Tag: AI

Companies are starting to use emotional recognition technology for recruitment. But we know the results should not be accepted without further research. It simply isn’t fair for us to judge other people for what they’re feeling—especially if, as the science shows, we actually don’t know.
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.
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.
Superwise.ai 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 Superwise.ai caught my attention. I see both challenges and opportunities ahead for this AI startup.
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.
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.
New data on smart speakers show that the market is growing at a fast pace, but it is new entrants, mostly from China that are seeing the biggest growth.
The combination of the SaaS business model and AI services could help bring AI as a service to the masses without a heavy price tag.
If you want learn more about how the AIoT can benefit your organization and your industry, start by reading this guide SAS and the Artificial Intelligence of Things (AIoT) today!
AI and the IoT are the perfect example of two technologies that complement one another and should be tightly connected. Learn more about the AIoT in this whitepaper from SAS.
NVIDIA continues to flex its technical muscle in Artificial Intelligence (AI) to seize new opportunities in the fast-growing chipset market. Long known as the powerhouse of AI “training” solutions, the company has recently been pushing into the adjacent—and potentially much larger—market for AI “inferencing” products. What does that mean for the future of the AI wars? Let’s take a look.

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