AlphaGo defeated Li Shizhen for a while and attracted a lot of media attention, even though it has been a while. The words artificial intelligence, machine learning, and deep learning have become hot words in the media, and the media used them to describe how DeepMind succeeded. First of all, the relationship between artificial intelligence, deep learning, and machine learning is the simplest way to distinguish the three: imagine concentric circles, artificial intelligence (AI) is the concentric circle with the largest radius, and machine learning is inward ( Machine Learning), the most in-depth learning is Deep Learning. Since several computer scientists mentioned the word at the Dartmouth meeting in 1956, artificial intelligence has been lingering in the minds of experimental researchers. In the decades that followed, artificial intelligence was advertised as the key to a better future for human civilization. In the past few years, especially since 2015, artificial intelligence has begun to explode. This greatly improves the GPU's wide availability, making parallel processing faster, cheaper, and more powerful. The entire big data movement has unlimited storage and a lot of data: images, text, transactions, mapping data and more. About artificial intelligence: 1. Over the past two decades, advances in the digitization of big data sets, the establishment of a basic framework for managing large data sets, and the big data computing paradigm have been the main reasons why this century has focused on data science and artificial intelligence. 2. Once we digitize the data so that they can be processed by the program, the next step is to incite automation and predict the future. As the forecasting power increases, it seems that more "smart" aspects have emerged. So we changed the term "data science" to "artificial intelligence." In fact, there is no obvious difference between the two, but the sense of novelty and difficulty is different. The novelty and difficulty are normally distributed over time. Today, "artificial intelligence" gives people the same feeling as yesterday's "data science." 3. The AI ​​learned from the data is called Machine Learning. Traditional machine learning extracts features that people can recognize from raw data, and then learns these features to produce a final model. About machine learning: Machine learning is a way to implement artificial intelligence. 1. The most fundamental point of machine learning is the use of algorithms to analyze the practice and learning of data, and then make decisions or predictions about real events. Instead of using a set of hard-coded software programs generated by a specific set of instructions to solve a specific task, the machine "trains" by using a large amount of data and algorithms, thus giving it the ability to learn how to perform tasks. 2, machine learning is the product of early artificial intelligence crowd thinking, the algorithms formed over the years include decision tree learning, inductive logic programming, clustering, reinforcement learning, Bayesian network and so on. As we know, all of these have not achieved the ultimate goal of strong artificial intelligence, and early machine learning methods were not even touched by weak artificial intelligence. 4. It turns out that one of the best areas of application for machine learning over the years has been computer vision, although a lot of manual coding is still needed to do the job. People will write manual code classifiers, such as edge detection filters, so that the program can recognize the start and stop of a target; perform shape detection to determine if it has eight sides; and ensure that the classifier recognizes the letter "stop." In those hand-coded classifiers, the machine develops algorithms that make the image and "learning" more meaningful and are used to determine if this is a stop sign. The results are not bad, but it is not enough. Especially when the sign is not so clear in fog, or if a tree covers part of the sign, it is difficult to succeed. There is another reason why computer vision and image detection are not comparable to humans. It is too fragile and too susceptible to the surrounding environment. About deep learning: Deep learning is now a very hot concept in the field of machine learning, but after being reprinted by various media, this concept has gradually become a mythical feeling: for example, people may think that deep learning is a way to simulate the human brain. The neural structure of the machine learning way, so that the computer has the same human wisdom; and such a technology will undoubtedly have unlimited prospects in the future. So what kind of technology is deep learning in essence? Deep learning is a method of modeling patterns (sounds, images, etc.) in the field of machine learning. It is also a statistically based probability model. After modeling the various modes, various modes can be identified. For example, if the mode to be modeled is sound, then the recognition can be understood as speech recognition. The analogy is to understand that if the machine learning algorithm is compared to the sorting algorithm, then the deep learning algorithm is one of many sorting algorithms (such as bubble sorting), which in certain application scenarios will have certain Advantage. 