It’s #futurefridays and today we’ll be talking about the basics of Artificial Intelligence.
Previously, I talked about Natural Language Processing, and before that, Computer Vision.
But today we’ll tackle another field of Artificial Intelligence called Machine Learning.
Wikipedia defines Machine Learning as:
Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to perform the task. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop a conventional algorithm for effectively performing the task.
Let me show you some examples of how Machine Learning is being used in real-world scenarios.
But first, I would recommend you watch this video from Accenture that gives a really simple explanation of what Machine Learning is, in 1 just minute.
AI 101: What is Machine Learning?
Video © Accenture
Now that you have a basic understanding of what Machine Learning is, allow me to give some examples of it in action today.
Waze
Have you ever wondered how Waze is able to tell if traffic is heavy heading towards your destination, and it is able to forecast arrival times and recommended routes?
Well, that’s Machine Learning in action. It learns the patterns based on how much time users are spending on each road on the map and at specific times of the day, week, month, etc.
Chat Bots and Virtual Assistants
I’ve talked about this in another post talking about Natural Language Processing as this is actually a combination of Machine Language and NLP.
Check out the detailed article here: Chat Bots Making Phone Calls on Your Behalf using NLP AI (Use Case).
Email Spam Filtering
Did you know that your email software uses Machine Learning to determine which messages to filter which messages are Spam compared to those which are not?
And when you find something in the Spam folder that isn’t supposed to be there, when you flag it as “Not Spam” you’re essentially teaching the Machine Learning algorithm what to do next time.
Again this technology is a combination of Machine Learning and Natural Language Processing working in tandem with each other.
Face Recognition like in Social Media
Have you ever wondered how Facebook is able to auto-detect who the people are in a photo that you upload even if you haven’t tagged them yet?
Here’s an example of how Facebook uses this technology.
This is actually Machine Learning working in tandem with Computer Vision.
This can also be used to identify emotions in faces, like Anger, Sadness, Happiness, and so forth.
Object and Character Recognition using Computer Vision
This is pretty similar to face recognition but can also be utilized for other objects, like telling apart humans from animals, or telling apart cats from dogs, or squares from circles.
This can also be used to identify characters like numbers and letters. And when combined with Natural Language Processing, can be used to identify context and meaning.
Here’s an example of a Lego Brick Sorter using Computer Vision and Machine Learning.
Forecasting and Prediction Models like Product Recommendations
Have you ever wondered how social media sites are able to post ads that are relevant to stuff that you have been researching about?
That’s machine learning in action, where the algorithm is able to recognize your buying intent based on previous purchases and recent searches.
Machine Learning can also be used to identify patterns that can be used for forecasting, such as when NASCAR race cars should pit for fuel and tires, and when crops will grow in certain areas, given the weather and other factors.
Here’s an example of using AI to predict Heart Attacks before it happens.
Ending Note
I hope you found this to be of value, and gave you a sense of what Machine Learning is and where it is being utilized in real-world scenarios.
If you have any questions, reply with a comment. I’d love to hear from you.
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