SEATTLE – Cascadia Capital said it is launching one of the nation's first emerging growth investment banking practice groups dedicated to Robotics, Automation, and Artificial Intelligence (RAAI). Cascadia said its new RAAI group is well-positioned to provide the nuanced M&A and capital raising guidance business owners and entrepreneurs in this sector need as RAAI technology continues to upend critical industries, resulting in permanent market shifts. The RAAI group is built on the firm's history of advising companies in the RAAI space through its long-standing Industrials, Energy & Applied Technology, Healthcare, Consumer, and Food & Ag practice groups. The practice is led by Cascadia Chairman & CEO Michael Butler and Managing Directors Jamie Boyd and Firdaus Pohowalla, with support from Vice Presidents Yee Lee and Jason Lippenberger, Associate Tarek Elmasry, and Analysts Scott Whiting and Mikaela Slade. "We have been active in the RAAI space for several years with growing excitement and felt that the time was right to formally pull together the firm's expertise in a way that maximizes the service we deliver to our clients," Butler said.

LOS ALAMOS, N.M., Dec. 17, 2018--Machine-learning research published in two related papers today in Nature Geosciences reports the detection of seismic signals accurately predicting the Cascadia fault's slow slippage, a type of failure observed to precede large earthquakes in other subduction zones. Los Alamos National Laboratory researchers applied machine learning to analyze Cascadia data and discovered the megathrust broadcasts a constant tremor, a fingerprint of the fault's displacement. More importantly, they found a direct parallel between the loudness of the fault's acoustic signal and its physical changes. Cascadia's groans, previously discounted as meaningless noise, foretold its fragility. "Cascadia's behavior was buried in the data. Until machine learning revealed precise patterns, we all discarded the continuous signal as noise, but it was full of rich information. We discovered a highly predictable sound pattern that indicates slippage and fault failure," said Los Alamos scientist Paul Johnson. "We also found a precise link between the fragility of the fault and the signal's strength, which can help us more accurately predict a megaquake."

Machine-learning research published in two related papers today in Nature Geoscience reports the detection of seismic signals accurately predicting the Cascadia fault's slow slippage, a type of failure observed to precede large earthquakes in other subduction zones. Los Alamos National Laboratory researchers applied machine learning to analyze Cascadia data and discovered the megathrust broadcasts a constant tremor, a fingerprint of the fault's displacement. More importantly, they found a direct parallel between the loudness of the fault's acoustic signal and its physical changes. Cascadia's groans, previously discounted as meaningless noise, foretold its fragility. "Cascadia's behavior was buried in the data. Until machine learning revealed precise patterns, we all discarded the continuous signal as noise, but it was full of rich information. We discovered a highly predictable sound pattern that indicates slippage and fault failure," said Los Alamos scientist Paul Johnson. "We also found a precise link between the fragility of the fault and the signal's strength, which can help us more accurately predict a megaquake."