“90% of the world’s supercomputers run on Intel technology,” Diane Bryant tells me at SxSW. “And 95% of artificial intelligence solutions run on Intel Xeon and Xeon Phi processors.”

Bryant is an Intel veteran who joined the semiconductor giant right after getting an electrical engineering degree from U.C. Davis. Starting a a microprocessor design engineer, she quickly worked her way up the ranks, spending 4 years as Intel’s CIO before moving on to lead their Data Center Group.

With recent acquisitions of Nervana and Mobileye, Intel is building a solid position in the A.I. wars. Every major tech company in Silicon Valley, along with every automotive giant from Detroit, is battling to gain ground in self-learning and self-driving technologies. Artificial intelligence is now table stakes for technology enterprises.

“Intel is the only company with an end-to-end tech solution,” Bryant explains, citing the key advantage of Intel for A.I. “We power your consumer devices, from your phones and laptops to semi-automated vehicles. We also power the networks and data centers.”

GPUs used to be popular mainly with gamers chasing visual performance. Images are represented as matrices inside computers. Turns out the deep neural nets that power our current A.I. craze also heavily depend on matrix operations, making GPUs suddenly very useful for A.I. research. Nvidias stock price shot up 224% in 2016, making CEO Jen-Hsun Huang even richer.

Intel’s Xeon Phi competes directly with Nvidia’s GPUs. When asked how they are different, Bryant explains the difference between a general purpose GPU, or GPGPU, and specialized computing technology: “Nervana technology is designed specifically for neural networks. There’s no overhead of pixel processing like for GPGPUs, so there’s a 2-4x improvement with specialized computing.”

Consistent architecture and specialized hardware make life simpler for developers building A.I. applications. On stage at SxSW, Bryant invited several entrepreneurs and executives building on top of Intel technology, including Jason Mars of Clinc and Lucas Candela of FarmLogs, to demo their technology.

 

Jason May Clinc Intel AI

Jason May demos financial chatbot Finie made by his company Clinc. Image Credit: Intel Corporation.

 

Clinc built a powerful digital assistant named Finie to allow you to converse with your bank account. If you’re the type who hates accounting, you can simply ask Finie questions like “How much did I spend on my Vegas trip last month?” and she’ll intelligently parse your request and get an answer to you right away. FarmLogs applies A.I. to help farmers automatically track farm activity, analyze crop health, and answer questions about optimal crop rotations.

Bryant also highlighted several Intel AI projects advancing innovation in sports, healthcare, and humanitarian causes. An athlete seeking performance analytics normally needs to incur an expensive lab visit, where sensors are placed on his or her body for tracking. Using AI-assisted motion capture technology, athletes and their coaches can get real-time feedback on the spot with mobile devices.

 

Diane Bryant tests the AI-Assisted Motion Capture Technology made by the Intel Sports Group.

 

“Half of men and one third of women will be diagnosed with cancer in their lifetimes,” cautions Bryant. “The average accuracy for human radiologists is 75%. For machines, it’s 85%. Intel healthcare technology is already commercially deployed in China and has served over 5,000 patients.” Intel also has a partnership with Penn Medicine, which uses A.I. to mitigate hospital re-entry due to heart failure and correctly identify 20% more at-risk patients.

NCMEC is the National Center for Missing and Exploited Children. Many of their victims are sex trafficking victims who end up on pornography sites all over the internet. While NCMEC has a hotline for callers to report suspected child pornographic, they received over 10 million alerts in 2016. Each alert can take over 30 days to process due to the difficult of matching photos on porn sites to NCMEC’s huge database of missing children. With A.I., this month long process is reduced to a single day, since computers do visual matching much faster at scale than humans can and can shortlist the highest potential matches for staff review.

 

Matching outdated photos of missing children to possible photos of them found on the internet is a challenge A.I. helps solve. Image Credit: Intel Corporation.

 

“You need scale to achieve a meaningful A.I. result,” Bryant warns. While A.I. startups crop up every day, the enormous scale and infrastructure that tech titans like Intel, IBM, and Google have enable them to apply the requisite compute and storage capabities to tough problems in healthcare and social good.

Google’s DeepMind, originators of AlphaGo, the deep learning algorithm which defeated 17 time international Go champion Lee Sedol in 2016, also has a department dedicated to A.I. for healthcare research. To tackle the issue of legacy and paper-based systems in hospitals, DeepMind Health’s first product is a mobile app called Streams which promises to centralize essential patient information and serve timely alerts at the moment of care. Similarly IBM Watson is prioritizing healthcare with A.I. solutions for genomics, oncology, drug discovery, personalized care, patient engagement, and clinical trials.

While technology giants may be fighting a zero-sum game for A.I. dominance, humanity and society still benefit from their constant, rapid-pace investment in powerful new hardware and algorithms.