This post was done in partnership with Wirecutter. When readers choose to buy Wirecutter's independently chosen editorial picks, Wirecutter and Engadget may earn affiliate commission. Despite what I tell my son, I really don't have eyes in the back of my head. But I do have Wi-Fi security cameras with smartphone apps, which allow me to keep tabs on him, as well as my dog, my car, the front door, and the yard. Picking the right one (or two, or three) depends on what you want to do with it.
AI and machine learning disruption for Enterprises started happening in the areas such as IT operations management (ITOPs) and Cloud management and SaaS apps. In 2019 CIOs will see disruptive solutions for Cloud & Devops, AI/ML driven IT Ops and Cloud Ops. Customers want AI-driven multi-cloud operations for monitoring, detection, prevention of disruptions. Disruptions cause revenue loss, unhappy users, impacts brand reputation etc. AI and Machine learning had a tremendous impact and success in cyber security solutions. This similar trend will happen for IT Ops, DevOps, Cloud Ops.
HP on Tuesday announced a bevy of PC updates and new features, as well as the formal release of HP Sure Sense, the company's new software that uses AI to prevent and block malware in near-real-time, including ransomware and previously unknown malware. HP says Sure Sense is different from other market offerings because it uses deep learning to understand what malware looks like and shuts down threats in seconds. The deep learning engine boils down terabytes of data into a lightweight agent that's installed directly on notebooks to scan for malware with minimal impact on PC resources. HP said Sure Sense is 99 percent effective for catching malware, including malware that was created just yesterday, and requires minimal updates. It also has behavioral detection in the system, meaning that it looks for ransomware behavior and blocks it if it sees something happening with rapid encryption of files.
The smartest companies now approach cybersecurity with a risk management strategy. Learn how to make policies to protect your most important digital assets. The Royal Melbourne Institute of Technology (RMIT) has announced a new online course on cybersecurity in a bid to address Australia's cybersecurity skills shortage. As part of the course, RMIT Online has partnered with the National Australia Bank (NAB) and Palo Alto Networks, with both organisations to provide mentors for the course. The course, called Cyber Security Risk and Strategy, will cover topics such as the fundamentals of cybersecurity and how to apply cybersecurity risk mitigation strategies to an organisation.
While there are innumerable cybersecurity threats, the end goal for many attacks is data exfiltration. Much has been said about using machine learning to detect malicious programs, but it's less common to discuss how machine learning can aid in identifying other types of notable threats. Critically, machine learning can be key in detecting one of the most insidious types of malicious actors – one with legitimate access to your network and systems. When properly trained, machine-learning algorithms can be used to identify insider threats and frauds before they become dangerous. When people hear the term "insider threat," many of them imagine an employee gone rogue, a disgruntled member of your team committing corporate espionage and leaking sensitive data or documents to competitors or criminals.
Amazon has admitted that employees listen to customer voice recordings from Echo and other Alexa-enabled smart speakers. The online retail giant said its staff "reviewed" a sample of Alexa voice assistant conversations in order to improve speech recognition. "This information helps us train our speech recognition and natural language understanding systems, so Alexa can better understand your requests, and ensure the service works well for everyone," Amazon said in a statement. We'll tell you what's true. You can form your own view.
When Ivan Medvedev joined Google as a privacy engineering manager in 2013, the company had rogue data anxiety. Its user base and set of services had become so massive that it seemed inevitable that sensitive data could accidentally crop up in unexpected places, like customers filing support tickets with more personal information than necessary. So Medvedev worked with colleagues on Google's privacy team to develop an internal tool that could scan large amounts of data and automatically home in on identifying information or other sensitive data. Whether it was an old tax form accidentally captured in a photo or patient data embedded in the pixels of an ultrasound, the team designed the tool to find the unexpected. "It should not be misunderstood as a comprehensive, privacy-proofed solution in itself."
Artificial intelligence applied to information security can engender images of a benevolent Skynet, sagely analyzing more data than imaginable and making decisions at lightspeed, saving organizations from devastating attacks. In such a world, humans are barely needed to run security programs, their jobs largely automated out of existence, relegating them to a role as the button-pusher on particularly critical changes proposed by the otherwise omnipotent AI. Such a vision is still in the realm of science fiction. AI in information security is more like an eager, callow puppy attempting to learn new tricks – minus the disappointment written on their faces when they consistently fail. No one's job is in danger of being replaced by security AI; if anything, a larger staff is required to ensure security AI stays firmly leashed.
The new world of marketing is personalized, contextualized, and dynamic. Increasingly, this world is orchestrated not by outside parties but by chief marketing officers partnering with their technology organizations to bring control of the human experience back in-house. Together, CMOs and CIOs are building an arsenal of experience-focused marketing tools that are powered by emerging technology. Their goal is to transform marketing from a customer acquisition-focused activity to one that enables a superb human experience, grounded in data. In experiential marketing, companies treat each customer as an individual by understanding their preferences and behaviors. Analytics and cognitive capabilities illuminate the context of customers' needs and desires, and determine the optimal way to engage with them. Experience-management tools tailor content and identify the best method of delivery across physical and digital touchpoints, bringing us closer to truly unique engagement with each and every human. Imagine a world in which a brand knows who you are and what you want, and can deliver the product, service, or experience that best suits your needs seamlessly and in real time, across physical or digital channels. Marketing technology is undergoing a renaissance. Channel-focused solutions such as websites, social and mobile platforms, content management tools, and search engine optimization are fast becoming yesterday's news. As part of the growing beyond marketing trend, organizations are adopting a new generation of martech systems that deliver unprecedented levels of customer intimacy, targeted engagement, and precision impact.
A red circle has been added to this image to highlight the location of the camera. The Tesla Model 3 has eight cameras on the outside of it to help enable its driver aids and security features, but there's also one inside that doesn't do anything…yet. But it may help you earn money one day, if Telsa's plans for an autonomous ride-hailing network come to fruition. CEO Elon Musk has previously detailed the long-term goal for such a system, which would theoretically allow owners of fully self-driving Teslas to hire them out as taxis when they're not using them for themselves. Responding to a Twitter question about it from one of his followers, Musk revealed that the camera above the rearview mirror is primarily there to keep an eye on passengers using the service and provide evidence if anyone "messes up" a car.