The “cloud” in cloud computing originated from the habit of drawing the internet as a fluffy cloud in network diagrams. No wonder the most popular meaning of cloud computing refers to running workloads over the internet remotely in a commercial provider’s data center—the so-called “public cloud” model. AWS (Amazon Web Services), Salesforce’s CRM system, and Google Cloud Platform all exemplify this popular notion of cloud computing.
But there’s another, more precise meaning of cloud computing: the virtualization and central management of data center resources as software-defined pools. This technical definition of cloud computing describes how public cloud service providers run their operations. The key advantage is agility: the ability to apply abstracted compute, storage, and network resources to workloads as needed and tap into an abundance of pre-built services.
From a customer perspective, the public cloud offers a way to gain new capabilities on demand without investing in new hardware or software. Instead, customers pay their cloud provider a subscription fee or pay for only the resources they use. Simply by filling in web forms, users can set up accounts and spin up virtual machines or provision new applications. More users or computing resources can be added on the fly—the latter in real time as workloads demand those resources thanks to a feature known as auto-scaling.
The array of available cloud computing services is vast, but most fall into one of the following categories:
SaaS (software as a service)
This type of public cloud computing delivers applications over the internet through the browser. The most popular SaaS applications for business can be found in Google’s G Suite and Microsoft’s Office 365; among enterprise applications, Salesforce leads the pack. But virtually all enterprise applications, including ERP suites from Oracle and SAP, have adopted the SaaS model. Typically, SaaS applications offer extensive configuration options as well as development environments that enable customers to code their own modifications and additions.
IaaS (infrastructure as a service)
At a basic level, IaaS public cloud providers offer storage and compute services on a pay-per-use basis. But the full array of services offered by all major public cloud providers is staggering: highly scalable databases, virtual private networks, big data analytics, developer tools, machine learning, application monitoring, and so on. Amazon Web Services was the first IaaS provider and remains the leader, followed by Microsoft Azure, Google Cloud Platform, and IBM Cloud.
PaaS (platform as a service)
PaaS provides sets of services and workflows that specifically target developers, who can use shared tools, processes, and APIs to accelerate the development, test, and deployment of applications. Salesforce’s Heroku and Force.com are popular public cloud PaaS offerings; Pivotal’s Cloud Foundry and Red Hat’s OpenShift can be deployed on premises or accessed through the major public clouds. For enterprises, PaaS can ensure that developers have ready access to resources, follow certain processes, and use only a specific array of services, while operators maintain the underlying infrastructure.
Note that a variety of PaaS tailored for developers of mobile applications generally goes by the name of MBaaS (mobile back end as a service), or sometimes just BaaS (back end as a service).
FaaS (functions as a service)
FaaS, the cloud instantiation of serverless computing, adds another layer of abstraction to PaaS, so that developers are completely insulated from everything in the stack below their code. Instead of futzing with virtual servers, containers, and application runtimes, they upload narrowly functional blocks of code, and set them to be triggered by a certain event (e.g. a form submission or uploaded file). All the major clouds offer FaaS on top of IaaS: AWS Lambda, Azure Functions, Google Cloud Functions, and IBM OpenWhisk. A special benefit of FaaS applications is that they consume no IaaS resources until an event occurs, reducing pay-per-use fees.
The private cloud downsizes the technologies used to run IaaS public clouds into software that can be deployed and operated in a customer’s data center. As with a public cloud, internal customers can provision their own virtual resources in order to build, test, and run applications, with metering to charge back departments for resource consumption. For administrators, the private cloud amounts to the ultimate in data center automation, minimizing manual provisioning and management. VMware’s Software Defined Data Center stack is the most popular commercial private cloud software, while OpenStack is the open source leader.
A hybrid cloud is the integration of a private cloud with a public cloud. At its most developed, the hybrid cloud involves creating parallel environments in which applications can move easily between private and public clouds. In other instances, databases may stay in the customer data center and integrate with public cloud applications—or virtualized data center workloads may be replicated to the cloud during times of peak demand. The types of integrations between private and public cloud vary widely, but they must be extensive to earn a hybrid cloud designation.
