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Last Week, I launched my website by the name "Alpha Intelligence" dedicated to the fields of Artificial Intelligence, Machine Learning & Deep Learning....
The website features a few of the AI based interactive demos and intelligent agents that I have programmed myself...Today I will attempt to delve a little deeper into 'behind the scenes' view of things and my logic and rationale for choosing those specific technology use cases for building my AI agents.....
The first interactive demo that recognises handwritten characters that are chosen by the visitor to the site is the fundamental building block of Artificial Intelligence and has been used as an international benchmark that is accepted globally for determining the quality of the AI algorithm based on the accuracy of its predictions. The program showcased by me on the Alpha Intelligence website has 94% accuracy in recognising the handwritten characters....I have also experimented with a more advanced version of this algorithm that gives close to 98.5% accuracy....
The large sample of handwritten digits for training and testing of these algorithms have been picked up from real life based on the handwriting samples of a wide pool of people during the process of collecting data for the United States Census. This sample is referred to as the MNIST dataset and acts as a global bench mark in the field of AI....
Though this is a simple example of AI in action, it is not difficult to see the benefits that can accrue to us by this technology...The first successful real life use case that has been in vogue is the automatic reading and processing of cheques by some of the large American and European Banks....The accuracy is usually close to 100% as people are more careful in making sure that they write more legibly on bank cheques compared to a mundane task such as filling up data in a Federal Census questionnaire....
There are a number of other use cases such as automatic processing of handwritten application forms et al. The success rate is very high when it comes to areas where one expects a limited set of inputs and therefore the range of outputs to be given by the handwriting recognition algorithms are also naturally limited in scope...Needless to say processing structured data is far more easier than processing unstructured data...
The second demo that I have built and showcased on the Alpha Intelligence website is very pivotal to the field of AI as the concepts and algorithms used for the interactive demo are the brain behind more complex functionality such as robotic vision and other advanced image and object recognition tasks including recognition of objects in motion such as video footage....
I did receive feedback from a few of you that the quality of images used in the second demo on the website that automatically identifies and labels objects was not very good and that the resolution was low.... That was actually intentional as the benchmark data used here which is the CIFAR-10 dataset is meant to test the limits of the AI capabilities and the AI system has an objective ultimately of achieving a vision that is far better than the human vision. The intent being that robots and other AI systems should be able to recognise and label even blurred and low resolution or even distant images or partial images so that they can act as vision enhancing aids to human beings. Such robots can be used in military applications such as carrying out reconnaissance missions or even act as an intelligent assistant to visually impaired people thereby greatly enhancing the quality of their lives and giving them a huge boost of independence....
The program that I had developed for the demo on the website is only 85% accurate and there is some more distance to be travelled for enhancing the accuracy rates....The algorithm implemented here follows very similar concepts as those used by Google for automatically labelling images on Flickr. A few months ago there as a furore on the social media when the Google Image Recognition algorithm mistakenly classified an African-American young lady as a 'Gorilla'. Image recognition in its inception stages and we have quite some distance to travel in this space before we can put it to use in real life for mission critical applications....
And the final AI demo that I had programmed and showcased on my website was that of automatic translation from English to French.....This is infact the most complex of all the three demos that I had showcased on my website....
This is based on the concept of back to back Recurrent Neural Networks with LSTM and Sequence to Sequence modelling.
If we indicate the computational complexity of the first demo (character recognition) as X then the complexity of the second demo (automatic labelling of images) would be 10 X and that of the third demo (language translation) would be 100 X....
Language and other cognitive skills that come so naturally and easily to humans are on the other hand very very difficult for the computer systems to comprehend and decipher the code.... It was only in mid-2015 that the 'Google Translation Engine' and 'Microsoft Skype Translation Service' have been able to achieve near real time responses and near human level accuracy...
So what's next on the Alpha Intelligence website?
I am planning to work on adding additional functional capability to the 'Alpha Translation Engine' such as translation from 'English to German' and 'English to Spanish' in the coming days....
The other endeavour that I intend to undertake is the replication of the "Google Street View" advanced vision and image recognition algorithm that automatically scans the streets for house numbers and automatically recognises them and then stores them in character format in the "Google Street View" database....This was one of the biggest breakthroughs in the field of AI during the year 2015 and I am sure it will be real fun trying to take a shot at it and see where it will lead me to.....😊
Have a great Weekend !!!
