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Despite initial results, are executives and employees growing frustrated with working from home and concerned that it’s killing innovation? Yes, but there are other ways to foster innovation.

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Working from home is affecting all teams differently, but some believe it’s stifling innovation. A recent article in The Wall Street Journal featured excerpts from interviews with various CEOs regarding remote work. More than 60% of the comments were negative about the value and sustainability of working from home, and interestingly only one of those negative comments was related to the logistics and infrastructure of working from home, a common complaint among workers.

SEE: TechRepublic Premium editorial calendar: IT policies, checklists, toolkits, and research for download (TechRepublic Premium)

Also interesting was that concerns about productivity and quality of work only concerned a single CEO of a startup. The other executives who expressed a negative opinion of working from home were concerned with two overarching themes: Innovation and interpersonal interactions. Much has been made of the initial productivity concerns around working from home, and the surprisingly consistent or even increased productivity in the initial wave of home working. However, concerns around innovation and physical proximity are interesting and seemingly related.

Some say remote innovation isn’t possible

The pairing of concerns around innovation and interpersonal interaction is intriguing, as historically, innovation was considered a contact sport that required close physical interaction. The fact that many of the CEOs interviewed by the WSJ were concerned about their company’s ability to innovate while working remotely isn’t surprising. 

SEE: 3 ways to help your team stay connected while WFH (TechRepublic)

We’ve seen this movie before; in the early days of outsourcing and offshoring, a major impediment to those practices was innovation, and most teams and executives assumed that physical distance was the root cause for this inability to innovate. It was therefore assumed that you could outsource rote work, but innovation required a physically co-located team.

Explore the problem behind the problem

It’s easy to cite the biggest and most obvious change in the last year as the root cause for concerns around inability to innovate. Most organizations no longer have their entire team a dozen footsteps down the hall, making it easy to throw up one’s hands and assume that until this changes, innovation is a casualty of COVID-19 that won’t be resuscitated until some undefined return to normalcy.

However, there are hundreds of organizations that continue to innovate and were able to produce significant innovations through teams that span not only remote and in-person workers, but workers scattered across geographies, languages, and time zones. One core ingredient to innovation that physical proximity has always provided is focus. If you were gathering key personnel from across the globe to perform a targeted task, it was easy to get everyone in the same room, close the door, and through the sheer force of everyone being physically proximate, watch as the best brains in your company closed down laptops, stashed phones, and were ready to engage in 30- to 90-minute sessions of uninterrupted focus. If you were lucky, you might even get a half-dozen of these focused sessions over the course of a couple days, advancing key initiatives, developing new concepts, and blowing up roadblocks.

SEE: If you’re asking for feedback, make sure you use it—or at least explain why you’re not (TechRepublic)

In the work-from-home era, at best you might get 10 minutes of focus after the late joiners finally arrive and figure out how to turn on their video, or someone gets an Outlook notification and begins ploughing through email while appearing to be engaged. While we humans are wired for physical interaction, innovation remotely is not impossible, and it’s this lack of highly focused energy that’s driving the executive perception of inability to innovate, rather than some inherent magic to physical proximity.

If you want to rekindle that magical feeling you get when you have your top team in a room, and it feels like previous challenges are melting away, and new strategies and tactics are being effortlessly created before your eyes, it’s going to take more work than just ordering everyone onto airplanes on a specific date. In fact, there are some benefits to the remote world, in particular an ability to connect with far-flung minds in minutes rather than scheduling logistics months in advance.

Try diligence rather than exasperation

Rather than blaming the unchangeable shifts to remote work, try exploiting the tools you have. Instead of booking a full-day video conference, which quickly descends into half-focused individuals doing other things, book 90 minutes with your team. Send a short pre-read, ask that everyone closes Outlook, shuts off their devices, and commits to focusing on the task at hand. Request 100% video participation, don’t be afraid to use virtual (or even real) whiteboards, assuming everyone can see them, and target a group of no more than nine before you start creating virtual breakout rooms.

