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 What is Machine Learning?

Machine learning is a type of artificial intelligence (and therefore a subset of it) that specializes in parsing and analyzing given data in order to adapt from it and make adequately intelligent decisions. To put it simply, this AI is made to observe and notice a lot of stuff, and then take one or more courses of action based on the information it received.

Typical machine learning tasks today could have:-

  1.       Link recommendations
  2.       Content moderation
  3.       Search results display
  4.       Curating timelines (in social media)

A machine learning system is capable of analyzing an enormous amount of data in a short time, creating solutions or conclusions from it. It optimizes its algorithm to give accurate interpretations, much more than what humans can do with the same time constraints. For example, we want to determine automatically if a certain email is spam or not. A machine learning system will sift through thousands upon thousands of emails in order to find patterns that would help it determine a spam email. It would then give a rough classification of either spam and regular email, the data of which it would use yet again to find even more patterns that would help it refine its analysis even further.

When given newer and newer sets of data, machine learning systems could adapt and update its algorithms to get even better at what it does. Or at the very least, minimize the likelihood of mistakes. This is what makes machine learning very important in our current data-driven era.

machine learning vs deep learning iitmind

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What is Deep Learning?

Deep learning is, yet again, another subset, this time of machine learning. The basic design of deep learning systems is based on an organic brain. Whereas we form new memories using a complex web of neural patterns, this kind of system weaves its own complex web of decisions using an artificial neural network, which is composed of countless algorithmic layers.

A few fairly notable deep learning systems are:-

  1.       Watson (defeated contestants at Jeopardy!)
  2.       AlphaGo (defeated professional Go player Lee Sedol in March 2016)
  3.       Deepfake (generating realistic but artificial representations of actual people)
  4.       OpenAI Five (a gaming deep learning project, defeated pro DOTA player Dendi last 2017)

Unlike standard machine learning systems, which can still perform quite well even given relatively basic sets of data, a starting deep learning system would literally start from scratch. It is characterized by its ‘limping period’, where the first few generations of its AI would only start providing actual results after an adaptation period from several countless failed generations.

When it does reach a fairly complex level of efficiency, deep learning systems simply start to overwhelm everything else before it. DeepMind’s AlphaGo, for example, started using an initial set of 160,000 amateur Go matches before it stumbled its way towards beating professional Go players by playing millions of times against itself.

Deep learning systems, unlike other previously designed machine learning systems, hugely rely on matrix multiplications to generate data. As such, commercial GPUs are usually the best hardware for these systems, as they are capable of delivering the high-level parallel processing requirements needed to maintain operability.

deep learning iitmind

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Machine Learning and Deep Learning

Perhaps the more important distinction that we need to learn is the difference between machine learning and deep learning. First of all, as mentioned earlier, deep learning is machine learning, technically one type, or a subset of it. Machine learning, however, is not always deep learning. The distinction largely has to do with the way both are built.

Machine learning has been developed within the same computer environment as many of our software during the last few decades. As such, it is in a way, linear, and even if it is built to adapt to Moore’s Law, it is still limited by its decision trees and algorithms. Deep learning, on the other hand, meshes all of its algorithms within a neural network. It is designed for high-level parallel computing, which we can now consider as the next generation in machine learning.

Regardless of whether the differentiation is clear or not, it is absolutely certain that deep learning is the future. For our purposes, however, separating deep learning AI from standard machine learning AI is essential in understanding just how different it truly is, and just how advanced it could actually be. Despite still being at its developmental stages today, it is almost already incomparable to everything else that came before it.

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