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2021前沿科技预测报告 Emerging Technology Predictions 2021(3)


Speaking of augmented and virtual reality (AR/VR): If ever there was a time when people might yearn to retreat into a digital reality, 2020 was surely that time. But both the hardware and the software were still finding their footing, and like anything that wasn’t rock solid at the start of the year, COVID-19 presented a setback.

But the setback will be temporary. In July, IDC took stock of the developing technology and estimated that from 2019 to 2024, the compound annual growth rate in global AR/VR spending would be 76.9% worldwide, reaching $136.9 billion.2

AR and VR are naturally lumped together in many discussions, but they’re not the same thing. Augmented reality overlays data on live video, often through a phone or tablet camera. (Think Pokémon Go, or watch this Splunk video demonstrate an industrial repair scenario.) Virtual reality is what we’ve been expecting at least since Tron hit movie theaters in 1982: Interactive worlds created entirely from computer graphics. VR requires headsets, currently on the expensive and cumbersome side, to create true immersion.

Virtual and augmented reality improve collaboration and access to knowledge. As a greater percentage of our retail activity and business communication move online, there will be demand for more immersive experiences. Additionally, industries not nearly as well-established as ecommerce are making massive shifts to digital platforms: Remote learning and telemedicine are entering many school districts, universities and medical practices for the first time.

New medical applications are already appearing. Doctors at George Washington University Hospital have been using VR to uncover lung damage from COVID-19 since March.

“Telemedicine has certainly accelerated, with medical practices working in partnership with insurance providers,” says Simon Davies, vice president of Splunk in the Asia-Pacific region.

“One of the most interesting VR advancements I’m seeing is in data visualization and event management. The ability to effectively consume data is critical to timeliness. Organizations are using virtual reality to consume 20 to 30 dashboards, coupling human intelligence with machine intelligence to distill meaningful insights in real time.”

An AR/VR survey of venture capitalists, released in March by law firm Perkins Coie, predicted that AR will continue to outpace VR, in part because mobile devices are already everywhere, and they’re better suited to AR overlays than full-immersion VR.


According to the World Economic Forum

According to the World Economic Forum, AR does three things very well: visualization, annotation and storytelling — each of which can be very useful to workplaces, schools and institutions during and after the pandemic. Further, AR/VR experiences could help mitigate the isolating nature of lockdowns and lingering effects on, for instance, business and leisure travel.The WEF links to news stories about the following examples:
? Virtual job training for young adults with autism
AR visualization of air pollution worldwide
? “ All 185 first-year medical students at Case Western Reserve University (CWRU) are using HoloLens and HoloAnatomy, an award-winning AR app by CWRU and Cleveland Clinic, to learn from their own homes.”
? “ London’s National Theatre is using AR to help make its performances more accessible for people who are deaf and hard of hearing.”
? Microsoft’s Project Tokyo helps visually impaired people to “see” using AR and AI and the HoloLens. The device can detect the location of people in the user’s environment, and recognize faces, relaying the information to the wearer via audio


AR/VR’s breakthrough application will be immersive collaborative communication.

“I think of it as Zoom on steroids,” Jesse Chor says. The toll remote work takes on collaborative communication means we’ll want more immersive solutions that make interaction easier and more effective. “Not like a Zoom call today, where everyone talks over each other: ‘Oh, sorry, you go ahead.’ ‘No, you go.’”

5G bandwidth and more powerful laptops will reduce latency problems and provide opportunity for new ways to collaborate, to share visual presentations, on-the-fly notes and more. Post-COVID, more workers are going to continue to work remotely, and tech companies have long believed that innovation arises from the live, daily mix of coworkers, and that the hallway and kitchen are as important, if not moreso, than designated conference rooms.“

So the question becomes, how do remote teams effectively replace actually being in front of each other? We’re going to want virtual collaboration to be as close to the real thing as possible,” Chor says. “I think the next iterations of video conferencing will incorporate AR and VR technology.”


We’ll see a breakout hit in consumer/entertainment VR by early 2022, or virtual reality will drop off the radar.

Any consumer entertainment model requires a combination of hardware and content. The hardware element, Jesse Chor says, is there. But that isn’t enough.

“Virtual reality is at a very, very interesting spot,” Chor says. “It’s not new — it has been around forever, and it’s at a very vulnerable time now.”

He notes that the success of entertainment technology is driven by content. A new device might be an obsession only for the hardcore hobbyist until a viral hit drives mainstream adoption. Chor says VR’s hardware is there. The Oculus Quest, released in 2019 and sold out everywhere in 2020, is the Nintendo Switch of VR: A cheap, attractive device to drive adoption. Now we need the critical-mass software, the breakthrough game.

“It’s do-or-die; either that breakthrough comes in 2021, early ’22, or it’ll be another 10 years before it gets visited again.” So VR needs its “Legend of Zelda: Breath of the Wild” or “Super Mario Odyssey.” Otherwise, Chor says, companies will lose patience and stop investing in the technology. He’s optimistic that a hit will emerge to capture interest. “Because with COVID, there’s an audience for anything that’s driving immersion at home.”



Biometrics was already taking off before the pandemic. Apple devices had been asking for your thumbprint for years, and the newest iPhones really want to be unlocked by your face.

In addition to biological attributes such as thumbprint, voice and facial scans, biometrics includes behavioral measurements, and that goes back centuries: Verifying identity by comparing signatures is not exactly cutting edge. Similarly, what time you regularly log into a system, your typing style, how you walk — all are being used today to identify individuals.

UK schools have used fingerprinting or face scanning for access control, recording attendance, buying lunch, checking out library books. One school, University Technical College, Leeds, has had to abandon its fingerprint sensor access controls due to the coronavirus pandemic, replacing them with proximity card sensors, facial recognition and, for health purposes, a thermal camera. (On the other hand, a French court blocked high schools from using facial recognition.)

