Update: The authors gave a good rebuttal, I have increased my score to 6. Original comments: In this paper, the authors considered the problem of learning directed acyclic graphs via optimizing a score. In particular, they have developed a new approach that requires O(s log p) samples to learn a DAG from the data. The proposed a approach is an optimization based approach that learns a DAG via optimizing a nonconvex scoring function. The theoretical analysis of this paper is complete. In addition, the analysis techniques developed in this paper seems to be helpful to solve other related problems in structure learning and high-dimensional statistics.

While the abundance of rich and vast datasets across numerous fields has facilitated the advancement of natural language processing, sectors in need of specialized data types continue to struggle with the challenge of finding quality data. Our study introduces a novel guidance data augmentation technique utilizing abstracted context and sentence structures to produce varied sentences while maintaining context-entity relationships, addressing data scarcity challenges. By fostering a closer relationship between context, sentence structure, and role of entities, our method enhances data augmentation's effectiveness. Consequently, by showcasing diversification in both entity-related vocabulary and overall sentence structure, and simultaneously improving the training performance of named entity recognition task.

ChatGPT is a type of artificial intelligence language model that uses deep learning algorithms to generate human-like responses to text-based prompts. The introduction of the latest ChatGPT version in November of 2022 has caused shockwaves in the industrial and academic communities for its powerful capabilities, plethora of possible applications, and the great possibility for abuse. At the time of writing this work, several other language models (e.g., Google Bard and Meta LLaMA) just came out in an attempt to get a foothold in the vast possible market. These models have the ability to revolutionize the way we interact with computers and have potential applications in many fields, including education, software engineering, healthcare, and marketing. In this paper, we will discuss the possible applications, drawbacks, and research directions using advanced language Chatbots (e.g., ChatGPT) in each of these fields. We first start with a brief introduction and the development timeline of artificial intelligence based language models, then we go through possible applications of such models, after that we discuss the limitations and drawbacks of the current technological state of the art, and finally we point out future possible research directions.

Back in April 2021, the European Commission published its proposal for the Artificial Intelligence Regulation ("AI Regulation), which is currently making its way through the European legislative process. This draft AI Regulation seeks to harmonise rules on artificial intelligence by ensuring AI products are sufficiently safe and robust before they enter the EU market. The AI Regulation is intended to apply to what the EU terms "AI systems". The most recent iteration of this concept is defined (in summary) as all systems developed through machine learning approaches and logic, and knowledge-based approaches. This is a wide definition aimed to accommodate future developments in AI technology but extends to much of modern AI software. The broad scope of this definition is narrowed by the operational impact of the draft legislation, as the AI Regulation takes a'risk-based approach' to governing AI systems.

AI engines utilizing deep learning neural networks provide excellent tools for analyzing traditional board games. Here we are interested in gaining new insights into the ancient game of Go. For that purpose, we need to define new numerical measures based on the raw output of the engines. In this paper, we develop a numerical tool for automated move-by-move performance evaluation in a context-sensitive manner and for recognizing game features. We measure the urgency of a move by the cost of passing, which is the score value difference between the current configuration of stones and after a hypothetical pass in the same board position. Here we investigate the properties of this measure and describe some applications.

Soon, computers could sense that users have a problem and come to the rescue. This is one of the possible implications of new research at University of Copenhagen and University of Helsinki. "We can make a computer edit images entirely based on thoughts generated by human subjects. The computer has absolutely no prior information about which features it is supposed to edit or how. Nobody has ever done this before," says Associate Professor Tuukka Ruotsalo, Department of Computer Science, University of Copenhagen.

Soon, we won't need to use the Help function. The computer will sense that we have a problem and come to the rescue by itself. This is one of the possible implications of new research at University of Copenhagen and University of Helsinki. "We can make a computer edit images entirely based on thoughts generated by human subjects. The computer has absolutely no prior information about which features it is supposed to edit or how. Nobody has ever done this before," says Associate Professor Tuukka Ruotsalo, Department of Computer Science, University of Copenhagen.

Soon, we won't need to use the Help function. The computer will sense that we have a problem and come to the rescue by itself. This is one of the possible implications of new research at University of Copenhagen and University of Helsinki. "We can make a computer edit images entirely based on thoughts generated by human subjects. The computer has absolutely no prior information about which features it is supposed to edit or how. Nobody has ever done this before," says Associate Professor Tuukka Ruotsalo, Department of Computer Science, University of Copenhagen.

While pole beans and other twining plants use their touch-sensitive shoots to wrap themselves around supports like ropes and rods to grow upward, the UGA team's robot is designed to firmly but gently grasp objects as small as 1 millimeter in diameter. "We had tried different designs but we were not happy with the results, then I recalled the pole beans I grew in our garden few years ago," said Mable Fok, an associate professor and the study's lead author. "This plant can hold onto other plants or rope so tightly. So, I did some research on twining plants and thought it was a good design from nature for us to explore." In a new study published in the journal Optics Express, the researchers say their soft robotic spiral gripper offers several advantages over existing robotic devices. "Our robot's twining action only requires a single pneumatic control, which greatly simplifies its operation by eliminating the need for complex coordination between multiple pneumatic controls," said Fok. "Since we use a unique twining motion, the soft robotic gripper works well in confined areas and needs only a small operational space."

Artificial intelligence, or AI, has long been the object of excitement and fear. In July, the Financial Times Future Forum think-tank convened a panel of experts to discuss the realities of AI -- what it can and cannot do, and what it may mean for the future. Entitled "The Impact of Artificial Intelligence on Business and Society", the event, hosted by John Thornhill, the innovation editor of the FT, featured Kriti Sharma, founder of AI for Good UK, Michael Wooldridge, professor of computer sciences at Oxford university, and Vivienne Ming, co-founder of Socos Labs. For the purposes of the discussion, AI was defined as "any machine that does things a brain can do". Intelligent machines under that definition still have many limitations: we are a long way from the sophisticated cyborgs depicted in the Terminator films. Such machines are not yet self-aware and they cannot understand context, especially in language. Operationally, too, they are limited by the historical data from which they learn, and restricted to functioning within set parameters. Rose Luckin, professor at University College London Knowledge Lab and author of Machine Learning and Human Intelligence, points out that AlphaGo, the computer that beat a professional (human) player of Go, the board game, cannot diagnose cancer or drive a car.