Abstract: Conceptual abstraction and analogy-making are key abilities underlying humans' abilities to learn, reason, and robustly adapt their knowledge to new domains. Despite of a long history of research on constructing AI systems with these abilities, no current AI system is anywhere close to a capability of forming humanlike abstractions or analogies. This paper reviews the advantages and limitations of several approaches toward this goal, including symbolic methods, deep learning, and probabilistic program induction. The paper concludes with several proposals for designing challenge tasks and evaluation measures in order to make quantifiable and generalizable progress in this area.

This paper focuses on a core task in computational sustainability and statistical ecology: species distribution modeling (SDM). In SDM, the occurrence pattern of a species on a landscape is predicted by environmental features based on observations at a set of locations. At first, SDM may appear to be a binary classification problem, and one might be inclined to employ classic tools (e.g., logistic regression, support vector machines, neural networks) to tackle it. However, wildlife surveys introduce structured noise (especially under-counting) in the species observations. If unaccounted for, these observation errors systematically bias SDMs. To address the unique challenges of SDM, this paper proposes a framework called StatEcoNet. Specifically, this work employs a graphical generative model in statistical ecology to serve as the skeleton of the proposed computational framework and carefully integrates neural networks under the framework. The advantages of StatEcoNet over related approaches are demonstrated on simulated datasets as well as bird species data. Since SDMs are critical tools for ecological science and natural resource management, StatEcoNet may offer boosted computational and analytical powers to a wide range of applications that have significant social impacts, e.g., the study and conservation of threatened species.

Combining drone imagery with weather data and planting schemes to forecast how much fresh vegetables a harvest is going to yield; that's what predictive modelling intern Berend Klaver from TU Delft is sweating on at VanBoven, while his bosses are entertaining the American west coast. VanBoven is one of the ten winners of the Academic Startup Competition 2020, currently on tour in Silicon Valley for a 4-week incubator programme. "The market of fresh vegetables is one of constant shortages and surpluses. VanBoven predicts the harvest of fresh produce to perfectly align supply and demand. The result is decreased food waste, a resilient value chain and fair prices," says the startup on its website.

Seniors and first responders try to snag one of 800 doses available at a vaccination site in Fort Myers, Fla. Octavio Jones/Getty Images hide caption Seniors and first responders try to snag one of 800 doses available at a vaccination site in Fort Myers, Fla. With millions of older Americans eligible for coronavirus vaccines and limited supplies, many continue to describe a frantic and frustrating search to secure a shot, beset by uncertainty and difficulty. The efforts to vaccinate people who are 65 and older have strained under the enormous demand that has overwhelmed cumbersome, inconsistent scheduling systems. The struggle represents a shift from the first wave of vaccinations -- health care workers in health care settings -- which went comparatively smoothly. Now, in most places, elderly people are pitted against each other competing on an unstable technological playing field for limited shots.

One of the earliest images in Natalia Almada's virtuoso documentary Users is of an infant, tightly wrapped and strapped to a Snoo smart crib, robotically being rocked to sleep to the sound of manufactured white noise. By recreating many of the sensations of being in the womb, the Snoo has become a popular gadget for new parents who need help tucking their little ones in. In many ways, it's the pinnacle of a smart gadget: Developed by Dr. Harvey Karp, with product design by the renowned Yves Behar, the Snoo solves a problem that parents have faced for millennia. But what do we lose if a robot can automatically soothe a crying baby, effectively replacing a nurturing parent. That's the question at the heart of Users, which premiered at the Sundance Film Festival this week.

In many cases, it is difficult to generate highly accurate models for time series data using a known parametric model structure. In response, an increasing body of research focuses on using neural networks to model time series approximately. A common assumption in training neural networks on time series is that the errors at different time steps are uncorrelated. However, due to the temporality of the data, errors are actually autocorrelated in many cases, which makes such maximum likelihood estimation inaccurate. In this paper, we propose to learn the autocorrelation coefficient jointly with the model parameters in order to adjust for autocorrelated errors. For time series regression, large-scale experiments indicate that our method outperforms the Prais-Winsten method, especially when the autocorrelation is strong. Furthermore, we broaden our method to time series forecasting and apply it with various state-of-the-art models. Results across a wide range of real-world datasets show that our method enhances performance in almost all cases.

We consider the problem of forecasting multiple values of the future of a vector time series, using some past values. This problem, and related ones such as one-step-ahead prediction, have a very long history, and there are a number of well-known methods for it, including vector auto-regressive models, state-space methods, multi-task regression, and others. Our focus is on low rank forecasters, which break forecasting up into two steps: estimating a vector that can be interpreted as a latent state, given the past, and then estimating the future values of the time series, given the latent state estimate. We introduce the concept of forecast consistency, which means that the estimates of the same value made at different times are consistent. We formulate the forecasting problem in general form, and focus on linear forecasters, for which we propose a formulation that can be solved via convex optimization. We describe a number of extensions and variations, including nonlinear forecasters, data weighting, the inclusion of auxiliary data, and additional objective terms. We illustrate our methods with several examples.

