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Author Archives: Bill Catchings

Machine learning in 2018

We are almost to the end of 2017 and, as you have probably guessed, we will not have a more detailed proposal of our machine learning benchmark ready by the end of the year. The key aspects of the benchmark proposal we wrote about a few months ago haven’t changed, but we are running behind [...]

Machine learning performance tool update

Earlier this year we started talking about our efforts to develop a tool to help in evaluating machine learning performance. We’ve given some updates since then, but we’ve also gotten some questions, so I thought I’d do my best to summarize our answers for everyone. Some have asked what kinds of algorithms we’ve been looking [...]

Machine learning everywhere!

I usually think of machine learning as an emerging technology that will have a big impact on our lives in the not too distant future through applications like autonomous driving. Everywhere I look, however, I see areas where machine learning will affect our lives much sooner in a myriad of smaller ways. A recent article [...]

Thoughts from MWC Shanghai

I’ve spent the last couple days walking the exhibition halls of MWC Shanghai. The Shanghai New International Expo Centre (SNIEC) is large, but smaller than the MWC exhibit space in Barcelona or the set of exhibit halls in Las Vegas for CES. (SNIEC is not even the biggest exhibition space in Shanghai!) Further, MWC here [...]

Evaluating machine learning performance

A  few weeks ago, I discussed the rising importance of machine learning and our efforts to develop a tool to help in evaluating its performance. Here is an update on our thinking. One thing we are sure of is that we can’t cover everything in machine learning. The field is evolving rapidly, so we think [...]

Learning about machine learning

Everywhere we look, machine learning is in the news. It’s driving cars and beating the world’s best Go players. Whether we are aware of it or not, it’s in our lives–understanding our voices and identifying our pictures. Our goal of being able to measure the performance of hardware and software that does machine learning seems [...]

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