Neural networks and artificial intelligence

Sondak, Norman E.; Sondak, Vernon K.

doi:10.1145/65294.71221

Cited by 7 publications

(4 citation statements)

References 4 publications

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“…If the model of traditional word vector combined with neural network structure is used, it is difficult to accurately understand its semantic information only through a small number of samples in the training set. Using the pre training model can effectively enhance the performance of the model, and the pre training model has strong generalization, which is conducive to the model's understanding of some rare categories of samples [19].…”

Section: Pre Training Model Based On Deepmentioning

confidence: 99%

Artificial Intelligence-Assisted Decision-Making Method for Legal Judgment Based on Deep Neural Network

2022

Mobile Information Systems

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With the arrival of the third revolution of artificial intelligence, the applications of artificial intelligence in the fields of automatic driving, image recognition, smart home, machine translation, medical services, e-sports, and so on can be seen everywhere, and topics about artificial intelligence are constantly emerging. Since 2017, the discussion on artificial intelligence in the field of law has become more and more active. In this context, the application of artificial intelligence in the field of legal judgment and the hypothetical system based on this technology in court judgment has also become the object of discussion from time to time. In this paper, based on the artificial intelligence decision-making method of the deep neural network, aiming at the three subtasks of legal judgment prediction, namely, crime prediction, law recommendation, and sentence prediction, a multi-task judgment prediction model BERT12multi and a sentence interval prediction model BERT-Text CNN are proposed, which improve the prediction accuracy and adopt the knowledge distillation strategy to compress the model parameters and improve the reasoning speed of the judgment model. Experiments on the CAIL2018 data set show that the performance of the deep neural network model in crime prediction and law recommendation tasks can be significantly improved by adopting the pre training model adaptive training, grouping focus loss, and gradient confrontation training strategies. Using a step-by-step sentence prediction strategy can realize the weight sharing of pre training model and make use of the prediction results of charges and laws in sentence prediction. The recall training-prediction strategy can avoid error accumulation and improve the accuracy of sentence prediction. By integrating the artificial intelligence decision-making method, the case reasoning speed can be greatly improved, the highest compressible model volume can be about 11% of the original one, and the reasoning speed can be increased by about 8 times. At the same time, performance close to that of the deep neural model can be obtained, which is superior to other legal decision prediction models based on word embedding.

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Section: Pre Training Model Based On Deepmentioning

confidence: 99%

Artificial Intelligence-Assisted Decision-Making Method for Legal Judgment Based on Deep Neural Network

2022

Mobile Information Systems

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“…The strength ofNNs lie in their ability to derive information from complicated orimprecise data. They can also extract patterns and detect trendstoo complex for a human or other computer techniques [17].…”

Section: Neural Networkmentioning

confidence: 99%

Using perception in managing unstructured documents

Cheng¹,

Pan²

2005

XRDS

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“…Sondak and Sondak argue [8] the importance of adding the topic of neural networks into the computer science curriculum. They cite the intense media attention and the wide breadth of research interest in the topic as the basis for their conclusion.…”

Section: Introductionmentioning

confidence: 99%

Teaching neural networks using LEGO handy board robots in an artificial intelligence course

Imberman

2003

Proceedings of the 34th SIGCSE Technical Symposium on Computer Science Education

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In this paper we propose a novel method for teaching neural networks with back propagation in an undergraduate Artificial Intelligence course. We use an agent based approach in the course as outlined in the textbook Artificial Intelligence A Modern Approach by Stuart Russell and Peter Norvig [7]. The students build a robot agent whose task is to learn path following behavior using a neural network. Robot agents are constructed from standard lego pieces and use the MIT Handy Board as a controller.

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Neural networks and artificial intelligence

Abstract: Neuralnetworks have been called "more important than the atomic bomb" and have received a major funding commitment

Cited by 7 publications

References 4 publications

Artificial Intelligence-Assisted Decision-Making Method for Legal Judgment Based on Deep Neural Network

Artificial Intelligence-Assisted Decision-Making Method for Legal Judgment Based on Deep Neural Network

Using perception in managing unstructured documents

Teaching neural networks using LEGO handy board robots in an artificial intelligence course

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