The Upstream and Downstream of China's Instrumentation Network Industry After several years of development, the Internet of Things has entered a new phase. Consumers, businesses, and governments have seen the benefits of connecting various devices to the Internet. Today, Shen Xiaowei, former president of IBM China Research, talks about his understanding of the Internet of Things.
From the perspective of IBM, it should be said that the Internet of Things has gone through three phases today and we may wish to make a relatively simple review. In the beginning, we talked about the Internet of Things more to talk about perception of the physical world, to perceive the world, to bring this information to the data center for processing whether it is wired transmission or wireless communication. This is the most primitive concept of the Internet of Things. Today we look back at the concept of Internet of Things 1.0. The core of this concept is to build a system of Internet of Things. After this, we realized that the Internet of Things requires a lot of solutions, and we need to build a universal or near-universal Internet of Things platform so that different solutions can operate on such platforms. Such a platform is a very special technology that requires the processing of data on the Internet of Things, so this is a concept of the Internet of Things 2.0. In this stage, we spent a lot of effort, including IBM, including many of our partners, and spent a lot of effort to build a lot of Internet of Things technologies, making us better able to do data on such Internet of Things. deal with.
Today, from IBM's perspective, the Internet of Things has gradually entered the stage of the Internet of Things 3.0, a very interesting feature of this stage. One is the very, very deep integration of Internet of Things technology and cloud computing technology. Another point is that technology such as the simulation of the physical world will be brought into the application of the Internet of Things. Based on the in-depth analysis of the simulation of the physical world, it will allow us to continue to perceive the physical world, and it will also enable very deep insights. This is the stage of the Internet of Things (IoT) 3.0, which has surpassed the traditional Internet of Things. Whether it is a system or a platform, it is more about looking at the business optimization of the Internet of Things.
We look at the development trend of the Internet of Things and grasp two major development trends. One is the expansion of the Internet of Things to the cloud. Today, there is already a lot of data showing that very many solutions will be directly designed and displayed by the cloud in the coming years. This is a trend in the development of IoT solutions to the cloud. Another trend, from the collection and transmission of data to simple analysis, to a trend of very profound data processing and data analysis, is to generate insights that can be implemented, meaning that this insight can be implemented. We can manage this physical world based on such insights. This is a large macro trend from the perspective of the Internet of Things from 1.0 to 3.0.
Simply talk about what kind of innovation the Internet of Things requires from technology. One is a cloud-based solution. A lot of progress on this aspect of cloud computing, including how to conduct cloud computing with security, is a very important research direction.
Through big data analysis, here I would like to talk about three points. One is the traditional IT data processing technology, because a very large amount of data generated by the Internet of Things needs to be greatly improved. A very important aspect is the unstructured processing. The traditional data is a kind of structured data. For example, the data operated by the enterprise, many of them exist in the traditional relational database. The new Internet of Things data, including social media data, is an interactive system. Whether it is a human interaction system or an interactive system with the physical world, the data generated is unstructured. How do we put traditional IT solutions such as structured data or such technologies, middleware technologies, and system technologies to expand it into unstructured aspects? This is a very, very important direction.
Second, the Internet of Things is a very important direction. Technically, it is necessary to introduce physical models into data analysis. Most of the time when we talk about data, it is more about understanding the relationship between the data itself. It does not use data to describe the physical world. In reality, the physical model has been used for many years, regardless of weather forecast. Air pollution has such a physical model. How do we introduce physical models into the perception and management of the physical world in the new form?
Another very important thing is that the Internet of Things can bring us change, that is, the world is more connected and more relevant than ever before. Traditionally, a physical model may be used to analyze, for example, weather or analyze pollution, but today we may need to link such models with economic models and link such models with healthy models because they are related to each other. This will produce a super physical model. The model contains a very large number of different areas of the model, they need to work with each other to jointly manage and optimize such a physical world.
If we believe that this is a future development approach, it involves the direction of the development of the Internet of Things technology that I talked about. When this world becomes very, very complicated, and the data is very, very large, the model is very, very much, and today's manpower has been very difficult to understand about managing such things, even today's ordinary algorithms. At this time we may need to introduce, including such systems as cognitive learning with machine learning. This ability allows us to continuously learn through historical data or real-time data, continuous training, to continuously debug this model of the physical world, so that a better understanding of the world. Such an Internet of Things system is self-learning. This is the rise of cognitive computing represented by cognitive computing, a very important announcement IBM made several years ago.
