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March 28, 2018

AI Reborn


During my academic days we had a dedicated subject for Artificial Intelligence (Al) in our Final year. Of what I could remember, it was all about Algorithms and a unique less known language called LISP (list Processing) for which we had labs back then. It was one of my favorite subject as it was futuristic and not normal science. But coming out of college I believed that Al was something that would continue to be a research topic rather being a practical implementation. Why? Because how would you come up with an algorithm that could mimic Intelligence? It is one thing to write a code for Chess that could analyze all the "n" possible moves and choose the one that has the smallest high probable path for victory but it's totally a different case to provide a generic Intelligence. Those were the days post IBM Deep Blue's victory stories against Human Grand Masters. So, called intelligence in domain like chess is quite possible as it has boundaries. There are limited rules and probable moves. A good set of algorithm and powerful processing power would give any grandmaster a run for his money. But in a more variable domain, things get more fiction than being realistic.

Fast forward seventeen years and Al seems to be more realistic and evolving towards realization. And I get a feeling YES, it's possible. So, what has changed? To answer this in two words -"Data Analytics".  Today's AI is driven by Data Analytics, Algorithms being developed are focused more on Data. Other than this most of the advancement in Today's Information Technology landscape is aiding Data Analytics.

The foundation of Today's AI has been data; algorithms under the field of Machine Learning and Data Mining are focused around data and harvesting patterns from it, these harvested patterns forms the building blocks of today's AI. Unlike yesteryears, today data is available in huge volume, the digital world around us has been spewing data all around, and this data in form of Big Data provide the mining field for patterns which usually remain unseen on surface until right analytics has been applied. With real time analytics, intelligence no longer need to be harvested from historical data but can be attained and recognized as it happens.

Transformation in technology landscape has aided the evolution of AI as well. Cloud computing is providing virtually unlimited storage and computing power required to run data analytics at scale. Another crucial element that is contributing to the success of AI today is "Connected System" through Web services and API's. AI yesteryears was conceived to be more of a monolithic, an AI system back then was supposed to house most of its intelligence capability, but today intelligence is distributed. Today if you want your system to recognize human speech than you need not build it from scratch but can leverage existing speech recognition services from Microsoft, Google or Amazon. Similarly if you need an image recognition capability than you can look forward for Vision API's again from players like Microsoft, Google and Amazon.

Just like distributed architecture the evolvement of AI has also been distributed and community driven. Popular statistical computing language "R" widely used for Data Analytics is open source. What makes "R" so popular for data analysis is its vast range of packages developed and contributed by independent developers solving certain problem domains and making it available for reuse this collective effort has been contributing towards the advancement of Data Analysis and AI.   

These are some of the factors providing the right ecosystems that is making AI to thrive and become a reality today. As hinted above no one entity is building the whole AI ecosystem but it's been evolving gradually in bits and pieces. Bunch of these services are baked locally to provide certain AI implementation like "Self Driving Car". Gradually such focused implementation can be augmented with one another to give rise to more intelligent system with broader capability that could someday rival humans. And probably one day, tables might turn around and machines might be working on making humans more intelligent and efficient so that we humans can serve their purpose, Scary uh!

March 10, 2016

New Generation of Data Analytics and New Generation of Opportunities


As someone once said "Intelligence is nothing but amount of meaningful information harvested from the data available".  So it's the data and the skill/talent/pattern to extract the meaningful information that defines the intelligence, knowledge and statistical predictions.

Traditionally analytics were driven by deriving trends and patterns from the historically accumulated data, these data normally included conventional data that were logged or recorded as they occurred and were done for this specific purpose. With the advancement of technology and its penetration into different turf things have changed, today application of technology in different space is in a way digitizing the offerings where the actual primary outcome of the technology inadvertently serves as a data for breakthrough analytics that could be derived by connecting different dots. The dots that are meaningless independently but when connected gives you the "Big Picture". Here in this blog I will cite some example where information is extracted from an unconventional and innovative way that otherwise were not obvious.

There is a saying that "A picture is worth a thousand words" and for Data Analytics what this means is that every picture has a treasure of information waiting to be harvested. Traditionally it was difficult to deal with images and extract meaningful information out of it as you would have done with character data's but the advancement of technology is breaking this barrier. Google Goggles was among one of the earlier efforts that ventured into this field of unlocking this data that hid within images and today their latest mobile app Google Photos leverages this capability of searching image for their contents, through Image processing and geotagging information.  This pattern of extracting information from image has today paved way to cutting edge analytics where Satellite images of parking lots of big retailers like Walmart, Home Depot and others are used to predict their quarterly earnings thus giving research analyst actual raw data that could be trusted. Similar pattern on a bigger scale is helping predict global economy where satellite images of Oil Storage, Movement of Trucks in Mines, Agriculture fields and Night lights are reflecting the current status of world economy as they shape, this all has been possible by having real times images transformed into precious data.

Showing real time traffic has been one of the common features of GPS devices and apps used today, initially this information were sourced through traffic sensors placed by government authorities and other concerned departments as a result only few roads had this privileges for their traffic data to be analyzed, all this changed with company like Google going the crowdsourcing way, with crowdsourcing google was able to improve the reach and accuracy of its traffic information and predictions, when users use google maps for navigation on their phone, the phone sends back the data anonymously which help the company determine how fast the cars are moving in any specific roads, with acquisition of Waze Google was able to add human touch to this algorithm as drivers could provide real-time feedback of their driving experience on specific routes. So does this stop here? Nope, with more and more users using google maps for navigation, Google has access to vast amount data pertaining to end users driving habits - How much the user drives, where does he drive, what time he drives, the speed, the critical points the user navigates and much more, with this immense amount of data company like Google is well positioned to provide Auto insurance based on unique data analytics that could rival traditional actuarial methodology.

To end this post I would like to cite a personal example and opportunity that I have been lucky to come across. Being tech freak, last year, I bought a car adapter called Automatic it comes with a companion mobile app and helps your car to be connected, it tracks your driving behavior, miles driven fuel consumed and routes taken, basically a handy device if you are interested in analyzing your driving habits at leisure. As the new year 2016 arrived, I got a report from Automatic that had some key aspects based on my driving habits for the gone year like, average fuel consumed, States travelled e.t.c, but one of the fact reported was interesting, the report mentioned that I left my home to work 16 minutes earlier than an average Automatic user did and I arrived home 44 minutes later than the average Automatic user did (huh.. me working hard J), Automatic had enough data and pattern to determine my office commute, now when I think about this, there is a huge potential of connecting the other dots and harvesting some incredible facts and opportunities. For example based on my parking location my place or organization where I work could be determined or I could be given an incentive to let reveal where I work and in turn would get insights to how my fellow workers (of course anonymous details) fared with their working hours. This collective data can give out quite some interesting facts about the work place culture like, average hours an employee works,  average data of employee's commuting hours and timings in a day can determine how flexible the organization working hours were, average regular data of employee commuting to work place can determines organizations policies and support for Work From Home, thus the data which are logged for certain aspect can be traversed to different context and augmented with other details to gain wealth of information, which in turn can turned into tremendous opportunities. Imagine company like Glassdoor taping into such data by forming an alliance, it could be a goldmine of actual raw data to augment their current analytics.

This is just some of the examples how today data no longer come from traditional intended sources but are harvested from wide ranges of technology offerings and then augmented and enriched with other factors to give some incredible depths and insights. Today with analytics, anymore it's not the sky but the Imagination that's your limit, hence it's time to get Datatized!!

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