are modular. We actually put a data center at the bottom of the ocean to see how that would work. We try a lot of different things from an innovation perspective just to see what works. How energy-efficient can we get our data centers by the standard of power usage effectiveness (PUE)? We met our goal globally of 1.2 PUE, where 1 is the ideal power usage effectiveness. I won’t say it’s an industry standard, but it is a way that most data center providers look at how efficiently they’re running a data center from a power perspective. Moret: What are some fields you see as key pipelines to work for a company like Microsoft in the cloud space? Adams: There’s a wide variety of roles inside Microsoft for people with all kinds of backgrounds. If we look at where the IT industry is going with cloud computing, most people are very comfortable now with the concept. The next question is how do we take the fact that it’s pennies on the dollar to host limitless data in a cloud and apply artificial intelligence and machine learning to that data to democratize AI? The cloud is what’s enabling this innovation too. The opportunities there are limitless. Imagine if a data scientist could simply go into a tool and use dropdown menus to choose machine learning algorithms that have already been tested by other data scientists and not just engineers essentially democratizing data science. You could even give this capability to citizen scientists or employees who don’t have a data science background. I tell my nieces and nephews, if you want to get a great job in computers these days, go into cybersecurity or data science. We don’t have the human capital we need as a country to be able to excel here. We’re playing around with some really cool technology right now, using AI for
translation services, and we have a project called InnerEye. It’s a research project that employs AI and machine learning to build innovative tools for automatic, quantitative analysis of three-dimensional radiological images to figure out where a cancer tumor is and delineate where the organs are relative to the tumor. You have an X-ray, MRI, or CT scan, and a human radiologist looks at that. What if you could use artificial intelligence through InnerEye to assist the doctor and augment their capability to help them deliver the radiation to the exact target, sparing organs from needless radiation. From a cybersecurity perspective, you start to look at how a combination of AI and Big Data science can augment a cybersecurity professional’s daily activities. The big thing here is being able to look at all the telemetry that’s coming from all the systems. Not just the network, not just NetFlow data, but all telemetry from every device. Your watch, phone, refrigerator — everything is a sensor these days. You’ll be able to start seeing anomalies and trends you normally wouldn’t. I think the next big trend will be an uptick in these capabilities to help us better secure our data and use that technology for good to augment human capacity. And then there is quantum computing. I’m not sure exactly how far we are as an industry from that. We’re working on quantum computing ourselves at Microsoft. But just imagine, computers today rely on 1s and 0s. Each bit has to be a 1 or a 0. With qubits, you can have both states at once. What this means is you can do calculations in minutes or hours that would today take a lifetime to compute. The innovation we are seeing today has all been enabled by the cloud and hyperscale computing. Moret: It seems as if computer science made the biggest single talent pipeline into hardcore tech jobs. But data science is definitely on its heels. It feels like absolute demand compared to computer science is not as large, but it’s growing much faster, perhaps. Does that resonate?
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