Building trust and intelligence in a digitalised world

Universities play a key role, not only in advancing technology, but also in educating future practitioners to consider the human element and impact. Banks, insurance companies, energy grids and many other central authorities control and manage much of the infrastructure of society and largely contribute to the Swiss economy. The world depends on these authorities to serve as intermediaries and ensure a level of trust in financial, contractual, and other societal interactions.

When these systems fail us, as they did in the global financial crisis of 2007 – 2008, researchers and developers turned to the Blockchain - a new language for accounting that enables the decentralisation of conventional systems using cryptography to foster trust and transparency.

To Ashley Taylor, LO3 Energy, Blockchain is more than just a technology, “At its core, Blockchain was a cultural reaction to the problems with centralised decision making power…a means to empower people and move them closer to the decisions that influence their daily lives.”

To others, like Tadge Dryja, MIT Media Lab, the applications are still an open question – an opportunity for ETH Zurich “to bridge the knowledge gap that exists between the developers of Bitcoin and academic literature.”

Blockchain technology allows us to bypass central authorities, “For the first time in human history we can have a banking system without a central bank – one that can execute contracts among mutually mistrusting peers".

"We are on the frontier of a new technology that allows people to interact directly with more transparency,” says Arthur Gervais, an Information Security researcher at ETH Zurich.

Machine learning reinvents the old adage, “knowledge is power.” As academics and industry leaders apply machine learning in new and diverse ways it liberates them from the tasks that computers can perform faster and with a much higher-level of efficiency.

Creating more than just efficiency, machine learning spawns deep and meaningful collaborations across academic disciplines from computer science to astrophysics and neuroscience. ETH professors Ce Zhang, a computer scientist and Kevin Schawinski, a professor of Galaxy and Black Hole astrophysics are leveraging machine learning tools to clean up images from space.

“Artificial intelligence is now allowing us to focus on the science of astrophysics research – to extract more knowledge from images taken by telescopes than with conventional methods. Even more exciting, AI is on the verge of giving us a completely new way to look at the universe as we watch neural networks make sense of objects such as galaxies,” says Schawinski.

Have we really thought about the consequences of machine learning? How will its applications influence our lives? Joanna Bryson, University of Bath suggests that “While many scan the horizon looking for killer robots with glowing eyes, or amorphous super intelligent puppet masters, humanity has been quietly augmenting itself with artificial intelligence…to understand the long-term consequences of AI, we need first to better examine our present.”

Examining the “present” is exactly what The New York Times did when the advent of the internet challenged the existing business model of major print newspapers. Chris Wiggins, Chief Data Scientist at The New York Times spoke about how he and his collaborators use machine learning to “empower the journalistic mission.”

As a neuroscientist who studied at ETH Zurich, Pascal Kaufmann builds artificial brains for large companies. He sees ETH as perfectly positioned to take on a leading role in the world in terms Artificial Intelligence.

Hoping ETH will crack the brain code, Kaufmann says, “ETH Zurich is able to attract the best and brightest talents in the world. If we laser-focused them on the right questions, I think that Switzerland could become the AI epicentre of the world.”