Over the past several years, our world has been confronted with a range of unprecedented and, at times, deadly challenges—from the covid-19 pandemic to severe weather conditions, and a concurrent rise of societal issues including aging population, urban congestion, and unequal access to health care.
But as the development of artificial intelligence (AI) and its applications grow, AI technologies are playing an instrumental role in addressing socio-economic and environmental challenges faced by the modern world, ultimately helping us to reach a better standard of living.
Filling gaps, providing quality care
One of the most promising applications of AI in recent years has been in augmenting human workers in key sectors that are chronically understaffed, contributing to major advances in solving challenging social issues.
In China, for instance, the medical system has long grappled with a shortage of health-care professionals, with an average of just 17.9 doctors for 10,000 people. The situation is even more imbalanced in small towns and rural areas, forcing many patients to travel long distances to cities to access quality medical care and specialist treatments.
Baidu’s Clinical Decision Support System (CDSS) was developed to address this urgent need. Built on a foundation of medical natural language processing and knowledge graph technology, the system provides real-time assistance to doctors, informing their judgments, helping them more accurately recognize symptoms, and providing corresponding treatment options. By bringing the expertise and resources of top-tier medical institutions to local clinics, the system offers patients a quality of treatment that would otherwise be out of reach. To date, Baidu’s CDSS has been applied in thousands of primary care facilities, and the number is rapidly growing.
“In the diagnosis stage, sometimes young doctors may miss or ignore some symptoms due to a lack of experience,” says one doctor from a hospital in Beijing that has been using CDSS for two years. “Through the consultation support and real-time alert functions of CDSS, which provide more suggestions and references to physicians, we were able to significantly improve the quality of our medical department.”
Accessible solutions through humanized technology
By 2022, approximately 14% of China’s population will be aged 65 and over, according to forecasts by the China Development Foundation, with the number expected to grow to more than 30% by 2050.
For older populations, AI-powered smart speakers and displays can serve as a vital lifeline. Baidu has developed a popular smart display unit with computer vision capabilities and voice interaction technology, called Xiaodu, which can provide a wide range of essential services, including offering health tips, arranging shopping and transportation assistance, providing access to emergency care, and even daily conversation and emotional support.
The success of Xiaodu made it one of the stars of the recent Baidu World, the company’s annual flagship technology conference, which explored how local welfare associations are increasingly distributing Xiaodu installations to seniors.
Xiaodu’s popularity among the elderly highlights another key potential of AI: breaking down barriers and inequalities in access to technology in today’s world. While previous generations saw older populations disenfranchised by the advent of new technologies, AI offers the possibility of applications that will be accessible to all.
Transforming infrastructure, revolutionizing society
Beyond solving targeted problems, new developments show how AI has even greater potential to reduce errors and improve efficiency in the systems that permeate our daily lives, including urban infrastructure in a growing number of cities.
In China’s Shandong province, Baidu AI Cloud supports safety inspections of the electric power grid, providing instant alerts to avert power outages that could affect millions. In Quanzhou, Baidu AI Cloud is being used to accurately forecast water consumption needs at the city’s main water treatment plant for its population of 8 million people. The system analyzes a range of factors, from weather patterns to holidays, helping to boost the plant’s efficiency and cutting its electricity usage by 8%.
“We always need to make sure the system is functioning and the water quality is stable, but it would be impossible for a worker to stay awake and alert for 24 hours a day, never sleeping,” says Shen Peikun, a worker at the Quanzhou plant. “But now this system can handle the equipment and alert us if there are any sudden changes.”
Baidu’s AI technology has revolutionized one of the most ordinary but vital features of city life: the traffic light. Smart traffic systems can monitor vehicle and pedestrian flows, analyzing a vast array of data to predict future traffic conditions and optimize the traffic flow. In the northern Chinese city of Baoding, the use of Baidu’s smart traffic lights has reduced waiting times by up to 20% during peak rush hours, giving people back more time in their daily lives.
With the rapid development of autonomous driving, including Baidu’s Apollo Moon robotaxis unveiled earlier this year, a more comprehensive smart traffic infrastructure is taking shape, with each component building upon the other to enable safer and more efficient travel for all.
In its research on smart traffic solutions, for example, Baidu has found that even a 15% improvement in traffic efficiency correlates to a 2.4% growth in GDP for a given area, as time and resources formerly ensnared in daily inconvenience are freed up to drive economic productivity. In economies grasping for new levers of growth and competitive advantage, optimization like this can provide an invaluable solution. Greater efficiency can also lead to a better use of the earth’s resources, and a reduction in carbon emissions.
