The journey and implications of Ethereum infrastructure

A car with a bitcoin in front
A car with a bitcoin in front
Photo by Executium on Unsplash

As the Bitcoin revolution was in full swing, young computer scientists had the idea to generalize this new technology to create digital applications.

Taking the open-source structure, Turing complete and peer-to-peer transaction of the blockchain, they created Ethereum, a digital infrastructure based on smart contracts and decentralized exchanges.

But this journey has not been a smooth one, and Ethereum has experienced the many threats of DOS attacks, scammers and fundraising to excess, which have put its fundamental mission at risk : enabling everyone to create and invest in a organization.

Nevertheless, these new technologies show the way to a new…


Learning is not only about feeding on bottom-up data

a strangely colored structure
a strangely colored structure
Photo by JJ Ying on Unsplash

While computer models need large amounts of data to identify a word, a human child only needs two or three occurrences to understand it. Where does this incredible ability to generalize knowledge come from?

From birth, the human brain relies on metacognitive rules to foster the learning of language, physical laws and human relationships. Whether to move intuitively in its environment or to guess a word meaning, human intelligence does not start from simple data but structure its knowledge on strong cognitive foundations.

Here is what these foundations tell us about machine learning models' capabilities and limitations.

The power of human innate meta-knowledge

There is a…


And how digital clocks boost this process

Photo by Héctor Achautla on Unsplash

To understand a speaker’s words or to perceive a melody, the human brain needs to spot precise delays of hundreds of milliseconds. Yet neurons have a lot of trouble counting accurately. So how do they achieve to beat and assess the tempo so reliably?

Actually, our neural structures perceive different time scales depending on the behaviors they regulate. This has enabled them to specialize on very specific times frames. More sophisticated tempo systems (like modern watches) have also helped them to memorize and predict their actions better. …


The prior linguistic revolution of the compiler

Computer screens displaying “no signal”
Computer screens displaying “no signal”
Photo by Rubenz Arizta on Unsplash

We all know the huge progress made by neural networks in understanding natural language. But we know less about how coders have taught machines to speak our language.

This revolution in data-processing compiling has made possible the many programming languages that we know today. By translating binary codes into a more usable language, they have made coding accessible to almost everyone.

According to Nick Polson and James Scott in AIQ, this revolution led to a second, which is the invention of natural language learning and recognition models. Here is how we got from one to the other.

The Invention of the compiler languages


The road from Big data to Open Data

A researcher in front of his computer
A researcher in front of his computer
Photo by National Cancer Institute on Unsplash

Big Data has so far mainly benefited to the profitability of private companies’ processes. A new social science now wants to put data at the service of human innovation.

Realizing the potential of computing machines to perform large-scale social experiments, they have invented new measurement tools to closely study and understand human behavior, all in transparency and for the benefit of users.

According to Alex Pentland in Social Physics, by giving back to individuals the control of their data, we can gain new insights on how ideas spread in human societies.

Here are the ambitious and open data analytics solutions…


New ideas spread between social groups like a virus

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The Big Data revolution is not just benefiting to computer researchers and IA studies. With the ability to measure and track behavior at any time and place, social sciences are also using the power of data to conduct experiments on a scale never before imagined.

According to Alex Pentland in Social Physics, by being able to collect, generalize and count a large set of data, they are free from traditional research limitations and human bias. With digital technologies, they especially noticed how ideas spread epidemically among social groups, finding their way within close social bonds.

Here are the new insights…


Deep Blue 1996’s victory was just a fresh start.

Garry Kasparov vs Deep Blue rematch
Garry Kasparov vs Deep Blue rematch
Garry Kasparov vs Deep Blue rematch

If Deep Blue 1996 rematch against champion Gary Kasparov was a victory for machine intelligence, it also showed how close computer science has always been to the study of chess. Researchers have constantly sought algorithmic refinements by testing them in this intuitive but very complex game. But it’s not just machines that have benefited from this stimulating playground.

According to Gary Kasparov in Deep Thinking, despite the domination of modern chess software, contemporary players are relying a lot on the cognitive power of these computers. They used them to deepen their game understanding, and learned to upgrade their own cognitive…


The virtues and limits of the brain-machine metaphor

A doctor looking at an MR
A doctor looking at an MR
Photo by National Cancer Institute on Unsplash

From the early 20th century, computer scientists have found great inspiration in the neural structures that neuroscientists were investigating.

According to Matthew Cobb in The Idea of the Brain, this is where the computational experts got the idea of thinking intelligence in a binary way. As neurons evolving and communicating together through nerve impulses, they imagined and conceived artificial systems that grow through binary feedbacks.

But current neuroscience has also shown how this metaphor can be misleading and doesn’t account fully for how human intelligence works. The brain is not a digital but analogic system, based on continuous neural signals…


We’ll still need very human ways to communicate and connect

a futuristic bar
a futuristic bar
Photo by Andreeew Hoang on Unsplash

Computers are seizing human cognitive abilities, but they won’t replace all human jobs.

Reaching a common agreement, taking care of other people, or triggering emotions are all very human ways to communicate and interact. In this sense, it’s not that these tasks have little chance of being replaced, it is that we need them precisely for their human presence. As, for many more years to come, a robot won’t ever have the same reassuring, human-like presence, some jobs will definitely stand the test of time.

Here are the purely human jobs that will most probably thrive in future robot-dominated societies…


Human and machine predictive abilities are complementary

A guy handling a keyboard on a factory machine
A guy handling a keyboard on a factory machine
Photo by ThisisEngineering RAEng on Unsplash

We find many ways to oppose AI and human intelligence. More powerful and cheaper, machine automation will replace workers. Conversely, workers with human and flexible skills put themselves against automation.

But few dare to consider humans and AI as allies who collaborate to make better decisions and increase their impact. According to Agrawal, Goldfarb and Gans in Prediction Machines, this is a pity, because these two types of intelligence are complementary, and would allow the creation of predictive devices with unprecedented accuracy.

Human cognitive flexibility and machines predictive’ scalability would help together to make more accurate and impactful decisions. …

Jean-marc Buchert

Man + Machine. Learn about human/machine interface and the human skills of the future : https://manplusmachines.com

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