First, the development of artificial intelligence 1.1 Concept According to Wikipedia's explanation, artificial intelligence is the intelligence displayed by machines. In contrast to the natural intelligence of humans and other animals, AI research is defined in computer science as an "agent software program": anything that feels the surroundings and can be maximized It succeeds in the opportunity of the equipment. 1.2 Major Events In October 2016, the White House released two heavy reports, "Be ready for future artificial intelligence" and "US National Artificial Intelligence Research and Development Strategic Planning", which elaborated on the future of artificial intelligence development planning and artificial intelligence in the United States. The challenges and opportunities brought by government work. VentureBeat summarized these two reports and came up with seven easy-to-understand points: Artificial intelligence should be used to benefit humanity 2. The government should embrace artificial intelligence 3, need to control automatic cars and drones 4, Let all children keep up with the development of technology 5. Use artificial intelligence to supplement rather than replace human workers; 6. Eliminate biases in the data or do not use biased data; 7, consider safety and global impact In 2016, Double 11 and Luban (Alibaba's Artificial Intelligence Design System) first served Double Eleven, produced 170 million chapters of merchandise display ads, and increased the clickthrough rate of products by 100%. If you rely solely on the designer's hands to complete, suppose that each drawing takes 20 minutes, and full play requires 100 designers for 300 consecutive years. In 2017, Luban's design level has significantly improved. Currently, he has learned mega-level designer creative content and has developed a design capability of hundreds of millions of dollars. In addition, Luban has achieved the ability to produce 40 million posters a day, and no one will be exactly the same. In May 2017, AlphaGo Master beat world champion Ke Jie. On October 18, 2017, the DeepMind team announced the strongest version of AlphaGo, codenamed AlphaGo Zero. On October 25, 2017, at the Saudi Arabia’s Future Investment Plan Conference, Saudi Arabia granted the “female†robot Sofia citizenship produced by Hanson Robotics. As the world’s first robot to acquire citizenship, Sofia said on the same day that “she†hopes to use artificial intelligence “to help humans to lead a better life†and at the same time to support Muskek, who supports the “AI threat theory,†that “people do not make me, I don't make anyone!" After the meeting, Musk said on Twitter: “Entering the film “The Godfather†into the artificial intelligence system, what else can be worse than this?†The godfather is a Hollywood classic movie, and the story is full of betrayal and murder. The ethical issues that have arisen after Sofia was granted citizenship are also something people have to consider. There are too many big news in the field of artificial intelligence in recent years. Second, what is the relationship between artificial intelligence, deep learning, machine learning, and enhanced learning? Artificial intelligence is a large category, including expert systems, knowledge representation, machine learning, etc. Among them, machine learning is currently the best fire and development branch. Machine learning includes supervised learning, non-supervised learning, and deep learning. Learning and so on. Supervised learning is what people often say about classification. Through an existing training sample (that is, known data and its corresponding output), it is trained to get an optimal model (this model belongs to a certain set of functions, and optimality indicates that Under the evaluation criteria is the best). This model is then used to map all inputs to the corresponding output, and the output is simply judged to achieve the purpose of classification, which also has the ability to classify unknown data. For example, one of the activities that we often do when we are in kindergarten is called literacy. As shown in the above picture, the teacher will show us many pictures and the following words will be written. After a long time, abstract concepts will form in our brains. , two horns, a short tail, fat (features)... Such animals are cattle; round, yellow, glowing, hung in the sky ... is the sun; people like this. When we see something similar, we can recognize it, even if it is not exactly the same as what we saw before, but it conforms to the concept that is formed in our brains. Non-supervised learning is another method of learning more. It differs from supervised learning in that we do not have any training samples in advance and we need to model the data directly. For example, as shown in the figure, in the absence of any hints (no training set), you need to divide the following six figures into two categories. How do you divide, of course, the first row and the second row? The class, because the shape of the first row is closer, the shape of the second row is closer. Unsupervised learning is to find features in the data