There are weaknesses and obstacles in the way, but in lieu of a vaccine and widespread testing for COVID-19, contact-tracing apps might be at least a useful interim tactic.
Pandemics stir up mass panics that are hard to control. They inject massive amounts of fear, uncertainty, and doubt into the general population.
The current COVID-19 emergency has produced an infodemic of overwhelming proportions. Though there’s plenty of authoritative advice for coping with the novel coronavirus, scared human beings are grabbing desperately for any advice, no matter how suspect.
Protecting ourselves in a pandemic may come down to following simple rules of thumb. We should stay home, wash and sanitize our hands constantly, wear surgical masks in public, keep a six-foot distance from others, avoid touching our faces, and seek prompt medical attention if we believe we’ve been infected.
Groping for actionable counter-contagion analytics
Data-driven analytics can help us protect ourselves in a pandemic. However, it’s not clear what kinds of analytic-driven apps might best help individuals to ward off invisible pathogens.
Contact-tracing apps have grabbed a lot of headlines recently, and many people seem to regard them as possible tool for flattening COVID-19’s infection curve. These apps have been in the IT industry news recently, thanks to Apple and Google’s announcement that they are partnering to build mobile-based apps that will work on both IOS and Android devices. Also, the European Commission earlier this month announced a strategy for COVID-19 mobile contact tracing and alerting.
Essentially, these apps, which may be implemented in smartphones or Internet of Things devices, alert people to the risk that they have been exposed to a person who is either ill, infected, or asymptomatically carrying the virus. Using sophisticated data analytics, the apps alert users when they come close to another user who has tested positive. Optionally, the apps may guide impacted users on the best next steps, such as testing, quarantining, and social distancing.
Too little, too late?
Contact-tracing apps can only be effective in countering a pandemic’s spread if they provide useful advice to many people before the contagion would have petered out on its own. However, the Apple-Google initiative released its first deliverable — an API for integration with third-party contact-tracing apps — just as the hoped-for curve flattening had already begun in many regions of world.
What that means is that API-integrated contact-tracing apps will hit the market weeks or months after the demand for them has started to wither away. In addition, the promised follow-on deliverable –embedding of common contact-tracing technology in both IOS and Android devices — will be much too late to make a serious dent in a pandemic that (we all hope and pray) will be history by then.
Even if Apple-Google had started their initiative back in December when the pandemic started in Wuhan, China, and had delivered their planned API and app promptly at that time, their efforts probably would not have made a difference in slowing the contagion’s spread. Any of the other efforts around the world to address the pandemic with a contact-tracing app would have faced the same challenge of getting a solution into people’s hands fast enough.
Rapid enough adoption of the Silicon Valley dynamic duo’s handiwork would have depended on the lightning convergence of several trends.
For starters, most of a population needs to use a common contact-tracing application for it to have reasonable chance of stalling a pandemic. An estimate in a recent Oxford University study put the threshold adoption rate at 60%.
For that to happen, most people would need to use phones that are compatible with the dominant contact-tracing app. That’s a tall order, considering that people tend to take years to change out their mobiles, and they’re definitely not going to do it just to use one application of uncertain value.
Furthermore, the dominant contact-tracing app in a country or region would have to be certified and/or sponsored by a government health authority. This is practically the only way that a large enough proportion of people in each country would download, install, and use such an app. One problem with this is that it might take precious weeks or months for different countries’ apps to integrate with a common Apple-Google offering, by which time the crisis may be long over.
Also, any top-down effort to get people to download and use a contact-tracing app in a crisis would run up against popular distrust of government agencies. There would need to be an accelerated public service campaign to address people’s concerns and encourage them to participate. And it would be especially difficult to make this case in countries such as China, Singapore, France, Australia, and Israel, where the apps inform health authorities that you may be infected and they in turn notify you on the proper next steps.
In addition, the well-known privacy and civil rights implications of contact-tracing apps may deter many users from adopting them in the first place. Let’s note that even in a paternalistic surveillance state such as Singapore, only about 12% of the population downloaded the government-issued TraceTogether contact-tracing app.
Reliance on Bluetooth
Another potential obstacle to the success of contact-tracing apps is that many rely on Bluetooth, though some use the less precise GPS or Wi-Fi signals for proximity sensing. There are several downsides to Bluetooth that will dilute any contact-tracing app’s ability to deliver high-quality data on COVID-19 exposure and infection:
There are only so many critical alerts that the average user can tolerate before they tune it all out in order to get on with their life. The likelihood that contact-tracing apps will bombard users with false positives may cause many people to abandon them.
