A main benefit of RPA solutions is that they reduce human error while enabling employees to feel more human by engaging in conversations and assignments that are more complex but could also be more rewarding. For instance, instead of having a contact center associate enter information while also speaking with a customer, an RPA solution can automatically collect, upload, or sync data into with other systems for the associate to approve while focusing on forming an emotional connection with the customer. Another impact of RPA is it can facilitate and streamline employee onboarding and training. An RPA tool, for instance, can pre-populate forms with the new hire’s name, address, and other key data from the resume and job application form, saving the employee time. For training, RPA can conduct and capture data from training simulations, allowing a global organization to ensure all employees receive the same information in a customized and efficient manner. RPA is not for every department and it’s certainly not a panacea for retention and engagement problems. But by thinking carefully about the benefits that it offers to employees, RPA can transform workflows—making employees’ jobs less robotic and more rewarding.
The idea is to quickly detect whether a command given to a device is live or is prerecorded. It's a tricky proposition given that a recorded voice has characteristics similar to a live one. "Such attacks are known as one of the easiest to perform as it simply involves recording a victim's voice," says Hyoungshick Kim, a visiting scientist to CSIRO. "This means that not only is it easy to get away with such an attack, it's also very difficult for a victim to work out what's happened." The impacts can range from using someone else's credit card details to make purchases, controlling connected devices such as smart appliances and accessing personal data such home addresses and financial data, he says. The voice-spoofing problem has been tackled by other research teams, which have come up with solutions. In 2017, 49 research teams submitted research for the ASVspoof 2017 Challenge, a project aimed at developing countermeasures for automatic speaker verification spoofing. The ASV competition produced one technology that had a low error rate compared to the others, but it was computationally expensive and complex, according to Void's research paper.
Cognitive bias means that individuals think subjectively, rather than objectively, and therefore influence the design of the product they're creating. Humans filter information through their unique experience, knowledge and opinions. Development teams cannot eliminate cognitive bias in software, but they can manage it. Let's look at the biases that most frequently affect quality, and where they appear in the software development lifecycle. Use the suggested approaches to overcome cognitive biases, including AI bias, and limit their effect on software users. A person knowledgeable about a topic finds it difficult to discuss from a neutral perspective. The more the person knows, the harder neutrality becomes. That bias manifests within software development teams when experienced or exceptional team members believe that they have the best solution. Infuse the team with new members to offset some of the bias that occurs with subject matter experts. Cognitive bias often begins in backlog refinement. Preconceived notions about application design can affect team members' critical thinking. During sprint planning, teams can fall into the planning fallacy: underestimating the actual time necessary to complete a user story.
A different approach to bridging the worlds of on-prem data centers and the growing variety of cloud computing services is offered by a company called Alluxio. From their roots at the Berkeley Amp Labs, they've been focused on solving this problem. Alluxio decided to bring the data to computing in a different way. Essentially, the technology provides an in-memory cache that nestles between cloud and on-prem environments. Think of it like a new spin on data virtualization, one that leverages an array of cloud-era advances. According to Alex Ma, director of solutions engineering at Alluxio: "We provide three key innovations around data: locality, accessibility and elasticity. This combination allows you to run hybrid cloud solutions where your data still lives in your data lake." The key, he said, is that "you can burst to the cloud for scalable analytics and machine-learning workloads where the applications have seamless access to the data and can use it as if it were local--all without having to manually orchestrate the movement or copying of that data."
Speaking of the intersection of open source software development and cloud services, open source luminary Tim Bray has said, “The qualities that make people great at carving high-value software out of nothingness aren’t necessarily the ones that make them good at operations.” The same can be said of maintaining open source projects. Just because you’re an amazing software developer doesn’t mean you’ll be a great software maintainer, and vice versa. Perhaps more pertinently to the Sanfilippo example, developers may be good at both, yet not be interested in both. (By all accounts Sanfilippo has been a great maintainer, though he’s the first to say he could become a bottleneck because he liked to do much of the work himself rather than relying on others.) Sanfilippo has given open source communities a great example of how to think about “career” progression within these projects, but the same principle applies within enterprises. Some developers will thrive as managers (of people or of their code), but not all. As such, we need more companies to carve out non-management tracks for their best engineers, so developers can progress their career without leaving the code they love.