Public APIs (application programming interfaces)
Just as SaaS delivers applications to users over the internet, public APIs offer developers application functionality that can be accessed programmatically. For example, in building web applications, developers often tap into Google Maps’ API to provide driving directions; to integrate with social media, developers may call upon APIs maintained by Twitter, Facebook, or LinkedIn. Twilio has built a successful business dedicated to delivering telephony and messaging services via public APIs. Ultimately, any business can provision its own public APIs to enable customers to consume data or access application functionality.
iPaaS (integration platform as a service)
Data integration is a key issue for any sizeable company, but particularly for those that adopt SaaS at scale. iPaaS providers typically offer prebuilt connectors for sharing data among popular SaaS applications and on-premises enterprise applications, though providers may focus more or less on B-to-B and ecommerce integrations, cloud integrations, or traditional SOA-style integrations. iPaaS offerings in the cloud from such providers as Dell Boomi, Informatica, MuleSoft, and SnapLogic also enable users to implement data mapping, transformations, and workflows as part of the integration-building process.
IDaaS (identity as a service)
The most difficult security issue related to cloud computing is the management of user identity and its associated rights and permissions across private data centers and pubic cloud sites. IDaaS providers maintain cloud-based user profiles that authenticate users and enable access to resources or applications based on security policies, user groups, and individual privileges. The ability to integrate with various directory services (Active Directory, LDAP, etc.) and provide is essential. Okta is the clear leader in cloud-based IDaaS; CA, Centrify, IBM, Microsoft, Oracle, and Ping provide both on-premises and cloud solutions.
Collaboration solutions such as Slack, Microsoft Teams, and HipChat have become vital messaging platforms that enable groups to communicate and work together effectively. Basically, these solutions are relatively simple SaaS applications that support chat-style messaging along with file sharing and audio or video communication. Most offer APIs to facilitate integrations with other systems and enable third-party developers to create and share add-ins that augment functionality.
Key players in such industries as financial services, healthcare, retail, life sciences, and manufacturing provide PaaS clouds to enable customers to build vertical applications that tap into industry-specific, API-accessible services. Vertical clouds can dramatically reduce the time to market for vertical applications and accelerate domain-specific B-to-B integrations. Most vertical clouds are built with the intent of nurturing partner ecosystems.
Cloud computing attractions and objections
The cloud’s main appeal is to reduce the time to market of applications that need to scale dynamically. Increasingly, however, developers are drawn to the cloud by the abundance of advanced new services that can be incorporated into applications, from machine learning to internet-of-things connectivity.
Although businesses sometimes migrate legacy applications to the cloud to reduce data center resource requirements, the real benefits accrue to new applications that take advantage of cloud services and “cloud native” attributes. The latter include microservices architecture, Linux containers to enhance application portability, and container management solutions such as Kubernetes that orchestrate container-based services. Cloud-native approaches and solutions can be part of either public or private clouds and help enable highly efficient devops-style workflows.
Objections to the public cloud generally begin with cloud security, although the major public clouds have proven themselves much less susceptible to attack than the average enterprise data center. Of greater concern is the integration of security policy and identity management between customers and public cloud providers. In addition, government regulation may forbid customers from allowing sensitive data off premises. Other concerns include the risk of outages and the long-term operational costs of public cloud services.
Yet cloud computing, public or private, has become the platform of choice for large applications, particularly customer-facing ones that need to change frequently or scale dynamically. More significantly, the major public clouds now lead the way in enterprise technology development, debuting new advances before they appear anywhere else. Workload by workload, enterprises are opting for the cloud, where an endless parade of exciting new technologies invite innovative use.
On – 10 Jul, 2017 By Eric Knorr
Did social media actually counter election misinformation?
