Today, most quantum computers can only handle the simplest and shortest algorithms, since they're so wildly error-prone. And in recent algorithmic benchmarking experiments executed by the U.S. Quantum Economic Development Consortium, the errors observed in hardware systems during tests were so serious that the computers gave outputs statistically indiscernible from random chance. That's not something you want from your computer. But by employing specialized software to alter the building blocks of quantum algorithms, which are called "quantum logic gates," the company Q-CTRL discovered a way to reduce the computational errors by an unprecedented level, according to the release. The new results were obtained via several IBM quantum computers, and they also showed that the new quantum logic gates were more than 400 times more efficient in stopping computational errors than any methods seen before. It's difficult to overstate how much this simplifies the procedure for users to experience vastly improved performance on quantum devices.
Design patterns for ML pipelines have evolved several times in the past decade. These changes are usually driven by imbalances between memory and CPU performance. They are also distinct from traditional data processing pipelines (something like map reduce) as they need to support the execution of long-running, stateful tasks associated with deep learning. As growth in dataset sizes outpace memory availability, we have seen more ETL pipelines designed with distributed training and distributed storage as first-class principles. Not only can these pipelines train models in a parallel fashion using multiple accelerators, but they can also replace traditional distributed file systems with cloud object stores. Along with our partners from the AI Infrastructure Alliance, we at Activeloop are actively building tools to help researchers train arbitrarily large models over arbitrarily large datasets, like the open-source dataset format for AI, for instance. ... Even though the problem of transfer speed remained, this design pattern is widely considered as the most feasible technique for working with petascale datasets.
Standup are not about technical details, even though some technical context can help to frame the arisen complexity and allow PjM and Leads to take the necessary steps to enable you achiving your tasks ( additional meetings, extending the deadline, reestimating the task within the sprint, set up pair programming sessions etc). According to the official docs the Daily Scrum is a 15-minute event for the Developers of the Scrum Team. The purpose of the Daily Scrum is to inspect progress toward the Sprint Goal and produce an actionable plan for the next day of work. This creates focus and improves self-management. Daily Scrums improve communications, identify impediments, promote quick decision-making, and consequently eliminate the need for other meetings. Honestly I am not so sure about the last point, due to the short time allocated to it, it can indeed generate other meetings, follow-ups between Tech Lead and (some of ) the developers, or between the team and the stakeholders, or among developers which decide to tackle an issue with pair programming.
Is waterfall ever better? The short answer is yes. Waterfall is more efficient, more streamlined, and faster when it comes to specific types of projects like these: Generally speaking, the smaller the project, the better suited it is to waterfall development. If you’re only working with a few hundred lines of code or if the scope of the project is limited, there’s no reason to take the continuous phased approach; Low priority projects – those with minimal impact – don’t need much outside attention or group coordination. They can easily be planned and knocked out with a waterfall methodology; One of the best advantages of agile development is that your clients get to be an active part of the development process. But if you don’t have any clients, that advantage disappears. If you’re working internally, there are fewer voices and opinions to worry about – which means waterfall might be a better fit; Similarly, if the project has few stakeholders, waterfall can work better than agile. If you’re working with a council of managers or an entire team of decision makers, agile is almost a prerequisite.
Focusing on a pipeline using infrastructure-as-code allows security teams to build in static analysis tools to catch vulnerabilities early, dynamic analysis tools to catch issues in staging and production, and policy enforcement tools to continuously validate that the infrastructure is compliant, Leitersdorf said. "If you think about how security can be done now, instead of doing security at the tail end of the process ... you can now do security from the beginning through every step in the process all the way to the end. Most security issues will be caught very early on, and then a handful of them will be caught in the live environment and then remediated very quickly," he said. Developers get to retain their speed of development and deployment of applications and, at the same time, reduce the time to remediate security issues. And security teams get to collaborate more closely with DevOps teams, he said. "From a security team perspective, you feel better, you feel more confident, you have guardrails around your developers to reduce the chance of making mistakes along the way and building insecure infrastructure and you now have visibility into their DevOps process, a huge bonus," Leitersdorf said.