The uptick in the need for data science, across industries, comes with the need for data science teams. While hiring may have slowed down in the tech sector – Google slowed its hiring efforts during the pandemic – data scientists professionals are still in high demand. However, it’s important to keep a close eye on how these teams continue to evolve. One position which is increasingly in-demand as businesses become more data-driven is the role of the Algorithm Translator. This person is responsible for translating business problems into data problems and, once the data answer is found, articulating this back into an actionable solution for business leaders to apply. The Algorithm Translator must first break down the problem statement into use cases, connect these use cases with the appropriate data set, and understand any limitations on the data sources so the problem is ready to be solved with data analytics. Then, in order to translate the data answer into a business solution, the Algorithm Translator must stitch the insights from the individual use cases together to create a digestible data story that non-technical team members can put into action.
Why the change? Companies that have established open source programs say the most important factor is developer recruitment. "We want to have a good reputation in the open source world overall, because we're hiring technical talent," said Bloomberg's Fleming. "When developers consider working for us, we want other people in the community to say 'They've been really contributing a lot to our community the last couple years, and their patches are always really good and they provide great feedback -- that sounds like a great idea, go get a job there.'" While companies whose developers contribute code to open source produce that code on company time, the company also benefits from the labor of all the other organizations that contribute to the codebase. Making code public also forces engineers to adhere more strictly to best practices than if it were kept under wraps and helps novice developers get used to seeing clean code.
The Ekans ransomware begins the attack by attempting to confirm its target. This is achieved by resolving the domain of the targeted organization and comparing this resolved domain to a specific list of IP addresses that have been preprogrammed, the researchers note. If the domain doesn't match the IP list, the ransomware aborts the attack. "If the domain/IP is not available, the routine exits," the researchers add. If the ransomware does find a match between the targeted domain and the list of approved IP addresses, Ekans then infects the domain controller on the network and runs commands to isolate the infected system by disabling the firewall, according to the report. The malware then identifies and kills running processes and deletes the shadow copies of files, which makes recovering them more difficult, Hunter and Gutierrez note. In the file stage of the attack, the malware uses RSA-based encryption to lock the target organization's data and files. It also displays a ransom note demanding an undisclosed amount in exchange for decrypting the files. If the victim fails to respond within first 48 hours, the attackers then threaten to publish their data, according to the Ekans ransom recovered by the FortiGuard researchers.
If performance is paramount and price is no object, Intel’s Optane SSD 905P is the best SSD you can buy, full stop—though the 8TB Sabrent Rocket Q NVMe SSD discussed above is a strong contender if you need big capacities and big-time performance. Intel’s Optane drive doesn’t use traditional NAND technology like other SSDs; instead, it’s built around the futuristic 3D Xpoint technology developed by Micron and Intel. Hit that link if you want a tech deep-dive, but in practical terms, the Optane SSD 900P absolutely plows through our storage benchmarks and carries a ridiculous 8,750TBW (terabytes written) rating, compared to the roughly 200TBW offered by many NAND SSDs. If that holds true, this blazing-fast drive is basically immortal—and it looks damned good, too. But you pay for the privilege of bleeding edge performance. Intel’s Optane SSD 905P costs $600 for a 480GB version and $1,250 for a 1.5TB model, with several additional options available in both the U.2 and PCI-E add-in-card form factors. That’s significantly more expensive than even NVMe SSDs—and like those, the benefits of Intel’s SSD will be most obvious to people who move large amounts of data around regularly.
Failure will happen, incidents will occur, and SLOs will be breached. These things may be difficult to face, but part of adopting SRE is to acknowledge that they are the norm. Systems are made by humans, and humans are imperfect. What’s important is learning from these failures and celebrating the opportunity to grow. One way to foster this culture is to prioritize psychological safety in the workplace. The power of safety is very obvious but often overlooked. Industry thought leaders like Gene Kim have been promoting the importance of feeling safe to fail. He addresses the issue of psychological insecurity in his novel, “The Unicorn Project.” Main character Maxine has been shunted from a highly-functional team to Project Phoenix, where mistakes are punishable by firing. Gene writes “She’s [Maxine] seen the corrosive effects that a culture of fear creates, where mistakes are routinely punished and scapegoats fired. Punishing failure and ‘shooting the messenger’ only cause people to hide their mistakes, and eventually, all desire to innovate is completely extinguished.”
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