IBM in the Race for AI Leadership

“There’s no doubt that artificial intelligence (AI) and its underlying technology building blocks are top of mind for enterprises today,” according to Forrester Research in a recent infographic. Adds Gartner: “AI technologies will be the most disruptive class of technologies over the next 10 years due to radical computational power, near-endless amounts of data, and unprecedented advances in deep neural networks,” said Mike Walker, Gartner’s research director.

It certainly is shaping up to be an intense race. Back in June, Datamation listed the top 20 players at that time based on their AI acquisitions. Apparently Datamation editors believe the emerging AI leaders will be those buying their way into the market. The list is alphabetical, not ranked, but most of the big technology players are there: Amazon, Apple, Facebook, Google, Intel, Salesforce.

IBM staff brainstorm Watson AI services

Of course, IBM is there; described in the listing as a leader in the field of AI since the 1950s. It has kept busy in the AI space, with 3 recent acquisitions in addition to its AI service, Watson, noted as a supercomputer that reveals insights from unstructured big data through machine learning and natural language processing. It shortly can boast of its role in the government’s new Summit and Sierra supercomputers, which will be AI beasts for sure.  Guess IBM can finally move beyond its Jeopardy triumph.

In early November IBM announced new offerings to its Watson Data Platform, including data cataloging and data refining. The objective is to make it easier for developers and data scientists to analyze and prepare enterprise data for AI applications, regardless of its structure or where it resides.

By 2018, nearly 75 percent of developers will build AI functionality into their apps, according to IDC. However, they also face the obstacle of making sense of increasingly complex data that lives in different places, and that must be securely and continually ingested to power these apps.

Addressing these challenges, IBM has expanded the functionality of its Watson Data Platform, an integrated set of tools, services and data on the IBM Cloud. The platform strives to enable data scientists, developers, and business teams to gain intelligence from the data most important to their roles, as well as easily access services like machine learning, AI, and analytics.

Specifically, this expansion includes:

• New Data Catalog and Data Refinery offerings, which bring together datasets that live in different formats on the cloud, in existing systems and in third party sources; as well as apply machine learning to process and cleanse this data so it can be ingested for AI applications;
• The ability to use metadata, pulled from Data Catalog and Data Refinery, to tag and help enforce a client’s data governance policies. This gives teams a foundation to more easily identify risks when sharing sensitive data.
• The general availability of Analytics Engine to separate the storage of data from the information it holds, allowing it to be analyzed and fed into apps at much greater speeds. As a result, developers and data scientists can more easily share and build with large datasets.

Built on open source technologies and fueled by IBM Cloud, the Watson Data Platform brings together IBM’s cloud infrastructure, powerful data services, and decades of experience helping clients across industries solve their data challenges, according to IBM. Linked closely with the most popular Watson communities including Python and Spark, IBM is expecting the Platform to evolve and build the most open and complete data operating system on the cloud.

Driving this in part, notes Forrester,  come the emerging organizational structure of the business technology decision makers who keep business aligned with a functional unit, a line of business, or a firm’s strategic priorities as well as challenges. The structure is ideally situated to understand the impact of emerging technologies to solve business problems. These technologists within business units are also beginning to realize that AI isn’t just big data or advanced analytics on steroids, nor is it best applied to gain operational efficiencies or achieve incremental gains. Rather, AI can be wholly disruptive, and, ideally, each firm will use it to completely reinvent its business model.

Adds Gartner: In the realm of AI, machine learning has the potential to benefit industries from supply chain to drug research. It will soon become impossible for conventional engineering solutions to handle the increasing amounts of available data. Machine learning offers the ability to extract certain knowledge and patterns from a series of events and observations and make better business decisions faster.

This is the final DancingDinosaur piece for this year. DancingDinosaur will be back the week of Jan. 8. In the meantime, best wishes for enjoyable and peaceful holidays.

