Shinobu Yamanaka apologized the moment this reporter arrived for an interview at a day care facility in the city of Konan, Kochi Prefecture, one muggy morning in July. "Sorry, I had completely forgotten about it," she said with a smile at Day Service Happy, a traditional Japanese-style house converted into a day care center for people with dementia and other health conditions in need of nursing care. "I must leave for another appointment at a local elementary school soon." Yamanaka, a vivacious 46-year-old woman who has her short hair dyed ash blonde, has early-onset Alzheimer's. She often has memory lapses like the one that morning, she later confided.

Nadia Ady and Faun Rice are working on a research project exploring where artificial intelligence (AI) researchers find inspiration and ideas about human intelligence and what approaches they use to translate ideas from the disciplines that study human intelligence (e.g. We spoke to Nadia and Faun about the project, what they've learnt so far, and how they plan to further develop the work. Faun: We are doing a multidisciplinary project – I'm from the social sciences, while Nadia works on artificial intelligence. We're interviewing other artificial intelligence researchers who work with direct analogs from human psychology and try to translate them for machines in some way. For one, we talk to them about how they find the definitions that they're working with.

Supervised learning of image classifiers distills human knowledge into a parametric model through pairs of images and corresponding labels (X,Y). We argue that this simple and widely used representation of human knowledge neglects rich auxiliary information from the annotation procedure, such as the time-series of mouse traces and clicks left after image selection. Our insight is that such annotation byproducts Z provide approximate human attention that weakly guides the model to focus on the foreground cues, reducing spurious correlations and discouraging shortcut learning. To verify this, we create ImageNet-AB and COCO-AB. They are ImageNet and COCO training sets enriched with sample-wise annotation byproducts, collected by replicating the respective original annotation tasks. We refer to the new paradigm of training models with annotation byproducts as learning using annotation byproducts (LUAB). We show that a simple multitask loss for regressing Z together with Y already improves the generalisability and robustness of the learned models. Compared to the original supervised learning, LUAB does not require extra annotation costs. ImageNet-AB and COCO-AB are at https://github.com/naver-ai/NeglectedFreeLunch.

We're more distracted than ever. Why remember anything when I can just Google it? Why summon the attention to read a book when I can just scroll through Twitter? Some philosophers believe that ChatGPT and its siblings will further diminish our ability to do the kind of "deep work" needed to spark creativity and breed big ideas. What good are the tools if we begin to rely on them so much that we no longer have the capacity to think bigger?

Artificial Intelligence is very much on the news agenda right now. The unstoppable rise of ChatGPT and the seemingly imminent prospect of generalised AI able to re-create broad human thinking processes has seen concerns raised by everyone from major business CEOs to Geoffrey Hinton, one of the godfathers of AI research. AI has been an element of video game design and production for at least two decades, but now with AI art programs and the rise of procedurally generated game dialogue, there are growing questions over how AI is going to effect not just the content of games, but the teams that make them. Talking at the Xbox games showcase in Los Angeles recently, Xbox chief Phil Spencer played down concerns that AI could be used to streamline the game production process and therefore lead to smaller teams. "Actually, that isn't an area we're thinking about a ton with AI," he said.

OneDoor Studios CMO Dan Cobb discusses the adaption of the YA series'Calculated.' A Hollywood film studio is leveraging a new real-time design and artist development process to adapt a popular young adult (YA) series, including an industry-first application of artificial intelligence (AI) that gives fans and artists active input in creating character design, sets and special effects. Dan Cobb, the Chief Marketing Officer (CMO) of OneDoor Studios, said development is underway on "Calculated," an adaption of the YA sci-fi series by Nova McBee. On a mission to become the "World's First Fan-Funded and Fan-Created Film Studio," Cobb and his team have developed a relationship with AI artists on the WeGo.One's Discord channel. The artists, who are required to have deep knowledge of the source material, liaise with investors and the author to spawn images using MidJourney V5 Pro and a combination of other similar generative image technologies to build the film's storyboard, enhance concept art and develop shot lists.

Kurt "CyberGuy" Knutsson explains whether robot security guards are better or worse for society. A new study is suggesting that robots with more "charismatic" voices – as opposed to flat, matter-of-fact ones – can help people be more creative. Scientists from Denmark found that students who are given a task by a robot with a voice programmed to be more "engaging" and "inspiring" performed better. These students were also more creative than students who received instructions from an identical robot with a flat voice, according to the findings from researchers in Denmark as published by Frontiers in Communication, a peer-reviewed, open-access science journal. Increasingly, social robots are being used for support in educational settings, as SWNS, the British news service, noted.

Physics is a field of science that has traditionally used the scientific method to answer questions about why natural phenomena occur and to make testable models that explain the phenomena. Discovering equations, laws and principles that are invariant, robust and causal explanations of the world has been fundamental in physical sciences throughout the centuries. Discoveries emerge from observing the world and, when possible, performing interventional studies in the system under study. With the advent of big data and the use of data-driven methods, causal and equation discovery fields have grown and made progress in computer science, physics, statistics, philosophy, and many applied fields. All these domains are intertwined and can be used to discover causal relations, physical laws, and equations from observational data. This paper reviews the concepts, methods, and relevant works on causal and equation discovery in the broad field of Physics and outlines the most important challenges and promising future lines of research. We also provide a taxonomy for observational causal and equation discovery, point out connections, and showcase a complete set of case studies in Earth and climate sciences, fluid dynamics and mechanics, and the neurosciences. This review demonstrates that discovering fundamental laws and causal relations by observing natural phenomena is being revolutionised with the efficient exploitation of observational data, modern machine learning algorithms and the interaction with domain knowledge. Exciting times are ahead with many challenges and opportunities to improve our understanding of complex systems.

In recent years, ML researchers have wrestled with defining and improving machine learning (ML) benchmarks and datasets. In parallel, some have trained a critical lens on the ethics of dataset creation and ML research. In this position paper, we highlight the entanglement of ethics with seemingly ``technical'' or ``scientific'' decisions about the design of ML benchmarks. Our starting point is the existence of multiple overlooked structural similarities between human intelligence benchmarks and ML benchmarks. Both types of benchmarks set standards for describing, evaluating, and comparing performance on tasks relevant to intelligence -- standards that many scholars of human intelligence have long recognized as value-laden. We use perspectives from feminist philosophy of science on IQ benchmarks and thick concepts in social science to argue that values need to be considered and documented when creating ML benchmarks. It is neither possible nor desirable to avoid this choice by creating value-neutral benchmarks. Finally, we outline practical recommendations for ML benchmark research ethics and ethics review.

Gabrielle Zevin likes talking about failure. Her first novel for adults, released almost 20 years ago, did "really badly," by her account. "I had really never had a failure like that in my life," Zevin said. At the time, she was living in New York City, and it seemed as though the whole world was bearing witness to her defeat. "I thought I would go into a store and they would be like, 'Here is your bagel, here is your lox and sorry your novel failed so badly,'" she said.