Software writing is slowly moving from human-written to computer generated using large amounts of data. Blockchain allows for crowdsourcing of cheap and diverse high quality data which wasn’t possible before.

The “IF” “THEN” approach

The Von Neuman computer model I used in all the computer we are familiar with from Desktop to Laptops via Smartphones and Tablets. To make the computer do what we want the typical computer programmer will, elegantly and through more advanced rules, basically write a list of “if” “then” conditions where all possible cases will hopefully be covered. For example: if you type W, move the character up. If you type S, move it down. If there is a wall in the direction of movement stop. And so on. The results are the computers we are familiar with today who use keyboards, mice, and in general digital inputs that are 0s or 1s and very clear. This also includes capacity sensing on smartphones which are “is the finger here or not”.

However, as seen in self-driving cars, recent advances in computer science are opening the door to computers using other inputs like “what they see”.

Neural Network programming

Programming these new computers is very different. A training environment is being setup. In this training environment a lot of known, labeled, organized data is being fed to the computer and the computer is being told what output is expected for this given data. And the computer itself organizes its code to make sure that when this data is fed as input, the output matches expectations. This so called “code” now is in fact very different. It is not human readable. It’s just a series of numbers that humans can not interpret. To be more precise it is often a neural network and the coding itself is about attributing numbers to the connections between neurons from adjacent layers.

Once a few sets of such data has been inputted and the desired outputs have been achieved then a copy of this “code” is being installed in the production environment. And as long as data that is similar to the training data is being fed, the outputs as the ones expected nearly always.

Data is the key

What is important here however is that the data, the amount of data and the quality of the data, and the clear outputs related to this data are in fact what is programming the computer. The human who feeds this data to the machine has a very limited impact on the resulting code and the code’s quality. It is instead crucial that the data be as big as possible, that it covers all possible inputs the computer can be presented with, and that it is labeled properly so that the desired outputs can be tested.

Therefore, the data is the key.

Let’s take computer visions, and more precisely enabling a computer to recognize indoor objects. The applications start with augmented reality glasses and go on and on, via software that provides posture advice to enabling robots to cook or clean for you.

To create the software that allows these systems to work one needs hundreds of thousands of pictures of home interiors. As well as hundreds of thousands of pictures of all the type of objects one usually finds inside a home. In the kitchen. In the office. In the bedroom. And ideally not only in an apartment in New York. But also on a farm in California. Also in an apartment in Tokyo or an apartment in Moscow. How can a company launch such a product without the data? Where can a company find the data?

Blockchain is the key to data

Blockchain allows for micro payments fast, efficiently and safely to anybody on the planet with an internet connection.

This allows companies to crowdsource data, for example labeled pictures, from anybody who is interested in participating and being compensated for it. Of course to assure that the data is accurate and clean, one can also use blockchain to make payments to other people, lets call them voters, to confirm that the pictures and its data are accurate. To make sure that the picture’s quality is good enough. And so on.

This is only possible now, as blockchain becomes more familiar to the data miner and to the companies who need the data. How else could a Silicon Valley startup pay hundreds of thousands of people $1 each for a bunch of pictures?

Furthermore the blockchain system ensures that once one company has obtained the data, the data miner can also sell the data to other companies. No one large company can take control and dominate this data marketplace or hike the prices. This data ecosystem is decentralized and enables a new function that was not possible without blockchain.

PIX ecosystem

We have started work on such an ecosystem in September 2017. We have developed the PIX ecosystem (https://www.pixtoken.co/) which is in beta stage at this time. We have started building an ecosystem around pictures to be used for computer vision. At a later stage this ecosystem can be also used to mine all kind of other user generated data from sound to video, to be used for computer voice recognition like Alexa or perhaps future machine learning and AI computer tools that haven’t even been invented yet.

About the author

George Popescu has been in the crypto space since 2011.

George is the co-founder and CEO of Lampix https://lampix.com/ , a table-top augmented reality company.

George is also the co-founder of the PIX ecosystem https://www.pixtoken.co/ and of Block X Ventures https://blockxventures.com/ , a blockchain-focused investment bank.

Previously he sold and exited his most successful company, Boston Technologies (BT) group, in 2014. George was the Founder and CEO of BT which he boot-strapped from $0 to a $20+ million business. BT has been #1 fastest growing company in Boston in 2011 according to the Boston Business Journal and has been on the Inc. 500/5000 list of fastest growing companies in the US for 4 years in a row ( #143, #373, #897 and #1270).

George earned the best journalist aware for Lending Times at the Lendit Industry Awards, 2017.

Previously George obtained 3 Master’s Degrees: a Master’s of Science from MIT working on 3D printing, a Master’s in Electrical Engineering and Computer Science from Supelec, France and a Master’s in Nanosciences from Paris XI University.