We demonstrate how language can improve geolocation: the task of predicting the location where an image was taken. Here we study explicit knowledge from human-written guidebooks that describe the salient and class-discriminative visual features humans use for geolocation. We propose the task of Geolocation via Guidebook Grounding that uses a dataset of StreetView images from a diverse set of locations and an associated textual guidebook for GeoGuessr, a popular interactive geolocation game. Our approach predicts a country for each image by attending over the clues automatically extracted from the guidebook. Supervising attention with country-level pseudo labels achieves the best performance. Our approach substantially outperforms a state-of-the-art image-only geolocation method, with an improvement of over 5% in Top-1 accuracy. Our dataset and code can be found at https://github.com/g-luo/geolocation_via_guidebook_grounding.

Most enterprises that implement AI solutions have learned a bitter lesson along the way: The path to organizationwide AI adoption is far from simple, intuitive or easy. Arguably, the hardest thing about it is the lack of clear guidance. The absence of a simple, best practice guide has deeply frustrated companies all over the world over for the last decade, resulting in billions of dollars (in both direct investment and in people hours) going down the drain. The "AI guidebook" wasn't written yet, because it simply doesn't exist. These two letters, "AI," can mean natural language processing or computer vision or time series analysis -- each of which can be useful across a broad range of use cases. Combine this with the diversity of organizations that wish to deploy AI, each having their specific data, business needs and pain points, and you get an immensely diverse universe of AI solutions.

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Originally, Maysam Moussalem dreamed of being an architect. "When I was 10, I looked up to see the Art Nouveau dome over the Galeries Lafayette in Paris, and I knew I wanted to make things like that," she says. "Growing up between Austin, Paris, Beirut and Istanbul just fed my love of architecture." But she found herself often talking to her father, a computer science (CS) professor, about what she wanted in a career. "I always loved art and science and I wanted to explore the intersections between fields. CS felt broader to me, and so I ended up there."

This idea could be interpreted as being rather bleak; are we doomed to repeat the errors of the past until we correct them? We certainly do need to learn and re-learn life lessons--whether in our work, relationships, finances, health, or other areas--in order to grow as people. Zooming out, the same phenomenon exists on a much bigger scale--that of our collective human history. We like to think we're improving as a species, but haven't yet come close to doing away with the conflicts and injustices that plagued our ancestors. What might happen over the course of this year, and what information would we use to make educated guesses about it? The editorial team at The Economist took a unique approach to answering these questions.

In May 2020, Fernanda Viégas, Jess Holbrook, and Martin Wattenberg -- who cofounded Google Research's People AI Research (PAIR) initiative in 2017 -- sat down to talk about participatory machine learning, a core idea central to the direction PAIR's research and projects have taken. They took the conversation as an opportunity to further articulate and explore the concept in theory and especially in practice. David Weinberger, PAIR's writer-in-residence, prompted them with questions. Fernanda: From the beginning, PAIR has had a broad research agenda focused on putting humans at the center of building AI technology. For instance, we were building tools to help developers understand their data and model behaviors, but we were also working on how doctors do or don't trust AI-assisted diagnoses. We were bringing Tensorflow to the web and publishing human-centered AI guidance for UXers.

The developers of AI systems have entered a phase where tweaking algorithms and pumping up accuracy will do no good. Questions such as fairness and privacy are more important now than ever. But, an organisation cannot afford or expect a machine learning engineer to develop tools from scratch that can cater to the different demands at different stages of building a pipeline. Google is now offering a one-stop solution to all these challenges through its TensorFlow community. The team at TensorFlow have built tools to assist and overcome the errors that surface in data collection, processing, loading and deployment.

So says the famed quote from Shakespeare's The Tempest, alleging that we can look to what has already happened as an indication of what will happen next. This idea could be interpreted as being rather bleak; are we doomed to repeat the errors of the past until we correct them? We certainly do need to learn and re-learn life lessons--whether in our work, relationships, finances, health, or other areas--in order to grow as people. Zooming out, the same phenomenon exists on a much bigger scale--that of our collective human history. We like to think we're improving as a species, but haven't yet come close to doing away with the conflicts and injustices that plagued our ancestors. What might happen over the course of this year, and what information would we use to make educated guesses about it?

So says the famed quote from Shakespeare's The Tempest, alleging that we can look to what has already happened as an indication of what will happen next. This idea could be interpreted as being rather bleak; are we doomed to repeat the errors of the past until we correct them? We certainly do need to learn and re-learn life lessons--whether in our work, relationships, finances, health, or other areas--in order to grow as people. Zooming out, the same phenomenon exists on a much bigger scale--that of our collective human history. We like to think we're improving as a species, but haven't yet come close to doing away with the conflicts and injustices that plagued our ancestors.

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