You need to be able to orchestrate the ebb and flow of data among multiple nodes, either as multiple sources, multiple targets, or multiple intermediate aggregation points. The data integration platform must also be cloud native today. This means the integration capabilities are built on a platform stack that is designed and optimized for cloud deployments and implementation. This is crucial for scale and agility -- a clear advantage the cloud gives over on-premises deployments. Additionally, data management centers around trust. Trust is created through transparency and understanding, and modern data integration platforms give organizations holistic views of their enterprise data and deep, thorough lineage paths to show how critical data traces back to a trusted, primary source. Finally, we see modern data analytic platforms in the cloud able to dynamically, and even automatically, scale to meet the increasing complexity and concurrency demands of the query executions involved in data integration. The new generation of some data integration platforms also work at any scale, executing massive numbers of data pipelines that feed and govern the insatiable appetite for data in the analytic platforms.
While outsiders view testing as simple and straightforward, it's anything but true. Until as recently as the 1980s, the dominant idea in testing was to do the same thing repeatedly and write down the results. For example, you could type 2+3 onto a calculator and see 5 as a result. With this straightforward, linear test, there are no variables, looping or condition statements. The test is so simple and repeatable, you don't even need a computer to run this test. This approach is born from thinking akin to codeless test automation: Repeat the same equation and get the same result each time for every build. The two primary methods to perform such testing are the record and playback method, and the command-line test method. Record and playback tools run in the background and record everything; testers can then play back the recording later. Such tooling can also create certification points, to check the expectation that the answer field will become 5. Record and playbook tools generally require no programming knowledge at all -- they just repeat exactly what the author did. It's also possible to express tests visually. Command-driven tests work with three elements: the command, any input values and the expected results.
It’s certainly possible that the scales are tipping in favor of those who believe AGI will be achieved sometime before the century is out. In 2013, Nick Bostrom of Oxford University and Vincent Mueller of the European Society for Cognitive Systems published a survey in Fundamental Issues of Artificial Intelligence that gauged the perception of experts in the AI field regarding the timeframe in which the technology could reach human-like levels. The report reveals “a view among experts that AI systems will probably (over 50%) reach overall human ability by 2040-50, and very likely (with 90% probability) by 2075.” Futurist Ray Kurzweil, the computer scientist behind music-synthesizer and text-to-speech technologies, is a believer in the fast approach of the singularity as well. Kurzweil is so confident in the speed of this development that he’s betting hard. Literally, he’s wagering Kapor $10,000 that a machine intelligence will be able to pass the Turing test, a challenge that determines whether a computer can trick a human judge into thinking it itself is human, by 2029.
The opportunity here really cannot be underestimated. It is there for the taking by organisations who are willing to approach technological transformation in a radically different way. This involves breaking away from monolithic technology platforms, obstructive governance procedures, and the eye-wateringly expensive delivery programmes so often facilitated by traditional large consulting firms. The truth is, you simply don’t need hundreds of people to drive significant change or digital transformation. What you do need is to adopt new technology approaches, re-think operating models and work with partners who are agile experts, who will fight for their clients' best interests and share their knowledge to upskill internal staff. Hand picking a select group of top individuals to work in this way provides a multiplier of value when compared to hiring greater numbers of less experienced staff members. Of course, external partners must be able to deliver at the scale required by the clients they work with. But just as large organisations have to change in order to embrace the benefits of the digital age, consulting models too must adapt to offer the services their clients need at the value they deserve.
the internal IT team needs to work closely with the service provider. To thoroughly understand and outline the project requirements and deliverables. This is to ensure that there is no aspect that is overlooked, and both sides are up to speed on the security and regulatory compliance requirements. Not just the vendor, but the team members and all the tools used in the migration need to meet all the necessary certifications to carry out a government project. Of course, certain territories will have more stringent requirements than others. Finally, an effective transition or change management strategy will be important to complete the transition. Proper internal communications and comprehensive training for employees will help everyone involved be aware of what’s required from them, including grasping any new processes or protocols and circumnavigating any productivity loss during the data migration. While the nitty-gritty of a public sector migration might be similar to a private company’s, a government data migration can be a much longer and unwieldy process, especially with the vast number of people and the copious amounts of sensitive data involved.
