Fifty years ago, when Intel was founded, the integrated circuits (microprocessors) we created forever changed how computers were made while boosting their processing power. We are now entering the age of artificial intelligence (AI), a time when machines are using algorithms that give them superhuman abilities and learning powers. The rise of AI is intertwined with the history of faster, more robust microprocessors—with ever-improving memory, storage and communications technologies—which make AI and machine learning possible. Here at Intel, we continue to research, work on technology solutions and deliver products that allow your companies to break through the barriers of deploying AI applications.
We understand it’s tricky to run day-to-day operations and strategize for the future in the age of AI, when change is exponential and seems unpredictable. Multiple studies from Forbes Insights over the years have shown that top executives are concerned about their ability to recognize the best uses of new technologies for their organizations, as well as how to best integrate them with their current infrastructures.
While we recognize that, as executives, you cannot dig deep into the weeds of new technologies, it is crucial to understand the possibilities and opportunities these technologies bring to the table—what they can and cannot do, how they can transform your business models, and the resources you’ll need to successfully introduce them.
That’s why we have joined up with Forbes Insights to create this first-ever series of AI publications. Over the next year, we’ll unveil new research and provide business leaders like you with the information you need to make the most of AI.
In this first issue, we focus to a large degree on explaining the main concepts that underlie artificial intelligence, and how these concepts are being translated into business applications. You will also hear from some industry leaders, including top scientists and executives from Facebook, Microsoft and Stitch Fix, about their vision for AI and their practical advice about how to best leverage these technologies.
We hope this yearlong exploration will help demystify artificial intelligence, debunk the myths that have been gathering around it and clarify the opportunities it can present to your organization. In other words, we see it as a pragmatic guide for business executives coupled with some scientific and historical background—for a full and actionable understanding of the possibilities of AI.
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About the Author
Naveen G. Rao is corporate vice president and general manager of the Artificial Intelligence Products Group at Intel Corporation. Rao’s team focuses on deep learning, a subset of machine learning and artificial intelligence, and works to develop the hardware and software ingredients needed to enable its scalable deployment. Intel uses deep learning to accelerate complex, data-intensive processes, such as image recognition and natural language processing, to improve the performance of Intel® Xeon® and Intel® Xeon Phi™ processors in various business segments, including autonomous driving and personalized medicine. Trained as both a computer architect and neuroscientist, Rao joined Intel in 2016 with the acquisition of Nervana Systems. As chief executive officer and co-founder of Nervana, he led the company to become a recognized leader in the deep learning field. Before founding Nervana in 2014, Rao was a neuromorphic machines researcher at Qualcomm Inc., where he focused on neural computation and learning in artificial systems. Rao’s earlier career included engineering roles at Kealia Inc., CALY Networks and Sun Microsystems Inc. Rao earned a bachelor’s degree in electrical engineering and computer science from Duke University, then spent a decade as a computer architect before going on to earn a Ph.D. in computational neuroscience from Brown University. He has published multiple papers in the area of neural computation in biological systems. Rao has also been granted patents in video compression techniques, with additional patents pending in deep learning hardware and low-precision techniques and in neuromorphic computation.
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