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Uber for Money and UChicago Microbiome Center


Richard Waters,, Artificial intelligence in the cloud promises to be the next great disrupter, here. Maybe useful for Uber for Money.

With such capabilities, the thinking goes, companies will be able to infuse their own services with this intelligence thanks to the “AI in the cloud”. Using the public cloud to provide services over the internet is the first essential move. “You have to use the cloud to access large pools of publicly accessible information,” says Gartner’s Mr Austin.

According to Eric Schmidt, chairman of Google’s parent company Alphabet, operating in the cloud will allow companies to grow rapidly by tapping into crowdsourced data, from social media for example. Having access to machine learning and other forms of AI that companies like his can provide means the next truly disruptive businesses will emerge from this potent mix, he said.

U Chicago News, New Microbiome Center to merge expertise of Uchicage, MBL and Argonne, here. Reverse the BioSphere.

The new center dovetails with the White House Office of Science and Technology Policy’s National Microbiome Initiative, launched May 13 with the goal of bringing together public and private entities to advance the understanding of microbiome behavior and enable protection and restoration of healthy microbiome function.

Nicole Hemsoth, The Next Platform, Large Scale Weather Prediction at The Edge of Moore’s Law, here. Simply simulate all the funds with NIMo – make it like weather prediction. The computational form advantage in NIMo is no underlying PDE solvers so the program has better control of the L2 cache misses. You run Uber for Money Monte Carlo at  near peak throughput.

What is interesting on the computational front is that the future of weather prediction accuracy, timeliness, efficiency, and scalability seems to be riding a curve not so dissimilar to that of Moore’s Law. Big leaps, followed by steady progress up the trend line, and a moderately predictable sense of how progress could move along are no longer such a sure bet. As the weather modeling agencies look to the future of both systems and software, a reality check seems to be settling in, especially for what might be possible in 2025—just under a decade from now.

Applied to weather prediction, however, this parallel is not as simple as transistor counts and economies as scale. But just as other application areas are floundering in the post 2020 prediction pool in part because of declining computational efficiencies set against more complex modeling requirements and possibilities, weather has its own sets of concerns.

As we have described here in the context of new machine and research programs at NOAA, the national weather service in the U.S. and elsewhere in the world, the challenges over the next ten years for calculating in higher resolution over smaller geographic distances are far less focused on the ability to procure more “pure compute” and far more centered on scaling existing approaches in the wake of even more data from satellites and other instruments and the need for more focused resolution. For instance, one of the most highly respected global forecasting centers can run simulations at 9 km resolution, with an eye on 5 km over the next ten years. But because of limited scalability of numerical prediction models and the relative inefficient of available compute in the face of those demands, 1 km resolution is still out of range.


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