Not only software and hardware vendors, but also many cloud service providers have stuck in the artificial intelligence (AI) market, but the fierce competition is not a bad thing for developers. Together with the cooperation between the big manufacturers, it also promotes the whole. The AI ​​ecosystem is moving toward an open type, at least in the market, the resources will be richer and more complete, thus reducing the barriers to entry for developers.
Lack of integration of resources makes it easy for development to have faultsAlthough there are quite a lot of resources in the market for developers to use, it is simple, but it is not easy to invest in development. Under the deep and complex knowledge system, it is difficult for developers to specialize in each technical level. It is like a person who is good at collating and analyzing big data. Little is known about the hardware architecture and IT technology in the bottom computing environment.
A person with a background in data science can still train a deep learning model. After all, there are many popular deep learning open source frameworks available on the market, such as Tensor Flow, Caffe, CNTK, Theano, etc. These frameworks each have their own advantages. Disadvantages.
In addition to the imageNet image recognition competition held by Stanford University in the past, large companies including Google, Microsoft, Baidu and other companies will take the opportunity to test the performance of their own systems in addition to the image recognition throne. With the limit, in the course of the game, some new neural network algorithms are trained through their own deep learning framework. These resources are “supervised†and “verified†by the big manufacturers, and developers can take them if they have the needs.
It's not difficult to train with mature resource-input models, but the situation becomes more complicated if developers are to explore further computing environments, including choosing the right hardware assembly or building a basic computing environment. . Liu Houyi, assistant of Advantech's Intelligent Systems Business Group, observed that in the development environment of deep learning in the past, resources were more difficult to integrate due to the clear barriers between the various technical levels.
Even if the developer wants to seek assistance from outside, there is a lack of a complete solution in the market. Through a one-time import, the cumbersome project and the software development design can be solved through one-time import, which makes the company like SI. It is easy to stop in the process of building the most basic computing system, forcing the development process to have a fault.
Deep learning complete solutions reduce development time and increase efficiencyTherefore, integrated equipment is critical to reducing system build time. The most representative of the market is NVIDIA's DGX-1, which was launched in 2016. It combines hardware, deep learning software, and program development tools. It is the world's first integrated device for AI analysis. .
The DGX-1 integrates the resources and tools needed for a basic computing environment to reduce the time required for system integration and software engineering. With such a device, enterprises can quickly and easily deploy related computing environments. However, the cost of DGX-1 is not high, especially for small-scale companies or individual developers, cost-based considerations are easily discouraged.
Fortunately, Advantech also found that the market is facing the problem of scattered resources and difficult system integration. In order to reduce the complexity and technical threshold of the establishment of training systems, Advantech has also actively invested in the artificial intelligence market in recent years, and cooperated with Dachang technical cooperation to develop deep learning. Complete solution.
Advantech integrates the resources required for the basic computing environment, including hardware devices, deep learning software development kits, and model training platforms, to help the industry accelerate the training system and reduce manpower and time costs. It also enables companies such as SI to devote more resources to address the needs of end users.
The only thing developers need to do is to "collect data and organize data" so easily, and the most cumbersome system construction project can be completed in the future through a fully deployed solution on the market. This is why Advantech actively participates in the artificial intelligence market and joins the big manufacturers to integrate the lowest computing resources. "The purpose is to enable developers to invest limited development time in the most valuable data processing and analysis." Advantech Intelligence Bao Zhiwei, the associate of the system business group, emphasized this.
However, "training" is only the beginning of deep learning. The bigger challenge is how to apply deep learning technology to various scenarios for prediction and analysis. The key to this part is the terminal's reasoning system. Various terminal application scenarios are such as transportation, robotics, healthcare, and public safety, and each application scenario actually faces some special needs.
For example, traffic scenes require extremely secure and reliable hardware devices for sensing, and in the public safety field, quality-stable photographic lenses are required to maintain reliable execution efficiency, as well as challenges in demanding and demanding applications. Therefore, if the industry cannot grasp the characteristics of the industry end, the first step in system construction will be easy to get stuck and encounter obstacles.
For Advantech, which has been deeply cultivated in the industry for a long time, in addition to the industry know-how that has accumulated for many years, it is more familiar with the needs of market customers than the average industry. Advantech also provides an inference system based on industry-side requirements in the market to accelerate the landing of AI in terminal application scenarios.