Agreeing with the fact that the technologies are captivating us completely with their interesting innovations and gadgets. From Artificial intelligence to machine learning, IoT, big data, virtual and augmented reality, Blockchain, and 5G; everything seems to take over the world way too soon. Keeping it to the topic of Artificial Intelligence, this technology has expanded its grip on our lives without even making us realize that fact. In the days of the pandemic, the IT experts kept working from home and the tech-grounds kept witnessing smart ideas and AI-driven innovations. Artificial Intelligence is also the new normal. Artificial Intelligence is going to be the center of our new normal and it will be driving the other nascent technologies to the point towards success. Soon, AI will be the genius core of automated and robotic operations. In the blink of an eye, Artificial Intelligence can be seen adopted by companies so rapidly and is making its way into several sectors. 2020 has seen this deployment on a wider scale as the AI experts were working from home but the progress didn’t see a stop in the tech fields.
There are some underlying trends in the following vignettes. The internet of things and related technologies are in early use in smart cities and other infrastructure applications, such as monitoring warehouses, or components of them, such as elevators. These projects show clear returns on investment and benefits. For instance, smart streetlights can make residents’ lives better by improving public safety, optimizing the flow of traffic on city streets, and enhancing energy efficiency. Such outcomes are accompanied with data that’s measurable, even if the social changes are not—such as reducing workers’ frustration from spending less time waiting for an office elevator. Early adoption is also found in uses in which the harder technical or social problems are secondary, or, at least, the challenges make fewer people nervous. While cybersecurity and data privacy remain important for systems that control water treatment plants, for example, such applications don’t spook people with concerns about personal surveillance. Each example has a strong connectivity component, too. None of the results come from “one sensor reported this”—it’s all about connecting the dots.
Sam Chatman, VP of IT Ops at OneMain Financial, explains the impact of levering AIOps is, “Being able to understand what is released, when it’s released, and the potential impacts of that release. We are overcoming alert fatigue, and BigPanda will be our Watson of the Enterprise Monitoring Center (EMC) by automating alerts, opening incident tickets, and identifying those actions to improve our mean time to recovery. This helps us keep our systems up when our users and customers need them to be.” For other organizations, it might help to visualize what naturally happens to IT operations’ monitoring programs over time. Every time systems go down and IT gets thrown under the bus for a major incident, they add new monitoring systems and alerts to improve their response times. As new multicloud, database, and microservice technologies emerged, they add even more monitoring tools and increased observability capabilities. Having more operational data and alerts is a good first step, but then alert fatigue kicks in when tier-one support teams respond and must make sense over dozens to thousands of alerts.
Demand for gaming hardware blew up during the pandemic, with everyone bored and stuck at home. In the early days of the lockdowns in the United States and China, Nintendo’s awesome Switch console became red-hot. Even replacement controllers and some games became hard to find. ... Beyond the AMD-specific TSMC logjam, the chip industry in general has been suffering from supply woes. Even automakers and Samsung have warned that they’re struggling to keep up with demand. We’ve heard whispers that the components used to manufacture chips—from the GDDR6 memory used in modern GPUs to the substrate material fundamentally used to construct chips—have been in short supply as well. Seemingly every industry is seeing vast demand for chips of all sorts right now. ... High demand and supply shortages are the perfect recipe for folks looking to flip graphics cards and make a quick buck. The second they hit the streets, the current generation of GPUs were set upon by “entrepreneurs” using bots to buy up stock faster than humans can, then selling their ill-gotten wares for a massive markup on sites like Ebay, StockX, and Craigslist.
Production is when AI models prove their value, and as AI use spreads, it becomes more important for businesses to be able to scale up model production to remain competitive. But as Shlomo notes, scaling production is exceedingly difficult, as this is when AI projects move from the theoretical to the practical and have to prove their value. “While algorithms are deterministic and expected to have known results, real world scenarios are not,” asserts Shlomo. “No matter how well we will define our algorithms and rules, once our AI system starts to work with the real world, a long tail of edge cases will start exposing the definition holes in the rules, holes that are translated to ambiguous interpretation of the data and leading to inconsistent modeling.” That’s much of the reason why more than 90% of c-suite executives at leading enterprises are investing in AI, but fewer than 15% have deployed AI for widespread production. Part of what makes scaling so difficult is the sheer number of factors for each model to consider. In this way, HITL enables faster, more efficient scaling, because the ML model can begin with a small, specific task, then scale to more use cases and situations.
Quote for the day:
"A true dreamer is one who knows how to navigate in the dark" -- John Paul Warren