Declarative modeling uses symbolic expressions to represent models. With such expressions one can formalize high-level mathematical computations on models that would be difficult or impossible to perform directly on a lower-level simulation program, in a general-purpose programming language. Examples of such computations on models include model analysis, relatively general-purpose model-reduction maps, and the initial phases of model implementation, all of which should preserve or approximate the mathematical semantics of a complex biological model. Multiscale modeling benefits from both the expressive power of declarative modeling languages and the application of model reduction methods to link models across scale. Based on previous work, here we define declarative modeling of complex biological systems by defining the semantics of an increasingly powerful series of declarative modeling languages including reaction-like dynamics of parameterized and extended objects, we define semantics-preserving implementation and semantics-approximating model reduction transformations, and we outline a "meta-hierarchy" for organizing declarative models and the mathematical methods that can fruitfully manipulate them.

Every year, more than a billion people around the world celebrate Chinese New Year and engage in a subtle linguistic dance with luck. You can think of it as a set of holiday rituals that resemble a courtship. To lure good fortune into their lives, they may decorate their homes and doors with paper cutouts of lucky words or phrases. Those who need a haircut make sure to get one before the New Year, as the word for "hair" (fa) sounds like the word for "prosperity"--and who wants to snip away prosperity, even if it's just a trim? The menu of food served at festive meals often includes fish, because its name (yu) sounds the same as the word for "surplus"; a type of algae known as fat choy because in Cantonese it sounds like "get rich"; and oranges, because in certain regions their name sounds like the word for "luck."

I have two children, and they are a study in contrasts: My son works at a gym designing and building rock-climbing walls; In his spare time, he climbs them. My daughter is a Ph.D. student in immunology; In her spare time, she writes novels. My son is the sort of person you want around in a crisis, cool-headed and springing to action. Let's just say my daughter is not. My son spends money as soon as he earns it.

The machine reading task, where a computer reads a document and answers questions about it, is important in artificial intelligence research. Recently, many models have been proposed to address it. Word-level models, which have words as units of input and output, have proven to yield state-of-the-art results when evaluated on English datasets. However, in morphologically richer languages, many more unique words exist than in English due to highly productive prefix and suffix mechanisms. This may set back word-level models, since vocabulary sizes too big to allow for efficient computing may have to be employed. Multiple alternative input granularities have been proposed to avoid large input vocabularies, such as morphemes, character n-grams, and bytes. Bytes are advantageous as they provide a universal encoding format across languages, and allow for a small vocabulary size, which, moreover, is identical for every input language. In this work, we investigate whether bytes are suitable as input units across morphologically varied languages. To test this, we introduce two large-scale machine reading datasets in morphologically rich languages, Turkish and Russian. We implement 4 byte-level models, representing the major types of machine reading models and introduce a new seq2seq variant, called encoder-transformer-decoder. We show that, for all languages considered, there are models reading bytes outperforming the current state-of-the-art word-level baseline. Moreover, the newly introduced encoder-transformer-decoder performs best on the morphologically most involved dataset, Turkish. The large-scale Turkish and Russian machine reading datasets are released to public.

Here are 90 upcoming meetings and conferences, for January 2018 and beyond. You can also find the latest list on KDnuggets Meetings page Most popular topics: AI/Artificial Intelligence, 23 top countries: top cities: Toronto, Stockholm: 4 Color code: Business-Oriented meetings in Blue, Research meetings (with calls for papers and program committee) in green January 2018 Americas: Jan 17-19, BAFI 2018: 3rd Conf. on Business Analytics in Finance and Industry. Use code KDNUGGETS to save. Feb 8, AI Evolution brings together experts, developers, technologists, and business leaders. Feb 11-16, TDWI, 4 core learning tracks on Business Analytics & BI / Data Science / Data Management/ Data Strategy.

Former Indy car racer Sam Schmidt has a million-dollar car that allows him to do something that people said he would never be able to do again – drive on his own. But he still can't wait for fully autonomous vehicles to arrive. Not for driving on the track, where he feels fully safe manoeuvring his modified 2016 Corvette Stingray by using special gears created for quadriplegics. Rather, Schmidt says he needs the safety features found in autonomous cars to face the intimidating streets of Las Vegas, where he lives. "I don't feel comfortable on the street," says Schmidt, who lost the use of his four limbs in a 2000 crash on a racetrack in Orlando.

There's a higher degree of accuracy in this more specialized context, when someone types in a query the machine will translate it accurately. This is especially important when dealing with real-world business data.

In his session at 21st Cloud Expo, James Henry, Co-CEO/CTO of Calgary Scientific Inc., introduced you to the challenges, solutions and benefits of training AI systems to solve visual problems with an emphasis on improving AIs with continuous training in the field. He explored applications in several industries and discuss technologies that allow the deployment of advanced visualization solutions to the cloud. Speaker Bio James Henry is Co-CEO/CTO of Calgary Scientific Inc., a company specializing in bringing real time interactive software to cloud and mobile platforms. He has 25 years of experience leading software teams in many industries including the oil and gas, healthcare, telecommunication, geolocation, construction and simulation industries. His current interest is in enabling people, data and AIs to interact in real time to solve complex problems.

"[[[When in the course of human events it becomes necessary for one people [to dissolve the political bands [which have connected them with another]] and [to assume among the powers of the earth, the separate and equal station [to which the laws of Nature and of Nature's God entitle them]]], a decent respect to the opinions of mankind requires [that they should declare the causes [which impel them to the separation]]]." But how did it ever make its way into the world? At 71 words, it is composed of eight separate clauses, each anchored by its own verb, nested within one another in various arrangements. The main clause (a decent respect to the opinions of mankind requires …) hangs suspended above a 50-word subordinate clause that must first be unfurled. To some linguists, Noam Chomsky among them, sentences like these illustrate an essential property of human language. These scientists have argued that recursion, a technique that allows chunks of language such as sentences to be embedded inside each other (with no hard limit on the number of nestings) is a universal human ability, perhaps even the one uniquely human ability that supports language. It's what allows us to create--literally--an infinite variety of novel sentences out of a limited inventory of words.

The absence of Amazon's Alexa and Prime Music services in Canada has been a strange oversight, given that the nation's share a border and (one of two) common languages. That has now been corrected, as Amazon has finally launched the Echo family, Prime Music and Alexa Voice services and skills in the land of hockey and poutine. "We're excited to bring [Alexa] to Canada with an experienced designed from the ground up for our Canadian customers," said Amazon Senior VP Tom Taylor in a statement. Amazon has introduced a new English voice for Alexa with a Canadian accent, though even we Canadians aren't exactly sure what that is. However, Alexa doesn't seem to be available yet in French, which is bit surprising considering that it's one of Canada's two official languages (Engadget has reached out for more information).