Ahead of the election, Facebook, Twitter, and YouTube promised to clamp down on election misinformation, including unsubstantiated charges of fraud and premature declarations of victory by candidates. And they mostly did just that — though not without a few hiccups.
But overall their measures still didn’t really address the problems exposed by the 2020 U.S. presidential contest, critics of the social platforms contend.
“We’re seeing exactly what we expected, which is not enough, especially in the case of Facebook,” said Shannon McGregor, an assistant professor of journalism and media at the University of North Carolina.
One big test emerged early Wednesday morning as vote-counting continued in battleground states including Wisconsin, Michigan, and Pennsylvania. President Donald Trump made a White House appearance before cheering supporters, declaring he would challenge the poll results. He also posted misleading statements about the election on Facebook and Twitter, following months of signaling his unfounded doubts about expanded mail-in voting and his desire for final election results when polls closed on Nov. 3.
So what did tech companies do about it? For the most part, what they said they would, which primarily meant labeling false or misleading election posts in order to point users to reliable information. In Twitter’s case, that sometimes meant obscuring the offending posts, forcing readers to click through warnings to see them, and limiting the ability to share them.
The video-sharing app TikTok, popular with young people, said it pulled down some videos Wednesday from high-profile accounts that were making election fraud allegations, saying they violated the app’s policies on misleading information. For Facebook and YouTube, it mostly meant attaching authoritative information to election-related posts.
For instance, Google-owned YouTube showed a video of Trump’s White House remarks suggesting fraud and premature victories, just as some traditional news channels did. But Google placed an “information panel” beneath the videos noting that election results may not be final and linking to Google’s election results page with additional information.
“They’re just appending this little label to the president’s posts, but they’re appending those to any politician talking about the election,” said McGregor, who blamed both the tech giants and traditional media outlets for shirking their responsibility to curb the spread of misinformation about the election results instead of amplifying a falsehood just because the president said it.
“Allowing any false claim to spread can lead more people to accept it once it’s there,” she said.
Trump wasn’t alone in attracting such labels. Republican U.S. Sen. Thom Tillis got a label on Twitter for declaring a premature reelection victory in North Carolina. The same thing happened to a Democratic official claiming that former Vice President Joe Biden had won Wisconsin.
The flurry of Trump’s claims that began early Wednesday morning continued after the sun rose over Washington. By late morning, Trump was tweeting an unfounded complaint that his early lead in some states seemed to “magically disappear” as the night went on and more ballots were counted.
Twitter quickly slapped that with a warning that said “some or all of the content shared in this Tweet is disputed and might be misleading about an election or other civic process.” It was among a series of such warnings Twitter applied to Trump tweets Wednesday, which makes it harder for viewers to see the posts without first reading the warning.
Much of the slowdown in the tabulation of results had been widely forecasted for months because the coronavirus pandemic led many states to make it easier to vote by mail, and millions chose to do so rather than venturing out to cast ballots in person. Mail ballots can take longer to process than ballots cast at polling places.
In a Sept. 3 post, Facebook CEO Mark Zuckerberg said that if a candidate or campaign tries to declare victory before the results are in, the social network would label their post to note that official results are not yet in and direct people to the official results.
But Facebook limited that policy to official candidates and campaigns declaring premature victory in the overall election. Posts that declared premature victory in specific states were flagged with a general notification about where to find election information but not warnings that the information was false or misleading.
Facebook also issued a blanket statement on the top of Facebook and Instagram feeds on Wednesday noting that the votes for the U.S. presidential election are still being counted.
Twitter was a bit more proactive. Based on its “ civic integrity policy,” implemented last month, Twitter said it would label and reduce the visibility of Tweets containing “false or misleading information about civic processes” in order to provide more context. It labeled Trump’s tweets declaring premature victory as well as claims from Trump and others about premature victory in specific states.
The Twitter and Facebook actions were a step in the right direction, but not that effective — particularly in Twitter’s case, said Jennifer Grygiel, a professor at Syracuse University and social media expert.