From an industry perspective, there are two major types of industries that will involve a very large number of positive effects. One is traditional heavy asset industry or asset intensive industry. No matter energy or transportation, these industries have a lot of assets. These assets are also very expensive. How can we manage these assets better and make it more effective? use. The other type is connected-device-type industries. Whether it is a car network or an intelligent home, a large number or a large number of devices are interconnected. How can we better manage and generate better value?
As we move toward the era of the Internet of Things, traditional computer IT systems still exist. The future IT system, in the opinion of IBM today, is actually building an insightful system. The traditional IT system is a record-based system, because those systems are more structured data, plus new systems, it is designed for interactive systems. Regardless of the interaction with the physical world just mentioned, or the social interaction between people. The core value of an IT system is to generate insights. How do we combine this traditional data with emerging data to produce executive insights. It is a very, very important challenge and an opportunity for IT development in the coming years.
Last year, IBM announced that we have integrated the power of IBM's 12 global research institutes to bring in our best technologies, best capabilities, and what to do. Put IT technologies, including technologies such as the Internet of Things, cloud computing, and big data. Bringing in, to solve some of the very important issues affecting the national economy and the people's livelihood, especially the environmental and energy issues. Including several aspects, one is how we can use the Internet of Things, big data technology to do a good use of renewable energy. We all know that renewable energy, such as wind power, although wind power itself is a kind of clean energy, but one of the great challenges it faces is the difficult predictability of wind power itself and how it can use real-time sensor data in real time. The health cloud map data, real-time weather data to do the precise next 72 hours, every interval of 15 minutes may specify 200 meters by 200 meters may generate wind. Only by making such accurate predictions can we apply these wind powers effectively to the grid.
In the second step, even if we can maximize the use of renewable energy, how can we make better use of the Internet of Things technology, cloud computing technology, energy conservation and emission reduction. Why does the same company generate a dollar of GDP for such energy, and another company only has half of it. How can we find such a problem, trace its origin and do some processing.
While we did a highly efficient use of renewable energy and corporate energy-saving emission reduction, we also know that because pollution control itself is also a problem, but also a very very big challenge. How can we make use of the technologies mentioned earlier, including cognitive computing, to make better predictions of pollution, air quality predictions, and decision support systems that can provide actionable solutions to take appropriate measures. This is also the direction in which we are now working hard, and we have also made some progress.
Next, I will use a few minutes to talk about one of our forecasts for the Internet of Things in the coming years. The core of the Internet of Things that I just talked about was the development of the cloud, and the wisdom of it. The Wisdom of the Internet of Things is a very important direction for the development of the Internet of Things. At the same time, there are several very important trends that we must notice. One is that we see that today a large amount of data is generated at the terminal, and the speed at which the terminal generates data is much faster than the increase in bandwidth. This means that no matter what you do, you cannot always send this data back to the data center or back to the cloud computing center.
At the same time, we also have a forecast that within two years, the sum of computing power and storage capacity of global smartphones in 2017 will exceed the computing power and storage capacity of global servers. What is the enlightenment to us? We should be able to think that in the near future, a lot of computing, the so-called computing of the Internet of things will happen at the edge, this edge may be a mobile phone, it may be a sensor, it may be a camera . The future world is such a world of cloud computing edge computing. Some of the operations need to be done in the cloud. Some of the processing needs to be done at the edge. How do we construct such a system? This is a very large-scale, ultra-complex distributed system. This system is how we can make it safer and more reliable. This is a very good and interesting issue for many industries. It is also a good opportunity.
In fact, the development of the Internet of Things involves many aspects, not only in cloud computing or algorithms, but also in algorithms such as cognitive computing and machine learning. I will give another example here. Last year, IBM announced that it was IBM's SyNAPSE chip. The traditional computer is a computer based on Von Neumann system. It is more adept at logical computing and is not good at image thinking. In this regard, IBM has also invested huge resources in the chip layer and system layer. We hope to have a comprehensive understanding and discussion of issues on the Internet of Things.
IBM announced last year that the chip's 5.4 billion transistors could roughly simulate 100 neurons and 250 million programmable neurites. Although there are 5.4 billion transistors, the power consumption of this chip is lower than 1/10 watts, and if it is correct, it should be 70 milliwatts. Such an ultra-low-power chip that handles many things is completely different from traditional computing, and it provides us with a lot of imagination. In the future, even at the edge of the system, you may have completely new technology. Whether it is from software or hardware, including from chips, there will be very different technologies to make future IoT or IoT systems available to everyone. Different values.
