The ideas presented in George Lakoff and Srini Narayanan's The Neural Mind are fascinating, but the writing is far less compelling This is a book review in two parts. The first is about the ideas presented in The Neural Mind: How brains think, which are fascinating. The second is about the actual experience of reading it. The book tackles one of the biggest questions in neuroscience: how do neurons perform all the different kinds of human thought possible, from planning motor actions to composing sentences and musing about philosophy? The authors have very different perspectives.

Within the text analysis and processing fields, generated text attacks have been made easier to create than ever before. To combat these attacks open sourcing models and datasets have become a major trend to create automated detection algorithms in defense of authenticity. For this purpose, synthetic text detection has become an increasingly viable topic of research. This review is written for the purpose of creating a snapshot of the state of current literature and easing the barrier to entry for future authors. Towards that goal, we identified few research trends and challenges in this field.

Robots have continued to make inroads into the construction industry. Whether due to a lack of skilled labor, or simply new tech becoming commercially available, companies continue to market robots and drones to contractors as options to perform important tasks faster and more efficiently. Here, Construction Dive looks at recent innovations in robotics that could potentially pick up extra slack or speed up jobsite work. This small, four-wheeled robot could aid contractors by carrying tools and materials around the site. A group of engineers at the Fraunhofer Italia Innovation Engineering Center (IEC) in Bolzano, Italy, are working on a way to adapt the Husky A200 to do just that, according to New Atlas.

This paper presents several strategies that can improve neu - ral network-based predictive methods for MOOC student course trajectory modeling, applying multiple ideas previ - ously applied to tackle NLP (Natural Language Processing) tasks. In particular, this paper investigates LSTM network s enhanced with two forms of regularization, along with the more recently introduced Transformer architecture.

In 2008, Political Analysis published a groundbreaking special issue on the analysis of political text, examining some of the initial efforts in political science to consider text as a data source and to develop methods for analyzing text data. In their introduction to the special issue, Monroe and Schrodt (2008) note that text one of the most common mediums through which political phenomenon are documented is underutilized in the social sciences and they argue for further research. They suggest the research discussed in the special issue should be a jumping-off point, or "departure lounge" for future text as data research. Answering their call, in the last eight years, the fi eld of "text as data" in social science has grown dramatically. As the number of sources and types of textual data documenting social science phenomenon has exploded, so too have methods for, and the use of, text analysis in social science research.