Summary Bullets:

• Looking to build good artificiaI intelligence (AI)? Don’t let the speed and availability of open source frameworks, modules, libraries, and languages lull you into a false sense of confidence.

• Good AI needs to start with good data and good data needs to be ingested, registered, described, validated, and processed well before it reaches the ready hands of AI practitioners.

These are heady times. Enterprises have at their disposal both the raw materials and the necessary tools to achieve great things with AI, be that something grandiose as self-driving cars or unassuming as a fraud detection algorithm. The trouble with an abundance of materials (e.g., data) and tools (e.g., open source machine learning models), however, is speed. Speed kills, as they say.

For AI practitioners, this means learning to run before learning to walk by hastily automating decisions via AI models that are built on unsound data. With a few simple open source frameworks, modules, libraries, and languages, seemingly useful but ultimately erroneous predictions and conclusions can be readily drawn from any old data set in very short order. What’s the answer? More or better tools? No. As with most human problems, good old human knowhow and understanding are necessary. And that begins with data.

Continue reading “When It Comes to Building Trusted AI Outcomes, Knowledge Must Precede Speed” →

Summary Bullets:

• Rather than try to predict wide-scale market trends, we share five very specific 2020 “wishes” for enterprise data and analytics practitioners.

• Our list centers on cross-cloud data portability, the benefits of structured query language, AI software development, and the death of business intelligence.

The deepening absence of daylight and the seemingly consistent sprinkle of snow now falling gently across the northern hemisphere heralds many annual customs — imbibing eggnog and other odd drinks, lighting holiday lights, visiting family, and of course, forecasting the future. Continue reading “A 2020 Wish List for Data and Analytics Technology Providers” →

Summary Bullets:

• At its annual user conference, Tableau rolled out several data prep and management capabilities, highlighted by the ability for Tableau Prep Builder users to write out to third-party data stores, such as the popular Snowflake solution.
• This, along with several ongoing cloud and database initiatives, marks a significant philosophical shift for the vendor away from pure analytics and toward a more complete solution to help buyers establish a company-wide data culture.

For a company powered by analytics, Tableau put very few on display during its annual user conference in Las Vegas last week. Certainly, there were numerous stats to be found, particularly relating to the adoption of Tableau Online, where there are now 15,000+ active customer accounts. What’s more, Tableau Online is maintaining 100% YoY growth, as reported by CEO Adam Selipsky – a fitting fact given that Tableau and its new, cloud-first parent company, Salesforce, are now free to talk integration and rationalization. Continue reading “Tableau Tackles Analytics at Scale, Not Through Tech Alone but with a ‘Culture of Data’” →

Summary Bullets:

• Competition in the AI chipset space is heating up; new players are looking to join the fray and they are raising impressive amounts of capital.

• New vendors face stiff competition from tech heavyweights such as Nvidia, hyper-scale cloud providers such as Google and Amazon, and well-funded Chinese organizations.

Just as the market for AI platforms is heating up, so is competition in the AI chipset space. And it isn’t only the large well-established competitors such as Nvidia, Google, and Huawei vying for market share. New players are looking to join the fray as well, and they are raising impressive amounts of capital. Untether, a Toronto-based chip manufacturing start-up, announced in early November that it had raised $20 million in series A funding. The one year old company plans to release a chip designed for AI inference using near-memory design, reducing the distance data must travel, thereby moving data to processors at 2.5 petabits per second, which improves overall processing efficiency. Across the pond, Graphcore, a UK-based organization, has raised a substantial $200 million to develop its Intelligence Processing Units (IPUs), parallel processors designed for machine learning.

Continue reading “Is There Room for Start-ups in the Lucrative AI Chip Market?” →

Summary Bullets:

• Just one short year after an internal reorganization to more fully meld artificial intelligence (AI) with data and analytics, IBM is back with a new, more accessible vision for IBM Watson.
• This time around, the company isn’t focused on game shows or scientific discovery but instead on solving very basic, often human-centric challenges.

When it comes to chasing the market’s heated but somewhat unrequited love affair with AI, IBM has certainly done its part in terms of generating hype for its multi-billion dollar investment in IBM Watson. That hype, which has taken aim at some rather lofty goals such as identifying and diagnosing cancer, has not fully panned out, with some early adopters scaling back or halting operations altogether due to concerns over cost and efficacy. Continue reading “IBM Data and AI Forum: Say Hello to a More Accessible IBM Watson” →

Summary Bullets:

• Nutanix marked its ten-year anniversary with an expanded commitment to providing new on-premises support for emerging workloads, and solutions that enable hybrid and multi-cloud architectures.

• New solutions like Nutanix Xi Clusters on AWS promise to further intensify the growing competition in the fast-changing market for hybrid and multi-cloud solutions.

This year’s Nutanix .NEXT conference in Copenhagen was a milestone event for the company, marking just over ten years since it was first established, and giving Nutanix a timely forum in which to map out its strategy for the next ten years.
Continue reading “Nutanix Is Reasserting Itself as an Alternative to VMware in the Data Center and in the Cloud” →

Summary Bullets:

• Huawei announced the commercial launch of the Ascend 910, an AI chip optimized for model training, as well as MindSpore, a training and inference framework.

• The announcements come during escalating tensions between the U.S. and China, underscoring that Huawei will forge ahead in emerging technologies, with or without the U.S.

In the realm of AI, Huawei is readying itself to go head to head with global leaders, including Google and Amazon. The company’s recent commercial launches of an AI chip optimized for model training, as well as a training and inference framework, come hot on the heels of the unveiling of its internally developed operating system for mobile devices. The timing of the announcements is opportune; they come during a period of escalating tensions between the U.S. and China, serving to underscore that Huawei will forge ahead in emerging technologies, with or without the U.S. Continue reading “Huawei’s Ascend 910 Chip and MindSpore Framework Underscore its Ability to Forge Ahead with AI, Despite Escalating Tensions with the U.S.” →