1. Over the past decade, neural networks, a machine learning structure similar to the synaptic connections in mammalian brains, have been revived. Neural networks do not require artificial extraction of features. After the raw data enters the learning algorithm, it does not require any artificial work. We call it "deep learning." 2. Although deep learning techniques and learning models have existed for decades, we are now seeing theoretical innovations and breakthroughs based on experience, as the practicality of infrastructure and data is just beginning to mature. In 2006, NVIDIA launched the GPU-based CUDA development platform, which became a feng shui in the history of deep learning. 3. It is precisely because deep learning is separated from the artificial construction feature that it can become a natural learning tool. A lot of skills, we have learned before we have the ability to extract features in complex mathematical ways. These skills are naturally learned by us and are difficult to generalize with high-level features. Through traditional machine learning methods, it is difficult to think of human beings directly, or to construct high-dimensional precise features. 4. We have achieved good results in machine vision and natural language processing long before we were able to build complex semantics (semanTIc). But learning these skills does not require us to have the ability to make mathematical inferences, let alone the high-level semantics that people intentionally construct. 5. Deep learning has shown breakthrough results in the generalized high-dimensional machine learning problem. The areas covered include genomics, oil and gas, digital pathology and even the public market. About strong artificial intelligence: ArTIficial General Intelligence (AGI) refers to strong artificial intelligence, which is a kind of artificial intelligence at the human level. It can compete with human beings in all aspects, and human brains can do it. Not only the local and specific problems that artificial intelligence is dealing with today, the complicated and cumbersome tasks that may occur in the future are not for them. The current artificial intelligence requires human programming, but it does not rule out that it can be automatically one day in the future. program. Therefore, the definition of AGI is actually not very accurate. 1. If the AGI definition is still difficult, it is not yet predictable. Lecun and Baidu's chief scientist Wu Enda and others believe that it is not necessary to waste time on AGI's predictions, because it is still far from human beings to reach this level. Artificial intelligence also needs to span a few years or even decades of winter. Wu Enda made a metaphor. Human concerns about future artificial intelligence are comparable to the fantasy of the Alpha Centauri alpha galaxy. (Note: Because the Centaurus alpha star system is very close to the Earth, many science fiction novels "think" that there is a developed universe. civilization). 2, Google DeepMind co-founder Sean Legg believes that it is absolutely beneficial to start researching artificial intelligence security. It helps us to establish a framework for researchers to develop smarter in this direction in a positive direction. artificial intelligence. 3, AGI will have human-like intelligence, but it will not have a human-like appearance, because we do not understand our own "objective function". Currently, we train computers in specific areas to minimize the mistakes they make. Unless we know how our own objective function is calibrated, even if AGI is smart or even conscious, it will never be exactly like a human. 4. People will limit and standardize the behavior of AGI through input and output channels. There will be a lot of debates about AGI's good and evil in the future, whether increasing its good ability will also cause potential malicious behavior. Driverless cars are an early but powerful example. Computer vision company about artificial intelligence: 1. Despise technology: let the machine understand the world The company specializes in face recognition technology and related product application research, providing services to developers, providing a complete set of face detection, face recognition, face analysis and face 3D technology visual technology services, mainly through the provision of cloud API, The offline SDK and the user-oriented self-developed product form have widely applied face recognition technology to Internet and mobile application scenarios. 