NVIDIA's meteoric growth in the datacenter, where its business is now generating some $1.6B annually, has been largely driven by the demand to train deep neural networks for Machine Learning (ML) and Artificial Intelligence (AI)--an area where the computational requirements are simply mindboggling. First, and perhaps most importantly, Huang announced new TensorRT3 software that optimizes trained neural networks for inference processing on NVIDIA GPUs. In addition to announcing the Chinese deployment wins, Huang provided some pretty compelling benchmarks to demonstrate the company's prowess in accelerating Machine Learning inference operations, in the datacenter and at the edge. In addition to the TensorRT3 deployments, Huang announced that the largest Chinese Cloud Service Providers, Alibaba, Baidu, and Tencent, are all offering the company's newest Tesla V100 GPUs to their customers for scientific and deep learning applications.
This was followed by the implementation of NVIDIA DGX-1 systems with NVIDIA Tesla P100 graphics processing units (GPUs) in SAP's production data center in St. Leon-Rot, Germany and in SAP's Innovation Labs in Palo Alto, California, and Singapore in September 2017. From the outset of SAP's machine learning efforts, NVIDIA's computing platform has promoted the company's training of data sets and algorithms – the core of intelligent machine learning applications in the SAP Leonardo Machine Learning portfolio. With SAP Leonardo Machine Learning, SAP brings digital intelligence to enterprise offerings and creates tremendous opportunities for customers to realize greater benefits through automated processes, targeted results-driven marketing, superior customer service, as well as increased agility and process efficiency. The partnership between SAP and NVIDIA to bring DGX-1 systems with Volta to production in the SAP Data Center will give SAP customers access to machine learning services and applications from SAP's own Data Center infrastructure.
The Pegasus line will be available by the middle of 2018 for automakers to begin developing vehicles and testing software algorithms needed to control future driverless cars, NVIDIA executives told a developers' conference in Munich on Tuesday. The deal between Deutsche Post, ZF and NVIDIA will include future Deutsche Post StreetScooter delivery trucks. In Munich, the three partners are showcasing a prototype StreetScooter running NVIDIA Drive PX chips used to control sensors including six cameras, one radar and one lidar, or 3D laser camera. De Ambroggi said NVIDIA's Pegasus automotive platform was the first with the processing power for automakers to begin developing truly autonomous vehicles, which could be upgraded with software improvements ahead of actual roadway deployments.
And Deep Instinct doesn't require frequent updates, said Eli David, chief technology officer at Deep Instinct. It trains the deep learning neural network on hundreds of millions of files. The software runs efficiently on the combination of central processing units (CPUs) and graphics processing units (GPUs) and Nvidia's CUDA software for running non-graphics software on graphics chips. Cape Analytics can collect aerial images that reveal a lot about a home, if they are properly analyzed, said Busy Cummings, vice president of sales.
Shares of graphics chip maker Nvidia are up 69% this year and have risen almost fivefold since the end of 2015. With Nvidia's share price up 1% to about $180 on Monday, Stein on Monday increased his price target on the stock to $200 from $181. Stein expects Nvidia's revenue from the server market, which more than doubled to $830 million last year, to increase at an average rate of 61% annually through 2020. While the new Volta V100 chip is two to six times faster than competing chips on some machine learning tasks, with the addition of the new TensorRT3 compiler for software, the set up is 40 to 140 times faster.
Automated inspection company Avitas Systems, which is a GE Venture company, is using Nvidia's DGX-1 and DGX Station to train its neural-network-based artificial intelligence to be able to quickly and consistently identify defects in industrial equipment. Alex Tepper, Avitas founder and head of corporate and business development, explained in an interview that GE has been helping customers with industrial inspections for a long time, and has found that these customers are spending hundreds of millions of dollars on inspections that involve a person driving out to, or flying a helicopter above an asset. Additionally, Avitas can provide reliable replication of observation conditions with automated inspection methods – robots can take the same photograph or sensor reading from the same perspective over and over again. "Avitas started with a prototype version of our station, and soon they'll be getting an upgrade to our DGX Station with Volta [launched in May], and that'll be a huge performance gain," explained Nvidia GM of DGX Systems Jim Hugh.
Technologies like artificial intelligence, machine learning, big data, Internet of things (IOT), and deep learning will come together to help realize Industry 4.0. Similarly, PTC plans this year to link its Creo computer-aided design system, to the company's ThingWorx IoT development platform. Fusion Connect Internet of Things software from Autodesk can help connect factory applications across a number of industrial machines and make sense of information returned from the connected machines. Introduced last summer, Autodesk's Design Graph is another machine learning system that helps users manage 3D content, offering Google search-like functionality for 3D models, says Mike Haley, who leads the machine intelligence group at Autodesk.
In my view, though impossible to quantify exactly, Amazon's AI investments and capabilities should sustain and increase its competitive advantage over time, leading me to believe that the stock has a much longer runway than what investors are giving it credit for. Looking at Microsoft's Q2 call (CYQ4), Nadella, MSFT's CEO, began by briefly summarizing earnings and then described FY17 as "a tremendous year of customer momentum with Cloud, AI, and digital transformation". In the press release, the only direct mention of machine learning and AI came in bullet number 20: "AWS customers continue to ramp their use of Amazon Machine Learning and Artificial Intelligence services…" Even sell side analysts do not fully appreciate how important AI is to Amazon. For example, in the Q2 earnings call last month, AI was only mentioned towards the end of the call and it came from a sell side analyst: "As you continue to scale operations and you bring data to bear and robotics and Kivas and AI machine learning, are you finding that kind of new fulfillment center optimization curve is accelerating?"
Element AI -- a Montreal-based platform and incubator that wants to be the go-to place for any and all companies (big or small) that are building or want to include AI solutions in their businesses, but lack the talent and other resources to get started -- is announcing a mammoth Series A round of $102 million. They include Fidelity Investments Canada, Korea's Hanwha, Intel Capital, Microsoft Ventures, National Bank of Canada, NVIDIA, Real Ventures, and "several of the world's largest sovereign wealth funds." But the basic model is not: Element AI is tackling this problem essentially by leaning on trends in outsourcing: systems integrators, business process outsourcers, and others have built multi-billion dollar businesses by providing consultancy or even fully taking the reins on projects that businesses do not consider their core competency. Element AI says that initial products that can be picked up there include predictive modeling, forecasting models for small data sets, conversational AI and natural language processing, image recognition and automatic tagging of attributes based on images, 'aggregation techniques' based on machine learning, reinforcement learning for physics-based motion control, compression of time-series data, statistical machine learning algorithms, voice recognition, recommendation systems, fluid simulation, consumer engagement optimization and computational advertising.
HPE and NVIDIA are delivering IT solutions with superhuman intelligence to harness the full power of AI and pioneer the next generation of HPC systems. Topping this list of solutions, the NVIDIA Volta is fueling some of the most powerful supercomputers in the U.S. By combining AI with traditional HPC applications on a single platform, Volta rapidly accelerates workloads for HPC, AI training, AI inference, and virtual desktops. Powered by Volta, the NVIDIA Tesla V100 pairs 5,120 CUDA cores and 640 NEW tensor cores to deliver 120 TeraFLOPS of Deep Learning, 7.5 TeraFLOPS of double precision performance, and 15 TeraFLOPS of single precision performance to turbocharge both HPC and AI, making it the most advanced data center GPU ever built. In addition to enhancing speed and accessibility, the NVIDIA TensorRT, a Deep Learning inference optimizer and runtime engine, enables 3.5X faster inference performance and delivers dramatic throughput gains, even at less than 7 milliseconds of latency required by real-time AI services.