On 21 November 2015, James Bates had three friends over to watch the Arkansas Razorbacks play the Mississippi State Bulldogs. Bates, who lived in Bentonville, Arkansas, and his friends drank beer and did vodka shots as a tight football game unfolded. After the Razorbacks lost 51–50, one of the men went home; the others went out to Bates's hot tub and continued to drink. Bates would later say that he went to bed around 1am and that the other two men – one of whom was named Victor Collins – planned to crash at his house for the night. When Bates got up the next morning, he didn't see either of his friends. But when he opened his back door, he saw a body floating face-down in the hot tub. A grim local affair, the death of Victor Collins would never have attracted international attention if it were not for a facet of the investigation that pitted the Bentonville authorities against one of the world's most powerful companies – Amazon. Collins' death triggered a broad debate about privacy in the voice-computing era, a discussion that makes the big tech companies squirm.
AI fuzzing uses machine learning and similar techniques to find vulnerabilities in an application or system. Fuzzing has been around for a while, but it's been too hard to do and hasn't gained much traction with enterprises. Adding AI promises to make the tools easier to use and more flexible. The good news is that enterprises and software vendors will have an easier time finding potentially exploitable vulnerabilities in their systems so they can fix them before bad guys get to them. The bad news is that the bad guys will have access to this technology as well and will soon start to find zero-day vulnerabilities on a massive scale.
If you didn't comb through the 15 press releases issued by Oracle at their co-located Modern Business Experience (MBX) and Modern Customer Experience (MCX) events, you're forgiven. Digging further, the underlying theme is really the problem/opportunity of data. Both topics spark debates on data privacy, the enormous need for integrated data platforms, and how AI can have real business impact. Brian Sommer and I got into those issues – and more – in our Oracle MBX and CX event review podcast. After Brian describes Oracle MBX as "crawling with chatbots," we aired out pet peeves grievances on customer experience hype.
You can teach an old house new tricks. Add convenience and energy-saving perspective to your home with these intelligent gadgets. It may look like a smoke detector, but the RoomMe is really a human detector. The sensor-laden fixture can tell I just walked into the room (by sniffing my smartphone over Bluetooth) and will command connected thermostats, lights, and speakers to calibrate the environment to match my preferences. Here's the clever bit: One person gets to be the "Room Master" of each sensor, and their desires override everyone else's--no matter who was in the room first.
In the context of vandalism and terrorist activities, video surveillance forms an integral part of any incident investigation and, thus, there is a critical need for developing an "automated video surveillance system" with the capability of detecting complex events to aid the forensic investigators in solving the criminal cases. As an example, in the aftermath of the London riots in August 2011 police had to scour through more than 200,000 hours of CCTV videos to identify suspects. Around 5,000 offenders were found by trawling through the footage, after a process that took more than five months. With the aim to develop an open and expandable video analysis framework equipped with tools for analysing, recognising, extracting and classifying events in video, which can be used for searching during investigations with unpredictable characteristics, or exploring normative (or abnormal) behaviours, several efforts for standardising event representation from surveillance footage have been made [9, 10, 11, 22, 23, 28, 30, 37]. While various approaches have relied on offering foundational support for the domain ontology extension, to the best of our knowledge, a systematic ontology for standardising the event vocabulary for forensic analysis and an application of it has not been presented in the literature so far. In this paper, we present an OWL 2  ontology for the semantic retrieval of complex events to aid video surveillance-based vandalism detection.
Enterprise security has always been a cat and mouse game, with cyber adversaries constantly evolving their attack systems to get past defenses. Can AI based systems help in warding off new age threats and zero day attacks. To get a perspective, we spoke with Vikas Arora, IBM Cloud and Cognitive Software Leader, IBM India/South Asia, who shares his view on how AI can impact enterprise security. What are your views on the cyber security landscape in India? Which sectors do you think are the most vulnerable today?
The Redmond, Washington-based tech giant is competing with Alphabet Inc.'s Google, International Business Machines Corp. and a clutch of small, specialized companies to develop quantum computers – machines that, in theory, will be many times more powerful than existing computers by bending the laws of physics. Two recent articles caught my eye. The first was in Financemagnates.com It was an interview with Michael Bancroft of Bloomberg TV, in which he spoke of the spreading popularity of blockchain technology, not just for protecting cryptocurrencies but for a growing number of uses including cybersecurity. He said that before too long, "what we're likely to see is blockchain being employed for cybersecurity… [in] governments who are looking to secure important files and records safe from hackers."
It can be wise to secure your Android phone with antivirus software, but which ones can you count on? You can rule out most of them, apparently. AV-Comparatives has tested 250 antivirus apps for Google's platform, and only 80 of them (just under one third) passed the site's basic standards -- that is, they detected more than 30 percent of malicious apps from 2018 and had zero false positives. Some of the apps that fell short would even flag themselves, according to the researchers. In some cases, the failure is a simple one: they're not really scanning app code.
Privacy advocates have placed an unfair stigma on machine learning. Despite what you may have heard through the mass media, ML is not some fiendish tool for invading people's privacy. Regardless, now that European Union's General Data Protection Regulation has taken effect, there's an even stronger scrutiny of ML applications in target marketing, customer engagement, experience optimization and other use cases that touch personally identifiable information, or PII. But in fact, ML is becoming a key element in how organizations manage compliance with GDPR and other privacy mandates. The core of ML's role in GDPR compliance is in its use as a tool for discovering, organizing, curating and controlling enterprise PII assets across complex, distributed application environments.