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.
Shares of Nvidia (NVDA) are up $4.09, or 4%, at $105.72, after Goldman Sachs's Toshiya Hari this morning elected the stock to the firm's "Conviction Buy List," and raised the price target on the stock to $129 from $92, writing that the company is a "unique growth story in semis" because of trends in gaming, virtual reality, and machine learning, as well as automotive electronics. Hari believes Nvidia will continue to benefit from machine learning build-out at cloud computing giants, given that "competition continues to face high barriers to entry with new entrants not expected until the mid-2017/2018 timeframe." Hari's discussions recently with people in industry suggest the machine learning, and artificial intelligence, fields, imply a total addressable market of $5 billion to $10 billion. Nvidia has almost 90% share of the market for chips for "training," which is one part of the machine learning market, and which made up 78% of the machine learning market "acceleration" in 2015. Competitors with offerings include privately held Graphcore and Cerebras.
This is the first installment in a three-part review of 2016 in machine learning and deep learning. In Part Two, we cover developments in each of the leading open source machine learning and deep learning projects. Part Three will review the machine learning and deep learning moves of commercial software vendors. As organizations expand the use of machine learning for profiling and automated decisions, there is growing concern about the potential for bias. In 2016, reports in the media documented racial bias in predictive models used for criminal sentencing, discriminatory pricing in automated auto insurance quotes, an image classifier that learned "whiteness" as an attribute of beauty, and hidden stereotypes in Google's word2vec algorithm.
Nvidia was granted a permit yesterday by the California Department of Motor Vehicles to start testing self-driving vehicle technology on the state's public roads. The company joins a growing list of autonomous vehicle testers in California that features the likes of Google, Mercedes-Benz, Tesla, Ford, and GM, as well as startups like Faraday Future and NextEV. Apparently Nvidia didn't waste any time once the permit was approved, too. While Nvidia is best known for its graphics cards, the company has spent the last few years steadily pushing into artificial intelligence, with an emphasis on autonomous driving. It announced a computer vision system tailor-made for self-driving at the Consumer Electronics Show in 2015, and then followed that up with a more advanced version -- called the Drive PX2 -- at this past year's CES.