Strategic implications of Chinese/US AI development

Discussion in 'Strategic Defense' started by shifty_ginosaji, Nov 13, 2018.

  1. Hendrik_2000

    Hendrik_2000 Brigadier

    Dec 20, 2006
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    Via Taishang
    Half of world's top AI unicorns come from China

    Six of the 11 artificial intelligence (AI) startups that are considered to be unicorns – which means to have a value of one billion U.S. dollars or above – come from China, according to CB Insights, a research firm that tracks venture capital and startups.

    SenseTime took the top spot with a valuation of 4.5 billion U.S. dollars, followed by Yitu Technology at 2.3 billion U.S. dollars and smaller unicorns 4Paradigm, Horizon Robotics and Momenta.

    The annual report published by CB Insights compiles a list of 100 of the most promising private companies. The selection is based on several factors, including patent activity, investor profile and market potential. From emerging startups to established unicorns, the cohort is a mix of startups in different stages of funding and product commercialization.

    Twenty-three startups on the list are headquartered outside the U.S., including six each from China, Israel, and the United Kingdom.

    China accounted for 17 of the top 20 academic institutions involved in patenting AI and was particularly strong in the fast-growing area of “deep learning” – a machine-learning technique that includes speech recognition systems.

    The country unveiled a national AI development plan in July 2017, aiming to build an AI technologically world-leading domestic industry by 2030. The value of China's core AI industries is expected to exceed 150 billion yuan (22.15 billion US dollars) by 2020 and 400 billion yuan (59.07 billion US dollars) by 2025.

    China and the United States are ahead of the global competition to dominate AI, according to a study by the UN World Intellectual Property Organization (WIPO) published January.
  2. Hendrik_2000

    Hendrik_2000 Brigadier

    Dec 20, 2006
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    From NYT. This could be the answer to bridge the gap between urban and rural health care in china

    A.I. Shows Promise as a Physician Assistant

    Doctors competed against A.I. computers to recognize illnesses on magnetic resonance images of a human brain during a competition in Beijing last year. The human doctors lost.CreditMark Schiefelbein/Associated Press
    Doctors competed against A.I. computers to recognize illnesses on magnetic resonance images of a human brain during a competition in Beijing last year. The human doctors lost.CreditCreditMark Schiefelbein/Associated Press

    By Cade MetzFeb. 11, 2019
    Each year, millions of Americans walk out of a doctor’s office with a misdiagnosis. Physicians try to be systematic when identifying illness and disease, but bias creeps in. Alternatives are overlooked.

    Now a group of researchers in the United States and China has tested a potential remedy for all-too-human frailties: artificial intelligence.

    In a paper published on Monday in Nature Medicine, the scientists reported that they had built a system that automatically diagnoses common childhood conditions — from influenza to meningitis — after processing the patient’s symptoms, history, lab results and other clinical data.

    The system was highly accurate, the researchers said, and one day may assist doctors in diagnosing patients with complex or rare conditions.

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    On Monday, President Trump signed an executive order meant to spur the development of A.I. across government, academia and industry in the United States. As part of this “American A.I. Initiative,” the administration will encourage federal agencies and universities to share data that can drive the development of automated systems.

    Pooling health care data is a particularly difficult endeavor. Whereas researchers went to a single Chinese hospital for all the data they needed to develop their artificial-intelligence system, gathering such data from American facilities is rarely so straightforward.

    “You have go to multiple places. The equipment is never the same. You have to make sure the data is anonymized,” said Dr. George Shih, associate professor of clinical radiology Weill Cornell Medical Center and co-founder of, a company that helps researchers label data for A.I. services. “Even if you get permission, it is a massive amount of work.”

    After reshaping internet services, consumer devices and driverless cars in the early part of the decade, deep learning is moving rapidly into myriad areas of health care. Many organizations, including Google, are developing and testing systems that analyze electronic health records in an effort to flag medical conditions such as osteoporosis, diabetes, hypertension and heart failure.

