Popular "CHAGPT" and the prospect of AI

1. what is chagpt?

It is a natural language model GPT (Generative Pre-trained Transformer) released by OpenAI a few years ago. The principle of GPT is: first, provide it with a huge corpus (directly grabbed from the Internet), and let the model break up, mark and learn these texts through hundreds of billions (175 billion) parameters to build a complex prediction model; Then, according to this prediction model, it is judged which word a word should take in this situation. In this way, one word is strung together to form a paragraph or an article.

Its core is: building GTP model (structure)-model pre-training and self-learning to build internal feedback (motivation)-Chat is to build a friendly user interface and interactive way to realize external feedback (interactive layer).

2. What can chatgpt do now?

A, answer questions; Chatgpt’s actual expression ability is better than many people; Because of the "breadth of self-learning", the content it answers can often give the questioner a new angle or a perfect framework;

B, write an article; Also based on the "breadth of feeding", we can give quality results such as emails, reports, papers, planning schemes, etc.

C, summarizing and refining; Can help you listen to videos, podcasts, articles, etc., and summarize the main points in concise language;

D, generating codes; Also based on "feeding professionalism", its code generation ability is also very strong;

3. Disadvantages of chatgpt:

A, the level of input information: ChatGPT can’t generate information out of thin air, and all its knowledge can only come from the corpus it is fed. Obviously, the answers it can provide and the contents it can output will not exceed the average level of these corpora; It is essentially a kind of second-hand information. It is neither accurate nor traceable.

B, moral and ethical issues: under the current modeling conditions, there are negative and "deviation" phenomena;

C, the answer error rate is high (about 5%): it is difficult for us to see where the answer given by ChatGPT is wrong, which will lead to potential risks to users;

4. the prospect of chatgpt:

A. Search: Looking back at people’s search for information, in the search 1.0 stage, they can only go to the library to look up information; Search 2.0 stage

Information is digitized, and information can be queried and exchanged through the Internet; However, the information is too complicated and needs to be effectively screened, refined and integrated, which is the search 3.0 stage; It solves the contradiction between "too much information" and "too little attention"

B. Reading: In the future, each of us may have our own "exclusive doctor, lawyer, financial manager", etc. No matter what questions we want to consult, ask artificial intelligence directly, and it will give the latest, most comprehensive and accurate answer;

C. Writing: We no longer need to spend a lot of energy on writing plans and documents. We just need to think and come up with all kinds of ideas, themes, ideas, etc., and then tell these ideas to artificial intelligence, give it enough information, and it can be automatically output.

5. Future prospects of AI:

A. The future of AI will become the same infrastructure as water and electricity; Just as the first industrial revolution harnessed coal, the second industrial revolution harnessed oil, electricity and the third industrial revolution harnessed information; The next revolution will be to control "data and computing power", and the future intelligence will become the most basic facilities and mass goods of the whole society.

B. For individuals, AI may not make you unemployed, but those who will use AI in the future will make you unemployed. That is to say, if employees can’t strive to upgrade to High Concept (high concept, responsible for deep thinking) or High Touch (high experience, responsible for interpersonal interface), they will be gradually eliminated.

After VAR awarded OPE a penalty for Liverpool handball, Salah shot wide and missed the penalty.

Mohamed Salah missed a penalty for OPE against Bournemouth at noon on Saturday.

VAR awarded Liverpool handball

Salah broke the reds’ first goal of the season.

Klopp lost to relegation fighter Bournemouth.

What happened? Liverpool were awarded a penalty for Adam Smith’s handball, which gave them a chance to equalize the score against Bournemouth. Salah stood up for Liverpool’s first penalty of the season and uncharacteristically missed the ball!

The bigger picture: Liverpool hope to take advantage of last week’s 7-0 victory over Manchester United on the South Coast. Philip Bill scored the only goal of the game, and the Red Army fell behind. They thought they had a chance to return to the game before Salah missed the penalty.

Quantum machine learning: the intersection of quantum computing and artificial intelligence

With the continuous progress of technology, the concept of quantum computing is more and more widely known. As a new computing paradigm, quantum computing is very different from traditional computing methods. It can deal with problems that traditional computers can’t handle, which makes quantum computing have broad application prospects in the field of artificial intelligence. Quantum machine learning, as an important field where quantum computing and artificial intelligence intersect, has a wide and far-reaching application prospect. This paper will introduce the basic concept, principle and application of quantum machine learning, and analyze its future development trend.

First, the basic concepts of quantum machine learning

Quantum machine learning is a technology that uses quantum computing to realize machine learning. Its main purpose is to use the advantages of quantum computing to deal with problems that traditional computers can’t handle and improve the efficiency and accuracy of machine learning. The main difference between quantum machine learning and traditional machine learning is that it uses qubits to store and process data instead of classical bits used in traditional machine learning.

Second, the principle of quantum machine learning

The principles of quantum machine learning mainly include quantum data coding, quantum state preparation and quantum algorithm design. Among them, quantum data coding is the process of coding classical data into quantum States, so that the efficiency and accuracy of machine learning can be improved by using the characteristics of superposition and entanglement of quantum States. Preparation of quantum states is a process of putting qubits into the required quantum states. By controlling and operating qubits, the conversion between different quantum states can be realized, thus realizing various algorithms in machine learning. The design of quantum algorithms is the process of designing and implementing quantum algorithms, which can be optimized on quantum computers, thus achieving the purpose of machine learning.

Third, the application of quantum machine learning

Quantum machine learning is widely used, including classification, clustering, regression, dimensionality reduction and other fields. Here are some applications:

  1. Quantum neural network

Quantum neural network is a new type of neural network, which uses quantum bits to store and process data. Quantum neural network can deal with complex nonlinear problems, which makes it have a wide application prospect in image recognition, speech recognition and other fields.

  1. Quantum support vector machine

Quantum support vector machine is a support vector machine algorithm based on quantum computing, which can process high-dimensional and nonlinear data sets faster and improve the accuracy and efficiency of classification. Quantum support vector machine is widely used in bioinformatics, image processing, financial forecasting and other fields.

  1. Quantum clustering

Quantum clustering is a method to realize clustering analysis by quantum computing, which can process a large number of data faster and improve the accuracy of clustering. Quantum clustering is widely used in biology, image processing, market analysis and other fields.

Quantum dimensionality reduction is a method to realize dimensionality reduction analysis by quantum computing, which can process high-dimensional data faster and reduce the complexity and storage space of data. Quantum dimensionality reduction is widely used in data mining, image processing, natural language processing and other fields.

Fourth, the future development trend of quantum machine learning

With the continuous progress of quantum computing technology, the application prospect of quantum machine learning will be more and more extensive. In the future, the development trend of quantum machine learning mainly includes the following aspects:

  1. Further improvement of hardware technology

At present, the performance of quantum computer needs to be improved, and the development of hardware technology will help to improve the efficiency and accuracy of quantum machine learning.

  1. Innovation of algorithm design

With the deepening and development of quantum machine learning theory, algorithm design will become more and more important. In the future, quantum machine learning algorithms will be more complex and efficient.

  1. Expansion of application scenarios

With the continuous expansion of the application scenarios of quantum machine learning, the future will involve more fields, including physics, chemistry, biology, finance, transportation and so on.

To sum up, quantum machine learning, as an important field where quantum computing and artificial intelligence intersect, has a very broad application prospect. In the future, quantum machine learning will continue to develop and innovate in hardware technology, algorithm design and application scenarios, thus bringing more benefits and development opportunities to human society.