Author: James Kobielus

How AI can potentially be used in the battle against pandemics – exploring the potential use cases of AI for battling coronarivus COVID-19 and beyond.
If we don’t understand how machine learning works, how can we trust it? Increasing model transparency creates risks as well as rewards.
Microsoft Research AI ethics checklist is a set of published principles for designing ethics checklists that can be readily operationalized in AI DevOps processes. We commend Microsoft Research’s recent effort to catalyze consensus within the practitioner community for the purpose of developing clear principles for designing operationalizable AI ethics checklists. Though the researchers don’t publish a one-size-fits-all operationalizable AI ethics checklist, they provide a useful discussion of the scenarios within which checklists can be helpful, and also within which they can be counterproductive or irrelevant to their intended users. This is important as the abstract nature of AI ethics principles makes them difficult for practitioners to operationalize. As befits the scope of this topic, AI applications and tool vendors are still trying to bring their ethics-assurance frameworks into coherent shape. Everybody—even the supposed experts—are groping for a consensus approach and practical tools to make ethics assurance a core component of AI DevOps governance.
Big changes in AI, machine learning applications, tools, techniques, platforms, and standards are on the horizon. addresses a growing enterprise need for AI model assurance. That said, there are both challenges and opportunities ahead for this AI startup. Model assurance is the ability to determine whether an AI application’s machine learning (ML) models remain predictively fit for their assigned tasks. This is a critical feature of any operational AI DevOps platform, which is one of the reasons caught my attention. I see both challenges and opportunities ahead for this AI startup.
Google’s latest announcement lands in a fast-moving, but still immature, quantum computing marketplace. By extending the most popular open-source ML development framework, Google will almost certainly catalyze use of TensorFlow Quantum in a wide range of ML-related initiatives. The Google-developed quantum ML framework will find its way into a wide range of other solution providers’ quantum computing environments. So what will Google’s likely next move be in the Quantum ML space? Read on to see what I think.
Multicloud computing has become the hottest theme in enterprise networking this year. What does this mean for the future?
It is apparent that NVIDIA recognizes its current market-leading status in AI chipsets won’t necessary last forever. By focusing on the enterprise stack—the data center, enterprise computing, and all things related to the future of AI in the enterprise, the company plots a path forward that allows it to sustain its impressive growth record. The company’s acquisition of SwiftStack provides NVIDIA with a software-driven data storage and management platform for acceleration of deep learning, analytics, and high-performance computing applications across multiclouds.
Google Cloud partnering with AT&T to build 5G edge computing solutions is a move toward helping telcos optimize for 5G wireless applications. It’s also a move that’s central to Google’s strategy of moving the company beyond its core ad business and working to claim a larger share of the enterprise cloud business. What’s most noteworthy about this announcement from Google Cloud is the breadth of its strategy for partnering with 5G carriers, including, but not limited to, AT&T. Here’s a look at that in greater depth.
BMC’s Compuware acquisition is a clear sign that the mainframe era is winding down. Recognizing that their enterprise customers are distributing IT assets across clouds and need tooling to automate management of it all, they have wisely hitched wagons and beefed up their focusing on self-managing closed-loop IT operations management.
As governments everywhere grapple with issues surrounding use of AI-driven facial recognition, they’ll have to consider how to factor facial deepfaking into their regulatory frameworks. Even if there were a surefire way to identify deepfakes, banning them would run afoul of free-speech guarantees in democratic nations.