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Demystifying AI, Machine Learning and Deep Learning

Demystifying key buzzwords like Artificial intelligence, machine learning, artificial neural networks and deep learning is simple and complex task at the same time.

Let us attempt to melt down the thick confusion of how all-encompassing terms like artificial intelligence, machine learning, and deep learning speaks to each other.

Almost every technology (now even non technology) company on this planet is claiming the share of extra revenue by putting these buzz words on display.

What is getting lost here is with all the buzzwords swirling around, it’s easy to get lost and not see the difference between hype and reality.

Demystifying key buzzwords like Artificial intelligence, machine learning, artificial neural networks and deep learning is simple and complex task at the same time.

“Artificial neural systems, or neural networks, are physical cellular systems which can acquire, store, and utilize experiential knowledge” – Zurada (1992).

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Demystifying AI, Machine Learning and Deep Learning

Artificial intelligence, machine learning, artificial neural networks and deep learning are simple and complex terms at the same time. All these buzz words are agents of the future analytics.

Almost every technology (now even non technology) company on this planet is claiming the share of extra revenue by putting these buzz words on their product display.

This is what getting lost here in race of these buzzwords swirling around every thing. All these terms are representative of the future of analytics. Sometimes it is good to un-develop something existing to uncover the hidden gems underneath.

In scenarios like example 2 as above where artificial intelligence has to play critical role to show its power. Animal picture is a dog or cat etc.

No algorithm can be considered good or bad but surely can be data greedy or resource hungry. Algorithms need to be trained to learn how to classify and process information.

To simulate the mapping of inputs to outputs as it happens in a human brain which makes very difficult tasks for computers like image recognition, sarcasm detection, voice recognition, etc.

It has the conceivable potential to introduce new sources of growth, reinvent existing business, changing the style of work.

AI powered computers has started simulating the human brain’s sensation, action, interaction, perception and cognition abilities.

AILabPage defines Machine Learning  as “A focal point where business, data and experience meets emerging technology and decides to work together“. Machine learning is responsible for assessing the impact of data.  In machine learning algorithms are used for gaining knowledge from data sets.

Machine learning and data mining follow the relatively same process. Algorithms are built through which input is received and after statistical analysis output value is predicted. There are three general classes of machine learning —

In this specific transmitter substances are released from the sending side of the junction.The biological neuron model is widely used in artificial neural networks with some minor modifications on it.

Deep learning’s main driver are artificial neural networks system or neural networks. Deep learning is based on multiple levels of features or representation in each layer with the layers forming a hierarchy of low-level to high-level features.

Speech/Text and image processing can make perfect robot to start with and actions based on triggers makes it the best. It has to pass basic 4 tests.

Turning test i.e needs to acquire college degree, needs to work as an employee for at-least 20 years and perform well to get promotions and attain ASI status.Traditional machine learning focuses on feature engineering but deep learning focuses on end-to-end learning based on raw features.

The analogy to deep learning is that the rocket engine is the deep learning models and the fuel is the huge amounts of data we can feed to these algorithms.

Sir Andrew Ng Decision tree learning, inductive logic programming, clustering, reinforcement learning, and Bayesian networks, among others are key components here.

Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction.

Fundamentally solving intelligence means the artificial seeking and production of knowledge that answers questions whether they were asked or not, known or unknown.

As mentioned above artificial intelligence is much broader concept than machine learning, which addresses the use of computers to mimic the cognitive functions of humans.

Machine learning is a subset of AI and focuses on the ability of machines to receive a set of data and learn for themselves, changing algorithms as they learn more about the information they are processing.

Data Science does not necessarily involve big data, but the fact that data is scaling up makes big data an important aspect of data science.

It has extreme potentials to take business intelligence to competitive intelligence that can infer competitive measures using augmented site-centric data.

One of the best strength of DeepLearning does not require predefined features to find peculiar patterns that humans will always struggle or probably would never be able to define before hand.

as reason is very simple and known which is machine learning models are not sufficiently accurate or can’t be accurate without lots of data and lots of training.

Tech giants like Google, Microsoft, Apple and Baidu known for their dominance in digital technologies globally are spending couple of billion united state dollars on AI.  90% of this is going to RnD &

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