Today everyone is talking about Artificial Intelligence, Machine Learning, and Deep Learning. But does everybody understand what the difference is? In this blog post, we will give simple answers to this question so that you are not confused when someone tells you the smart sentence “does this technology use AI?” Let me give you a short description just at the start.
Artificial Intelligence is the big idea. It’s a mechanism that incorporates human intelligence into machines. Machine Learning is smaller than AI. It is a subset of AI. Machine Learning allows machines to learn from their own experience. And deep learning is basically a sub-part of the broader family of Machine Learning which makes use of Neural Networks(similar to the neurons working in our brain) to mimic human brain-like behavior. So, AI comes first then come ML, and DL.
Does this make things clear for you? I guess, not yet. So, let's dive into each separately.
To understand AI, let’s look at the two words – “artificial” and “intelligence”. By artificial scientists mean that it is made by a human and that it is non-natural. By intelligence, scientists think that it can think. In other words, machines (computers) are taught to mimic a human brain and its thinking capabilities.
Let’s take the example of Google Maps. It has become a common thing to use Google Maps to get from Point A to Point B. But do you know how much data is processed to show you this direction? AI-powered technology maps Google Maps to estimate the time of arrival, traffic delay time, and unexpected delays. So, AI-powered technology is given a task: “how can I get from point A to point B?”. To answer this question, it thinks – “what is the best way to offer to the client?” The technology processes information to estimate the traffic flow in order to offer the optimal route to take in order to get to the desired destination as quickly as possible. Quite intelligent, ha? No human brain could do the same in the same amount of time! No wonder the whole business community considers having an Artificial Intelligence app.
If you think of artificial intelligence as the body, then machine learning is the vessels that provide blood to the body. How is it different from regular computer programming? In fact, machine learning apps allow software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. This means that computers can learn without being programmed to perform specific tasks, in other words, computers can learn from data.
Let’s take a medical diagnosis. Machine Learning can help with diagnosing diseases. For example, physicians can use chatbots with speech recognition capabilities to discern patterns in symptoms. Today, oncology and pathology use machine learning to recognize cancerous tissue. It goes so far that machines can now recommend a treatment option.
Machine Learning is used in every moment of our life starting from Google recommending pages or Netflix or Amazon offering a better user experience.
So why is this important? We must accept that the world consumes a massive amount of data. It is not possible to process this data by humans alone. Machine Learning offers cheaper and more powerful ways of processing the growing volumes and varieties of available data. It also allows affordable data storage.
Who can benefit? Just anyone! Shops, factories, services, industries. There is no limit to how Machine Learning can be applied in everyday life.
Just as Machine Learning is a subfield of Artificial Intelligence, so Deep Learning is a subfield of Machine Learning. Deep Learning algorithms are more complex forms of Machine Learning algorithms. What does Deep Learning do? DL analyzes a vast amount of data and finds patterns similar to how a human would draw conclusions. But unlike humans, DL is capable of processing such big volumes of information that no human would be able to do. To do that, DP applications use a layered structure of algorithms called an artificial neural network (ANN). ANN is interconnected nodes or neurons in a layered structure that resembles the human brain. This means that DL can learn from itself. For example, if the system finds patterns, it will remember the pattern, and make use of it in future information processing.
To understand DL, you need to know about Natural language processing (NLP). It is a sector of deep learning that has recently come to the forefront. The main task of NLP is to understand language as it is spoken naturally. And this is not an easy task owing to the various colloquialisms, slang, and syntax. With NLP, DL is able to identify the language and also the human voice. The rate of NLP accuracy is 98% accuracy.
An example is Virtual assistants or driverless cars.
So, what’s the deal? Why are Artificial Intelligence, Machine Learning, and Deep Learning gaining momentum? The fact is that they will help many industries to grow at a faster speed than the human brain is capable to allow. This relates to anything from the car industry, to retail, to agriculture, and more.
And after all, how are they different? In fact, they are subsets of a big family but AI is the parent, and ML and DL are subsets. Here is an image to look at.
Having said that, we should admit that AI, ML, and DL are still in their infancy in some areas but their power is already enormous. In addition, they are not affordable for all, and only large companies with vast financial and human resources can build AI algorithms since they are complex and expensive.
However, this is changing. We believe cheaper and more affordable solutions will come into the market, and everyone will be able to benefit from the new form of technology.