Recent developments in large language models (LLMs) have been accompanied by rapidly growing public interest in natural language processing (NLP). This attention is reflected by major news venues, which sometimes invite NLP researchers to share their knowledge and views with a wide audience. Recognizing the opportunities of the present, for both the research field and for individual researchers, this paper shares recommendations for communicating with a general audience about the capabilities and limitations of NLP. These recommendations cover three themes: vague terminology as an obstacle to public understanding, unreasonable expectations as obstacles to sustainable growth, and ethical failures as obstacles to continued support. Published NLP research and popular news coverage are cited to illustrate these themes with examples. The recommendations promote effective, transparent communication with the general public about NLP, in order to strengthen public understanding and encourage support for research.

To address this ever-growing issue, the White House today released a Blueprint for an AI Bill of Rights. This outlines five principles that should guide the design, use and deployment of automated systems to protect Americans in this age of AI.

James Hendler is the Director of the Institute for Data Exploration and Applications and the Tetherless World Professor of Computer, Web and Cognitive Sciences at RPI. He also is acting director of the RPI-IBM Artificial Intelligence Research Collaboration and serves on the Board of the UK's charitable Web Science Trust. Hendler has authored over 400 books, technical papers and articles in the areas of Semantic Web, artificial intelligence, agent-based computing and high-performance processing. Hendler was the recipient of a 1995 Fulbright Foundation Fellowship, is a former member of the US Air Force Science Advisory Board, and is a Fellow of the AAAI, BCS, the IEEE, the AAAS and the ACM. He is also the former Chief Scientist of the Information Systems Office at the US Defense Advanced Research Projects Agency (DARPA) and was awarded a US Air Force Exceptional Civilian Service Medal in 2002.

Recent research efforts aiming to bridge the Neural-Symbolic gap for RDFS reasoning proved empirically that deep learning techniques can be used to learn RDFS inference rules. However, one of their main deficiencies compared to rule-based reasoners is the lack of derivations for the inferred triples (i.e. explainability in AI terms). In this paper, we build on these approaches to provide not only the inferred graph but also explain how these triples were inferred. In the graph words approach, RDF graphs are represented as a sequence of graph words where inference can be achieved through neural machine translation. To achieve explainability in RDFS reasoning, we revisit this approach and introduce a new neural network model that gets the input graph--as a sequence of graph words-- as well as the encoding of the inferred triple and outputs the derivation for the inferred triple. We evaluated our justification model on two datasets: a synthetic dataset-- LUBM benchmark-- and a real-world dataset --ScholarlyData about conferences-- where the lowest validation accuracy approached 96%.

Over the last year, the amount of research in hierarchical planning has increased, leading to significant improvements in the performance of planners. However, the research is diverging and planners are somewhat hard to compare against each other. This is mostly caused by the fact that there is no standard set of benchmark domains, nor even a common description language for hierarchical planning problems. As a consequence, the available planners support a widely varying set of features and (almost) none of them can solve (or even parse) any problem developed for another planner. With this paper, we propose to create a new track for the IPC in which hierarchical planners will compete. This competition will result in a standardised description language, broader support for core features of that language among planners, a set of benchmark problems, a means to fairly and objectively compare HTN planners, and for new challenges for planners. Introduction When the International Planning Competition (IPC) started out in 1998 it aimed to include both classical and hierarchical planners as competitors (McDermott 2000).

When you're watching a modern blockbuster such as The Avengers, it's hard to escape the feeling that what you're seeing is almost entirely computer-generated imagery, from the effects to the sets to fantastical creatures. But if there's one thing you can rely on to be 100% real, it's the actors. We might have virtual pop stars like Hatsune Miku, but there has never been a world-famous virtual film star. Even that link with corporeal reality, though, is no longer absolute. You may have already seen examples of what's possible: Peter Cushing (or his image) appearing in Rogue One: A Star Wars Story more than 20 years after his death, or Tupac Shakur performing from beyond the grave at Coachella in 2012.

Fear of successful cyberattacks meets fear of unintended consequences when machine learning is your first line of defense. Fear can be a great motivator. If you are afraid that a human cannot make a decision fast enough to stop a cyberattack, you might opt for an artificial intelligence (AI), machine learning system. But although fear, uncertainty and doubt -- the FUD factor -- of not responding quickly enough might motivate you to take this action, that same FUD factor that the action your automated system takes might be wrong is an equally strong motivator not to employ this technology. Welcome to this year's Catch 22. In the 1983 sci-fi classic War Games, a computer was employed to replace the soldiers who manned the intercontinental ballistic missile silos because, it was believed, the computer could launch the missiles dispassionately and not be swayed by indecision in case of a nuclear attack. A teenager hacked the system thinking it was an unreleased video game.

When a class in Mandarin Chinese starts next summer at Rensselaer Polytechnic Institute, students will be practicing their spoken dialogues with a different sort of teaching assistant: an artificial intelligence chatbot. Capable of conversing with students in simulated settings -- a restaurant, garden or even a Tai Chi class -- the bot is part of a future where artificial intelligence (AI) will perform more of the tasks, and potentially the jobs, now done by humans. Part of a so-called "situations room" at RPI, the chatbot is an example of what are called "cognitive and immersive systems," in which the burgeoning field of AI is melded with rapidly growing torrents of financial, health and education information as well as so-called "unstructured data" like social media posts spreading across an expanding constellation of networked computers, smartphones and other electronic devices. RPI is developing the room under a partnership with the technology giant IBM and its supercomputer Watson, which first gained worldwide attention in 2011 when it beat humans in the TV game show "Jeopardy." It's too early to predict how much impact AI will have on how New Yorkers work, but a recent report by the Albany-based Rockefeller Institute of Government projects that large numbers of jobs being replaced or changed -- particularly in jobs that involve basic, repetitive actions.

Ask poverty attorney Joanna Green Brown for an example of a client who fell through the cracks and lost social services benefits they may have been eligible for because of a program driven by artificial intelligence (AI), and you will get an earful. There was the "highly educated and capable" client who had had heart failure and was on a heart and lung transplant wait list. The questions he was presented in a Social Security benefits application "didn't encapsulate his issue" and his child subsequently did not receive benefits. "It's almost impossible for an AI system to anticipate issues related to the nuance of timing," Green Brown says. Then there's the client who had to apply for a Medicaid recertification, but misread a question and received a denial a month later.

The next time you sit down to watch a movie, the algorithm behind your streaming service might recommend a blockbuster that was written by AI, performed by robots, and animated and rendered by a deep learning algorithm. An AI algorithm may have even read the script and suggested the studio buy the rights. It's easy to think that technology like algorithms and robots will make the film industry go the way of the factory worker and the customer service rep, and argue that artistic filmmaking is in its death throes. For the film industry, the same narrative doesn't apply -- artificial intelligence seems to have enhanced Hollywood's creativity, not squelched it. It's true that some jobs and tasks are being rendered obsolete now that computers can do them better.