New generation geostationary satellites make solar reflectance observations available at a continental scale with unprecedented spatiotemporal resolution and spectral range. Generating quality land monitoring products requires correction of the effects of atmospheric scattering and absorption, which vary in time and space according to geometry and atmospheric composition. Many atmospheric radiative transfer models, including that of Multi-Angle Implementation of Atmospheric Correction (MAIAC), are too computationally complex to be run in real time, and rely on precomputed look-up tables. Additionally, uncertainty in measurements and models for remote sensing receives insufficient attention, in part due to the difficulty of obtaining sufficient ground measurements. In this paper, we present an adaptation of Bayesian Deep Learning (BDL) to emulation of the MAIAC atmospheric correction algorithm. Emulation approaches learn a statistical model as an efficient approximation of a physical model, while machine learning methods have demonstrated performance in extracting spatial features and learning complex, nonlinear mappings. We demonstrate stable surface reflectance retrieval by emulation (R2 between MAIAC and emulator SR are 0.63, 0.75, 0.86, 0.84, 0.95, and 0.91 for Blue, Green, Red, NIR, SWIR1, and SWIR2 bands, respectively), accurate cloud detection (86\%), and well-calibrated, geolocated uncertainty estimates. Our results support BDL-based emulation as an accurate and efficient (up to 6x speedup) method for approximation atmospheric correction, where built-in uncertainty estimates stand to open new opportunities for model assessment and support informed use of SR-derived quantities in multiple domains.

Artificial intelligence might sound like a futuristic concept, and it may be true that we're years or decades away from a generalized form of AI that can match or exceed the capabilities of the human brain across a wide range of topics. But the implications of machine learning, facial recognition and other early forms of the technology are already playing out for companies, governmental agencies and people around the world. This is raising questions about everything from privacy to jobs to law enforcement to the future of humanity. On this episode of the GeekWire Podcast, we hear several different takes from people grappling right now with AI and its implications for business, technology and society, recorded across different sessions at the recent GeekWire Summit in Seattle. Listen to the episode above, or subscribe in your favorite podcast app, and continue reading for edited excerpts. Smith: I think it's fair to say that artificial intelligence will reshape the global economy over the next three decades probably more than any other single technological force, probably as much as the combustion engine reshaped the global economy in the first half of the 20th century. One of our chapters is about AI in the workforce, and we actually start it by talking about the role of horses, the last run of the fire of horses in Brooklyn in 1922. And we trace how the transition from the horse to the automobile changed every aspect of the economy. I think the same thing will be true of AI, so we should get that right.

The year is 1989 and we're introduced to the World Wide Web. The Berlin Wall is coming down. The Exxon Valdez is spilling oil in Prince William Sound, Alaska. Students are calling for democracy and free speech in Tiananmen Square. Crockett and Tubbs are clearing the mean streets of Miami.

Organizations today are going through a variety of digital hardships. They are trying to find ways to derive value from data through which they want to achieve specific business outcomes. But doing so is not a cakewalk, it takes a lot of effort from data scientists' end to mine data to carve analytics application for driving innovative and efficient decision-making. In order to make analytics more effective, businesses are replacing traditional data management with an emerging set of practices. These practices are focused on collaboration and automation and are known as data operations, or DataOps. DataOps is a junction of advanced data governance and analytics delivery practices that incorporates the data life cycle.

I'm publishing this series to discuss a topic that I follow closely - cloud stocks, trends, strategy, acquisitions, and more. Please subscribe to my Cloud Stock Analysis series and never miss an article. Earlier this week, IBM (Nasdaq: IBM) declared its third quarter results that exceeded earnings expectations despite missing revenue estimates. The stock fell nearly 5% post the result announcement in the after-hours session. Revenues for the third quarter fell 3.9% over the year to $18.03 billion, missing the Street's forecast of $18.23 billion for the quarter.

Time series forecasting with limited data is a challenging yet critical task. While transformers have achieved outstanding performances in time series forecasting, they often require many training samples due to the large number of trainable parameters. In this paper, we propose a training technique for transformers that prepares the training windows through random sampling. As input time steps need not be consecutive, the number of distinct samples increases from linearly to combinatorially many. By breaking the temporal order, this technique also helps transformers to capture dependencies among time steps in finer granularity. We achieve competitive results compared to the state-of-the-art on real-world datasets.

Researchers from China and South Korea have created an AI that can predict El Niño up to 18 months before it occurs. El Niño is a weather event that can occur every 2-7 years, where the area of warmer water in the western Pacific Ocean around Australia spreads across the Pacific. This leads to warmer air rising across the Pacific, causing severe rainfall and drastically changing wind direction and strength across the Pacific. This has huge knock on effects on weather worldwide. El Niño can cause colder winters in northern Europe and droughts in countries such as Australia and Malaysia.

FILE - In this June 4, 1942 file photo provided by the U.S. Navy shows the USS Yorktown listing heavily to port after being struck by Japanese bombers and torpedo planes in the Battle of Midway. Researchers scouring the world's oceans for sunken World War II ships are honing in on debris fields deep in the Pacific.(AP MIDWAY ATOLL, Northwestern Hawaiian Islands (AP) -- Deep-sea explorers scouring the world's oceans for sunken World War II ships are focusing on debris fields deep in the Pacific, in an area where one of the most decisive battles of the time took place. Hundreds of miles off Midway Atoll, nearly halfway between the United States and Japan, a research vessel is launching underwater robots miles into the abyss to look for warships from the famed Battle of Midway. Weeks of grid searches around the Northwestern Hawaiian Islands have already led the crew of the Petrel to one sunken warship, the Japanese ship the Kaga.

Newspapers are a popular form of written discourse, read by many people, thanks to the novelty of the information provided by the news content in it. A headline is the most widely read part of any newspaper due to its ap - pearance in a bigger font and sometimes in colour print. In this paper, we sug - gest and implement a method for computing inferences from English news headlines, excluding the information from the context in which the headlines appear. This method attempts to generate the possible assumptions a reader formulates in mind upon reading a fresh headline. The generated inferences could be useful for assessing the impact of the news headline on readers includ - ing children. The understandability of the current state of social affairs depends greatly on the assimilation of the headlines. As the inferences that are indepen - dent of the context depend mainly on the syntax of the headline, dependency trees of headlines are used in this approach, to find the syntactical structure of the headlines and to compute inferences out of them.

In wondering what can be done to steer civilization away from the abyss, I confess to being increasingly puzzled by the central enigma of contemporary cognitive psychology: To what degree are we consciously capable of changing our minds? I don't mean changing our minds as to who is the best NFL quarterback, but changing our convictions about major personal and social issues that should unite but invariably divide us. As a senior neurologist whose career began before CAT and MRI scans, I have come to feel that conscious reasoning, the commonly believed remedy for our social ills, is an illusion, an epiphenomenon supported by age-old mythology rather than convincing scientific evidence. If so, it's time for us to consider alternate ways of thinking about thinking that are more consistent with what little we do understand about brain function. I'm no apologist for artificial intelligence, but if we are going to solve the world's greatest problems, there are several major advantages in abandoning the notion of conscious reason in favor of seeing humans as having an AI-like "black-box" intelligence. To believe that we can accurately determine whether or not consciousness contains causal properties is sheer folly. But first, a brief overview as to why I feel so strongly that purely conscious thought isn't physiologically likely.