Category: IT Infra

Unified Communication

Meta reminds people that the metaverse will probably be awesome, one day

Insights Desk

In context: With all the excitement, headlines, and talk about generative AI, Meta is reminding people that these systems aren’t as important as the area it has plowed over $24 billion into over the years: the metaverse. Nick Clegg, the company’s head of global affairs, just held a press conference in the virtual world to hail the metaverse as the future of computing.

Bloomberg reports that Clegg held a small press conference within Meta’s Horizon Workrooms. He was in London and spoke to Washington-based reporters who were wearing borrowed Meta Quest headsets. The publication notes that the group appeared as torsos seated around a large wooden table, and only Clegg’s avatar resembled the person wearing the headset.

“We’re going to stick with it, because we really believe, all the early evidence suggests, that something like this will be the heart of the new computing platform,” Clegg said. “But it’s going to take a while.”

It wasn’t that long ago when Facebook went all-in on the metaverse, going so far as to change its corporate name to Meta and pour billions into its Reality Labs division. But getting consumers to feel the same level of excitement has always been an uphill battle. Even teens aren’t interested in the concept.

Things have gotten even worse for Meta in recent times. The global economic slump means expensive VR headsets are pretty low on people’s shopping lists – shipments slumped more than 12% year-over-year in 2022.

But the biggest blow to Meta’s ambitions has been the rise of generative AI over the last few months. Tech firms are rushing to implement the likes of ChatGPT into their services; even Meta said it would be introducing AI-powered chat in WhatsApp and Messenger. It’s taken what little focus was on the metaverse away from that area and placed it squarely onto AI – Zuckerberg rarely mentions the virtual world anymore.

Big companies seem to have realized that spending a lot of money on the metaverse is becoming a pointless endeavor. Microsoft’s round of 10,000 layoffs saw its industrial metaverse project killed off, and Disney laid off its entire metaverse team as part of cost-cutting plans. There are also the cuts Meta has made that impacted the Reality Labs division, and senators demanding the metaverse be a place for adults only.

One supporter in Meta’s corner is Tim Sweeney. The Epic Games boss recently explained why he thinks the concept still has promise.

Clegg believes advertising and commerce will help Meta recoup the billions it has invested in the metaverse – Zuckerberg famously said it could be earning billions or even trillions of dollars in ten years – but then people actually have to be using these virtual worlds to buy things or be served ads.

There were plenty of obvious bugs during Clegg’s event, including all the avatars’ mouths moving when one person spoke. He believes the hardware is another area that will improve in time. “I just really want to stress that we’re going to look back on the headware we’re wearing now and think, ‘Gosh, do you remember the days when you would wear a Quest Pro?'” Clegg said. “We’ve always been very clear that we’re in this for the long haul. This is not going to happen overnight.”

Networking

US national lab is using machine learning to detect rogue nuclear threats

Insights Desk

In context: While the entire technology world is focused on generative AI and its alleged capabilities to destroy the economy and the job market, researchers are employing neural networks to tackle challenges in science, energy, health and security, such as detection of rogue nuclear weapons.

The Pacific Northwest National Laboratory (PNNL) is trying to hunt for unknown nuclear threats by using machine learning (ML) algorithms. PNNL, which is one of the United States Department of Energy national laboratories, said that ML is everywhere now, and that it can be used to create “secure, trustworthy, science-based systems” designed to give people and nations answers to different kinds of difficult scientific challenges.

The official public debut of an ML algorithm dates back to 1962, PNNL said, when an IBM 7094 computer won against a human opponent in checkers. The system was able to learn by itself, thanks to the aforementioned algorithm, without being explicitly programmed to change its strategy against chess player Robert Nealey.

Today, PNNL said, machine learning is everywhere as it powers personalized shopping recommendations and voice-driven assistants like Siri and Alexa. Generative AI tools like ChatGPT are just the latest public face of a technology that has had many decades to mature and evolve.

PNNL researchers are employing machine learning for national security, too, as the laboratory’s experts are combining their knowledge in nuclear nonproliferation and “artificial reasoning” to detect and (possibly) mitigate nuclear threats. The main target of their research is to employ data analytics and machine learning algorithms to monitor nuclear materials that could be used to produce nuclear weapons.

The AI employed by PNNL can be useful for the International Atomic Energy Agency (IAEA), which is monitoring nuclear reprocessing facilities in non-nuclear weapon nations to see if the plutonium separated from spent nuclear fuel is later employed for nuclear weapons production. The IAEA uses sample analysis and process monitoring in addition to in-person inspections, which can be a time-consuming and labor-intensive process.

PNNL’s algorithms can create a virtual model of the facility inspected by the IAEA, tracking “important temporal patterns” to train the model and predict the pattern belonging to normal use of the various areas in the facility. If data collected on-site doesn’t match the virtual prediction, the inspectors could be called to check the facility once more.

Another ML-powered solution designed in PNNL labs can process images of radioactive material through an “autoencoder” model, which can be trained to “compress and decompress images” into small descriptions that are useful for computational analysis. The model looks at images of microscopic radioactive particles, searching for the unique structure that the radioactive material develops because of the environmental conditions or purity of the source materials at its production facility.

Law enforcement agencies (i.e., the FBI) can then compare the microstructures of field samples with a library of electron microscope images developed by university and national laboratories, PNNL said, so that they can speed the identification process up. Machine learning algorithms and computers “will not replace humans in detecting nuclear threats any time soon,” PNNL researchers warn, but they can be useful in detecting and averting a potential nuclear disaster on US soil.