That’s because tweets from major figures can get almost instant traction, Grygiel said. So even though Twitter labeled Trump’s tweets about “being up big,” and votes being cast after polls closed and others, by the time the label appeared, several minutes after the tweet, the misinformation had already spread. One Wednesday Trump tweet falsely complaining that vote-counters were “working hard” to make his lead in the Pennsylvania count “disappear” wasn’t labeled for more than 15 minutes and was not obscured.
“Twitter can’t really enforce policies if they don’t do it before it happens, in the case of the president,” Grygiel said. “When a tweet hits the wire, essentially, it goes public. It already brings this full force of impact of market reaction.”
Grygiel suggested that for prominent figures like Trump, Twitter could pre-moderate posts by delaying publication until a human moderator can decide whether it needs a label. That means flagged tweets would publish with a label, making it more difficult to spread unlabeled misinformation, especially during important events like the election.
This is less of an issue on Facebook or YouTube, where people are less likely to interact with posts in real-time. YouTube could become more of an issue over the next few days, Grygiel suggested, if Trump’s false claims are adopted by YouTubers who are analyzing the election.
“Generally, platforms have policies in place that are an attempt to do something, but at the end of the day it proved to be pretty ineffective,” Grygiel said. “The president felt empowered to make claims.”
How artificial intelligence may be making you buy things
The shopping lists we used to scribble on the back of an envelope are increasingly already known by the supermarkets we frequent.
Firstly via the loyalty cards, we scan at checkouts, and more and more so from our online baskets, our shopping habits are no longer a secret.
But now more retailers are using AI (artificial intelligence) – software systems that can learn for themselves – to try to automatically predict and encourage our very specific preferences and purchases like never before.
Retail consultant Daniel Burke, of Blick Rothenberg, calls this “the holy grail… to build up a profile of customers and suggest a product before they realize it is what they wanted”.
So the next time you dash into your local shop to buy certain snacks and a particular wine on a Friday night, perhaps you can blame AI and a computer that has learned all about you, for the decision.
Will Broome is the founder of Ubamarket, a UK firm that makes a shopping app that allows people to pay for items via their phones, make lists, and scan products for ingredients and allergens.
“Our AI system tracks people’s behavior patterns rather than their purchases, and the more you shop the more the AI knows about what kinds of products you like,” he says.
“The AI module is designed not only to do the obvious stuff, but it learns as it goes along and becomes anticipatory. It can start to build a picture of how likely you are to try a different brand, or to buy chocolate on a Saturday.”
And it can offer what he calls “hyper-personalized offers”, like cheaper wine on a Friday night.
Ubamarket has struggled to persuade the UK’s biggest supermarkets to adopt the app, so it has instead done deals with smaller convenience shop chains in the UK including Spar, Co-op, and Budgens, stores not traditionally associated with hi-tech.
Take-up of the app remains low but it is growing, in part thanks to the coronavirus pandemic, which has made people more reluctant to touch tills or stand in queues.
“With the app, we have found that the average contents of a basket are up 20%, and people with the app are three times more likely to return to shop in that store,” says Mr. Broome.
In Germany, a Berlin start-up called SO1 is doing similar things with its AI system for retailers. It claims that nine times more people buy AI-suggested goods than those offered by traditional promotions, even when the discounts are 30% less.
Getting offers on goods that you actually might want to buy rather than random coupons is great for consumers. However, Jeni Tennison, who heads up the UK’s Open Data Institute, a body that campaigns against the misuse of data, remains cautious about the vast amounts of information on people that is being collected.
“People are happy to be recommended products, but start to feel more uncomfortable when they are being nudged, or manipulated, into particular buys based on a caricature of who they are rather than the full complexity of their personality,” she says.
And she adds that there are bigger societal questions raised by the use of AI in retail.
“We need to ask how equitable and ethical the data collection is. So, for example, are middle-class white women being offered money off fresh vegetables, but it is not being offered to someone who could really benefit from it?” says Ms. Tennison.