Japan’s Seven Bank was testing ATMs that use facial recognition in late 2019, and in 2020 trials were under way to use facial recognition for cashierless checkouts in Japanese 7-Eleven stores.


Biometrics will move up in the world. And into the cloud.

“I’m a big fan of biometrics,” says Splunk Head of Mobile Engineering Jesse Chor. He predicts that biometric adoption, like (and as an example of) two-factor identification, will increase sharply in the Data Age. And he predicts that a key evolution in biometric verification will be solutions that don’t store your data on your device. Because if the app that verifies your thumb print is stored on your phone, bad guys need only hack your phone (which may be preferable to hacking your thumb, actually).

“The data won’t be stored on your phone,” Chor says. “It’s going to live in the cloud. The phone just sends the thumbprint it receives to another entity, and it’s up to that entity to decide, is this the right one? And your device won’t send your actual biometric data, but a hash, just like we do with passwords now.”

While biometric identification is a great way to minimize security breaches that depend on account or identity theft, the field is highly controversial, particularly around facial recognition. In September, the city of Portland, Oregon, banned use of facial recognition by government or businesses, a more stringent ban than bans on government agency usage already enacted by San Francisco, Oakland and Boston. Controversies around the technology have led to calls for greater regulation. A 2019 profile of one facial recognition provider’s practices ran in The New York Times under the headline, “The Secretive Company That Might End Privacy as We Know It,” and use of facial recognition and other biometric data collection (including DNA samples) as part of China’s oppression of its Uighur minority generated international condemnation.

Chor notes that legal and ethical guidelines will have to be worked out on the national and international level, and permissible use of biometric data may vary. (A September article in the MIT Technology Review discusses such efforts.) But biometric technologies are too important and useful to kill. Whether we’re doing our jobs or managing our personal finances, the most important aspects of our lives are digital, and increasingly under threat of cyberattack.



The enterprise blockchain ecosystem has rapidly matured, and Splunk’s head of blockchain, Nate McKervey, measures that in a very hands-on way.

“2020 has been the first year where I have not had to explain blockchain once to organizations,” he says. “They come in understanding it to a high-enough degree that we can show them what problems we’re solving, which is a wonderful thing. Looking back, 2018 was the year of ‘what is blockchain’ conversations. 2019 was full of ‘nobody needs blockchain’ discussion, and in 2020 we’ve reached, ‘Okay, I understand where it’s useful and valuable. Now, how can you help me be successful?’”

The 2021 conversation, he predicts, will be, “What is your blockchain strategy?”

“In two years,” he says, “some companies will realize that if they don’t have a blockchain strategy, they’ll be less competitive because they won’t be as efficient as their competitors.”

With that in mind, he provided a rapid-fire series of predictions around digital ledger technologies.


COVID will accelerate blockchain adoption.

McKervey says that when the pandemic lockdowns began, he assumed that organizations would conserve resources and focus on core technology investments.

“I even told our leadership that I expected emerging technologies to be the first ones to be cut,” he says. But by autumn, not one of the more than 100 organizations he was talking to had suspended their blockchain initiatives. “In fact, we’ve had more inbound interest.”

An August article at CIODive.com backs him up. The publication spoke to experts who also had expected 2020 to be a bad year for blockchain, but were seeing continued interest, particularly in supply chain and other use cases where value is obvious. As Congress continues to urge the utilization of blockchain technology, we expect no slow down in interest.


Successful blockchain implementations will focus on efficiencies.

As blockchain emerges from that 2019 trough of disillusionment, McKervey expects adoption to accelerate in the pandemic/post-pandemic era. The degree to which the pandemic interrupted supply chains will particularly drive interest in blockchain technologies, which can improve visibility into the source of goods, and where they are at any time, and how overlapping supply chains interact.

“If my supply chain is 10 times more efficient than yours, your vendors aren’t going to want to work within your supply chain,” McKervey says. “We’re seeing organizations decrease paperwork and manual processes by 97%. If their competition doesn’t do the same, they won’t be able to compete.”

The financial services industry was our big pick last year for blockchain strides. That sector continues to be a strong area of blockchain interest, with particular interest in central bank digital currencies (CBDCs), but expect supply chain applications to see the greatest near-term improvements, largely driven by the pandemic.


In the short term, organizations will struggle to turn blockchain test projects into full-scale successes.

Blockchain initiatives still start with small proof-of-concept projects that prove value on a small scale, and success drives full implementation … which is where the trouble often starts.

“The move to production is where the real challenge comes,” McKervey says. “When you do a proof of concept, you just have to show that it functions. When you move to production, it needs to be secure, stable and to perform at scale.”

The trouble, he says, is often a lack of observability. Problems in production have to be diagnosed. You need visibility into data from the digital ledger itself, your infrastructure, your applications. Often organizations stitch together a mix of individual tools to analyze and visualize each type of data. And they may have to write proprietary code to analyze the ledger data. It reminds him of the days before Splunk, when sysadmins would write scripts to awk and grep through log files.

“The problem is, then they have this code that they have to support and scale and modify as the ledgers get upgraded and modified, and that’s a huge hurdle we’ve worked with customers on,” he says. As a result, he expects to see vendors respond with more interoperative monitoring solutions, or, as Splunk provides, cohesive observability solutions that combine logs, metrics, traces and ledger data


While blockchain consortiums are a leading model, they’ll be hampered by coordination and visibility challenges.

A lot of enterprise blockchain experience right now is through consortiums, in which companies in a certain industry or supply chain collaborate via a digital ledger. The consortium operator, which is not a stakeholder in the partnership itself, may be coordinating the solution, but lacks visibility, as do the members of the consortium. And visibility can be a challenge when some data may be proprietary to a specific participant, and when some of the participants may be rivals.

Among the biggest challenges, McKervey says, is getting to a decentralized state. Often the consortium starts with centralized control by the operator, with the goal of decentralizing the control. This can only be achieved when sufficient visibility across the consortium is obtained. Sufficient visibility is a difficult target, since the members use different infrastructure, cloud providers and monitoring solutions. This parallels the challenge individual organizations find as they power up from proof of concept to full production, and is another force that will drive a more cohesive approach to observability.


