Question answering on unstructured data is considered a task, worthy of evaluating even a human learning a new language, and expected to be tough for AI systems to do so, with high precision. There has been a lot of progress in the field lately, the recent state of art algorithms have shown to be more accurate than human performance on some specific datasets like Squad.

While these algorithms are effective, it has been observed that most of these rely on superficial information like local context similarity, global term frequency etc to extract answer from the documents. …

Meet Neo ! Neo is a talented developer who loves building stuff.One fine morning , Neo decides to take up a road, less travelled, decides to build a chatbot !

After a couple of keyword searches and skimming through dozens of articles with titles “build a chatbot in 5 mins” , “chatbot from scratch” etc, Neo figures out the basic components to be intent detection, Named Entity Recognition ,Text Matching for QnA . Another 30 mins of Google search and Neo has collected his arsenal, the state of the art implementations for these 3 components. …

Ashish Kumar, VP AI Product

At NIPS 2016, there was an unprecedented story building up. Something that got every AI enthusiast agog about an unknown AI startup ‘Rocket AI’.

The names associated with the hot startup were pioneers in the AI field and it was informed to the media that there was major announcement soon to come. There was even a workshop held, where one of the researchers explained about the concept of Temporal Recurrent Optimal Learning to the house full of researchers and media personnel.

The whole community was abuzz with the jargons “Temporally Recurrent Optimal Learning”, “Jacobian Optimized…

We are indeed living in interesting times, where we celebrate human-built machines defeating the best human minds at variety of activities. IBM Deep Blue’s win against Chess champion Gary kasparov in 1997, IBM watson acing Jeopardy in 2011 and now Google DeepMind reportedly wining ‘Go’ with high precision, being cited as a major breakthrough in AI, which even Facebook claims their team came close to acing the game as well.
DeepMind goes against the ‘Go’ champion, to be streamed live for the world to witness.

While these feats are undoubtedly remarkable, and as understandable its creating quite a buzz in…

Earlier it was Random forest , the go-to algorithm for classification problems in most of the data science competitions. Correctly formulated problem , with smart feature engineering and minimal tuning of the RF algorithm ( ntree, mtry) using grid search could get you past the bulk of the crowd .

Then came Xgboost and it soon became the hot favorite. It isn’t very tough to say Deep learning is running the show at the moment. Although, GPU powered deep learning frameworks, weren’t accessible to everyone . The ones who could use it were reaping the benefits.

Then arrived H2o, bringing…

Researchers state that the neurological activities in the brain (nucleus accumbens, responds strongly to rewards) of a gift recipient about to open the gift box is just as high as the subject who is about to scratch the lottery coupon!

Within split-seconds of opening the box, the recipient would either be ecstatic, neutral or disappointed based on how they perceived the gift , to what extent his/her expectations were met , to what level their anticipation was rewarded irrespective of the monetary value or the utility of the product. …

It’s a known fact that bagging (an ensemble technique) works well on unstable algorithms like decision trees, artificial neural networks and not on stable algorithms like Naive Bayes. The well known ensemble algorithm Random forest thrives on the ability of bagging technique which leverages the ‘instability’ of decisions trees, to help build a better classifier.
Even though, random forest attempts to handle the issues caused by highly correlated trees, does it completely solve the issue? Can the decision trees be made more unstable than what random forest does, so that the learner be even more accurate?

Lets talk about random forest, subsequently, I will introduce the concept of “Randomized forest” : for the lack of a better name :)

1. Discards pruning: No…