Academics, industry and the media may have different views on how far AI has progressed in today’s tech world. One argument I often hear is that artificial intelligence based on big data and deep learning is a completely new form of technology, and its emergence can completely change the social form of human beings in the future, because it can “learn” autonomously, thus replacing human labor in a large number. I think there are two misconceptions: First, deep learning is not a new technology; Second, the “learning” involved in deep learning techniques is not the same as human learning, because it does not really understand the “depth” of the information it is facing. Deep learning is not a new technology From the perspective of the history of technology, the predecessor of deep learning technology is actually the “artificial neural network” technology (also known as “connectionism” technology) which has been very popular in the 1980s. The essence of this technology is to build a simple artificial neural network structure by means of mathematical modeling, and a typical such structure generally consists of three layers: input unit layer, intermediate unit layer and output unit layer. After receiving information from the outside world, the input cell layer “decides” whether to send further data to the intermediate cell layer according to the aggregation algorithm and excitation function built into each cell. The process is similar to that of human neurons receiving electrical pulses from other neurons. It can “decide” whether to send electrical impulses to other neurons based on changes in the electrical potential within its own nucleus. It should be noted that no matter the overall task performed by the whole system is about image recognition or natural language processing, it is impossible for the observer to know the nature of the relevant overall task only from the operation state of a single computing unit in the system. Rather, the whole system actually took the form of “pieces”, the recognition on the macro level of task decomposition to system components between the microscopic information transfer activities, and the microscopic information transfer activities of these trends, to simulate the human mind in the process of symbol of the level of information processing. The basic way engineers adjust the trend of a system’s microinformation transmission activity is as follows: first, the system randomizes the input information and then compares the results with the desired results. If the two do not match well, the system triggers its own “back propagation algorithm” to adjust the weight of each computing unit in the system, so that the output given by the system is different from the previous output. The greater the weight of connection between two elements, the more likely co-excitation will occur between them, and vice versa. Then, the system compares the actual output with the ideal output again. If the coincidence degree is still not good, the system starts the back propagation algorithm again until the actual output and the ideal output match each other. The system that completes this training process can not only perform accurate semantic classification for the training samples, but also perform relatively accurate semantic classification for the input information that is close to the training samples. For example, if a system has been trained to recognize which photos in an existing library are the faces of Three, the system can quickly recognize even a new photo that has never been in the library as the face of three.

 Cognitive intelligence is a branch of computer science and an advanced stage of intelligence science. It is based on human cognitive system, aims to imitate human core capabilities, and focuses on the understanding, storage and application of information. Based on the deep understanding of perceptual information and natural language information, and guided by the interdisciplinary theoretical system, a new generation of theory, technology and application system of technical science is formed. The core research areas of cognitive intelligence include: 1. The relationship among universe, information and brain; 2. 2. Structure, function and mechanism of human brain; 3. Philosophy system, liberal arts system and science system; 4. Core systems of cognitive integration, intelligence integration, and bi-brain (human brain and computer) integration. Four steps of cognitive intelligence: 1. Cognition of the universe. The supporting theoretical systems include three-body theory (universe, information, brain), Tao theory, ontology, ontology, cognitive theory, fusion theory, HNC and so on. 2. Understand the structure, function and mechanism of human brain. The supporting disciplines include brain science, psychology, logic, emotion, biology, chemistry and so on. 3. Clear the rules of information connotation. Supporting disciplines include semiotics, linguistics, cognitive linguistics, formal linguistics and other disciplines. 4. System landing capability. Supporting disciplines include computer science, mathematics and so on. Interface application website address: www.weilaitec.com  interface address (example) :  a239p06512. Zicp. VIP/Web/BuAppJa…” + IP  User message content.  String MSG =” What are you doing?” ;  APIkey parameter. The apikey is the apikey  apikey =””;  // The client IP address, the unique identifier of the end user (the IP address of the end user, the phone number, the wechat number, the QQ number, etc.)  String IP =””;  // Do put encode in place. To GBK.  MSG = URLEncoder. Encode (MSG, “GBK”);  The three parameters are all lowercase  MSG parameters.  The MSG parameter must be encoded as GBK. Otherwise, it will be garbled.  Interface code:  Package ai.nlp. Jiekou.test;  import Java. IO. ByteArrayOutputStream;  import Java. IO. IOException;  import Java. IO. InputStream;  import Java. IO. UnsupportedEncodingException;  import java.net.HttpURLConnection;  import java.net.URL;  import java.net.URLEncoder;  import ai. NLP. Util. Changliang. ChangLiangZi;  public class ApiTest { /** 

  •  The return data is returned
  • @ param urlStr 
  •  */  private static String opUrl(String urlStr)  { URL URL = null;  HttpURLConnection conn = null;  InputStream is = null;  ByteArrayOutputStream baos = null;  try  { url = new url (urlStr);  conn = (HttpURLConnection) url.openConnection();  conn. SetReadTimeout (5 * 10000);  conn. SetConnectTimeout (5 * 10000);  conn. SetRequestMethod (” POST “);  If (conn.getresponsecode () == 200)  { is = conn.getinputStream ();  Baos = new ByteArrayOutputStream();  int len = -1;  byte[] buf = new byte[128];  While ((len = is.read(buf))!  { baos.write(buf, 0, len);  } baos. Flush ();  String result = baos.tostring ();  return result; } else  { throw new Exception(” Server connection error!” ); }  catch (Exception e)  { e.intstackTrace (); } finally  { try  { if (is!  is = null). The close (); } catch (IOException e)  { e.intstackTrace (); }  try  { if (baos! = null)  baos. Close (); } catch (IOException e)  { e.intstackTrace ();  } conn. Disconnect (); }  return ChangLiangZi.WU; }  public static void main(String args []){ //  The // MSG parameter must be coded as GBK; otherwise, it will be garbled.  String MSG =” What are you doing?” ;  //apikey parameter.  String apikey = “UTNJK34THXK010T566ZI39VES50BLRBE8R66H5R3FOAO84J3BV”;  // The client IP address, the unique identifier of the end user (the IP address of the end user, the phone number, the wechat number, the QQ number, etc.)  String IP =”127.0.0.1″;  // Do put encode in place. To GBK.  try  { MSG = URLEncoder. Encode (MSG, “GBK”);  } the catch (UnsupportedEncodingException e) { e.p rintStackTrace ();  } System. Out.println (opUrl (” a239p06512. Zicp. VIP/Web/BuAppJa…” +ip)); } } 