“What we really need to understand is what impact data collection and profiling has on different sectors of society. Is it profiling people based on race, social-economic status, sexuality?”
Online giant Amazon is no stranger to data collection. It has vast amounts of information on its customers from their online purchases, and via its products such as Ring doorbells and Echo speakers. It is now making a move into physical retailing, with bricks-and-mortar shops packed full of AI-aided computer vision technology.
It means that in its Amazon Go grocery stores, currently up and running at 27 locations in the US, people can shop with no interaction with a human or a till.
They simply swipe their smartphones on the scanner when they enter the supermarket, pick up what they want to buy, and then just walk out. The AI is watching of course and sends you a bill at the end.
The first Amazon Go stores were small sites, because of the expense of the sensors and equipment needed, but the company is gradually expanding to larger stores.
Amazon is also working on tech for supermarkets that don’t want to retrofit their stores with such costly systems. This is where its Dash Cart comes in, a supermarket trolley that is packed with sensors to detect and collate everything you put in.
In the Los Angeles store where it is being tested, it has a special fast lane to check out, without the need for a human, of course.
Another US retailer, Kroger, is experimenting with smart shelves fitted with LCD displays that beam contextualized content designed to draw customers towards them. Some display offers and personalized content by connecting via Bluetooth to loyalty apps on phones.
More than three-quarters of large retailers around the world either have AI systems now in place or plan to install them before the end of the year, according to research group Gartner.
Its analyst Sandeep Unni says the global pandemic has accelerated this trend because it has dramatically changed consumer habits.
“People panic bought, and focused on essential rather than non-essential goods, which in turn created a huge supply-demand imbalance,” he says. “This meant that we saw shelves becoming empty, and demand forecasting was suddenly not working.”
New Tech Economy is a series exploring how technological innovation is set to shape the new emerging economic landscape.
US firm Afresh makes AI-based supply systems for supermarkets to help the best plan for what stock levels are required.
Afresh founder Matt Schwartz says that staff has to teach the AI systems about key events in the calendar, such as the recent Halloween.
“Historically taking account of things like holidays [and other events] has been one of the biggest challenges for AI,” he says.
“[And] we can’t fully automate away the humans. The AI may suggest 20 cases of pumpkins for October, and the humans can adjust that if they need to.”
Microsoft to permanently close nearly all of its retail stores
Microsoft is about to (mostly) give up on retail. Today the company announced plans to permanently close all Microsoft Store locations in the United States and around the world, except for four locations that will remain open.
Those locations are New York City (Fifth Ave), London (Oxford Circus), Sydney (Westfield Sydney), and the Redmond campus location. All other Microsoft Store locations across the United States and globally will be closing.
The decision partially explains why Microsoft had yet to reopen a single store after they were all closed in light of the COVID-19 pandemic. Last week, Microsoft told The Verge that its “approach for re-opening Microsoft Store locations is measured and cautious, guided by monitoring global data, listening to public health and safety experts, and tracking local government restrictions.” The company declined to offer an update on when any stores might open again.
Since many Microsoft stores are in shopping centers and malls, the continued closure hasn’t stood out as unusual. In US states that are taking a cautious approach to restore retail operations — to avoid a resurgence of the novel coronavirus — most malls remain closed. There have already been spikes of COVID-19 cases in regions with more relaxed guidelines, which has led Apple to re-close some stores where it had only recently welcomed customers back in.
In April, Microsoft outlined in a blog post how many retail store associates had shifted to remote work after their everyday jobs were sidelined. The company has continued to provide regular pay for team members through the pandemic. “Our retail team members will continue to serve customers working from Microsoft corporate facilities or remotely and we will continue to develop our diverse team in support of the overall company mission and objectives,” the company said.
The Microsoft Store debuted in 2009 and closely adhered to Apple’s successful retail playbook. Each store is a showcase for the company’s Surface and Xbox hardware, plus a selection of third-party PCs. Employees are well-versed in all things Windows, and the company also offers in-store events, workshops, customer service, and repairs.
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