Data Science vs. Artificial Intelligence vs. Machine Learning vs. Deep Learning

Artificial Intelligence

Artificial intelligence, or AI for short, has been around since the mid 1950s. It’s not necessarily new. But it became super popular recently because of the advancements in processing capabilities. Back in the 1900s, there just wasn’t the necessary computing power to realise AI. Today, we have some of the fastest computers the world has ever seen. And the algorithm implementations have improved so much that we can run them on commodity hardware, even your laptop or smartphone that you’re using to read this right now. And given the seemingly endless possibilities of AI, everybody wants a piece of it.

But what exactly is artificial intelligence? Artificial intelligence is the ability that can be imparted to computers which enables these machines to understand data, learn from the data, and make decisions based on patterns hidden in the data, or inferences that could otherwise be very difficult (to almost impossible) for humans to make manually. AI also enables machines to adjust their “knowledge” based on new inputs that were not part of the data used for training these machines.

Another way of defining AI is that it’s a collection of mathematical algorithms that make computers understand relationships between different types and pieces of data such that this knowledge of connections could be utilised to come to conclusions or make decisions that could be accurate to a very high degree.

But there’s one thing you need to make sure, that you have enough data for AI to learn from. If you have a very small data lake that you’re using to train your AI model, the accuracy of the prediction or decision could be low. So more the data, better is the training of the AI model, and more accurate will be the outcome. Depending on the size of your training data, you can choose various algorithms for your model. This is where machine learning and deep learning start to show up.

In the early days of AI, neural networks were all the rage. There were multiple groups of people across the globe working on bettering their neural networks. But as I mentioned earlier in the post, the limitations of the computing hardware kind of hindered the advancement of AI. But from the late 1980s all the way up to the 2010s, machine learning it was. Every major tech company was investing heavily in machine learning. Companies such as Google, Amazon, IBM, Facebook, etc. were virtually dragging AI and ML PhD. people straight from universities. But these days, even machine learning has taken a back seat. It’s all about deep learning now. There’s definitely been an evolution of AI in the last few decades, and it’s getting better with every passing year. You can visualise this evolution from the image below.