The manufacturing industry is exploring 5G technology at an accelerated pace, largely to enable AI-driven use cases such as closed-loop manufacturing, adaptive manufacturing, predictive analytics for maintenance, and extended reality (XR)-based worker training and safety, says Jagadeesh Dantuluri, general manager for private and dedicated networks at Keysight Technologies. “It’s not about a static assembly line performing the same action time and time again, but one that can change based on their needs,” he says. “Private networks essentially enable new business models in manufacturing.”
Yet, the benefits of 5G private networks extend beyond manufacturing. Because the technology offers more reliable connectivity, faster data rates and lower latency, and greater scalability, security, and network control than previous communications technologies, 5G private networks will drive innovations in many industrial and enterprise sectors.
The benefits of 5G private networks
A private cellular network is built on 3rd Generation Partnership Project (3GPP)-defined standards (such as LTE or 5G), but it offers dedicated on-premise coverage. This is important for remote facilities where public networks do not exist, or where indoor coverage is not robust. A private network also makes exclusive use of the available capacity; there is no contention from other network users, as on a public network. Private operators can also deploy their own security policies to authorize users, prioritize traffic, and, most importantly, to ensure that sensitive data does not leave the premises without authorization.
The dedicated nature of 5G private networks coupled with a customized service, intrinsic control, and URLLC capabilities provides more reliable industrial wireless communication for a wide variety of use cases, Dantuluri says “Applications include wireless, real-time, closed-loop control and process automation, and AI-based production and AR/VR-based design for onsite and remote workers,” he explains. “In addition, low-cost connectivity allows sensors to become easily deployed in a wider variety of scenarios, allowing businesses to create innovative applications and collect real-time data.”
The industrial sector is driving toward a massive digital transformation, and the integration of information-technology (IT) systems with operational-technology (OT) systems will speed up this process. Digital technologies will also enable many new use cases, such as automated manufacturing.
A 5G private network enables a facility to synchronize and integrate tracking data into its workflow, allowing production lines to be configured in real time, says Dantuluri. “Since the factory’s assembly lines and infrastructure, such as robotic arms, autonomous mobile robots (AMRs), autonomous guided vehicles (AGVs), and sensors, are wirelessly connected, configuring or moving assembly elements on demand is much easier. This use case demands highly reliable, low-latency wireless connectivity and coverage, and potentially high data rates in both the uplink and downlink, and maybe support for Time Sensitive Networks (TSN) in the future. This use case application can only be achieved with 5G private networks.”
Outside the industrial sector, 5G private networks enable mobile augmented-reality (AR) and virtual-reality (VR) applications, allowing, for example, engineers to view superimposed blueprints, soldiers to have heads-up displays, and businesses to have virtual meetings in the field or working remotely. “If a machine has to be repaired, and a technician or a factory worker has AR goggles, they can have technical information superimposed on the real-world device to see what is wrong,” says Dantuluri. “And the data center can send instructions about how to do the repairs, step by step.”
As enterprises realize the benefits of pervasive, low-latency, high-bandwidth, and secure connectivity, the applications of 5G private networks will expand. By the end of 2024, analysts expect investment in 5G private networks will add up to tens of billions of dollars. A separate analysis by the research arm of investment firm JP Morgan predicts that the global enterprise opportunity for 5G will exceed $700 billion by 2030.
Time is running short to limit global warming to 1.5°C (2.7 °F) above preindustrial levels, but there are feasible and effective solutions on the table, according to a new UN climate report.
Despite decades of warnings from scientists, global greenhouse-gas emissions are still climbing, hitting a record high in 2022. If humanity wants to limit the worst effects of climate change, annual greenhouse-gas emissions will need to be cut by nearly half between now and 2030, according to the report.
That will be complicated and expensive. But it is nonetheless doable, and the UN listed a number of specific ways we can achieve it. Read the full story.
—Casey Crownhart
How people are using GPT-4
Last week was intense for AI news, with a flood of major product releases from a number of leading companies. But one announcement outshined them all: OpenAI’s new multimodal large language model, GPT-4. William Douglas Heaven, our senior AI editor, got an exclusive preview. Read about his initial impressions.
Unlike OpenAI’s viral hit ChatGPT, which is freely accessible to the general public, GPT-4 is currently accessible only to developers. It’s still early days for the tech, and it’ll take a while for it to feed through into new products and services. Still, people are already testing its capabilities out in the open. Read about some of the most fun and interesting ways they’re doing that, from hustling up money to writing code to reducing doctors’ workloads.
Google has a lot riding on this launch. Microsoft partnered with OpenAI to make an aggressive play for Google’s top spot in search. Meanwhile, Google blundered straight out of the gate when it first tried to respond. In a teaser clip for Bard that the company put out in February, the chatbot was shown making a factual error. Google’s value fell by $100 billion overnight.
