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 Commits $1B to Drive Watson into the Mainstream

IBM is ready to propel Watson beyond Jeopardy, its initial proof-of-concept, and into mainstream enterprise computing. To that end, it announced plans to spend more than $1 billion on the recently formed Watson business unit, an amount that includes $100 million in venture investments to build an ecosystem of entrepreneurs developing Watson-powered apps.

In addition, companies won’t need racks of Power servers to run Watson. With a series of announcements yesterday IBM unveiled plans to deliver Watson capabilities as business-ready cloud services. The announcement focused on three Watson services: 1)  Watson Discovery Advisor for research and development projects in industries such as pharmaceutical, publishing and biotechnology; 2) Watson Analytics to deliver visualized big data insights based on questions posed in natural language by any business user; and 3) IBM Watson Explorer to more easily uncover and share data-driven insights across the enterprise.

DancingDinosaur has been following Watson since its Jeopardy days. Having long since gotten over the disappointment that Watson didn’t run on the Power side of a hybrid zEnterprise, it turns out that IBM has managed to shrink Watson considerably.  Today Watson runs 24x faster, boasts a 2,400% improvement in performance, and is 90% smaller.  IBM has shrunk Watson from the size of a master bedroom to three stacked pizza boxes, and you don’t even need to locate it in your data center; you can run it in the cloud.

Following the introduction of Watson IBM was slow to build on that achievement. It focused on healthcare and financial services, use-cases that appeared to be no-brainers.  Eventually it experienced success, particularly in healthcare, but the initial customers came slowly and the implementations appeared to be cumbersome.

Watson, at least initially, wasn’t going to be a simple deployment. It needed a ton of Power processors. It also needed massive amounts of data; in healthcare IBM collected what amounted to the entire library of the world’s medical research and knowledge. And it needed applications that took advantage of Watson’s formidable yet unusual capabilities.

The recent announcements of delivering Watson via the cloud and committing to underwrite application developers definitely should help. And yesterday’s announcement of what amounts to three packaged Watson services should speed deployment.

For example, Watson Analytics, according to IBM, removes common impediments in the data discovery process, enabling business users to quickly and independently uncover new insights in their data. Using sophisticated analytics and aided by Watson’s natural language interface, Watson Analytics automatically prepares the data, finds the most important relationships, and presents the results in an easy to interpret interactive visual format. As a result, business users are no longer limited to predefined views or static data models. Better yet, they can feel empowered to apply their own knowledge of the business to ask and answer new questions as they emerge. They also will be able to quickly understand and make decisions based on Watson Analytics’ data-driven visualizations.

Behind the new Watson services lies IBM Watson Foundations, described as a comprehensive, integrated set of big data and analytics capabilities that enable enterprises to find and capitalize on actionable insights. Basically, it amounts to a set of user tools and capabilities to tap into all relevant data – regardless of source or type – and run analytics to gain fresh insights in real-time. And it does so securely across any part of an enterprise, including revenue generation, marketing, finance, risk, and operations.  Watson Foundations also includes business analytics with predictive and decision management capabilities, information management with in-memory and stream computing, and enterprise content management packaged into modular offerings. As such it enables organizations of any size to address immediate needs for decision support, gain sustainable value from their initial investments, and grow from there.

This apparently sounded good to Singapore’s DBS Bank, which will deploy Watson cognitive computing capabilities to deliver a next- generation client experience.  For starters, DBS intends to apply Watson to its wealth management business to improve the advice and experience delivered to affluent customers.  The bank is counting on cloud-based Watson to process enormous amounts of information with the ability to understand and learn from each interaction at unprecedented speed. This should greatly increase the bank’s ability to quickly analyze, understand and respond to the vast amounts of data it is accumulating.

Specifically, DBS will deploy IBM’s cloud-based Watson Engagement Advisor solution, to be rolled out in the second half of the year. From there the bank reportedly plans to progressively deploy these capabilities to its other businesses over time.

For fans of cognitive computing and Watson, the announcements represent a much awaited evolution in IBM’s strategy. It promises to make cognitive computing and the natural language power of Watson usable for mainstream enterprises. How excited fans should get, however, depends on the specifics of IBM’s pricing and packaging for these offerings.  Still, faced with having to recoup a $1 billion investment, don’t expect loss-leader pricing from IBM.

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