SEE: Launch a 90-day experiment to get business and IT projects off the ground (TechRepublic)

You may find that with a bit more planning than “everyone get on the plane,” and tempering your expectations from half-day sessions to 60- to 90-minute bursts of energy, you can create that flow state so many executives are missing. In fact, you may be able to get four to six such sessions, spread over a number of days or weeks, in the time it would take to harmonize travel schedules for a physical meeting.

Once there’s a return to normalcy, the remote innovation muscles you develop now might even allow your teams to lay the groundwork for wildly productive in-person meetings, creating an unbeatable combination of physical and virtual. Don’t be tempted to assume innovation is impossible remotely; rather, try tweaking when, how, and how often you collaborate, and you may be surprised by the quality of the result.

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Preserving privacy of machine learning models

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When you see headlines about artificial intelligence (AI) being used to detect health issues, that’s usually thanks to a hospital providing data to researchers. But such systems aren’t as robust as they could be, because such data is usually only taken from one organization.

Hospitals are understandably cautious about sharing data in a way that could get it leaked to competitors. Existing efforts to handle this issue include “federated learning” (FL) a technique that enables distributed clients to collaboratively learn a shared machine learning model while keeping their training data localized.

Preserving privacy of machine learning models

However, even the most cutting-edge FL methods have privacy concerns, since it’s possible to leak information about datasets using the trained model’s parameters or weights. Guaranteeing privacy in these circumstances generally requires skilled programmers to take significant time to tweak parameters – which isn’t practical for most organizations.

A team from MIT CSAIL thinks that medical organizations and others would benefit from their new system PrivacyFL, which serves as a real-world simulator for secure, privacy-preserving FL. Its key features include latency simulation, robustness to client departure, support for both centralized and decentralized learning, and configurable privacy and security mechanisms based on differential privacy and secure multiparty computation.

MIT principal research scientist Lalana Kagal says that simulators are essential for federated learning environments for several reasons.

  1. To evaluate accuracy. SKagal says such a system “should be able to simulate federated models and compare their accuracy with local models.”
  2. To evaluate total time taken. Communication between distant clients can become expensive. Simulations are useful for evaluating if client-client and client-server communications are beneficial.
  3. To evaluate approximate bounds on convergence and time taken for convergence.
  4. To simulate real-time dropouts. With PrivacyFL clients may drop out at any time.

Using the lessons learned with this simulator, the team we are in the process of developing an end-to-end federated learning system that can be used in real-world scenarios, For example, such a system could be used by collaborating hospitals to train privacy-preserving robust models to predict complex diseases.

Written by Adam Conner-Simons, MIT CSAIL

Source: Massachusetts Institute of Technology




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Oppo A33 (2020) With Triple Rear Cameras, 5,000mAh Battery Launched in India: Price, Specifications

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Oppo A33 (2020) has been launched in India, featuring a large 5,000mAh battery, a a Qualcomm Snapdragon 460 SoC, and a hole-punch display with a 90Hz refresh rate. The smartphone also has a rear fingerprint scanner and a triple camera setup at the back. The Oppo A33 (2020) was unveiled in Indonesia last month, and will go on sale via Flipkart later this month though it is already available via offline retail stores, the company announced.

Oppo A33 (2020) price in India, launch offers (expected)

The Oppo A33 (2020) has been priced at Rs. 11,990 for its 3GB RAM + 32GB storage option. Oppo says it is available via offline retail stores, and will go on sale from Flipkart in its “next Big Billion Day sale.” Offers include 5 percent cashback on Kotak Bank, RBL Bank, Bank of Baroda, and Federal Bank cards. If users buy the phone from Paytm, benefits worth Rs. 40,000 will be listed. Offline, there are also going to be schemes options from banks like Bajaj Finserv, Home Credit, HDB Financial Services, IDFC First Bank, HDFC Bank, and ICICI Bank. The Oppo A33 (2020) was launched in Indonesia in September,

Oppo A33 (2020) specifications

The Oppo A33 (2020) runs on ColorOS 7.2 based on Android 10 and features a 6.5-inch HD+ (720×1,600 pixels) hole-punch display with 90Hz refresh rate. Under the hood, there is the octa-core Qualcomm Snapdragon 460 SoC. Internal storage is at 32GB with the option to expand further using a microSD card (up to 256GB).