Another weakness of contact-tracing apps is that they rely on users to manually opt-in and self-report if they believe they’ve been exposed. Obviously, someone who has just been notified that they may have fatal disease may have other, more important things on their mind than the need to altruistically report this to the world at large. In such circumstances, they may rush to a hospital and entirely forget to self-report.
Or a user may wait until after being tested before self-reporting, and only do so if they’ve tested positive. But that delay may reduce their likelihood of self-reporting even further. And if they live in an area where it’s difficult or impossible to find a competent healthcare professional to test them, they may never get around to self-reporting.
That same user may eventually download the app, in anticipation of the dreaded second wave of infections months later. By that time, however, society may have started to take this outbreak in stride and healthcare institutions may have access to ample testing, vaccination, and treatment programs that obviate the need for people to vigilantly self-report their exposure.
Globally, the many contact-tracing applications under development are laudable. In lieu of a vaccine and widespread testing for COVID-19, contact-tracing apps can be a useful interim tactic.
Contact-tracing apps can function as supplements to the contract-tracing exercises that healthcare professionals routinely perform when a contagion has begun to spread within a population. They can also serve as a personal early warning system and recommendation engine for individuals.
However, it’s unlikely that the apps — even if delivered to market the moment a pandemic takes root –can make a serious dent in preventing its spread.
James Kobielus is an independent tech industry analyst, consultant, and author. He lives in Alexandria, Virginia. View Full Bio
IT teams are facing a new normal with the shift to remote work. Here are some key steps they may not be considering (but should) to improve the digital experience.
With the current global environment, the shift to remote work has taken place almost overnight for many companies, creating enormous challenges for both IT and employees. IT teams are being asked to implement massive changes daily, creating unprecedented pressure to support an exponentially larger number of employees working away from the office. Rather than having four or five offices to worry about, IT now has thousands of home offices to support.
While many enterprises have over the years been adapting to remote work, for many businesses, remote work is causing significant damage to their bottom line and the well-being of their employees. The lack of IT preparedness for a remote work environment can pose serious threats and challenges to a business’s productivity, and security so ensuring systems work well for all remote employees is mission critical.
So, how should IT teams handle this new normal, especially as many IT departments are unfamiliar with handling a remote workforce? Here are seven key steps IT teams may not be considering as they look to improve remote employees’ digital experience.
1. Manage your remote workers’ experience
In order to fully understand if your remote workers can be successful, IT should have visibility into how employees are experiencing IT services on a daily basis. Consider comprehensive ongoing measurement that calculates accurate, real-time data from your employee’s devices, web browsing, security, productivity & collaboration tools, and business applications. Only by seeing the experience employees have can you take active steps to improve it.
2. Ready your digital foundation
If you are transitioning to a remote working-friendly environment, it’s critical that you get the basics right on your infrastructure. To prevent any backend impact to your remote workers, IT departments need to confirm who is working remotely, what different device are being used and what are the critical applications they’re connecting to. It’s important to also manage compliance to ensure the business stays secure for example VPNs for certain applications, disk encryption.
3. Find and fix incidents proactively
With a remote workforce, time to resolution will only be getting worse due to complex network and application environments. IT service desk staff aren’t equipped with accurate information and tools to quickly resolve the wide range of issues that arise, resulting in downtime, disruption and lost productivity. Better investment in a proactive approach to incident resolution with real data around how the employees are experiencing IT will greatly improve how IT can service their remote employees quickly and effectively — before employees have to report it themselves.
4. Promote employee self-help
IT teams often have their hands full trying to manage both on-premise and remote employees. With the right engagement and automation tools, IT can offset their workload by establishing an easy-to-use employee self-help system. With this, employees can handle quick fixes that might otherwise have had a good amount of IT downtime.
5. Facilitate employee collaboration
One of the main disadvantages of remote working is the lack of direct communication employees have with their colleagues. Collaboration tools have gained considerable traction as enterprises attempt to scale their remote workforce. However, without the right IT visibility and support, managing an existing collaboration tool or migrating to a new one can be difficult. The performance of all collaboration tools is tied to local device and network performance — two things that IT typically has less visibility of in a remote work environment. Consider solutions to give IT this visibility to enable a seamless collaboration experience.
6. Be aware of Shadow IT
The jump to remote work has thrown millions of employees onto popular collaboration tools. Working from home also makes it easier for employees to access collaboration tools that sit outside their corporate IT policy. In order to take back control without alienating shadow IT users, IT departments should get visibility of all the shadow IT services employees are using. Typically, shadow IT isn’t employees trying to be malicious, they’re just trying to get their jobs done and existing IT services can’t provide them what they need. By engaging with employees to understand the underlying reasons for non-standard adoption, IT can work with them over time to serve their needs with a more standardized approach.