2, cloud from technology: the face recognition technology derived from the father of computer vision Guangzhou Yunshang Information Technology Co., Ltd. (referred to as Yun Cong Technology) is a high-tech enterprise specializing in computer vision and artificial intelligence. The core technology is derived from the academician of the Fourth Academy and the father of computer vision, Professor Thomas S. Huang. 3, Green squat: let the computer understand the world Gling is a technology company that applies computer vision and deep learning technology to the commercial field. The self-developed squat technology is the world leader in the detection, tracking and identification of people and vehicles. Beijing Moshanghua Technology Co., Ltd.: Artificial Intelligence Computer Vision Engine Clothing + is the leading artificial intelligence computer vision engine, and won five world firsts in ImageNet2015, the world's top computer vision competition. 4, according to technology: with you to build the future of computer vision Currently working on computer vision, intelligent understanding of image and video, and distributed systems and big data applications, providing users with computer vision products based on image and video understanding. 5, code Long Technology: the most fashionable artificial intelligence Malong Technologies is an artificial intelligence company focused on leading deep learning and computer vision technology breakthroughs, creating a world-leading visual decision engine and providing companies with leading international, customized computer vision solutions. 6, Linkface face cloud technology: the world's leading face recognition technology services Founded in 2014, it has created an innovative face detection algorithm based on deep learning, and built a set of efficient and stable face analysis system, including face detection, face key point detection, face recognition, face attribute analysis. , complete testing and other technologies required for identity authentication. 7. çæ³·Intelligence: a leader in fatigue driving warning technology The core team of the company was initiated by a former Chinese People's Liberation Army and a military graphic image technology transfer expert. The company's visual identity technology has passed the comprehensive testing requirements of many multinational electronics companies and domestic car manufacturers in Japan and Europe. 8, Sense TIme business soup technology: church computer to understand the world We have successfully gathered the best and most influential deep learning, computer vision scientists, and leading figures from a number of industries including Google, Baidu, Microsoft and Lenovo. Under the background of the rise of the artificial intelligence industry, Shangtang Group has rapidly become a leading enterprise in the artificial intelligence industry with more than ten years of accumulation in technology, talents and patents. 9, Tuptech: focus on image recognition Guangzhou Tupu Network Technology Co., Ltd. is an entrepreneurial technology company standing at the forefront of artificial intelligence, focusing on the overall solution for image recognition. Piezoelectric Elements For Inkjet Piezo Transducer
Piezoelectric ceramic ring
Inkjet Piezo Transducer,Piezoelectric Vibration Transducer,Piezoelectric Rings,Piezoelectric Elements For Inkjet Piezo Transducer Zibo Yuhai Electronic Ceramic Co., Ltd. , https://www.yhpiezo.com
Applications: ultrasonic vibration tranducer for inkjet printer
Vibration mechanism of inkjet printer:
Generally, it is composed of piezoelectric ceramics and driving rods. By high-frequency electric excitation, piezo ceramics produce high-frequency ultrasonic vibration (above 60 kHz or higher), which is transmitted to the driving rod and generates high-frequency micro-displacement (back and forth expansion) at its front end.
Piezo ceramics components features :
1. High vibration amplitude and can withstand higher power.
2. The product has high reliability, strong maintainability, and is not easy to break down or off-line.
3. The frequency can be adjusted in a wide range, generally within the range of 10KHz.
Yuhai support all the new developping transducer, Welcome the customized elements inquiry.
The present piezoelectric elements for Inkjet piezo transducer is following :
Piezo rings OD4*ID2*2.5mm price USD1.20/pc, 2000pcs
Material: PBaS-4
Fr.: 694 KHz ±5KHz
K33: ≥0.55
Tg loss <0.5%
Ct 60pF ±12.5%
Piezo rings OD4*ID2*2.5mm price USD1.20/pc, 2000pcs
Material: PSnN-5
Fr.: 626KHz ±5KHz
K33: ≥0.57
Tg loss < 2%
Ct 53pF ±12.5%
Piezo rings OD6*ID2.5*2mm price USD1.50/pc, 2000pcs
Material: PZT-41
Fr.: 785 KHz ±5KHz
K33: >0.53
Tg loss < 0.5%
Ct 107 pF ±12.5%
Piezo rod OD3*7mm price USD1.20/pc, 2000pcs
Material: PLiS-51
Fr.: 192 KHz ± 3KHz
K33: >0.62
Tg loss < 2%
Ct 18.7 pF ±12.5%