When wireless charging went mainstream a few years back, it was expected to usher in a new era of convenience for consumers. It sounded irresistibly cool -- one of those things you just HAD to try. But when people finally did get their hands on a wireless charger, opened the box, and gave it a shot, the sparks just didn’t fly. This prophesied technology had finally arrived … and it was a letdown.
Before we go any further, let’s get one thing straight: wireless charging itself was not the problem. The concept wasn’t flawed, but the implementation was. We had been promised unparalleled convenience, but what we got was a solution with a fairly large margin of error.
Though they seem like something out of a futuristic sci-fi movie, deepfakes are very much a reality. In fact, developers have been experimenting with deepfake technology as far back as the late 1990s. Today, deepfakes have become so advanced and believable that they can cause some serious damage in the wrong hands.
"Deepfake" is a term used for the process of replacing someone in an existing image or video with someone else’s likeness. If you’ve watched Former U.S. President Barack Obama giving a lecture about cybercrime or follow the hilarious thread of videos in which various actors have their faces replaced with that of Nicolas Cage, you’ve seen a deepfake. As entertaining as they are, deepfake algorithms are getting more sophisticated every day. When we stop being able to differentiate this synthetic media from reality, a real problem arises.
Your friends and followers aren't the only ones looking at what you Like. In 2017, CareerBuilder revealed that 70 percent of employers review social media during the hiring process. Today that number may be even higher: one survey by First Advantage found that nearly two-thirds of employers choose to screen all employees and applicants for "red flag" behavior on websites such as Facebook and Twitter.
The primary peril of vetting candidates by examining their online profiles remains the same as when this trend first rose to prominence: employers performing "social media background checks" may inadvertently uncover sensitive or protected information which compromises their ability to make an unbiased hiring decision. Not hiring a candidate based on such information, from sexual orientation to religion to race, could expose a business to accusations of unfair or discriminatory hiring practices.
The cloud has revolutionized making it easy to build, launch, and scale a service. That's driven a wave of spending on the major cloud providers, as made evident by the latest earnings reports from Microsoft (Azure), Amazon (AWS), and Google (Google Cloud). Microsoft just reported 62 percent QoQ growth for Azure, AWS brought in nearly $10 billion for Amazon in Q4 2019, and Google reported cloud earnings for the first time in January.
Companies of all sizes are clearly investing billions on the cloud and there doesn't seem to be a ceiling. Gartner predicts that by 2022 overall cloud spend will reach more than $330 billion, and that number grows every year. But at the same time, current estimates reveal that billions of this spend is the result of needless and wasted outlay. A recent survey of companies that spend at least $5 million on the cloud annually found that a vast majority (69 percent) regularly overspend on their cloud budget by 25 percent or more.
From forecasting the weather each day, predicting the future price of an asset, or identifying seasonality in a company’s sales revenue, time series forecasting plays an incredibly important part of our personal and professional lives.
Forecasting the future is never an easy task, although in this article we’ll introduce several statistical and machine learning techniques that can help us with the task. In particular, we’ll cover the following topics:
In 2019, global private investment in AI technology reached $70 Billion, representing a massive buy-in from start-ups and big corporations alike. From self-driving cars to advanced medical diagnosis, the potential for AI to shift the very foundations of our lifestyles is growing.
And yet, 65 percent of companies have not seen business gains from their AI investments. That potential, it seems, is being stymied. But why?
Business leaders rely on business intelligence. This is underpinned by the hard work of their DataOps teams who ensure the complicated connections of the data science BI processes keep flowing. Given the centrality of data to the whole process, the technical team cannot be expected to manage this without getting business leadership to first understand just what effective DataOps entails. In failing to do so, friction grows as strategic direction meets the physical limits of technology, time, skills, and budget. The fundamental promise of big data and data processing is to provide organizations with the insights to make more intelligent decisions. Yet, only infrequently do we see organizations focusing on how they can collect their data as much as they focus on how they can act on it. In other words, they don’t give due consideration to the data they have about... well... collecting data. This appears an oversight given how much of a game-changer these insights can be in terms of operations, reliability and optimizing resource use.
Before diving into how we can collate and make use of this data, however, it is instructive to look at what kind of inputs can be collected on data processing and storage. These, if used properly, can drastically improve processes and improve workload management.
If you’re looking for a strategy to get ahead when it comes to customer acquisition, machine learning can be your secret weapon. While machine learning does fall under the larger category of artificial intelligence (AI), it’s a bit more specific and can be extremely effective technology to pair with your customer and prospect database. True AI can think for itself like Lieutenant Commander Data from Star Trek. Machine learning, however, can automate tasks and apply predictive analytics that drive meaningful growth.
