Citi Ventures, Salesforce, Tencent Holdings, and NVIDIA GPU Ventures were frequently spotted in the deals we investigated, while VC firm Kleiner Perkins Caufield & Byers and private equity firm Warburg Pincus were common co-investors. The unicorn startup (valued at $1.2B) welcomed Roche Venture Fund -- the CVC arm of the healthcare company Roche -- into its ownership in its $175M Series C, which is the company's largest raise to date. At the heart of the platform is "Neuralytics" -- the company's big data, predictive analytics, and artificial intelligence engine -- which leverages sales interaction data from across the company's global network to make predictive and prescriptive recommendations (with the aim of shortening sales cycles and improving internal sales processes). In the startup's $100M Series C in Q3'15, IT firm Rackspace Hosting participated alongside CVC capitalG (formerly Google Capital) and follow-on investors Accel Partners and Warburg Pincus.
Kimberly Powell, who leads Nvidia's efforts in health care, says the company is working with medical researchers in a range of areas and will look to expand these efforts in coming years. Most notably, a machine-learning technique called deep learning is being applied to processing medical images and sifting through large amounts of medical data. Nvidia is, for example, working with Bradley Erickson, a neuro-radiologist at the Mayo Clinic, to apply deep learning to brain images. There are, however, significant challenges in applying techniques like deep learning to medicine.
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.
AI helped triple NVIDIA's data center revenue in the most recent quarter, with the company's CFO, Colette Kress, saying: "AI has quickly emerged as the single most powerful force in technology. If you have an Amazon (NASDAQ:AMZN) Echo speaker in your home, have ever used Alphabet's (NASDAQ:GOOG) (NASDAQ:GOOGL) Google Assistant, or talked to Apple's Siri, then you've interacted with artificial intelligence on some level already. Amazon's most lucrative business, its Amazon Web Services (AWS), now offers machine-learning services (part of the broader AI market) to improve natural-language processing, image analysis, and speech generation across apps and services that use AWS. The Motley Fool owns shares of and recommends Alphabet (A shares), Alphabet (C shares), Amazon, Apple, Facebook, and Nvidia.
Nvidia has benefitted from a rapid explosion of investment in machine learning from tech companies. Can this rapid growth in the use cases for machine learning continue? Recent research results from applying machine learning to diagnosis are impressive (see "An AI Ophthalmologist Shows How Machine Learning May Transform Medicine"). Your chips are already driving some cars: all Tesla vehicles now use Nvidia's Drive PX 2 computer to power the Autopilot feature that automates highway driving.
"We invented a computing model called GPU accelerated computing and we introduced it almost slightly over 10 years ago," Huang said, noting that while AI is only recently dominating tech news headlines, the company was working on the foundation long before that. Nvidia's tech now resides in many of the world's most powerful supercomputers, and the applications include fields that were once considered beyond the realm of modern computing capabilities. Now, Nvidia's graphics hardware occupies a more pivotal role, according to Huang – and the company's long list of high-profile partners, including Microsoft, Facebook and others, bears him out. GTC, in other words, has evolved into arguably the biggest developer event focused on artificial intelligence in the world.
Increasingly affordable AI maintenance and the increased speed of calculations thanks to GPU are significant factors in the unbridled growth of AI. The astonishing results that were achieved on training a neural network on GPU cards made Nvidia a key player, with 70 percent of the market share that Intel failed to gain. Compared with the results from the analog algorithms, and thanks to the combination of machine learning and big data, previously "unsolvable" problems are now being solved. Machine learning algorithms can directly analyze thousands of previous cases of different types of diseases and make their own conclusions as to what constitutes a sick individual versus a healthy individual, and consequently help diagnose dangerous conditions including cancer.
The notion of cars that drive themselves is one that becomes more and more real with each passing day. Acquisitions seem to be happening left and right, and almost every major auto manufacturer is devoting resources to bring us a self driving car. Companies like Google, Uber, and Tesla are all devoting significant investments to the self driving car with the universal target date of "2020" for commercialization being forecasted by nearly all of these players. Mobileye, about the only pure-play self driving car stock out there, recently announced a partnership with Delphi and a target date of 2019. While all eyes remain fixed on the big names in this game, there are some new entrants to this space that you may never heard of but that are getting closer and closer to making the self driving car a reality.
SANTA CLARA, CA--(Marketwired - Apr 17, 2017) - NVIDIA ( NASDAQ: NVDA) today announced that its deep learning platform is now available as part of Baidu Cloud's deep learning service, giving enterprise customers instant access to the world's most adopted AI tools. The new Baidu Cloud offers the latest GPU computing technology, including Pascal architecture-based NVIDIA Tesla P40 GPUs and NVIDIA deep learning software. It provides both training and inference acceleration for open-source deep learning frameworks, such as TensorFlow and PaddlePaddle. "Baidu and NVIDIA are long-time partners in advancing the state of the art in AI," said Ian Buck, general manager of Accelerated Computing at NVIDIA. "Baidu understands that enterprises need GPU computing to process the massive volumes of data needed for deep learning.
SQL Server 2017, which will run on both Windows and Linux, is inching closer to release with a set of artificial intelligence capabilities that will change the way enterprises derive value from their business data, according to Microsoft. The Redmond, Wash., software giant on April 19 released SQL Server 2017 Community Technology Preview (CTP) 2.0. Joseph Sirosh, corporate vice president of the Microsoft Data Group, described the "production-quality" database software as "the first RDBMS [relational database management system] with built-in AI." Download links and instructions on installing the preview on Linux are available in this TechNet post from the SQL Server team at Microsoft. It's no secret to anyone keeping tabs on Microsoft lately that the company is betting big on AI, progressively baking its machine learning and cognitive computing technologies into a wide array of the company's cloud services, business software offerings and consumer products. "In this preview release, we are introducing in-database support for a rich library of machine learning functions, and now for the first time Python support (in addition to R)," stated Sirosh, in the April 19 announcement.