From the perspective of IBM, it should be said that the Internet of Things has gone through three phases today and we may wish to make a relatively simple review. In the beginning, we talked about the Internet of Things more to talk about perception of the physical world, to perceive the world, to bring this information to the data center for processing whether it is wired transmission or wireless communication. This is the most primitive concept of the Internet of Things. Today we look back at the concept of Internet of Things 1.0. The core of this concept is to build a system of Internet of Things. After this, we realized that the Internet of Things requires a lot of solutions, and we need to build a universal or near-universal Internet of Things platform so that different solutions can operate on such platforms. Such a platform is a very special technology that requires the processing of data on the Internet of Things, so this is a concept of the Internet of Things 2.0. In this stage, we spent a lot of effort, including IBM, including many of our partners, and spent a lot of effort to build a lot of Internet of Things technologies, making us better able to do data on such Internet of Things. deal with.
Today, from IBM's perspective, the Internet of Things has gradually entered the stage of the Internet of Things 3.0, a very interesting feature of this stage. One is the very, very deep integration of Internet of Things technology and cloud computing technology. Another point is that technology such as the simulation of the physical world will be brought into the application of the Internet of Things. Based on the in-depth analysis of the simulation of the physical world, it will allow us to continue to perceive the physical world, and it will also enable very deep insights. This is the stage of the Internet of Things (IoT) 3.0, which has surpassed the traditional Internet of Things. Whether it is a system or a platform, it is more about looking at the business optimization of the Internet of Things.
We look at the development trend of the Internet of Things and grasp two major development trends. One is the expansion of the Internet of Things to the cloud. Today, there is already a lot of data showing that very many solutions will be directly designed and displayed by the cloud in the coming years. This is a trend in the development of IoT solutions to the cloud. Another trend, from the collection and transmission of data to simple analysis, to a trend of very profound data processing and data analysis, is to generate insights that can be implemented, meaning that this insight can be implemented. We can manage this physical world based on such insights. This is a large macro trend from the perspective of the Internet of Things from 1.0 to 3.0.
Simply talk about what kind of innovation the Internet of Things requires from technology. One is a cloud-based solution. A lot of progress on this aspect of cloud computing, including how to conduct cloud computing with security, is a very important research direction.
Through big data analysis, here I would like to talk about three points. One is the traditional IT data processing technology, because a very large amount of data generated by the Internet of Things needs to be greatly improved. A very important aspect is the unstructured processing. The traditional data is a kind of structured data. For example, the data operated by the enterprise, many of them exist in the traditional relational database. The new Internet of Things data, including social media data, is an interactive system. Whether it is a human interaction system or an interactive system with the physical world, the data generated is unstructured. How do we put traditional IT solutions such as structured data or such technologies, middleware technologies, and system technologies to expand it into unstructured aspects? This is a very, very important direction.
Second, the Internet of Things is a very important direction. Technically, it is necessary to introduce physical models into data analysis. Most of the time when we talk about data, it is more about understanding the relationship between the data itself. It does not use data to describe the physical world. In reality, the physical model has been used for many years, regardless of weather forecast. Air pollution has such a physical model. How do we introduce physical models into the perception and management of the physical world in the new form?
Another very important thing is that the Internet of Things can bring us change, that is, the world is more connected and more relevant than ever before. Traditionally, a physical model may be used to analyze, for example, weather or analyze pollution, but today we may need to link such models with economic models and link such models with healthy models because they are related to each other. This will produce a super physical model. The model contains a very large number of different areas of the model, they need to work with each other to jointly manage and optimize such a physical world.
If we believe that this is a future development approach, it involves the direction of the development of the Internet of Things technology that I talked about. When this world becomes very, very complicated, and the data is very, very large, the model is very, very much, and today's manpower has been very difficult to understand about managing such things, even today's ordinary algorithms. At this time we may need to introduce, including such systems as cognitive learning with machine learning. This ability allows us to continuously learn through historical data or real-time data, continuous training, to continuously debug this model of the physical world, so that a better understanding of the world. Such an Internet of Things system is self-learning. This is the rise of cognitive computing represented by cognitive computing, a very important announcement IBM made several years ago.
