Artificial intelligence has been touted by some in the security community as the silver bullet in malware detection. Its proponents say it's superior to traditional antivirus since it can catch new variants and never-before-seen malware--think zero-day exploits--that are the Achilles heel of antivirus. One of its biggest proponents is the security firm BlackBerry Cylance, which has staked its business model on the artificial intelligence engine in its endpoint PROTECT detection system, which the company says has the ability to detect new malicious files two years before their authors even create them. But researchers in Australia say they've found a way to subvert the machine-learning algorithm in PROTECT and cause it to falsely tag already known malware as "goodware." The method doesn't involve altering the malicious code, as hackers generally do to evade detection.
Your resume may not be the only deciding factor in landing your next job – it could be an'employability score' created by artificial intelligence that has the final vote. More than 100 big name firms are using HireVue's AI-driven assessment, which is technology that ranks candidates based on their facial movements, choice of words and speaking voice. Although employers can pursue any candidate, some have told The Washington Post that they usually focus on those the computer system liked best -- leading some experts to question how bias the process may be. More than 100 employers are using HireVue's AI-driven assessment that ranks candidates based on their facial movements, choice of words and speaking voice HireVue's technology is employed by many large name companies such as Hilton Hotels, Unilever and Goldman Sachs, according to The Washington Post. And with hundreds of applications flooding in for just a single position, the AI has made it easy for human employers to find the perfect candidate what i- but some experts believe the technology can do more harm than good.
Mashable's series Algorithms explores the mysterious lines of code that increasingly control our lives -- and our futures. That's become the go-to refrain for why your Instagram feed keeps surfacing the same five people or why YouTube is feeding you questionable "up next" video recommendations. But you should blame the algorithm -- those ubiquitous instructions that tell computer programs what to do -- for more than messing with your social media feed. Algorithms are behind many mundane, but still consequential, decisions in your life. The code often replaces humans, but that doesn't mean the results are foolproof.
New technologies and solutions have been applied to a number of long-running services. These always try to make them more accurate, faster and issues-free as far as they can go, and they have achieved (in most cases) an impressively high hit rate. The latest comes in the shape of a new revolutionising solution able to sort out a psychographic credit score based on cultural and behavioural principles besides the well-known economic data. AdviceRobo's team are behind such invention and it is said to give another twist to a controversial metric measure. AdviceRobo demoed at FinovateEurope 2019 its revolutionizing solution to "assess credit quality by applying smart psychographic credit processing, artificial intelligence and the internet of things. This is world's first solution that integrates lifestyles, attitudes and beliefs of people and other behavioural data into smart credit software," they said in a press release.
Whether it's diagnosing patients or driving cars, we want to know whether we can trust a person before assigning them a sensitive task. In the human world, we have different ways to establish and measure trustworthiness. In artificial intelligence, the establishment of trust is still developing. In the past years, deep learning has proven to be remarkably good at difficult tasks in computer vision, natural language processing, and other fields that were previously off-limits for computers. But we also have ample proof that placing blind trust in AI algorithms is a recipe for disaster: self-driving cars that miss lane dividers, melanoma detectors that look for ruler marks instead of malignant skin patterns, and hiring algorithms that discriminate against women are just a few of the many incidents that have been reported in the past years.