New metaverse standards to address lack of interoperability

Big-name tech companies such as Meta, Microsoft, and Epic Games have formed a standards organization called the Metaverse Standards Forum (MSF). This is meant to be a group that creates open standards for all things metaverse, including virtual reality, augmented reality, and 3D technology.

Over 30 companies have signed on, some of which are deep in metaverse technology like Meta itself. Others include Nvidia, Unity (the creators of the popular game engine), Qualcomm, Sony, and even the web standards organization itself — the Worldwide Web Consortium (W3).

Meta Quest

According to the official press release:

“The Forum will explore where the lack of interoperability is holding back metaverse deployment and how the work of Standards Developing Organizations (SDOs) defining and evolving needed standards may be coordinated and accelerated. Open to any organization at no cost, the Forum will focus on pragmatic, action-based projects such as implementation prototyping, hackathons, plugfests, and open-source tooling to accelerate the testing and adoption of metaverse standards, while also developing consistent terminology and deployment guidelines.”

This seems to imply that many of the future technologies created for the metaverse will include some level of interoperability between companies. That doesn’t mean the metaverse will be the Internet 2.0, but it may allow users to use certain profiles or data across metaverse platforms. In fact, this is directly stated in the press release:

“The metaverse will bring together diverse technologies, requiring a constellation of interoperability standards, created and maintained by many standards organizations,” said Neil Trevett, Khronos president. “The Metaverse Standards Forum is a unique venue for coordination between standards organizations and industry, with a mission to foster the pragmatic and timely standardization that will be essential to an open and inclusive metaverse.”

A vision of Meta's metaverse in the work setting.

Besides the W3, other standards organizations have also joined the Forum, such as the Open AR Cloud, Spatial Web Foundation, and the Open Geospatial Consortium. This gives a lot of weight and much needed legitimacy to the organization, as the metaverse is very much a burgeoning field of technology.

Interestingly, major VR/AR players are conspicuously missing at the moment. Apple, who has already invested much in AR technology and is planning its own headset, has not yet joined the MSF. Niantic, maker of popular AR game Pokemon Go, is also missing from the roster. Protocol also points out that the Roblox Corporation, maker of the wildly successful Roblox game, has also declined to join for now.

While not considered a “metaverse” in the popular usage, Roblox in particular has been able to create an immersive 3D world where people can create entire games within it.

The exclusion of Apple, Niantic, and Roblox isn’t a forgone conclusion, however, as the MSF has just begun. The good thing is that most of the major players in the metaverse tech are agreeing to create some kind of unified standard to make development much easier. The press release named several important technology fields, including avatars, privacy and identity management, and financial transactions.

The Metaverse Standards Forum is scheduled to begin meeting next month.

Editors’ Choice

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Lack of AI implementation may have cost enterprises $4.26T, Signal AI finds

Elevate your enterprise data technology and strategy at Transform 2021.

AI’s potential impact on the U.S. economy could reach into the trillions of dollars, according to a report published this week.

Signal AI, which offers a decision augmentation platform infused with AI, interviewed 1,000 C-suite executives in the U.S. for the study. The report found 85% of respondents estimate upwards of $4.26 trillion in revenue is being lost because organizations lack access to AI technologies to make better decisions faster.

According to the Signal AI survey, 96% of business leaders said they believe AI decision augmentation will transform decision-making, with 92% agreeing companies should leverage AI to augment their decision-making processes.

More than three-quarters of respondents (79%) also noted that their organizations are already using AI technologies to help make decisions.

In general, 96% of business leaders said they believe they can leverage AI to improve their business decision-making processes, with 80% noting they already feel they have too much data to weigh when making decisions. On average, 63% of respondents said they spend upwards of 40 hours a week on decisions.

Reputations and expectations

More than two-thirds of respondents (69%) ranked data higher than instinct in terms of influence on business decisions, even though many execs have been skeptical of the quality of data being employed within analytics and business intelligence (BI) applications.

Arguably the most surprising survey result is that just over 85% ranked reputation as a bigger priority than profit margins, Signal AI CEO David Benigson said. There’s a growing appreciation for the impact reputation has on both profitability and revenues, he noted.

But some business leaders may have unrealistic AI expectations, Benigson reported. “Just like with other technologies, they are overestimating the impact of AI in the short term and underestimating it in the longer term,” he said.

Estimating the potential revenue impact of AI is an inexact science. But a lot of complex business processes are occurring in near real time that are impossible for humans to optimize with AI augmentation. The challenge is building AI models that accurately reflect those business processes. Many of the data science teams that have been hired to build AI models lack a deep understanding of the process they are being tasked with automating. Many AI models, as a consequence, never get deployed in a production environment.

Nevertheless, the volume of AI models being deployed continues to increase. The next big challenge for organizations will be the maintenance of all those AI models, many of which are subject to drift as new data sources become available. This means an AI model may not be as efficient as it once was because it needs to be retrained or replaced altogether.

Regardless of the path forward, AI models will increasingly become just another type of artifact to be incorporated into the application development process. The challenge will be aligning the efforts of application developers with the data science teams that build AI models to ensure neither is waiting for the other to finish a project before an application can be deployed.

