In a typical team scenario, a Dropbox user shared a file with a team member for review or approval. If they wanted to check the progress of this process, the only way to do it up until now was to send an email or text message explicitly asking if the person looked at it yet — not a terribly efficient workflow.
Dropbox recognized this and has built in a fix in the latest mobile release. Now users can can simply see who has looked at or taken action on a file directly from the mobile application without having to leave the application.
In addition, those being asked to review files can see those notifications right at the top of the Home screen in the mobile app, making the whole feedback cycle much more organized.
Joey Loi, product manager at Dropbox says this is a much more streamlined way to understand activity inside of Dropbox. “With this feature, we think about the closing loop on collaboration. At its heart, collaboration is feedback flows. When I change something on a file, there are a few steps before [my co-worker] knows I’ve changed it,” Loi explained. With this feature that feedback loop can close much faster.
The company also changed the way it organizes and displays files putting the files that you opened most recently at the top of the Home screen, which is somewhat like Recents in Google Drive. It also provides a way to favorite a file and puts those files that are most important at the top of the list, making it easier to find the files that are likely most important to you more quickly when you access the mobile app.
Finally you can now drag and drop a file from an email into a Dropbox folder in a mobile context.
While none of these individual updates are earth shattering changes by any means, they do make it easier for users to access, share and work with files in Dropbox on a mobile device. “All the features are to help teams collaborate and be efficient on mobile,” Loi said.
News Source = techcrunch.com
Oracle could be feeling cloud transition growing pains
Oracle is learning that it’s hard for enterprise companies born in the data center to make the transition to the cloud, an entirely new way of doing business. Yesterday it reported its earnings and it was a mixed bag, made harder by changing the way the company counts cloud revenue.
In its earnings press release from yesterday, it put it this way: “Q4 Cloud Services and License Support revenues were up 8% to $6.8 billion. Q4 Cloud License and On-Premise License revenues were down 5% to $2.5 billion.”
Let’s compare that with the language from their Q3 revenue in March: “Cloud Software as a
Service (SaaS) revenues were up 33% to $1.2 billion. Cloud Platform as a Service (PaaS) plus Infrastructure as a Service (IaaS) revenues were up 28% to $415 million. Total Cloud Revenues were up 32% to $1.6 billion.”
See how they broke out the cloud revenue loudly and proudly in March, yet chose to combine their cloud revenue with license revenue in June.
In the post-reporting earnings call, Safra Catz, Oracle Co-CEO, responding to a question from analyst John DiFucci, took exception to the idea that the company was somehow obfuscating cloud revenue by reporting it in this way. “So first of all, there is no hiding. I told you the Cloud number, $1.7 billion. You can do the math. You see we are right where we said we’d be.”
She says the new reporting method is due to the new combined licensing products that lets customer use their license on-premises or in the cloud. Fair enough, but if your business is booming you probably want to let investors know about that. They seem to be uneasy about this approach with the stock down over 7 percent today as of publishing this article.
Oracle could of course settle all of this by spelling out their cloud revenue, but instead chose a different path. John Dinsdale, an analyst with Synergy Research, a firm that watches the cloud market was dubious about Oracle’s reasoning.
“Generally speaking, when a company chooses to reduce the amount of financial detail it shares on its key strategic initiatives, that is not a good sign. I think one of the justifications put forward is that is becoming difficult to differentiate between cloud and non-cloud revenues. If that is indeed what Oracle is claiming, I have a hard time buying into that argument. Its competitors are all moving in the opposite direction,” he said.
Indeed most are. While it’s often hard to tell exactly the nature of cloud revenue, the bigger players have been more open about this. For instance in its most recent earnings report, Microsoft reported its Azure cloud revenue grew 93 percent. Amazon reported its cloud revenue from AWS was up 49 percent to $5.4 billion in revenue, getting very specific about the revenue number.
Further you can see from Synergy’s most recent market share cloud growth numbers from the 4th quarter last year, Oracle was lumped in with “the Next 10,” not large enough to register on its own.
That Oracle chose not to break out cloud revenue this quarter can’t be seen as a good sign. To be fair, we haven’t really seen Google break out their cloud revenue either with one exception in February. But when the guys at the top of the market shout about their growth, and the guys further down don’t, you can draw your own conclusions.
News Source = techcrunch.com
Google injects Hire with AI to speed up common tasks
Since Google Hire launched last year it has been trying to make it easier for hiring managers to manage the data and tasks associated with the hiring process, while maybe tweaking LinkedIn while they’re at it. Today the company announced some AI-infused enhancements that they say will help save time and energy spent on manual processes.
“By incorporating Google AI, Hire now reduces repetitive, time-consuming tasks, like scheduling interviews into one-click interactions. This means hiring teams can spend less time with logistics and more time connecting with people,” Google’s Berit Hoffmann, Hire product manager wrote in a blog post announcing the new features.
The first piece involves making it easier and faster to schedule interviews with candidates. This is a multi-step activity that involves scheduling appropriate interviewers, choosing a time and date that works for all parties involved in the interview and scheduling a room in which to conduct the interview. Organizing these kind of logistics tend to eat up a lot of time.
