Robotics with deep learning

Robotics with machine learning / deep learning may be the next big thing from a venture capital perspective. Incidentally, it is pretty complete and affordable as an indie r&d, modular and scalable project, so let’s get in! A couple of nice projects to perform some tests are the RobotArm from ftobler and the mBot from makeBlock. I am more focused on Python environments and computer vision / deep learning for the arm,  though, so something like this semi-professional Dorna, now on kickstarter, might come better at under €1000.

Terrestrial molten salt

Generation IV nuclear reactors incoming with the “Terrestrial Energy’s molten-salt nuclear reactor approved by national regulator” in Canada. Not good for the UK because integral so they can go with Rolls Royce but good enough for Canada? Ha, geopolitics! “Terrestrial has now completed the first phase of a prelicensing review, which provides a regulatory opinion that, given its design features, the company could obtain a licence to construct such a reactor.” Congrats, Terrestrial Energy and keep going!

IAEA Bulletin Nov 2017

“This edition of the IAEA Bulletin , Vol. 58-4, November 2017covers some of the most relevant topics on nuclear power and its role in contributing to sustainable development.” Among the many interesting articles: “Clean energy for a sustainable future: the role of nuclear power” by Yukiya Amano; “Going long term: US nuclear power plants could extend operating life to 80 years” by May Fawaz-Huber; “How China has become the world’s fastest expanding nuclear power producer” by Laura Gil. Divulgative read by the leading atomic and nuclear agency worldwide.

Deep Neural Networks 2017

“Deep neural networks (DNNs) are currently widely used for many artificial intelligence (AI) applications including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Accordingly, techniques that enable efficient processing of DNNs to improve energy efficiency and throughput without sacrificing application accuracy or increasing hardware cost are critical to the wide deployment of DNNs in AI systems. This article aims to provide a comprehensive tutorial and survey about the recent advances towards the goal of enabling efficient processing of DNNs.” Vivienne Sze, Yu-Hsin Chen, Tien-Ju Yang, Joel Emer. Full article here: > cs > arXiv:1703.09039

Inspire and be Inspired

Lemn Sissay, poet, is the Chancellor of The Manchester University since 2015. This is his institutional poem for the university and the city.

Inspire and be Inspired – A poem from Lemn Sissay VIDEO

“Open the dawn in the open sky the laboratory – open the book, open the challenge, with open eyes. Open. Out. Look. Open all minds, open all dreams, research, question Open all doors, open all senses Open all defences, ask: What were these closed for? In the possibilities of light, the nature of trust, the strength of unassailable us. How strong the night lies as light aeriates the dark and atomic dreams multiply from a graphene heart We who have walked the world in the name of here and where we came from stand in this great city and say: I belong here, I belong I bring my past, I bring my future, I bring my rights and I bring my song I stand atop The University of Manchester – we belong here, we belong”

Deep Learning with Python

A new book by Francois Chollet, the inventor of, is going to be released in Jan 2018 under the title Deep Learning with Python from Manning. It will present the state-of-the-art neural networks architectures and implementations for supervised learning, relying upon the TensorFlow library as the main backend engine of his own excellent Keras wrapper. The ebook is in progress but complete and available in pre-release already at a discount, coupon code from the free materials on the Manning webpage itself. Short tip from the late 2017 forefront: use gradient boosting machines for shallow learning and keep deep learning for perceptual problems.

PyTorch for Windows

Even if PyTorch for deep learning does not support Windows natively, there is an unofficial version being followed through this issue #494 topic on their GitHub repo. After one month away without my laptop, it is possible I’m going to need an update, provided that it does not break my hard earned stability with Anaconda on Python 3.5 and packages. PyTorch is the forefront these days, but I am not convinced feature engineering is past its best, so a small technological debt may save my stable install.


Getting older and realising time is limited, makes me have an eye only on people doing or trying something exceptional with a measurable impact. Work is not work anymore, just a playground to test tools and produce advancements. This should be the mission of research and it is fantastic I can do without the burden of administration, teaching or toxic environments. It is at the forefront of the cliff towards the unknown, with the need to build a way that is actually suspended in air until reinforcement comes from people behind and their communities, possibly not in my lifetime.

Nuclear failure?

The Bulletin of the Atomic Scientists is always interesting and provoking, even when the anti-nuclear stance prevails. The latest opinion by Mark Cooper, though, called “A dozen reasons for the economic failure of nuclear power” is so biased it is embarrassing at the least. Mind, the pro-nuclear camp should always read detractors in order to improve its practices, but when fundamentals are both wrong and used to support a narrative, nothing can be done.

Julia language

I was a fan of the Julia language a couple of years ago, I could see distributed computing and data science as natural targets for that kind of semantics. That said, I have struggled a lot to follow on, mostly because “Language level is what developers care about, but the majority of programmers are not developers.” and I am not a developer indeed. That’s why I am now a full Python language adept and very happily so.