Networking plays a critical role in one's professional life, particularly for people who depend on experts' opinions and hold curiosity about lived experiences.LinkedIn lets users connect to people of their choice, exchange ideas, or collaborate on projects. However, communicating directly with them comes at a price. Email messages for LinkedIn where users can message users directly is a premium feature. LinkedIn groups play a vital role in bringing together the tech enthusiasts to collaborate and brainstorm ideas; and it works particularly well in areas like artificial intelligence and machine learning, where you can get inputs from people who are experienced enough to instill a sense of direction and provide significant insights. It is a collaborative group of AI researchers who work on next-generation machine intelligence.

Do we give importance to the privacy of our profile photos publicly available around social media? Have we ever bothered about privacy when we share innumerable photos of friends and family members on Facebook or Instagram? But why should we pay importance to the privacy protection of photos in the first place? We should because our publicly available photos could be utilized for unauthorized facial recognition and that can invade our private lives. There is little doubt that facial recognition is a serious threat to privacy.

LinkedIn is the premier place for enterprise technology professionals to gather, connect with one another, share ideas, and network. If you are a business person who works in the machine learning or artificial intelligence field, or you're just looking for additional insights into what the smartest in the industry are talking about, LinkedIn professional groups are a great place to start. As a result, we've compiled a list of the top 10 forums to join to discover new insights, best practices and learn which software tools the experts are using in their daily operations. This is a community for artificial intelligence, machine learning and analytics experts. The group brings together business communities across industry, enterprises, academic, and government sectors representing all of those with interests in artificial intelligence and machine learning techniques, technologies and applications.

Artificial Intelligence (AI) has been seen as a potential solution for automatically detecting and combating malware, and stop cyber attacks before they affect any organization. However, the same technology can also be weaponized by threat actors to power a new generation of malware that can evade even the best cyber-security defenses and infects a computer network or launch an attack only when the target's face is detected by the camera. To demonstrate this scenario, security researchers at IBM Research came up with DeepLocker--a new breed of "highly targeted and evasive" attack tool powered by AI," which conceals its malicious intent until it reached a specific victim. According to the IBM researcher, DeepLocker flies under the radar without being detected and "unleashes its malicious action as soon as the AI model identifies the target through indicators like facial recognition, geolocation and voice recognition." Describing it as the "spray and pray" approach of traditional malware, researchers believe that this kind of stealthy AI-powered malware is particularly dangerous because, like nation-state malware, it could infect millions of systems without being detected. The malware can hide its malicious payload in benign carrier applications, like video conferencing software, to avoid detection by most antivirus and malware scanners until it reaches specific victims, who are identified via indicators such as voice recognition, facial recognition, geolocation and other system-level features. Also Read: Artificial Intelligence Based System That Can Detect 85% of Cyber Attacks "What is unique about DeepLocker is that the use of AI makes the "trigger conditions" to unlock the attack almost impossible to reverse engineer," the researchers explain. "The malicious payload will only be unlocked if the intended target is reached." To demonstrate DeepLocker's capabilities, the researchers designed a proof of concept, camouflaging well-known WannaCry ransomware in a video conferencing app so that it remains undetected by security tools, including antivirus engines and malware sandboxes. With the built-in triggering condition, DeepLocker did not unlock and execute the ransomware on the system until it recognized the face of the target, which can be matched using publicly available photos of the target. "Imagine that this video conferencing application is distributed and downloaded by millions of people, which is a plausible scenario nowadays on many public platforms.

After an eight-year-long absence from the most populated country in the world, Google search is going to dramatically make a comeback in China. Google is reportedly planning to launch a censored version of its search engine in China that is going to blacklist certain websites and search terms to comply with Chinese government's attempts to censor the Internet, a whistleblower revealed. According to leaked documents obtained by The Intercept, CEO Sundar Pichai met with a Chinese government official in December 2017 to re-enter the world's largest market for internet users. Project Dragonfly -- Censored Google Search Engine Since spring last year Google engineers have been secretly working on a project, dubbed "Dragonfly," which currently includes two Android mobile apps named--Maotai and Longfei--one of which will get launched by the end of this year after Chinese officials approve it. The censored version of Google search engine in the form of a mobile app reportedly aims to "blacklist sensitive queries" and filter out all websites (news, human rights, democracy, religion) blocked by the Chinese government, including Wikipedia, BBC News, Instagram, Facebook, and Twitter.

The recent explosion of data set size, in number of records and attributes, has triggered the development of a number of big data platforms as well as parallel data analytics algorithms. At the same time though, it has pushed for usage of data dimensionality reduction procedures. Indeed, more is not always better. Large amounts of data might sometimes produce worse performances in data analytics applications. One of my most recent projects happened to be about churn prediction and to use the 2009 KDD Challenge large data set.