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.

This paper explores the current trending research areas in the field of Computer Science (CS) and investigates the factors contributing to their emergence. Leveraging a comprehensive dataset comprising papers, citations, and funding information, we employ advanced machine learning techniques, including Decision Tree and Logistic Regression models, to predict trending research areas. Our analysis reveals that the number of references cited in research papers (Reference Count) plays a pivotal role in determining trending research areas making reference counts the most relevant factor that drives trend in the CS field. Additionally, the influence of NSF grants and patents on trending topics has increased over time. The Logistic Regression model outperforms the Decision Tree model in predicting trends, exhibiting higher accuracy, precision, recall, and F1 score. By surpassing a random guess baseline, our data-driven approach demonstrates higher accuracy and efficacy in identifying trending research areas. The results offer valuable insights into the trending research areas, providing researchers and institutions with a data-driven foundation for decision-making and future research direction.

Computer science degrees are a good choice for students as the range of roles open to graduates continues to grow. From supporting IT infrastructure at a company, to creating apps or to working in banks and financial services, there is a huge range of paths available to computer science graduates. The UK is home to some of the most prestigious universities in the world, many of which are involved at the cutting edge of computer science research and are bolstered by a steady stream of dedicated grants and funding. There are full-time, part-time and flexible-study options, as well as courses with a placement year in industry, known as sandwich courses. Below are the best universities in the UK for computer science degrees.

Andy Cockburn (andy.cockburn@canterbury.ac.nz) is a professor at the University of Cantebury, Christchurch, New Zealand, where he is head of the HCI and Multimedia Lab. Pierre Dragicevic is a research scientist at Inria, Orsay, France.

The challenge of speeding up deep learning models during the deployment phase has been a large, expensive bottleneck in the modern tech industry. In this paper, we examine the use of both regularization and pruning for reduced computational complexity and more efficient inference in Deep Neural Networks (DNNs). In particular, we apply mixup and cutout regularizations and soft filter pruning to the ResNet architecture, focusing on minimizing floating-point operations (FLOPs). Furthermore, by using regularization in conjunction with network pruning, we show that such a combination makes a substantial improvement over each of the two techniques individually.

Two new $1 million awards from the National Science Foundation aim to help researchers take advantage of the latest advances in data science, networking and computation while protecting the integrity of their scientific work. The Renaissance Computing Institute (RENCI) of the University of North Carolina at Chapel Hill will serve as lead institution on both projects. Many scientists today use sophisticated data-intensive approaches to combine and analyze large data sets from scientific instruments and data stores all over the country. While these techniques hold great value for discovery and innovation, integrating the necessary data and tools into a scientist's workflow is often a complex undertaking. In addition, errors can be introduced when data is moved or analyzed; if those errors go undetected, it can compromise the science.

A researcher recently laid out a controversial proposal to add to a round of peer reviews for journals and conferences that would look at the societal consequences of any computer science research. In an interview published in Nature, Brent Hecht, who is an assistant professor at Northwestern University, director of the People, Space, and Algorithms Research Group, and the chair of the ACM Future of Computing Academy, said that the "peer reviewers must ensure that researchers consider negative societal consequences of their work." He is also of the strong opinion that the review process for any research should have the researcher to assess how the technology can be used in the future. If the researcher does not perform such an analysis then the journal should reject the paper. In March of 2018, Hecht wrote a proposal titled It's Time to Do Something: Mitigating the Negative Impacts of Computing Through a Change to the Peer Review Process where he said that the current research community only thinks of the benefits a research paper can have no impact on the society.

The Machines now have X-ray vision. A new piece of software has been trained to use wifi signals -- which pass through walls, but bounce off living tissue -- to monitor the movements, breathing, and heartbeats of humans on the other side of those walls. The researchers say this new tech's promise lies in areas like remote healthcare, particularly elder care, but it's hard to ignore slightly more dystopian applications. While it's easy to think of this new technology as a futuristic Life Alert monitor, it's worth noting that at least one member of the research team at the Massachusetts Institute of Technology behind the innovation has previously received funding from the Pentagon's Defense Advanced Research Projects Agency (DARPA). Another also presented work at a security research symposium curated by a c-suite member of In-Q-Tel, the CIA's high-tech venture capital firm.

What are some underdeveloped areas in computer science research right now (2018)? Over the past few decades, computer science research, either in industry or academia, has led to ground breaking technology innovations such as the internet, which continues to change our lives. In the post-Moore's Law era, advances in cloud computing affected so many sub-areas of computer science like operating systems and database systems. Furthermore, solid state drives (SSDs) changed the way we design storage systems, which were previously tailored for the mechanical hard drive (HDD). Recently, quantum computing promises lightning-speed calculations as opposed to classic electronics-based computers.

IEEE Spectrum: Last Friday you had a discussion about AI with Yann LeCun, one of the most important computer scientists working on AI. LeCun said that since we don't know what form a superintelligent AI would take, it's premature to start researching safety mechanisms to control it. Max Tegmark: Just because we don't know quite what will go wrong doesn't mean we shouldn't think about it. That's the basic idea of safety engineering: You think hard about what might go wrong to prevent it from happening. But when the leaders of the Apollo program carefully thought through everything that could go wrong when you sent a rocket with astronauts to the moon, they weren't being alarmist. They were doing precisely what ultimately led to the success of the mission.