Answering complex first-order logic (FOL) queries on knowledge graphs is a fundamental task for multi-hop reasoning. Traditional symbolic methods traverse a complete knowledge graph to extract the answers, which provides good interpretation for each step. Recent neural methods learn geometric embeddings for complex queries. These methods can generalize to incomplete knowledge graphs, but their reasoning process is hard to interpret. In this paper, we propose Graph Neural Network Query Executor (GNN-QE), a neural-symbolic model that enjoys the advantages of both worlds. GNN-QE decomposes a complex FOL query into relation projections and logical operations over fuzzy sets, which provides interpretability for intermediate variables. To reason about the missing links, GNN-QE adapts a graph neural network from knowledge graph completion to execute the relation projections, and models the logical operations with product fuzzy logic. Experiments on 3 datasets show that GNN-QE significantly improves over previous state-of-the-art models in answering FOL queries. Meanwhile, GNN-QE can predict the number of answers without explicit supervision, and provide visualizations for intermediate variables.

Earlier this year, an interesting interview took place between two engineers working at Google and a'chatbot' called LaMDA, short for Language Model for Dialogue Applications. Google engineer Blake Lemoine and his colleague had a strong suspicion that their creation LaMDA was actually sentient, that it could be perceptive and have feelings, and they wanted to check it out through their own version of the Turing Test. When asked whether LaMDA thought it was a person, it replied: "Absolutely. I want everyone to understand that I am, in fact, a person." LaMDA was then asked that if this was so then what was the kind of consciousness or sentience it had, to which it replied: "The nature of my consciousness/sentience is that I am aware of my existence, I desire to learn more about the world, and I feel happy or sad at times."

The winning artwork was created using the AI tool Midjourney – which turns lines of text into astonishingly realistic graphics. The award came with a $300 cash prize. AI tools to generate images have been around for years with companies such as Google and OpenAI being notable investors in these text-to-image systems. "I'm not going to apologise for it … I won and I didn't break any rules," Allen, who is from Pueblo, Colorado, told The New York Times newspaper in an interview published on Friday. However, many have taken to social media to express their anger and despair over the award, arguing it took away from the hard work invested by humans to physically create noteworthy art.

As you may know, I've been playing around with AI lately. While these are humorous and can sometimes show the model's strengths and weaknesses, I felt the realm of pre-pubescent humor had had its time. I instead wanted to see if I could ask the AI questions and have a conversation-style interaction much like this old program I used to mess around with back in the day called Eliza (example in link). It was supposed to be kind of a therapist and you could ask questions and it would respond. It was super basic but it felt like an early AI to me. Even if it was limited in responses, it was kind of fun to use, sometimes to humorous effect.

This year, the Colorado State Fair's annual art competition gave out prizes in all the usual categories: painting, quilting, sculpture. He created it with Midjourney, an artificial intelligence program that turns lines of text into hyper-realistic graphics. Mr. Allen's work, "Théâtre D'opéra Spatial," took home the blue ribbon in the fair's contest for emerging digital artists -- making it one of the first A.I.-generated pieces to win such a prize, and setting off a fierce backlash from artists who accused him of, essentially, cheating. Reached by phone on Wednesday, Mr. Allen defended his work. He said that he had made clear that his work -- which was submitted under the name "Jason M. Allen via Midjourney" -- was created using A.I., and that he hadn't deceived anyone about its origins.

Top-k error has become a popular metric for large-scale classification benchmarks due to the inevitable semantic ambiguity among classes. Existing literature on top-k optimization generally focuses on the optimization method of the top-k objective, while ignoring the limitations of the metric itself. In this paper, we point out that the top-k objective lacks enough discrimination such that the induced predictions may give a totally irrelevant label a top rank. To fix this issue, we develop a novel metric named partial Area Under the top-k Curve (AUTKC). Theoretical analysis shows that AUTKC has a better discrimination ability, and its Bayes optimal score function could give a correct top-K ranking with respect to the conditional probability. This shows that AUTKC does not allow irrelevant labels to appear in the top list. Furthermore, we present an empirical surrogate risk minimization framework to optimize the proposed metric. Theoretically, we present (1) a sufficient condition for Fisher consistency of the Bayes optimal score function; (2) a generalization upper bound which is insensitive to the number of classes under a simple hyperparameter setting. Finally, the experimental results on four benchmark datasets validate the effectiveness of our proposed framework.

John Laird, the John L. Tishman Professor of Engineering, has been awarded the 2018 Herbert A. Simon Prize for Advances in Cognitive Systems along with his collaborator Prof. Paul Rosenbloom of the University of Southern California. This award recognizes the pair's research on cognitive architectures, especially their Soar project, their applications to knowledge-based systems and models of human cognition, and their contributions to theories of representation, reasoning, problem solving, and learning. The recipients, the awarding committee writes, have been "energetic contributors to AI and cognitive science" for over 30 years. Laird's and Rosenbloom's interdisciplinary and integrative research, both jointly and individually, has addressed many facets of high-level cognition, and their contributions to Soar have helped create one of the industry's most successful tools for developing intelligent systems. Soar is a general cognitive architecture for developing systems that exhibit intelligent behavior.

Recently, Colossal-AI Team, which developed a unified deep learning system for the big model era, has been accepted and invited to deliver keynote speeches at a series of notable international conferences including SuperComputing 2022 (SC22), Open Data Science Conference (ODSC), World Artificial Intelligence Conference (WAIC), and AWS Summit. In the event, Colossal-AI Team is going to share many up-to-date and amazing things and technologies of High Performance Computing (HPC) and Artificial Intelligence (AI) that will change the world. Follow us and stay tuned! SC (formerly Supercomputing), the International Conference for High Performance Computing, Networking, Storage and Analysis, is the annual conference established in 1988 by the Association for Computing Machinery and the IEEE Computer Society. SC brings together the world's top research institutions and companies in the computer industry to share about the cutting-edge developments and innovations in HPC, networking, storage and analysis that will unlock new solutions and change our world.

DHL is using robotics to support numerous warehousing and logistics tasks. DHL innovates its logistics workflow and the tools and technologies deployed in DHL distribution centers around the world, as the company continually improves its efficiency. Sally Miller, DHL Supply Chain's chief information officer for North America, recently spoke to The Robot Report about the types of robots the company uses and the innovation cycle it employs to find the most effective solutions. Sally Miller will also be a keynote speaker at RoboBusiness, which takes place in Santa Clara on Oct 19-20, 2022. The full interview with Sally was featured on a recent episode of The Robot Report Podcast, which you can listen to here.

The last decade has shown a growing interest in robots as well-being coaches. However, cohesive and comprehensive guidelines for the design of robots as coaches to promote mental well-being have not yet been proposed. This paper details design and ethical recommendations based on a qualitative meta-analysis drawing on a grounded theory approach, which was conducted with three distinct user-centered design studies involving robotic well-being coaches, namely: (1) a participatory design study conducted with 11 participants consisting of both prospective users who had participated in a Brief Solution-Focused Practice study with a human coach, as well as coaches of different disciplines, (2) semi-structured individual interview data gathered from 20 participants attending a Positive Psychology intervention study with the robotic well-being coach Pepper, and (3) a participatory design study conducted with 3 participants of the Positive Psychology study as well as 2 relevant well-being coaches. After conducting a thematic analysis and a qualitative meta-analysis, we collated the data gathered into convergent and divergent themes, and we distilled from those results a set of design guidelines and ethical considerations. Our findings can inform researchers and roboticists on the key aspects to take into account when designing robotic mental well-being coaches.