#theCUBE #Exascale #HPE AI can be a game changer for your business. But it can also be quicksand if you go into it without a clear understanding of your goal and strategy. How do you transform the potential of AI into something practical for your organization? A discussion on how to identify impactful AI projects, sidestep pitfalls, prototype and productionize a solution and extract business value from AI. Q&A: How to train your data (at exascale speed) BY BETSY AMY-VOGT https://siliconangle.com/2020/10/16/qa-how-to-train-your-data-at-exascale-speed-exascaleday/ Having data and having insights are two very different things. To transform data into information that can actually help drive better decisions and scientific breakthroughs is a proactive task. And a daunting one. So what are the steps data scientists recommend to turn that stagnant data lake into a sparkling flow of insights? “Step back from the data questions; the infrastructure questions; all of these technical questions that can seem very challenging to navigate,” said Arti Garg (pictured), head of AI solutions and technologies at Hewlett Packard Enterprise. “And first ask: What problems am I trying to solve? It’s really no different than any other type of decision you might make in an organization.” Garg spoke with Jeff Frick, host of theCUBE, SiliconANGLE Media’s livestreaming studio, during Exascale Day 2020. They discussed the challenges facing modern data scientists, the moral implications of giving artificial intelligence autonomous control, and how exascale computing will change the field of data science. (* Disclosure below.) [Editor’s note: The following content has been condensed for clarity.] You bring up such a good point: It’s all about asking the right questions. You’ve got to shape the data to the question, and then you’ve got to start to build the algorithm to answer that question. How should people think when they’re actually building algorithms and training algorithms? Garg: I like to think about AI solutions as they get deployed being part of a workflow. And the workflow has multiple stages associated with it. The first stage being generating your data. Then starting to prepare and explore your data. Then building models for your data. But sometimes what we don’t always think about are the next two phases. First is deploying whatever model or AI solution you’ve developed. What will that really take? Is it going to live in a secure and compliant ecosystem? Or as we’re seeing more applications on the edge, is it actually going to live in an outdoor ecosystem? Then, finally, who’s going to use it and how are they going to drive value from it? Because it could be that your AI solution doesn’t work because you don’t have the right dashboard that highlights and visualizes the data for the decision-maker who will benefit from it. I think it’s important to sort of think through all of these stages upfront. Think through what some of the biggest challenges you might encounter are so that you’re prepared when you meet them, and you can refine and iterate along the way and even upfront tweak the question you’re asking. October 18, 2020 is Exascale Day, celebrating high-performance computing making the leap from petascale trillions (1015) to exascale quintillions (1018) in floating-point operations per second. Can you share your thoughts on being a data scientist and suddenly having all this massive compute power at your disposal? Garg: Only time will tell exactly all of the things that we’ll be able to unlock from these new massive computing capabilities that we’re going to have. But a couple of things that I’m very excited about are that in addition to these very large investments in large supercomputers, exascale supercomputers, we’re also seeing investment in the other types of scientific instruments driving pharmaceutical drug discovery. I’m talking about what they call light sources which shoot X-rays at molecules and allow you to really understand their structure. Historically, you would go take your molecule to one of these light sources and you shoot your X-rays at it and you would generate just masses and masses of data — terabytes of data with each shot. Understanding what you were looking at was a long process of getting computing time and analyzing the data. [With exascale computing,] we’re on the precipice of being able to do that, if not in real time much closer to real time...... for more, click the article link above Watch the complete video interview below, and be sure to check out more of SiliconANGLE’s and theCUBE’s coverage of Exascale Day 2020. (* Disclosure: TheCUBE is a paid media partner for Exascale Day 2020. Neither Hewlett Packard Enterprise, the sponsor for theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)