robot could communicate with people, read musical scores and also play music on a keyboard [4].
Wabot-1(left) and Wabot-2(right)
Since the 50s many scientists tried to develop new AI systems, however, they found it quite difficult because at that time the available amount of data was really small. Today we have millions and millions of records being generated every day, every hour, every second. Back then, computers were starting to become more usable, being smaller and more efficient. Still, they did not have the power we can find today in the most regular computer. Internet? It started to become a real thing only in the 70s. As you can see, when AI started, it was really hard to find enough data to train the AI models.  It was only at the end of the 90s that AI grew popular again. And that was when the Deep Blue defeated the world chess champion, in 1997.
AI started with some issues off course, however, now we are having great achievements. Today, we have algorithms capable of overcome doctors regarding breast cancer detection [5], robots working with humans, systems that suggest products based on your needs and so many other AI applications. Now, AI is where it belongs, on top of the business world.


References
  1. History of AI - Towards Data Science. (n.d.). Retrieved January 2, 2020, from https://towardsdatascience.com/history-of-ai-484a86fc16ef
  2. History of Artificial Intelligence. (n.d.). Retrieved January 2, 2020, from https://www.coe.int/en/web/artificial-intelligence/history-of-ai
  3. Smith, C., McGuire, B., & Huang, T. (2006). The History of Artificial Intelligence. The University of Washington, (December), 27. https://doi.org/10.1016/j.chembiol.2009.06.005, https://courses.cs.washington.edu/courses/csep590/06au/projects/history-ai.pdf
  4. A Complete History of Artificial Intelligence. (n.d.). Retrieved January 2, 2020, from https://learn.g2.com/history-of-artificial-intelligence
  5. Google AI system can beat doctors at detecting breast cancer - CNN. (n.d.). Retrieved January 3, 2020, from https://edition.cnn.com/2020/01/02/tech/google-health-breast-cancer/index.html

Comments

  1. This comment has been removed by the author.

    ReplyDelete
  2. The evolution of data before implementing AI has seen a remarkable progression from its early collection in physical formats like paper and punch cards to the digital age, where data is stored electronically. Initially, data was primarily structured, organized in specific formats like spreadsheets and relational databases.

    ReplyDelete

Post a Comment

Popular posts from this blog

How does COVID-19 continue to spread? - A simulation 2.0 (How it was built)

 Unfortunately, the days we are living right now are still bad, or even worse than ever. Millions of people are being killed by this "new virus", as they called it once. COVID-19 is here and will be among us for too long. Some of us thought, incorrectly, 2021 will be the year, we will have vaccines, that's it! No more problems related to COVID-19! Let's start living as before!  No, no, no! If you still think this way, please stop it right now. By not respecting the known procedures to avoid the COVID-19 infection you will keep the virus spreading chain. Consequently, the virus will kill more people, being them related to you or not. Many apparently  healthy humans are having severe "side effects" by getting infected with this virus. Stop thinking the virus provokes just flu and help to stop the spread!  Millions of healthcare professionals are giving their lives to help in this war. You are neglecting them and all the people around you! Keep yourself safe

How does COVID-19 continue to spread? - A simulation 2.0 (Results)

This post shows some of the results we can find by using the simulation. As in the first version I made some tests, now I focused the new tests on the travelling and vaccination processes. These two processes were added in the last simulation version and represent some critical behaviour and processes in the virus spread. Photo by Sharon McCutcheon on Unsplash Vaccination process impact Using the standard static configuration values we can find the following results: The vaccination process does not have a considerable impact if we close our borders. By not receiving new agents with the infection, the simulation reaches the number of 0 infected agents on the 38th day using a vaccination percentage of 0.1 If we increase the vaccination percentage to 0.9 the 0 infected agents threshold is reached on the 39th day. Thus, we can infer that if we control the flow of agents in a city/simulation, the vaccination process does not have a considerable impact as it takes some time until the people