Will go to MECSPE Parma on Saturday and I am having a look at the nearly 2000 exhibitors websites for introductory e-mails about r&d external collaboration, where opportune. Apart of industrial buildings, very often paraded with pride, one useful piece of info is that many shops in the mechanical field use Dassault SolidWorks as a 3D software package for their duties. It is quite expensive to sustain a single-seat license without a stream of work, but the freeware FreeCAD is not ready at commercial level yet.
The most comprehensive resource I found about blockchain for industrial IoT is a late 2016 paper by Bahga and Madisetti, which implements an Ethereum application for the supply chain in the manufacturing field on top of a cloud-based model. The problem here is adoption, with big corps like IBM working on their own version. This solution may well stay on paper and die, but they are funded by unis so ok. On the other hand, for indie devs the point is in getting paid some gas / cryptocurrency fraction for providing the apps and then get real money for it. While b2b is going towards private networks of ledgers, b2c may be more open, a la bitcoin, and I want to do something here.
Can you treat deep learning as a blackbox? It is not advisable but perfectly doable if you use Keras 2, released three days ago and now better integrated with Google TensorFlow (other than the much simpler Theano). How to have it then? Install Anaconda using this tutorial and you are off to go, Anaconda is the reference package to many Windows users and me. What to do with deep learning in the industrial or IoT field? Told you that already and more to come, ehehe.
Going to a seminar at Polo Tecnologico about startup laws in Italy for 2017 this afternoon, for DronesBench. Basically, it is a set of concessions the fiscal regulator makes to startups in order to favour their growth for three to five years after incorporation: less taxes, credits for services, easier path to regulate affairs. Italy is full of these for new business entities, the package for tech and r&d startups is a more tailored dress that may really help proper r&d ideas flourish.
It is a long shot for small nuclear services firms from Italy to work in the nuclear field abroad. The market is illiquid, very few and big projects for which consortia is the only way. Building relationships is much tougher than business though, it is geopolitics and also corporate or academic interests at play. Venture capital is not financing these and banks do not lend to such firms. The only way to grow is own r&d and conventional business in the mechanical industry.
Many thanks to the IT insider I have in Pisa! He introduced the blockchain tech to me and the Ethereum API for development https://www.ethereum.org , being Ethereum the tool of choice for big b2b firms worldwide. I am going to have a look at two or three possible cases in the industrial fields I follow and start development on Monday. Target: an MVP to sell as soon as possible, with 10% of the proceedings going to the insider above.
A big faire named MECSPE http://www.mecspe.com in Parma on 23-25 March will be dedicated to technologies for innovation and the industry 4.0. With almost 2,000 exhibitors enrolled, it is an opportunity for me to interact directly with the most suitable for a r&d collaboration. That’s why I’m checking the list in full, having a look at websites and sending speculative e-mails. Fingers crossed for an interesting trip there.
I am trying to understand if the blockchain technology can help me produce a fast application for industrial uses. It was a nice hint given by an old friend in Pisa, in that fully justifying my trip here already. Blockchain for sensors in the nuclear industry as a way to avoid power plants hacking then? Something more, I hope, that is to say blockchain as a way to manage outliers from the monitoring levels through secure IoT. Will it work? Literature review this week.
Very good article by Datafloq here about industrial data science. Big manufacturing firms use real-time monitoring systems to ensure their products are without defects. They need neither false negatives nor false positives because the components they produce are expensive. Implementation of reliable and repeatable process-oriented data science architectures is mandatory: CRISP-DM, process mining, graphical analyses. Domain knowledge gives depth, while an understanding of corporate politics helps to be compliant with limits and laws. Still way to go before protocols are defined, so ample room for innovation. I might try something in the line with my DataBot.it.
It is a pity one nuclear startup, a darling of the media in the US, has been exposed for overpromising and therefore forced to backtrack from their foundational claims. What is it going to happen next time? Young & smug is not good in the nuclear industry, which is slow and considerate, and rightly so. They may find solace in the old adage from Beckett and fail better. On a side note, paper reactors always seem easy & cheap.