Wi-Fi 6 is a new technology and costs a premium to companies making devices like phones or other devices in your home. The added cost and few clear benefits to in-home users will likely make the industry slow to adopt Wi-Fi 6 chips. It’s no surprise that IDC research indicates mainstream adoption of Wi-Fi 6 will take place only by 2023. As someone who works in the Wi-Fi industry, I’m excited about Wi-Fi 6 and its capabilities. Yes, Wi-Fi 6 has the potential to improve speeds and more. But it’s still early in the adoption phase and too early for consumers to see immediate improvements. In-home Wi-Fi users and small businesses would be better holding off until Wi-Fi 6 costs come down and more devices adopt the necessary, compatible 11ax chips. At that point, we will start to see some of Wi-Fi 6’s benefits deliver meaningful user benefits at the right price points for accessible adoption. In the meantime, you can get the most out of Wi-Fi 5 by being smart about where you place your router, using a mesh system, and following healthy Wi-Fi hygiene, like using a router that auto-updates to the latest software.
Google Assistant is a fun tool to use when you want instant access to data to save time. And British bank NatWest wants you to bank on your voice to have such immediate access to your financial data. On Monday, the bank has announced a new pilot scheme for its customers. The three-months long trial would initially be available to 500 customers only. They would be able to communicate with their Google Home smart speaker or smartphone about their financial details. The possible questions would include those about banking balance, recent transactions, pending transactions or contact details of the bank’s helpline. Customers’ existing PIN code and online banking passwords would be the verification required to use the voice assistant. When prompted, a customer would provide a portion of their pin to confirm their identity. The bank expects that the full-scale rollout of voice banking would follow after the trial is evaluated. ... Indeed, NatWest seems to be capitalizing on trends really well, personalization and voice tech being the latest in line.
Unlike human drivers, autonomous vehicles don’t have an instinctive understanding of the world. Instead, they rely on training data to learn about conditions they are likely to encounter and how to react to them. The more high-quality data AI models have to train on, the better. ... It contains 1,000 segments, each capturing 20 seconds of continuous driving. The data comes from four locations: San Francisco and Mountain View in California; Phoenix in Arizona (where Waymo has launched a small-scale robotaxi service); and Kirkland in Washington. It also comes from multiple sources, including cameras as well as radar and lidar, which bounce lasers off nearby objects to create 3D maps of their surroundings. Helpfully, the company has labelled things like pedestrians, bikes, and signals in the data set, which means other researchers won’t have to do this grunt work. ... While Waymo deserves some credit for its move, it’s sharing just a tiny sliver of the information it has gathered.
Another crucial change heralded by 5G will be the way in which networks are managed. Organisations will be able to simultaneously manage different types of access networks (wired, wireless, optical, copper), technologies (fieldbus, ethernet, wireless), protocols and equipment. This will allow them to create a ‘network of networks’. Even better, private networks can be created to cover a specific area, wherever they make sense. 5G offers the ability for an organisation to have its own dedicated ‘slice’ of a network, putting it in much greater control of its own connectivity, security and quality of service. 5G will enable organisations to have secure, reliable, real time ‘edge cloud’ capabilities. This means that data storage and processing capability can be much closer to the point where they is needed, which reduces reduces latency and increases speed. In applications such as robotics, this can eliminate the need for on-board intelligence, allowing cheaper, smaller, dumber robots to be deployed that are controlled in real time by intelligent processing in the edge cloud.
For customers, the immediate impact of the acquisition is minimal and positive. They can still turn to Pivotal for its leading cross-cloud development platform and application development and transformation services, and they can still turn to VMware for its leading software-defined infrastructure software and growing cross-cloud migration and management software. Both companies were already members of the Dell Technologies "family" of companies and sought to cooperate in servicing customers. Most recently, both Pivotal and VMware were investing heavily in helping customers transition from legacy development processes to modern, Agile software development and from traditional, virtualized, mostly on-premises infrastructure to container-based, cloud-deployed, and Kubernetes-orchestrated infrastructure. VMware can now streamline that cooperation. It already jointly developed Kubernetes products with Pivotal while it steadily added container support to its own vSphere product line -- it hasn't always been clear which company built or sold the emerging container platform products.
