Current Issue : October-December Volume : 2024 Issue Number : 4 Articles : 5 Articles
Neuromorphic computing, a brain-inspired non-Von Neumann computing system, addresses the challenges posed by the Moore’s law memory wall phenomenon. It has the capability to enhance performance while maintaining power eciency. Neuromorphic chip architecture requirements vary depending on the application and optimising it for large-scale applications remains a challenge. Neuromorphic chips are programmed using spiking neural networks which provide them with important properties such as parallelism, asynchronism, and on-device learning. Widely used spiking neuron models include the Hodgkin–Huxley Model, Izhikevich model, integrate-and- re model, and spike response model. Hardware implementation platforms of the chip follow three approaches: analogue, digital, or a combination of both. Each platform can be implemented using various memory topologies which interconnect with the learning mechanism. Current neuromorphic computing systems typically use the unsupervised learning spike timing-dependent plasticity algorithms. However, algorithms such as voltage-dependent synaptic plasticity have the potential to enhance performance. This review summarises the potential neuromorphic chip architecture specications and highlights which applications they are suitable for....
Background: OpenAI’s ChatGPT (San Francisco, CA, USA) and Google’s Gemini (Mountain View, CA, USA) are two large language models that show promise in improving and expediting medical decision making in hand surgery. Evaluating the applications of these models within the field of hand surgery is warranted. This study aims to evaluate ChatGPT-4 and Gemini in classifying hand injuries and recommending treatment. Methods: Gemini and ChatGPT were given 68 fictionalized clinical vignettes of hand injuries twice. The models were asked to use a specific classification system and recommend surgical or nonsurgical treatment. Classifications were scored based on correctness. Results were analyzed using descriptive statistics, a paired two-tailed t-test, and sensitivity testing. Results: Gemini, correctly classifying 70.6% hand injuries, demonstrated superior classification ability over ChatGPT (mean score 1.46 vs. 0.87, p-value < 0.001). For management, ChatGPT demonstrated higher sensitivity in recommending surgical intervention compared to Gemini (98.0% vs. 88.8%), but lower specificity (68.4% vs. 94.7%). When compared to ChatGPT, Gemini demonstrated greater response replicability. Conclusions: Large language models like ChatGPT and Gemini show promise in assisting medical decision making, particularly in hand surgery, with Gemini generally outperforming ChatGPT. These findings emphasize the importance of considering the strengths and limitations of different models when integrating them into clinical practice....
This paper introduces a numerical methodology for classifying and identifying types of biobased materials through experimental thermal characterization. In contrast to prevailing approaches that primarily focus on thermal conductivity, our characterization methodology encompasses several thermal parameters. In this paper, the physical characteristics of seven types of bio-based concrete were analyzed, focusing on the thermal properties of palm- and esparto-fiber-reinforced concrete. The proposed method uses artificial intelligence techniques, specifically the k-means clustering approach, to segregate data into homogeneous groups with shared thermal characteristics. This enables the elucidation of insights and recommendations regarding the utilization of bio-based insulation in building applications. The results show that the k-means algorithm is able to efficiently classify the reference concrete (RC) with a performance of up to 71%. Additionally, the technique is more accurate when retaining only six centroids, which, among other things, allows all the characteristics associated with each type of concrete to be grouped and identified. Indeed, whether for k clusters k = 7 or k = 5, the technique was not able to predict the typical characteristics of 2% or 3% esparto concrete (EC)....
With the gradual maturity of artificial intelligence technology, artificial intelligence products continue to emerge in the market, which poses new challenges to the current copyright law system. In particular, whether artificial intelligence products are copyrightable is a hot and difficult issue in the current theoretical and practical circles. Based on this, there are two mainstream viewpoints in the current theoretical circle, that is, the viewpoint that supports copyright protection for artificial intelligence products from the perspective of "reader-centered", and the viewpoint that opposes copyright protection for artificial intelligence products from the perspective of “author-centered". However, the question of whether artificial intelligence products can be protected by copyright involves verifying the reasonableness and forward-looking nature of the "open" regulation of copyright objects in China on one hand. On the other hand, it is also relates to determining the future legislative direction of high-level artificial intelligence. Therefore, by comparing the similarities and differences in originality between the copyright law system and the copyright law system, the conclusion is drawn that artificial intelligence products should be protected by copyright. The evaluation criteria of subject-object separation established by "reader-centered doctrine" should be adopted....
With the rapid development of artificial intelligence technology, artificial intelligence has developed into “expressive artificial intelligence”, artificial intelligence-generated content (AIGC) is more and more widely used in various fields. However, there are still some disputes and confusion about the copyright ownership of these machine-generated content. This paper first introduces the basic concepts and characteristics of artificial intelligence-generated content under the current background. Secondly, this paper discusses the positioning of AIGC in the copyright law and the difficulties in protecting the rights and interests through the different views and legislative practices on the copyright recognition of artificial intelligent-generated content in the world. Finally, in view of the current disputes, such as "creative requirements" and "human participation", this paper puts forward the possible ways to solve this problem in the future, including improving the copyright law to clarify the right ownership and responsibility of AIGC, learning from foreign experience, and establishing the copyright ownership system of AIGC, etc., which provides a useful reference for the formulation and practice of relevant laws and regulations....
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