Showing posts with label deep learning. Show all posts
Showing posts with label deep learning. Show all posts

Monday, June 7, 2021

Why is Artificial Intelligence a big fab hype in the recent IT and computing technology world - a collective article on current standpoint on AI

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Why is Artificial Intelligence  a big fab hype in the IT and technology world

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* The most comprehensible perception about artificial Intelligence in the current technological biome is a distopian future coupled with human-like looking and performing robots and realistic looking holograms that throw a completely realistic idea of a certain picture or scenario in which non-human like androids and droids would be acting and talking like real people and having human-  level or even superhuman intelligence and capablities for performing a task which would be much complex and strenuous to humans . This concept is still beyond the aegis of reality as of now and this term is called as "Artificial General Intelligence(AGI)" and this does not exist anywhere on earth till now as depicted by some high-level CGI based movies in their content .

 

* But what is actually existing as of now is that of a fractional level implementation of Deep Learning which is shortly known as DL which can do some small level specific tasks which are better than people doing the computing task .. one   needs to keep in mind over here is that it is only restricted to computing activity and factory work . Still is believed that there are multiple fundamental level limitations that does not properly get developed into AGI - Artificial General Intelligence (AGI) . So the next level of algorithmic data science and machine learning development is that of innovating the present technology to come up with better networks and better methods for shaping into an artificial Intelligence product .

 

* So inorder to get a bird's eye view of where the development stands at , one might need to look at the following persisting scenarios :

 1)     Where Deep Learning and Reinforcement Learning are standing today


 2)    What are the limitations of Deep Learning and Reinforcement Learning algorithms and  where does the current trend stand at ?


 3)    Underlying principles and applications of Human Neuroscience and human intelligence


4)     A possible architecture to achieve artificial general Intelligence

 

Last modified: 14:46

Machine Learning Algorithms used in solving some of the popular real world problems

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Machine Learning Algorithms used in solving some of the popular

real world problems

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 *  The majority of all the real world problems in machine learning are classification and regression problems which are to be performed over structured data ( that means it doesn't take into account much of unstructured data into the account )  


*  In the real world machine learning environment , deep learning also plays a very shallow and limited role in the field of machine learning and therefore it is considered as one of the smallest factors that go into building of applications over machine learning


 *  One of the the mostly used algorithm to augment the model performance is XGBoost algorithm .This was created by developer and machine learning engineer Tianqi Chen who changed the face of applied machine learning which as already mentioned is the best case use of machine learning algorithms and the way augmentation of built model performances is done which come under the category of gradient boosting algorithms which also happens to work over and on top of structured data .

  

*  Therefore , gradient boosters like XGBoost are considered as the gold standard for modelling over structured data problems used in data competitions .Again it needs to be re-iterated and kept in memory eternally that almost all of the data problems use XGBoost and other gradient boosters for working over any data problem .

 

 *  It is also believed that the top cloud based companies like Google , Microsoft  and Amazon also use gradient boosters over their cloud paltform for analysis into structured data problems

 *  Google's Cloud based Machine Learning Platform is called as AutoML . In this platform and application , the AutoML tables use gradient boosters and deep learning models for hyperparameter tuning


Last modified: 14:20