As AI applications multiply—including in smart cities and autonomous driving—and become more integrated with one another, their potential to unlock positive value and to help find solutions to some of the world’s most pressing social concerns will continue to grow.
This content was produced by Baidu. It was not written by MIT Technology Review’s editorial staff.
When Anton Korinek, an economist at the University of Virginia and a fellow at the Brookings Institution, got access to the new generation of large language models such as ChatGPT, he did what a lot of us did: he began playing around with them to see how they might help his work. He carefully documented their performance in a paper in February, noting how well they handled 25 “use cases,” from brainstorming and editing text (very useful) to coding (pretty good with some help) to doing math (not great).
ChatGPT did explain one of the most fundamental principles in economics incorrectly, says Korinek: “It screwed up really badly.” But the mistake, easily spotted, was quickly forgiven in light of the benefits. “I can tell you that it makes me, as a cognitive worker, more productive,” he says. “Hands down, no question for me that I’m more productive when I use a language model.”
When GPT-4 came out, he tested its performance on the same 25 questions that he documented in February, and it performed far better. There were fewer instances of making stuff up; it also did much better on the math assignments, says Korinek.
Since ChatGPT and other AI bots automate cognitive work, as opposed to physical tasks that require investments in equipment and infrastructure, a boost to economic productivity could happen far more quickly than in past technological revolutions, says Korinek. “I think we may see a greater boost to productivity by the end of the year—certainly by 2024,” he says.
Who will control the future of this amazing technology?
What’s more, he says, in the longer term, the way the AI models can make researchers like himself more productive has the potential to drive technological progress.
That potential of large language models is already turning up in research in the physical sciences. Berend Smit, who runs a chemical engineering lab at EPFL in Lausanne, Switzerland, is an expert on using machine learning to discover new materials. Last year, after one of his graduate students, Kevin Maik Jablonka, showed some interesting results using GPT-3, Smit asked him to demonstrate that GPT-3 is, in fact, useless for the kinds of sophisticated machine-learning studies his group does to predict the properties of compounds.
“He failed completely,” jokes Smit.
It turns out that after being fine-tuned for a few minutes with a few relevant examples, the model performs as well as advanced machine-learning tools specially developed for chemistry in answering basic questions about things like the solubility of a compound or its reactivity. Simply give it the name of a compound, and it can predict various properties based on the structure.
Data collected in 2020—and kept from public view since then—potentially adds weight to the animal theory. It highlights a potential suspect: the raccoon dog. But exactly how much weight it adds depends on who you ask. New analyses of the data have only reignited the debate, and stirred up some serious drama.
The current ruckus starts with a study shared by Chinese scientists back in February 2022. In a preprint (a scientific paper that has not yet been peer-reviewed or published in a journal), George Gao of the Chinese Center for Disease Control and Prevention (CCDC) and his colleagues described how they collected and analyzed 1,380 samples from the Huanan Seafood Market.
These samples were collected between January and March 2020, just after the market was closed. At the time, the team wrote that they only found coronavirus in samples alongside genetic material from people.
There were a lot of animals on sale at this market, which sold more than just seafood. The Gao paper features a long list, including chickens, ducks, geese, pheasants, doves, deer, badgers, rabbits, bamboo rats, porcupines, hedgehogs, crocodiles, snakes, and salamanders. And that list is not exhaustive—there are reports of other animals being traded there, including raccoon dogs. We’ll come back to them later.
But Gao and his colleagues reported that they didn’t find the coronavirus in any of the 18 species of animal they looked at. They suggested that it was humans who most likely brought the virus to the market, which ended up being the first known epicenter of the outbreak.
Fast-forward to March 2023. On March 4, Florence Débarre, an evolutionary biologist at Sorbonne University in Paris, spotted some data that had been uploaded to GISAID, a website that allows researchers to share genetic data to help them study and track viruses that cause infectious diseases. The data appeared to have been uploaded in June 2022. It seemed to have been collected by Gao and his colleagues for their February 2022 study, although it had not been included in the actual paper.