7. Stay in touch with employees
IT communications and outreach has never been more important than now. By keeping the lines of communication open with employees — even subtle communication efforts — provide employees with a better remote experience.A continuous dialog with employees offers significant opportunities to gather feedback. IT departments should look to provide comprehensive feedback and to do this on an ongoing basis. This dialogue should also include employee education if needed. For example, which applications need to use a VPN, and which don’t, to avoid the VPN becoming overloaded.
As VP, Global Solution Consulting, Jon Cairns leads the team responsible for promoting technical excellence to Nexthink’s customers and partners and helping them understand the value they can get from our solution portfolio. He has over 20 years of experience in the technology industry, having held a range of leadership roles across consulting, presales, sales and marketing. Cairns holds a bachelor’s degree in Electronics Engineering from the University of Southampton, UK and an MBA degree from London Business School.
The InformationWeek community brings together IT practitioners and industry experts with IT advice, education, and opinions. We strive to highlight technology executives and subject matter experts and use their knowledge and experiences to help our audience of IT … View Full Bio
Open source tools are crucial to machine learning development, but if you want to manage those models in the enterprise, you need a platform.
Trying to do more with less during the pandemic? While organizations may not be jumping into big investments right now, everyone is looking to conserve cash and maximize revenue in these uncertain times. Artificial intelligence and machine learning can be a part of achieving those goals, but there are some challenges to gaining the benefits.
“Machine learning relies on open source, ” Bradley Shimmin , Omdia analyst for data plus analytics, told InformationWeek. (Omdia in addition to InformationWeek are both owned by Informa) “In terms of turning that open source into an actual solution in the enterprise, it takes some doing. inch
A new report from Omdia can help provide a roadmap for organizations looking to gain those benefits quickly. The analyst research firm broke out some of the leading platforms to help organizations move early efforts to machine learning at scale with a platform approach.
The report names a handful of vendors from across the spectrum of system providers as leaders in the space, to give organizations a sense of their options for managing machine learning at level in the enterprise.
Shimmin noted that the vendors selected as leaders don’t always compete with each other, and they may represent different specialties in the field.
But what all of these players will help organizations do is “turn what is a multi-year investment into something you can do in a shorter time. AI and ML can optimize business and drive new areas of innovation, ” Shimmin said.
“Given the fact that so many industries are trying to respond to a global pandemic makes that idea even more important, ” he said. “If your survival as a company depends on your ability to innovate quickly, find a new revenue stream, and extract every bit of value you can, AI and ML really can offer that. ”
The platform approach is a little different from where many machine learning professionals started. In school and at startups they built their project portfolios by using open source tools and libraries. But evolving any project from experimentation with a series of models to something that can be integrated with enterprise decision-making and operations takes a whole other level of effort.
Some pundits have argued that the wide array of open source tools, while brilliant for developing these individual projects, don’t meet muster when it comes to coordinating and managing a machine learning practice for deployment at scale.
Organizations are coming to recognize that these free tools and libraries hold an important place in a larger ecosystem of machine learning technology within enterprises. Yet the real power of these tools can only be felt when a full platform can be deployed to wrangle the tools and even models. Open source and enterprise systems must be used together.
“To create meaningful ML applications, it is necessary to understand the data that goes into an application, its provenance, how it is pre- and post-processed, ” wrote statement author Michael Azoff. “… We talk of platforms rather than tools because these solutions span the whole ML development lifecycle and typically encompass multiple tools that are ideally accessed from one studio or console environment. inches
Omdia looked at a selection of eight companies across the spectrum of machine learning platforms. For public cloud companies it considered Microsoft and IBM. For a long-established analytics and ML vendor it looked at SAS. For relatively new ML suppliers for general development it looked over C3. ai, Dataiku, H20. ai, and Petuum. And for a relatively new ML vendor dedicated to one task it looked at Evolution AI.
While the list is not exhaustive, Azoff notes, it “should provide a starting point for shortlisting vendors for further evaluation and proof-of-concept trials. very well All the platforms covered in the record provide support for the full ML lifecycle, according to Azoff.
That said, most of the companies included in the review were ranked as leaders, including Microsoft, SAS, IBM, C3. aje, and Dataiku. H20. ai together with Petuum were challengers, and Evolution AI was a follower. Shimmin said that future reports will look at other technologies for machine learning, which includes Amazon SageMaker suite.