In about three years, blockchain gets really exciting.

The short term for blockchain is in security and efficiency, but looking a few years out, new business-transformative blockchain-based solutions will emerge and most won’t even know blockchain is part of the solution. Secure voting, tracking of political donations, tracking of disease outbreaks and securing medical supply chains are all happening now, but business-transformative use cases are hard to predict. (Did the taxi industry see the Uber model coming? Exactly.) Decentralized identity will be an enabler of these new business models. “That stuff is a ways out, though,” McKervey says. “Right now we’re still seeing blockchain develop as a strategic technology for businesses and the public sector. We’re at the stage where blockchain is beginning to increase efficiencies, but most CIOs aren’t yet seeing blockchain as a top initiative.”When will they get the message? “When leaders see their business model disrupted,” he says, “and by then it may be too late for their organization.”Decentralized identity is especially interesting, McKervey says. Individuals and organizations use many globally unique identifiers, such as: communications addresses (email address, user name, etc.), ID number (passport, driver license, tax ID), product identifiers (serial numbers, barcodes, RFIDs). The vast majority of these globally unique identifiers are not under our control, and may be open to fraudulent use: identity theft. The idea of a means of identification that is secure, global and not controlled by a central body has many appeals and applications.

“That stuff is a ways out, though,” McKervey says. “Right now we’re still seeing blockchain develop as a strategic technology for businesses and the public sector. We’re at the stage where blockchain is beginning to increase efficiencies, but most CIOs aren’t yet seeing blockchain as a top initiative.”When will they get the message? “When leaders see their business model disrupted,” he says, “and by then it may be too late for their organization.


Edge Computing

Edge computing is an inevitable progression. We’re already managing a great deal of the data and interactions of our smartphones and laptops via the cloud, with software that’s delivered as a service rather than installed on the endpoint. And we’re constantly digitizing more stuff. We’re putting sensors into warehouses, onto trucks and freight trains, in industrial machinery.

The result is that we’re measuring things and making decisions about devices on the edges of our networks by shipping the data to a central, probably cloud-based datacenter, doing the analytics, and sending back automated instructions. All that back and forth takes time, and latency is a problem.

(That’s right, a quarter-century ago, we were all on dialup, and now we’re complaining about the speed of light. Sounds funny, but half-second latency won’t be a laugh when your car is driving itself through rush-hour traffic.)

Our main prediction about edge computing (in which analytics and automation do all the “thinking,” and take action, at the network periphery rather than reporting back to the mother ship) is that it’s here, it’s necessary, and it’s getting better. What’s interesting is the way in which the edge provides a perfect arena for every emerging technology we’ve been discussing.


Emerging technologies come together at the edge.

John Sabino says that the real power in emerging technologies is not any one of them, but the combinations. “I think the keys are AI/ML and automation, and when you add them to IoT, edge computing and 5G, you can transform entire industries — logistics, manufacturing, healthcare, energy.”

Tim Tully sees the same effect, and notes that smarter AI, and more powerful hardware and robust connectivity will be transformative for applications of edge computing. “More and more is happening at the edge, because we can do more and more computation as the hardware and software gets more sophisticated,” Tully says. “Local processing reduces the latency of moving the data to the cloud to process, and you get the same results.”

“When I was at GE, we had this concept of an industrial internet that is only gaining steam,” Sabino adds. “You can see it on brewery lines right now. You might have four master brewers that might operate a multimillion-gallon line.”Progress. We’ll drink to that.


Future Steaks, Working Assumptions

We asked Jesse Chor for his most far-out, decade-plus prediction, and he gave us a pretty wild vision. Today’s 3D printers can handle machine parts and numerous consumer goods. Chor says that’s just the foot in the door for digital printing.

“I look at COVID-19 as an example,” he says. “Without a doubt, it’s going to accelerate vaccine production and testing, but I’m also looking ahead to a world where we digitally print vaccines, we digitally print medicine. It’s going to be world-changing, and even that’s just a start. Imagine digitally printing food.”

Sounds amazing, though if we’re predicting Star Trek futures, we’re more excited about visiting a VR holodeck than eating a filet mignon printed by a food replicator. To ground us back in the present, we asked Tim Tully for his most immediate, prosaic prediction.

“We’re all going to have to update our home networks,” he says. So many people are working from home, perhaps while a working spouse and distance-learning kids compete for bandwidth. And even if that’s not you, you’re probably consuming a lot more digital entertainment at home during the COVID era, in place of a more on-the-go social life.

“If you have three kids in the house on Zoom doing school plus two working parents, that’s probably 5x the amount of traffic you’ve ever had to have before,” he says. “I’ve helped a number of our execs upgrade to enterprise-level or prosumer tech.”So bring on the WiFi 6, and get on the Oculus Quest waiting list. The future is now.

2021前沿科技预测报告 Emerging Technology Predictions 2021(2)


Smarter AI will work wonders, and challenge human workers.

AI/ML, drawing insights from data and acting on it through automation, will transform just about every digital interaction in our lives. And at this point, nearly every interaction in our lives has a digital component. As the above trends come together — ethically sound algorithms that are robust against adversaries, and that can learn on their own — we’ll see them act more like humans, making more consequential decisions and actions.

Humans, however, will still have work to do in a world so transformed by AI. Ram Sriharsha says that the human disruption will be substantial. Many current jobs will be eliminated or fundamentally altered, and many new jobs will be created. In both cases, a new workforce will be needed, and organizations should begin retraining now.

Companies should be training their workforces, Sriharsha says, noting that such training used to be more common. “Companies are going to realize that it’s a value-add for them to train their employees now. In-house training on new methodologies, new techniques and so on, is going to be important.”