In the meantime, business leaders may want to temper their AI expectations. Implementing an AI model is roughly akin to hiring a junior member of a team that needs some time to learn how processes work. Unlike a human, however, that AI model never takes a day off, quits, or forgets what it learns unless it is retrained. The only real issue is that when an AI model does make a mistake it may be at a level of scale that is difficult for the business to recover from unless the proper guardrails are in place.


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This thin and light $310 Acer laptop is loaded with goodies most budget laptops lack

Today’s deal features a star from Amazon Prime Day, and while today’s price isn’t as good last month’s all-time low, this is still pretty sweet. Amazon is selling the Acer Aspire 5 Slim laptop for $310. That’s $40 below this sleek laptop’s MSRP, and $20 below its previous price of $330.

The notebook features a 15.6-inch 1080p IPS display (a rarity in a cheap laptop), a 2.6GHz dual-core AMD Ryzen 3 3200U processor, 4GB of RAM, and a 128GB SSD. It’s also rocking a backlit keyboard (another rarity in this price range), a speedy USB 3.1 port, two USB 2.0 ports, and one HDMI. For Wi-Fi, the laptop has 802.11ac. 

A loadout like this should be able to handle day-to-day tasks easily. Heck, you could even play esports and other low-intensity games thanks to the Radeon Vega graphics cores infused in the Ryzen processor. You can’t say that about budget laptops very often!

The overall design of this laptop is very nice, with an aluminum lid atop a slim aluminum-looking chassis. It’s very reminiscent of Acer’s Chromebooks, but it’s packing Windows 10 in S Mode instead. If there is a downside to this laptop, that’s it. Windows 10 in S Mode means you can only use Windows Store apps on your device. The good news is getting the full version of Windows requires a few clicks in the Windows Store and no extra fees.

Overall, this should be a great-looking, well-performing laptop capable of a few unique tricks for this price range, and it’s available at a solid price today.

[Today’s deal: Acer Aspire 5 Slim for $310 on Amazon.]

Note: When you purchase something after clicking links in our articles, we may earn a small commission. Read our affiliate link policy for more details.

Ian is an independent writer based in Israel who has never met a tech subject he didn’t like. He primarily covers Windows, PC and gaming hardware, video and music streaming services, social networks, and browsers. When he’s not covering the news he’s working on how-to tips for PC users, or tuning his eGPU setup.

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Tech News

How the lack of pay-as-you-go SIMs are thwarting the always-connected PC

Anyone who’s used an LTE-connected laptop understands the sheer joy of mobile computing: always-on connectivity, wherever you are. But this vision will never be broadly accessible unless the wireless carriers sell us prepaid SIMs that can be topped up cheaply.

American consumers and carriers alike are comfortable with the notion that you own a phone and a wireless plan, and pay for one to power the other. But when you start adding devices to the mix, such as an Apple Watch or a 4G laptop, there are both technical issues and billing issues to overcome. 

The U.S. wireless carriers have convinced customers that a fixed monthly bill is in their best interest. Everywhere else in the world, pay-as-you-go SIMs flourish, allowing wireless devices to connect as needed, without unnecessary fees. 

Users who roam among wired and wireless access points don’t necessarily need an enormous stable of mobile minutes or data, just the flexibility to connect when needed at an affordable price. Until American consumers have access to that, “always connected” PCs are going to be more trouble than they’re worth.

Acer Swift 7 sim slot Mark Hachman / IDG

Not many laptops ship with a SIM slot like this older Acer Swift 7, but they’re exceedingly handy when you’re on the go. Why have all-day battery life if you don’t have all-day connections, too?

What others get

The best wireless experience I’ve had was in Taiwan, where you can pick up a local wireless SIM at the airport, pay about $10 for 5 days of unlimited high-speed data, then toss the SIM in the garbage on the way home. You can also buy a pay-as-you-go SIM and top up in any convenience store on the island. ‘Topping up’ is like filling your car with gas: You pay a few bucks, and add to your pool of available minutes and data. Depending upon the country and the service, topping up can be done either in-store or online, and sometimes just via text.

There’s a difference between the SIMs tourists buy, however, and the SIMs permanent residents need to connect their laptops for 4G use. Case in point: this Vodafone (UK) plan that offers unlimited data, unlimited texts, and 500MB of data per day, capped at just one UK pound per day. 

vodafone pay as you go

Vodafone’s plan may be capped at 500MB per day, but for just over a dollar?

That’s just a start. I think it’s fair to say more and more people are turning away from voice and even SMS messaging, moving to data-only plans and apps. I’d love a data-only version of this: a SIM that I could simply leave in my laptop, with a small pool of data that could be periodically topped up. Everything else I could do on my phone.

What we get

American customers have been trained to pay for a fixed bundle of voice, texts, and data per month, with more and more opting for unlimited plans from the four major carriers. 

