Neural attention (NA) is an effective mechanism for inferring complex structural data dependencies that span long temporal horizons. As a consequence, it has become a key component of sequence-to-sequence models that yield state-of-the-art performance in as hard tasks as abstractive document summarization (ADS), machine translation (MT), and video captioning (VC). NA mechanisms perform inference of context vectors; these constitute weighted sums of deterministic input sequence encodings, adaptively sourced over long temporal horizons. However, recent work in the field of amortized variational inference (AVI) has shown that it is often useful to treat the representations generated by deep networks as latent random variables. This allows for the models to better explore the space of possible representations. Based on this motivation, in this work we introduce a novel regard towards a popular NA mechanism, namely soft-attention (SA). Our approach treats the context vectors generated by SA models as latent variables, the posteriors of which are inferred by employing AVI. Both the means and the covariance matrices of the inferred posteriors are parameterized via deep network mechanisms similar to those employed in the context of standard SA. To illustrate our method, we implement it in the context of popular sequence-to-sequence model variants with SA. We conduct an extensive experimental evaluation using challenging ADS, VC, and MT benchmarks, and show how our approach compares to the baselines.

Digital texts have become an increasingly important source of data for social studies. However, textual data from open platforms are vulnerable to manipulation (e.g., censorship and information inflation), often leading to bias in subsequent empirical analysis. This paper investigates the problem of data distortion in text classification when controlling type I error (a relevant textual message is classified as irrelevant) is the priority. The default classical classification paradigm that minimizes the overall classification error can yield an undesirably large type I error, and data distortion exacerbates this situation. As a solution, we propose the Neyman-Pearson (NP) classification paradigm which minimizes type II error under a user-specified type I error constraint. Theoretically, we show that while the classical oracle (i.e., optimal classifier) cannot be recovered under unknown data distortion even if one has the entire post-distortion population, the NP oracle is unaffected by data distortion and can be recovered under the same condition. Empirically, we illustrate the advantage of NP classification methods in a case study that classifies posts about strikes and corruption published on a leading Chinese blogging platform.

Artificial intelligence, which is bringing us everything from self-driving cars to personalized ads on the web, is also invading the world of medicine. In radiology, this technology is increasingly helping doctors in their jobs. A computer program that assists doctors in diagnosing strokes garnered approval from the U.S. Food and Drug Administration earlier this year. Another that helps doctors diagnose broken wrists in X-ray images won FDA approval on May 24. One particularly intriguing line of research seeks to train computers to diagnose one of the deadliest of all malignancies, pancreatic cancer, when the disease is still readily treatable.

Google, hoping to head off a rebellion by employees upset that the technology they were working on could be used for lethal purposes, will not renew a contract with the Pentagon for artificial intelligence work when a current deal expires next year. Diane Greene, who is the head of the Google Cloud business that won a contract with the Pentagon's Project Maven, said during a weekly meeting with employees on Friday that the company was backing away from its A.I. work with the military, according to a person familiar with the discussion but not permitted to speak publicly about it. Google's work with the Defense Department on the Maven program, which uses artificial intelligence to interpret video images and could be used to improve the targeting of drone strikes, roiled the internet giant's work force. Many of the company's top A.I. researchers, in particular, worried that the contract was the first step toward using the nascent technology in advanced weapons. But it is not unusual for Silicon Valley's big companies to have deep military ties.

Google is ending its involvement with Project Maven, the controversial Pentagon research program that sought to use AI to improve object recognition in military drones. Diane Greene, head of Google Cloud, told employees during a Friday meeting that the company will let its current contract with the Defense Department lapse in 2019 and that it will not pursue a new one, according to the New York Times and Gizmodo. The announcement comes shortly after Google said it would draft an ethics policy to guide its involvement in future military projects -- one that would explicitly ban the use of artificial intelligence in weaponry. "It is incumbent on us to show leadership [in the ethical use of AI]," Green reportedly said during the meeting. I am happy about this decision.

If that works out as planned, the auto maker expects to double operating profit to €16 billion ($18.71 billion) by 2022 and hit double-digit profit margins from 6.8% today. Chief Executive Sergio Marchionne said the company will invest €9 billion to develop and deploy electric engines as it expands its lineup of electric-powered vehicles, part of a €45 billion spending plan over the next five years focused on four core brands: Jeep SUVs, Ram pickups and Alfa Romeo and Maserati luxury cars. "This plan will provide the portfolio of products aligned with our brands that will ensure our ability to comply in each region" with stricter emissions and fuel-economy standards, Mr. Marchionne told financial analysts and media at a meeting on a company test track located outside Milan. In the U.S., the company is expanding its bet on bigger SUVs and trucks, reflecting consumer demand and a more relaxed approach to increasing fuel economy standards in Washington. Mr. Marchionne chided his peers for appearing to back away from what he said was a unified request to President Donald Trump by auto industry leaders to ease fuel economy regulations.

The UK is planning to leverage its NHS to improve its cancer diagnosis procedures, by combining its vast array of data with AI-based technologies to spot early signs of cancer. Combining medical records with population data, the hope is to prevent 20,000 cancer-related deaths annually, by 2023. It's ambitious, but the UK is well-placed in such projects. Critics will argue that the current government's economic austerity policies and its budget cuts to the regional NHS trusts are a bigger hindrance than a lack of AI, and that it should look to rectify those aspects before trying something shiny and new.

In announcing the investigation, China's Culture and Tourism Ministry said Jinri Toutiao has allowed a martyr to be portrayed in a negative light. It didn't make any statement on potential penalties. The incident caught the public's attention two weeks ago, leading the comic's producer to apologize on his Twitter-like Sina Weibo account. Jinri Toutiao issued an apology on its own platform and removed the offending content. In April, a Bytedance comedy app was shut down by Chinese authorities on the grounds it had hosted lewd content.

SAN FRANCISCO – Google workers Friday got word that the internet titan will retreat from a deal to help the U.S. military use artificial intelligence to analyze drone video, according to reports. The collaboration with the U.S. Department of Defense was said to have sparked rebellion inside the California-based company. An internal petition calling for Google to stay out of "the business of war" garnered thousands of signatures, and some workers reportedly quit to protest a collaboration with the military. The New York Times and the tech news website Gizmodo cited unnamed sources as saying a Google's cloud team executive told employees on Friday that the company will not seek to renew the controversial contract after it expires next year. The contract was reported to be worth less than $10 million to Google but was thought to have potential to lead to more lucrative technology collaborations with the military.

AI-powered chatbots come with many benefits for the businesses that adopt them, but in some instances, they can have greater impact for the everyday user. In this blog, we list four recent articles giving examples of where AI-powered applications and chatbots have been put into practice to help customers and the common man. The developer of the'world's first robot lawyer' application, which helped overturn more than one-hundred parking fines, is now adapting the functionality of the integrated chatbot to provide legal aid to refugees seeking asylum in the US and Canada, as well as asylum support in the UK. The original DoNotPay AI-powered application gives legal aid through a simple chat interface, where a chatbot asks a series of questions to help determine which application a refugee needs to fill out and whether they are eligible for asylum protection under international law. After this, the chatbot takes note of the relevant details required for asylum applications in the US or Canada, auto-fills the application form and sends.