Smart speakers, such as Amazon Echo, have been adopted by millions of users. However, the privacy impacts of smart speakers have not been well examined. We investigate the privacy leakage of smart speakers under an encrypted traffic analysis attack, referred to as voice command fingerprinting. In this attack, an adversary eavesdrops encrypted voice traffic from and to a smart speaker and infers which voice command a user says without decrypting encrypted traffic. We design our attacks based on neural networks and collect two large-scale datasets on Amazon Echo and Google Home by using an automatic traffic crawler.

A novel machine learning model could help predict mortality and neurological outcomes post-cardiac arrest, according to a new Johns Hopkins study. Presented at the Society of Critical Care Medicine's 49th Annual Critical Care Congress in Orlando, FL, study results indicate the new model demonstrated significantly improved prediction capabilities compared to the reference APACHE model. "The objectives of our study were to first predict the neurological outcome and mortality at discharge using data only from the first 24 hours of ICU admission and the second objective was to determine whether utilizing physiologic time series (PTS) data, specifically just features from the bedside monitoring data, are useful in terms of model performance," said lead investigator Hanbiehn Kim, MBE, of Johns Hopkins University, during his presentation. Using the Philips eICU database, which includes over 200,000 patients from 208 hospitals, Kim and colleagues from Johns Hopkins Hospital extracted data on cardiac arrest patients who were mechanically ventilated. Of note, this database includes PTS data from patient bedside bio-monitors that recorded heart rate, oxygen saturation, blood pressure, and respiratory rate at 5-minute intervals.

Feature Engineering is one of those terms that, on the surface, seems to mean exactly what it is saying: you want to refactor or create something from the data that you have. Okay, fine…but what does that actually mean in real life when you're sitting in front of your data set and wondering what to do? The term encompasses a variety of methods that each have a variety of sub-methods associated with them. I'm just going to cover some of the main ones to give you an idea of the sort of thing Feature Engineering contains, with some indication of widely used methods. Encoding -- I think this is one of the most simple and commonly used aspects of Feature Engineering.

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The European Commission has said it intends to draw up new rules to protect citizens against misuses of artificial intelligence (AI) tech. It likened the current situation to "the Wild West" and said it would focus on "high-risk" cases. But some experts are disappointed that a white paper it published did not provide more details. A leaked draft had suggested a ban on facial recognition's use in public areas would be proposed. Industry Commissioner Thierry Breton suggested the new legislation would be comparable to the General Data Protection Regulation.

While artificial intelligence offers opportunities to automate and innovate, just 30% of workplaces are actually using it. Combined with a lack of understanding of the technology, employers don't have the internal structure and personnel needed to launch the power of AI into their business model, says Augustine Walker, senior director of product management for Veritone, an AI solutions provider. "There isn't a lot of focus on what tools are out there so that I can make my business better with AI," Walker says. "The ubiquity of the talent pool and the capabilities are not out there yet -- it's still maturing." Walker spoke with Employee Benefit News on how AI can actually be a catalyst for creativity and why data scientists are a critical piece to the puzzle.

AI is changing more than what computers can do and how we communicate and interact with technology. AI is changing the very nature of work, of hiring, reinforcing the imperative for life-long learning, and serving as a catalyst for organisation-wide change. How to Prepare a Generation of AI-first Workers The NHS is set to lose an estimated 350,000 staff by 2030 in the UK--a quarter of its workforce. It's a Catch 22 situation: staff are leaving because of their intolerable workloads, caused by an already acute level of staff shortages. While the National Health Service (NHS) has no magic wand to summon up a small army of suitably qualified staff, they are looking to Artificial Intelligence (AI) to help.

The debate over artificial intelligence's role in HR--from recruiting to workforce planning to performance--has become moot: There's no doubt that AI has arrived and is expanding rapidly in the HR space. But, not so fast, some experts say. While AI represents a fantastic opportunity to drive HR success (and by extension, bottom-line growth), ethical issues tied to AI represent a potential dark side of these technologies. The good news is, chief HR and people officers can successfully navigate this rapidly changing, growing trend by steering clear of those ethical speed bumps in the first place. They must take a smart, steady, planned approach to circumvent negative outcomes.

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As AI and machine learning permeate every sphere of our lives today, it gets easier to celebrate these technologies. From entertainment to customer support to law enforcement, they provide humans with considerable help. Certain things they are capable of are so amazing that they seem almost like magic to an outside observer. However, it's necessary to remember that as astonishing as machine learning-powered tech advancements are, they are still a product created by us, humans. And we can't simply shed our personalities when developing anything, much less an AI – an algorithm that has to think on its own.