Sensor data has been playing an important role in machine learning tasks, complementary to the human-annotated data that is usually rather costly. However, due to systematic or accidental mis-operations, sensor data comes very often with a variety of missing values, resulting in considerable difficulties in the follow-up analysis and visualization. Previous work imputes the missing values by interpolating in the observational feature space, without consulting any latent (hidden) dynamics. In contrast, our model captures the latent complex temporal dynamics by summarizing each observation’s context with a novel Iterative Imputing Network, thus significantly outperforms previous work on the benchmark Beijing air quality and meteorological dataset. Our model also yields consistent superiority over other methods in cases of different missing rates.

Cost-Sensitive Online Classification has drawn extensive attention in recent years, where the main approach is to directly online optimize two well-known cost-sensitive metrics: (i) weighted sum of sensitivity and specificity; (ii) weighted misclassification cost. However, previous existing methods only considered first-order information of data stream. It is insufficient in practice, since many recent studies have proved that incorporating second-order information enhances the prediction performance of classification models. Thus, we propose a family of cost-sensitive online classification algorithms with adaptive regularization in this paper. We theoretically analyze the proposed algorithms and empirically validate their effectiveness and properties in extensive experiments. Then, for better trade off between the performance and efficiency, we further introduce the sketching technique into our algorithms, which significantly accelerates the computational speed with quite slight performance loss. Finally, we apply our algorithms to tackle several online anomaly detection tasks from real world. Promising results prove that the proposed algorithms are effective and efficient in solving cost-sensitive online classification problems in various real-world domains.

In this paper, we address the problem of reconstructing a time-domain signal (or a phase spectrogram) solely from a magnitude spectrogram. Since magnitude spectrograms do not contain phase information, we must restore or infer phase information to reconstruct a time-domain signal. One widely used approach for dealing with the signal reconstruction problem was proposed by Griffin and Lim. This method usually requires many iterations for the signal reconstruction process and depending on the inputs, it does not always produce high-quality audio signals. To overcome these shortcomings, we apply a learning-based approach to the signal reconstruction problem by modeling the signal reconstruction process using a deep neural network and training it using the idea of a generative adversarial network. Experimental evaluations revealed that our method was able to reconstruct signals faster with higher quality than the Griffin-Lim method.

For all the joking we do about Skynet-scenarios and killer robots, there's some truth to the worrisome creations. To prevent Terminators from becoming a real threat, some 50 robotics experts are boycotting the Korea Advanced Institute of Science and Technology (KAIST), a university in South Korea, given its decision to open an artificial intelligence weapons lab, according to Financial Times. The fear is that it'll trigger a next-gen arms race and that ultimately, any safeguards put in place will be circumvented by terrorists and, more specifically, North Korea. Since February, FT says KAIST has been working on a quartet of experiments at the Research Center for the Convergence of National Defense and Artificial Intelligence: AI-based command-and-decision systems, navigation algorithms for underwater drones, smart aircraft-training systems (with AI) and AI-based object tracking and recognition tech. While this might sounds normal for an academic setting, KAIST has a partnership with Korean arms company Hanwha Systems, whose parent company has apparently been blacklisted by the UN for making cluster munitions.

Google is betting that algorithms that understand images and text will draw business to its cloud services, make augmented reality popular, and prompt us to search using our smartphone cameras. From a report: The search company's machine learning systems work best on material from a few rich parts of the world, like the US. They stumble more frequently on data from less affluent countries -- particularly emerging economies like India that Google is counting on to maintain its growth. "We have a very sparse training data set from parts of the world that are not the United States and Western Europe," says Anurag Batra, a researcher at Google. When Batra travels to his native Delhi, he says Google's AI systems become less smart.

CIOs and those charged with running IT Operations are challenged to deliver secure, audited, and reliable compute environments for the applications and data for the business. Behind the scenes these tasks are often accomplished by following onerous time-consuming processes and often the management of these environments and processes will be outsourced to multiple IT service providers. In addition, the division of work is often siloed into traditional "towers" that are not well integrated for cross-functional purposes. So, when traditional IT Service Management (ITSM) meets the cloud, and equally, DevOps, there is invariably going to be conflict. As a factor of IT Transformation and Digital Transformation, IT no longer stands alone - or at least in a modern workplace it should not stand alone - it is the hub through which all other business services will see their efforts delivered.

In past @ThingsExpo presentations, Joseph di Paolantonio has explored how various Internet of Things ( IoT) and data management and analytics (DMA) solution spaces will come together as sensor analytics ecosystems. This year, in his session at @ThingsExpo, Joseph di Paolantonio from DataArchon, added the numerous Transportation areas, from autonomous vehicles to "Uber for containers." While IoT data in any one area of Transportation will have a huge impact in that area, combining sensor analytics from these different areas will impact government, industry, retail and other processes, as well as life-style choices. These changes to transportation are happening now, but at varying maturity levels as we advance along a model of Connect, Communicate, Collaborate, Contextualize and Cognition. Attendees will come away with specific ideas on identifying value from transportation IoT data and sensor analytics in their own industry.

The developments in Google's Cloud Computing segment, especially the Cloud Machine Learning service, have been so rapid that Google calls it one of its fastest growing product areas. Google has been ramping up their Cloud Platform quite aggressively in recent months. Just a few weeks ago, the Google Cloud Platform opened its newest zone in Tokyo, increasing the total number of regions they are present in to six - three in the US and one each in Belgium and Taiwan and Tokyo. Not long ago, the company announced its acquisition of Orbitera, a cloud commerce company. The developments in Google's Cloud Computing segment, especially the Cloud Machine Learning service, have been so rapid that Google calls it one of its fastest growing product areas.

A recent article titled "You Can't Outsource Digital Transformation" discusses what GE is doing to prepare for–if not lead–digital business transformation disruption.

AI research is increasingly being used by militaries around the world for offensive and defensive applications. This past week, groups of AI researchers began to fight back against two separate programs located halfway around the world from each other, generating tough questions about just how much engineers can affect the future uses of these technologies. From Silicon Valley, the New York Times published an internal protest memo written by several thousand Google employees, which vociferously opposed Google's work on a Defense Department-led initiative called Project Maven, which aims to use computer vision algorithms to analyze vast troves of image and video data. As the department's news service quoted Marine Corps Col. Drew Cukor last year about the initiative: "You don't buy AI like you buy ammunition," he added. "There's a deliberate workflow process and what the department has given us with its rapid acquisition authorities is an opportunity for about 36 months to explore what is governmental and [how] best to engage industry [to] advantage the taxpayer and the warfighter, who wants the best algorithms that exist to augment and complement the work he does."