Nowadays, artificial intelligence is the technology darling of all major industries. Many giants have entered the game, and entrepreneurs have only increased. Under this big wave, machine learning has become a key point.
One of my favorite things about the technology industry is that technology is growing rapidly from high-end products to low-priced products. The plagiarism between small companies is not so terrible. Just as Facebook treats its own incubator lab like Snapchat, this is how big companies want to replicate the results of a small company. But from technology giants such as Apple and Google, to hardware and software vendors in the middle and lower reaches of the industry, there are not much problems. However, Apple and Google have already shown a sign that technologies and methods developed by Apple and Google cannot be easily imitated and copied.
For example, one of the most popular words in the field of smartphones recently is "machine learning" technology. Just like virtual reality and augmented reality, machine learning has been seen as a promise. Machine learning is also the core competitiveness of Apple's iPhone X and Google's Pixel 2/XL. It is also the driving force for differentiated competition between today and tomorrow, and the companies that fall behind will find themselves under pressure.
The advantage of machine learning technology is that it cannot be easily copied, cloned or reverse compiled. Competing with Apple and Google in this game requires as much computing power and user data as possible, as well as more time and investment. Simply put, machine learning technology will become the "Holy Grail" of big companies, and small businesses can't reach the height that big companies can achieve. It requires a lot of resources and users and foundations, and it will get better and better over time, and competitors have to continue to improve, trying not to be too far away from big companies.
I don't mean that machine learning technology is a blessing, but using OLED screens, all vendors will do it, but machine learning is a technology that enables differentiated competition.
Google's HDR+ cameraLet's start with the consumer machine learning technology that has left us the most impressive impression to date, the cameras on Google Pixel 2 and Pixel 2 XL. It is amazing that it can achieve similar SLR effects through algorithms, especially in low light conditions. Google's imaging software has surpassed the physical limitations of mobile photography devices (ie, there is not enough room to accommodate large sensors and lenses), combined with clever algorithms and machine learning techniques. As Google has said, the company has turned an optical problem into a data problem, and few companies are now able to handle this data as well as Google.
I spoke with Marc Levoy, a scientist from Stanford University. He led Google's computational photography team, which highlighted how Google is helping machine-cameras with machine learning technology: As time goes by, the effect will get better and better. . Even if Google itself does not make any improvements on the Pixel 2 and Pixel 2 XL cameras, machine learning technology will make the camera better by extending the time. Levoy said that the more you can invest in machine learning settings, the better the output will be, and the time and processing power (both the device itself and the server) are critical factors.
Google AssistantAt the CES International Consumer Electronics Show in January this year, Yu Chengdong, president of Huawei's Consumer Division, was asked if he would launch his own voice assistant in the US market: "Alexa and Google Assistant are good enough, how does Huawei compete?" This atypical but very real answer (especially for competitor CEOs) succinctly illustrates the heights Amazon and Google have achieved in machine learning. The two US technology giants have invested huge resources in the fields of natural language processing and speech recognition, and they have all achieved initial success. They have maintained a leading position in the global competition in this field, so even Huawei is not as good as it is. Long-term investment in machine learning technology.
Google Assistant is not a hardware differentiation feature because Google wants to run the voice assistant on all hardware. However, this assistant service can direct users to Google search or other company services, and almost everyone will benefit from a variety of machine learning technologies, whether you are considering using Google Maps or YouTube video recommendations. For the mobile market, Google Assistant's role is to enhance Google's influence on hardware partners. So in the Android smartphone that debuted next year, it would be a shame if you don't support the Google Play Store or Google Assistant.
Apple's Face ID
On the other side of Apple, machine learning technology has also begun to penetrate most of the software on the iPhone, and Apple's Core ML tool allows developers to easily add it to the entire library. However, the biggest highlight of the new iPhone X is the “Qi Liu Hai†at the top of the screen and the technology it contains. In Liu Haizhong you will find a complete set of infrared sensors, equivalent to Microsoft's Kinect system, which helps Face ID, a new face recognition method.
Now I am still not sure how to balance between safety and convenience (especially without the Touch ID). I know that all users who use iPhone X are satisfied with the accuracy of Face ID. It works in the dark and thanks to machine learning techniques, it can constantly adapt to changes in the appearance of our face. If you remove all the usual changes and adjustments, then Face ID is basically the only bright spot of iPhone X, it also relies on machine learning technology to play its own magic.
For enhanced machine learning, the technology that really wants to become a mass market now supports the initial phase. For users who buy iPhone X, Face ID obviously has no borderless full screen and is more attractive. Although the Google Pixel 2 camera works well, there are not many users who care about this. But the most important thing is that smartphone manufacturers need to integrate into their product learning solutions to improve the core user experience and remain competitive. Chinese smartphone makers may be very fast in the iterative speed of hardware, but it is very difficult to copy machine learning technology, even months or even years.
Huawei artificial intelligence chip and Samsung BixbyIn addition to Apple and Google, Huawei is also the biggest supporter of machine learning and artificial intelligence technologies on mobile devices. Huawei's latest generation of Mate 10 is defined by the company as a "real artificial smartphone." Huawei has been moving in the right direction in the field of artificial intelligence. Although Apple and Google use machine learning systems in different tangible areas, Huawei's approach is to deepen the machine learning body into the system to optimize the long-term Android system. Using performance, this is a very commendable attempt, but it's hard to imagine it will be the biggest attraction for users to buy Mate 10. In addition, Huawei has also carried out some marketing of artificial intelligence concepts on the camera, hoping to automatically improve the image quality while taking photos, but I have not seen the results that are too satisfactory.
Huawei's example reminds us that machine learning itself is not the only selling point, but how to derive a real selling point in machine learning technology.
Another outstanding example is Samsung's Bixby voice assistant. Bixby's positioning is similar to that of Google Assistant, but Samsung is under-prepared, under-planned, and has no large amounts of data and technology to accumulate. However, from the next year, Bixby assistants will have a great progress, and many companies are studying how to use machine learning technology to enhance the potential of equipment.
When you first see the iPhone X, you may be experienced with its gorgeous OLED screen. Although expensive, it still has a unique appeal. Apple has always been very good at making adjustments on hardware and producing more complex technologies. Such as TapTIc Engine tactile feedback, 3D Touch screens and Touch Bar touch bars on the new MacBook Pro, but these are very easy to copy or emulate by competitors. In 2014, Apple invested heavily in the sapphire screen industry, hoping to create its own sapphire screen supply chain, which will be a huge advantage in the future, but the result is that this effort failed, and related companies also face bankruptcy.
In the past few months, the days of smartphone vendors making major updates on hardware have returned. In the later stages of the development of the smartphone industry, machine learning technology is the only way to achieve differentiation. I personally think that Google's camera technology is still generally underestimated, the main reason is the long-term Pixel limited sales. In the face of numerous competitors, the Face ID feature may also be copied, after all, many Apple competitors. But the difference between true mobile innovators and imitators, although the boundaries have slowly blurred, but with the advent of the era of machine learning technology, this boundary will become clear again.
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