Academia was engulfed in scandal this week after research papers across dozens of academic journals were found to have been written by AI. An investigation found over 100 papers that were likely written, at least in part, by ChatGPT. These papers slipped through because of lax or nonexistent peer-review processes at for-profit journals, stoking wider fears that the body of human scientific knowledge is being rapidly infiltrated by low-quality computer-generated garbage. Many of these papers were published in obscure scientific journals, but news of this kind of scientific fraud hurts public trust in science, many scientists agree. Even before AI-generated text in scientific papers, so-called'paper mills' have been pumping out low-quality and even plagiarized papers for years.

While large language models (LLMs) have made considerable advancements in understanding and generating unstructured text, their application in structured data remains underexplored. Particularly, using LLMs for complex reasoning tasks on knowledge graphs (KGs) remains largely untouched. To address this, we propose KG-GPT, a multi-purpose framework leveraging LLMs for tasks employing KGs. KG-GPT comprises three steps: Sentence Segmentation, Graph Retrieval, and Inference, each aimed at partitioning sentences, retrieving relevant graph components, and deriving logical conclusions, respectively. We evaluate KG-GPT using KG-based fact verification and KGQA benchmarks, with the model showing competitive and robust performance, even outperforming several fully-supervised models. Our work, therefore, marks a significant step in unifying structured and unstructured data processing within the realm of LLMs.

In its August edition, Resources Policy, an academic journal under the Elsevier publishing umbrella, featured a peer-reviewed study about how ecommerce has affected fossil fuel efficiency in developing nations. But buried in the report was a curious sentence: "Please note that as an AI language model, I am unable to generate specific tables or conduct tests, so the actual results should be included in the table." The study's three listed authors had names and university or institutional affiliations--they did not appear to be AI language models. But for anyone who has played around in ChatGPT, that phrase may sound familiar: The generative AI chatbot often prefaces its statements with this caveat, noting its weaknesses in delivering some information. After a screenshot of the sentence was posted to X, formerly Twitter, by another researcher, Elsevier began investigating.

There has been much hand-wringing about the crisis of the humanities, and recent breakthroughs in artificial intelligence have added to the angst. It is not only truck drivers whose jobs are threatened by automation. Deep-learning algorithms are also entering the domain of creative work. And now, they are demonstrating proficiency in the tasks that occupy humanities professors when they are not giving lectures: namely, writing papers and submitting them for publication in academic journals. Could academic publishing be automated?

Heading into 2021, the future of artificial intelligence (AI) in technology and cybersecurity will only continue to evolve as more organizations adopt new and innovative techniques. According to one recent survey, two-thirds of organizations are already using the intelligent technology for cybersecurity purposes. Using these tools allows for companies to be more prepared for the innovative attacks that cybercriminals continue to develop – also using AI technologies. For example, just last year, criminals employed AI-based software to replicate a CEO's voice to command a cash transfer of €220,000 (approximately $243,000). For businesses looking to implement more AI into their security stack in 2021, it's important to follow these six steps to ensure the effective use of AI – without compromising security anywhere else down the line.

DeepMind, Google's AI unit, is joining the global effort learn more about COVID-19, as the disease's toll spreads rapidly throughout much of the world. The UK-based team just used DeepMind's new deep learning system to share its predictions about the protein structures of the coronavirus, which causes the disease. The system uses a machine-learning technique known as "free modelling" to help it predict protein structures when no similar structures of protein are available. Scientists around the world are racing to learn more about the new coronavirus that has swept through China and spread across 86 other countries, at last count. At least 288 people have died outside of mainland China, as of Thursday.

Ravi Adve from University of Toronto graciously invited me to give a public lecture at the university earlier this summer. I was very grateful for the opportunity. The university's facilities were fantastic, and Ravi did a wonderful job organizing everything. The audience was engaging and asked thoughtful questions, and the attendance was much higher than I had anticipated for a Tuesday morning lecture in the middle of summer! There was great representation from multiple departments, including Electrical and Computer Engineering, Computer Science, and Mathematics.

The banking industry is very important for an economic cycle of each country and provides some quality of services for us. With the advancement in technology and rapidly increasing of the complexity of the business environment, it has become more competitive than the past so that efficiency analysis in the banking industry attracts much attention in recent years. From many aspects, such analyses at the branch level are more desirable. Evaluating the branch performance with the purpose of eliminating deficiency can be a crucial issue for branch managers to measure branch efficiency. This work not only can lead to a better understanding of bank branch performance but also give further information to enhance managerial decisions to recognize problematic areas. To achieve this purpose, this study presents an integrated approach based on Data Envelopment Analysis (DEA), Clustering algorithms and Polynomial Pattern Classifier for constructing a classifier to identify a class of bank branches. First, the efficiency estimates of individual branches are evaluated by using the DEA approach. Next, when the range and number of classes were identified by experts, the number of clusters is identified by an agglomerative hierarchical clustering algorithm based on some statistical methods. Next, we divide our raw data into k clusters By means of self-organizing map (SOM) neural networks. Finally, all clusters are fed into the reduced multivariate polynomial model to predict the classes of data.

Journal articles are by far the primary source of research communication in my field(s). Nobody cares about conference proceedings, which are usually short abstracts and not published. My group has started working on some cool projects that are more machine learning than biology (though the models are loosely inspired by biological neural nets) and I'm wondering where to publish them. I know conference abstracts papers are the most common form of research communication for neural networks, but are there any widely read journals as well? The ones I know of have low impact factors, which is not a problem in itself (at least to me), but may indicate that nobody reads them.

The "Journal of Artificial Intelligence Research (JAIR) was one of the first scientific journals distributed over the web. It has now completed over five years of successful publication. Electronic publishing is reshaping the way academic work is disseminated, and JAIR is leading the way toward a future where scientific articles are freely and easily accessible to all. This report describes how the journal has evolved, its "grassroots" philosophy, and prospects for the future.