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ChatGPT’s Enlightenment to Future Battlefield Intelligence Perception and Decision-making

2023年4月12日 | admin | sdsadas

Author: Liu Xinyu

The combination of artificial intelligence technology and unmanned aerial vehicle will greatly change the existing theory and mode of military operations. The power of artificial intelligence plays a role at all levels of the national defense industry, and the rapid development of artificial intelligence will define the next generation of war. Recently, ChatGPT, a generative artificial intelligence technology developed by OpenAI, has been paid attention to and used all over the world. ChatGPT is a large-scale generative pre-training language model based on transformer. It can train in existing data sets and generate texts similar to human language. This unique ability makes it an ideal tool for military applications. The underlying natural language model and technology that provides power for ChatGPT may completely change the artificial intelligence on the battlefield and have a great impact on the situation awareness and independent decision-making methods in future wars.

Intelligent situational awareness in the whole information domain

In the future military field, there will be a battlefield environment with complex information, high confrontation and changeable tasks. The highly uncertain combat environment puts forward extremely high requirements for the independent perception and cognitive ability of combat equipment. Military equipment needs to have the ability of automatic target detection and identification and multi-sensor data fusion. It can detect and fuse enemy target information and its own support information through autonomous and receiving information acquisition methods, and perceive the battlefield situation and extract important information for subsequent decision-making on the basis of obtaining full information domain.

Based on the demand for intelligent situational awareness, the generative artificial intelligence technology ChatGPT can be integrated into military vehicles, aircraft and other combat systems. With the application of artificial intelligence language robots trained by a large number of models, the required real-time information can be effectively coordinated in a multi-domain environment, and the input data from various sensors can be analyzed to generate a complete, comprehensive and real-time updated operational environment map.

Intelligent technology can play a key role in future military operations. Generative artificial intelligence technology can rely on its strong creativity, understanding and response speed to obtain cross-domain intelligence and battlefield situation data, improve the ability of insight into intelligence, form a highly simulated situation scene, realize dialogue between people and battlefield environment, provide real-time information and situation prediction, enhance the real-time and decision-making of battlefield situation perception, and better support real-time decision-making in military operations.

Real-time and efficient intelligent independent decision-making ability

At present, the autonomous decision-making ability of aircraft has been initially characterized by intelligence and independence. The US Air Force uses artificial intelligence "decision-making assistant tools" in distributed common ground system (DCGS) to help sort out and integrate a large amount of data. This artificial intelligence system connects most airborne intelligence and monitoring and reconnaissance platforms of the US Air Force, and integrates artificial intelligence technology into training to expand into other fields.

If the generative artificial intelligence technology ChatGPT is introduced into the decision-making method of aircraft, it can provide real-time information about enemy positions, movements and capabilities, as well as the advantages and disadvantages of friendly forces in tactical situations based on the prior information database and real-time signal, data and image databases, and at the same time based on the real-time interaction between aircraft and environment, so as to analyze, reason and make decisions, and realize rapid response to battlefield decision-making.

The generative artificial intelligence robot can generate multiple sets of operational plans in a short time when the aircraft is faced with complex and uncertain operational conditions, and preview the battlefield process and results for each set of plans, so as to generate real-time optimal decisions in the face of complex requirements in terms of information acquisition, reaction time, calculation speed, tactical evolution and comprehensive evaluation, and support decision makers with diversified decision plans and deduction results.

Real-time and efficient intelligent autonomous decision-making method can cover the complex situation in real combat environment, play a similar role to human brain in high uncertainty environment, dynamically adjust attack and protection strategies according to real-time situation awareness and operational effectiveness evaluation, and realize efficient confrontation through closed loop of process.

Enlightenment and prospect of intelligent military

Generative artificial intelligence technology breaks the logic of time series calculation, and makes artificial intelligence in multiple sub-fields begin to merge technically. As a new technology in the field of artificial intelligence, deploying ChatGPT in military operations may enhance cross-domain combat capability and realize situational awareness and real-time independent decision-making in all information domains. In the future battlefield, if we can deploy the top-level layout and bottom-level algorithm of generative artificial intelligence with the guidance of military operations and equipment development, with its ability of understanding, responding and interacting with people, we can greatly improve the cognitive and decision-making methods in the battlefield, promote the technical upgrading in key areas, and realize the optimization iteration of combat capability.

