When you are a world-renowned pioneer in smells, it's somewhat inevitable you will end up sticking your face into peculiar places: the burned rubber tire of a Chevy lowrider, a rotting hunk of wall insulation from an abandoned home, a cupped palmful of cool water from the Detroit River. It's also inevitable that the trailing documentary crew (sent by the local gallery behind your next odor-based installation) and photographer (sent, in this case, by Engadget) will home in on this money shot, jostling ahead of and around you to capture the famous nose in intimate proximity with prosaic, occasionally distasteful, objects. Along with these very words, those images are a critical way to visualize how Sissel Tolaas, who flew to Detroit from Berlin, does the unique fieldwork that has made her a legend in the colossal yet somewhat invisible world of modern olfaction. Yet there's also no denying that the sight of this -- the sniff shot, ubiquitous in casual Google image searches of Tolaas' name -- is not only curious but also comical. The idea of placing one's grown, adult face in close communion with the fluff spilling out of a blighted house to deeply inhale its surely unhealthy molecules and have them wash over you on an emotional level... well, it's something dogs do. But how else are we, with the linguistic and visual tools at our disposal, supposed to communicate what the great Sissel Tolaas is really about to you, the reader? Anyway, Tolaas hates being shadowed by cameras this way, although she's being a terrific sport about it. On her first day in Detroit, she arrives at a former tobacco factory in Poletown.

History is rife with fakes. In 1983, the German magazine Stern announced that it had acquired previously undocumented diaries written by Hitler, a find British historian Hugh Trevor-Roper initially heralded as "an archive of great historical significance." In reality, however, an illustrator named Konrad Kujau had penned the volumes himself. Thanks to the scrutiny of historians at the German Federal Archive, they were soon revealed to be forgeries and the so-called "Hitler Diaries" became a cautionary tale about media frenzies. Imagine, however, if experts couldn't readily identify the diaries as fraudulent.

The conversation about unconscious bias in artificial intelligence often focuses on algorithms that unintentionally cause disproportionate harm to entire swaths of society--those that wrongly predict black defendants will commit future crimes, for example, or facial-recognition technologies developed mainly by using photos of white men that do a poor job of identifying women and people with darker skin. But the problem could run much deeper than that. Society should be on guard for another twist: the possibility that nefarious actors could seek to attack artificial intelligence systems by deliberately introducing bias into them. According to a U.S. government study on big data and privacy, biased algorithms could make it easier to mask discriminatory lending, hiring or other unsavory business practices. Academics and industry observers have called for legislative oversight that addresses technological bias.

Natural language processing (NLP) is a field of computer science, artificial intelligence and computational linguistics concerned with the interactions between computers and human (natural) languages, and, in particular, concerned with programming computers to fruitfully process large natural language corpora.

Global software provider Katabat has released Katabat Engage, which delivers data-driven debt collections powered by machine learning to consumer lenders. Katabat Engage helps lenders collect more dollars through a platform of personalized, digital communications tailored to customer preferences. Powered by a proprietary machine learning platform that has performed well in Google Kaggle competitions, Engage enables lenders to deploy customized e-mail and SMS text collection messages and continuously tune customer outreach and response strategies. "We are very excited to present Engage to our clients and the broader marketplace," said Katabat CEO Ray Peloso, who recently did an interview with Thrive Global about how machine learning can improve the customer journey. "Our data science team has built a mature and reliable data pipeline for machine learning and continues to demonstrate the power of the platform through its success in several Google Kaggle machine-learning competitions."

That's the response you'll hear from self-proclaimed music purists talking about technological innovation in song creation. Sampling, synthesizers, drum machines, Auto-Tune--all have been derided as lazy ways to make chart-topping hits because they take away the human element. The new argument among fans and musicians will be about the use of artificial intelligence in songwriting. According to several estimates, in the next decade, between 20% and 30% of the top 40 singles will be written partially or totally with machine-learning software. Today, recording pros can use A.I.-powered programs to cue an array of instrumentation (from full orchestral arrangements to hip-hop beats), then alter it by mood, tempo, or genre (from heavy metal to bluegrass).

We introduce an extension of the multi-instance learning problem where examples are organized as nested bags of instances (e.g., a document could be represented as a bag of sentences, which in turn are bags of words). This framework can be useful in various scenarios, such as text and image classification, but also supervised learning over graphs. As a further advantage, multi-multi instance learning enables a particular way of interpreting predictions and the decision function. Our approach is based on a special neural network layer, called bag-layer, whose units aggregate bags of inputs of arbitrary size. We prove theoretically that the associated class of functions contains all Boolean functions over sets of sets of instances and we provide empirical evidence that functions of this kind can be actually learned on semi-synthetic datasets. We finally present experiments on text classification and on citation graphs and social graph data, showing that our model obtains competitive results with respect to other approaches such as convolutional networks on graphs.

ABSTRACT Typical spoken language understanding systems provide narrow semantic parses using a domain-specific ontology. The parses contain intents and slots that are directly consumed by downstream domain applications. In this work we discuss expanding such systems to handle compound entities and intents by introducing a domain-agnostic shallow parser that handles linguistic coordination. We show that our model for parsing coordination learns domain-independent and slot-independent features and is able to segment conjunct boundaries of many different phrasal categories. We also show that using adversarial training can be effective for improving generalization across different slot types for coordination parsing. Index Terms-- spoken language understanding, chunking, coordination 1. INTRODUCTION A typical spoken language understanding (SLU) system maps user utterances to domain-specific semantic representations that can be factored into an intent and slots [1, 2]. For example, an utterance, "what is the weather like in boston" has one intent WeatherInfo and one slot type CityName whose value is "boston." Thus, parsing for such systems is often factored into two separate tasks: intent classification and entity recognition whose results are consumed by downstream domain applications.

We propose a method to perform audio event detection under the common constraint that only limited training data are available. In training a deep learning system to perform audio event detection, two practical problems arise. Firstly, most datasets are "weakly labelled" having only a list of events present in each recording without any temporal information for training. Secondly, deep neural networks need a very large amount of labelled training data to achieve good quality performance, yet in practice it is difficult to collect enough samples for most classes of interest. In this paper, we propose a data-efficient training of a stacked convolutional and recurrent neural network. This neural network is trained in a multi instance learning setting for which we introduce a new loss function that leads to improved training compared to the usual approaches for weakly supervised learning. We successfully test our approach on two low-resource datasets that lack temporal labels.

We are on the verge of the AI gold rush. Like the prospectors of the infamous historical gold rush, however, only a few leading organizations will strike gold. Real economic growth will be achieved by the companies selling the equivalent of picks, food, supplies, shovels, and jeans for artificial intelligence and machine learning. Think of all the tools required: training data, governance tools, consulting and integration services, and most critical, the creation of new sustainable revenue models. Startups, incumbent tech companies, and corporate innovation centers have already started using artificial intelligence and machine learning to solve real business problems across nearly every industry, including manufacturing, healthcare, transportation, and energy.