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Event Diary - GE TechNEXT 2017: Delivering a Digital Transformation

Posted on December 13, 2017
By Avinash Patchava
and Rajesh Pantina, Marketing Lead, Performance SolutionsMarketing Lead, Performance Solutions

InMobi regularly hosts, participates in and contributes to a series of events, meetups, webinars, sharing best practices with partners and thought leaders, across the globe. In this edition of the InMobi Event Diary, we are looking at the highlights and developments from “TechNEXT,” an event organized by GE research at their offices in Bangalore, India.



In this edition of the InMobi event diary, we cover our presence at the GE Research hosted event “TechNEXT.” TechNEXT is a forum at GE that familiarizes a deep technologist audience with what’s next and what’s cool in technology by bringing them face to face with industry leaders and their insights. InMobi was among the few invited to participate in GE’s inaugural TechNEXT event. The panel aimed to unravel the process of building a digital DNA through the age of digital-industrial transformation by leveraging Artificial Intelligence & data insights in particular.

Delivering a Digital transformationAvi Patchava, VP, Machine Learning and Artificial Intelligence, at InMobi was part of the panel discussion that was moderated by Vinay Jammu, Technology Leader for Physical-Digital Analytics at GE Global research, and featured Dr. Shankar Venkatagiri - Associate Professor at Indian Institute of Management Bangalore - and Sameer Dhanrajani - Chief Strategy Officer at Fractal Analytics.

Digital transformation - Setting Up for Success

The panel noted that a digital transformation is a narrowly focused initiative around one or two technologies that can deliver the most value for the business given its maturity, and not an ‘all over the place’ measure. Given there are a large set of disruptive digital technologies such as IoT, Blockchain, Cloud technology, 3D printing and Artificial Intelligence, a digital transformation can mean many things. Speaking on leveraging Artificial Intelligence (AI), Avi emphasized the need to stay laser-focused on the ‘use case.’ A use case is the sole unit of measuring the impact of AI transformation. Leaders need to have clarity on what the specific near-term use cases are, and how they will be delivered. Citing InMobi’s Machine Learning Accelerator programs to bolster platforms such as algorithm deployment, data strategy, talent acquisition and talent development, Avi highlighted the need for strengthening enablers to become an A.I.-led company.

‘De-jargoning’ Artificial Intelligence

Avi shared the formulaic four questions that can help evaluate any opportunity or validate a success story, and help distinguish between what’s real and what’s hype. There is a lot of jargon and buzzwords used in the field of Artificial Intelligence, and one needs to break through it. One should always ask:

  1. What is the specific use case that A.I. is being used for?
  2. What is edge or advantage provided by the data being used in delivering the use case?
  3. Is there clarity on what algorithmic techniques will be used and why are they suitable?
  4. And, does the business have clarity on the derived value for a targeted use case? While, for a completed use-case, has the exact financial impact been measured?

The Role of Humans in an AI Dominated World

For an audience largely from an engineering background, Vinay wanted the panelists’ to opine upon the growth prospects for individuals and the relevant roles. Avi explained that in a world driven by Machine Learning and A.I., the role of the model-builder becomes critical in addition to several supporting roles that help model-builders succeed. The supporting roles include: the Big-Data engineer who sets up the data flows or plumbing to ensure big data sets are available and accessible when a model-builder needs them; the Production engineer who writes and sets up the algorithm code for deployment in large scale environments; and the analytics-savvy Product Manager who applies the understanding of modelling techniques to ensure models are addressing relevant problems and can also effectively evaluate models.

Managing data privacy

“How will companies manage concerns of data privacy when data is so valuable and therefore seductive?” was one of the burning questions from the audience. Dr. Shankar introduced the audience to the role of the ‘Algorithm auditor’ and its growing relevance. An algorithm auditor is an external party that ensures algorithms across businesses are really doing what they claim to be doing and do not indulge inappropriate data usage. Avi, reinforcing Dr. Shankar’s view, talked about the need for companies to have legal teams that check on modelling activities and approve of the data-usage guidelines being followed. The panel also discussed how a world with greater personalization initiate a loop of clear benefits for users. For instance, in the field of personalized medicine, successful treatments for users leads to users becoming more and more comfortable with data-sharing, thereby driving tangible benefits through better personalization and so on.

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