Google won’t share many details about how Bard works: large language models, the technology behind this wave of chatbots, have become valuable IP. But it will say that Bard is built on top of a new version of LaMDA, Google’s flagship large language model. Google says it will update Bard as the underlying tech improves. Like ChatGPT and GPT-4, Bard is fine-tuned using reinforcement learning from human feedback, a technique that trains a large language model to give more useful and less toxic responses.
Google has been working on Bard for a few months behind closed doors but says that it’s still an experiment. The company is now making the chatbot available for free to people in the US and the UK who sign up to a waitlist. These early users will help test and improve the technology. “We’ll get user feedback, and we will ramp it up over time based on that feedback,” says Google’svice president of research, Zoubin Ghahramani. “We are mindful of all the things that can go wrong with large language models.”
But Margaret Mitchell, chief ethics scientist at AI startup Hugging Face and former co-lead of Google’s AI ethics team, is skeptical of this framing. Google has been working on LaMDA for years, she says, and she thinks pitching Bard as an experiment “is a PR trick that larger companies use to reach millions of customers while also removing themselves from accountability if anything goes wrong.”
Google wants users to think of Bard as a sidekick to Google Search, not a replacement. A button that sits below Bard’s chat widget says “Google It.” The idea is to nudge users to head to Google Search to check Bard’s answers or find out more. “It’s one of the things that help us offset limitations of the technology,” says Krawczyk.
“We really want to encourage people to actually explore other places, sort of confirm things if they’re not sure,” says Ghahramani.
This acknowledgement of Bard’s flaws has shaped the chatbot’s design in other ways, too. Users can interact with Bard only a handful of times in any given session. This is because the longer large language models engage in a single conversation, the more likely they are to go off the rails. Many of the weirder responses from Bing Chat that people have shared online emerged at the end of drawn-out exchanges, for example.
Google won’t confirm what the conversation limit will be for launch, but it will be set quite low for the initial release and adjusted depending on user feedback.
Bard in action
GOOGLE
Google is also playing it safe in terms of content. Users will not be able to ask for sexually explicit, illegal, or harmful material (as judged by Google) or personal information. In my demo, Bard would not give me tips on how to make a Molotov cocktail. That’s standard for this generation of chatbot. But it would also not provide any medical information, such as how to spot signs of cancer. “Bard is not a doctor. It’s not going to give medical advice,” says Krawczyk.
Perhaps the biggest difference between Bard and ChatGPT is that Bard produces three versions of every response, which Google calls “drafts.” Users can click between them and pick the response they prefer, or mix and match between them. The aim is to remind people that Bard cannot generate perfect answers. “There’s the sense of authoritativeness when you only see one example,” says Krawczyk. “And we know there are limitations around factuality.”
Hoffman got access to the system last summer and has since been writing up his thoughts on the different ways the AI model could be used in education, the arts, the justice system, journalism, and more. In the book, which includes copy-pasted extracts from his interactions with the system, he outlines his vision for the future of AI, uses GPT-4 as a writing assistant to get new ideas, and analyzes its answers.
A quick final word … GPT-4 is the cool new shiny toy of the moment for the AI community. There’s no denying it is a powerful assistive technology that can help us come up with ideas, condense text, explain concepts, and automate mundane tasks. That’s a welcome development, especially for white-collar knowledge workers.
However, it’s notable that OpenAI itself urges caution around use of the model and warns that it poses several safety risks, including infringing on privacy, fooling people into thinking it’s human, and generating harmful content. It also has the potential to be used for other risky behaviors we haven’t encountered yet. So by all means, get excited, but let’s not be blinded by the hype. At the moment, there is nothing stopping people from using these powerful new models to do harmful things, and nothing to hold them accountable if they do.
Deeper Learning
Chinese tech giant Baidu just released its answer to ChatGPT
So. Many. Chatbots. The latest player to enter the AI chatbot game is Chinese tech giant Baidu. Late last week, Baidu unveiled a new large language model called Ernie Bot, which can solve math questions, write marketing copy, answer questions about Chinese literature, and generate multimedia responses.
A Chinese alternative: Ernie Bot (the name stands for “Enhanced Representation from kNowledge IntEgration;” its Chinese name is 文心一言, or Wenxin Yiyan) performs particularly well on tasks specific to Chinese culture, like explaining a historical fact or writing a traditional poem. Read more from my colleague Zeyi Yang.
Even Deeper Learning
Language models may be able to “self-correct” biases—if you ask them to
Large language models are infamous for spewing toxic biases, thanks to the reams of awful human-produced content they get trained on. But if the models are large enough, they may be able to self-correct for some of these biases. Remarkably, all we might have to do is ask.
