Category: AI

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.
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.
With the rise of PyTorch, TensorFlow’s dominance may be waning. While PyTorch scale advantages are tipping the scales, deployability is still TensorFlow’s strength. Here’s a look at what I think is ahead with these two for deep learning dominance.
Ericsson’s AI-powered Energy Infrastructure Operations is blueprinted to decrease OPEX and CO2 emissions for operators using AI and data analytics to improve energy efficiency and improve site availability.

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