AI Could Provide Moment-by-Moment Nursing for a Hospital’s Sickest Patients

Behnood Gholami, Wassim M. Haddad and James M. Bailey, AI Could Provide Moment-by-Moment Nursing for a Hospital’s Sickest Patients, IEEE Spectrum, 24 Sep 2018

At our company, Autonomous Healthcare, based in Hoboken, N.J., we’re designing and building some of the first AI systems for the ICU. These technologies are intended to provide vigilant and nuanced care, as if an expert were at the patient’s bedside every second, carefully calibrating treatment. Such systems could relieve the burden on the overtaxed staff in critical-care units. What’s more, if the technology helps patients get out of the ICU sooner, it could bring down the skyrocketing costs of health care. We’re focusing initially on hospitals in the United States, but our technology could be useful all around the world as populations age and the prevalence of chronic diseases grows. 

The benefits could be huge. In the United States, ICUs are among the most expensive components of the health care system. About 55,000 patients are cared for in an ICU every day, with the typical daily cost ranging from US $3,000 to $10,000. The cumulative cost is more than $80 billion per year.

Models Will Run the World

Steven A. Cohen and Matthew W. Granade, Models Will Run the World, Wall Street Journal, Aug. 19, 2018  

Tencent, the Chinese social-media giant and maker of WeChat , is one of our favorite examples of this new business model. A Tencent executive told us last fall: “We are the only company that has customer data across social media, payments, gaming, messaging, media, and music, and we have this information on [several hundred] million people. Our strategy is to put this data in the hands of several thousand data scientists, who can use it to make our products better and to better target advertising on our platform.” That unique data set powers a model factory that constantly improves user experience and increases profitability—attracting more users, further improving the models and profitability. That’s a model-driven business....

好好投資科技的 AI 深度學習演算系統

陳君毅，曾為上流社會理財，兩位外資金童拚創業，把頂級財富管理帶給普羅大眾，數位時代，2018.09.25

和碩布局車用電子

王郁倫，奧迪跨入電動車，和碩代工練兵十年、迎來春天，數位時代，2018.09.21

和碩技術長黃中于是和碩集團技術研發幕後推手，內部同仁暱稱「黃博」，他接受《數位時代》訪問指出，未來汽車將有四大趨勢，分別是：自駕車、新能源、通訊技術（如SOS智慧緊急求助Intelligent Emergency Call、道路救援Breakdown cover）及共乘。 

和碩布局車用電子多年，今年COMPUTEX上展示曲面玻璃接合技術，用在車內裝儀表板電腦，並預告開發自駕車關鍵行動車聯網技術V2X（Vehicle-to-everything）及5G車載通訊技術，該通訊技術由高通、奧迪、福特及5G汽車協會（5GAA）共同推出，是自駕車關鍵技術。 

V2X車聯網技術預計2020年成熟，被視為自駕車的關鍵技術，是因可以讓行駛於道路上的自駕車輛，對周遭的車輛、行人與各項設施進行資料傳輸與分享，讓駕駛提早獲得潛在危險警示，避免意外事故，比方在十字路口，預先知道對向車道有來車，特別的是，警訊靠的不是影像技術，而是通訊技術，所以不會有機器視覺「盲點」。...

Efficient tuning of online systems using Bayesian optimization by Facebook

Ben Letham, Brian Karrer, Guilherme Ottoni, and Eytan Bakshy, Efficient tuning of online systems using Bayesian optimization, Facebook Research, September 17, 2018

A/B tests are often used as one-shot experiments for improving a product. In our paper Constrained Bayesian Optimization with Noisy Experiments, now in press at the journal Bayesian Analysis, we describe how we use an AI technique called Bayesian optimization to adaptively design rounds of A/B tests based on the results of prior tests. Compared to a grid search or manual tuning, Bayesian optimization allows us to jointly tune more parameters with fewer experiments and find better values. We have used these techniques for dozens of parameter tuning experiments across a range of backend systems, and have found that it is especially effective at tuning machine learning systems.... 

We have used the approach described in the paper to optimize a number of systems at Facebook, and describe two such optimizations in the paper. The first was to optimize 6 parameters of one of Facebook’s ranking systems. These particular parameters were involved in the indexer, which aggregates content to be sent to the prediction models. The second example was to optimize 7 numeric compiler flags for HHVM. The goal of this optimization was to reduce CPU usage on the web servers, with a constraint on not increasing peak memory usage.