Machine learning is the AI focal point for your customer relationship management (CRM) tool and can be the key to boosting your customer acquisition.
The threat of a global pandemic is alarming, but at least in this case, IT has some advance notice to prepare for the worst-case scenario. You do not want to be caught without a plan if local governments institute a quarantine or local schools are closed for several weeks. And even if we avoid a pandemic -- fingers crossed -- the planning you did won’t be in vain.
It’s important for every organization to always have a plan to deal with disasters large and small, whether it’s flooding, inclement winter weather or a particularly bad cold that sends half your team home. Here are the steps you should take to put together your plan and prepare for a potential pandemic.
It’s been over a month since CCPA was implemented and businesses are struggling to comply. Smart organizations, however, know that compliance doesn’t have to be a sunk cost; in fact, it can be used as a competitive differentiator. Instead of playing catch up with global, national, and state data privacy regulations, businesses should consider implementing broad policies and protections for consumer information that will prepare the organization for any future legislation.
For all the criticisms of GDPR -- and there are many -- the EU legislation set an important precedent for data privacy laws internationally. Businesses that are already GDPR-compliant are in a good position to satisfy requirements from new national and state data privacy laws.
When I started my 10-year career in IT, it was common in smaller companies to see IT departments managing physical security. IT teams knew when new staff were onboarded and offboarded, and access control was just another task to add to their processes. While larger organizations had IT departments as well, they also may have leaned more on a facilities department or even dedicated physical security staff.
Since then, the worlds of physical security and IT have converged. While some of this system has stayed the same, there’s been one major change: regardless of who’s in charge of managing physical security, IT is involved by either owning the system or individual parts of it -- tasks like network or server provisioning, database management, backups and firmware upgrades. Enterprises are starting to understand this convergence and that they must take a more active role in security and where it fits in the organization’s overall strategy. Teams responsible for security -- both physical security and IT -- will face increased calls to work together and address their companies’ ever-evolving security needs.
The continuing conflict among nations around the globe has been fueling a surge in state-sponsored cyberattacks.
Magnallium, a known advanced persistent threat (APT) group linked to Iran, has allegedly been trying to hack the US power grid by password-spraying private networks to potentially gain entry into this critical infrastructure. The act is believed to be part of a possible retaliation by Iran against the US in response to the escalation of military hostilities between the two countries earlier this year. Israel also recently reported to have thwarted hacking attempts on airport systems and planes of world leaders attending the World Holocaust Forum.
Businesses are embracing automation. But you’re most likely to find it thriving in discrete pockets throughout the organization, rather than integrated into every process. One initiative might automate the routing of customer emails to the right agent, while another might help the payroll team process timecards faster. Whilst these solutions to point problems are innovative, their limited scope means they fall short of full-scale automation.
For proof, consider that only 17 percent of businesses have scaled their intelligent automation technologies, according to a recent report from HFS Research, and 90 percent aren’t taking an integrated approach. If organizations want to grow their business, it’s critical they abandon working in silos in favor of a strategic, holistic approach to automation.
Digital criminals are increasingly pivoting to the network after initially attacking an endpoint or publicly accessible cloud. Indeed, a network foothold enables attackers to move laterally to more valuable cloud workloads. They can then steal their target organization’s sensitive information and monetize it in whatever way they deem fit.
Many of us are fighting back against the threat of lateral movement by augmenting our visibility over the network. However, we’re constantly running into challenges in the cloud. When using AWS Virtual Private Cloud (VPC) or Azure Virtual Networks (VNets) to detect threats in network traffic, for instance, we’re missing packets’ application-level context. We thus can’t detect the malicious activity that hides within them. In this post, we will discuss why achieving visibility into the cloud continues to pose a challenge. We’ll then explore how we can gain the requisite level of visibility in the cloud.
One of the biggest things an organization can do to stay afloat and thriving in our current competitive landscape is to be readily adaptable to change. In contrast, one of the biggest mistakes I often see organizations make is not having a strong enough grasp on their IT infrastructure to do so.
This means that when a problem arises, decision makers will often act out of instinct and make changes that then create further problems or cause unexpected side effects. Once you can quickly and properly survey the technological and/or organizational landscape you’re working with, being adaptable to change can be almost effortless. Below are a few tactics to help you remain adaptable to change and avoid potential catastrophe in the face of an uncertain market.