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Interview With Saket Kumar, Chief Data Scientist At Google

'The most important applications will likely be analyzing consumer behavior'

12May

The race to lead the way in AI is hotting up. Amazon, Microsoft, Google, and IBM are among those to have invested heavily in research of the technology, with applications ranging from driverless cars to improved cancer treatment.

Arguably leading the way is Google, whose main focus has been on acquiring innovative startups in the field and bringing them under their umbrella. Sundar Pichai, Google’s chief executive officer, recently said that the company was ‘really transitioning to becoming an AI-first company.’ Perhaps its most eye catching demonstration was the victory last year of Google Deepmind’s AlphaGo over Go star Lee Sedol, but even more exciting things are happening behind the scenes.

Dr. Saket Kumar is Chief Data Scientist at Google. He has more than 15 years experience as an innovative analytics practitioner and thought leader, with a focus on translating data into insights for decision makers. He has led successful analytics assignments in multiple industries, including advertising, oil & gas, healthcare, and manufacturing. At Google, he leads a team of data scientists focused on improving marketing effectiveness for top tier clients. 

We sat down with him ahead of his presentation at the Machine Learning Innovation Summit, taking place this June 5-6 at the Marriott Union Square in San Francisco.

Where do you think machine learning’s most important applications will be in the near future?

This is a hard question. We see tons of business and consumer activities being digitized. The amount of data that gets digitized continues to grow. Machine learning is great for situations where there are large data sets and cases to learn from. Examples of this include Image identification, voice transcription, translation etc. The most important applications will likely be analyzing consumer behavior as companies like Google, Facebook, Amazon, and others have tons of such data and have developed large knowledge base that they can leverage to build ML solutions.

In a recent KDnuggets poll, 51% of respondents said that they expect most expert-level Predictive Analytics/Data Science tasks currently done by human Data Scientists to be automated to happen within the next decade. Do you think the data scientist’s role is really under threat? What kind of impact do you think it will have on the job market in general, and are governments prepared?

There is going to be automation and improvement in the tools that help with predictive analytics. However, I do not see any threat to the work done by human data scientists. A lot of drudgery of processing and cleaning data will hopefully go away. The base analysis and modeling is likely going to be commoditized. Despite this, there will still be a role for people who know data, algorithms, deep domain knowledge, and can effectively communicate math based insights to business leaders.

Are there any new technologies or ideas in the machine learning space that you find particularly exciting or believe will be especially important in the next few years?

I am excited about the intersection of video/multi-media consumption and analytics. Image and video recognition is still work in progress. There is a lot of exciting stuff that can be done with respect to what ML sees in videos and actual consumption/interaction response of the consumers.

What challenges do you foresee holding back machine learning from achieving its potential? How do you think these could be overcome?

There are a lot of positive trends (computing and storage costs going down). There are still data silos with and across organizations. One obvious one is the lack of qualified data scientists. Most companies - with the exception of large silicon valley companies - struggle to get right talent as the pool to draw from is not large.

You can hear more from Saket, along with other leading experts in the field from the likes of Etsy, Amazon, and Facebook, at the Machine Learning Innovation Summit. View the full agenda here.

BONUS CONTENT: Sayantan Mukhopadhyay, Head of Sales Analytics at Twitter, discusses how he built an analytics team at the social media giant

 

Sources

Image credit: Benny Marty/Shutterstock.com

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