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.
It was in this same dingy diner in April 1993 that three young electrical engineers--Malachowsky, Curtis Priem and Nvidia's current CEO, Jen-Hsun Huang--started a company devoted to making specialized chips that would generate faster and more realistic graphics for video games. "We've been investing in a lot of startups applying deep learning to many areas, and every single one effectively comes in building on Nvidia's platform," says Marc Andreessen of venture capital firm Andreessen Horowitz. Starting in 2006, Nvidia released a programming tool kit called CUDA that allowed coders to easily program each individual pixel on a screen. From his bedroom, Krizhevsky had plugged 1.2 million images into a deep learning neural network powered by two Nvidia GeForce gaming cards.
NVIDIA Corp announced the debut of a smaller and more efficient artificial intelligence (AI) system for self-driving cars that would operate Baidu's navigation and autonomous vehicle technology. According to a Reuters report, Nvidia debuted its new Drive PX 2 at the GPU Technology Conference in Beijing. Chinese web giant Baidu will roll out the in-vehicle computer as its autonomous driving computer system. Nvidia's latest Drive PX 2 computer, which launched in January, uses 10 watts of power and is half the size of the original version.
Today, when Intel announced a new generation of Xeon Phi server chips, the emphasis was on their ability to handle A.I. Of all those servers, 7 percent were handling deep learning, while 95 percent were doing machine learning, she said. Of servers doing machine learning or deep learning, "the vast, vast majority of workloads are machine learning. They offer "advanced acceleration capabilities" for workloads like Google's TensorFlow deep learning framework, Google has said.