In general, the "minor problems" are precisely those that result in the most extensive security disasters, and we can say that failures usually present two gaps: We encountered flaws such as insecure code design, injection, and configuration issues through a code vulnerability intentionally or unintentionally: within the TOP 10 demonstrated by OWASP; Through the vulnerability of operations: among the most common problems, we can mention the choice for "weak passwords", default or even the lack of password. A second common failure is the mismanagement of people's permission to a document or system. These types of problems, unfortunately, are pretty standard. Not by chance, 75% of Redis servers have issues of this type. In an analogy, we can say that security flaws are like the case of the Titanic. Considered one of the biggest wrecks, most people are unaware that the ship had a "small problem": the lack of a simple key that could have opened the compartment with binoculars and other devices to help the crew visualize the iceberg in time and prevent a collision.
XDR fundamentally brings all the anchor tenants that are required to detect and respond to threats into a simple, seamless user experience for analysts that automates repetitive work. Bringing together all the required context enables analysts to take action quickly, without getting lost in a myriad of use cases, different screens and workflows and search languages. It can also help security analysts respond quickly without creating endless playbooks to cover every possible scenario. XDR unifies insights from endpoint detection and response (EDR), network data and security analytics logs and events as well as other solutions, such as cloud workload and data protection solutions, to provide a complete picture of potential threats. XDR incorporates automation for root cause analysis and recommended response, which is critical in order to respond quickly with confidence across a complex IT and security infrastructure. Whether your primary challenge is the complexity of tools, data and workflows or preventing a ransomware actor from laterally moving across your environment, quickly detecting and containing threats is of the essence.
By creating your abstraction in one layer, everything related to it is centralized so any changes can be made in one place. Centralization is related to the “Don’t repeat yourself” (DRY) principle, which can be easily misunderstood. DRY is not only about the duplication of code, but also of knowledge. Sometimes it’s fine for two different entities to have the same code duplicated because this achieves isolation and allows for the future evolution of those entities separately. ... By creating the abstraction layer, you expose a specific piece of functionality and hide implementation details. Now code can interact directly with your interface and avoid dealing with irrelevant implementation details. This improves the code readability and reduces the cognitive load on the developers reading the code. Why Because policy is less complex than its details, so interacting with it is more straightforward. ... Abstraction layers are great for testing, as you get the ability to replace details with another set of details, which helps isolate the areas that are being tested and properly create test doubles.
When it comes to the demand side, although much progress has been made in terms of escalating diversity up the corporate agenda, there still needs to be a seismic shift in employment practices. Specifically, there needs to be a sweeping change in mindset amongst hiring managers, who still often hire based on ‘cultural fit’, as well as a new approach to how companies help women to break the glass ceiling, through mentoring, career development support and family friendly policies. In the case of recruitment, hiring for cultural fit tends to favour the status quo in a company, whether that relates to race, gender, age, socioeconomic level and so on. That makes it harder for anyone who doesn’t ‘fit the mould’ to get into sectors where they are currently under-represented. Instead, hiring managers ought to look towards integrating ‘value fit’ in their hiring process. The values of a business are the things that drive the way they work and can include everything from teamwork and collaboration, to problem-solving and customer focus. Yes, you need to ensure your employees don’t clash with one another, but a value-fit approach will often lead to this outcome too.
When our work and personal life interact or become a blur between what is personal life and what is work life, this is when we have to look at our setup. Working from home allows employees to have more flexibility and focus on their work. It also cuts down on commute hours. However, some of us are still trying to separate our work life from our personal life throughout the pandemic. Think about it. We take calls in our kitchen or bedroom at times. Kitchen and bedroom are personal life spaces, not work life spaces. Having a separate space for work helps a lot for balancing. But not everyone has this privilege; as a consequence, the blurriness of work and personal life can impact us, and burnout can creep in. However, if you section a part of the room as a work space, and only use that particular spot for work, it does help. Lastly, no matter what, have work boundaries set, such as turning off work equipment at 6PM during the week and off the whole weekend, and keep those boundaries in place.
Quote for the day:
"The actions of a responsible executive are contagious." - Joe D. Batton