More than half of the children in the United States play video games on Roblox.

In fact, the massively popular gaming platform has grown so much during the coronavirus pandemic, the company’s valuation has skyrocketed from $4 billion in early 2020 to $30 billion in early 2021.

What makes Roblox so popular?

In this talk, Phillip Ball explains why quantum mechanics is not weird.

Quantum computers rely on concepts such as superposition and entanglement that defy our intuitions about how things can behave. It’s often said that the world is quantum-mechanical and weird at small scales, and classical and familiar at human scales.

I will challenge that idea, arguing that the classical world isn’t distinct from the quantum but emerges from it. While we don’t yet have a full understanding of how that happens, the outlines are becoming clear – and in one view, the concept of quantum information lies at the heart of that account. In this talk – which is not-technical and requires no specialist scientific knowledge – I will show address some popular misconceptions about what quantum mechanics means, and explain what we can currently say about what it does mean.

Microsoft Research hosts this talk on Automating ML Performance Metric Selection

From music recommendations to high-stakes medical treatment selection, complex decision-making tasks are increasingly automated as classification problems. Thus, there is a growing need for classifiers that accurately reflect complex decision-making goals.

One often formalizes these learning goals via a performance metric, which, in turn, can be used to evaluate and compare classifiers. Yet, choosing the appropriate metric remains a challenging problem. This talk will outline metric elicitation as a formal strategy to address the metric selection problem. Metric elicitation automates the discovery of implicit preferences from an expert or an expert panel using relatively efficient and straightforward interactive queries.

Beyond standard classification settings, I will also outline early work on metric selection for group-fair classification.