January 26, 2022
Kannan Subbiah
FCA | CISA | CGEIT | CCISO | GRC Consulting | Independent Director | Enterprise & Solution Architecture | Former Sr. VP & CTO of MF Utilities | BU Soft Tech | itTrident
Exascale computing is unimaginably faster than that. “Exa” means 18 zeros. That means an exascale computer can perform more than 1,000,000,000,000,000,000 FLOPS, or 1 exaFLOP. That is more than one million times faster than ASCI Red’s peak performance in 1996. Building a computer this powerful isn’t easy. When scientists started thinking seriously about exascale computers, they predicted these computers might need as much energy as up to 50 homes would use. That figure has been slashed, thanks to ongoing research with computer vendors. Scientists also need ways to ensure exascale computers are reliable, despite the huge number of components they contain. In addition, they must find ways to move data between processors and storage fast enough to prevent slowdowns. Why do we need exascale computers? The challenges facing our world and the most complex scientific research questions need more and more computer power to solve. Exascale supercomputers will allow scientists to create more realistic Earth system and climate models.?
Most of the time, organizations struggle to exercise their incident response and vulnerability management plans. An organization can have the best playbook out there, but if it doesn’t exercise it on a regular basis, well, ‘If you don’t use it, you lose it’. It needs to make sure that its playbooks have the proper scope so that everyone from executives to everyone else within the organization knows what they need to know… When I say ‘exercise’, it’s important that organizations test their plans under realistic conditions. I’m not saying they need to unplug a device or bring in simulated bad code. They just need to make sure everyone tasked in the playbook knows what’s going on, understands what their roles are and periodically tests the plans. They can take the lessons they’ve learned and refine them. Incident response exercises don’t end with victory. They end with lessons for the future. Ultimately, documents that sit on a shelf rarely get read. To be high-performing, industry, government and critical infrastructure organizations need to continue to test their technology, processes and people.
While the concept of a route is not new in any web framework really, the definition of one begins in Remix by creating the file that will contain its handler function. As long as you define the file inside the right folder, the framework will automatically create the route for you. And to define the right handler function, all you have to remember, is to export it as a default export. ... For static content, the above code snippet is fantastic, but if you’re looking to create a web application, you’ll need some dynamic behavior. And that is where Loaders and Actions come into play. Both are functions that if you export them, the handler will execute before its actual code. These functions receive multiple parameters, including the HTTP request and the URLs params and payloads.?The loader function is specifically called for GET verbs on routes and it’s used to get data from a particular source (i.e reading from disk, querying a database, etc). The function gets executed by Remix, but you can access the results by calling the useLoaderData function.?
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User consent is the foundation of open banking, whilst transparency as to where their data goes and who it is shared with is a necessary pre-condition of customer trust. The fintech sector should avoid following in the footsteps of the ad-tech industry, where entire ecosystems were built with a disregard for individuals’ rights and badly worded consent requests. Here, data collected by tracking technologies sunk into the ad-tech ecosystems without a trace, leaving privacy notices so confusing and complex that even seasoned data protection lawyers struggled to understand them. The full potential of open banking can only happen if financial ecosystems are built on transparency which gives users control over who can access their financial data and how it can be used. ... Innovative fintech solutions will need to strike the right balance between the need for regulatory compliance regarding consent, authentications, security and transparency on the one hand, and seamless user experience on the other, in particular when more complex ecosystems and relationships between various products start emerging.
“While organisations understand that data governance is important, many in the region feel that they have invested enough. And that's why data governance implementations are failing because it's still seen largely as an expense,” says Budge in an exclusive interview with Data & Storage Asean. “There's no doubt that it is a significant expense but rightly so, given that so much of digital transformation success is hinged on the proper deployment and consistent execution of a data governance program. Essentially, data governance is not a one-off investment—something you build and walk away—but requires actual ongoing practice and oversight.” Budge adds: “Executives often see only the upfront costs. For the short-sighted, the costs alone are reason enough to curtail further investment. ...” This short-sightedness, though, is not the only reason data governance is largely failing. Another pain point is what Budge describes as “the lack of understanding of the importance of a sound data governance strategy and the value that it can drive.”
According to Meta, realizing the benefits of self-supervised learning and transformer-based models requires various domains — whether vision, speech, language, or for critical applications like identifying harmful content. AI at Meta’s scale will require massively powerful computing solutions capable of instantly analyzing ever-increasing amounts of data. Meta’s RSC is a breakthrough in supercomputing that will lead to new technologies and customer experiences enabled by AI, said Lee. “Scale is important here in multiple ways,” said Lee. ... “Secondly, AI projects depend on large volumes of data — with more varied and complete data sets providing better results. Thirdly, all of this infrastructure has to be managed at the end of the day, and so space and power efficiency and simplicity of management at scale is critical as well. Each of these elements is equally important, whether in a more traditional enterprise project or operating at Meta’s scale,” Lee said.