A McKinsey article in May says that the COVID-19 pandemic has illustrated the effects of sudden change on a workforce, and underscored the need for training as companies must match workers to rapidly evolving roles:

This dynamic is about more than remote working — or the role of automation and AI. It’s about how leaders can reskill and upskill the workforce to deliver new business models in the post-pandemic era.

To meet this challenge, companies should craft a talent strategy that develops employees’ critical digital and cognitive capabilities, their social and emotional skills, and their adaptability and resilience. Now is the time for companies to double down on their learning budgets and commit to reskilling. Developing this muscle will also strengthen companies for future disruptions.And every organization’s strategy coming out of 2020 is to build resilience to further disruptions.

And every organization’s strategy coming out of 2020 is to build resilience to further disruptions.



Pretty much since the invention of the smartphone, the focus of our digital lives has been the screen in our hands. For software developers, mobile first was a mantra for years until now it’s so basic that it needn’t be said. Splunk’s chief technology officer, Tim Tully, notes that he can do 80% of his job on his smartphone (but adds that, since COVID-19 sequestered him at home, he’s reverted to a heavy, powerful desktop machine for the first time in a decade).

The rise of the mobile device was significantly driven by the arrival of 4G networks that allowed mobile data streaming, which in turn powered the success of Netflix and YouTube, Uber and Lyft, all the social media networks and more.

(Before 4G, we still mostly used phones for talking. Now, we use our phones for data and bark instructions at an AI-enabled hockey puck that lives on our coffee table.)

5G is next on the horizon, but there are significant roadblocks that will slow its rollout. Before we consider the longer term, there are immediate mobile trends that were greatly accelerated by the pandemic. To name two: two-factor authentication and digital payments.


Two-factor authentication will become the widespread norm, not an option.

The sudden wave of office workers logging in from home raises security concerns, because now there are more people logging in from outside your network who might not be who they said they are.

It’s a challenge that should be keeping security experts and mobile software engineers awake at night. “The surface area of security has expanded because of COVID and mobile,” says Splunk Head of Mobile Engineering Jesse Chor, “and I think it’s definitely a concern.”

Expect to see more adoption of two-factor authentication, whether by a phone app that asks, “Did you just try to log in?” or a biometric scan. Mick Baccio, a Splunk security advisor who has worked for the Dept. of Health and Human Services and the White House, and was CISO for the Pete Buttigieg presidential campaign, agrees that 2FA is growing, and he sees hardware tokens as the likeliest solution. Hardware tokens include little USB security keys, or can be incorporated into mobile phones.

“A hardware token pretty much shuts down the risk of account takeover,” Baccio notes. “Who doesn’t want to shut that down? It’s one of the biggest problems security teams face. Just shut it down and move more resources to your next biggest problem.”

“The scary part now is that there are only two incumbent mobile operating systems,” Chor says. “If Apple or Google screw up their operating system, think about how devastating a vulnerability could be. A simple bug around my PIN, say, could let you get into my work network, hack my email, use my ecommerce accounts, hit my bank. You can basically be me.”

And as for the form factor of the two-factor, Chor says he’s “a big fan of biometrics. I think COVID is going to really accelerate the adoption of biometric identification for security and payments.”

An important value of biometric logins, Chor says, is that it replaces the physical device. If the mobile phone is the interface for the biometric identification, but does not store biometric data, it’s useless to a thief. (See below for more on biometrics.)

“Just like we don’t send passwords over the air anymore, we send hashes, the devices will send a hash of your biometric data,” Chor says. The device becomes a conduit for information that is confirmed in the cloud. A lost or stolen phone is no security threat, as long as you still have your thumb on hand. “I think security is going to head that way, where the phone is just a conduit.”


Contactless payment will rise faster than expected. (Like, really fast.)

COVID has driven adoption of digital payments, in terms of contactless payment apps such as Apple Pay, Google Pay, Samsung Pay, Square Cash, Venmo and PayPal One Touch. And after the pandemic recedes and we achieve a “new normal,” expect the convenience and no-touch benefits to continue to gain traction.

“People still go out and buy things, and when they do, they’re more often using contactless payments, which generally involves a phone,” Jesse Chor says. “And ideas like the digital wallet — Apple Pay — will make a lot more sense to a lot more people.”

In an April report, Bain & Co. noted that disruptions to businesses from restaurants to retail to the entire travel industry meant that payment volumes were down, and payment services providers were suffering. But not so, the contactless payment industry. Bain observed signs of growth, including hiring at several of the payment providers, and predicted faster-than-anticipated adoption even after the pandemic ends and the economy recovers. Bain’s pre-COVID estimate was that 57% of transaction value would be done digitally by 2025. Since the pandemic, Bain has revised the prediction to 67%.

Simon Davies, vice president of Splunk in APAC, says COVID has pushed several emerging technologies to center stage in the Asia-Pacific region. “Before COVID, people were already doing things like blockchain, mobile technologies, etc. but weren’t seen as being mainstream,” he says. “Now contactless payments have become much more prevalent — essentially the norm. That wouldn’t have happened quite as quickly if it weren’t for the pandemic.”


Despite rising appetite, 5G won’t hit in 2021. Expect rollout to be held up by hardware challenges at least into 2022.

Pre-COVID, the telecom industry was eager to roll out 5G, the next-generation telecommunications standard that promises greater bandwidth and lower latency, and a cell tower every eight feet or so. The technology could make our mobile devices more powerful, and fuel other emerging technologies, including virtual and augmented reality. These days, robust deployment of 5G looks more like a three-to-five-year proposition.

Tech industry news site EPS News predicts slow and uneven 5G adoption: “Before the pandemic struck, the mobile industry was rushing to bring 5G networks and technologies to market. With declining revenue and a shrinking market, this shift is less likely to take place soon. Some carriers and manufacturers already delayed upcoming releases of 5G devices and services.”

