How You Can Enable Modern Enterprise Data Science with Armen Kerlopian


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Armen Kerhlopain serves today as the chief science officer for Genpact, which is a New York stock exchange listed with over 90,000 employees globally. They are fortune companies that focus on gaining value from data, data science, analytics, machine learning, AI, and digital transformation.

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Here’s the timestamps for the episode: 

(00:00) – Introduction

(02:13) – Hackathon for AI and Social good, everything from using computer vision, teaching computers to see, to assess urban greenery, the project being to help understand the experience of citizens to that of making governments more transparent using natural language processing, teaching computers to read

(04:01) – Sustainability around accessibility. Whether it’s information around languages

(05:26) – It’s increasingly clear that the business model is around compute and storage,  and what that leaves us is a gap in domain specific applications

(07:19) – Revenue pulled through the AI led capabilities and how much storage and compute is used to support that

(10:45) – The highest performance systems are actually a combination of the two, whether the human is a fail safe or there is a specific instruction from the human or the more rote aspects of the operation 

(15:11) – When we move from level zero, which is no automation, to level one, which is driver assistance, that’s a lot of where we’re at today. So the human is still in control, but there are certain automation capabilities. And tying it to different industry areas like in this to a kind of alerts 

(17:38) – We have instances at this level of autonomy where a little bit of extra data goes a long way for the user experience

(20:27) – From supply chain to areas in finance and accounting, like order to cash, these very disciplined process-focused areas that are in some ways the backbones of industry that really do matter for getting value from data and a focus on customer experience

(28:02) – The preciousness of human time will be the ones that succeed, and organizations that view time as a commodity are missing actually the most formidable mark of the AI revolution

(34:01) –  Humans plus algorithms working in creative ways with data, as opposed to just call it automation level

(39:47) –  Bringing even a little bit of unstructured data is non-trivial, like text or images, pulling out structured information, useful information from that  unstructured data