From the point of view of data and subsequent development, generative artificial intelligence technology is a more advanced neural network deep learning algorithm, which has high requirements for training data, depends on the authenticity of training data and is easily disturbed by external information. Because of the long training time and billions of parameters, automatic machine learning is needed for multiple lines to generate better calculation results. Therefore, when transforming the technological achievements of generative artificial intelligence, it is necessary to take into account the parameterization requirements of scientific research technology development and the automation requirements of equipment application development, so as to achieve the balance between scientific research liquidity and industrial productization.

Generative artificial intelligence has brought a new paradigm to military applications and set a new route for the next generation of military operations. It is the main problem to apply generative artificial intelligence technology to the military field to actively explore the representation form of situation awareness and decision-making tasks for different military problems and consider how to use effective information for large-scale pre-training. The combination of artificial intelligence technology and military operations and technological innovation will reserve new ways for situation awareness and independent decision-making in future operations, and realize intelligent support for the development of new military equipment.

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

2023年3月13日 | admin | restert

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:

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.

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.

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:

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.

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.

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.

Messi assists Mbappé to beat brest 2-1 away.

2023年3月12日 | admin | khjgjgh

Messi assists Mbappé in the lore.

In the early morning of March 12th, Beijing time, brest played at home in the 27th round of Ligue 1 against Paris Saint-Germain. As there are probably four relegation teams in Ligue 1 this season, brest ranks 15th, but only one point ahead of the relegation zone. In terms of total value, the Paris team is as high as 890 million, while brest is only 80 million.

In the 37th minute of the game, Messi instigated the attack, and Mbappé shot hard and was saved, but the ball was not far away, and Sohler made up the shot to break the goal.

In the 44th minute, brest got the chance through a simple long pass, and Hornot faced the defense of the two men and shot from a small angle to equalize the score.

In the 90th minute, Messi sent a wonderful assist, Mbappé passed the goalkeeper and finished the lore. Messi completed the milestone of 300 assists and Mbappé scored 2000 goals.

In the end, Paris beat brest 2-1 away, got rid of the depression of the Champions League and ushered in a four-game winning streak.

At 22: 30 pm on March 11th, Bayern beat augsburg 5-3 in the 24th round of the Bundesliga.

In the second minute, Berisha turned and shot low, hitting the ball into the lower right corner of the goal. Sommer couldn’t save it, and Bayern scored 0-1.

In the 16th minute, Sane divided the ball to the right in front of the penalty area. Cancelo caught the ball and entered the penalty area. He swung past the defender’s right foot and volleyed at a small angle. Bayern equalized the score 1-1.

In the 19th minute, Manet passed the ball with a barb on the right side of the penalty area, and pawar shot the ball in front of the door. Bayern overtook augsburg 2-1.

In the 35th minute, Bayern’s right corner kicked into the penalty area, and Delicht grabbed the header in the middle, and the goalkeeper flew to save the ball. pawar’s right foot volley in front of the door scored twice, and Bayern scored 3-1 augsburg.

In the 45th minute, Manet volleyed his left foot from the restricted area and was saved. Sanet headed into the empty net in front of the door, and Bayern led augsburg 4-1.

In the 55th minute, Berisha took the penalty. He tricked Sommer into hitting the right side of the goal and scored twice. augsburg was 2-4 behind Bayern.

In the 74th minute, Bayern made a reactionary fast break after stealing in the frontcourt. Cancelo passed the back point on the right side of the penalty area, Alfonso Davidson volleyed the goal, and Bayern led augsburg 5-2.

In the 93rd minute, Vargas made a high-speed dash with the ball on the left, and came to the front of the bottom line on the left side of the penalty area. cardona followed him and scored a goal. augsburg scored 3-5 Bayern.

Bayern started: 27- Sommer, 2- Yu Pamekano, 4- Driget (83′ 23-Blind), 5- pawar, 19- Alfonso-Davies (77′ 40- Mazravi), 22- Cancelo, 6- kimmich, 7- Gnabry (71′ 25-Muller).

Augsburg starting: 1- Ji Chivici, 2- Gumny (46′ 22-Yago), 6- Gouweleeuw, 23- Bauer, 3- pedersen, 27- engers (46′ 13- Rexbeke), 14- Baumgartlinger, 10-A- Mayer (77′ 34