Analysts at 451 Research also see the delays, but note that this is no time to sleep in, writing1 in July, “5G will not move the needle in the enterprise in 2020, but planning must start now. … A couple of years from now, 5G networks will support ultra-low latency and mission-critical communications that enable the applications and processes supporting the digital transformation of industries, some of which will see acceleration due to COVID-19.”

“5G’s problem is the chicken and the egg,” says Jesse Chor. “You need demand that’s realized in devices that can use 5G, and you need the carrier infrastructure. But who’s going to build one without the other?

“I think the biggest obstacle for 5G is the physical limitations of the technology,” he says. Not only does the new standard require new cell towers, but 5G has a much shorter range than 4G, and is not great at penetrating walls, meaning we’ll need a lot more towers than earlier standards required. That’s expensive, and also, there’s a global pandemic.

“Especially with COVID, manufacturing has been slowed, and it’s really hard now to get fleets of people to install things, to even get the proper permits,” Chor says. “But despite the real barriers to adoption right now, the demand for higher bandwidth is going up. Between work-from-home and the hunger for streaming entertainment, people are very bandwidth-hungry.”

Chor sees the tipping point coming from the device side: “I think phone manufacturers need to get on it. We’ve just seen it with Apple, which launched the 5G iPhone 12 in October. That could increase the total addressable market by 30% or so. Other carriers will follow, especially if they have assurances from carriers about when 5G coverage is coming.”

Another impediment to rollout is that the pandemic has shuttered key locations for initial rollout. Chor expects corporate and college campuses to work with carriers to roll out the infrastructure.

“Once offices broadly reopen, I think a lot of tech companies will start instituting 5G within their campuses or buildings and pump it up that way, because there’s a lot of productivity they can gain with connected devices with low latency,” Chor says. And if tech employees or college students and staff are enjoying high-speed bandwidth as they work, they’ll drive demand where they live.

Look for initial networks to roll out in Asia, he adds. “China is already rolling it out in a lot of places. I was there just prior to COVID, and I saw the towers popping up everywhere,” he says. “And developing economies, in Asia and elsewhere, have an advantage in terms of lacking legacy infrastructure. It’s just a different discussion when you’re starting from near-zero, versus upgrading from an extensive legacy investment.”

The World Economic Forum lists 5G as one of 10 emerging technologies to watch in the wake of COVID-19, from distance learning and telehealth to drone deliveries and contactless payments, and notes that nearly every other technology on the list depends upon stable, affordable, high-speed internet. But while acknowledging the power of the technology, it also notes that “the adoption of 5G will increase the cost of compatible devices and the cost of data plans. Addressing these issues to ensure inclusive access to the internet will continue to be a challenge as the 5G network expands globally.


Never Mind the 5G, Here’s the WiFi 6

Jesse Chor says that a lot of the benefits we want from 5G will be delivered first by WiFi 6, an available but not-yet-widespread technology.

“WiFi 6 is at least 10 times faster than regular WiFi, so it will bring the bandwidth and low latency we talk about with 5G,” he says. “If you’re a business, you want to roll it out fast, and you can have it today — you just need connected devices that can use it.”

Many new devices, such as iPads and iPhones, are ready for WiFi 6. Schools and corporate campuses can benefit from the technology, and it will be a major part of the bandwidth equation that includes 5G.

“WiFi 6 is great for internal devices you control, manage and own. 5G would be for outside devices that you don’t necessarily control and own, but you want them to be connected,” he says. A shopping mall would want 5G available for shoppers, for instance, but might use WiFi 6 internally for retailers and management.“

Just like 5G, WiFi 6 will soon be ubiquitous,” he says.

In a few years, pretty much any WiFi enabled device you buy will be WiFi 6.

Chor notes that 5G promises much more than just higher bandwidth. Both 3G and 4G networks ushered in new services, and whole new industries, from Lyft to Door Dash to the kaleidoscope of digital streaming channels. Telecommunications bandwidth unlocks tremendous economic power.

“And I’m sure that 5G will do the same, with greater bandwidth and lower latency unlocking new things we didn’t think of,” he says. “If you look at the market caps of internet companies, they exploded every time another telecom standard was unlocked.”

Among the new possibilities to be unlocked: new mobile form factors. (Prediction: In the future, we’re all Tony Stark.) “The future of mobile is hands-free, wearable devices,” Chor says. “Connected glasses, like a phone on your face, totally makes sense, especially given the rise of AR/VR.”

It will take a few years, say five to 10, and it’s not just about use cases.“

You need advancement in material sciences, battery technology, a lot of stuff that needs to catch up,” Chor says. “But once we have them, who would not want glasses that do everything? Then you’re like Iron Man. It will be a while before we get there, but it’s definitely coming.

2021前沿科技预测报告 Emerging Technology Predictions 2021(1)

Emerging Into the Data Age 走入数据时代

What we really want to see emerging in 2021 is all of us, from our homes. 其实,2021年我们最希望看到的是,我们自己站起来,走出来 – 从我们各自蜗居了差不多一年的家里。By summer, a semblance of normalcy and stability should begin asserting itself globally. 到今年夏天,全球范围内应该开始出现至少在表面上回归正常和稳定。Old patterns will return, but with changes. 旧模式会回归,但是带来一些变化。Other regions may maintain the habit of wearing face masks in public even after the pandemic 其他地方可能会在疫情过后仍然坚持公共场合带口罩的习惯。. Temperature checks as you board a plane may be a thing, 以后坐飞机时,检查体温可能会成为正式登机流程之一,and restaurants might never feel crowded again 另外,餐馆可能再也不会出现拥挤的场景了。.

A pattern that wasn’t interrupted by the pandemic 有一种模型并没有被疫情打断, but instead shifted into overdrive 相反还进入了高速通道, is digital transformation 这就是数据转型. The Data Age 数据时代— defined by greater interconnectedness through ubiquitous digital technologies 具体特征就是无处不在的数字科技,还有大范围的互联互通 — was already here 对此我们都不陌生. Now it’s really here 现在,它已经来到了我们所有人的身边.