DancingDinosaur is Alan Radding, a veteran information technology analyst, writer, and ghost-writer. Please follow DancingDinosaur on Twitter, @mainframeblog. See more of his IT writing at technologywriter.com and here.

IBM Nvidia GPU Speed Workloads in Face of Zettabyte Volumes

Last week IBM became the  first major global cloud provider to make the NVIDIA Tesla P100 GPU accelerator available on the cloud. By combining NVIDIA’s acceleration technology with IBM’s Cloud platform, businesses can expect to more quickly and efficiently run compute-heavy workloads.

A few compute-heavy workloads, however, will barely be noticed in the aggregate volume of data that analyst firms like IDC see coming by 2025. IDC analysts David Reinsel, John Gantz and John Rydning, in their latest Digital Universe study conclude: The 163 zettabyte (ZB) global datasphere projected in Data Age 2025 is only the beginning.

Courtesy: VMware

Today, IBM and Nvidia see the big zettabytes driver as cognitive analytics, but that’s just one part and not even the largest. As IBM explains: The combination of IBM’s connectivity and bare metal servers with the Tesla P100 GPUs enables higher throughput than traditional virtualized servers. This high level of performance can allow organizations to deploy fewer, more powerful cloud servers to more quickly deliver increasingly complex simulations and big data workloads. Even before 2025, you can’t deploy enough NVIDIA technology to matter.

The IDC analysts take a more visionary view: Imagine being awoken and tended to by a virtual personal assistant that advises you on what clothing from your wardrobe is best suited to the weather report and your schedule for the day or being transported by your self-driving car. Or maybe you won’t need to commute to an office at all as technology will allow you to conjure workspaces out of thin air using interactive surfaces, and holographic teleconferencing becomes the norm for communicating virtually with colleagues.

Behind this vision are data and processing. As the IDC analysts put it: In earlier periods, data growth stemmed largely from the rise of the personal computer and the consumption of digital entertainment. The world today, however, contains more consumer devices (PCs, phones, game consoles, and music players) than human beings, and all these devices require data.

The embedding of computing power in a large number of endpoint devices also contributes to data growth. Today, the number of embedded system devices feeding into datacenters is less than one per person globally, but over the next 10 years, that number will increase to more than four per person.  And the number of files generated will be unimaginable, measured in quintillions per year. BTW, it would take Niagara Falls 210,000 years to move one quintillion gallons of water.

Don’t worry about storing all these zettabytes. As the analysts note: The vast majority of the global datasphere is used and discarded rather than stored. This is primarily a reflection of the fact that most data is fundamentally disposable. Here is where metadata comes into play. From the huge amount of data created, you can prioritize which data has sufficient value to be stored through metadata, classification, tagging, and such.

The key: apply cognition to the selection of data to store. Let cognition determine the smart criteria to identify which data to retain, in what form, and for how long. That way you can hang onto critical information without the need to store all the data produced.

Even still, the quantity of data will outpace any reasonable expectation to store all the data. For example, it would take roughly 16 billion of today’s largest 12TB enterprise HDDs to store the 163ZB data expected to be created in 2025. But over the past 20 years, the entire disk drive industry shipped only 8 billion HDDs amounting to nearly 4ZB of storage.

Obviously, you can’t store 163 ZB of data or even a miniscule piece of it. The solution points to intelligence embedded throughout the system. Start with the application of smart criteria described above. And don’t ignore intelligent automation; people can’t possibly handle this without assistance.

And the 163ZB global datasphere projected in Data Age 2025 is only just the beginning. Another decade in technology years will likely bring about unforeseen advancements, use cases, businesses, and life- changing services that rely on yet more data. As IDC notes, the quantity of data generated will continue to outpace any notion of storing all of the data. Start now incorporating cognition to determine metadata, file tagging, data classification, and expiration dating.

DancingDinosaur is Alan Radding, a veteran information technology analyst, writer, and ghost-writer. Please follow DancingDinosaur on Twitter, @mainframeblog. See more of his IT writing at technologywriter.com and here.