A leader is defined by a purpose that is bigger than themselves. When that purpose serves a greater good, it becomes the platform for great leadership. Gandhi summed up his philosophy of life with these words: “My life is my message.” That one statement speaks volumes about how he chose to live his life and share his message of non-violence, compassion, and truth with the world. When you have a purpose that goes beyond you, people will see it and identify with it. Being purpose-driven defines the nobility of one’s character. It inspires others. At its core, your leadership purpose springs from your identity, the essence of who you are. Purpose is the difference between a salesman and a leader, and in the end, the leader is the one that makes the impact on the world. ... The earmark of a great leader is their care and concern for their people. Displaying compassion towards others is not about a photo-op, but an inherent characteristic that others can feel and hear when they are with you. It lives in the warmth and timbre of your voice. It shows in every action you take. Caring leaders take a genuine interest in others.
It is now well established that the modern AI/ML systems’ success has been critically dependent on their ability to process massive amounts of raw data in a parallel fashion using task-optimized hardware. Therefore, the use of specialized hardware like Graphics Processing Units (GPUs) played a significant role in this early success. Since then, a lot of emphasis has been given to building highly optimized software tools and customized mathematical processing engines (both hardware and software) to leverage the power and architecture of GPUs and parallel computing. While the use of GPUs and distributed computing is widely discussed in the academic and business circles for core AI/ML tasks (e.g. running a 100-layer deep neural network for image classification or billion-parameter BERT speech synthesis model), they find less coverage when it comes to their utility for regular data science and data engineering tasks. These data-related tasks are the essential precursor to any ML workload in an AI pipeline and they often constitute a majority percentage of the time and intellectual effort spent by a data scientist or even an ML engineer.
Simply put, Deep Learning is an ML technique where very large Neural networks are used to learn from the large quantum of data and deliver highly accurate outcomes. The more the data, the better the Deep Learning model learns and the more accurate the outcome. Deep Learning is at the centre of exciting innovation possibilities like Self Driven Cars, Image recognition, virtual assistants, instant audio translations etc. The ability to manage both structured and unstructured data makes this a truly powerful technology advancement. ... Differentiation not only comes from product proposition and comms but also how consumers experience the brand/service online. And here too strides in Deep Learning are enabling marketers with more sophisticated ways to create differentiation. Website Experience: Based on the consumer profile and cohort, even the website experience can be customized to ensure that a customer gets a truly relevant experience creating more affinity for the brand/service. A great example of this is Netflix where no 2 users have a similar website experience based on their past viewing of content.
When it comes to cybersecurity teams and leadership, the report findings revealed no strong differences between the security function having a CISO or CIO at the helm and organizational views on increased or decreased cyberattacks, confidence levels related to detecting and responding to cyberthreats or perceptions on cybercrime reporting. However, it did find that security function ownership is related to differences regarding executive valuation of cyberrisk assessments (84 percent under CISOs versus 78 percent under CIOs), board of director prioritization of cybersecurity (61% under CISOs versus 47% under CIOs) and alignment of cybersecurity strategy with organizational objectives (77% under CISOs versus 68% under CIOs). The report also found that artificial intelligence (AI) is fully operational in a third of the security operations of respondents, representing a four percent increase from the year before. Seventy-seven percent of respondents also revealed they are confident in the ability of their cybersecurity teams to detect and respond to cyberthreats, a three-percentage point increase from last year.
The world is somewhere on the rapid growth part of the S-curve of the information revolution. It is at the point that the S-curve is going into maturity that speed of change slows down. It is at that point that the gap between expectation of change capacity by upper management and the reality of the actual capacity for change is going to increase. And Enterprise/IT Architects–Strategists are amongst other things tasked with bridging that worsening gap. Which means that — for Enterprise/IT Architects–Strategists and many more people who actually are active in shaping that digital landscape — as long as there is no true engagement of top management, the gap between them and their upper management looks like this red curve, which incidentally also represents how enjoyable/frustrating EA-like jobs are ... We’re in the period of rapid growth, the middle of the blue curve. That is also the period where (IT-related, which is much) change inside organisations (and society) gets more difficult every day, and thus noticeably slows down. Well run and successful projects that take more than five years are no exception.