“To streamline this process, Hire now uses AI to automatically suggest interviewers and ideal time slots, reducing interview scheduling to a few clicks,” Hoffmann wrote.
Another common hiring chore is finding keywords in a resume. Hire’s AI now finds these words for a recruiter automatically by analysing terms in a job description or search query and highlighting relevant words including synonyms and acronyms in a resume to save time spent manually searching for them.
Finally, another standard part of the hiring process is making phone calls, lots of phone calls. To make this easier, the latest version of Google Hire has a new click-to-call function. Simply click the phone number and it dials automatically and registers the call in call a log for easy recall or auditing.
While Microsoft has LinkedIn and Office 365, Google has G Suite and Google Hire. The strategy behind Hire is to allow hiring personnel to work in the G Suite tools they are immersed in every day and incorporate Hire functionality within those tools.
It’s not unlike CRM tools that integrate with Outlook or GMail because that’s where sales people spend a good deal of their time anyway. The idea is to reduce the time spent switching between tools and make the process a more integrated experience.
While none of these features individually will necessarily wow you, they are making use of Google AI to simplify common tasks to reduce some of the tedium associated with every-day hiring tasks.
News Source = techcrunch.com
Amazon starts shipping its $249 DeepLens AI camera for developers
Back at its re:Invent conference in November, AWS announced its $249 DeepLens, a camera that’s specifically geared toward developers who want to build and prototype vision-centric machine learning models. The company started taking pre-orders for DeepLens a few months ago, but now the camera is actually shipping to developers.
Ahead of today’s launch, I had a chance to attend a workshop in Seattle with DeepLens senior product manager Jyothi Nookula and Amazon’s VP for AI Swami Sivasubramanian to get some hands-on time with the hardware and the software services that make it tick.
DeepLens is essentially a small Ubuntu- and Intel Atom-based computer with a built-in camera that’s powerful enough to easily run and evaluate visual machine learning models. In total, DeepLens offers about 106 GFLOPS of performance.
The hardware has all of the usual I/O ports (think Micro HDMI, USB 2.0, Audio out, etc.) to let you create prototype applications, no matter whether those are simple toy apps that send you an alert when the camera detects a bear in your backyard or an industrial application that keeps an eye on a conveyor belt in your factory. The 4 megapixel camera isn’t going to win any prizes, but it’s perfectly adequate for most use cases. Unsurprisingly, DeepLens is deeply integrated with the rest of AWS’s services. Those include the AWS IoT service Greengrass, which you use to deploy models to DeepLens, for example, but also SageMaker, Amazon’s newest tool for building machine learning models.
These integrations are also what makes getting started with the camera pretty easy. Indeed, if all you want to do is run one of the pre-built samples that AWS provides, it shouldn’t take you more than 10 minutes to set up your DeepLens and deploy one of these models to the camera. Those project templates include an object detection model that can distinguish between 20 objects (though it had some issues with toy dogs, as you can see in the image above), a style transfer example to render the camera image in the style of van Gogh, a face detection model and a model that can distinguish between cats and dogs and one that can recognize about 30 different actions (like playing guitar, for example). The DeepLens team is also adding a model for tracking head poses. Oh, and there’s also a hot dog detection model.
But that’s obviously just the beginning. As the DeepLens team stressed during our workshop, even developers who have never worked with machine learning can take the existing templates and easily extend them. In part, that’s due to the fact that a DeepLens project consists of two parts: the model and a Lambda function that runs instances of the model and lets you perform actions based on the model’s output. And with SageMaker, AWS now offers a tool that also makes it easy to build models without having to manage the underlying infrastructure.
You could do a lot of the development on the DeepLens hardware itself, given that it is essentially a small computer, though you’re probably better off using a more powerful machine and then deploying to DeepLens using the AWS Console. If you really wanted to, you could use DeepLens as a low-powered desktop machine as it comes with Ubuntu 16.04 pre-installed.
For developers who know their way around machine learning frameworks, DeepLens makes it easy to import models from virtually all the popular tools, including Caffe, TensorFlow, MXNet and others. It’s worth noting that the AWS team also built a model optimizer for MXNet models that allows them to run more efficiently on the DeepLens device.
So why did AWS build DeepLens? “The whole rationale behind DeepLens came from a simple question that we asked ourselves: How do we put machine learning in the hands of every developer,” Sivasubramanian said. “To that end, we brainstormed a number of ideas and the most promising idea was actually that developers love to build solutions as hands-on fashion on devices.” And why did AWS decide to build its own hardware instead of simply working with a partner? “We had a specific customer experience in mind and wanted to make sure that the end-to-end experience is really easy,” he said. “So instead of telling somebody to go download this toolkit and then go buy this toolkit from Amazon and then wire all of these together. […] So you have to do like 20 different things, which typically takes two or three days and then you have to put the entire infrastructure together. It takes too long for somebody who’s excited about learning deep learning and building something fun.”
So if you want to get started with deep learning and build some hands-on projects, DeepLens is now available on Amazon. At $249, it’s not cheap, but if you are already using AWS — and maybe even use Lambda already — it’s probably the easiest way to get started with building these kind of machine learning-powered applications.
News Source = techcrunch.com
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