Most technologies have a clear line drawn between them. Because of this, it is quite easy for people to learn and understand them. But, when it comes to data-related technologies, there are a lot of misconceptions. In fact, there’s a fair chance that you yourself might not completely understand what the difference between artificial intelligence (AI), machine learning (ML) and deep learning (DL) is. With that in mind, let’s take a few minutes to address this confusion.
To properly understand the differences between these three technologies, let’s first look at how they stack together in terms of hierarchy.
In very simple terms, AI is the mothership that houses both machine learning and deep learning. Going further down in the tree, deep learning is a subset of machine learning. The following diagram sums this up perfectly.
Now that you understand how these technologies are related, let’s define each of them and look at their differences one by one.
Artificial Intelligence is a technique that allows machines to take autonomous decisions without any human intervention to complete a set of tasks. The term was first coined in the year 1956 and was a dream for many computer scientists. It wasn’t feasible at the time because of the deficiency of usable data and processing power.
The idea is to program a machine to learn from experience and take decisions depending upon these experiences. This is achieved by processing large quantities of data and recognize patterns in them. Using these patterns, the machine can anticipate the proceeding steps to perform the given task.
Machine learning is a subset of AI which allows a machine to analyze a given dataset and make predictions based on the insights. Unlike AI which is process or task-oriented, machine learning focusses on trends and predictions that can help a professional define other algorithms to improve the performance of the process and get better results.
This trending technology doesn’t have much of a clear history. Most enthusiasts believe that the first machine learning algorithms were seen way back in the late 80s or early 90s. It basically tries to answer three of AI’s most intriguing questions in different fields:
- Statistics: How can you efficiently train large and complex models?
- Computer Science and Artificial Intelligence:How to modify simple AI systems to more robust ones?
- Neuroscience: How can you program a machine to function like an organic brain?
Deep learning is targeted machine learning technique that achieves great power and flexibility by allowing the machine to learn and represent the world as a nested hierarchy of concepts or abstraction. Seems like a mouthful, but it’s not that difficult.
Deep learning, at its core, uses humongous amounts of data which the machine analyzes. But, unlike general machine learning, the machine tries to build hierarchy among the datasets and relate one group of data to another. So, instead of treating a problem as a complex task, deep learning enables a user to divide the main task into a collection of simple tasks that are interwoven.
While machine learning answers the first two questions raised by AI-experts, the third question remained untouched. That’s when the deep learning approach was invented. Just like organic intelligence where neurons are responsible for data connections, deep learning utilizes what we now call artificial neural networks for its functioning.
Let’s now take a few examples to understand these technologies even better.
Building the First Car
Imagine a world without cars or any other kind of automobile and you are part of a team that is planning to build the first ever car. As there this is going to be a new invention, you do not have a blueprint or any other means to derive inspiration. This means that the team will have to try different approaches until you finally build the most efficient, economical and stable car. This is how AI works. The computer derives insights from the given data and generates different outcomes by applying separate algorithms. This is then boosted to complete the given task with the best possible statistics.
Some examples of real-world applications of AI are virtual assistants like Apple Siri, Samsung Bixby, Microsoft Cortana, Amazon Alexa, etc. Away from the niche assistants space, Tesla’s self-driving cars also use AI.
Predicting Currency Rates
Currency conversion rates tend to be a bit erratic but using historical analysis and a hefty amount of data, one can get an estimate of how much each currency will be worth in the future. This is achieved by using simple machine learning models.
Examples of real-world machine learning applications include sports analytics, weather insight apps, stock market predictors etc.
How can you differentiate the letter ‘A’ to be ‘A’ and not anything else?
Using past experiences, our brains are trained to analyze visual inputs and compare it with proven datasets to recognize patterns and images. In a similar manner, deep learning algorithms work by using complex models that compare a given input with an existing data set.
Real-world applications of deep learning include fingerprint sensors, image recognition software etc.
In conclusion, we can say that machine learning is an improvement in traditional artificial intelligence and deep learning is machine learning’s next iterative upgrade.
Now you know the differences between three of the most interrelated technologies in the market. Which one do you think would be the most suitable career for you? If you would like a head-start and learn any of these technologies, why not take up an online course that will help you understand these technologies better?