Artificial intelligence is very hyped up, and for good reasons, but many pop culture information sources lose the "how" of it all, and instead focus in on the dream of what may come next, in some far-off future. Vaporware is common in the industry. It's not a good state of affairs because regular people don't see the connection between the research and the resulting products, and people fear what they don't understand. ... Artificial intelligence as it is developed today, is primarily programming, data gathering, and mathematics. It isn't sexy and it works poorly at first. Sometimes it doesn't work at all. The scary results you see in demonstrations are the product of a lot of hard work to hide the shortcomings of a narrow machine intelligence. I like to think of the artificial intelligence field like "Charlotte's Web", in the sense that Wilbur (the artificial intelligence agent) gets all of the attention at the fair, but Charlotte (the programmer) stays up all night spinning the web. When Wilbur gets all the attention at the fair, as was intended, you have to ask yourself what exactly the pig did to deserve the attention.
By banning the government's root certificate in Chrome, Firefox, and Safari, the three browser vendors are making sure the Kazakh government won't be able to secretly utilize the certificate in the future and restart its web surveillance program when things quiet down and everyone's attention and scrutiny has moved to other things. "Apple believes privacy is a fundamental human right, and we design every Apple product from the ground up to protect personal information," an Apple spokesperson told ZDNet. "We have taken action to ensure the certificate is not trusted by Safari and our users are protected from this issue." "We will never tolerate any attempt, by any organization—government or otherwise—to compromise Chrome users' data. We have implemented protections from this specific issue, and will always take action to secure our users around the world," said Parisa Tabriz, Senior Engineering Director on Google Chrome. "People around the world trust Firefox to protect them as they navigate the internet, especially when it comes to keeping them safe from attacks like this that undermine theirsecurity.
From on-device security, like face unlock, facepay and fingerprint recognition, to smartphone camera and audio functions that allow users to have more intelligent and fun experiences through apps such as Socratic, Snapchat, FaceApp and Shazam, there are a variety of ML-based features used regularly by consumers. However, for ML-based tasks that create massive amounts of data, these are often shifted to the cloud for processing before being sent back to the device with the action. For example, Socratic and Shazam both use ML processing in the cloud, not on the device. This begs the question: wouldn’t it be simpler and quicker for ML processing to happen on the device? Being able to perform ML-based tasks on the device – or the edge – instead of sending them to the cloud for processing is described by many as the “next stage of the ML evolution.” There are significant constraints that make the back and forth of ML data between the cloud and device impractical – power, cost, latency and privacy.
As a data engineer, you may run the pipelines in batch or streaming mode – depending on your use case. Standardizing names of all new customers once every hour is an example of a batch data quality pipeline. Validating the address of a customer in real time as part of approving a credit card application is an example of a real-time data quality pipeline. You may also receive complex structured and unstructured documents, such as NACHA and EDI documents, SWIFT and HIPAA transactions, and so on. You can receive documents from partners for processing or process documents to send out to partners. This is an example of a B2B data exchange pipeline. Data matching and merging is a crucial technique of master data management (MDM). This technique involves processing data from different source systems to find duplicate or identical records and merge records in batch or real time to create a golden record, which is an example of an MDM pipeline.
It is important to note that the Lewis Model is relative - people in different cultures will show a mixture of behaviour of the three types; it's just a question of how dominant each are. Applied on a national scale, North European countries and the USA are strongly linear-active, Latino countries like Spain, Italy and those in South America are very multi-active, while countries in the far east such as Japan, Vietnam and China are highly reactive. The model is a living artefact and is regularly updated as the global landscape changes. We did see common challenges and patterns all over the world. We did however also observe differences between the cultural types that Lewis describes. A few examples: the companies and teams we visited in Argentina seemed comfortable with experimenting and they really embraced the Agile value of customer collaboration over contract negotiation. Teams were not afraid to come up with ideas, but extra attention was needed to ensure focus.
Quote for the day:
"Never give up on something that you can’t go a day without thinking about." -- Winston Churchill