From an industry perspective, there are two major types of industries that will involve a very large number of positive effects. One is traditional heavy asset industry or asset intensive industry. No matter energy or transportation, these industries have a lot of assets. These assets are also very expensive. How can we manage these assets better and make it more effective? use. The other type is connected-device-type industries. Whether it is a car network or an intelligent home, a large number or a large number of devices are interconnected. How can we better manage and generate better value?
As we move toward the era of the Internet of Things, traditional computer IT systems still exist. The future IT system, in the opinion of IBM today, is actually building an insightful system. The traditional IT system is a record-based system, because those systems are more structured data, plus new systems, it is designed for interactive systems. Regardless of the interaction with the physical world just mentioned, or the social interaction between people. The core value of an IT system is to generate insights. How do we combine this traditional data with emerging data to produce executive insights. It is a very, very important challenge and an opportunity for IT development in the coming years.
Last year, IBM announced that we have integrated the power of IBM's 12 global research institutes to bring in our best technologies, best capabilities, and what to do. Put IT technologies, including technologies such as the Internet of Things, cloud computing, and big data. Bringing in, to solve some of the very important issues affecting the national economy and the people's livelihood, especially the environmental and energy issues. Including several aspects, one is how we can use the Internet of Things, big data technology to do a good use of renewable energy. We all know that renewable energy, such as wind power, although wind power itself is a kind of clean energy, but one of the great challenges it faces is the difficult predictability of wind power itself and how it can use real-time sensor data in real time. The health cloud map data, real-time weather data to do the precise next 72 hours, every interval of 15 minutes may specify 200 meters by 200 meters may generate wind. Only by making such accurate predictions can we apply these wind powers effectively to the grid.
In the second step, even if we can maximize the use of renewable energy, how can we make better use of the Internet of Things technology, cloud computing technology, energy conservation and emission reduction. Why does the same company generate a dollar of GDP for such energy, and another company only has half of it. How can we find such a problem, trace its origin and do some processing.
While we did a highly efficient use of renewable energy and corporate energy-saving emission reduction, we also know that because pollution control itself is also a problem, but also a very very big challenge. How can we make use of the technologies mentioned earlier, including cognitive computing, to make better predictions of pollution, air quality predictions, and decision support systems that can provide actionable solutions to take appropriate measures. This is also the direction in which we are now working hard, and we have also made some progress.
Next, I will use a few minutes to talk about one of our forecasts for the Internet of Things in the coming years. The core of the Internet of Things that I just talked about was the development of the cloud, and the wisdom of it. The Wisdom of the Internet of Things is a very important direction for the development of the Internet of Things. At the same time, there are several very important trends that we must notice. One is that we see that today a large amount of data is generated at the terminal, and the speed at which the terminal generates data is much faster than the increase in bandwidth. This means that no matter what you do, you cannot always send this data back to the data center or back to the cloud computing center.
At the same time, we also have a forecast that within two years, the sum of computing power and storage capacity of global smartphones in 2017 will exceed the computing power and storage capacity of global servers. What is the enlightenment to us? We should be able to think that in the near future, a lot of computing, the so-called computing of the Internet of things will happen at the edge, this edge may be a mobile phone, it may be a sensor, it may be a camera . The future world is such a world of cloud computing edge computing. Some of the operations need to be done in the cloud. Some of the processing needs to be done at the edge. How do we construct such a system? This is a very large-scale, ultra-complex distributed system. This system is how we can make it safer and more reliable. This is a very good and interesting issue for many industries. It is also a good opportunity.
In fact, the development of the Internet of Things involves many aspects, not only in cloud computing or algorithms, but also in algorithms such as cognitive computing and machine learning. I will give another example here. Last year, IBM announced that it was IBM's SyNAPSE chip. The traditional computer is a computer based on Von Neumann system. It is more adept at logical computing and is not good at image thinking. In this regard, IBM has also invested huge resources in the chip layer and system layer. We hope to have a comprehensive understanding and discussion of issues on the Internet of Things.
IBM announced last year that the chip's 5.4 billion transistors could roughly simulate 100 neurons and 250 million programmable neurites. Although there are 5.4 billion transistors, the power consumption of this chip is lower than 1/10 watts, and if it is correct, it should be 70 milliwatts. Such an ultra-low-power chip that handles many things is completely different from traditional computing, and it provides us with a lot of imagination. In the future, even at the edge of the system, you may have completely new technology. Whether it is from software or hardware, including from chips, there will be very different technologies to make future IoT or IoT systems available to everyone. Different values.
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