Housebound by a pandemic, humanity slowed its emissions of greenhouse gases in 2020. But Earth paid little heed: Temperatures last year tied the modern record, climate scientists reported last week. Overall, the planet was about 1.25°C warmer than in preindustrial times, a trend that puts climate targets in jeopardy, according to jointly reported assessments from NASA, Berkeley Earth, the U.K. Met Office, and the National Oceanic and Atmospheric Administration. The annual update of global surface temperatures—an average of readings from thousands of weather stations and ocean probes—shows 2020 essentially tied records set in 2016. But the years were nothing alike. Temperatures in 2016 were boosted by a strong El Niño, a weather pattern that warms the globe by blocking the rise of cold deep waters in the eastern Pacific Ocean. Last year, however, the Pacific entered La Niña, which has a cooling effect. That La Niña didn't provide more relief is an unwelcome surprise, says Nerilie Abram, a climate scientist at Australian National University. “It makes me worried about how quickly the global warming trend is growing.” The past 6 years are the six warmest on record, but the warming of the atmosphere is unsteady because of its chaotic nature. The ocean, which absorbs more than 90% of the heat from global warming, displays a steadier trend, and here, too, 2020 was a record year. The upper levels of the ocean contained 20 zettajoules (1021 joules) more heat than in 2019, and the rise was double the typical annual increase, scientists reported last week in Advances in Atmospheric Sciences . The subtropical Atlantic Ocean was particularly hot, fueling a record outbreak of hurricanes, says Lijing Cheng, a climate scientist at the Chinese Academy of Sciences's Institute of Atmospheric Physics who led the work. This heat, monitored down to 2000 meters by a fleet of 4000 robotic probes, is spreading deeper into the ocean while also migrating toward the poles. An extreme heat wave struck the northern Pacific, killing marine life. For the first time, warm Atlantic waters were seen penetrating into the Arctic Ocean, melting sea ice from below and reducing its extent nearly to a record low ( Science , 28 August 2020, p. [1043][1]). The warming ocean and melting ice sheets are raising sea levels by 4.8 millimeters per year, and the rate is accelerating ( Science , 20 November 2020, p. [901][2]). On land, 2020 was even more relentless, with temperatures rising 1.96°C above preindustrial levels, a clear record, Berkeley Earth reported. It was the warmest year ever in Asia and Europe and tied for the warmest in South America. Russia was particularly hot, breaking its previous record by 1.2°C, while swaths of Siberia were 7°C warmer than in preindustrial times, leading to large-scale fires and thawing permafrost that caused buildings to founder and set off oil spills ( Science , 7 August 2020, p. [612][3]). “Siberia was crazy,” says Zeke Hausfather, a climate scientist at the Breakthrough Institute and co-author of the Berkeley Earth analysis. “That heat would effectively be impossible without the warming we've seen.” In Australia, record-setting heat and drought fueled catastrophic bushfires at the start of 2020. Fires torched nearly one-quarter of southeastern Australia's forests and destroyed 3000 homes. Climate change was to blame for the country's “Black Summer,” Abram and co-authors concluded in a study published this month in Communications Earth & Environment . Meanwhile, in the United States, unprecedented heat came to the desert Southwest, which is already warming faster than the rest of the country. Phoenix wilted under its hottest summer ever, averaging 36°C. Arizona's Maricopa county, home to Phoenix, is a leader in addressing heat exposure, yet its heat deaths have hit a new record each year since 2016. In 2020, the number approached 300, a jump of some 50% over the previous year, says David Hondula, a climatologist who studies heat mortality at Arizona State University, Tempe. “It was just off the charts in terms of heat.” ![Figure][4] Turning up the heatCREDITS: (GRAPHIC) N. DESAI/ SCIENCE ; (DATA) MET OFFICE; NASA; BERKELEY EARTH; NOAA Although the global economic slowdown of the COVID-19 pandemic cut carbon dioxide (CO2) emissions by some 7%, atmospheric CO2 is long-lived, and warming from previous emissions is preordained. In any case, the drop in emissions is unlikely to last. Later this year, in May, before photosynthesis in the Northern Hemisphere draws down CO2, the U.K. Met Office predicts that levels of atmospheric CO2 will pass 417 parts per million for several weeks, 50% higher than preindustrial levels. Only dramatic action by the world's countries, far beyond existing efforts, can begin to halt this build up, Cheng says. Should the current rate of warming continue, the world will breach the targets set in the Paris climate agreement—limiting warming to 1.5°C or 2°C—by 2035 and 2065, respectively. But Hausfather says it's quite possible that warming, which has largely held steady for the past few decades at 0.19°C per decade, will actually speed up. The rate of warming over the past 14 years is well above the long-term trend. The debate now, he says, is whether that is an omen of an even darker future. [1]: https://www.sciencemag.org/content/369/6507/1043.full [2]: https://www.sciencemag.org/content/370/6519/901.full [3]: https://www.sciencemag.org/content/369/6504/612.full [4]: pending:yes

If you happen to be crossing the San Francisco Bay or Golden Gate bridges this week, look for a massive surfboard with a red sail on top cruising slowly across the water. Don't flinch if you don't see anyone on board. It's actually an autonomous research vessel known as the Saildrone Surveyor and it's being steered remotely from shore. The 72-foot-long vessel is launching this week into the bay from its dock at a former naval base in Alameda, California. It is designed to spend months at sea mapping the seafloor with powerful sonar devices, while simultaneously scanning the ocean surface for genetic material to identify fish and other marine organisms swimming below.

Dynamic time warping (DTW) plays an important role in analytics on time series. Despite the large body of research on speeding up univariate DTW, the method for multivariate DTW has not been improved much in the last two decades. The most popular algorithm used today is still the one developed seventeen years ago. This paper presents a solution that, as far as we know, for the first time consistently outperforms the classic multivariate DTW algorithm across dataset sizes, series lengths, data dimensions, temporal window sizes, and machines. The new solution, named TC-DTW, introduces Triangle Inequality and Point Clustering into the algorithm design on lower bound calculations for multivariate DTW. In experiments on DTW-based nearest neighbor finding, the new solution avoids as much as 98% (60% average) DTW distance calculations and yields as much as 25X (7.5X average) speedups.