“COVID-19 has been a catalyst 新冠病毒是一个催化剂, greatly accelerating digital transformation 极大地加速了电子转型的进程,” says Ammar Maraqa 说, Splunk’s senior vice president 他是SPLUNK的副总裁and chief strategy officer 和首席战略官. “For business and IT leaders对商业和IT领袖们而言, the strategic long view has been dramatically compressed 他们的长期战略计划被大大地压缩了. Disruptions anticipated in five or 10 years 5到10年一遇的意外情况 have been compressed to a horizon of months or weeks. 被压缩到了几个月甚至几个星期之内”

For a lot of organizations 对很多组织来说, high-speed transformation is going to look like a lot more cloud 高速转型很可能就是向云端进发. Organizations that were already fairly mature in their cloud adoption 那些已经在云端发展得很成熟的组织 are pushing into automation 如今在朝自动化前进 and machine learning 还有机器学习. Orgs that can really push the envelope 还有一些走在队伍最前列的组织机构 are planning for 5G 则瞄准了5G在做规划, investigating augmented reality or blockchain 或者在研究应用增强虚拟现实和区块链的可能性, or deploying edge computing solutions 又或者,在部署边缘计算技术解决方案.

A lot of powerful software 很多强大的软件 is in our near future 离我们已经很近了 — in large part 很大程度上, says Splunk’s head of mobile engineering SPLUNK的移动技术部门负责人, Jesse Chor 说, because of a key hardware development 是因为一种关键硬件设备的发展: the rise of the graphics processing unit (GPU) 也就是显卡的崛起. The central processing unit 中央处理器, the computer’s brain 是电脑的大脑 and a fundamental determiner of how fast a program can run 也是决定一个程序可以运转多快的最核心部件, continues to steadily improve 仍然在稳定发展. But in recent years 但是近几年, the GPU 显卡— originally designed for 3D graphics 本来是为3D绘图设计,and therefore central to gaming 因此对游戏竞技举足轻重 — is increasingly important 正在变得越来越重要 to use cases beyond “first-person shooter.”应用范围不仅限于第一人称视角设计游戏 Playing the GameSure 在玩 GAMESURE这个游戏时, gaming isn’t the only use of advancing GPUs 游戏不是显卡的唯一用途, but it’s still a big one 但仍然是非常重要的应用领域.

Splunk’s chief technology officer SPLUNK 公司的首席技术官, Tim Tully, says the improvement in GPUs 说显卡的发展 makes gaming a more interesting tech space than ever 使得游戏成了一个前所未有的有趣科技领域. He also admits 他也承认,that since the pandemic 自从疫情爆发以来, he has become a more regular gamer 他自己也成了一个花不少时间玩游戏的玩家. “GPU advancements are allowing developers to make these super-immersive games 显卡的发展允许游戏开发者制作出一些非常逼真的游戏, and VR will be an interesting element there 虚拟现实技术将会是这个领域一个中很有趣的元素,” he says 他说道. “And since the pandemic 自从疫情爆发以来, gaming has become an even bigger activity 游戏已经成了一个影响很大的活动, a more important escape mechanism 一个更重要的逃离现实的途径, than ever 从未有过的重要途径. I think it will consume more of the budget we used to spend in restaurants or movie theaters 我想,它会让我们以后在餐馆和电影院少花钱,在游戏中多花钱.”

“The evolution of GPU 显卡的进化 is going to be a big one 将是一个大事件,” Chor says 说道, “because that’s what unlocks machine learning on edge 因为那将是解开机器学习枷锁的钥匙, that’s what unlocks AR 还可以解开增强现实技术的应用, that’s what unlocks all these great future experiences 它可以解开所有这些未来的美妙体验. And then if you combine that with the low latency and high bandwidth of 5G and WiFi 6 如果你在把它和5G以及WIFI 6的低延迟和高带宽结合在一起, you’ve got everything you need 那就万事俱备了.”This year’s predictions for emerging technologies include a number of ways to keep the most powerful GPUs busy 今年的前沿科技预测报告包括一系列可以让最强大的显卡忙碌起来的方法.


Predictions and Survival Strategies for 2021

2021 大预测和生存策略

Artificial Intelligence/Machine Learning (AI/ML) 人工智能/机器学习 Self-learning ML 自我学习 will help us see beyond buzzwords to value Challenges 将会帮助的视线穿越迷雾,看清前面的挑战: Adversarial attacks 来自对手的攻击, AI ethics 人工智能的道德, human training 人类的培训


5G will be delayed 5G在今年的发展会被延迟, then big 然后是高速发展. Meanwhile 与此同时, look at WiFi 6 先来看看WIFI 6这种技术. Also 另外还值得关注的有: Contactless payments 无接触付款, two-factor authentication 双因素认证and biometrics 还有生物测定技术.


AR 增强现实: Pandemic setbacks 疫情的牵扯 may lead to immersive collaborative tools 可能会使得一些逼真的合作工具的出现,and healthcare 还有这种技术在医疗领域的应用发展.

VR 虚拟现实: The hardware’s there 硬件已经有了. It’s do-or-die on the software front 在软件这一块,则是一个要么做,要么死的选择 .

Biometrics 生物测定

Biometrics was already taking off before the pandemic 生物测定技术在疫情之前已经起步了.

Blockchain 区块链

The marketplace finally gets blockchain 市场上终于出现了区块链的身影; it’s focused on efficiencies today 目前来说,它的应用集中在提高效率方面, and will see wilder possibilities in the years ahead 未来的几年里,我们会看到更多的应用可能性.

Edge Computing 边缘技术技术

Where it all comes together 这是一个所有前沿技术综合应用的场景.


Artificial Intelligence 人工智能/Machine Learning 机器学习 (AI/ML)

The adoption of AI/ML technologies AI和ML 技术的应用 was already under way when the pandemic hit 在疫情爆发时已经开始上路, but since COVID-19 disrupted public health 但是既然新冠病情打扰了公共卫生, the economy 经济 and pretty much every other aspect of how we live and work 还有我们工作生活中的几乎所有方面, organizations have significantly sped up their incorporation of machine learning algorithms 组织机构纷纷加快了他们将机器计算算法融入工作中的进程.

