Some basics of artificial intelligence and deep learning

Before we dive straight into the concepts of artificial intelligence and deep learning, let us first try to understand the core motives and purposes behind the need and requirements of such technologies around the globe. Have you ever wondered how big technical giants like Apple and Google summarize your attributes and keen interests just on the tips of your fingers on the smartphones?.

How they analyze your voice and carry out the task just like that and how your interests of certain products and likes come out to be the frequent advertisements on your screens?. No idea well this much pretty how artificial intelligence and deep learning works. To sum up, all the basic needs of artificial intelligence came into existence for the betterment of humans and to replicate the delicate biology of thinking with the Machine itself. So that to carry out the projects or challenging tasks more accurately with less or no errors and Faster than a human mind.

 Or in technical terms It 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 Artificial Intelligence does not have to con ne itself to methods that are biologically observable

How does artificial intelligence work?

To understand human nature and behavior perhaps impacts artificial intelligence most as Artificial Intelligence (AI) fascinates, challenges and disturbs us. There are many voices in society that predict drastic changes that may come as a consequence of AI – a possible apocalypse or Eden on earth. However, only a few people truly understand what AI is, what it can do and what its limitations are. Understanding Artificial Intelligence explains, through a straightforward narrative and amusing illustrations, how AI works.

It is written for a non-specialist reader, adult or adolescent, who is interested in AI but is missing the key to understanding how it works. The creation of the so-called “intelligent” machine and it explains the different methods that are used in AI. It presents new possibilities offered by algorithms and the difficulties that researchers, engineers and users face when building and using such algorithms. Each new aspect allows the reader to discover a new aspect of AI and to become fully aware of the possibilities offered by this rich field.

Let us further understand this by a perfect example in the modern history and beginning of the computer era. A Turing machine, some algorithms, a bit of complexity, some graphs, and two or three heuristics: voila, the computer scientist’s complete survival kit. Everything began in 1843, when Ada Lovelace wrote the first algorithm for Charles Babbage’s analytical engine, a precursor to the computer. In a way, this is the prehistory of computer science. The first programmable machines did not quite exist yet: Jacquard’s loom, invented in 1801, was already using perforated cards to guide needles, but it was purely a matter of controlling mechanical commands. People imagined machines would soon be capable of surpassing humans in every activity.

They thought people would be able to automatically translate Shakespeare to Russian and replace general practitioners with computers that could make a diagnosis based on a patient’s information. Playing chess requires intelligence, without a doubt. It takes several hours to understand how each piece moves and years of training to play well. But artificial intelligence can mimic but in terms can play better in just a really short period of time beating the so long practiced human mind.

Deep learning and its aspects

 It’s been a while since you have been reading this particular fascinated dialogue about artificial intelligence and perhaps got a better and slightly understandable mind set about artificial intelligence and its basic working. So it’s now a good for you to introduce you to the deep learning title or a part of artificial intelligence

Have you ever think about it that back in 60s when there are no means of Graphic interface and user friendly operating systems people have to put down the codes for any specific task they want to get done repeating same process daily typing codes exactly the same over and over and still don’t get bored unless one of them must have thought that what if we just put the all amount of data in the machine and let it decide what kind of task we want it to perform based on the analytics we have feed in. Well sort of that idea evolved into the modern values of deep learning which is basically connected to the terms of Machine learning.

Deep learning is giving machines the ability to learn itself without getting programmed again and again. Which simply demonstrates that it can predict and carry out the task exactly as we need by observing data and experiences and making decisions for our expectations.

Applications of artificial intelligence and deep learning in modern days

Artificial intelligence techniques empowered edge-cloud architecture for brain CT image analysis

1. A robust feature extraction method for brain CT image analysis.
2. Mapping of brain regions with clearer and smoother details.
3. Brain image feature extraction based on Edge and IoT devices.

Emotion recognition using speech and neural structured learning to facilitate edge intelligence

1. A novel emotion recognition is proposed based on robust features and machine learning from audio speech. 
2. Audio data is used as input to the system from which, Mel Frequency Cepstrum Coefficients (MFCC) are calculated as features.
3. The proposed approach can be applied in different practical applications such as understanding peoples’ emotions in their daily life and stress from the voice of the pilots or air traffic controllers in air traffic management systems.

Distributed gas concentration prediction with intelligent edge devices in coal mine

1. This work proposes to address the issue through a novel method for predicting gas concentrations by taking full advantage of multidimensional data in an intelligent edge system. 
2. To significantly reduce the time consumed during model training and facilitate real-time predictions.
3. It conducts extensive experiments by using actual industrial data collected from a company to demonstrate the superior performance of the proposed method.

Deep learning with nonlocal and local structure preserving stacked autoencoders for soft sensors in industrial processes.

1. Deep learning-based soft sensors have been widely used for quality prediction in modern industry.
2. A nonlocal and local structure preserving stacked autoencoder (NLSP-SAE) is proposed for soft sensors.
3. The application on an industrial hydrocracking process demonstrates that it can improve the prediction accuracy for quality variables.

And etc. are the numerous examples of how Artificial Intelligence and deep learning can sum up to add in the betterment of human society. But along with it AI also comes up with its negative points too. which is also a point or matter of discussion among the intellectuals but let’s just not hope that humanity one day develops its very own Skynet and ends up us all extinct.