As for enterprise response to the outbreak, Shimmin said the anecdotal evidence he’s seen so far is that investment in AI and machine studying has not slowed, and that it may be increasing.
“Those solutions can optimize your business to cut costs and make you more resilient to the change we are seeing now, ” this individual said. “It can also help drive new business which can also make you more resilient. It really can drive resiliency across highly disruptive market changes. ”
Jessica Davis has spent a career covering the intersection of business and technology at titles including IDG’s Infoworld, Ziff Davis Enterprise’s eWeek and Channel Insider, and Penton Technology’s MSPmentor. She’s passionate about the practical use of business intelligence,… View Full Bio
IBM, Red Hat, and others combine resources to help lay the foundation for the next era of wireless connectivity and edge computing.
The escalation to a new caliber of wireless network is expected to push more organizations to advance their digital transformation strategies as IBM and other technology providers work to drive the surge. During the recent IBM Think Digital online conference , details emerged of solutions and ecosystems in development intended to boost the deployment of AI, internet of things, and analytics workloads at the edge.
Rob High, vice president and CTO for IBM Edge Computing, says IBM is focusing on the core 5G network and backbone, including telcos, orchestrating virtual network functions, and network modernization. The other side of the coin, he says, is around enterprise compute being applied in edge computing scenarios that is accelerated by 5G. “That is the edge-side of 5G, with respect to how enterprises get value by lowering latencies and getting better control of bandwidth growth, ” he says.
IBM announced new edge resources and edge-enabled apps as well as the IBM Edge Ecosystem, where independent software vendors can offer services to enterprises, and the IBM Telco Network Cloud Ecosystem, which brings together varied providers to drive the application of network cloud platforms. Companies within those ecosystems include Cisco, Dell Technologies, Intel, Juniper Networks, NVIDIA, and Samsung.
High says 5G’s promises include lower latencies, improved bandwidth density, and network slicing. That could mean a 50% improvement in latency over 4G technology, he says, though the separation between a device and the cloud also needs to be accounted for. “The only way you can overcome the laws of physics in the distance between the device and the cloud is by moving the work closer to the endpoint, ” High says.
Accomplishing that means telcos need to consider how they deploy the 5G network as well as enterprise compute capacity in the system facilities, he says, so the workloads can be performed much closer physically to the devices. “That whole movement towards the community edge is an important feature of 5G deployment, ” High says.
Advancing to a new networking tier could still be stymied by the way the system functions. In the backhaul there could be bottlenecks in the network, he says, which eventually prevent the full potential of the bandwidth being exercised. This may occur in the distance between cloud data centers. “You want to leapfrog over those bottlenecks and get the compute facilities closer to where those devices are, ” High says. “The multilevel edge is an essential element to 5G achieving its purpose. ” He says 5G can extend the cloud out to the network facility and reduce that distance.
Some organizations plus uses cases are cloud agnostic, High says, and the focus is on lower latency, edge, in addition to edge computing. Many automobiles, for example , are being rearchitected to enable for more open compute capabilities, he says. This allows applications to be loaded into the vehicle for new use cases such as analytics that monitor vehicle performance and can predict when service will be necessary or detect the presence of an object in the path of the vehicle and apply the particular brakes. “That stuff needs to work even when you’re not connected to the multi-level, ” High says. The car might be in the countryside, away from wireless towers, and will still need to perform such analytics. “You need that processing occurring on the vehicle, ” he admits that.
The edge includes deploying workloads out to devices such as vehicles, industrial robots, conveyor belts, and even assembly machines. It can also include remote on-premise, locations such as factories, retail stores, or bank branches that have calculate hosted onsite.
The mechanics and dynamics of 5G could provide a substantial push for commercial applications. “5G is likely to disproportionally benefit business use cases much more so than 4G or LTE, ” High says, where the consumer saw the greater gains for their personal wireless needs.
There is potential for 5G to unlock options that were once difficult or thought impossible, High says, such as wireless communications in factories. The spectrum some other wireless protocols use, he says, operate at a frequency that has the potential for disrupting factory operations and equipment. The protocols 5G employs are usually safer in such settings, High says, for use among the devices and systems in factories. “Now you can wirelessly connect a lot of the intelligent equipment in the factory, ” he says.
For more on the affect 5G may have on enterprise, follow up with these stories:
Joao-Pierre S. Ruth has spent his career immersed in business and technology journalism first covering local industries in New Jersey, later as the New York editor for Xconomy delving into the city’s tech startup community, and then as a freelancer for such outlets as… View Full Bio
Even before so many schools across the country pivoted to online learning in the wake of the COVID-19 coronavirus crisis, some schools had already pioneered how to do it.