“We’re seeing 我们看到的情况是, that particularly, but not exclusively, with security use cases 这种技术主要应用在安全领域,但不是只在安全领域,” says Ram Sriharsha 说, Splunk’s head of machine learning 他是SPLUNK的机器学习部门负责人. For algorithms already in use 对那些已经进入应用领域的算法, the pandemic has created challenges 疫情让它们面临着挑战. Predictive retail algorithms falter when our behaviors change suddenly and significantly 当我们的行为突然发生突然的变化时,那些预测性的零售领域算法就变得不适应了. As the MIT Technology Review noted in May 麻省理工学院的科技评论杂志在五月份一篇文章中指出, “Machine-learning models trained on normal human behavior 基于正常的人类行为训练出来的机器学习模型 are now finding that normal has changed 现在发现所谓的正常已经改变了, and some are no longer working as they should 有些算法根本就不再有作用.”

In other words 换句话说, online retailers’ recommendation engines weren’t quite ready back in March 三月份的时候,那些在线零售网点的货品推荐就没发挥什么作用 for everyone to suddenly care about nothing but toilet paper and hand sanitizer 因为在那个时候,人们唯一想购买的就是卷筒纸和洗手液.

At this point 当下, machine learning has mostly caught up with the changes caused by the pandemic and recession 机器学习基本上已经跟上了疫情和经济萧条所带来的改变. And across industries 各行各业, algorithms have been, if not commoditized 如果不是已经被商业化了, then democratized就是被民主化了. The major cloud providers 那些主要的云服务提供商 are offering the hardware and software 开始将硬件和软件 to bring the power of machine learning 携带者机器学习的力量 to their customers 带给顾客. Those providers and other third-party vendors 那些主要提供商和第三方分销商 are delivering “AI as a service.” 开始将人工智能当成一项服务出售 And, more importantly 更重要的是, the idea of artificial intelligence 人工智能的主张 has taken hold in the corporate imagination 已经在一些大公司的发展计划当中了.


AI/ML will be held back by its own limitations AI/ML 将被其自身的限制所牵扯— until it can learn on its own 直到它可以靠自己学习.

One thing holding back AI/ML adoption is the resource overhead 限制AI/ML 应用的情况之一是资源消耗, says Ram Sriharsha 说, Splunk’s head of machine learning 他是SPLUNK 的机器学习部门的负责人.

“The problem with the traditional ML pattern 传统的机器学习模型的问题, in which people spend a lot of time building and deploying models 人们花费很多时间建造和安置的那些模型, is that it just doesn’t scale 是他们跟不上节奏,” he says 他说. “Organizations are evolving at a much faster rate 组织机构的发展速度要快得多, and the questions you’re asking your data are evolving 他们希望这些数据也快速进化. You can’t hire data science teams fast enough to keep up 但他们找不到速度足够快的数据工程师团队.”

The answer 解决这个问题的钥匙, he says 他说, is to automate the learning in machine learning 就是让机器学习自动化. “You not only have to automate the process of creating models and deploying them 你不仅需要让创建模型和配置模型的过程自动化, you have to automate the process of learning and relearning 还需要让学习和再学习的过程自动化.”

A challenge closely connected to the human-intensive process 对那些需要大量人力的程序来说,一个挑战, he notes 他说道, is the fact that most machine learning models 就是大多数机器学习模型rely on well-structured, clearly labeled data to learn都需要结构良好,清晰标注的数据作为基础.

“You’re going to see increasing amounts of R&D energy 你会看到,越来越多的科研资源 trying to solve these two problems 在尝试解决这两个问题, which is how to make the algorithms learn with as few labels and as little human input as possible 就是研究如何让算法尽可能使用少一些标签,少一些人工投入,来学习,” Sriharsha says 说道. “The more I can throw ML at unstructured data 如果我能丢给机器学习算法很多没有结构的数据,and have the algorithm figure out how to extract what it needs from the data 让算法自己从数据中整理出所需要的信息, the more powerful its contribution to the organization will be 那么它给组织带来的贡献就会大很多.”

It’s a lot harder to get an algorithmic model to work with the shifting, unstructured sources of data 要让算法模型去对付那些变化的、没有结构的数据资源,很难 that would drive better retail recommendations or supply chain refinements 这些模型需要提供更聪明的零售建议,或者提供连锁调整 than it is 下面这种情况就要容易得多,to get a model to master structured board games 让模型去掌握那些有结构的棋类 like chess and go 比如说国际象棋和围棋, but that’s what CIOs want 但这就是那些CIO们想要的. It’s definitely what our CIO wants 他们一心一意就想要这个.

“I’m keeping my eyes on self-learning systems 我一直在关注自我学习系统,” says Splunk 的 CIO Steve McMahon 说. “I want my process automation largely to be self-aware 我希望我的程序自动化能够尽可能地具有自我意识, so to speak 可以这么说, and learning 还有自我学习 so that it can identify the greatest opportunities 所以它能够识别最大的机会.”


Defense against adversarial learning will improve in the next few years. Because it has to.

Last year, our predictions report warned of the potential threat of AI sabotage: You can poison the outcomes of AI-driven automation by poisoning the data it learns from. We gave the example of tricking an autonomous vehicle into misunderstanding a stop sign. In September, researchers found that a tiny sticker on an object the size of a fighter jet could hide it from an AI processing drone footage. The threat of data deception remains on the horizon, and a new area of research will have to rise to the challenge, because today’s AI is as naive as a week-old puppy.

“Machine learning algorithms trust the data they learn from,” Ram Sriharsha says. “But what happens if people are trying to hack you? As an industry, we haven’t thought carefully about how to learn in the presence of adversaries.”

