GAFAM businesses (Google, Amazon, Facebook, Apple, Microsoft) are now showing their full supremacy on the web. By providing unlimited computing and data resources through low-cost services, they have conquered the data that fuels their business model.

Yet, according to George Gilder in The Life After Google, this model looks structurally energy-intensive and unreliable, based on highly-centralized computers and databases locations. It might be hitting a wall: an ever-growing data consumption marginal cost.

Instead, blockchain technologies are promising open and globally distributed computational resources, which thus offer much better safety and computing power opportunities. …

Since the development in the 2000s of user-friendly digital products, software designers have tried to keep their user by bringing them immediate and constant rewards. And this strategy has always been quite effective to quickly scale products.

But by stimulating short-term cognitive motivations, customers can often feel like being stuck in superficial interpretation of their needs. They might want products that they can use and customize to their intentions, that help them set clear goals and achieve deeper aspirations.

Here are 4 stories that show how product design can help users learn, excel and grow in their fields.

The tribalisitic journey of social media users

Since the advent of industrial design in the 1960s, designers have fully learned to adapt their products to the implicit needs of users.

This user-friendliness of machines is now pushed to its very end. As they talk with us, anticipate our mistakes, are polite and empathetic, smart devices and apps have literally become our best friends.

According to Cliff Kuang and Robert Fabricant in User Friendly, product designers have learned to create a real human-robot connection. However, not without some serious limitations : users have psychological expectations and cultural imaginary that still cannot be broken easily.

Here is how these…

Since the discovery of neurons, neuroscientists have kept inventing new ways to visualize patterns of brain activity. With fMRI technology, they have found a tool worthy of their ambition to literally decode brains. Neuroscientists can now predict and accurately connect neural functions with cognitive behaviors by running computing models like a data scientist. But according to Russell. A. Poldrack in The New Mind Readers, these techniques have the same biases and limitations as artificial intelligence models, and require a rigorous scientific methodology.

Here is the history and implications of this technology, and what it can teach us about the brain-machine…

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…

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…

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. …

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