"Many researchers … speculate that the information-processing abilities of biological neural systems must follow from highly parallel processes operating on representations that are distributed over many neurons. [Artificial neural networks] capture this kind of highly parallel computation based on distributed representations"
– from Machine Learning (Section 4.1.1; page 82) by Tom M. Mitchell, McGraw Hill Companies, Inc. (1997).
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.
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.
H2O.ai and Nvidia today announced that they have partnered to take machine learning and deep learning algorithms to the enterprise through deals with Nvidia's graphics processing units (GPUs). Mountain View, Calif.-based H20.ai has created AI software that enables customers to train machine learning and deep learning models up to 75 times faster than conventional central processing unit (CPU) solutions. H2O.ai is also a founding member of the GPU Open Analytics initiative that aims to create an open framework for data science on GPUs. As part of the initiative, H2O.ai's GPU edition machine learning algorithms are compatible with the GPU Data Frame, the open in-GPU-memory data frame.
Audi and Nvidia have been collaborating for some time, but at CES 2017, the companies made their biggest joint announcement yet. Using artificial intelligence and deep learning technology, the companies will bring fully automated driving to the roads by 2020. To achieve this, Audi will leverage Nvidia's expertise in artificial intelligence, the fruits of which are already being shown at CES. Audi's Q7 Piloted Driving Concept is fitted with Nvidia's Drive PX 2 processor and after only four days of "training," the vehicle is already driving itself over a complex road course. This is due to the Drive PX 2's incredible ability to learn on the go, which is a far cry from the first driverless cars that needed pre-mapped routes to function properly. "Nvidia is pioneering the use of deep learning AI to revolutionize transportation," Nvidia CEO Jen-Hsun Huang said.
In case you missed it, TensorFlow is now available for Windows, as well as Mac and Linux. This was not always the case. For most of TensorFlow's first year of existence, the only means of Windows support was virtualization, typically through Docker. Even without GPU support, this is great news for me. I teach a graduate course in deep learning and dealing with students who only run Windows was always difficult.
NVIDIA (NASDAQ:NVDA) is primarily known as the company that revolutionized computer gaming. The debut of the Graphics Processing Unit (GPU) in 1999 provided gamers with faster, clearer, and more lifelike images. The GPU was designed to quickly perform complex mathematical calculations that were necessary to accelerate the creation of realistic graphics. It achieved this feat by performing many functions at the same time, known as parallel computing. This resulted in faster, smoother motion in game graphics and a revolution in modern gaming.
Without a doubt, 2016 was an amazing year for Machine Learning (ML) and Artificial Intelligence (AI). I have opined on the 5 things to watch in AI for 2017 in another article, however the potential dynamics during 2017 in processor and accelerator semiconductors that enable this market warrant further examination. It is interesting to note that shares of NVIDIA roughly tripled in 2016 due in large part to the company's technology leadership in this space. While NVIDIA GPUs currently enjoy a dominant position for Machine Learning training today, the company's latest quarter growth of 197% YoY, in a market now worth over a half billion dollars, has inevitably attracted a crowd of potential competitors, large and small. And semiconductors remain one of the few pure AI plays for public equity investors seeking a position in this fast growing market.
Without a doubt, 2016 was an amazing year for Machine Learning (ML) and Artificial Intelligence (AI). During the year, we saw nearly every high tech CEO claim the mantel of becoming an "AI Company". However, only a few companies were actually able to monetize their significant investments in AI, notably,,,,,, and . But 2016 was nonetheless a year of many firsts. As a posterchild for the potential for ML, Google Deep Mind mastered the subtle and infinitely complex game of GO, soundly beating the reigning world champion.
Intelligent machines powered by artificial intelligence (AI) computers that can learn, reason and interact with people and the surrounding world are no longer science fiction. Thanks to a new computing model called deep learning using powerful graphics processing units (GPUs), AI is transforming industries from consumer cloud services to healthcare to factories and cities. Many of these are in place already, providing new services to millions around the world. However, no industry is poised for such a significant change as the $10 trillion transportation industry. The automotive market is next, and the opportunity to develop advanced self-driving vehicle holds the promise to the world of dramatically safer driving and new mobility services.