Like so many other organizations, schools across the US, from kindergarten through college, scrambled in March to move from what has almost always been an on-premises educational experience to something new — some kind of online learning program.
Some schools went with recorded online tutorials and set up office hours for students to “meet” with teachers, via phone or video conference. Other schools conducted live video conferenced classrooms and discussions. In some cases, schools shut down their on-premise teaching for the rest of the school year immediately. In other cases what started as a 2-week closure turned into the remainder of the school year — months of instruction.
But at least one school degree program was doing the whole online learning thing really well from the moment that first stay-at-home government shutdown order was issued. That’s because the University of California Berkeley School of Information Master of Information and Data Science Degree program has been online only from the time it began in January 2014.
Unlike MOOCs (massive open online courses), the UC Berkeley Data Science program was created in the image of a typical advanced education course, just delivered remotely. The school has the same small classes and discussion groups you would expect from an on-campus school. The expectations of students are also high.
“Just because it is online doesn’t mean that you can disengage and not pay attention to the discussion,” said Kyle Hamilton, a lecturer with the program who teaches four sections of Machine Learning at Scale for the program each semester. Hamilton is also a graduate of the program.
Over the last 6 years since its inception, the program has gotten smarter with its technology, refining the tools it uses to connect with students and the tools students use to do their work. The program is remote and attracts students from all over the world, yet most students still hail from the San Francisco Bay area where companies like Google are based. UC Berkeley School of Information has often partnered with tech giants to provide tools for student use. For instance, Google would grant credits to students to use data science tools in the Google cloud.
But just because it was free doesn’t mean it was easy. Students would need to use the credits to set up their own infrastructure to complete the coursework, yet the setup itself was often challenging. You could argue that dealing with the frustrations of setting up the technology is something the students would need to learn in order to deal with tech in the real world after graduation. But the frustrations took away from time students had to apply to the actual course content in Hamilton’s course — Machine Learning at Scale.
“When the course started, there were so many moving parts,” she said. “Students were expected to do their own infrastructure set up. What I’ve tried to do over the semesters is to simplify that.”
The program has used several different clouds over the years including Google and AWS, “but I cannot personally support all these clouds,” Hamilton said.
In the most recent semester, however, UC Berkeley School of Information has been piloting a new partnership with cloud-based big data platform provider, Databricks, a company that got its start as a cloud-based provider of open source Apache Spark. It has since expanded its cloud-based platform to incorporate a full host of open source data science tools. This month Databricks made the college partnership program official, announcing the launch of the Databricks University Alliance, a global program offered to educational institutions at no cost to help their students graduate with the skills they need to land jobs in data science. In addition, students working from home on their own can download and use the free Databricks Community Edition and access educational content from the company.
In conjunction with UC Berkeley School of Information, the alliance program also provides students with access to tutorials, content and training materials on open source tools including Apache Spark, Delta Lake, and MLflow. It is powered by public cloud providers such as Microsoft Azure and Amazon Web Services (AWS).
The tech support on the infrastructure was a huge help for Hamilton, who worked with Denny Lee and Rob Reed of Databricks to provide students with a stable environment to use.
“I’ve had good reviews from students about the infrastructure,” Hamilton said. “They took a list of my students to onboard to the platform. They provided technical support. If I had questions, I could email Denny and Rob and they would get back to us within a day.”
Such partnerships between educational institutions and tech companies go back a long way. Apple famously offered special packages and discounts to schools in the 1980s and 1990s, making the brand the choice of a generation even after graduation. Google is now leading in that space with so many schools choosing inexpensive Chromebooks as a way to deliver curriculum to students.
The UC Berkeley School of Information program and the Databricks partnership together target a subset of advanced education students. But such programs could be a forerunner of a new way to deliver education in a post-pandemic world. Maybe a new class of elite cyborg universities will emerge. Such programs could disrupt the education market by being less dependent on physical location and potentially enrolling many more students at a lower price after an era when many graduates spent decades trying to pay off massive college debt.
In any case, the students in Hamilton’s Machine Learning at Scale class will have an easier time setting up their infrastructure so they can focus more on the material they are there to learn.
Jessica Davis has spent a career covering the intersection of business and technology at titles including IDG’s Infoworld, Ziff Davis Enterprise’s eWeek and Channel Insider, and Penton Technology’s MSPmentor. She’s passionate about the practical use of business intelligence, … View Full Bio