Multivariate time series forecasting is extensively studied throughout the years with ubiquitous applications in areas such as finance, traffic, environment, etc. Still, concerns have been raised on traditional methods for incapable of modeling complex patterns or dependencies lying in real word data. To address such concerns, various deep learning models, mainly Recurrent Neural Network (RNN) based methods, are proposed. Nevertheless, capturing extremely long-term patterns while effectively incorporating information from other variables remains a challenge for time-series forecasting. Furthermore, lack-of-explainability remains one serious drawback for deep neural network models. Inspired by Memory Network proposed for solving the question-answering task, we propose a deep learning based model named Memory Time-series network (MTNet) for time series forecasting. MTNet consists of a large memory component, three separate encoders, and an autoregressive component to train jointly. Additionally, the attention mechanism designed enable MTNet to be highly interpretable. We can easily tell which part of the historic data is referenced the most.

Researchers from Google's AI division and Harvard University have created an AI model capable of predicting the location of aftershocks up to one year after a major earthquake. The model was trained with 199 major earthquake events in recent decades followed by 130,000 aftershocks, and was found to be more accurate than a method used to predict aftershocks today. Aftershocks included in the dataset used to train the neural network took place in a perimeter that stretches 50 kilometers vertically and 100 kilometers horizontally from each earthquake epicenter. "We found that after feeding these model stress changes into the neural network, the neural network could sort of predict aftershock locations in the testing dataset more accurately that the sort of baseline Coulomb failure stress change criterion that's used a lot in studies of aftershock locations," Phoebe DeVries of the Department of Earth and Planetary Sciences at Harvard University told VentureBeat in a phone interview. Data used to train the model came from noteworthy earthquakes such as the 2004 Sumatra earthquake, the 2011 earthquake in Japan, the 1989 Loma Prieta earthquake in the San Francisco Bay Area, and the 1994 Northride earthquake near Los Angeles.

"In two years, China will be ahead of the United States in AI (artificial intelligence)," states Denis Barrier, CEO of global venture firm Cathay Innovation. If so, China will largely determine how this technology transforms the world. Today's contest is more than a race for dominance in a new technology -- it's one between authoritarianism and democracy. "AI is the world's next big inflection point," says Ajeet Singh, CEO of ThoughtSpot in Palo Alto. Artificial intelligence is machine learning, which self-learns programmed tasks, using data, and the more it gets, the more learned it becomes.

This post was featured in our Cognilytica Newsletter, with additional details. When people hear about the race for Artificial Intelligence (AI) dominance, they often think that the main competition is between the US and China. After all, the US and China have most of the largest and most well funded AI companies on the planet, and the pace of funding, company growth, and adoption doesn't seem to be slowing anytime soon. However, if you look closely, you'll see that many other countries have a stake in the AI race, and indeed, some countries have AI efforts, funding, technologies, and intellectual property that make them serious contenders in the jostling for AI dominance. In this newsletter, we'll take a look at how countries are strategically positioned with regards to their AI capabilities and ambitions, and see if AI is truly like that of the space race or simply like any other technology trend we've seen come and go.

Uber has been seeking ways to lower development costs and losses in its autonomous-vehicle unit following a fatal crash involving one of its cars earlier this year in Arizona. Last year, the Uber division spent about $750 million on self-driving car development before making cuts this year, according to people familiar with the matter. In recent months, Uber has closed its Arizona autonomous-vehicle operations and laid off about 400 test drivers, some of whom it will rehire after undergoing new training. Uber also has taken its self-driving vehicles off the roads in the San Francisco Bay Area, Pittsburgh and Toronto while investigators look into the circumstances of the Arizona crash. For ride-sharing concerns like Uber and Lyft Inc., autonomous vehicles could cut their biggest expense: paying human drivers.

In the U.S., 15,498 of the more than 88,000 dams in the country are categorized as having high hazard potential--meaning that if they fail, they could kill people. As of 2015, some 2,000 of these high hazard dams are in need of repair. With a hefty price tag estimated at around $20 billion, those repairs aren't going to happen overnight. A project out of the Columbia Water Center aims to help guide the process of repairing or decommissioning these dams. The team is pinpointing the riskiest dams, using climate models, GIS data, and artificial intelligence to predict the likelihood that rainfall will overtop a dam and cause significant downstream damages to population and critical infrastructure.

In the U.S., 15,498 of the more than 88,000 dams in the country are categorized as having high hazard potential--meaning that if they fail, they could kill people. As of 2015, some 2,000 of these high hazard dams are in need of repair. With a hefty price tag estimated at around $20 billion, those repairs aren't going to happen overnight. A project out of the Columbia Water Center aims to help guide the process of repairing or decommissioning these dams. The team is pinpointing the riskiest dams, using climate models, GIS data, and artificial intelligence to predict the likelihood that rainfall will overtop a dam and cause significant downstream damages to population and critical infrastructure.

For four years I've waited for a spiritual successor to Never Alone. Part game, part documentary, E-Line Media and Upper One Games managed to adapt traditional Inupiaq folklore into an accessible platformer while educating players about the culture in question. It was flawed, but beautiful and memorable. Memorable enough, I guess, that the BBC got in touch and asked E-Line to do it again. At Gamescom this week I got to take a look at Beyond Blue, a spiritual successor to Never Alone and a companion piece to the BBC documentary Blue Planet 2. As you might expect from the Blue Planet 2 pairing, Beyond Blue takes you deep under the ocean.

Common-sense and background knowledge is required to understand natural language, but in most neural natural language understanding (NLU) systems, this knowledge must be acquired from training corpora during learning, and then it is static at test time. We introduce a new architecture for the dynamic integration of explicit background knowledge in NLU models. A general-purpose reading module reads background knowledge in the form of free-text statements (together with task-specific text inputs) and yields refined word representations to a task-specific NLU architecture that reprocesses the task inputs with these representations. Experiments on document question answering (DQA) and recognizing textual entailment (RTE) demonstrate the effectiveness and flexibility of the approach. Analysis shows that our model learns to exploit knowledge in a semantically appropriate way.

Alphabet is broken out into its core Google business and a number of other subsidiaries, which it deems "Other Bets." The majority of Google's business comes from advertising revenues, which the company generates through its search engine as well as a number of other Google-affiliated and partnership websites. Outside of search and advertising, Google generates revenue from products including cloud and enterprise, consumer hardware, mapping, and YouTube. In addition to Google, Alphabet encompasses a host of other subsidiaries called "Other Bets." These companies are more experimental in nature, and as a result are not material to Alphabet's bottom line.