James Kobielus
Contributor
James Kobielus is research director and lead analyst at Futurum Research.
How data analysis, AI, and IoT will shape the post-pandemic ‘new normal’
Only a common public health infrastructure of AI, cloud computing, streaming, and the Internet of Things can marshal our data against pandemics
GPipe and PipeDream: Scaling AI training in every direction
New frameworks Google GPipe and Microsoft PipeDream join Uber Horovod in distributed training for deep learning
Coding together apart: Software development after COVID-19
Distance collaboration may become standard practice, aided by group collaboration tools
TensorFlow deepens its advantages in the AI modeling wars
Despite complaints about its complexity, TensorFlow supports every AI development, training, and deployment scenario you can imagine
AI companies plant the seeds for quantum machine learning
Quantum computing promises to accelerate analytics faster than the speed of light, but it still feels slightly unreal, in spite of the first signs of broad commercialization
Boosting AI’s smarts in the absence of training data
Zero-shot learning repurposes knowledge through statistical or semantic approaches without needing huge amounts of fresh training data
Keeping your enterprise AI expertise up to speed
AI is such a significant force, you can’t afford not to stay on top of the ever-changing developments
Making AI’s arcane neural networks accessible
Data scientists remain in hot demand, but they will give up more of their core functions this year and beyond to automated tools
Mitigating the risks of the AI black box
If we don’t understand how machine learning works, how can we trust it? Increasing model transparency creates risks as well as rewards
Artificial intelligence predictions for 2020
Big changes in machine learning applications, tools, techniques, platforms, and standards are on the horizon
Boutique clouds rise on multicloud and microservices
Hoping to avoid cloud vendor lock-in, enterprises are moving workloads to niche providers
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