'Ecommerce businesses have a problem - one that causes lost customer revenue, yet has been historically nearly impossible to solve' Geoff Huang, VP of Product at Sift The problem stems from the inability to know their false-positive rate, which is the percentage of orders from legitimate customers that are mistakenly blocked as fraud. According to a survey conducted by CNP, 42% of ecommerce merchants don't know their false-positive rate (also known as customer insult rate). That is a startling statistic--nearly half of online sellers have no visibility into the number of good orders they inadvertently block or the subsequent revenue lost from those orders. And the news, unfortunately, doesn't get much better. Sift polled 1,000 adult consumers and found roughly 25% of insulted online shoppers--those who were falsely declined--will take their business to a competitor.
A bad malware signature caused Sophos antivirus products to detect a critical Windows file as malicious on Sunday, preventing some users from accessing their computers. Because the file was blocked, some users who attempted to log into their computers were greeted by a black screen. Sophos issued an update to fix the problem within a few hours and said that the issue only affected a specific 32-bit version of Windows 7 SP1 and not Windows XP, Vista, 8 or 10. "Based on current case volume and customer feedback, we believe the number of impacted systems to be minimal and confined to a small number of cases," the company said in a support article. One Twitter user who was affected by the issue said that he highly doubts only a small number of customers were affected, while another one reported that he's been on hold trying to reach Sophos Support by phone for over two hours. "An email would have been nice," one user told Sophos via Twitter.
A new study suggests that common settings used in software for analyzing brain scans may lead to false positive results. Researchers led by Anders Eklund, an electrical engineer at Linköping University in Sweden, analyzed functional magnetic resonance imaging (fMRI) data from several public databases. Certain software settings, the team found, could give rise to a false positive result up to 70% of the time. In the context of a typical fMRI experiment, that could lead researchers to wrongly conclude that activity in a certain area of the brain plays a role in a cognitive function such as perception or memory.
Imagine a machine learning algorithm is tasked with identifying the number of bananas within a bowl of fruit. In total, the bowl contains 10 pieces of fruit, 4 of which are bananas, and 6 are apples. The algorithm determines that there are 5 bananas, and 5 apples. The number of bananas that were counted correctly are known as true positives, while the items that were identified incorrectly as bananas are called false positives. In this example, there are 4 true positives, and one false positive, making the algorithms precision 4/5, and its recall is 4/10.