Photo Source: ClimateAi
The consequences of climate change have never felt more real. Canada recorded three consecutive days of record breaking heat. California is entering its worst drought in 1200 years. Wildfires are once again raging in the northwest. And flash floods in Germany and China in the last two weeks have left a devastating and tragic toll.
The urgent need to adapt to the climate crisis was an important consideration in Radical’s decision to lead the series A funding round in ClimateAi with follow-on investment from Robert Downey Jr.’s FootPrint Coalition, a fund dedicated to technologies that combat Climate Change.
The world’s biggest companies face $1 trillion in climate change risks. Although every major supply chain relies on weather forecasting to inform operational and strategic decisions, traditional forecasting is highly inaccurate beyond two-week timeframes and climate modelling can only inform vague long-term risks. ClimateAi’s enterprise climate platform (ECP) leverages best-in-class weather simulations and proprietary deep learning (AI) methods to make better weather predictions over a longer period: starting at 2 weeks and stretching to 10 years. Initial customers include agricultural firms which can adjust to adverse weather sooner, ensuring continuity of the food supply chain. Increasing the medium to long-term accuracy in weather forecasting means communities and supply chain stakeholders are better equipped to face the realities and risks of climate change.
5 Noteworthy AI and Deep Tech Articles: week of July 26, 2021
1) Untether AI nabs $125M for AI acceleration chips (VentureBeat)
The next-generation of AI breakthroughs will depend on next generation hardware. Radical was an early investor in Untether AI, co-leading its Series A fundraising and leading an extension round, as it developed groundbreaking new technology for the acceleration of AI inference workloads. Radical is excited to announce its participation in Untether AI’s latest funding round as its world-leading AI chips are commercialized and the next iteration is developed.
2) DeepMind says it will release the structure of every protein known to science (MIT Technology Review)
Last week we noted two important advances in the field of computational biology with the open source release of the AlphaFold and the competing RoseTTAFold system. This week, DeepMind has shared high-quality predictions for the shape of every single protein in the human body, as well as for the proteins of 20 additional organisms that scientists rely on for their research. Most of the predicted shapes have not yet been verified in the lab, but the proposed protein almanac could have extraordinary benefits to science, accelerating research across sectors from drug discovery to plastics recycling.
3) Can AI grade your next test? (New York Times)
Neural networks could give online education a boost by providing automated feedback to students.The automated system built by a team of Stanford researchers has been used as a means to reach more students than instructors could otherwise reach on their own. Although the system has only been implemented for a coding course, a system like this could help instructors better understand which students need help and how to help them.
4) AI backpack concept gives audio alerts to blind pedestrians (Mashable)
An AI housed in a backpack outfitted with cameras could assist people with low or no vision better navigate cities. Jagadish Mahendran, a computer vision researcher at the University of Georgia’s Institute for Artificial Intelligence, found it “ironic that he had helped develop machines — including a shopping robot that could ‘see’ stocked shelves and a kitchen robot — but nothing for people.”
5) Mind Map — best science images (Nature)
An intricately detailed image charting the connections between thousands of cells in a tiny sliver of a human brain has been made possible with the help of AI. The sample was taken from the cortex and cut into thin slices that were imaged using electron microscopes. Scientists then stitched the images back together digitally and analysed them with the help of AI programs.
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