H2O.ai has announced that its Driverless AI automated machine learning platform and H2O4GPU open source GPU-accelerated machine learning package are now both fully optimised for the latest-generation NVIDIA Volta architecture GPUs — the NVIDIA Tesla V100 — and CUDA 9 software.
It’s been said that more data was generated in 2017 than in the previous 5,000 years. According to Statista, this figure will increase 10 times in less than a decade.
Global IT spending is expected to grow to US$3.7 trillion in 2018, an increase of 4.5 percent from 2017, according to Gartner.
Advances in various technologies will drive users to interact with their smartphones in more intuitive ways, said Gartner. It expect that, by 2019, 20 percent of all user interactions with the smartphone will take place via virtual personal assistants (VPAs).
“The role of interactions will intensify through the growing popularity of VPAs among smartphone users and conversations made with smart machines,” said Annette Zimmermann, Research Director of Gartner.
Gartner’s annual mobile apps survey conducted in Q4 among 3,021 consumers across three countries (US, UK and China) found that 42 percent of respondents in the US and 32 percent in the UK used VPAs on their smartphones in the last three months. More than 37 percent of respondents (average across US and UK) used a VPA at least one or more times a day.
As the first embedded computer designed to process deep neural networks, the new NVIDIA Jetson TX1 is set to enable a new wave of smart devices. Drones will evolve beyond flying by remote control to navigating through a forest for search and rescue. Security surveillance systems will be able to identify suspicious activities, not just scan crowds. Robots will be able to perform tasks customised to individuals’ habits.
That’s what the credit-card sized module can do. It can harness the power of machine learning to enable a new generation of smart, autonomous machines that can learn.
Deep neural networks are computer software that can learn to recognise objects or interpret information. This new approach to program computers is called machine learning and can be used to perform complex tasks such as recognising images, processing conversational speech, or analysing a room full of furniture and finding a path to navigate across it. Machine learning is a groundbreaking technology that will give autonomous devices a giant leap in capability.
NVIDIA has collaborated with a research team at Stanford University to create the world’s largest artificial neural network built to model how the human brain learns. The network is 6.5 times bigger than the previous record-setting network developed by Google in 2012.
Computer-based neural networks are capable of “learning” how to model the behaviour of the brain – including recognising objects, characters, voices, and audio in the same way that humans do.
Yet creating large-scale neural networks is extremely computationally expensive. For example, Google used approximately 1,000 CPU-based servers, or 16,000 CPU cores, to develop its neural network, which taught itself to recognise cats in a series of YouTube videos. The network included 1.7 billion parameters, the virtual representation of connections between neurons.