#Musings #Tech #AI #Robotics
Last Week, I launched my website by the name "Alpha Intelligence" dedicated to the fields of Artificial Intelligence, Machine Learning & Deep Learning....
The website features a few of the AI based interactive demos and intelligent agents that I have programmed myself...Today I will attempt to delve a little deeper into 'behind the scenes' view of things and my logic and rationale for choosing those specific technology use cases for building my AI agents.....
The first interactive demo that recognises handwritten characters that are chosen by the visitor to the site is the fundamental building block of Artificial Intelligence and has been used as an international benchmark that is accepted globally for determining the quality of the AI algorithm based on the accuracy of its predictions. The program showcased by me on the Alpha Intelligence website has 94% accuracy in recognising the handwritten characters....I have also experimented with a more advanced version of this algorithm that gives close to 98.5% accuracy....
The large sample of handwritten digits for training and testing of these algorithms have been picked up from real life based on the handwriting samples of a wide pool of people during the process of collecting data for the United States Census. This sample is referred to as the MNIST dataset and acts as a global bench mark in the field of AI....
Though this is a simple example of AI in action, it is not difficult to see the benefits that can accrue to us by this technology...The first successful real life use case that has been in vogue is the automatic reading and processing of cheques by some of the large American and European Banks....The accuracy is usually close to 100% as people are more careful in making sure that they write more legibly on bank cheques compared to a mundane task such as filling up data in a Federal Census questionnaire....
There are a number of other use cases such as automatic processing of handwritten application forms et al. The success rate is very high when it comes to areas where one expects a limited set of inputs and therefore the range of outputs to be given by the handwriting recognition algorithms are also naturally limited in scope...Needless to say processing structured data is far more easier than processing unstructured data...
The second demo that I have built and showcased on the Alpha Intelligence website is very pivotal to the field of AI as the concepts and algorithms used for the interactive demo are the brain behind more complex functionality such as robotic vision and other advanced image and object recognition tasks including recognition of objects in motion such as video footage....
I did receive feedback from a few of you that the quality of images used in the second demo on the website that automatically identifies and labels objects was not very good and that the resolution was low.... That was actually intentional as the benchmark data used here which is the CIFAR-10 dataset is meant to test the limits of the AI capabilities and the AI system has an objective ultimately of achieving a vision that is far better than the human vision. The intent being that robots and other AI systems should be able to recognise and label even blurred and low resolution or even distant images or partial images so that they can act as vision enhancing aids to human beings. Such robots can be used in military applications such as carrying out reconnaissance missions or even act as an intelligent assistant to visually impaired people thereby greatly enhancing the quality of their lives and giving them a huge boost of independence....
The program that I had developed for the demo on the website is only 85% accurate and there is some more distance to be travelled for enhancing the accuracy rates....The algorithm implemented here follows very similar concepts as those used by Google for automatically labelling images on Flickr. A few months ago there as a furore on the social media when the Google Image Recognition algorithm mistakenly classified an African-American young lady as a 'Gorilla'. Image recognition in its inception stages and we have quite some distance to travel in this space before we can put it to use in real life for mission critical applications....
And the final AI demo that I had programmed and showcased on my website was that of automatic translation from English to French.....This is infact the most complex of all the three demos that I had showcased on my website....
This is based on the concept of back to back Recurrent Neural Networks with LSTM and Sequence to Sequence modelling.
If we indicate the computational complexity of the first demo (character recognition) as X then the complexity of the second demo (automatic labelling of images) would be 10 X and that of the third demo (language translation) would be 100 X....
Language and other cognitive skills that come so naturally and easily to humans are on the other hand very very difficult for the computer systems to comprehend and decipher the code.... It was only in mid-2015 that the 'Google Translation Engine' and 'Microsoft Skype Translation Service' have been able to achieve near real time responses and near human level accuracy...
So what's next on the Alpha Intelligence website?
I am planning to work on adding additional functional capability to the 'Alpha Translation Engine' such as translation from 'English to German' and 'English to Spanish' in the coming days....
The other endeavour that I intend to undertake is the replication of the "Google Street View" advanced vision and image recognition algorithm that automatically scans the streets for house numbers and automatically recognises them and then stores them in character format in the "Google Street View" database....This was one of the biggest breakthroughs in the field of AI during the year 2015 and I am sure it will be real fun trying to take a shot at it and see where it will lead me to.....😊
Have a great Weekend !!!
#Musings #Tech #AI #Robotics