Although artificial intelligence has great potential, it is still in its infancy, and it is still a long way from its end goal. Its goal is to add value to advanced computers based on algorithmic procedures and simple and feasible automation.
The development of artificial intelligence is quite rapid, but it is still in its infancy. The current automation is entirely based on human wisdom, but in the future, with the development of technology, we may usher in a wave of large-scale automation, and this progress is based on the common human and machine. Work hard.
This article analyzes how deep learning accelerates enterprise automation from three aspects: 1) IT automation; 2) software development automation; 3) the evolution of automation.
Although the potential of artificial intelligence is huge, it is still in its infancy, and there is still a long distance from its end goal. Its goal is to add value to advanced computer-based programs and simple and feasible automation. At present, every point of automation of computer programs is entirely brought by human intelligence. This form of human intelligence is represented by intelligent algorithms, more complicated flowcharts and uninterrupted indoor experiments.
This also means that the next large-scale wave of automation not only requires human effort, it also requires "machine effort." This is the case of machine learning, because the transformational forces driving the development of artificial intelligence have become enablers of large-scale automation.
Three concepts
artificial intelligence
Any technology, method, and program that allows a computer to simulate human intelligence and use it to drive a positive digital response is artificial intelligence. Some components of artificial intelligence include decision trees, if-then rules, multi-step logic and machine learning (also including deep learning).
Machine learning
Machine learning is a subset of artificial intelligence, including all technologies that focus on improving the efficiency of computer programs through experience. These techniques include a feedback loop that records command (current process) and response (result) information, and logic to adjust the response based on that information.
Deep learning
Deep learning is a subset of machine learning and includes techniques designed to improve software by exposing software to large data streams and using multiple layers of neural networks. Today, neural networks are composed of increasingly complex code layers. Neural networks enable software to learn from the hundreds, thousands, or millions of data-driven simulations it encounters.
How can deep learning accelerate enterprise automation?
IT automation
Is IT automation an IT team that studies automation? Can IT itself be automated? Well, along with deep learning, artificial intelligence is slowly making this vague concept more clear and structured.
Consider the situation of Apache Web server technology. In the 1990s, server crashes were completely handled by humans. Then with Nagios and other monitoring systems reporting crashes, even the server can restart itself.
Immediately, we ushered in the wave of cloud and DevOps, as well as configuration management tools like Chef and Puppet. In addition to event-based decisions to start and stop servers, these tools can also handle server settings. The key is that the system is developing and the dependence on humans is decreasing.
Even today, if an enterprise needs to add more applications to the IT infrastructure and ecosystem, then the enterprise must follow a top-down approach. The central file or collection of files holds information about the architecture, which needs to be edited, and then the deployment of new applications needs to comply with this information.
Fortunately, since all this expansion and contraction information is recorded in one place, the neural network can be trained to understand patterns, predict and recommend instructions.
Software development automation
There has been a unified movement centered on letting computer programs understand human language to promote the application of artificial intelligence. The rest is the idea of ​​letting the computer better understand its own language!
In other words, let the program understand the code, understand the developer's methods, predict the expected results of the code, "magically" correct the errors, make the code safer, give best practices, and even complete it yourself. Does this sound unrealistic? But in fact there are already relevant examples:
GitHub has more than 66 million pull requests; in fact, each pull request means that some bad code is changing to good code.
Google is building an error prediction system, monitoring code repository, project management tools, and error reporting. It uses this information to predict possible errors in the code under development.
Siri's rival Samsung Viv is a very complex compiler that compiles human language into different algorithms and uses it to drive more instructions than Siri currently does.
All of these are moving in this direction, allowing computer software to use the power of large data sets and neural networks (essentially deep learning) to make the code more intelligent on its own.
The evolution of automation
The best application of deep learning points to automation, because it can make artificial intelligence better, cheaper, simpler, and faster. Study a computer program that can beat anyone in chess. Apart from this, this program will not achieve anything else now.
Increasing the power of neural computing networks, this program will gradually and easily defeat humans. Similarly, any successful examples of deep learning will subsequently bring more successful cases. Using artificial intelligence to run and manage computers will certainly improve other types of computing instructions. Before long, we can have smarter self-driving cars and more autonomous robots.
Progress is inevitable
In fact, on this journey, we are still driving at a low speed; compared to "through" deep learning, we spend more time doing deep learning. However, progress is inevitable, and within a few years, startups and companies will release commercial solutions that leverage the power of deep learning and autonomous driving.
An open source platform similar to TensorFlow will enhance these initial applications and will drive the creation of more complex and valuable deep learning systems. Deep learning will inevitably pull the power of big data, analytics and automation, and bring incredible results to enterprises and society. 
Editor in charge: Liu Yang
Stainless Steel Vacuum Sports Bottle
Stainless Steel Bottles are made from high-grade 18/8 stainless steel. This material is food grade, non-toxic, durable and easy to clean.
Our Stainless Steel Bottles are mostly Stainless Steel Vacuum Water Bottles. They have a double-wall vacuum treatment, so they can ensure your drink keeps as it is. And you don't need to worry about getting your hands cold or hot.
Stainless Steel Vacuum Sports Bottle,Solid Color Insulated Sports Water Bottle,Solid Color Vacuum Sports Water Bottle,Heat Transfer Vacuum Sports Water Bottle
Ningbo Auland International Co.,Ltd. , https://www.eversportsbottle.com