Understanding Basic of Artificial Intelligence

Inspiration

MLH-LHD Day4

Title

Some basis about Artificial Intelligence

BEGINNER

Let’s start with a little history. The timeline you see is taken from this article and it shows the most important milestones of Artificial Intelligence. The term AI goes back to Alan Turing who defined a test, Turing Test, to measure a machine’s ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human. A few years later it was John McCarthy who coined the term officially, at the famous Dartmouth Workshop, with the following phrase: “every aspect of learning or any other characteristic of intelligence can, in principle, be described with such precision that a machine can be made to simulate it. We will try to discover how to make machines use language, from abstractions and concepts, solve problems now reserved for humans, and improve themselves”. The rest of the milestones you see, mainly Deep Blue and AlphaGo, will appear throughout the article on several occasions. I recommend that you also watch the ColdFusion video where some more details about the history of Artificial Intelligence are nuanced.

Something really interesting that appears in the video are the 7 aspects of Artificial Intelligence defined in 1955 and that are still valid today, and in which we have currently reached (with some level of progress) only three of them: programming a computer to use general language, a way to determine and measure the complexity of problems and self-improvement. We could also say that we are starting with “randomness and creativity”, with some examples like Morgan’s trailer or the script of “Surprising” (2016), the perfumes of Watson, and projects like AIVA, Magenta or My Artificial Muse. Therefore, we could say that Artificial Intelligence are machines or computer programs that learn to perform tasks that require types of intelligence and that are usually performed by humans. And when we talk about types of intelligence, we need to rescue Jack Copeland’s reflection on what intelligence is: “the dominant thought in psychology considers human intelligence not as a single ability or cognitive process, but rather as a set of separate components. Research in AI has focused primarily on the following components of intelligence: learning, reasoning, problem solving, perception, and comprehension of language”.

That said, let’s go with the different types of Artificial Intelligence. In the video there were two: Weak AI or also called Artificial Narrow Intelligence (ANI), which allows computers to outperform humans in some very specific tasks (the most famous example is IBM Watson); and Strong AI or Artificial General Intelligence (AGI), the ability of a machine to perform the same intellectual tasks as a human being (we are far from reaching it). There is a third level called Artificial Superintelligence (ASI), when a machine possesses an intelligence that far surpasses the brightest and most gifted human minds in the world combined.

ADVANCED

We’re moving to the next level! Regardless of the category, the technological learning base of Artificial Intelligence is mainly based on two pillars: Symbolic Learning and Machine Learning. Curiously, the first pillar was the one that began everything but with the birth of Machine Learning and specifically with Deep Learning, all efforts have been focused on the second (although there are many technologists who are thinking on retaking the first). Before we move on, take a look at this video by Raj Ramesh: link

Really interesting how it synthesizes the different branches of Artificial Intelligence. I think it’s clear that the most promising branch that has come to stay is Machine Learning, which is nothing more than a system capable of taking large amounts of data, developing models that can successfully classify them and then make predictions with new data. To understand a little more this approach, watch this CGP Grey’s video: link

One of the most interesting thing is that these models are not programmed, they arise from training, and there is a point where no human, nor the programmers themselves, can understand how it works. By now, enough new “words” have come out, so I’ll leave you with an IA dictionary for beginners and one more reading: Difference between Machine Learning, Deep Learning and Artificial Intelligence.

Now it’s time to go deeper into Deep Learning, the most advanced approach to develop Artificial Intelligence today. After reading many articles, watching many videos and doing some courses, I can say with certainty that an ideal way to have a complete overview of Deep Learning, handling basic concepts, technical terminology and even starting to know some tools and platforms is DeepLearningTV. I don’t know how long it will be active (I recommend you download the videos) because it’s been a while since their last update and I don’t see any company or community behind it… Their videos are pure gold! Here you have the complete list with the 31 episodes: [link(https://www.youtube.com/watchv=b99UVkWzYTQ&list=PLjJh1vlSEYgvGod9wWiydumYl8hOXixNu)

TECHNICAL

And we’ve reached the final level! In this section I’m not going to explain any new concept in detail but I recommend you different online courses, free and paid, for you to do. Obviously, they are all technical courses, some require programming experience and others just a solid mathematical base. The important thing about these courses is that you acquire the knowledge you need. Some of you will want to go all the way until you can program an Alpha Zero and others, as is my case, understand the technological bases and extrapolate from there. Let’s start!

Google Machine Learning

Google has a lot of training content related to TensorFlow. I recommend the crash course of ML. On Youtube you have Machine Learning Recipes with Josh Gordon and AI Adventures, both also very recommendable. I advise you to also go through AI Experiments.

Udemy

Finally in Udemy you can find 3 paid courses of the same creators of the course I recommended on Blockchain: SuperDataScience. I recommend you to start with Artificial Intelligence: Artificial Intelligence A-Z™: Learn How To Build An AI Deep Learning A-Z™: Hands-On Artificial Neural Networks Machine Learning A-Z™: Hands-On Python & R In Data Science I’m closing this block with the cheatsheets published by Stanford and with Altexsoft’s Machine Learning Toolbox. Very useful! I hope you find this guide interesting and that it will help in your path of knowing more about AI. Please, tell me about your progress 🙂