| |April 201819CIOReviewAfter data, AI/ML is the most abused word in today's technology discussionsa. Everyone was connected on inter-net and was available for communi-cation / ideasb. Everyone was claiming to be in demand of something at that very momentPrevious analytical use cases were limited to predict consumer behav-ior needs in near future like credit scores, propensity to buy, propensity to leave, sales / demand forecasting etc. and none of the use cases were around real time context exchange or requirements. Information exchange with cloud computing gave birth to a new kind of analytics processing that is learn-ing and determining the answers/scores in near real time. Algorithms are trained with the following methods:a. Supervised learning ­ training data set have evidences of outcomeb. Unsupervised learning ­ outcomes are wide enough and can further become input datasetc. Start with supervised and later unsupervised ­ best of both approachesData is the soul for this entire ecosystem as algorithm can predict as best as the data inputs are, hence everything starts with data only and output is nothing else but again a data point.Below mentioned diagram depicts how AI/ML framework is used for different outcome use cases in industry on AWS cloud environment.Some of the key use cases came out from AI / ML evolutions which are changing our world around following areas: (each area is very big and can be discussed as separate discussion thread)- Customer Care & Marketing personalization- Financial Trading & Payments - Transportations & Optimizations- Healthcare and Medicines- Manufacturing and assembly linesNext Wave of Automation:With more and more data available for analysis and machines being more connected on faster networks; new use cases arrived which were not there before. This gave birth to new kind of ecosystem commonly known as IoT (internet of things). Connected devices, connected cars, connected homes hence connected minds.It is estimated that there will be more than 50bn connected devices by 2022 and humans may not be initiating each intent for need rather these devices will be taking actions/generating call for actions based on past behavior / interests shown by humans. Natural language processing (NLP), Voice enabled ecosystem (no more keyboard typing), censor data processing is taking consumer experience to next level; Alexa and SIRI will be known names in every household soon. Keeping those in minds, following three areas are the defining blocks of any ML/AI initiatives of future:· Contextual Consumer experience: Digital transactions require very high throughput and concurrency to operate whereas claimed TPS from ZIlliqa is around 2500 transactions per second only· Machine Assistance: Every technology benefit comes with its own setup and license costs; since there are not many success stories around no one really knows the tech-cost vs benefit study for client / agency documented yet· Less Human Errors: there are not many clients / agencies who can really claim of already implemented use cases on their premises using DLT hence a lot is only talked claims than implemented ones.Summary:After data, AI/ML is the most abused word in today's technology discussions; everyone talks about these areas but there are few who have really done it. To start AI / ML journey first key is to have "proper data strategy" and how this can be linked to overall business / consumer digital landscape.Second thing is about identifying right use case and how it's impacting internally / externally. Once these two are put together properly next thing is to identify right partner who can make this happen for you.
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