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.
A new training model, dubbed “KnowNo,” aims to address this problem by teaching robots to ask for our help when orders are unclear. At the same time, it ensures they seek clarification only when necessary, minimizing needless back-and-forth. The result is a smart assistant that tries to make sure it understands what you want without bothering you too much.
Andy Zeng, a research scientist at Google DeepMind who helped develop the new technique, says that while robots can be powerful in many specific scenarios, they are often bad at generalized tasks that require common sense.
For example, when asked to bring you a Coke, the robot needs to first understand that it needs to go into the kitchen, look for the refrigerator, and open the fridge door. Conventionally, these smaller substeps had to be manually programmed, because otherwise the robot would not know that people usually keep their drinks in the kitchen.
That’s something large language models (LLMs) could help to fix, because they have a lot of common-sense knowledge baked in, says Zeng.
Now when the robot is asked to bring a Coke, an LLM, which has a generalized understanding of the world, can generate a step-by-step guide for the robot to follow.
The problem with LLMs, though, is that there’s no way to guarantee that their instructions are possible for the robot to execute. Maybe the person doesn’t have a refrigerator in the kitchen, or the fridge door handle is broken. In these situations, robots need to ask humans for help.
KnowNo makes that possible by combining large language models with statistical tools that quantify confidence levels.
When given an ambiguous instruction like “Put the bowl in the microwave,” KnowNo first generates multiple possible next actions using the language model. Then it creates a confidence score predicting the likelihood that each potential choice is the best one.
The news: A new robot training model, dubbed “KnowNo,” aims to teach robots to ask for our help when orders are unclear. At the same time, it ensures they seek clarification only when necessary, minimizing needless back-and-forth. The result is a smart assistant that tries to make sure it understands what you want without bothering you too much.
Why it matters: While robots can be powerful in many specific scenarios, they are often bad at generalized tasks that require common sense. That’s something large language models could help to fix, because they have a lot of common-sense knowledge baked in. Read the full story.
—June Kim
Medical microrobots that travel inside the body are (still) on their way
The human body is a labyrinth of vessels and tubing, full of barriers that are difficult to break through. That poses a serious hurdle for doctors. Illness is often caused by problems that are hard to visualize and difficult to access. But imagine if we could deploy armies of tiny robots into the body to do the job for us. They could break up hard-to-reach clots, deliver drugs to even the most inaccessible tumors, and even help guide embryos toward implantation.
We’ve been hearing about the use of tiny robots in medicine for years, maybe even decades. And they’re still not here. But experts are adamant that medical microbots are finally coming, and that they could be a game changer for a number of serious diseases. Read the full story.
We haven’t always been right (RIP, Baxter), but we’ve often been early to spot important areas of progress (we put natural-language processing on our very first list in 2001; today this technology underpins large language models and generative AI tools like ChatGPT).
Every year, our reporters and editors nominate technologies that they think deserve a spot, and we spend weeks debating which ones should make the cut. Here are some of the technologies we didn’t pick this time—and why we’ve left them off, for now.
New drugs for Alzheimer’s disease
Alzmeiher’s patients have long lacked treatment options. Several new drugs have now been proved to slow cognitive decline, albeit modestly, by clearing out harmful plaques in the brain. In July, the FDA approved Leqembi by Eisai and Biogen, and Eli Lilly’s donanemab could soon be next. But the drugs come with serious side effects, including brain swelling and bleeding, which can be fatal in some cases. Plus, they’re hard to administer—patients receive doses via an IV and must receive regular MRIs to check for brain swelling. These drawbacks gave us pause.
Sustainable aviation fuel
Alternative jet fuels made from cooking oil, leftover animal fats, or agricultural waste could reduce emissions from flying. They have been in development for years, and scientists are making steady progress, with several recent demonstration flights. But production and use will need to ramp up significantly for these fuels to make a meaningful climate impact. While they do look promising, there wasn’t a key moment or “breakthrough” that merited a spot for sustainable aviation fuels on this year’s list.
Solar geoengineering
One way to counteract global warming could be to release particles into the stratosphere that reflect the sun’s energy and cool the planet. That idea is highly controversial within the scientific community, but a few researchers and companies have begun exploring whether it’s possible by launching a series of small-scale high-flying tests. One such launch prompted Mexico to ban solar geoengineering experiments earlier this year. It’s not really clear where geoengineering will go from here or whether these early efforts will stall out. Amid that uncertainty, we decided to hold off for now.