without knowing the data set classification. Deep learning is a new field that is extended based on machine learning. The neural network algorithm inspired by human brain structure is the origin of the increase in the depth of the model structure, and a series of new ones accompanied by the improvement of big data and computing power. The algorithm. The concept of deep learning was proposed and raised by well-known scientists such as Geoffrey Hinton et al.'s article published in 2006 and 2007 in Science. Deep learning, as an extension of machine learning, is used in image processing and computer vision, natural language processing, and speech recognition. Since 2006, research and application of deep learning in collaboration between academia and industry have made breakthroughs in the above areas. Taking ImageNet as the object recognition contest in the classic image of the database as an example, it defeated all the traditional algorithms and achieved unprecedented accuracy. Reinforcement learning is also an important branch of machine learning. It is through observation to learn how to make actions. Every action will have an impact on the environment. Learning objects make judgments based on the feedback of the observed surrounding environment. Third, how important is the basis of mathematics For the basic knowledge of mathematics, you need high school mathematics knowledge plus high numbers, linear algebra, statistics, and probability theory. Even if you do not master well, you must at least know the concept. When you use it, you know where to look. If the foundation is not good, you can first look at Wu Jun's "Mathematical Beauty," which is more straightforward and easy to understand. You can also learn by doing. Practice is the sole criterion for testing truth. After all, most people still focus on engineering practice. 1. Learn Python and use it for algorithm programming. Here, I strongly recommend that beginners learn Python. Python is not only very easy for beginners to master, but it also supports almost all library dependencies in machine learning. Although R language is useful, Python is generally more suitable for learning. In addition to basic programming, the most useful libraries for machine learning include Numpy, Pandas, and Matplotlib. 2. Learn more about the basics of machine learning. Andrew Ng's Machine Learning course is the most common machine learning course. Because the curriculum involves some concepts of partial derivatives (although the entire course does not require a thorough understanding of these concepts), this course may be somewhat difficult for high school students. 3. Learn various machine learning algorithms and understand how to apply them in real-world scenarios. In theory, it is impossible to directly understand some university mathematics knowledge and related machine learning algorithms. However, a research team in Australia solved this problem. Kirill Eremenko and Hadelin de Ponteves from the SuperDataScience team used various machine learning algorithms to find scenes in real life. This learning method is very effective. In addition, the proper understanding of the function of the algorithm in the application without having to contact complicated and advanced mathematical knowledge is undoubtedly a great welfare for learning machine learning algorithms. 4. Further explore the application of machine learning. So far, you have mastered a wide range of machine learning basics and learned a great deal of skills and programming knowledge. These are enough for you to complete some basic projects independently. Before you start a project, make sure that the data sets you use are simple and clean. They require too much data preprocessing or modification. I don't have much to say here. When you understand the basic knowledge in the field and master the relevant programming knowledge, you only need to practice through actual combat projects, become familiar with the overall process of solving problems, and practice in constant combat. And to improve your professional skills, it can only rely on your own efforts! 5. Find a field of particular interest and explore further. Although you now have a broad and in-depth understanding of the basics of machine learning, there are no clear boundaries for the practical application of these machine learning algorithms. Therefore, I suggest that you should find a machine learning application area that is of particular interest as soon as possible and conduct in-depth research. Below, I will list some areas that may be involved, but you should understand what you are learning before you begin. 1. Computer Vision: This may be the hottest area in machine learning/artificial intelligence. The computer uses a special type of deep neural network to identify, detect, and understand image content. 2. Natural language processing: Understanding how computers learn to speak is also a hot topic of research. 