The Oppo A33 (2020) smartphone also has the triple rear camera setup that includes a 13-megapixel primary sensor. The camera setup also has a 2-megapixel depth sensor and a 2-megapixel macro shooter. The Oppo A33 has an 8-megapixel selfie camera.

There is a 5,000mAh battery with 18W fast charging support on the Oppo A33 (2020). There is also the fingerprint sensor at the back of the handset. The phone also comes with dual stereo speakers. Connectivity options include Bluetooth v5, USB Type-C port, Wi-Fi 802.11ac, and more.


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Big data and DevOps: No longer separate silos, and that’s a good thing

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The pandemic has caused major shifts in the way IT and big data work. Now they may be working together for better outcomes.

Image: iStock/ RRice1981

The world has changed a lot since March 2020, and the coronavirus pandemic has affected nearly every aspect of our lives. While we’ve seen massive changes in technology already, another change happening right now is in big data and its role with DevOps.

“The COVID-19 pandemic has accelerated the blending of data analytics and DevOps, meaning developers, data scientists, and product managers will need to work more closely together than ever before,” said Bill Detwiler, editor in chief of TechRepublic. 

SEE: TechRepublic Premium editorial calendar: IT policies, checklists, toolkits, and research for download  (TechRepublic Premium)

Detwiler was interviewing managers at Tibco, a leader in big data integration and analytics. They said the coronavirus pandemic had caused organizations to rethink how they were using big data and analytics, generating what appears to be a movement toward merging IT DevOps methodologies with big data analytics.

For IT organizations, this is more than just a story about how the pandemic has altered how companies think about big data and analytics. The emergency of COVID has placed emphasis on getting analytics insights and results to market quickly. This has redefined analytics reporting as mission-critical, and not just as an ancillary tool for how companies operate and strategize.

SEE: Return to work: What the new normal will look like post-pandemic (free PDF) (TechRepublic)

The change is also creating revisions in operations and culture for IT. Here are some we’ve seen.

A move from waterfall to DevOps development

Developing, testing, and deploying big data applications is an iterative process. Because the process is iterative (i.e., develop-test-deploy until you get what you want), it doesn’t follow the more linear and assembly line-like development methodology of traditional IT waterfall development, which is a serial sequence of handoffs from development to QA (test) to an implementation staff.

SEE: Are you a big data laggard? Here’s how to catch up (TechRepublic)

A majority of IT departments are still organized around the waterfall development paradigm. There are separate silos within IT for development, testing, and deployment. These functions have to come together with each other and end users in the more collaborative and iterative process of big data application development. To do this, functional silos of expertise have to dissipate. 

Culturally (and perhaps organizationally) this changes the orientation of IT. The culture shift is likely to entail the creation of interdisciplinary functional teams instead of work handoffs from functional silo to functional silo. End users also become active participants on these interdisciplinary teams.

Fewer absolutes for quality

The testing of big data applications becomes more relative and less absolute. This is a tough adjustment for IT because in traditional transaction systems, you either correctly move a data field from one place to another, or you obtain a value based on data and logic that absolutely conforms to what the test script dictates. If you don’t attain absolute conformance, you retest until you do. 

SEE: Big data: How wide should your lens be? It depends on your use (TechRepublic)

Not so much with big data, which could start off with results being only 80% accurate, but with the business deeming them close enough to indicate an actionable trend.

Working in a context where less-than-perfect precision is acceptable is a challenging adjustment for IT pros, who are used to seeing an entire system blow up if a single character in a program or script is miskeyed.

The shift of big data into mission-critical systems

If you’re a transportation company, the ability to track your loads on the road and the health and safety of the cargo that they’re carrying becomes mission-critical. If you’re in the armed forces and you’re using drones on the battlefield to conduct and report reconnaissance in real-time flyovers, the data becomes mission-critical.

SEE: Big data success: Why desktop integration is key (TechRepublic)

This means that organizations must begin to attach the label of mission-critical to big data and analytics applications that formerly were classified as experimental. 

IT culture must shift to support mission-critical big data applications for failover, priority maintenance, and continuous development. This could shift IT personnel from traditional transaction support to big data support, requiring retraining to facilitate the change.

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