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Deep Learning Reinvents the Hearing Aid

Finally, wearers of hearing aids can pick out a voice in a crowded room

13 min read
Photo: Dan Saelinger/Trunk Archive
Photo: Dan Saelinger/Trunk Archive

My mother began to lose her hearing while I was away at college. I would return home to share what I’d learned, and she would lean in to hear. Soon it became difficult for her to hold a conversation if more than one person spoke at a time. Now, even with a hearing aid, she struggles to distinguish the sounds of each voice. When my family visits for dinner, she still pleads with us to speak in turn.

My mother’s hardship reflects a classic problem for hearing aid manufacturers. The human auditory system can naturally pick out a voice in a crowded room, but creating a hearing aid that mimics that ability has stumped signal processing specialists, artificial intelligence experts, and audiologists for decades. British cognitive scientist Colin Cherry first dubbed this the “cocktail party problem” in 1953.

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Meta Aims to Build the World’s Fastest AI Supercomputer

The AI Research SuperCluster could help the company develop real-time voice translations

3 min read
A brightly lit, high-ceilinged room with rows of silvery-black cabinets and yellow pipes near the ceiling.

Meta's new AI supercomputer.

Meta

Meta, parent company of Facebook, says it has built a research supercomputer that is among the fastest on the planet. By the middle of this year, when an expansion of the system is complete, it will be the fastest, Meta researchers Kevin Lee and Shubho Sengupta write in a blog post today. The AI Research SuperCluster (RSC) will one day work with neural networks with trillions of parameters, they write. The number of parameters in neural network models have been rapidly growing. The natural language processor GPT-3, for example, has 175 billion parameters, and such sophisticated AIs are only expected to grow.

RSC is meant to address a critical limit to this growth, the time it takes to train a neural network. Generally, training involves testing a neural network against a large data set, measuring how far it is from doing its job accurately, using that error signal to tweak the network’s parameters, and repeating the cycle until the neural network reaches the needed level of accuracy. It can take weeks of computing for large networks, limiting how many new networks can be trialed in a given year. Several well-funded startups, such as Cerebras and SambaNova, were launched in part to address training times.

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Rooftop Drones for Autonomous Pigeon Harassment

Have invasive flying rats met their match?

3 min read
A pigeon on a poop covered wooden plank.
iStock photo

Feral pigeons are responsible for over a billion dollars of economic losses here in the United States every year. They’re especially annoying because the species isn’t native to this country—they were brought over from Europe (where they’re known as rock doves and are still quite annoying) because you can eat them, but enough of the birds escaped and liked it here that there are now stable populations all over the country, being gross.

In addition to carrying diseases (some of which can occasionally infect humans), pigeons are prolific and inconvenient urban poopers, deploying their acidic droppings in places that are exceptionally difficult to clean. Rooftops, as well as ledges and overhangs on building facades, are full of cozy nooks and crannies, and despite some attempts to brute-force the problem by putting metal or plastic spikes on every horizontal surface, there are usually more surfaces (and pigeons) than can be reasonably bespiked.

Researchers at EPFL in Switzerland believe that besting an aerial adversary requires an aerial approach, and so they’ve deployed an autonomous system that can identify roof-invading pigeons and then send a drone over to chase them away.

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Adhesives Gain Popularity for Wearable Devices

Adhesive formulations help with challenging assembly of wearables and medical sensors

3 min read

A major challenge in wearable device assembly is to maximize the reliability of embedded circuits while keeping the package thin and flexible.

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This is a sponsored article brought to you by Master Bond.

Master Bond adhesive formulations provide solutions for challenging assembly applications in manufacturing electronic wearable devices. Product formulations include epoxies, silicones, epoxy-polyurethane hybrids, cyanoacrylates, and UV curing compounds.

There are some fundamental things to consider when deciding what is the right adhesive for the assembly of electronic wearable devices. Miniaturization of devices, and the need to meet critical performance specifications with multiple substrates, require an analysis of which chemical composition is most suitable to satisfy the required parameters.

These preliminary decisions are often predicated on the tradeoffs between different adhesive chemistries. They may vary widely, and in many cases are essential in achieving the needed goals in adhering parts and surfaces properly.

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