3. Reinforcement learning: This area focuses on how to let the machine learn in a specific way, and it is the most widely used in the field of video games. Once you have completed these courses, you can begin to download some basic projects from the Internet and try to add artificial intelligence elements to modify the agent's behavior and agent learning methods. More video tutorials can be searched on Youtube. 4. Data Science: This is an emerging field that has a wide range of applications in real life and also creates a large number of job opportunities. 5. After mastering the relevant knowledge in the field of data science, it is best to find internships for some companies because the actual business problems of the company will enable you to better consolidate the data science workflow through projects and quickly improve your own in actual combat. Ability, which will also greatly help future career development. 6. In addition, there are other application areas such as learning for learning (for recommending systems, generating confrontation networks (using AI to improve AI), and genetic algorithms (solutions that improve problems in a way that resembles natural evolution) are also worth your further However, from the current situation, many sub-application areas have not yet been fully explored and explored. For one, if you are particularly interested in one of these areas, then start exploring these areas. . For one person, if you want to work long-term in machine learning or artificial intelligence in the future, then a crucial issue is to understand what it is, where it is groundbreaking and its impact on society. In addition, you also need to identify an area of ​​interest as soon as possible, which will determine the direction of your future learning and research. Once you have identified the areas of research and you have the necessary understanding of the work in the field, you can follow the above steps to start targeted learning. Not only that, but also in the high school stage we still need to have a solid grasp of the knowledge in this field and have a general grasp of its development. For these I will give my suggestions: 1. Start reading research papers: This doesn't sound like a challenging job. Reading research papers is perfectly fine for most of them, and most of the papers are easy to grasp. If you encounter a problem that you do not understand, you can try to skip these obstacles as long as you can understand the overall thinking of the paper. 2. Listen to the opinions of the big names in the field: Industry bulls such as Andrew Ng, Ian Goodfellow and Yann LeCunn will be interviewed regularly and give their opinions on topics related to artificial intelligence and related fields. 3. Real-time tracking of domain dynamics: For example, publishing multiple articles related to machine learning and artificial intelligence in real time. 4. Understand what it means: TED talks are one of the best ways to learn about artificial intelligence. The keynote speakers are excellent workers in this field. They have unique insights on their respective fields of knowledge and future development trends, and can share some of their valuable work experience in the speech. 5. AI philosophy: The success of artificial intelligence has its supporters and opponents. 6. Contribution: If you like to learn from others' experiences, you can check out the artificial intelligence and deep learning topics introduced by the Facebook group. Micro USB 3.0 Male,Micro USB 3.0 Straddle Type,B Male Straddle Type 0.8mm,MICRO USB 3.0 B MALE ShenZhen Antenk Electronics Co,Ltd , https://www.antenkwire.com
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USB3.0 is a USB specification, which was initiated by Intel and other companies. The maximum transmission bandwidth of USB3.0 is as high as 5.0gbps (500MB / s).
While maintaining compatibility with USB2.0, USB3.0 also provides enhancements: significantly increased bandwidth (up to 5Gbps full duplex); better power management;
more power; faster device identification; and higher data processing efficiency.
The reason why USB 3.0 has the performance of "speeding" is entirely due to the improvement of technology.
Compared with USB 2.0 interface, USB 3.0 adds more physical buses in parallel mode.
You can pick up a USB Cable and look at the interface.
On the basis of the original 4-wire structure (power supply, ground wire, 2 pieces of data), USB 3.0 adds 4 lines for receiving and transmitting signals.
So there are eight lines in the cable and on the interface.
It is the additional 4 (2 pairs) of lines that provide the bandwidth required for "superspeed USB" to achieve "over speed".
Obviously, two (1 pair) lines on USB 2.0 are not enough.
In addition, in the signal transmission method, the host control mode is still used, but the asynchronous transmission is changed.
USB 3.0 makes use of two-way data transmission mode instead of half duplex mode in USB 2.0 era. In short, data only needs to flow in one direction, which simplifies the time consumption caused by waiting.
In fact, USB 3.0 does not take any rarely heard of advanced technology, but theoretically increases the bandwidth by 10 times. As a result, it is more friendly and friendly. Once superspeed USB products come out, more people can easily accept and make better customized products.