By: Rajvi Khanjan Shroff
Ever wondered how Siri on your phone works? Or Alexa?
Turns out it’s a bit of learning magic: Artificial Intelligence, or AI, is the key.
So how does it work?
First, let’s define what AI is. At its core, it’s about making machines (especially computer programs) “intelligent,’’ and teaching machines to learn in such a way that they can apply new concepts and use past experiences, or memory, to guide future decisions. This is done using machine learning.
For the sake of simplicity, let’s use computers as an example to help understand how AI works.
Usually, when a programmer needs to tell the computer to carry out a specific task, she would code the instructions. But machine learning is a revolution in that it uses examples, not instructions, as the algorithm. This means that instead of having to write truckloads of explanations for what you want, the process is shortened to you giving the computer concrete samples of what you want. Essentially, machines learn themselves from data rather than humans programming into them specific knowledge. It’s a dynamic learning process.
If you're confused, don't worry! An example should help clear this up.
Let’s assume you want to teach a computer how to identify a cat. You could do this by feeding the computer massive amounts of pictures that are classified into those that show a cat and others that don’t, and tell it to learn how to identify a cat on its own. It is using data, or pictures in this case, to learn instead of using a textbook of characteristics; in other words, statistics rather than reasoning.
So what does all of this have to do with your digital personal assistants? Well, they are prime examples of machine learning, and AI is at the center of how they work.
Let’s suppose that you want Siri to make a call. For this to be possible, she first needs to be able to understand you, and then reply to confirm. This is accomplished by using speech recognition (machines have algorithms that help them identify when humans are speaking and translate the conversation), natural language processing (how the machine is able to process human language or data), and natural language generation (how they apply the processing to speak like people).
Essentially, the assistants are software that use data stored on multiple computers connected to the internet to generate results similar to a human's.
As time goes on, machine learning is improving by leaps and bounds. For instance, Alexa learned how to carry on a conversation after a follow-up question similar to the way people do, and Siri was able to be accessed even when there were loud background noises. These features didn’t exist before--and represent the growth the industry has experienced.
Personal assistants are just one example in which AI is used. Other examples include self-driving cars, in which AI is used to control, navigate, and drive the vehicle; spam filters in
emails use AI to identify potential malicious content and to keep up with an ever-increasing amount of signals of malware over time; and social networking in social media uses AI to automatically identify your friends in a particular photo to suggest for tagging. AI can especially help in mitigating human error, as the advent of self-driving cars will help make everyone on the roads safer, and with much hacking and identity theft today, spam filters can help keep everyone from accidently clicking on a potentially life-altering scam. Clearly, AI is going to be an important aspect of the future, and its real world applications will have life changing effects.
In fact, as AI starts to expand and we start to use these technologies more and more, something known as Deep Learning will likely play a much bigger role as well. A subset of machine learning, Deep Learning aims to simulate the workings of the human brain, especially in data processing and higher-order thinking skills. In Deep Learning, machines use ANN, or artificial neural networks, to carry out machine learning; this means that the data goes through layers of the networks, and the machine learns at intervals by increasing its level of understanding one step at a time, and finally ends up with a conclusion, much like how a human would learn. If we go back to our cat example, then even if we don’t give the computer labelled pictures, it will be able to try to find identifiers on its own and be able to come up with an answer based on its own observations and findings.
Some real life examples of Deep Learning include advancements in customer experience, language identification and text generation.
With so much potential we have already seen possible thanks to AI, it’s impossible not to wonder: where will AI lead us next?
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