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AI exec says lack of automation is holding back AI progress

After more than a decade of providing a platform-as-a-service (PaaS) environment for building and deploying AI applications, launched an initial public offering (IPO) in December 2020. Earlier this month, in partnership with Microsoft, Shell, and the Baker Hughes unit of General Electric, the company launched the Open AI Energy Initiative to enable organizations in the energy sector to more easily share and reuse AI models.

Edward Abbo, president and CTO of, explained to VentureBeat why more fragmented alternatives to building AI applications that rely on manual processes not only take too long but also are, from an enterprise support perspective, unsustainable.

This interview has been edited for brevity and clarity.

VentureBeat: Where does fit in the ecosystem of all things AI?

Edward Abbo: There are two key products that we bring to market. One is an application platform as a service that accelerates the development, deployment, and operation of AI applications. Our customers can design, develop, deploy, and operate AI apps at scale. It runs on Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform as well as on private clouds and in a customer’s datacenter. The other is a suite or a family of industry-specific AI applications. Manufacturing customers, for example, can subscribe to AI applications for customer engagement.

VentureBeat: just launched an Open AI Energy Initiative alliance with Shell, Baker Hughes, and Microsoft. What’s the goal?

Abbo: The idea is that companies can develop their own AI models and applications and make them available via OSI in way that allows other companies to subscribe to them. This is the first AI marketplace for applications and AI models in that industry.

VentureBeat: Do you think organizations are struggling to operationalize AI?

Abbo: You often hear two things. Data scientists spend 95% of their time grappling with data. They need to access data from numerous different data stores and then [have] to unify that data. But an entity might be a person or a piece of equipment that has a different identifier in different systems. Almost all corporations are plagued with way too many systems, so their data is fragmented. Data scientists end up having to do that work. They need to unify data and normalize things based on time. They end up spending 95% of their time on data and data operations and only 5% of their time on machine learning. That’s obviously a huge inefficiency. It’s a great frustration for many data scientists.

The second thing is data scientists employ programming languages such as Python and R. They’re not computer scientists or programmers. They turn a model that they think has high value over to an IT organization that isn’t used to dealing with it. They need to figure out how to operationalize it and scale it. You can have two million machine learning models that you need to train, validate, put into operation, and then monitor for efficacy. After that, you might need to retrain that model or introduce another version into operation.

VentureBeat: How does change that equation?

Abbo: We’ve flipped it by handling the data operations. The data scientists can now spend 95% of their time on machine learning and only 5% retrieving data. We’re able to remove the barrier of going from endless prototypes to actually scaling and putting AI models in production. These are the hurdles we remove to scale and achieve enterprise AI.

We provide a product called Data Studio to integrate and rapidly unify data from disparate sources. By serving up data and analytic services, the data scientist doesn’t have to worry about doing all that work. We provide business analysts with drag-and-drop canvases they can use to bring data in and experiment with machine learning models without programming. They can then publish AI models and data services to downstream applications that might invoke those services.

VentureBeat: We hear a lot about machine learning operations (MLOps) and data operations (DataOps). Will these two disciplines need to converge?

Abbo: MLOps and DataOps need to converge. We’ve really brought data operations, IT operations, machine learning operations, business analysts, and applications onto a single platform. Data engineers are focused on aggregating the data and serving it up. Data scientists then use that to create models and publish them. Business analysts can then plug into the machine learning model library using the tools of their choice.

VentureBeat: That’s basically a no-code tool. Does that mean you don’t need to be a rocket scientist to do AI?

Abbo: We accommodate both universes. If you’re a programmer, you can publish our microservices in programming languages. But if you’re a business analyst or citizen data scientist, you don’t need to program. You can simply drag and drop, connect, and actually reference some sophisticated algorithms through a user interface without programming. We use a technique that’s referred to as a model-driven architecture. We’re representing the semantics of the application in a way that’s independent of the underlying technology. As Microsoft and AWS or Google introduce new technologies, we can basically plug those into a future-proof application.

VentureBeat: Do you think that AI platforms will by definition need to be hybrid in the sense of providing a level of abstraction that can be used to manipulate data regardless of where it resides?

Abbo: I definitely agree. Companies still have the majority of their systems in their datacenters. Being able to write your applications in a way where they can initially be deployed on-premises and then, without having to rewrite them, be moved into a cloud is a huge value to customers.

VentureBeat: What AI mistakes do you see organizations routinely making?

Abbo: The first inclination of the CIO is how hard could this be. I’ll just unleash my programmers to develop this capability. And then it’s 12 to 18 months down the road, and then they figure out it’s enormously difficult to pull off because of all the components you need to orchestrate. Data unification from dozens, sometimes hundreds, of different systems is a really challenging problem.

It’s not just a relational database anymore. It’s a multiplicity of data stores. Then you need an event model that handles data in batch, micro-batch, streaming, in memory, or interactive memory. Then there is a plethora of tools that need to interoperate. Underneath that, you have data encryption, data, transposition, and data persistence. You have to orchestrate all that.

The sooner people figure out they need a cohesive platform to accelerate the development and deployment of these AI apps, the better. We’re not talking about one or two apps here. We’re talking about hundreds of AI apps that leverage the existing systems in a way that delivers enormous economic value to companies. CEOs want them deployed as soon as possible.


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