AI vs Machine Learning vs. Deep Learning


Artificial Intelligence, Machine Learning and Deep Learning was very much of the hype words in 2016 and going in 2017. These techniques have existed for decades but the application to business world has recently been explored in main stream. Machine learning and Deep learning are both fundamentally a form of Artificial intelligence. While the concepts of Machine learning and Deep learning have been around as early as from 1950s, they both have evolved and separated from each other in every decade with technology and experiments.

As per Stanford University, AI is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.

So where exactly did AI start? Hmmm…After WWII, a number of people independently started to work on intelligent machines. The English mathematician Alan Turing may have been the first. He gave a lecture on it in 1947. He also may have been the first to decide that AI was best researched by programming computers rather than by building machines. By the late 1950s, there were many researchers on AI, and most of them were basing their work on programming computers.

AI has several branches. Some of them are – Search, Pattern Recognition, Logical AI, Heuristics, Genetic Programming, Epistemology, Ontology. Few of the common applications are Games, Speech Recognition, Vision, Expert Systems, Natural Language processing, Heuristic classification,

Machine Learning (ML) is a form of AI that facilitates a computer’s ability to learn and essentially teach itself to evolve as it becomes exposed to new and ever-changing data. The main components of ML software are statistical analysis and predictive analysis. These algorithms can spot patterns and find hidden insights based on observed data from previous computations without being programmed on where to look. They learn from every experience and interaction.

If you use Netflix, you will notice that the suggested movies change and personalize to your taste based on previous movie views. While machine learning has become an integral part of processing data, one of the main differences when compared to deep learning is that it requires manual intervention in selecting which features to process, whereas deep learning does it intuitively.

Some of the ML techniques are Linear Regression, K-means, Decision Trees, Random Forest, PCA, SVM and finally Artificial Neural Networks (ANN). Artificial Neural Networks is where the field of Deep Learning had its genesis from.

Deep Learning (DL) is an advanced, sophisticated branch of AI with predictive capabilities that is inspired by the brain’s ability to learn. Andrew Ng from Coursera and Chief Scientist at Baidu Research formally founded Google Brain that eventually resulted in the productization of deep learning technologies across a large number of Google services.Just as the human brain can identify an object in milliseconds, deep learning can mirror this instinct with nearly the same speed and precision. Deep learning has the nimble ability to assess an object, properly digest the information and adapt to different variants.

Deep Learning is used by Google in its voice and image recognition algorithms, by Netflix and Amazon to decide what you want to watch or buy next, and by researchers at MIT to predict the future. Extending deep learning into applications beyond speech and image recognition will require more conceptual and software breakthroughs, not to mention many more advances in processing power.

Here is Gartner’s August 2016 Hype Cycle and Deep Learning isn’t even mentioned on the slide:

Google had two deep-learning projects underway in 2012. Today it is pursuing more than 1,000, according to a spokesperson, in all its major product sectors, including search, Android, Gmail, translation, maps, YouTube, and self-driving cars. IBM’s Watson system used AI, but not deep learning, when it beat two Jeopardy champions in 2011. Now, though, almost all of Watson’s 30 component services have been augmented by deep learning. most of the deep-learning applications that have been commercially deployed so far involve companies like Google, Microsoft, Facebook, Baidu, and Amazon—the companies with the vast stores of data needed for deep-learning computations.

One of the startup consulting company 7F Consulting promises to leverage Machine Learning and Deep Learning in its Strategy Advisory practice for advising C-Suite on strategy decisions. Their framework is called ‘Rananiti’ – meaning War strategy in Sanskrit! Rananiti uses proprietary solution and framework that provides a significant access to market insights with probabilistic decision advising executives on critical business decisions!

Now, other companies have started experimenting and integrating deep learning into their own day-to-day processes. Are you the next one?

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