In the big data era, more and more cloud-based data-driven applications are developed that leverage individual data to provide certain valuable services (the utilities). On the other hand, since the same set of individual data could be utilized to infer the individual's certain sensitive information, it creates new channels to snoop the individual's privacy. Hence it is of great importance to develop techniques that enable the data owners to release privatized data, that can still be utilized for certain premised intended purpose. Existing data releasing approaches, however, are either privacy-emphasized (no consideration on utility) or utility-driven (no guarantees on privacy). In this work, we propose a two-step perturbation-based utility-aware privacy-preserving data releasing framework. First, certain predefined privacy and utility problems are learned from the public domain data (background knowledge). Later, our approach leverages the learned knowledge to precisely perturb the data owners' data into privatized data that can be successfully utilized for certain intended purpose (learning to succeed), without jeopardizing certain predefined privacy (training to fail). Extensive experiments have been conducted on Human Activity Recognition, Census Income and Bank Marketing datasets to demonstrate the effectiveness and practicality of our framework.

As the advancement of deep learning (DL), the Internet of Things and cloud computing techniques for biomedical and healthcare problems, mobile healthcare systems have received unprecedented attention. Since DL techniques usually require enormous amount of computation, most of them cannot be directly deployed on the resource-constrained mobile and IoT devices. Hence, most of the mobile healthcare systems leverage the cloud computing infrastructure, where the data collected by the mobile and IoT devices would be transmitted to the cloud computing platforms for analysis. However, in the contested environments, relying on the cloud might not be practical at all times. For instance, the satellite communication might be denied or disrupted. We propose SAIA, a Split Artificial Intelligence Architecture for mobile healthcare systems. Unlike traditional approaches for artificial intelligence (AI) which solely exploits the computational power of the cloud server, SAIA could not only relies on the cloud computing infrastructure while the wireless communication is available, but also utilizes the lightweight AI solutions that work locally on the client side, hence, it can work even when the communication is impeded. In SAIA, we propose a meta-information based decision unit, that could tune whether a sample captured by the client should be operated by the embedded AI (i.e., keeping on the client) or the networked AI (i.e., sending to the server), under different conditions. In our experimental evaluation, extensive experiments have been conducted on two popular healthcare datasets. Our results show that SAIA consistently outperforms its baselines in terms of both effectiveness and efficiency.

Training on only perfect Standard English corpora predisposes pre-trained neural networks to discriminate against minorities from non-standard linguistic backgrounds (e.g., African American Vernacular English, Colloquial Singapore English, etc.). We perturb the inflectional morphology of words to craft plausible and semantically similar adversarial examples that expose these biases in popular NLP models, e.g., BERT and Transformer, and show that adversarially fine-tuning them for a single epoch significantly improves robustness without sacrificing performance on clean data.

This article is part of Privacy in the Pandemic, a Future Tense series. Despite the famous French aversion to surveillance, the coronavirus pandemic has spurred France to integrate artificial intelligence tools into CCTV cameras in the Paris Metro. The new software, which is being trialed at the Châtelet-Les-Halles station in central Paris this week, will detect whether passengers are wearing face masks, Bloomberg reported. This is one of the ways that France, a country with one of the strictest lockdowns in Europe, is preparing for easing restrictions on Monday. While this decision shows France's willingness to use A.I. to help monitor people's behavior, the new system isn't as invasive as it sounds.

It may sound like a plot from a science fiction film, but NASA and the world governments are concerned about alien viruses contaminating Earth. As the first humans prepare for the Mars mission, experts warn that protocols are necessary to keep extraterrestrial pollutants from hitchhiking on space ships and astronauts when returning home from the Red Planet. Stanford professor of aeronautics and astronautics Scott Hubbard said in an interview that the solution is'planetary protection'. Mechanical systems will have to undergo a combination of chemical cleaning and heat sterilization, while the tubes containing samples from Mars need to be treated'as though they are the Ebola virus until proven safe.' Hubbard also suggests that astronauts must be quarantine once they touch down on our planet, as the first men who visited the moon in the Apollo mission did. As the first humans prepare for the Mars mission, experts warn that protocols need to be created to keep extraterrestrial pollutants from hitchhiking on space ships and astronauts when returning home.

The developers of a speech recognition bot assigned to analyze the public statements of politicians hit a major stumbling block when it tried to make sense of Donald Trump. Built by a tech startup called FactSquared, the bot AI was assigned to go through more than 11 million words Trump has spoken or tweeted since 1976--in interviews, campaign speeches, media appearances, and social media posts. According to FactSquared's CEO Bill Frischling, the bot failed to understand Trump's speeches until he brought in a specialist to strip out all of the bot's grammar and syntax coding. The tech startup FactSquared created an AI bot to try and catalog and analyze Donald Trump's public appearances and interviews, but they were so incoherent and rambling the bot actually crashed. 'It was still trying to punctuate it like it was English, versus trying to punctuate it like it was Trump,' Frischling told The LA Times.

The controversial facial recognition company Clearview AI says it will stop providing private entities with its technology. According to legal documents first reported by Buzzfeed, the company is ending non-government related contracts in response to class-action lawsuits and scrutiny from regulators. The court documents suggest that Clearview is voluntarily avoiding'transacting with non-governmental customers anywhere.' 'Clearview is cancelling the accounts of every customer who was not either associated with law enforcement or some other federal, state, or local government department, office, or agency,' the company said in a filing Buzzfeed reports that the lawsuit from which the documents stem relate to the companies use of biometric data that is being heard in a being heard in an Illinois federal court. The documents also show that Clearview will cease its contracts with all entities in Illinois as part of the lawsuit.

Boston Dynamics' robotic dog, Spot, is roving Singapore parks in an effort to remind pedestrians to remain a safe distance from one another. According to a statement from the country's National Parks Board, Spot will traverse a 4-mile swath of Bishan-Ang Mo Kio Park during off-peak hours while playing a recorded message that reminds park-goers'observe safe distancing measures.' The bot will also be fitted with cameras that are'enabled with... video analytics' which will be used to estimate the number of people in the park. According to a statement, the cameras will not track or record specific individuals, and no personal data will be collected. MailOnline has reached out to Singapore's Government Technology Agency to find out more about the video analytics system equipped to the bot and will update with further information.

From shortages of personal protective equipment to a variety of grocery items to electronics and apparel, coronavirus (COVID-19) has hit the global supply chain in expected and unforeseen ways, and it seems likely that it could take many months to recover. Bouncing back more quickly, said experts, will require supply chain managers to turn to new ways of managing the supply chain, including using Internet of Things (IoT) data, analytics and machine learning (ML). These tools will become the foundation on which supply chain managers gain insight into their markets and erratic supply and demand trends. "Having the right machine learning and AI technologies will help you understand the market and better manage your supply chain," said George Bailey, director of the Digital Supply Chain Institute. While the disruption is now global, its starting point was in China -- the 800-pound gorilla in global production.

Google today launched Read Along, an Android app that taps AI and machine learning to help children learn to read by providing verbal and visual feedback. Preliminary research suggests that apps like Read Along could significantly improve children's reading skills. A three-month pilot of Read Along's predecessor -- Bolo -- in the Unnao district of India involving 1,500 children across 200 villages found that, compared with a control group, 39% of the app's users reached the highest level of the Annual Status of Education Report (ASER) reading assessment test and 64% saw an increase in scores. Moreover, 92% of parents said they noticed some improvement in their child's skills. Read Along comes preloaded with around 500 stories and interactive games within those stories, for which kids earn stars and badges as they learn, practice, and progress.