Category: AI

We’ve talked a lot about how AI could take jobs, but it actually looks like AI is the new job creator. Here are the implications of this development.
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.
AI and predictive analytics are still making an impact for businesses in terms of identifying customer trends, building customer profiles, and constructing tighter potential target audiences.
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.
There are some big AI and analytics trends for 2020. The following are a few trends that can help grow and improve your company this year.
Microsoft announced Power Automate will include UI flows in the coming weeks along with a new pricing structure strongly positioning Microsoft in RPA.
Algorithmia integrates AI model governance with GitOps, integrating ML and code development into DevOps workflows that use Git as a source-code repository. With this announcement, Algorithmia has made it easier to use GitHub to break down the silos that traditionally have kept ML developers and application coders from integrating tightly within today’s continuous DevOps workflows.
With the retirement of DAWNBench, I decided to take a look at what’s next for not only the Stanford DAWN project as it reaches its midway point, but also for benchmarking the next gen infrastructure for industrialized data science.
For this week’s episode of the Futurum Tech Podcast, I was joined by my colleague and fellow analyst, Ron Westfall. We discussed what organizations need to know about the EU’s newly proposed guidelines for AI and Apple’s hit to the bottom line as a result of the coronavirus outbreak. We also covered the T-Mobile Sprint merger and what we think is ahead in the telecoms market and explored our thoughts about the role automation will play in 5G operations. Lastly, we covered news that Google is cracking down on Google Play apps that track location in the background, along with the FTC’s probe into high tech acquisitions over the course of the last decade and what that likely means (read: the headaches potentially ahead). Come on, have a listen. And if you’ve not yet subscribed, do — we cover all the tech news of the week in an easily digestible, definitely entertaining manner.