Chances are you've already seen the headline (or some variation thereof): "AI won an art contest, and artists are furious" Here's what happened: A Colorado man entered an art competition at the Colorado State Fair Fine Arts Competition in the category of "digital arts/digitally-manipulated photography". The problem, however, was that he produced the image using Midjourney, an online AI program that produces images based on user text input. He entered the piece using the name "Jason M. Allen via Midjourney", thus disclosing the use of the AI and meeting all competition rules. The judges were not initially aware that AI was used, yet later admitted that they still would have awarded Allen the prize even if they had. As the headline above attests, many artists the world over were not pleased with this outcome.

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We propose a manager-worker framework based on deep reinforcement learning to tackle a hard yet nontrivial variant of Travelling Salesman Problem (TSP), \ie~multiple-vehicle TSP with time window and rejections (mTSPTWR), where customers who cannot be served before the deadline are subject to rejections. Particularly, in the proposed framework, a manager agent learns to divide mTSPTWR into sub-routing tasks by assigning customers to each vehicle via a Graph Isomorphism Network (GIN) based policy network. A worker agent learns to solve sub-routing tasks by minimizing the cost in terms of both tour length and rejection rate for each vehicle, the maximum of which is then fed back to the manager agent to learn better assignments. Experimental results demonstrate that the proposed framework outperforms strong baselines in terms of higher solution quality and shorter computation time. More importantly, the trained agents also achieve competitive performance for solving unseen larger instances.

The role of the artist in creative endeavour has been a much-discussed topic due to recent artwork produced by AI programs such as Midjourney and DALL-E, including a controversial prize winner in Colorado this week. These programs are using artificial intelligence or diffusion machine learning to produce beautiful images. But is it art, and will it put human artists out of a job? Jason Allen's work, Théâtre D'opéra Spatial, created using AI, won a fine art prize in Colorado.Credit:Jason Allen The question "what is art?" has been seminal in the art world for decades. When Australia acquired Blue Poles, critics accused Jackson Pollock of throwing paint at the canvas while drunk.

Last month, a piece of art called Théâtre D'opéra Spatial (that's French for "Space Opera Theater") was entered into the Colorado State Fair's fine art competition by a man named Jason Allen. The piece is a gorgeous "painting" that depicts a giant baroque hall containing three women in flowing red and white robes. The image won first place in the digitally manipulated photography category, and the artist judges at the state fair said the work was the best of the best. At the fair, Allen said the piece was made with Midjourney, an artificial intelligence tool that can create art, but no one really understood what that meant. Once they realized an A.I.-generated piece of art had beaten human artist-created images, a debate opened up.

Professor Chad Jenkins has been elected a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI). Jenkins specializes in mobile manipulation robots and human-robot interaction. His research explores how to enable robots to learn from human demonstration in complex environments. His work has been supported through a number of prestigious awards, including a PECASE award, an NSF CAREER Award, an ONR Young Investigator Award, and a Sloan Research Fellowship. Jenkins is also devoted to ensuring that the fields of robotics and AI are accessible to everyone.

Well, every year, for the first week of this module, we engage in what I like to call our Data Science Bootcamp. This is an intensive preparatory week in which I present the students with a sample data science project, from start to finish, to give them a sense of what is expected from their group efforts. Every year I try to create a new project on a contemporary topic. In the past, I have covered topics such as marathon running (linked to Elide Kipchoge's efforts to break the 2-hour barrier), Hollywood movies (is it true that movies are rarely as good as the books on which they are based?), and the COVID pandemic, among others. During the bootcamp week, I describe how to take a topic from a vague project idea to a concrete set of suitable research questions, how to assemble an appropriate dataset, how to clean and analyze the data, and how to use the results of the analysis to carefully answer the research questions in a clear and compelling way.

A look at MuZero, the most advanced reinforcement algorithm ever. You are given this task- learn to play a game, but you won't be told the game's rules or have someone to play against. All you will be told is if a particular move is legal and if the game has ended. Do you think you can learn to play this game? And learn it to a superhuman extent.

Place recognition is the fundamental module that can assist Simultaneous Localization and Mapping (SLAM) in loop-closure detection and re-localization for long-term navigation. The place recognition community has made astonishing progress over the last $20$ years, and this has attracted widespread research interest and application in multiple fields such as computer vision and robotics. However, few methods have shown promising place recognition performance in complex real-world scenarios, where long-term and large-scale appearance changes usually result in failures. Additionally, there is a lack of an integrated framework amongst the state-of-the-art methods that can handle all of the challenges in place recognition, which include appearance changes, viewpoint differences, robustness to unknown areas, and efficiency in real-world applications. In this work, we survey the state-of-the-art methods that target long-term localization and discuss future directions and opportunities. We start by investigating the formulation of place recognition in long-term autonomy and the major challenges in real-world environments. We then review the recent works in place recognition for different sensor modalities and current strategies for dealing with various place recognition challenges. Finally, we review the existing datasets for long-term localization and introduce our datasets and evaluation API for different approaches. This paper can be a tutorial for researchers new to the place recognition community and those who care about long-term robotics autonomy. We also provide our opinion on the frequently asked question in robotics: Do robots need accurate localization for long-term autonomy? A summary of this work and our datasets and evaluation API is publicly available to the robotics community at: https://github.com/MetaSLAM/GPRS.

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