Visual 1st, the annual Silicon-Valley imaging conference for industry leaders and upstarts, once again brought together a worldwide audience for a day-and-a-half executive conference. The event, held Oct. 2-3 at the Golden Gate Club in San Francisco, addresses topics as far-reaching as artificial intelligence and as every day as printing. As with most conferences, the real meat of the event is the hallway discussions and informal meetings over a beer or wine at the reception. Below are some photos from the conference, courtesy of sponsor, Sweet Escapes. Each year, a panel of high-powered industry experts presented the four Visual 1st Awards to the most outstanding among 30 products competing in this year's show-and-tell demo sessions.

Alexa, Google Assistant, Siri, and Cortana can answer all sorts of questions that pop into users' heads, and they're improving every day. But what happens when a company like Amazon decides to crowdsource answers to fill gaps in its platform's knowledge? The result can range from amusing and perplexing to concerning. Alexa Answers allows any Amazon customer to submit responses to unanswered questions. When the web service launched in general availability a few weeks ago, Amazon gave assurances that submissions would be policed through a combination of automatic and manual review.

Learning with rejection (LWR) allows development of machine learning systems with the ability to discard low confidence decisions generated by a prediction model. That is, just like human experts, LWR allows machine models to abstain from generating a prediction when reliability of the prediction is expected to be low. Several frameworks for this learning with rejection have been proposed in the literature. However, most of them work for classification problems only and regression with rejection has not been studied in much detail. In this work, we present a neural framework for LWR based on a generalized meta-loss function that involves simultaneous training of two neural network models: a predictor model for generating predictions and a rejecter model for deciding whether the prediction should be accepted or rejected. The proposed framework can be used for classification as well as regression and other related machine learning tasks. We have demonstrated the applicability and effectiveness of the method on synthetically generated data as well as benchmark datasets from UCI machine learning repository for both classification and regression problems. Despite being simpler in implementation, the proposed scheme for learning with rejection has shown to perform at par or better than previously proposed methods. Furthermore, we have applied the method to the problem of hurricane intensity prediction from satellite imagery. Significant improvement in performance as compared to conventional supervised methods shows the effectiveness of the proposed scheme in real-world regression problems.

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.