My in-laws own a little two-bedroom beach bungalow. It's part of a condo development that hasn't changed much in fifty years. The units are connected by brick paths that wind through palm trees and tiki shelters to a beach. Nearby, developers have built big hotels and condo towers, and it's always seemed inevitable that the bungalows would be razed and replaced. But it's never happened, probably because, according to the association's bylaws, eighty per cent of the owners have to agree to a sale of the property.

We decided to have some fun with ChatGPT, the scary-good chatbot from OpenAI that's been garnering headlines. We fed it a fake letter, cobbled together with common tropes, and asked it to reply in a few different ways. I'm recently engaged and in the throes of planning my early 2024 wedding. My handsome fiancé, the timing, my mother's own hand-me-down ring--it's all felt like a perfect fairytale. Until I heard what my mother-in-law has in store for us.

Machine learning researchers are teaching neural networks how to superficially judge humans -- and the results are as brutal as they are familiar. A study about the judgmental AI, published in the prestigious Proceedings of the National Academy of Sciences journal, describes how researchers trained the model how to judge attributes in human faces, the way we do upon first meeting each other, and how they trained it to manipulate photos to evoke different judgments, such as appearing "trustworthy" or "dominant." "Our dataset not only contains bias," Princeton computer science postdoctoral researcher Joshua Peterson wrote in a tweet thread about the research, "it deliberately reflects it." We collected over 1 million human judgments to power a model that can both predict and manipulate first impressions of diverse and naturalistic faces! The PNAS paper notes that the AI so mirrored human judgment that it tended to associate objective physical characteristics, such as someone's size or skin color, with attributes ranging from trustworthiness to privilege.

As pandemic-led isolation triggers an epidemic of loneliness, Japanese are increasingly turning to "social robots" for solace and mental healing. At the city's Penguin Cafe, proud owners of the electronic dog Aibo gathered recently with their cyber-pups in Snuglis and fancy carryalls. From camera-embedded snouts to their sensor-packed paws, these high-tech hounds are nothing less than members of the family, despite a price tag of close to $3,000 -- mandatory cloud plan not included. It's no wonder Aibo has pawed its way into hearts and minds. Re-launched in 2017, Aibo's artificial intelligence-driven personality is minutely shaped by the whims and habits of its owner, building the kind of intense emotional attachments usually associated with kids, or beloved pets. Noriko Yamada rushed to order one, when her mother-in-law began showing signs of dementia several years ago.

Given a natural language phrase, relation linking aims to find a relation (predicate or property) from the underlying knowledge graph to match the phrase. It is very useful in many applications, such as natural language question answering, personalized recommendation and text summarization. However, the previous relation linking algorithms usually produce a single relation for the input phrase and pay little attention to a more general and challenging problem, i.e., combinational relation linking that extracts a subgraph pattern to match the compound phrase (e.g. In this paper, we focus on the task of combinational relation linking over knowledge graphs. To resolve the problem, we design a systematic method based on the data-driven relation assembly technique, which is performed under the guidance of meta patterns. We also introduce external knowledge to enhance the system understanding ability. Finally, we conduct extensive experiments over the real knowledge graph to study the performance of the proposed method. 1 Introduction Knowledge graphs have been important repositories to materialize a huge amount of structured information in the form of triples, where a triple consists of nullsubject, predicate, objectnull or null subject, property, value null. There have been many such knowledge graphs, e.g., DBpedia (Auer et al. 2007), Y ago (Suchanek, Kasneci, and Weikum 2007), and Freebase (Bollacker et al. 2008). In order to bridge the gap between unstructured text (including text documents and natural language questions) and structured knowledge, an important and interesting task is conducting relation linking over the knowledge graph, i.e., finding the specific predicates/properties from the knowledge graph that match the phrases detected in the sentence (also may be a question). Relation linking can power many downstream applications. As a friendly and intuitive approach to exploring knowledge graphs, using natural language questions to query the knowledge graph has attracted a lot of attentions in both academia and industrial communities (Berant et al. 2013; Bao et al. 2016; Das et al. 2017; Hu et al. 2018; Huang et al. 2019). Generally, the simple questions, e.g., who is the founder of Microsoft, are easy to answer since Figure 1: Example of combinational relations matching the compound phrase mother-in-law. it is straightforward to choose the predicate "founder" from the knowledge graph that matches the phrase "founder" in the input question.

Driving across the border into Canada late this summer, the CBC anchor on the radio announced that a glut of blueberries had pushed their prices down to historic lows. Having brought a fancy new juicer with me, I sensed an opportunity. The juicer in the back of the car was a Hurom H-AI, a sort of Maserati of juice machines, with a powerful motor that gives it a near-unflappable ability to liquefy whatever you throw in the hopper. The Hurom H-AI has a powerful motor that gives it a near-unflappable ability to liquefy whatever you throw in the hopper. It is a very effective machine, but it had a lot of convincing to do if I was going to like it, as the damn thing costs $700--a number that created a hurdle I was worried I couldn't clear.

It sounds counterintuitive, but there's a conversation taking place today about how businesses can use artificial intelligence to bring more human emotion and connection to customer service. In the past, people had to actually go to a store to buy a book, some jeans, or a potato. Sometimes these human lifeforms even forged personal relationships. But today, consumers frequently buy products and access other customer service through various online channels. That removes the human element – either completely (in the case of basic chatbots or other self-service mediums) or partially (because live agents are on script and remote).

"Justice League" opens in theaters today.Courtesy of Warner Brothers. Once upon a time, in the long long ago, this bad guy from a lava planet comes to Earth with these three magic boxes and tries to turn our pretty blue marble into a red marble, but is defeated by some people who live in the sea (Atlanteans), some warrior women who live on an island (Amazons), and "the tribes of man" (you, me and the bourgeoisie). The bad man escapes and loses these three precious boxes he really adores. One goes under the sea, one goes to the island, and then the humans bury one in some ditch in a forest. Are you still with me?

I was electrified by Jonathan Berger's music before I knew he wrote about music. His chamber works arise out of a lightning storm of modernist angles, dramatic and startling, though anchored to melodies that sail like a swallow, as one of his string quartets is called. His one-act operas Theotokia and The War Reporter, performed together in concert, match taut musical brocades to the hallucinations of, respectively, a schizophrenic, hearing voices of various mothers, and a photojournalist, based on Paul Watson, who won the 1994 Pulitzer Prize for his image of the corpse of an American soldier being dragged through the streets of Mogadishu. A few years ago, I read some of Jonathan's academic writing about music, which had a sharp focus on neurology and acoustics. He is a professor of music at Stanford, where he teaches composition, music theory, and cognition at the Center for Computer Research in Music and Acoustics. On a hunch that he could connect with a popular audience, I asked him to write an essay for Nautilus about how composers upend expectations to keep listeners off guard and engaged. That article, "Composing Your Thoughts," and his next one for Nautilus, "How Music Hijacks Our Perception of Time," which contain musical clips to illustrate his points, have been among our most popular articles. There's a certain amount of problem solving that happens in the context of a band of noise. For this month's issue I called Jonathan and was delighted to learn he had thought a lot about noise.