AI and Advanced Computing in Corporate Analytics, Training and Education

Brendan McGinty, Director of Industry, National Center for Supercomputing Applications (NCSA), University of Illinois, Urbana-Champaign and Dr. Eliu Huerta, Director, Center for Artificial Intelligence Innovation, National Center for Supercomputing Applications and Dr. Volodymyr Kindratenko, Senior Research Scientist, Center for Artificial Intelligence Innovation, National Center for Supercomputing Applications

Brendan McGinty, Director of Industry, National Center for Supercomputing Applications (NCSA), University of Illinois, Urbana-Champaign and Dr. Eliu Huerta, Director, Center for Artificial Intelligence Innovation, National Center for Supercomputing Applications and Dr. Volodymyr Kindratenko, Senior Research Scientist, Center for Artificial Intelligence Innovation, National Center for Supercomputing Applications

Artificial Intelligence (AI) is not new. It has been around for decades. What has caused the revolution in AI has been the data deluge. Data is everywhere. Companies now need more powerful machines to process and analyze all the data they are collecting and trying to utilize. As a result, AI is everywhere. People want to know what they can do with their ever-expanding data – what it can tell them, what intelligence they can derive from it, and how it can be utilized to improve the world.

In the corporate world, it is nearly impossible to find a sector that is not supremely impacted by data and, now, AI. To stay ahead in this constantly changing and ever-competitive environment, companies are now investing in AI talent, training that talent, and doing more with the data they are collecting to better serve their audience and remain competitive.

In healthcare and drug discovery, the human genome can now be analyzed, greatly reducing time to prognoses and treatment, improving care to the bedside, even improving wellness. Providers – doctors, nurses, PAs, etc. – are trained on leveraging data and analytics tools with built-in machine and deep learning – to translate the mass of historical and present patient data into treatment plans.

Agriculture now collects incredible volumes of data, from drones flying over fields so that precision applications can treat crops in targeted ways to the genomics of corn, which is much more extensive than the genomics of the human body. Farmers now operate combines that are supercomputers unto themselves, so those that are helping to feed us are being trained in these technological advancements. This is important because it won’t be long until we have to feed nine billion people on our planet, and the amount of farmland available is not growing.

Oil and gas companies are evolving into energy companies. Data and AI are playing a big part in helping that evolution happen through analyses from above the sky to under the earth, from maximizing the energy benefits of the sun to responsible and minimally invasive methods of oil discovery. Traditional education for atmospheric scientists, geologists, and geographers now entails massive data collection requiring modern training and tools to analyze and solve grand challenges while being good stewards of our planet.

To address and solve these grand challenges, having talent in the areas of applied AI, advanced computing and scientific visualization is now imperative. Understanding how to leverage big machines, including virtual machines known best in cloud computing, is also important. Many companies are increasingly aware of talented data scientists still in high school or begin to engage interns on real-world, applied challenges from freshman through post docs. Colleges now emphasize and promote interdisciplinary teaching and training, showcasing the transformative impact of the big data revolution across established fields of knowledge, from law and ethics to engineering. Companies, meanwhile, are providing continuing education to employees in these same disciplines. Computer science and statistics is now combined with economics, business, crop science, chemistry, and more to be able to analyze and deduce conclusions from massive data.

It is a fascinating time to live in, as data really is everywhere. Ethical and legal issues and ramifications aside, the training and education that goes into the developing the expertise necessary to extract meaningful solutions from data continues to evolve rapidly. Think about what it all may look like five or ten years from now. As AI pioneer, Prof. Andrew Ng from Stanford, has said, “AI is the new electricity.” It is a rare revolution indeed that has transformed every aspect of human interaction, science, engineering, tech, industry and finance. It is in our hands to ensure that this 4th revolution promotes prosperity. The future is in our hands.

 

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