And so I think a big part of it as a company, by setting these ambitious goals, it forces us to say if we want to be number one, if we want to be top tier in these areas, if we want to continue to generate results, how do we get there using technology? And so that really forces us to throw away our assumptions because you can’t follow somebody, if you want to be number one you can’t follow someone to become number one. And so we understand that the path to get there, it’s through, of course, technology and the software and the enablement and the investment, but it really is by becoming goal-oriented. And if we look at these examples of how do we create the infrastructure on the technology side to support these ambitious goals, we ourselves have to be ambitious in turn because if we bring a solution that’s also a me too, that’s a copycat, that doesn’t have differentiation, that’s not going to propel us, for example, to be a top 10 supply chain. It just doesn’t pass muster.
So I think at the top level, it starts with the business ambition. And then from there we can organize ourselves at the intersection of the business ambition and the technology trends to have those very rich discussions and being the glue of how do we put together so many moving pieces because we’re constantly scanning the technology landscape for new advancing and emerging technologies that can come in and be a part of achieving that mission. And so that’s how we set it up on the process side. As an example, I think one of the things, and it’s also innovation, but it doesn’t get talked about as much, but for the community out there, I think it’s going to be very relevant is, how do we stay on top of the data sovereignty questions and data localization? There’s a lot of work that needs to go into rethinking what your cloud, private, public, edge, on-premise look like going forward so that we can remain cutting edge and competitive in each of our markets while meeting the increasing guidance that we’re getting from countries and regulatory agencies about data localization and data sovereignty.
And so in our case, as a global company that’s listed in Hong Kong and we operate all around the world, we’ve had to really think deeply about the architecture of our solutions and apply innovation in how we can architect for a longer term growth, but in a world that’s increasingly uncertain. So I think there’s a lot of drivers in some sense, which is our corporate aspirations, our operating environment, which has continued to have a lot of uncertainty, and that really forces us to take a very sharp lens on what cutting edge looks like. And it’s not always the bright and shiny technology. Cutting edge could mean going to the executive committee and saying, Hey, we’re going to face a challenge about compliance. Here’s the innovation we’re bringing about architecture so that we can handle not just the next country or regulatory regime that we have to comply with, but the next 10, the next 50.
Laurel: Well, and to follow up with a bit more of a specific example, how does R&D help improve manufacturing in the software supply chain as well as emerging technologies like artificial intelligence and the industrial metaverse?
Art: Oh, I love this one because this is the perfect example of there’s a lot happening in the technology industry and there’s so much back to the earlier point of applied curiosity and how we can try this. So specifically around artificial intelligence and industrial metaverse, I think those go really well together with what are Lenovo’s natural strengths. Our heritage is as a leading global manufacturer, and now we’re looking to also transition to services-led, but applying AI and technologies like the metaverse to our factories. I think it’s almost easier to talk about the inverse, Laurel, which is if we… Because, and I remember very clearly we’ve mapped this out, there’s no area within the supply chain and manufacturing that is not touched by these areas. If I think about an example, actually, it’s very timely that we’re having this discussion. Lenovo was recognized just a few weeks ago at the World Economic Forum as part of the global lighthouse network on leading manufacturing.
And that’s based very much on applying around AI and metaverse technologies and embedding them into every aspect of what we do about our own supply chain and manufacturing network. And so if I pick a couple of examples on the quality side within the factory, we’ve implemented a combination of digital twin technology around how we can design to cost, design to quality in ways that are much faster than before, where we can prototype in the digital world where it’s faster and lower cost and correcting errors is more upfront and timely. So we are able to much more quickly iterate on our products. We’re able to have better quality. We’ve taken advanced computer vision so that we’re able to identify quality defects earlier on. We’re able to implement technologies around the industrial metaverse so that we can train our factory workers more effectively and better using aspects of AR and VR.
And we’re also able to, one of the really important parts of running an effective manufacturing operation is actually production planning, because there’s so many thousands of parts that are coming in, and I think everyone who’s listening knows how much uncertainty and volatility there have been in supply chains. So how do you take such a multi-thousand dimensional planning problem and optimize that? Those are things where we apply smart production planning models to keep our factories fully running so that we can meet our customer delivery dates. So I don’t want to drone on, but I think literally the answer was: there is no place, if you think about logistics, planning, production, scheduling, shipping, where we didn’t find AI and metaverse use cases that were able to significantly enhance the way we run our operations. And again, we’re doing this internally and that’s why we’re very proud that the World Economic Forum recognized us as a global lighthouse network manufacturing member.
Laurel: It’s certainly important, especially when we’re bringing together computing and IT environments in this increasing complexity. So as businesses continue to transform and accelerate their transformations, how do you build resiliency throughout Lenovo? Because that is certainly another foundational characteristic that is so necessary.