In the IVA (Intelligent Video Analysis) reasoning system developed by Advantech, users can use a variety of intelligent image analysis functions, including motion detection, face detection, crowd density detection, etc. Quickly import into the development of AI applications. Advantech's complete deep learning solution includes not only the training server required for the front end, but also the data storage devices and network devices required for the terminal inference environment.
80% of the time is spent on pre-development data processingFormer Baidu chief scientist Wu Enda once compared deep learning to a rocket. In the structure of the rocket, the most important part of propelling the rocket is the engine. At present, in the field of deep learning, the core of the engine is the neural network. In addition to the engine, the rocket needs fuel, so big data is an important part of the rocket - fuel.
Big data is critical to the development and advancement of deep learning. Associate Professor Bao Zhiwei of Advantech's intelligent system business group believes that the core of driving artificial intelligence comes from big data. Once the amount of data is obtained, the training model can be optimized to make the reasoning and analysis of the model more accurate. However, data with analytical value must be sorted in advance, otherwise it will end up being "Garbage in, garbage out."
However, processing data is very labor intensive and time-consuming. In the past, due to the lack of data processing tools, developers usually spend nearly 80% of their time processing data in the process of training models, such as collating or collecting data combinations, 10% of the time. In the calculation, and 10% of the time is in the optimization model.
Consuming a lot of data processing time will slow down the development process, and the market is trying to solve this annoying problem. For example, in Advantech's deep learning super workstation, because of its built-in automatic labeling tool, the only thing developers need to do is to check whether the label is correct, so developers can effectively save with the help of data processing tools. 80% of data processing time.
AI development is going to civilianThe reason why artificial intelligence has set off a boom is that, in addition to the improvement of hardware computing power and the breakthrough of neural network-like algorithms, the rise of cloud services has also greatly reduced the cost of system construction. The benefits of building a training and inference system in the cloud can be chosen to expand or reduce deployment quite flexibly, except that it does not cost a lot of money to build a physical machine.
Developers can now build training or inference systems through a variety of cloud providers, or they can choose to deploy directly in the terminal environment. But basically, the cost of choosing a terminal is undoubtedly the most expensive, in principle a bit like the difference between buyout and lease. If the inference system is built in the terminal, there is still a certain threshold for the industry because of the need to match the characteristics and needs of the industry.
In addition to providing computing resources, the cloud platform on the market today is packaged with a complete set of services including infrastructure, storage space and various APIs. The more well-known cloud computing services on the market today are AWS EC2, Azure or GCP platforms.
Amazon AWS has a first-mover advantage compared with other platforms because it was founded earlier. Today, it provides more than 70 kinds of computing, storage, database, analysis, and application services. It has a wide coverage and is naturally the first choice for users. Microsoft is in 2010. Azure was founded in the year, with 67 services and high integration with Microsoft products; GCP takes advantage of Google itself in the field of artificial intelligence, rushing to market pies with low-cost strategy; and IBM's Bluemix Many artificial intelligence APIs are released.
Screen Resolution
There are generally three screen resolutions for office projectors:
One is a 4:3 screen suitable for office PPT playback. The first is the SVGA machine with the lowest price and the highest cost performance. This kind of machine is generally priced at around 2500 and has powerful performance. The disadvantage is that the resolution is 800X600, which is relatively low. The second is the XGA machine, which is an upgraded version of the SVGA machine with a resolution of 1024X768.
The other is the 16:10 aspect ratio screen used by some foreign-funded enterprises, which is the 1280X800 resolution of WXGA. However, with the transparent price of 1080p projectors, more and more companies will choose full HD 1080p projectors as their office projection solutions.
Office Projector Features
According to the needs of different office environments, office projectors are roughly divided into three categories.
One is a conventional projector that is placed or hoisted in the conference room, the second is a Portable Projector that can be carried around, and the third is an ultra-short-throw projector that is convenient for work reports and speeches.
Wireless Office Projector
With the advent of the Internet era, the emergence of a series of wireless office series has added new members to the office projectors. The wireless office projector is realized by the built-in wireless module of the ordinary office projector. Wireless office makes people do not need to switch signal lines frequently in office meetings, allowing people to have a better experience in meetings!
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