The world is now heading into the Fourth Industrial Revolution, as Professor Klaus Schwab, Founder and Executive Chairman of the World Economic Forum, described it in 2016. Artificial Intelligence (AI) is a key driver in this revolution and with it, machine learning is critical. But critical to the whole process is the need to process a tremendous amount of data which in turns boosts the demand for computing power exponentially.

A study by OpenAI suggested that the computing power required for AI training surged by more than 300,000 times between 2012 and 2018. This represents a doubling of computing power every three months and two weeks; a number that is significantly quicker than Moore’s Law which has traditionally measured the time it takes to double computing power. Conventional methodology is no longer enough for such significant leaps, and we desperately need a different computing architecture to stay ahead in the game.

Huawei has developed a brand-new computing architecture to meet these challenges, and the Ascend series processor has been reinvented specifically for the deep learning needed by AI. The top-of-the-range Ascend 910 chip among current generation runs on just such an architecture, offering the highest possible computing density in just a single AI chip. The high-integration system-on-chip (SoC) processor delivers 256 TFLOPS of computing power yet requires relatively low power consumption – 310 watts maximum.

The Ascend 910 chip is the top choice for systems demanding high volume of data throughput and processing, such as the Square Kilometer Array which takes huge amount of data from a radio-telescope for the analysis of stars. For example, it would take just 10 seconds to locate and identify a specific type of star, as opposed to around 170 days currently required for the very same task by an astronomer.

The architecture allows chips to be more flexible, making it easy to fit a variety of situations. This is the elasticity we are aiming for – one single design fits a wide range of needs. The Ascend 310, for instance, is a programmable AI processor using the Huawei-developed architecture with relatively low power consumption at just 8 watts.

Another common application of the Ascend 310 chips is image recognition, an important part for autonomous vehicles. A single Ascend 310 chip can reach a top speed of 16TOPS on-site calculations. This can support simultaneous identification of 200 different objects such as cars, obstacles and traffic signs. The chip is powerful enough to process thousands of pictures in just a single second, helping vehicles to react to road conditions faster and more accurately.

All these features help make AI more affordable by supplying computing power in a reasonable cost range with an ability to work at low energy levels. Thanks to its unique architecture, Ascendbased architectures will be more accessible because they are highly flexible and can fit a wide range of scenarios. The architecture allows these chips to be flexible and scalable, making them very easy to use, with programmers easily able to adapt their use to a variety of scenarios.

We are excited to be leading the technological evolution driven by AI. There is much we have planned, and you can be sure Huawei will continue its rapid product development to support our customers’ needs. This includes the Ascend 610, built on Huawei’s self-developed architecture, and which is targeted at autonomous transport systems but covering a much wider range of operations.

The impact is massive, and it cannot be achieved solely by the machines. It takes a collective effort from Huawei and its partners to ensure the rapid advancement of AI. We are committed to significant investment, not just in monetary terms but also with a vast input of human resources. We are currently working on more than 300 customer deployments across 10 sectors including finance, transportation, healthcare and so on, using AI to drive efficiencies and cost-savings.

Artificial Intelligence may be smarter than us in many ways. But it is up to human beings to come up with the right design and architecture for AI to grow. After all, we can facilitate machines to produce calculations and predictions. But it is human beings, that create meaning rather than just numbers.

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Hortonworks, IBM and Red Hat today announced they’re banding together to build a consistent hybrid computing architecture for big data workloads. Dubbed the Open Hybrid Architecture Initiative, the program pledges simplicity of deployment and freedom of movement for data apps.

The rapid ascent of cloud computing platforms like AWS, Azure, and Google Cloud has given enterprises abundant new options for storing data and deploying processing-intensive applications, such as deep learning and real-time stream processing. Throw in the progress being made at the edge, with sensors and speedy ARM chips collecting and processing massive amounts of data, and you have the makings of a computing revolution.

While the computing possibilities in the cloud and on the edge may appear bountiful, the reality is that the underlying architectures for building apps that can span these three modes are just starting to come together. Enterprises today face a dearth of repeatable patterns to guide their developers, administrators, and architects, who are tasked with building, deploying and maintaining hybrid that span not just the cloud and the edge, but traditional on-prem data centers too.

That’s the underlying challenge that’s to be faced by the Open Hybrid Architecture Initiative. Launched today in advanced of the Strata Data Conference in New York City, the group outlined plans to integrate their respective technologies in such a way as to provide customers with greater freedom of movement and run-time options for their big data workloads.

In terms of actual deliverables, the initial phase of the initiative calls for the companies to integrate various products. Specifically, HortonworksData Platform (HDP), Hortonworks DataFlow (HDF), Hortonworks DataPlane (DPS) and IBMCloud Private for Data will be optimized to run on Red Hat OpenShift, the company’s distribution of Kubernetes for containerized applications.

The companies say the move will “provide the vast OpenShift community of developers and users – which include IBM and Hortonworks clients – fast access to robust analytics, data science, machine learning, data management and governance capabilities, fully supported across hybrid clouds.”

Enterprises will be able to access and process data no matter where it resides as part of the Open Hybrid Architecture Initiative, says Hortonworks co-founder and CTO Arun Murthy.

“Through the initiative, we deliver an architecture where it absolutely will not matter where your data is – in any cloud, on-prem or the edge – enterprises can leverage open-source analytics in a secure and governed manner,” he says in a blog post today. “The benefits of ensuring a consistent interaction model cannot be overstated and provides the key to unlocking a seamless experience.”

Kubernetes stands to play a starring role in the Open Hybrid Architecture Initiative. The open source container management software serves as a virtualization layer that decouples runtime environments from underlying hardware, while providing the capability to spin up, spin down, and move software applications at the administrator’s will.

“By building and managing their applications via containers and Kubernetes with OpenShift,” says Ashesh Badani, vice president and general manager of Cloud Platforms at Red Hat, “customers and the big data ecosystem have opportunities to bring this next generation of big data workloads to the hybrid cloud and deliver the benefits of an agile, efficient, reliable, multi-cloud infrastructure.”

IBM is currently working on achieving “primed” status for running IBM Cloud Private for Data on OpenShift, which is expected to occur later this month. “Scaling the ladder to AI demands robust data prep, analytics, data science and governance, all of which are easily scaled and streamlined in the kind of containerized, Kubernetes-orchestrated environments that we’re talking about today,” says Rob Thomas, general manager of IBM Analytics.

Hortonworks is following a similar path with its products, including DPS, which it launched a year ago and which will be called upon for spinning various engines like Hive and Spark up and down in a hybrid architecture while maintaining necessary controls that enterprises demand. “This allows customers to more easily adopt a hybrid architecture for big data applications and analytics, all with the common and trusted security, data governance and operations that enterprises require,” Murthy says.

It’s not clear if any cloud providers are working with the Open Hybrid Architecture Initiative at this point, or if there are any other members of the group. There is no website set up yet for the Open Hybrid Architecture Initiative, but a spokesperson for Hortonworks says there will be one soon.

The Hortonworks spokesperson says cloud platform vendors are welcome to join the group.  “We welcome participation from anyone who wants to collaborate to accelerate hybrid for customers,” the spokesperson says.

In any event, it’s not all about moving applications out of the data center and into the cloud. According to a recent IDC survey, more than 80% of respondents said they plan to move or repatriate data and workloads from public cloud environments behind the firewall to hosted private clouds or on-premises locations over the next year.

The reason for this “application repatriation” is that the “initial expectations of a single public cloud provider were not realized,” the group says.

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