He says that researchers will need to explore how to make their models robust against adversaries. And he says that now is the time to develop those techniques, because the potential power of such attacks will grow thanks to standard market forces.

“In time, there will not be hundreds of machine-learning startups selling hundreds of machine-learning platforms,” Sriharsha says. “There will be a few, or one.”

And just like the dominance of Microsoft’s operating system gave hackers one big target, a small number of dominant AI platforms would draw all the attacks.

“Once that market consolidates around one platform that almost everybody is using, hackers are really incentivized to figure out how to break it,” he says. “With that kind of adversarial attention, we have to spend a lot of energy right now to build robust algorithms that can withstand attack.”


Look beyond AI’s buzzword heat to get real, meaningful value from AI/ML.

AI is often a black box, a vague promise, a hope for a Star Trek future. Certainly vendors are slapping “Now with AI!” on products like it’s extra raisins in your Raisin Bran. And corporate customers are getting caught up in the ill-defined excitement.

“A lot of customers won’t even think about using your product if it doesn’t have AI built into it, or the potential for it to be integrated as soon as the initial use cases or outcomes are derived,” says John Sabino, Splunk’s chief customer officer. “But a lot of the time when people ask for AI, they’re just checking a box. They’re not sure what it is, but they’re afraid of being left behind.”

Simon Davies, vice president of Splunk in APAC, says that organizations in the Asia-Pacific region are already outgrowing the buzzword phase. “They’ve moved on from AI being something that you specifically think about to being a core part of any type of decision or technology,” he says. “Instead, the conversation is about ‘How can your platform assist us?’”

Delivering an AI-based product, that’s a vendor’s job. But how an organization uses AI to be more competitive or deliver better outcomes, that’s a strategic consideration. Which is important for an organization to consider, Sabino adds. “You’re looking to build relationships with providers who really understand how to leverage AI, and who really understand your business and use cases, because this is a strategic relationship. It’s how a company in a competitive industry can leapfrog the competition.”


Prediction:Increased attention to the challenges of ML bias will build ethical responsibilities into engineers’ job descriptions.

As we leave more decisions to algorithms, there will be increased attention to how they’re making those decisions. The utopian vision of fair outcomes derived from dispassionate examination of objective data overlooks the matter of who selects the data sets and designs the algorithms themselves: flawed and inevitably biased human beings.

We’ve seen examples of bias already. Our predictions last year noted the adversarial learning example of jerks (technical term) teaching a crowdsourced chatbot to be racist. Algorithmic bias in the mortgage industry is a well-known failing, and facial recognition hit a wall amid multiple controversies in 2020.

A July paper in Royal Society Open Science discussed the problem of “unethical optimization” and mathematical methods for detecting and eliminating such biases. Ram Sriharsha expects that pursuit to take on added importance in the next few years. But he says that the goal of completely eliminating bias is doomed to failure, so it has to be coupled with a goal to at least understand bias when it (inevitably) occurs.

“Take the classic example of loan applications being racially biased, even when it’s not an intention but an inadvertent result of the data you fed it,” Sriharsha says. “You can’t teach an algorithm, ‘Recognize race and don’t be a jerk about it.’ So explainability is going to be crucial.”

If we can understand how an algorithmic model produced an objectionable outcome, we can more quickly adjust it to produce better, fairer conclusions. Was the training data biased because it didn’t accurately reflect the full ethnic and gender makeup of society? Does a particular data set reflect societal biases that the model is then reinforcing? Are there other data sets that would contribute to a clearer picture of our society, and produce outcomes that better align with an organization’s or community’s values?

Explainability can help us fix errant models as they err, but the bigger question, Sriharsha says, is how we handle ethical issues up front, before the models are turned loose.

“It starts with education, evolving the course work we use to train future computer science engineers,” he says. “But more than that, we’re already seeing partnerships designed to prevent these unintended biases. If computer scientists in the past have worked in isolation, now they’re working with ethicists, economists and sociologists to understand the societal implications of certain models.”

If we can’t teach every software engineer to be a sociologist, urban planner, community activist and moral philosopher, we can at least make sure the engineer has them all on speed dial.

These consultations, and the very act of considering wider ranges of consequence, mean that better, fairer models will take more time and consideration to build. Problem: “Let’s slow down and think this through” is not typical Silicon Valley cocktail conversation.“

‘Move fast and break things’ has been the mantra of Silicon Valley,” Sriharsha notes. “And I think that for Silicon Valley entrepreneurship to remain at the top, you have to move fast. Especially in a rapidly evolving field like AI. But we’ll probably have to learn to move fast and not break things.”


Machine learning will help speed the discovery of new medicines — in part by looking at previous “failures.”

This prediction looks at one of the fields where data obsessed us all in 2020: healthcare. The current crisis is finding new applications of artificial intelligence, with the healthcare sector seen as a major growth area. For instance, the U.S. Centers for Disease Control developed a coronavirus chatbot that uses AI to tell you whether you need to go to the hospital.

But a challenge in applying machine learning to healthcare is that it’s a human-intensive field, in which a lot of what happens in terms of treatment and research moves, by necessity, at a human pace. And the smallish numbers involved in clinical trials or individual treatments don’t really cry out for advanced algorithmic support, Sriharsha notes.

“Where I feel AI/ML make the most sense in medicine today is not in current trials, but in the thousands that have already occurred,” he says. “Clinical trials are extremely expensive. Companies spend billions of dollars on them, and many fail. There are vast troves of data on the structure of drugs t that didn’t move forward, but might have undiscovered promise.

Some clinical trials are stopped not because a drug fails, he says, but because it wasn’t effective enough in an envisioned scenario. But it’s possible that those drugs, or new structural variations of them, could be more effective in other important scenarios. This existing, unused data could help researchers more quickly zero in on new, effective medicines.

“If you want to see the biggest impact of AI in healthcare and life sciences in the next 10 years,” Sriharsha says, “probably that is where it’s going to happen.”