SAN DIEGO--(BUSINESS WIRE)--AI Analytics company PredictX releases their brand new PredictX Digital Assistant to market at GBTA 2018. Managers can now ask the PredictX Travel voice app questions regarding their travel program - eliminating the need to navigate specialized travel data dashboards. Sophisticated travel apps have made it more convenient for travelers to book outside travel policy than ever before. Travelers book on-the-go leaving Travel managers at a distinct disadvantage when they have to spend time combing through reports and dashboards to find one policy violation or duty of care issue long after it has taken place. Travel Managers can use the voice command "Open PredictX" to access the PredictX Digital Assistant.

LAS VEGAS--Complexity is the enemy of security, but prompt patching is its strongest ally. Security professionals have made those points for years, but two presentations at the Black Hat USA conference here provided fresh arguments for them--and signs companies are getting snappier at fixing vulnerabilities. What that means for you: When your computer, phone or tablet says it has an update available, install it. Don't wait to benefit from the tighter focus of an Apple, Google or Microsoft on security issues. Support for that came in one Black Hat briefing covering a "vuln" in Apple's device-management system that lets organizations configure Macs from afar.

Some people worry that hackers could infiltrate their smart speakers and spy on them, but that hasn't been the practical reality -- not for Amazon's Echo, at least. A team of researchers from China's Tencent has come about as close as you can get right now, however. They've disclosed an attack on the Echo that uses both a modified speaker and a string of Alexa web interface vulnerabilities to remotely eavesdrop on regular models. It sounds nefarious, but it requires more steps than would be viable for most intruders. The team created a rogue Echo by removing a flash memory chip from the device, modifying its firmware to get root access, and soldering it back on its circuit board.

Since smart speakers like the Amazon Echo first began to appear in homes across the world, the security community has come to see them as a prime target. But that threat has remained largely hypothetical: No Echo malware has appeared in the wild, and even proof-of-concept attacks on the devices have remained impractical at best. Now, one group of Chinese hackers has spent months developing a new technique for hijacking Amazon's voice assistant gadget. But it may be the closest thing yet to a practical demonstration of how the devices might be silently hijacked for surveillance. At the DefCon security conference Sunday, researchers Wu Huiyu and Qian Wenxiang plan to present a technique that chains together a series of bugs in Amazon's second-generation Echo to take over the devices, and stream audio from its microphone to a remote attacker, while offering no clue to the user that the device has been compromised.

Amazon Echo owners use its voice-based digital assistant Alexa for a variety of purposes. Playing music and listening to the news typically top the list, but smart home control, general knowledge queries, and things like timers and alarms are also popular. Among its many built-in and third-party skills, one of the features least used by Echo owners is probably the one its maker wishes consumers used most: purchasing products through Amazon. Researchers have been predicting huge growth in shopping with virtual assistants. By 2022, the voice shopping space is supposed to be a $40 billion market.

It's All About EmojisEven before the event kicked off, CEO Sundar Pichai assured fixing bugs in things that matter the most, like burger emoji and floating foam on beer mug emoji. Better and smoother updates in AI, Assistant, Photos, News will soon make lives easy for most people. Here's a quick round-up of what Google has in store for your in the coming few days, weeks and months. The AI will convert JPEG to PDF. Google has trained its AI to help all the photography enthusiasts enjoy great features like adding colour to black and white images, making pictures pop by adjusting backdrop, suggesting brighter options for an under-processed picture.

Maybe you haven't noticed, but artificial intelligence has arrived. Gradually, over the last decade or so, it's become so ingrained into our everyday lives that most people don't think twice about it. Spotify assembles a "Discover Weekly" playlist tailored to your tastes. Netflix serves you options for your next binge-watching marathon. Alexa answers your questions and plays that nifty Spotify playlist on command.

Item recommendation is a personalized ranking task. To this end, many recommender systems optimize models with pairwise ranking objectives, such as the Bayesian Personalized Ranking (BPR). Using matrix Factorization (MF) --- the most widely used model in recommendation --- as a demonstration, we show that optimizing it with BPR leads to a recommender model that is not robust. In particular, we find that the resultant model is highly vulnerable to adversarial perturbations on its model parameters, which implies the possibly large error in generalization. To enhance the robustness of a recommender model and thus improve its generalization performance, we propose a new optimization framework, namely Adversarial Personalized Ranking (APR). In short, our APR enhances the pairwise ranking method BPR by performing adversarial training. It can be interpreted as playing a minimax game, where the minimization of the BPR objective function meanwhile defends an adversary, which adds adversarial perturbations on model parameters to maximize the BPR objective function. To illustrate how it works, we implement APR on MF by adding adversarial perturbations on the embedding vectors of users and items. Extensive experiments on three public real-world datasets demonstrate the effectiveness of APR --- by optimizing MF with APR, it outperforms BPR with a relative improvement of 11.2% on average and achieves state-of-the-art performance for item recommendation. Our implementation is available at: https://github.com/hexiangnan/adversarial_personalized_ranking.

The past 100 years have seen an incredible rise in technology advancement, and Artificial Intelligence is part of it. While humans strive to make their lives easier, like using self-driving cars or relying on Cortana, Alexa[1] and Siri to do their daily tasks, the computing technologies can also be used for far worse purposes. While others worry about machines taking over the world and destroying humanity, security researchers at IBM[2] considered a far likely scenario in the near future and created DeepLocker – an AI-powered malware that is capable of using evasive techniques to obfuscate its presence and avoid security software entirely. The most notorious malware like WannaCry, Trickbot,[3] and Zeus devastated the most influential organizations, resulted in millions of damages, and disrupted the work of vital sectors like hospitals all over the world. While such attacks can be prevented by using safety measures and adequate security software, the AI-based malware can result in an attack the world has never seen before.

The fast-rising power and sophistication of artificial intelligence is transforming how insurers engage with their customers. It is dramatically enhancing the experience insurance providers can offer their policyholders. Insurtech start-ups Lemonade, Insurify and Spixii, for example, are deploying intelligent virtual assistants to deliver a powerful customer experience and innovative digital services. Using artificial intelligence to delight customers is not the sole preserve of insurtechs. Large insurers, including Allianz, Credit Agricole, PNB MetLife, GEICO and USAA, are also applying artificial intelligence to heighten the customer experience delivered by their digital distribution channels.