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Honda is pulling away from a design practice that’s (literally) shaped auto making since the ’30s.

The $43 billion company still depends on life-size clay models to evaluate its designs, a tried and true method pioneered by GM designer Harley Earl. But Honda is gradually relying less on the practice, ever since the Coronavirus tore across the globe and resulting lockdowns divided its teams in Los Angeles, Ohio and Japan. The way Honda tells it, those early 2020 travel rules “threatened” its designers’ ability to work with engineers on the ’24 Prologue, creating a window for a deeper dive into virtual reality.

Researchers have discovered that salen can effectively bind a number of proteins of the coronavirus SARS-CoV-2.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the official name of the virus strain that causes coronavirus disease (COVID-19). Previous to this name being adopted, it was commonly referred to as the 2019 novel coronavirus (2019-nCoV), the Wuhan coronavirus, or the Wuhan virus.

In June 2022, Amazon re: MARS, the company’s in-person event that explores advancements and practical applications within machine learning, automation, robotics, and space (MARS), took place in Las Vegas. The event brought together thought leaders and technical experts building the future of artificial intelligence and machine learning, and included keynote talks, innovation spotlights, and a series of breakout-session talks.

Now, in our re: MARS revisited series, Amazon Science is taking a look back at some of the keynotes, and breakout session talks from the conference. We’ve asked presenters three questions about their talks, and provide the full video of their presentation.

On June 24, Alexa AI-Natural Understanding employees Craig Saunders, director of machine learning, and Devesh Pandey, principal product manager, presented their talk, “Human-like reasoning for an AI”. Their presentation focused on how Amazon is developing human-like reasoning for Alexa, including how Alexa can automatically recover from errors such as recognizing “turn on lights” in a noisy environment (instead of “turn off lights”) when the lights are already on.