When you think AI, what comes to your mind?
Some spell that sees the future,
Sci-fi movie scenes with computers automation at play,
Or something as literal as a smart computer that has the knowledge of your actions before you carry them out?.
When you think AI, think Algorithms.
Artificial intelligence ; a domain-specific illusion of intelligent behaviour displayed by machines, acting on the instructions of different chains of Algorithms wired together.
An Algorithm is a step-by-step information so detailed enough that something as literal-minded as a computer can follow them. On its own, an algorithm is no smarter than a frying pan (Haha, that was a joke!), it just does one thing very well, like sorting a list of numbers or searching the web for pictures of cute animals. But if wired together in a smart way, you can produce AI. For example, take a digital assistant like Siri, to which you might ask a question like “ Where can I find a Jazz Music Club in Lagos?” This query sets off a chain reaction of algorithms.
One Algorithm converts the raw sound wave of your speech into a digital signal.
Another algorithm translates the signal into a string of English phonemes, or perceptually distinct sounds. “Jaz-Muzik-klob”
The next algorithms segments those phonemes into words: “Jazz Music Club”
Those words are sent to a search engine — itself a massive network of algorithms that processes the query and sends back an answer.
Another algorithm formats the response into a coherent English sentence.
Results are being displayed in a standard text format according to users/web ranking or speech in a non-robotic-sounding way.
And that’s AI. Pretty much every AI system — whether it’s a self driving car, an automatic iris flower sorter, or a piece of software that monitors your online transaction for fraud — follows the same “network of algorithms” template. The network takes in data from some specific domain, performs a chain of calculation, and outputs a prediction or a decision.
There are two distinguishing features of algorithms used in AI. First, these algorithms practically deal with probabilities other than certainties. An algorithm in AI for example, won’t say outrightly that some online card transaction is fraudulent. Instead it will say the probability of fraud is 96% — or whatever it thinks, given the data. Second, there is the question of how these algorithms “know” what instructions to follow. In traditional algorithms, like the kind that run websites or applications, those instructions are fixed ahead of time by a programmer. In AI, however, those instructions are learned by the algorithms itself, directly from “training data”.Nobody tells an AI algorithm how to classify online card transactions as fraudulent or not. Instead, the algorithm sees lots of examples from each category (fraudulent, not fraudulent), and it finds the pattern that distinguishes one from the other. In AI, the role of the programmer is not to tell the algorithm what to do. It’s to tell the algorithm how to train itself what to do, using data and the rules of probability.
When John McCarthy, one of the founding founders of Artificial Intelligence first coined the word “Artificial Intelligence” in 1956, coupled with the buzzes of the technology in the 2000. Little did we know it will soon play a role in decisions that are much more important than the films people see on Netflix, the music they hear on Spotify, or the news stories they are recommended by Facebook. They will inform what medical treatment people receive, what jobs they compete for, what colleges they attend, what loans they qualify for —and yes, what jail sentences they receive when they commit a crime.
In conclusion, when you think AI, think algorithm. When you think AI, think Probability. When you think AI, think Statistics.
Excerpts from Nick Polson and James Scott : AIQ (How Artificial intelligence works and How we can harness its power for a better world).