In your day-to-day life, you must have heard the terms Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL). You might have also noticed that these terms are often used interchangeably.
But are these 3 the same? Do you have the liberty to use them interchangeably? Let’s find out!
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Way back in 1955, one of the earliest Godfather’s of AI, John McCarthy went on to define Artificial Intelligence as “the science and engineering of making intelligent machines that have the ability to achieve goals like humans do.” Then 4 years later, in 1959, Arthur Samuel described Machine Learning as a vast sub-field of AI dealing “that gives computers the ability to learn without being explicitly programmed.” What does this entail?
In Machine Learning, once a program is created, it will gradually learn how to accomplish intelligent tasks beyond the boundaries of programming – it learns through experience and data. In this respect, ML programs are unlike purpose-built programs with a pre-defined set of behavior and goals.
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Probably this picture will make things a little clearer for you:
Now, we’ll take an in-depth look at Artificial Intelligence, Machine Learning, and Deep Learning and their differences.
Artificial Intelligence vs Machine Learning
In simple words, AI seeks to design special computer programs and algorithms that allow machines to function like humans. The most common misconception that people have about AI is that it is a ‘system.’ The truth – AI is not a system; it is a study (like we mentioned before “science and engineering”) involving how to train computers to accomplish tasks done by humans.
AI can be classified into 3 types:
- Narrow AI: It refers to the AI that can program the machines to perform particular tasks, but in a much better way than a human.
- General AI: It refers to the AI that can perform an array of intellectual/intelligent tasks with the same accuracy level of a human.
- Active AI: Perhaps the most advanced form of AI, Active AI can outperform humans in specific tasks.
Since AI is a study or a concept of developing smart machines, it must have some technological means to achieve that end, right?
One of those means is Machine learning. Marks our words here, ML is one of the ways we can execute the concept of AI. According to Tom Mitchell, ML expert at the Carnegie Mellon University:
“Machine Learning is the study of computer algorithms that allow computer programs to automatically improve through experience.”
Thus, achieving the goal of AI – creating machines that can learn from experience and trial & error just as humans do. ML algorithms feed on data to learn from it. Whenever a new input is fed into the algorithm, it adjusts its response to this change to perform new tasks. A classical example of Machine Learning is the Recommendation Engine. You must have seen this on online platforms like Netflix and Amazon – based on the content you search for, these apps curate a personalized and customised recommendation list of the content you would ‘like’ to see.
Back when AI was in its earliest stages, the goal of scientists and researchers was to create machines that can perform tasks that demand intelligence. Initially, they developed programs (mind this again, programs are required to execute the plans of AI) that could solve basic logical problems and compete with humans in games like chess and checkers. Case in point – IBM’s Deep Blue that defeated world champion Gary Kasparov at chess.
This is one of the very first instances of the success of AI and needless to say, it caused a massive wave of excitement among computer and tech enthusiasts – it was the beginning of a world with brand new possibilities. Fast forward to this day, look at where AI stands now – today we can boast about real-life AI applications like Google’s self-driving car, Robot Sophia, smart virtual assistants like Apple’s Siri and Amazon’s Alexa, Smart Homes, and IoT, and so much more. All these AI inventions have one common thing that drives them – ML algorithms.
Machine Learning vs Deep Learning
Just as ML is a subset of AI, Deep Learning is a subset of ML.
The essence of Deep Learning is Artificial Neural Networks (ANN). Since the entire idea of AI was to mimic the capabilities of the human brain, artificial neural networks were created. You can consider Deep Learning as the evolutionary branch of ML wherein ANNs are complex webs of an incomprehensible amount of neurons, deeply interconnected with one another in layers.
ANNs are designed to tackle complex problems that traditional ML algorithms and simple neural nets cannot. This is not because conventional neural networks are connected in a simple fashion – they are unable to do the heavy weight-lifting of the human brain that has over 86 billion neurons, fashioning an intricate style of interconnectivity.
Now, as we stated earlier, Deep Learning can be said to be a subset of Machine Learning. In essence, Machine Learning is all about developing models that get progressively better at the task allocated to them, but they still need some sort of guidance. In case an ML algorithm returns an inaccurate result, the developers need to step in and make necessary amends. But, a Deep Learning model – as it uses Artificial Neural Networks that replicate a human brain’s working – determines the correctness of its predictions on its own, without the need for any human intervention.
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The above-discussion must have clarified a lot of queries that you might have had regarding AI, ML, or Deep Learning. In essence, the end goal for all three technologies is the same – making machines smarter. It’s the route that they take makes them different from one another.
If you are still confused regarding what is what, please feel free to drop us a comment – we’ll get you covered!