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Thinking about science and technology in terms of return on investment misses the point. Here's what kids really need to know. That's what led me to become a professor. As a high school student, one of my major life goals was to figure out how to build an actual light sword. Doing so is all but impossible, so it didn't really matter if I went into engineering or science, but I pursued STEM just the same.

Dual encoder (DE) models, where a pair of matching query and document are embedded into similar vector representations, are widely used in information retrieval due to their simplicity and scalability. However, the Euclidean geometry of the embedding space limits the expressive power of DEs, which may compromise their quality. This paper investigates such limitations in the context of hierarchical retrieval (HR), where the document set has a hierarchical structure and the matching documents for a query are all of its ancestors. We first prove that DEs are feasible for HR as long as the embedding dimension is linear in the depth of the hierarchy and logarithmic in the number of documents. Then we study the problem of learning such embeddings in a standard retrieval setup where DEs are trained on samples of matching query and document pairs. Our experiments reveal a lost-in-the-long-distance phenomenon, where retrieval accuracy degrades for documents further away in the hierarchy. To address this, we introduce a pretrain-finetune recipe that significantly improves long-distance retrieval without sacrificing performance on closer documents. We experiment on a realistic hierarchy from WordNet for retrieving documents at various levels of abstraction, and show that pretrain-finetune boosts the recall on long-distance pairs from 19% to 76%. Finally, we demonstrate that our method improves retrieval of relevant products on a shopping queries dataset.

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Could robots be part of the answer to alleviating teacher shortages (and other staffing issues) in the future? Lots of folks think so, and new research indicates kids might already be primed to accept a non-human information source. A group of researchers from Concordia University in Montréal, Canada, ran two experiments with groups of three- and five-year-old children, all recruited from a database of existing research participants. Families received gift cards and the children received certificates of merit for participating. Approximately half the sample was white, a quarter of the sample was mixed race, and the remainder consisted of various other ethnic groups (such as African, Asian, and South American).

Researchers have successfully developed an AI system capable of completing mathematics problems at a grade school level, a new report asserts. Traditionally, while AI models are proficient at manipulating language to formulate sentences, the multi-step reasoning required to solve math problems has been a step too far. However, researchers at OpenAI (the company behind language model GPT-3) say they have trained a model to recognize its own mistakes, which means it can repeatedly reassess until it discovers a workable solution. In testing, the AI system was able to solve almost as many problems as a sample of children between the ages of nine and twelve. The children scored 60% on a test drawn down from the OpenAI database, while the AI system scored 55%.

Hannah pulls up to the curb, opens the doors, and welcomes the kiddo inside. "We're headed to Darwin Elementary." That's where he goes to school, after all. For Milo's parents, there is good news and there is iffy news. The good news is that they don't have to cut babysitting checks for Hannah, because Hannah is a self-driving school bus.

This isn't a real example, but similar AI missteps have already become infamous in the tech industry and in social media. The industry is excited about AI for good reason -- big data and machine learning are lighting up powerful experiences that were unimaginable just a few years ago. But for AI to fulfill its promise, the systems must be trustworthy. The more trust people have, the more they interact with the systems, and systems use more data to give better results. But trust takes a long time to build, and bias can tear it down instantaneously, doing real harm to large communities.

Akron Public Schools officials have issued warnings to parents about a suspicious drone flying near township schools and playgrounds that is attempting to lure kids away. "Witnesses have claimed that the voice in the drone has attempted to lure children off school grounds," she wrote in the letter obtained by the Beacon-Journal. Akron Public Schools spokesman Mark Williamson reiterated that the drone was seen or reported in evenings and over the weekend, but has not been present during school hours. Akron Police spokesman Rick Edwards said local law enforcement has not received complaints about the suspicious drone speaking to children near school grounds.

If there's one truth that the tech revolution has proven itself repeatedly, it's this: just because we can build something doesn't mean we should. Take, for instance, Google Glass, iTunes Ping, and Clippy (to say nothing of the smart fork.) Likewise, imagine an app that can take a snapshot of a student's homework assignment, chew on the questions using cloud-based artificial intelligence, and spit out the answers. In theory, this sounds great for students, yet terrible for learning -- that is, until you put Socratic to the test. "Every student today goes to the Internet, goes to Google, to ask all of their questions -- this is something that's happening anyway," says Shreyans Bhansali, the co-founder and head of engineering for the free homework-helping app that's currently topping Apple's App Store for education software. "We read the question, we figure out what they need to learn to answer it, and then we teach them that stuff."