Our renewable future: favoring the next disruptive technologies

A solar concentrator.

We all agree on the necessity to switch to renewable energy sources. It will ensure energy security and climate change mitigation.

But a remaining question is to know what is the best power source option. There no good answer in that it really depends on several factors and even experts don’t share a common view.

The main factor to consider is certainly “where are you?”:

Some parts of the U.S. are windier than others, some are sunnier, and some have better access to hydroelectricity or geothermal resources…. You get the point. – Kate Gordon

It is in accordance with the increasing need of distributed energy (see here for the U.K.).

Distributed energy infrastructure requires the use of disruptive technologies that are able to locally enhance efficiency and energy storage.

The blockchain revolution

We are reaching some point where we will be required to pool resources. Not only to bring power to the developing world in a shared effort but also for optimizing energy distribution and allowing energy independency for both some legal entities at the top level and cities or districts at the lowest level.

Besides, the next electricity infrastructure will be far more resilient and be optimized in term of energy consumption. But the quest for energy optimization raised a concern about privacy. Smart meters are considered as a threat for privacy. GCHQ were forced to intervene because of insecure design. They are also pretty talkative: it is even possible to know what are your favorite TV shows.

Now, I’d like to say a few words on something new which have potential for revolutionizing the world.

I am convinced that blockchain is crucial for developing efficient locally distributed networks (see an initiative here). However, blockchain currently comes at a cost: it consumes a lot of power. But it could easily be tackled by well-optimized A.I. algorithms running on well-suited chips.

Besides the fact that research on every renewable energy sources should be favored to ensure resilience and adequate distribution, we have to develop better technologies, disruptive ones.

I will present you some initiatives that look promising.

Towards more efficient solar panels

First, Canadians have developed far more efficient solar panels using concentrators and efficient PV panels.

Canadian solar concentrators

Secondly, Chinese people have developed solar panels that work 24/7 (at least if it rains).

These two technologies are destined for a bright future. Especially since PV prices will keep dropping.

Hydropower and wave power… are some competitive sources

Harnessing marine current through its kinetic energy looks promising  since there are strong ocean currents.

A 2006 report from US department of Interior estimates that capturing just 1/1,000th of the available energy from the Gulf Stream would supply Florida with 35% of its electrical needs. – Wikipedia

EDF estimates that marine current power could generate up to 12,5 GW in Europe. It is equal to the energy that we can get from 14 nuclear reactors.

There are innovative open-flow devices just like this one were central rotor is the only moving part:


Credits: france info

It was invented by EDF and DCNS in France.

In order to both limiting impact on animals, sediments and plants & functioning even at low currents some initiatives were proposed. Like open-flow devices such as fish-friendly turbines in rivers.

I find this other one really interesting. It functions by converting the wave power and it is inspired by eels (an example of biomimetics) and is experimented in cooperation with ifremer… Its energy efficiency is really high and it works at low currents.


Credits: EEL energy

A better future?

As you may see there are plenty of disruptive technologies that can help us. Such technologies are more than necessary. They are of public interest. That is why both R&D and their use must be encouraged.

However technology is only a mean among many others. We also need a global awareness of the effets our habits have and public policies have an essential role to play.

A PM simply explains quantum computing

I’d like to share with you the following video that is extraordinary:

For your information, he’s the canadian PM.

One of the best chips is bad at maths

I will share with you a promising innovation in chips. A MIT project that was funded by DARPA resulted in a chip …

capable of processing frames almost 100 times faster than a conventional processor restricted to doing correct math—while using less than 2 percent as much power



The chip was actually doing imprecise summations but it was enough to perform very well on some hard tasks which do not required precise calculations. That is exactly what one does not expect from a chip’s functioning!

You can find more detail in this MIT review.

Human mind to solve quantum physics problems

I’d like to briefly share with you an interesting example of gamification in quantum physics.

schrodinger equation

You can find the corresponding article here.

The point is that computers used to fail at solving a quantum equation. So a team created a game ruled by quantum rules which was played by 10.000 people.

quantum moves

This game was an analogy of this quantum problem that may lead to the creation of a quantum computer: Quantum moves models the moves of an electron (its wave function corresponds to a “fluid” in the game) towards a crystal (the target). The tool at the bottom of the screen is actually a LASER that moves the electron to the target without impairing its quantum state.

Guess what?

Gamers outperformed algorithms in terms of speed and performance. Actually, they solved the problem leading to an accurate model.eyewire

It was not the only one example of gamification that have succeeded. In the past, Foldit let people explore the possibilities in protein structures. An other example is Eyewire, a game that lets you explore a brain and, actually, map it.

A geeky map

I’d like to say a few words about my latest impulsive buy: a geeky map about British TV shows.UK TV shows map


Graphic designer Tim Ritz has created The Great British Television Map, stretching from Nothern Ireland and Scotland down to London.

You can buy it (free shipping until today, 12 P.M) here.

An affordable credit card-sized supercomputer by NVIDIA

Jetson TX1

NVIDIA announced yesterday the Jetson TX1, a small form-factor Linux system-on-module, credit card sized for various application ranging from autonomous navigation to deep learning-driven inference and analytics.

Jetson TX1

It will soon be available as development kit, e.g. a mini-ITX carrier board that includes the pre-mounted module and has low power consumption which provides an out of the box desktop user experience (it comes with a linux’s ubuntu custom distribution). Unfortunately, the development kit requires a USB hub to work with a keyboard and a mouse and the 16GB eMMC memory storage is probably too few.

Since I really enjoyed performing artificial intelligence at the university and during an experience as contractor in a public research center, I think I will ask the developer kit for christmas. I plan to use it as media center, intelligent home automation and for personal deep learning projets.

You may wonder why I chose this solution? Just because this card packs several interesting characteristics:

  • a Tegra X1 SoC : an ARM A57 CPU and a Maxwell-based GPU packing 256 CUDA cores (delivering 1 teraflop at 5.7W, i.e. the same peak speed as a small 15 years old supercomputer!)
  • 4GB of RAM shared between the CPU and GPU

It sounds interesting to me.

Deep learning for everyone!


That’s great news! Google just open-sourced TensorFlow, its deep (machine) learning library.

The engine is widely used at Google: by speech recognition systems, in the new Google photo product, in Gmail, in search, etc.


From now on startups will be able to develop systems as intelligent as a 4 year old children. More interestingly, code sharing in python between researchers or data scientists has never been easier.

The limitations of the previous system no longer exist:

[DistBelief] was narrowly targeted to [artificial] neural networks, it was difficult to configure, and it was tightly coupled to Google’s internal infrastructure — making it nearly impossible to share research code externally. […] TensorFlow has extensive built-in support for deep learning, but is far more general than that — any computation that you can express as a computational flow graph, you can compute with TensorFlow (see some examples). Any gradient-based machine learning algorithm will benefit from TensorFlow’s auto-differentiation and suite of first-rate optimizers. And it’s easy to express your new ideas in TensorFlow via the flexible Python interface.

Maybe the engine will soon get available for its cloud-based service on a clustered architecture…