This is the fourth Synced year-end compilation of “Artificial Intelligence Failures.” Our aim is not to shame nor downplay AI research, but to look at where and how it has gone awry with the hope that we can create better AI systems in the future.
“Trust in AI systems is becoming, if not already, the biggest barrier for enterprises — as they start to move from exploring AI or potentially piloting or doing some proof of concept works into deploying AI into a production system”
It was announced yesterday in a PyTorch blog post that the PyTorch / XLA library, a package that enables PyTorch to connect to Google TPUs and use TPU cores as devices, is now generally available on Google Cloud, with support for a broad set of entry points for developers.
On July 27, 2020, Alibaba Cloud Research Center and Accenture jointly launched the “The Penetrative and Explosive Power of AI Dividend” white paper, as part of the “China Enterprise 2020” series report.
Toronto Machine Learning Society (TMLS) hosts MLOps, Production & Engineering 2020 through an interactive conference to enable attendees the opportunity to virtually engage with speakers and establish a stronger network within the AI community.
The 48-hour digital event kicked off yesterday, and the company wasted no time making impactful announcements that included a new supercomputer, a family of large AI models, and a Responsible ML on Microsoft Azure initiative.