Artificial Intelligence #17 - Top AI research resources I recommend

Hello all

?Welcome to Edition #17

?This is a relatively short newsletter (by my standards!) because I am on holiday / partly work in Berlin this week.

Some of you asked me how I cover such a wide range of topics in this newsletter

Firstly, my work and teaching at #universityofoxford - #articificialintelligence #cloud and #edge implementations allows me to engage with some very smart people and push the boundaries of AI. For example, this week. James Parr and Alison Lowndes (of Nvidia) invited me to a workshop on #AI in space. We follow this research and work with Nvidia on AI on Edge devices.

Also, I am working on models beyond #deeplearning on a paper.

Finally, I am working on Probabilistic Bayesian models (deep learning and graphical) at digitty.io

Hence, a range of these ideas are reflected in the newsletter

I follow some very insightful people/sources

In this letter, I cover my top AI research resources (which I recommend you should also follow if you are deeply interested in AI). If you want to suggest any more sources I should follow, please comment below (but see the narrow criteria I work with)

Before we proceed, we have all been shocked by the events in Afghanistan.

We believe in human rights, education, diversity, and education for all, especially gender equality in education. The Afghan all girl’s robotics team is trying hard to leave Afghanistan and the human rights lawyer Kimberley Motley (who is connected to me on LinkedIn) is doing some great work trying to get this team to Canada. I want to raise awareness of this issue and about the good work done by people here in our network

Now, considering the top AI resources I recommend, my criteria is:

• They should have original content and cover latest AI papers/ research
• I exclude resources from companies i.e. should be independent
• They should not be only ‘links of latest developments’

?These criteria exclude some excellent resources but also narrow down the field a lot (actually only four)

?So, here they are

Alpha signal has a following among researchers in Stanford, Google etc and with good reason. Its both topical and insightful?

Ben Dickson’s newsletter bdtechtalks This is free – but I would gladly pay for it! It covers themes on the evolution of AI in great detail and often simplifies concepts so it’s a good starting point to understand more complex material

Synced review covers AI & Machine Learning?developments - from China but also globally. Its coverage is detailed and technical

And finally, the sequence by Ksenia Semenova and Jesus Rodriguez – a bit more biased to NLP but very detailed work overall

So, these are my four top sources. And there are only four because of the criteria but also because AI research is far more complex.

Most sources cover AI from the perspective of AI entrants.

But no matter where you are in your AI journey, these resources will be helpful to you I hope

Finally, based on my previous post about cloud architectures Packt have come up with a free giveaway of a book Cloud Native Architectures: Design high-availability and cost-effective applications for the cloud

All you need to do is comment about the book in the comments of this post. The winners will be announced on 31 Aug. There will be 3 eBooks and 2 print copies (print only for US & CA). The packt website for this book is HERE (it gives more details and a TOC)

You could comment on any aspect ex your experiences in cloud native architectures based on what you read about this book. Note you should comment here (on this newsletter comments) else they cannot track comments

Image source Cnet