    Similar technologies are being built to automatically detect signs of illness and disease in X-rays, M.R.I.s and eye scans.

    The new system relies on a neural network, a breed of artificial intelligence that is accelerating the development of everything from health care to driverless cars to military applications. A neural network can learn tasks largely on its own by analyzing vast amounts of data.

    Using the technology, Dr. Kang Zhang, chief of ophthalmic genetics at the University of California, San Diego, has built systems that can analyze eye scans for hemorrhages, lesions and other signs of diabetic blindness. Ideally, such systems would serve as a first line of defense, screening patients and pinpointing those who need further attention.

    Now Dr. Zhang and his colleagues have created a system that can diagnose an even wider range of conditions by recognizing patterns in text, not just in medical images. This may augment what doctors can do on their own, he said.

    “In some situations, physicians cannot consider all the possibilities,” he said. “This system can spot-check and make sure the physician didn’t miss anything.”

    The experimental system analyzed the electronic medical records of nearly 600,000 patients at the Guangzhou Women and Children’s Medical Center in southern China, learning to associate common medical conditions with specific patient information gathered by doctors, nurses and other technicians.

    First, a group of trained physicians annotated the hospital records, adding labels that identified information related to certain medical conditions. The system then analyzed the labeled data.

    Then the neural network was given new information, including a patient’s symptoms as determined during a physical examination. Soon it was able to make connections on its own between written records and observed symptoms.

    When tested on unlabeled data, the software could rival the performance of experienced physicians. It was more than 90 percent accurate at diagnosing asthma; the accuracy of physicians in the study ranged from 80 to 94 percent.

    In diagnosing gastrointestinal disease, the system was 87 percent accurate, compared to the physicians’ accuracy of 82 to 90 percent.

    Able to recognize patterns in data that humans could never identify on their own, neural networks can be enormously powerful in the right situation. But even experts have difficulty understanding why such networks make particular decisions and how they teach themselves.

    As a result, extensive testing is needed to reassure both doctors and patients that these systems are reliable.

    Experts said extensive clinical trials are now needed for Dr. Zhang’s system, given the difficulty of interpreting decisions made by neural networks.

    “Medicine is a slow-moving field,” said Ben Shickel, a researcher at the University of Florida who specializes in the use of deep learning for health care. “No one is just going to deploy one of these techniques without rigorous testing that shows exactly what is going on.”

    It could be years before deep-learning systems are deployed in emergency rooms and clinics. But some are closer to real-world use: Google is now running clinical trials of its eye-scan system at two hospitals in southern India.

    Deep-learning diagnostic tools are more likely to flourish in countries outside the United States, Dr. Zhang said. Automated screening systems may be particularly useful in places where doctors are scarce, including in India and China.

    The system built by Dr. Zhang and his colleagues benefited from the large scale of the data set gathered from the hospital in Guangzhou. Similar data sets from American hospitals are typically smaller, both because the average hospital is smaller and because regulations make it difficult to pool data from multiple facilities.

    Dr. Zhang said he and his colleagues were careful to protect patients’ privacy in the new study. But he acknowledged that researchers in China may have an advantage when it comes to collecting and analyzing this kind of data.

    “The sheer size of the population — the sheer size of the data — is a big difference,” he said.
  3. tidalwave

    tidalwave Senior Member
    Registered Member

    Feb 10, 2015
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    This has nothing to do with AI.
    It has more to do with Huawei aspires to be a chipset component supplier.

    Beside 5G , AI, and Server chipsets, Huawei is the leading TV chipset supplier in China with 50% marketshare.

    I don't like the title of the thread, AI is getting overblown, it's only one field. In fact the price conscious folks, people in general are not willing to fork out the extra for AI accelerator such as in the phone. It still a luxury, not a must have feature.

    In the future, I could see Huawei provide GPU, Silicon photonics, and networking chipset.
    #103 tidalwave, Feb 25, 2019
    Last edited: Feb 25, 2019
    N00813 likes this.

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