Stephen Hawking Bridged Science and Popular Culture

WSJ.com: WSJD - Technology

The University of Cambridge professor was an iconic figure in both the scientific community and in popular culture, known for his keen mind and humor, as well as his striking physical challenges. Dr. Hawking had long battled with amyotrophic lateral sclerosis, which left him wheelchair-bound for most of his life. Commonly known as Lou Gehrig's disease or motor neuron disease, the condition damages the nerves that control movement and results in paralysis. Patients with ALS typically die within five years of diagnosis. Dr. Hawking, who was diagnosed in 1963 at the age of 21, is believed to have been the longest-living survivor, a fact that still perplexes neurologists.


Stephen Hawking - Wikipedia

@machinelearnbot

Stephen William Hawking CH CBE FRS FRSA (8 January 1942 – 14 March 2018)[14][15] was an English theoretical physicist, cosmologist, author and Director of Research at the Centre for Theoretical Cosmology within the University of Cambridge.[16][17] His scientific works included a collaboration with Roger Penrose on gravitational singularity theorems in the framework of general relativity and the theoretical prediction that black holes emit radiation, often called Hawking radiation. Hawking was the first to set out a theory of cosmology explained by a union of the general theory of relativity and quantum mechanics. He was a vigorous supporter of the many-worlds interpretation of quantum mechanics.[18][19] Hawking was an Honorary Fellow of the Royal Society of Arts (FRSA), a lifetime member of the Pontifical Academy of Sciences, and a recipient of the Presidential Medal of Freedom, the highest civilian award in the United States. In 2002, Hawking was ranked number 25 in the BBC's poll of the 100 Greatest Britons. He was the Lucasian Professor of Mathematics at the University of Cambridge between 1979 and 2009 and achieved commercial success with works of popular science in which he discusses his own theories and cosmology in general. His book, A Brief History of Time, appeared on the British Sunday Times best-seller list for a record-breaking 237 weeks. Hawking had a rare early-onset slow-progressing form of motor neurone disease (also known as amyotrophic lateral sclerosis and Lou Gehrig's disease), that gradually paralysed him over the decades.[20][21] Even after the loss of his speech, he was still able to communicate through a speech-generating device, initially through use of a hand-held switch, and eventually by using a single cheek muscle. Hawking was born on 8 January 1942[22] in Oxford to Frank (1905–1986) and Isobel Hawking (née Walker; 1915–2013).[23][24] Despite their families' financial constraints, both parents attended the University of Oxford, where Frank read medicine and Isobel read Philosophy, Politics and Economics.[24] The two met shortly after the beginning of the Second World War at a medical research institute where Isobel was working as a secretary and Frank was working as a medical researcher.[24][26] They lived in Highgate; but, as London was being bombed in those years, Isobel went to Oxford to give birth in greater safety.[27] Hawking had two younger sisters, Philippa and Mary, and an adopted brother, Edward.[28] In 1950, when Hawking's father became head of the division of parasitology at the National Institute for Medical Research, Hawking and his family moved to St Albans, Hertfordshire.[29][30]


Novel AI Model Predicts Breast Cancer as well as Doctors

#artificialintelligence

Published today in the peer-reviewed journal Radiology, an IBM Research team created a new artificial intelligence (AI) model that can predict breast cancer malignancy and identify normal digital mammography exams as accurately as radiologists. Mammography, a low-dose x-ray procedure to image breasts, is considered the best breast cancer screening test available according to the American Cancer Society. However, mammograms are not always accurate. According to a U.S. 10-year study published in the New England Journal of Medicine, 23.8 percent of study participants had at least one false positive mammogram where breast cancer was not actually present. Furthermore, the American Cancer Society estimates that one in five screening mammograms are false-negatives that fail to detect existing breast cancer.


Learning Optimal Personalized Treatment Rules Using Robust Regression Informed K-NN

arXiv.org Machine Learning

We develop a prediction-based prescriptive model for learning optimal personalized treatments for patients based on their Electronic Health Records (EHRs). Our approach consists of: (i) predicting future outcomes under each possible therapy using a robustified nonlinear model, and (ii) adopting a randomized prescriptive policy determined by the predicted outcomes. We show theoretical results that guarantee the out-of-sample predictive power of the model, and prove the optimality of the randomized strategy in terms of the expected true future outcome. We apply the proposed methodology to develop optimal therapies for patients with type 2 diabetes or hypertension using EHRs from a major safety-net hospital in New England, and show that our algorithm leads to the most significant reduction of the HbA1c, for diabetics, or systolic blood pressure, for patients with hypertension, compared to the alternatives. We demonstrate that our approach outperforms the standard of care under the robustified nonlinear predictive model.


Brain-inspired artificial intelligence in robots

#artificialintelligence

Research groups at KAIST, the University of Cambridge, Japan's National Institute for Information and Communications Technology, and Google DeepMind argue that our understanding of how humans make intelligent decisions has now reached a critical point in which robot intelligence can be significantly enhanced by mimicking strategies that the human brain uses when we make decisions in our everyday lives. In our rapidly changing world, both humans and autonomous robots constantly need to learn and adapt to new environments. But the difference is that humans are capable of making decisions according to the unique situations, whereas robots still rely on predetermined data to make decisions. Despite the rapid progress being made in strengthening the physical capability of robots, their central control systems, which govern how robots decide what to do at any one time, are still inferior to those of humans. In particular, they often rely on pre-programmed instructions to direct their behavior, and lack the hallmark of human behavior, that is, the flexibility and capacity to quickly learn and adapt.