Internet of Things (IOT) industry attracts many technology companies to invest, and whether the product has the ability of machine learning to decide whether it can get consumers.
According to Venture Beat, in 1996, Cook County Hospital emergency department in Chicago used an algorithm to see if patients had chest pain and had to move into bed when they had chest pain. The algorithm uses a systematic basic test, which is a fast, efficient and accurate method. Seventy percent of patients can be classified as low-risk, with 95% of other patients with heart disease, with accuracy over 75% to 89% as judged by the average physician. At that time, there was no deep computing technology.
There are currently 6.4 billion IoT devices worldwide, averaging almost one for each person on average. If 1% of them can collect a variety of heartbeat, diet or sleep data, analyzing their physical condition can greatly expand the popularity of medical treatment. The real key to expanding technology is machine learning. Machine learning can find out the law from the data collected, and even the average doctor may not have decades of experience. Imagine, Fitbit if you can detect changes in the heart of a heart disease, it will tell you that you should go to the hospital for treatment. Through the ordinary home equipment to solve the problem of the past can not create the value. At present, all kinds of leading intelligent devices have the ability of machine learning. To Nest, for example, most people buy Nest not because you want to use a cell phone to turn on the heating, to "press the switch" is never a problem for people, but the key is that it can save energy, adjust the temperature according to the indoor population, and solve the problem intelligently.
However, many IOT technologies lack true wisdom. Remote-controlled door locks or radios, at first glance, are advanced, but only gain the favor of high society, machine learning is to let people think that non-essential, universal equipment. Thermostats make you feel warm, wearable devices give you a personalized reminder, environmental monitoring will analyze the source of pollution, protect the health of family. And now with the cloud, to put machine learning technology into the device, whether network or computing can easily be achieved, the data is the real problem. Computer to find out the law from the data, the amount of data must be large enough. It must consider a variety of factors, including personal preferences, the environment, and more. Many factors may be related to time, including frequency of use, frequency of behavior, frequency of conditions, behavioral changes, seasonal changes, changes in sensor data accuracy, and the like. In this way, it is not easy to catch up with the leader because the leader has more and more accurate data.
However, only IBM and Google are currently selling machine learning products. Machine learning may be a too expensive investment for start-up companies, but not necessarily, the key is to take advantage of someone else's computing computer, and cloud computing is a good bet. Start-up companies can use the hour billing method to get a powerful computing power. Most importantly, your hardware does not need to have powerful capabilities for machine learning, and it does not have to be machine learning right from the start. Nest was not very smart at the beginning, but at the time there was only a simple algorithm that could predict how long it took for the temperature in your home to reach a certain level. Only afterwards did the other functions begin. For start-ups, the machine learning function can be slowly increased according to their own financial situation, but at the same time, it must be ensured that they can not be taken one step ahead of competitors.
Machine learning makes smart devices no longer just convenient, but powerful. Nest and Amazon Echo become leaders, not only increasing the value of new uses, but also adding hundreds of technology companies, the future of the world will be very different.
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