Friday, December 15, 2017

Microsoft Summit: AI Fear of Missing Out “FOMO”

Ozge Yeloglu @OzgeYeloglu, Data and AI Lead of Microsoft Canada has a core team of Data architects and Data Scientists located in central Canada. They have combined architects and scientists so that privacy and compliance is part of the AI implementation. Ozge was the first Data Scientist hired in Canada by Microsoft. Prior to Microsoft, Ozge was co-founder of a startup that analyzed logs to predict application failures.

What is artificial intelligence? The definition is “Intelligence exhibited by machines mimicking functions associated with human minds”. The three main pillars of human functions are reasoning (learning from data), understanding (interpreting meaning from data) and interacting (interacting with people in a human way). We are still very far away from natural human interaction with AI.
The reason AI is such a hot topic is because of advancements in the foundational components: Big Data, Cloud Computing, Analytics and powerful query algorithms. These are more universally available than at any other time in history. 

Digital Transformation in AI can be looked at in four pillars: Enable your customers through customer analytics and measuring customer experiences. Enable your employees through business data differentiation and organizational knowledge. Optimize your operations using intelligent predictions and deep insights (IoT). The final pillar is to transform your products by making them more dynamic?

The four foundational components for an AI platform is infrastructure, IT service, Digital Services and Cognitive data. The reality is that based on Gartner's research is that of the discussions happening on AI only 6% are at the implementing stage. Largely the majority of discussions are about knowledge gathering.

Ozge is doing a lot of lunch and learns to help people understand what AI is all about. Often once understood they realize that they need the foundational pieces in place before being ready for AI.
It is important to start with a single business problem, build the machine learning tooling and demonstrate the value. As you work through the use case you are educating your people. Essentially this applies a building block approach. Ozge recommends starting near future because the tools and technologies are emerging so quickly. Starting with a three year plan almost guarantees that the tools you select today will be obsolete by the time the project finishes.

It is important to know your data estate. If your data is not the right data your solutions will not be the right solutions. If it your data is not in the right place, it will take to long to run. Building the right data architecture is an enabler for AI. Great AI needs great data. It is important to also find the right people. Many Data Scientists are generalists so they may not have the right Domain expertise for your particular business. For this reason it may be better to take existing people and train them on Big Data management.

A good AI Solution is built on a AI platform, with comprehensive data, that resolves a business problem surrounded by the right people.

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