Welcome to our August 2022 monthly digest, where you can catch up with any AIhub stories you may have missed, get the low-down on recent events, and much more. This month, we continue our conference coverage, chat to winners of best paper awards, and listen to some interesting podcasts. Wouldn't it be handy to be able to automatically update information in an outdated article? Well, Robert Logan, Alexandre Passos, Sameer Singh and Ming-Wei Chang designed an algorithm to do just that in their paper FRUIT: Faithfully Reflecting Updated Information in Text. This work won them a best new task award at NAACL 2022 (Annual Conference of the North American Chapter of the Association for Computational Linguistics).

If you're like me, the growing drip, drip, drip of the content faucet is pushing you to the edge: posts, pings, notifications, alerts. Tech journalist Charles Arthur makes a compelling argument that future content is at a tipping point. Arthur is the author of the substack blog "Social Warming," about social networks' effects on society. "The approaching tsunami of addictive AI-created content will overwhelm us" warns Arthur. The tsunami he points to is the creation of what academics call synthetic media, media that is created entirely by artificial intelligence.

What could be more exciting for an AI artist to wake up early in the morning and see a new announcement in the Midjourney discord server about further testing again? Shortly after the public release of Stable Diffusion, Midjourney brought its photo-realistic beta version to the market just a few days ago. However, after a testing phase of only 24 hours, David and his team decided to turn this beta version off and make some improvements. Today it is back again with some new options. For the next 24-48 hours, depending on the user behaviors (guys, please, we all know what "ladies in bikinis" look like), we can create new, more cohesive AI images.

Mobile service robots have become increasingly ubiquitous. However, these robots can pose potential accessibility issues and safety concerns to people with visual impairments (PVI). We sought to explore the challenges faced by PVI around mainstream mobile service robots and identify their needs. Seventeen PVI were interviewed about their experiences with three emerging robots: vacuum robots, delivery robots, and drones. We comprehensively investigated PVI's robot experiences by considering their different roles around robots -- direct users and bystanders. Our study highlighted participants' challenges and concerns about the accessibility, safety, and privacy issues around mobile service robots. We found that the lack of accessible feedback made it difficult for PVI to precisely control, locate, and track the status of the robots. Moreover, encountering mobile robots as bystanders confused and even scared the participants, presenting safety and privacy barriers. We further distilled design considerations for more accessible and safe robots for PVI.

Revolutions often have humble origins, a small group with big ideas gathering to plant seeds of disruption. So, it was in the dog days of summer in 1956, when 10 academics gathered on the campus of Dartmouth College to discuss how to make machines use language and form abstractions and concepts to solve the kinds of problems now reserved for humans. The conference led to the founding of a new field of study, artificial intelligence. Six decades hence, we are in the midst of an AI revolution that is already dramatically changing entire sectors like healthcare, transportation, education, banking, and retail. But AI is not without its critics. Elon Musk famously said that, "With artificial intelligence, we're summoning the demon." While Stephen Hawking believed the development of full artificial intelligence could spell the end of the human race. So, whose job is it to make sure that such a vision never comes to pass? Today on Cold Call, we've invited Professor Tsedal Neeley to discuss her case entitled, "Timnit Gebru: Silenced No More on AI Bias and The Harms of Large Language Models." Tsedal Neeley's work focuses on how leaders can scale their organizations by developing and implementing global and digital strategies.

Charlie met with his co-worker Keith over lunch to plan a professional development day they were supposed to lead over April break, but Charlie kept losing the thread of the discussion. He couldn't stop thinking about who or what was maintaining backups of his old website. Keith sat opposite him with his compartmentalized lunchbox of raw ingredients; Keith only ever described the actions he performed on food as "meal prep," perhaps because cooking involved a willingness to adapt and surprise oneself. Charlie stabbed mindlessly at his corner-store Cobb salad, and by the second time he asked Keith to repeat something he'd just said, Keith's expression sank into sharp suspicion. "Charlie, come on," Keith said, but somewhat to Charlie's relief, Keith wasn't reading Charlie's mind and judging his salacious past.

This report documents the history of research on AI rights and other moral consideration of artificial entities. It highlights key intellectual influences on this literature as well as research and academic discussion addressing the topic more directly. We find that researchers addressing AI rights have often seemed to be unaware of the work of colleagues whose interests overlap with their own. Academic interest in this topic has grown substantially in recent years; this reflects wider trends in academic research, but it seems that certain influential publications, the gradual, accumulating ubiquity of AI and robotic technology, and relevant news events may all have encouraged increased academic interest in this specific topic. We suggest four levers that, if pulled on in the future, might increase interest further: the adoption of publication strategies similar to those of the most successful previous contributors; increased engagement with adjacent academic fields and debates; the creation of specialized journals, conferences, and research institutions; and more exploration of legal rights for artificial entities.

Some of the world's biggest companies are going all-in on the metaverse. One you may not know about is Ansys, a US public company that makes engineering simulation software and has been around since 1970. Dr. Prith Banerjee is its Chief Technology Officer, and I spoke to him last week about his vision for the metaverse -- and specifically, why he thinks the metaverse can't reach its full potential without "optimum physics-based modeling and simulation." Ansys, it turns out, already has a number of partnerships with companies building the metaverse -- including global telecoms companies, microchip and GPU manufacturers, data center and storage companies, and "all the cloud providers," according to Banerjee. He said that Ansys provides a mix of hardware and software expertise to these customers; everything from building hardware to designing a structural electromagnetics system.

There is a question, the answer to which could change the world. It is a simple question. The question is this: What the hell happened to graphene? It got big, oh, 10-ish years ago, around the time when two University of Manchester researchers won the Nobel Prize for "discovering" it. That's in scare quotes because all they really did, in a now famous example of serendipity in the sciences, was peel a piece of literal sticky tape off graphite--the stuff in literal pencils--and notice, basically by accident, that the residual flakes comprised a single layer of carbon atoms.

Dan Malkin, Tomasz Limisiewicz, Gabriel Stanovsky received an honourable mention for contribution to methods at NAACL 2022 for their work A balanced data approach for evaluating cross-lingual transfer: mapping the linguistic blood bank. We spoke to Dan, who told us about multilingual models, the cross-lingual transfer phenomenon, and how the choice of pretraining languages affects downstream cross-lingual transfer. The topic of this research is multilingual models. Multilingual models are interesting because of the cross-lingual transfer phenomenon, in which a multilingual model pre-trained on many languages is able to transfer knowledge about a downstream particular task from one language to another. So, if you train a big model on various languages and then test it on a task, for example question-answering, in a different language, it can perform in a non-trivial manner.