Nvidia used its first GPU Technology Conference in Europe (Amsterdam September 28-29) to announce Xavier, a system on chip (SOC) artificial intelligence (AI) “supercomputer”, based on Nvidia’s next-generation (after Pascal) GPU, Volta, to be released in 2017. Xavier is designed to accelerate the adoption and embedding of deep learning AI systems in advanced systems such as autonomous driving for automobiles, and will replace its current Deep Learning Drive PX2, offering greater computational capability, lower power consumption, and a smaller physical footprint. With public transport autonomous driving systems already in operation in cities including Singapore (taxis by nuTonomy) and Lyon, France (buses by Navya), the claim that these systems will be commonplace, at least in the public transport space, in three years’ time is beginning to look realistic. The main holdback will be regulations and safety testing, which are in a nascent state. However, governments have a strong incentive to introduce autonomous driving. Apart from the human tragedy of road accident carnage, there is the waste and cost to nation states.
To date, Nvidia’s high-end GPUs and deep learning systems are leading the market for deep learning accelerators, an area Ovum expects to become more competitive next year with new entrants emerging. However, Xavier will be tough to beat. Jen-Hsun Huang, Nvidia’s CEO, has reiterated that its GPUs, through what may be serendipity (as a technology its origins are in video processing), are an ideal technology for boosting training and inferencing in deep learning systems.
Society is facing a number of issues, such as providing a safer transport environment, as well as a healthcare system that can bear the strain of increasing demand from an aging population, and the ability to deal with the complexity of the digital age. AI systems, and deep learning in particular, can provide safer roads with AI-infused autonomous driving, a target application for Nvidia’s most advanced deep learning platform, Xavier. Car manufacturers can choose the extent to which they use features in the platform or can plug in their own customized solutions.
Ovum’s research shows that healthcare is the vertical ripest for disruption by digitalization. AI systems can assist doctors and medical researchers by mining the mountain of medical research publications, can relieve the strain on healthcare with AI-assisted doctor services on smartphone apps, and the combination of AI and robots will provide solutions for assisting in heavy lifting alongside humans. The other main application area is automating the analysis of big data generated by Internet of Things and streaming monitoring systems, including cyber-security applications.
Regulators need to pave the way for the AI-powered cyber-physical systems of the fourth industrial revolution. The recent EU General Data Protection Regulation (GDPR) and its restrictions on the use of “automated individual decision-making” could, however, impact machine learning systems. The directive is due to commence 2018 and was designed to protect citizen privacy from intrusive social media but could have an unintended consequence. The EU region needs to remain competitive in a world that will adopt AI systems because they reduce costs and create greater efficiencies. Regulators therefore need to introduce rules that will allow new AI-infused industries to grow.
Michael Azoff, Principal Analyst, IT Infrastructure Solutions
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