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.

Google Images is getting a big upgrade that is based on artificial intelligence.

Stacia L. Brown, a writer and podcaster, was scrolling through Netflix's recommendations and noticed a poster for Like Father, a summer release starring Kelsey Grammer and Kristen Bell--except that the poster she saw featured two lesser-known actors, Blaire Brooks and Leonard Ouzts. She took to Twitter: "Other Black @netflix users: does your queue do this? Generate posters with the Black cast members on them to try to compel you to watch?" Because of her astute eye, I took a look at my Netflix recommendations and suddenly noticed that Fyvush Finkel was popping up everywhere. Netflix responded to the controversy by saying that Brown's premise was wrong: The company couldn't target posters to subscribers' races, because "we don't ask members for their race, gender, or ethnicity."

Honda hasn't been shy about wanting an AI assistant in its cars, and it's bringing in a partner to ensure that this assistant is one you'll want to use. The automaker has tapped SoundHound to speed up the development of its AI companion. Thanks to the Houndify platform, your ride should understand natural, conversational voice commands, including ones that depend on contextual details like your location or past requests. If this sounds familiar, it should -- Hyundai and Kia are also using SoundHound's tech to power their respective assistants. You shouldn't get a cookie-cutter experience when Honda is free to customize the experience and add its own flourishes, but it could lead to your future ride sharing some major technology in common with a rival brand.

SINGAPORE--(BUSINESS WIRE)--Oct 25, 2018--DXC Technology (NYSE: DXC), the world's leading independent, end-to-end IT services company, today announced the opening of the DXC Digital Innovation Lab in Singapore. Developed with the support of the Singapore Economic Development Board (EDB), the DXC Digital Innovation Lab Singapore is an advanced environment for the incubation of ideas, learning and innovative technology solutions developed by data scientists and enterprise solution experts. The lab will benefit DXC employees, clients and partners, as well as the technology and business communities of Singapore, the region and beyond. The DXC Digital Innovation Lab Singapore is an extension of DXC Labs, whose goal is to ensure that DXC masters the emerging technologies it needs in order to lead clients through accelerating digital transformation. At the innovation lab, digital specialists will explore novel technologies, develop prototypes and create reference architectures for rapid business deployment.

Deep learning a subset of machine learning comes under the realms of artificial intelligence (AI) and works by gathering huge datasets to make machines act like humans. Deep learning having immense potential uses machine learning to tackle new complex problems like speech, language and image recognition by giving machines the power to learn how features in the data combine into increasingly higher level, abstract forms. The deployment of neural networks has aided deep learning to produce optimized results. Deep learning has immense adaptability, like how Facebook uses deep learning to automatically find friends in an image and suggests the user to tag them. According to a leading source, the deep learning market is expected to exceed $18 billion by 2024, growing at a CAGR of 42%.

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