Lifeboat Foundation: Safeguarding Humanity
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Category: biotech/medical – Page 1,877

1. AI-optimized manufacturing

Paper and pencil tracking, luck, significant global travel and opaque supply chains are part of today’s status quo, resulting in large amounts of wasted energy, materials and time. Accelerated in part by the long-term shutdown of international and regional travel by COVID-19, companies that design and build products will rapidly adopt cloud-based technologies to aggregate, intelligently transform, and contextually present product and process data from manufacturing lines throughout their supply chains. By 2025, this ubiquitous stream of data and the intelligent algorithms crunching it will enable manufacturing lines to continuously optimize towards higher levels of output and product quality – reducing overall waste in manufacturing by up to 50%. As a result, we will enjoy higher quality products, produced faster, at lower cost to our pocketbooks and the environment.

Anna-Katrina Shedletsky, CEO and Founder of Instrumental.

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For three months, Chelsea Alionar has struggled with fevers, headaches, dizziness and a brain fog so intense it feels like early dementia. She came down with the worst headache of her life on March 9, then lost her sense of taste and smell. She eventually tested positive for the coronavirus. But her symptoms have been stranger, and lasted longer, than most.

“I tell the same stories repeatedly; I forget words I know,” she told me. Her fingers and toes have been numb, her vision blurry and her fatigue severe. The 37-year-old is a one of the more than 4,000 members of a Facebook support group for Covid survivors who have been ill for more than 80 days.

The more we learn about the coronavirus, the more we realize it’s not just a respiratory infection. The virus can ravage many of the body’s major organ systems, including the brain and central nervous system.

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France has identified its first outbreak of tick-borne encephalitis from consumption of raw milk products, with more than 40 people affected.

The infections are linked to eating a brand of raw milk goat cheese in Ain, in the Rhone-Alpes region, between April and May this year, according to Santé publique France.

The cheese producer is GAEC des Chevrettes du Vieux Valey, based at Condamine in Haut-Bugey, Ain. It is thought ticks carrying the virus contaminated a goat, then its milk, then the cheeses, and finally consumers.

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If Dr. Mainprize felt proud of his role in the breakthrough, he didn’t show it.

He was well aware of the significance of this achievement; it was potentially the key to tackling a wide range of illnesses, from brain cancer to Parkinson’s disease and Alzheimer’s disease – illnesses that are currently impossible or hard to cure. But he also knew he and his team at Sunnybrook Health Sciences Centre still had a long way to go before their work translated into actual treatment for patients, said his close friend and colleague Nir Lipsman.

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The kickoff of Lyft’s second challenge comes months after Waymo expanded its public driving data set and launched the $110,000 Waymo Open Dataset competition. Winners were announced mid-June during a workshop at the 2020 Conference on Computer Vision and Pattern Recognition (CVPR), which was held online this year due to the coronavirus pandemic.

Following the release of the Perception Dataset and the conclusion of its 2019 object detection competition, Lyft today shared a new corpus — the Prediction Dataset — containing the logs of movements of cars, pedestrians, and other obstacles encountered by its fleet of 23 autonomous vehicles in Palo Alto. Coinciding with this, the company plans to launch a challenge that will task entrants with predicting the motion of traffic agents.

A longstanding research problem within the self-driving domain is creating models robust and reliable enough to predict traffic motion. Lyft’s data set focuses on motion prediction by including the movement of traffic types its fleet crossed paths with, like cars, cyclists, and pedestrians. This movement is derived from data collected by the sensor suite mounted to the roof of Lyft’s vehicles, which captures things like lidar and radar readings as the vehicles drive tens of thousands of miles:

Logs of over 1,000 hours of traffic agent movement.

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Alphabet’s Sidewalk Labs plans to spin out some of its smart city ideas into separate companies, CEO Daniel Doctoroff said today at Collision from Home conference. Doctoroff listed three potential companies: mass timber construction, affordable electrification sans fossil fuels, and planning tools optimized with machine learning and computation design.

Last month, Sidewalk Labs killed its Toronto smart city project, which envisioned raincoats designed for buildings, heated pavement, and object-classifying cameras. Privacy advocates celebrated that the Google sister company would not be getting invasive power to surveil residents. But as I argued in my column that week, the story was far from over. Sidewalk Labs was using the COVID-19 pandemic as a scapegoat for the Toronto project, but the company wouldn’t stay idle.

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Now that the world is in the thick of the coronavirus pandemic, governments are quickly deploying their own cocktails of tracking methods. These include device-based contact tracing, wearables, thermal scanning, drones, and facial recognition technology. It’s important to understand how those tools and technologies work and how governments are using them to track not just the spread of the coronavirus, but the movements of their citizens.

Contact tracing is one of the fastest-growing means of viral tracking. Although the term entered the common lexicon with the novel coronavirus, it’s not a new practice. The Centers for Disease Control and Prevention (CDC) says contact tracing is “a core disease control measure employed by local and state health department personnel for decades.”

Traditionally, contact tracing involves a trained public health professional interviewing an ill patient about everyone they’ve been in contact with and then contacting those people to provide education and support, all without revealing the identity of the original patient. But in a global pandemic, that careful manual method cannot keep pace, so a more automated system is needed.

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