Are AI and Machine Learning the same?
We have all heard or read about how AI is the next big thing. It seems like every 3rd article or blog is about AI and how companies are leveraging this shiny new tech to solve some of their biggest problems. And the same words are seen together. AI vs ML, Machine learning vs Deep Learning, Neural Networks ….. and so on.
Every so often I get asked (by engineers and non-engineers both) “Are AI and machine learning the same?” So let’s answer this.
What is AI? What are its goals?
I don’t want to give a textbook answer here on the AI meaning. I am sick of them myself. So here it goes…
AI’s primary goal is to create an artificial intelligence. This may or may not include a robotic body as such. That’s only for movies, not in real world always. An artificial intelligence needs to be capable of most, if not all, the things that humans do.
- It should be able to learn by observation or actions.
- It should be able to decide on a goal
- It should be able to create a plan to achieve this goal in an optimal way.
- It should be able to sense or feel and have an awareness of its surroundings
- It should be able to reason and draw conclusions and so on..
As you can see this is a pretty lofty goal. It is quite abstract as to how this can be achieved. And that’s the main point. AI is abstract in general. It has goals but needs ways of achieving them.
How does machine learning assist AI goals?
Machine learning is one of many tools that is used by an AI to achieve its goals. Machine learning unlike AI is very clear about its goals. Machine learning wants to create function approximations or models for some input and output combinations (we call this “training data”). Using these AI models, it can later generate outputs (we call this “predictions”) on unseen data. To create these models, it uses various AI algorithms which have hyper-parameters to help tune them.A very easy example is,
Input (X) Output (Y)
It’s clear from these combinations that the output is 3 times the input. So the function would be,
Y = 3X
Now this becomes our model. If we have unseen X values, we apply the model to predict the Y values. But of course, the AI algorithm is never this simple in real life. Here X could refer to a vector generated by text, image, audio, etc and Y could be any useful prediction.
These types of predictive models are used by AI to reach its goals.
Where exactly is Machine Learning used?
So we know that machine learning is used to create predictive models. But where exactly are these models used? What can you do with them? Let’s look at some relevant examples for businesses.
- Personalized recommendations – When netflix recommends movies based on your previous actions, when amazon recommends products for you to checkout, Machine learning is used to predict them
- Search engines – Google uses machine learning to provide you with search results for your queries
- Maps – Google uses machine learning to recommend the best route for your journey based to driving time calculations taking into consideration the live traffic and road closures data
- Virtual Assistants and Customer Service Chatbot – Natural language processing techniques of machine learning are used to provide a conversational AI experience to the user. This can be used in a wide variety of fields like customer service and customer engagement in ecommerce.
- Video Surveillance uses machine learning to reduce human effort by predicting any unusual behavior in video feeds
- Face Recognition – Many smartphone providers use machine learning to help you unlock your phone with your face
- Spam filtering – Email and SMS providers use machine learning to help filter out unwanted messages
AI vs Machine learning vs Deep learning
This is another familiar question – How is deep learning different from machine learning? If I had a nickel…
Machine learning’s goal is to create predictive models. Deep learning wants to do the same. Really. They’re like twins.
While machine learning likes to use traditional models like SVM, Decision trees, etc Deep learning uses fancier (read as “more complex”) neural network AI algorithms which work like the human brain (in theory). And that’s it. Everything else is pretty much the same.
I usually don’t refer to Deep Learning as a separate term. To me they are the same. Deep Learning is Machine Learning with updated AI Algorithms.
AI vs ML – AI is an abstract concept with many goals. Machine Learning is a specific tool used by AI to realise them.
Deep Learning is Machine Learning with updated algorithms.
For more content on AI and how businesses can leverage it, please check this article from Dinesh Sharma “Can AI make your business far more intelligent”