When it comes to online dating, Giovanni Wolfram, a 25-year-old living in Santa Fe, New Mexico, isn't all too worried about whether his fellow dating app users will find him attractive. Rather, his biggest fear is that he might come off as "cringey." "You can get away with being ugly," Wolfram says. "But being cringey is just like--that's a character that's imprinted on you." Since he first joined Hinge at 18, he has worked hard to scrub his profile of sincerity.

This paper proposes a methodology for constructing such corpora of child directed speech (CDS) paired with sentential logical forms, and uses this method to create two such corpora, in English and Hebrew. The approach enforces a cross-linguistically consistent representation, building on recent advances in dependency representation and semantic parsing. Specifically, the approach involves two steps. First, we annotate the corpora using the Universal Dependencies (UD) scheme for syntactic annotation, which has been developed to apply consistently to a wide variety of domains and typologically diverse languages. Next, we further annotate these data by applying an automatic method for transducing sentential logical forms (LFs) from UD structures. The UD and LF representations have complementary strengths: UD structures are language-neutral and support consistent and reliable annotation by multiple annotators, whereas LFs are neutral as to their syntactic derivation and transparently encode semantic relations. Using this approach, we provide syntactic and semantic annotation for two corpora from CHILDES: Brown's Adam corpus (English; we annotate ~80% of its child-directed utterances), all child-directed utterances from Berman's Hagar corpus (Hebrew). We verify the quality of the UD annotation using an inter-annotator agreement study, and manually evaluate the transduced meaning representations. We then demonstrate the utility of the compiled corpora through (1) a longitudinal corpus study of the prevalence of different syntactic and semantic phenomena in the CDS, and (2) applying an existing computational model of language acquisition to the two corpora and briefly comparing the results across languages.

Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, New Mexico 87501, USA Many datasets give partial information about an ordering or ranking by indicating which team won a game, which item a user prefers, or who infected whom. We define a continuous spin system whose Gibbs distribution is the posterior distribution on permutations, given a probabilistic model of these interactions. Using the cavity method we derive a belief propagation algorithm that computes the marginal distribution of each node's position. In addition, the Bethe free energy lets us approximate the number of linear extensions of a partial order and perform model selection. Ranking or ordering objects is a natural problem in In this case, the energy H(π) is the number of violations, many contexts.

Last week, the Brookings Institution published an examination of the "extent, location, and concentration" of AI activity in 400 US metro areas, hailing it as the "next great'general purpose technology,'" with the power to spur economic growth. Key takeaways: Although it already feels like AI is everywhere, the tech is still in its early days--and in the US, AI development and commercialization is mega-concentrated in a handful of mostly coastal locales. But, but, but: Brookings also identified 13 other metro areas with "above-average involvement" in AI, including hubs you may have seen coming--New York, Boston, Seattle, Los Angeles, Washington, D.C., San Diego, Austin, Texas, and Raleigh, North Carolina--as well as smaller metro areas like Boulder, Colorado, Lincoln, Nebraska, Santa Cruz, California, Santa Maria-Santa Barbara, California, and Santa Fe, New Mexico. Zoom out: The above 15 metro areas account for two-thirds of AI activity nationwide--and for that matter, more than 50% of the areas Brookings looked at make up just 5% of AI activity, Wired reported.

Satellite imagery holds a wealth of information that could be useful for industries, science and humanitarian causes, but one big and persistent challenge with it has been a lack of effective ways to tap that disparate data for specific ends. That's created a demand for better analytics, and now, one of the startups that has been building solutions to do just that is announcing a round of funding as it gears up for expansion. Descartes Labs, a geospatial imagery analytics startup out of Santa Fe, New Mexico, is today announcing that it has closed a $20 million round of funding, money that CEO and founder Mark Johnson described to me as a bridge round ahead of the startup closing and announcing a larger growth round. The funding is being led by Union Grove Venture Partners, with Ajax Strategies, Crosslink Capital, and March Capital Partners (which led its previous round) also participating. It brings the total raised by Descartes Labs to $60 million, and while Johnson said the startup would not be disclosing its valuation, PitchBook notes that it is $220 million ($200 million pre-money in this round).

On Friday afternoons, Caitlin Kontgis and some of the other scientists at Descartes Labs convene in their Santa Fe, New Mexico, office and get down to work on a grassroots project that's not part of their jobs: watching hurricanes from above, and seeing if they can figure out what the storms will do.* They acquire data from GOES, the Geostationary Operational Environmental Satellite operated by NOAA and NASA, which records images of the Western Hemisphere every five minutes. That's about how long it takes the team to process each image through a deep learning algorithm that detects the eye of a hurricane and centers the image processor over that. Then, they incorporate synthetic aperture data, which uses long-wave radar to see through clouds, and can discern water beneath based on reflectivity. That, in turn, can show almost real-time flooding, tracked over days, of cities in the path of hurricanes.

When Randy Bilyeu disappeared, he was hunting for the Fenn Treasure, a chest allegedly filled with gold, precious stones, and jewelry, supposedly hidden in the Rocky Mountains north of Santa Fe, New Mexico. In 2010, millionaire art dealer (and Former Vietnam fighter pilot) 79-year-old Forrest Fenn filled a bronze chest with rare metals, jewels, and artifacts, and then hid it in the mountains. Later that year, he published his autobiography, The Thrill of the Chase, which included a 24-line poem that he says contains the clues necessary to track down the treasure chest. Since then, he's become something of a global celebrity; in 2013, he appeared on NBC's Today Show to issue some new clues about the place where the chest had been hidden. Bilyeu happened to catch the episode on TV and became obsessed with finding the Fenn treasure--against all odds and his friends and family's better judgement.

We analyze a simple hill-climbing algorithm (RMHC) that was previously shown to outperform a genetic algorithm (GA) on a simple "Royal Road" function. We then analyze an "idealized" genetic algorithm (IGA) that is significantly faster than RMHC and that gives a lower bound for GA speed. We identify the features of the IGA that give rise to this speedup, and discuss how these features can be incorporated into a real GA. 1 INTRODUCTION Our goal is to understand the class of problems for which genetic algorithms (GA) are most suited, and in particular, for which they will outperform other search algorithms. Several studies have empirically compared GAs with other search and optimization methods such as simple hill-climbing (e.g., Davis, 1991), simulated annealing (e.g., Ingber & Rosen, 1992), linear, nonlinear, and integer programming techniques, and other traditional optimization techniques (e.g., De Jong, 1975). However, such comparisons typically compare one version of the GA with a second algorithm on a single problem or set of problems, often using performance criteria which may not be appropriate.