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.
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.
Every single sector of the economy will be transformed by AI and 5G in the next few years. Autonomous vehicles may result in reduced demand for cars and car parking spaces within towns and cities will be freed up for other usage. It maybe that people will not own a car and rather opt to pay a fee for a car pooling or ride share option whereby an autonomous vehicle will pick them up take them to work or shopping and then rather than have the vehicle remain stationary in a car park, the same vehicle will move onto its next customer journey. The interior of the car will use AR with Holographic technologies to provide an immersive and personalised experience using AI to provide targeted and location-based marketing to support local stores and restaurants. Machine to machine communication will be a reality with computers on board vehicles exchanging braking, speed, location and other relevant road data with each other and techniques such as multi-agent Deep Reinforcement Learning may be used to optimise the decision making by the autonomous vehicles.
In 2022, 8-gigabytes of memory is quite a low amount, but usually this is not such a big hindrance until the point where it would likely be a big hindrance for someone else. Really what has happened is that Julia has spoiled us. I know that I can pass fifty-million observations through something with no questions, comments, or concerns from my processor in Julia, no problem. It is the memory, however, that I am often running into the limits of. That being said, I wanted to explore some ideas on decomposing an entire feature’s observations into a “ canonical form” of sorts, and I started researching precisely those topics. My findings in regards to ways to preserve memory have been pretty cool, so I thought it might be an interesting read to look at what all I have learned, and additionally a pretty nice idea I came up with myself. All of this code is part of my project, OddFrames.jl, which is just a Dataframes.jl alternative with more features, and I am almost ready to publish this package.
No-code applied to cellular IoT management is an alternative to API, an accessible route to automation for non-developer teams. According to Gartner, 41% of employees outside the IT function are customizing or building data or technology solutions. The interest and willingness are there. The tools increasingly so. Automation tools enable teams with minimal to no hand-coding experience to automate workflows that would previously wait in a backlog for the attention of a specialist developer. IoT needs scale; there can be no hold-ups or bottlenecks in bringing projects to completion. Applying the benefits of no-code to cellular IoT addresses this. There will always be a high demand for skilled software developers to tackle complex development projects. The transition to the cloud did not drop system administrators, and no-code solutions will not replace specialist software developers; development ability is still needed. The no-code opportunity is in repetitive tasks such as activating an IoT SIM card. Using no-code, this workflow can easily be automated and free up developer resources for more complex integrations.
Regardless of the technology that eventually supports Web3, the key will be distribution; data can’t be trapped in a single place. Let me give you an example: data.world may seem like a Web 2.0 application. It’s collaborative, users generate content in the form of data and analysis, which can be loaded into our servers. That can feel like handing over control. However, unlike the case for today’s data brokers — Facebook, Amazon, etc. — you didn’t give up rights to your data; it is still yours to modify, restrict, or even delete at your discretion. More technically, data.world is built on the Semantic Web standards. This means that if you don’t want your data hosted by data.world, that’s just fine. Host it under some other SPARQL endpoint, give data.world a pointer to your data, and it will behave just the same as if it were hosted with us. Deny access to that endpoint — or just remove it — and it’s gone. This is not to say that data.world is the solution to Web3, here today; far from it. We still don’t really know what Web3 will turn out to be. But one thing is for certain — any Web3 platform will have to play in a world of distributed data.
Quote for the day:
"Small disciplines repeated with consistency every day lead to great achievements gained slowly over time." -- John C. Maxwell