Top Differences Between Artificial Intelligence, Machine Learning & Deep Learning by@devshankar.ganguly
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Top Differences Between Artificial Intelligence, Machine Learning & Deep Learning

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Artificial intelligence is the future of the technological football and now some of the world’s most advanced artificial intelligence and machine learning can be developed in hours on a personal computer with open-source frameworks, AI will become more pervasive and generalized than it is. Software will get smarter and more multi-capable as the latest in natural language processing, computer vision, recommending systems and more became much as easy to develop as a CMS.

Artificial intelligence in nowadays plays a significant role in our day to day life; from our smartphones to other electronic devices, the technology has given us the game-changing opportunities that will assist you in your work as well as in your lifestyle too. Few of the popular sci-fi movies like Terminator, Transformers and latest in this era was Avtar-in 2009 are the best examples of Artificial Intelligence, Machine Learning & Deep Learning.

What is artificial intelligence(AI)?


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The theory & development of computer systems that able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making and translation between languages.

What is machine learning?


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Machine learning is a subset of artificial intelligence in the field of computer science that often uses statistical techniques to give computers the ability to “learn” (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed. Arthur Samuel coined the phrase not too long after AI, in 1959, defining it as, “the ability to learn without being explicitly programmed.”

What is deep learning?

Deep learning is a subset of machine learning in Artificial Intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled. Also known as Deep Neural Learning or Deep Neural Network. Here is the link to explain with the evolution of AI:

Now it’s important to know what are the differences between Artificial Intelligence, Machine Learning & Deep Learning


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Artificial Intelligence -Human Intelligence Exhibited by Machines

Back in that summer of ’56 conference, the dream of those AI pioneers was to construct complex machines -enabled by emerging computers — that possessed the same characteristics of human intelligence. This is the concept we think of as “General AI” -fabulous machines that have all our senses (maybe even more), all our reason, and think just like we do. You’ve seen these machines endlessly in movies as the friend -C-3PO & foe-The Terminator. General AI machines have remained in the movies and science fiction novels for good reason; we can’t pull it off, at least not yet.

What we can do falls into the concept of “Narrow AI.” Technologies that are able to perform specific tasks as well as, or better than, we humans can. Examples of narrow AI are things such as image classification on a service like Pinterest and face recognition on Facebook.

Those are examples of Narrow AI in practice. These technologies exhibit some faces of human intelligence. But how? Where does that intelligence come from? That gets us to the next circle, Machine Learning.

Machine Learning — An Approach to Achieve Artificial Intelligence

Machine Learning at its most basic is the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world. So rather than hand-coding software routines with a specific set of instructions to accomplish a particular task, the machine is “trained” using large amounts of data and algorithms that give it the ability to learn how to perform the task.

Machine learning came directly from minds of the early AI crowd, and the algorithmic approaches over the years included decision tree learning, inductive logic programming. clustering, reinforcement learning, and Bayesian networks among others. As we know, none achieved the ultimate goal of General AI, and even Narrow AI was mostly out of reach with early machine learning approaches.

As it turned out, one of the very best application areas for machine learning for many years was computer vision, though it still required a great deal of hand-coding to get the job done. People would go in and write hand-coded classifiers like edge detection filters so the program could identify where an object started and stopped; shape detection to determine if it had eight sides; a classifier to recognize the letters “S-T-O-P.” From all those hand-coded classifiers they would develop algorithms to make sense of the image and “learn” to determine whether it was a stop sign.

Good, but not mind-bending great. Especially on a foggy day when the sign isn’t perfectly visible, or a tree obscures part of it. There’s a reason computer vision and image detection didn’t come close to rivaling humans until very recently, it was too brittle and too prone to error.

Time and the right learning algorithms made all the difference; that leads us to the next circle, Deep Learning.

Deep Learning — A Technique for Implementing Machine Learning

Another algorithmic approach from the early machine-learning crowd, Artificial Neural Networks, came and mostly went over the decades. Neural Networks are inspired by our understanding of the biology of our brains — all those interconnections between the neurons. But, unlike a biological brain where any neuron can connect to any other neuron within a certain physical distance, these artificial neural networks have discrete layers, connections, and directions of data propagation.

You might, for example, take an image, chop it up into a bunch of tiles that are inputted into the first layer of the neural network. In the first layer individual neurons, then passes the data to a second layer. The second layer of neurons does its task, and so on, until the final layer and the final output is produced.

Each neuron assigns a weighting to its input — how correct or incorrect it is relative to the task being performed. The final output is then determined by the total of those weightings. So think of our stop sign example. Attributes of a stop sign image are chopped up and “examined” by the neurons — its octagonal shape, its fire-engine red color, its distinctive letters, its traffic-sign size, and its motion or lack thereof. The neural network’s task is to conclude whether this is a stop sign or not. It comes up with a “probability vector,” really a highly educated guess, based on the weighting. In our example the system might be 86% confident the image is a stop sign, 7% confident it’s a speed limit sign, and 5% it’s a kite stuck in a tree, and so on — and the network architecture then tells the neural network whether it is right or not.

Even this example is getting ahead of itself because until recently neural networks were all but shunned by the AI research community. They had been around since the earliest days of AI, and had produced very little in the way of “intelligence.” The problem was even the most basic neural networks were very computationally intensive, it just wasn’t a practical approach. Still, a small heretical research group led by Geoffrey Hinton at the University of Toronto kept at it, finally parallelizing the algorithms for supercomputers to run and proving the concept, but it wasn’t until GPUs were deployed in the effort that the promise was realized.

Deep Learning has enabled many practical applications of Machine Learning and by extension the overall field of AI. Deep Learning breaks down tasks in ways that make all kinds of machine assists seem possible, even likely. Driverless cars, better preventive healthcare, even better movie recommendations, are all here today or on the horizon. AI is the present and the future. With Deep Learning’s help, AI may even get to that science fiction state we’ve so long imagined. You have a C-3PO, I’ll take it. You can keep your Terminator.



*Wrapping Up*

AI & IoT are intricately connected!

Over the past decade, deep learning is being used by enterprises to solve business level challenges. From face detection to product recommendation, customer segmentation, digit reorganization, machine translation, business intelligence, Internet of Things, network security and so on the use of deep and machine learning as a process has completely transformed the world we are living today.


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