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关键词： Law   Computer science   Policy   Technology  

摘要： A debacle at the company that built ChatGPT highlights concern that commercial forces are acting against the responsible development of artificial-intelligence systems.

关键词： Chemistry   Computer science   Machine learning   Materials science  

摘要： Tool from Google DeepMind predicts nearly 400,000 stable substances, and an autonomous system learns to make them in the lab.

关键词： Students  

摘要： It is imperative for the Computer Science (CS) community to ensure active participation and success of students from diverse backgrounds. This work compares CS to other areas of study with respect to success of students from three underrepresented groups: Women, Black and Hispanic or Latino. Using a data-driven approach, we show that trends of success over the years for underrepresented groups in CS are lagging behind other disciplines. Completion of CS programs by Black students in particular shows an alarming regression in the years 2011 through 2019. This national level decline is most concentrated in the Southeast of the United States and seems to be driven mostly by a small number of institutes that produce a large number of graduates. We strongly believe that more data-driven studies in this area are necessary to make progress towards a more equitable and inclusive CS community. Without an understanding of underlying dynamics, policy makers and practitioners will be unable to make informed decisions about how and where to allocate resources to address the problem. © 2023, CC BY.

关键词： Electrical and electronic engineering   Computer science  

摘要： Recent technological advances have contributed to the rapid increase in algorithmic complexity of applications, ranging from signal processing to autonomous systems. To control this complexity and endow heterogeneous computing systems with autonomous programming and optimization capabilities, we propose a unified, end-to-end, programmable graph representation learning (PGL) framework that mines the complexity of high-level programs down to low-level virtual machine intermediate representation, extracts specific computational patterns, and predicts which code segments run best on a core in heterogeneous hardware. PGL extracts multifractal features from code graphs and exploits graph representation learning strategies for automatic parallelization and correct assignment to heterogeneous processors. The comprehensive evaluation of PGL on existing and emerging complex software demonstrates a 6.42x and 2.02x speedup compared to thread-based execution and state-of-the-art techniques, respectively. Our PGL framework leads to higher processing efficiency, which is crucial for future AI and high-performance computing applications such as autonomous vehicles and machine vision.

关键词： Culture   Arts   Machine learning   Computer science   History  

摘要： From identifying disputed artworks to reconstructing lost masterpieces, artificial intelligence is enriching how we interpret our cultural heritage.

关键词： Education computing  

摘要： When the coding assignments step into more complicated and structured periods for individuals, how to cultivate the skills have become the urgent needs for people who are engaged in sophisticated coding development in both academics as well as industries, especially those who have some basic knowledge in their undergraduate studies. In this paper, we propose a state-of-art interactive tool: Programming Decomposition Tool(PDT) to help people enhance their complicated coding skills and further to broaden their CS learning horizons. The tools have two unique features: 1. The learning interface is adaptive and dynamic with respect to the detection of the knowledge depth of users. 2. Difficult learning parts will be decomposed into several easier sessions for better acquisition. © 2023, CC BY.

关键词： Students  

摘要： Misconceptions in the field of computer science can lead to confusion and loss of confidence for students. These misconceptions are based on students' concepts that are not scientifically accurate interpretations. One of the major reasons why students find certain computing and scientific concepts so tough to acquire is because they suffer an inequality or gap between their early classification of a concept and the new learning framework. Resolving misconception challenges in the teaching and learning of sciences is not new because according to the past studies, it is believed to manifest throughout the life of individuals where students are greatly involved when it comes to teaching and learning sciences. The study aims to resolve and correct students’ misconceptions using a practical approach and its impact on students’ gender differences. To address these misconceptions, an experimental design study was conducted in order to examine the effects of practical approach correcting misconceptions among students in computing. The study compared students' scores on Pre-Test and Post-Test, comparing their perceptions of computing and the effectiveness of practical methods to address them. The population of the study consisted of all undergraduate students in higher education institutions in Lagos State. Through the use of random sampling method, 75 undergraduate students from Lagos State University of Education were selected. The instrument was subjected to reliability index after it was validated by experts in computing. An index of .887 results showed significant highly reliable instrument. he results of findings established significant differences between the pre- and post-test scores, but only a slight difference between male and female scores observed. The study recommends that students should be given the opportunity to investigate and test their own findings of computer science components and software functions in order to build a deeper understanding without prec

摘要： Generative Artificial Intelligence (AI), particularly tools like OpenAI’s popular ChatGPT, is reshaping the landscape of computer science research. Used wisely, these tools can boost the productivity of a computer research scientist. This paper provides an exploration of the diverse applications of ChatGPT and other generative AI technologies in computer science academic research, making recommendations about the use of Generative AI to make more productive the role of the computer research scientist, with the focus of writing new research papers. We highlight innovative uses such as brainstorming research ideas, aiding in the drafting and styling of academic papers and assisting in the synthesis of state-of-the-art section. Further, we delve into using these technologies in understanding interdisciplinary approaches, making complex texts simpler, and recommending suitable academic journals for publication. Significant focus is placed on generative AI’s contributions to synthetic data creation, research methodology, and mentorship, as well as in task organization and article quality assessment. The paper also addresses the utility of AI in article review, adapting texts to length constraints, constructing counterarguments, and survey development. Moreover, we explore the capabilities of these tools in disseminating ideas, generating images and audio, text transcription, and engaging with editors. We also describe some non-recommended uses of generative AI for computer science research, mainly because of the limitations of this technology. Copyright © 2023, The Authors. All rights reserved.

关键词： Computer science   Machine learning   Technology  

摘要： Artificial intelligence is asking questions that humans hope to answer.