Z Advanced Computing, Inc. (ZAC), an AI (Artificial Intelligence) software startup, is developing its Smart Home product line through a paid-pilot for smart appliances for BSH Home Appliances, the largest manufacturer of home appliances in Europe and one of the largest in the world. BSH Home Appliances Corporation is a subsidiary of the Bosch Group, originally a joint venture between Robert Bosch GmbH and Siemens AG. ZAC Smart Home product line uses ZAC Explainable-AI Image Recognition. ZAC is the first to apply Explainable-AI in Machine Learning. "You cannot do this with other techniques, such as Deep Convolutional Neural Networks," said Dr. Saied Tadayon, CTO of ZAC.
An Associated Press investigation finds that Russian cyber spies exploiting a national vulnerability in cybersecurity are trying to break into the emails of scores of people working on military drone technology. An accused Russian hacker blamed for attacking LinkedIn, Dropbox and Formspring is finally facing American prosecutors after a lengthy extradition fight in the Czech Republic. Yevgeniy Aleksandrovich Nikulin is due to appear in U.S. federal court in California on Thursday for a detention hearing. It's unclear whether Nikulin has any connection to the Russian troll farm the Internet Research Agency, which is widely blamed by American authorities for interfering in the 2016 presidential election. But only two days after Nikulin's arrest, American officials for the first time publicly warned that the Russian government was directing efforts to influence the election by hacking and releasing private information.
Crowd-sourcing is a cheap and popular means of creating training and evaluation datasets for machine learning, however it poses the problem of `truth inference', as individual workers cannot be wholly trusted to provide reliable annotations. Research into models of annotation aggregation attempts to infer a latent `true' annotation, which has been shown to improve the utility of crowd-sourced data. However, existing techniques beat simple baselines only in low redundancy settings, where the number of annotations per instance is low ($\le 3$), or in situations where workers are unreliable and produce low quality annotations (e.g., through spamming, random, or adversarial behaviours.) As we show, datasets produced by crowd-sourcing are often not of this type: the data is highly redundantly annotated ($\ge 5$ annotations per instance), and the vast majority of workers produce high quality outputs. In these settings, the majority vote heuristic performs very well, and most truth inference models underperform this simple baseline. We propose a novel technique, based on a Bayesian graphical model with conjugate priors, and simple iterative expectation-maximisation inference. Our technique produces competitive performance to the state-of-the-art benchmark methods, and is the only method that significantly outperforms the majority vote heuristic at one-sided level 0.025, shown by significance tests. Moreover, our technique is simple, is implemented in only 50 lines of code, and trains in seconds.
Manuals on Reaper drones and details on how to defeat improvised explosive devices were leaked and it appears military personnel were hacked, according to cybersecurity researchers. The dark web drug trade might have depleted in recent months, but all manner of other black market trades continue to thrive in the underbelly of the internet. On Wednesday, researchers at cybercrime tracker Recorded Future reported that a hacker was trying to flog documents about the Reaper drone used across federal government agencies for between $150 and $200. It appeared they'd successfully hacked into at least two computers belonging to U.S. military personnel and the theft could have a significant impact on American campaigns abroad, Recorded Future warned. The company spoke directly with the hacker, learning the documents had been obtained by using a previously-disclosed vulnerability in Netgear routers.
If you've been scratching your head at the FAA's extensive efforts to regulate your personal (or company) drone use, consider the chaos when they start filling the skies. That's why the agency partnered with NASA for a series of nationwide tests to explore potential systems that could track and manage a wide range of drones simultaneously. Google parent company Alphabet's Project Wing tried out its own UAV air traffic control platform yesterday, a system that might one day guide a massive volume of airborne drones to keep them from crashing into buildings, people or each other. Unsurprisingly, Project Wing's UTM (UAS Air Traffic Management) leans heavily on other Google products like Maps, Earth and Street View to navigate drones around obstacles and plan routes. During yesterday's tests, UTM managed flight paths for multiple UAVs simultaneously, according to the group's blog post.