Testing is a complex activity, just like software engineering or any craft that takes study, critical thinking and commitment. It is not possible to encode everything that happens during testing into a document or artifact such as a test case. The best we can do is report our testing in a way that tells a compelling and informative story about risk to people who matter, i.e. those making the decisions about the product under test. ... SBTM is a kind of activity-based test management method, which we organize around test sessions. The method focuses on the activities testers perform during testing. There are many activities that testers perform outside of testing, such as attend meetings, help developers troubleshoot problems, attend training, and so on. Those activities don’t belong in a test session. To have an accurate picture of only the testing performed and the duration of the testing effort, we package test activity into sessions.
The answer is to combine the tools that tell you about the past and present states, with a tool that will shine a light on how the environment will behave in the future. Doing this requires the use of Computational Fluid Dynamics (CFD). A virtual simulation of an entire data center, CFD-based simulation enables operators to accurately calculate the environmental conditions of the facility. Virtual sensors, for instance, ensure the simulated data reflects the sensor data. Consequently, the results can be used to investigate conditions anywhere you want, in fine detail. CFD also extends beyond temperature maps and includes humidity, pressure and air speed. Airflow, for instance, can be traced to show how it is getting from one place to another, offering unparalleled insight into the cause of thermal challenges. Critically, CFD enables operators to simulate future resilience. A validated CFD model will offer information about any configuration of your data centre, simulating variations in current configurations, or in new ones you haven’t yet deployed.
Specific to the pandemic, a lot of technologies, endpoint security, cloud security, and zero trust, which have proliferated after the pandemic -- organizations have changed how they talk about how they are deploying these. Previously there may have been a cloud security team and an infrastructure security team, very soon the line started getting blurred. There was very little need for network security because not many people were coming to work. It had to be changed in terms of organization, prioritization, and collaboration within the enterprise to leverage technology to support this kind of workforce. ... Every team has to be constantly vigilant about what might be happening in their environment and who could be attacking them. The other side of it is constant learning. You constantly demonstrate awareness and vigilance and constantly learn from it. The red team can be a very effective way to train an entire organization and sensitize them to let’s say a phishing attack. As common as phishing attacks are, a large majority of people, including in the technology sectors, do not know how to fully prevent them despite the fact there are lot of phishing [detection] technology tools available.
The challenge comes when we stop looking at collections as independent elements and start looking at networks as collections of collections. A network isn’t an anthill, it’s the whole ecosystem the anthill is inside of including trees and cows and many other things. Trees know how to be trees, cows understand the essence of cow-ness, but what understands the ecosystem? A farm is a farm, not some arbitrary combination of trees, cows, and anthills. The person who knows what a farm is supposed to be is the farmer, not the elements of the farm or the supplier of those elements, and in your network, dear network-operations type, that farmer is you. In the early days, the developers of AI explicitly acknowledged the separation between the knowledge engineer who built the AI framework and the subject-matter expert whose knowledge shaped the framework. In software, especially DevOps, the management tools aim to achieve a goal state, which in our farm analogy, describes where cows, trees, and ants fit in. If the current state isn’t the goal state, they do stuff or move stuff around to converge on the goal. It’s a great concept, but for it to work we have to know what the goal is.
The microservice design of Milvus 2.0, which features read and write separation, incremental and historical data separation, and CPU-intensive, memory-intensive, and IO-intensive task separation. Microservices help optimize the allocation of resources for the ever-changing heterogeneous workload. In Milvus 2.0, the log broker serves as the system's backbone: All data insert and update operations must go through the log broker, and worker nodes execute CRUD operations by subscribing to and consuming logs. This design reduces system complexity by moving core functions such as data persistence and flashback down to the storage layer, and log pub-sub make the system even more flexible and better positioned for future scaling. Milvus 2.0 implements the unified Lambda architecture, which integrates the processing of the incremental and historical data. Compared with the Kappa architecture, Milvus 2.0 introduces log backfill, which stores log snapshots and indexes in the object storage to improve failure recovery efficiency and query performance.
Quote for the day:
"Expression is saying what you wish to say, Impression is saying what others wish to listen." -- Krishna Sagar