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I’m doing an abbreviated newsletter this week as I want to spend most of my energy amplifying the brave journalists on the ground reporting about this scary time. As so many have said — far more eloquently than me — the invasion of Ukraine is a story that impacts all of us, whether we’re on the ground there or not. And it’s hard to celebrate a funding round when scary times are the moment.

My brilliant colleagues put together a story on how the tech industry is responding to Russia’s invasion of Ukraine; I urge you to read it. While the situation is still ongoing, it’s clear that it’s already a tech story. And startups such as Grammarly, Ajax, People AI and Preply, backed by some of the world’s biggest VCs, are scrambling to support employees and operations amid the invasion.

Now, that’s a superpower!What if you could speak every language on Earth? You would travel around the globe with ease making new friends everywhere.

The firm is working on building an A.I.-focused universal instantaneous speech translator and it could forever change how we communicate.

A powerful translating A.I.

CEO of Meta Mark Zuckerberg announced this new development during an online event Wednesday. “The ability to communicate with anyone in any language — that’s a superpower people have dreamed of forever, and A.I. is going to deliver that in our lifetimes,” he said.

A new machine-learning technique can pinpoint potential power grid failures and cascading traffic bottlenecks, in real time.

A new machine-learning technique could pinpoint potential power grid failures or cascading traffic bottlenecks in real time.

Identifying a malfunction in the nation’s power grid can be like trying to find a needle in an enormous haystack. Hundreds of thousands of interrelated sensors spread across the U.S. capture data on electric current, voltage, and other critical information in real time, often taking multiple recordings per second.

Sounds provide important information about how well a machine is running. ETH researchers have now developed a new machine learning method that automatically detects whether a machine is “healthy” or requires maintenance.

Whether railway wheels or generators in a power plant, whether pumps or valves—they all make sounds. For trained ears, these noises even have a meaning: devices, machines, equipment or rolling stock sound differently when they are functioning properly compared to when they have a defect or fault.

The sounds they make, thus, give professionals useful clues as to whether a machine is in a good—or “healthy”—condition, or whether it will soon require maintenance or urgent repair. Those who recognize in time that a machine sounds faulty can, depending on the case, prevent a costly defect and intervene before it breaks down. Consequently, the monitoring and analysis of sounds have been gaining in importance in the operation and maintenance of technical infrastructure—especially since the recording of tones, noises and acoustic signals is made comparatively cost-effective with modern microphones.