According to John McCarthy, artificial intelligence (AI) is simply, “the science and engineering of making intelligent machines.” McCarthy was an American computer scientist and cognitive scientist, who helped develop the Lisp programming language family. (He was also very influential in the early stages of research on AI.) But what is artificial intelligence, really? In this nano-guide, I’ll try to give you a brief, but clear, insight on AI. So let’s start with the basics…
Artificial intelligence is a branch of computer sciences that studies and develops intelligent machines and software. As previously noted, researchers and experts define the field as “the study and design of intelligent agents”. Now, an intelligent agent is “a system that perceives its environment and takes actions that maximize its chances of success.” So why is this important? Easy; an AI designs and analyzes patterns every time we browse the web for information (like the information we scrounge up to complement our FQTQ articles). The GPS in that fancy-schmancy smartphone of yours gives you the best possible route to wherever you’re going, and it’s all thanks to AI! These routes are based on different sets of available data (distance, traffic, toll roads, etc.). If you’re into video-games, you will be able to see AI all over your screen. The animations of the characters (in addition to the interactions with the environment and any non-player character [NPCS]) is an example of the complex set of AI algorithms within the game. And of course, robotics are full of AI. Without AI, robots are simply linear mechanical devices. However, we want them to be able to utilize behaviors and develop decision making skills that are too similar to ones that humans (and other living creatures) use, that’s when AI gets sexy. And those are common examples. Technology develops in such a fast, unstoppable pace (Moore’s Law) that the uses of AI are getting more complex and refined, even as you’re reading this (not kidding).
The aim of AI is to produce machines that can mimic or simulate human intelligence. We want them to think so that they can anticipate errors and make split second decisions when new data is encountered. Well, in order to make a full AI, we need to be able to completely understand reasoning, knowledge, planning, learning, communication, perception, and the ability to move and manipulate objects (this last one is developing pretty quickly thanks to AI’s little brother, robotics). After we fully understand these concepts, we need to be able to abstract them, and turn them into efficient software that can be given different uses and approaches, because with AI, the possibilities can go from a common toaster, to a complex interplanetary rover and beyond.
For a more technical approach, you need a strong background in Linear Algebra, Probability, and I would highly recommend Mathematical Logic, Sets Theory, Graphs Theory and of course, Discrete Mathematics.
Now then..There are tons of concepts that I would love to go deeper into, but I would have to write a book. However, if you have any questions, you can comment this thread and I’ll get right on whatever people deem necessary. For now, I’ll briefly introduce you to one of the most complex and interesting projects concerning AI.
The Blue Brain Project is an attempt to reverse engineer the human brain and recreate it at the cellular level inside a computer simulation. The project was founded in May 2005 by Henry Markram at the EPFL in Lausanne, Switzerland (Switzerland, how surprising is that?).
Goals of the project are to gain a complete understanding of the brain and to enable better and faster development of brain disease treatments.
The research involves studying slices of living brain tissue using microscopes and patch clamp electrodes. Data is collected about all the many different neuron types. This data is used to build biologically realistic models of neurons and networks of neurons in the cerebral cortex. The simulations are carried out on a Blue Gene supercomputer built by IBM. Hence the name “Blue Brain”. The simulation software is based around Michael Hines’s NEURON, together with other custom-built components. (I took the abstract from here, so all the credit goes to EPFL on the insight.)
Sounds a lot like neuroscience, doesn’t it? But AI is closely intertwined with networks, and let me tell you that our mighty brain is a big and complex network.
Artificial Intelligence, in essence, is about creating or mimicking intelligent behavior in non-sentient entities. The wealth of knowledge that has been gathered in researching this topic reveals much about logic, perception, and common sense. In my own words, I’d say that AI is the science that highlights the basics of human thinking
(A very interesting TED Talk on AI by George John can be seen here. George John previously worked at NASA. He currently manages Rocket Fuel and has married big data with artificial intelligence to create the leading programmatic media buying engine in digital advertising.)