The Alibaba Damo Academy is a unique hybrid research and development (R&D) facility. An academically-oriented independent science organization established in 2017 in Hangzhou, China, it is also an arms-length research affiliate of its founder, Chinese internet technology giant Alibaba. Damo’s project development pipelines are positioned around developing data-enabled technologies for fundamental business and social challenges, such as alleviating traffic congestion in mega-cities and workforce productivity in logistics. But the approach to solving these foundational problems is purposefully focused on commercialization-centric principles and development, which Damo’s leaders believe help shorten their development cycle and improve the efficiency of their scientific research.
Damo’s approach to R&D is a founding principal linked to an even deeper strategic objective: that the academy must “outlast Alibaba,” to become an enduring, sustainable, and independent developer of all of the group’s technology innovation. Yet, while Damo grows “out from under the shade of Alibaba’s tree,” (to paraphrase one of company founder Jack Ma’s favorite metaphors), the parent is still vital to its success: the technological and operational challenges of Alibaba’s business ecosystem serve as a source of inspiration for Damo.
Damo categorizes its technology projects into “emergent” (cutting-edge technology), “mature discussion” (market-ready technology), and “commercialized” (ready to be a product). “Commercialized” projects and some “mature discussion” projects are tightly connected to Alibaba’s technology development processes through a virtuous R&D circuit, which aims to quickly bring concepts to market through a frugal innovation process that uses lightweight, scalable, and sharable development resources: more than 80% of the projects run by Damo’s Voice Recognition Lab, for instance, host their applications on the cloud.
Such applications include AI-enabled medical image analysis technology, which Damo says can conduct coronary pneumonia clinical tests in under two seconds and deliver a full diagnosis with 99% accuracy in 20 seconds, which helps hospitals greatly accelerate their diagnosis process. A robotics division at Damo is trialling an autonomous last-mile logistics robot, inspired by the strain China’s fast-growing e-commerce demand is placing on door-to-door delivery services. The robot can potentially make 500 deliveries over 100 kilometers a day on four kilowatt-hours of electricity, navigating complex road and urban conditions and distinguishing between the action of pedestrians and vehicles.
Big brains for big city problems
Still other Damo projects attempt to address a number of organizational and social challenges through cross-functional, multi-application programs. A primary example of this are Damo’s projects using natural language processing in AI-enabled digital assistants to increase operational efficiency in businesses. Seeking to lift the capabilities of office-based smart speakers beyond the fairly rudimentary interactions that consumers have with their devices (largely simple verbal commands to conduct internet searches or navigate e-commerce sites), Damo has developed a prototype natural conversation analysis tool called ting wu (Chinese for listen and understand). It is designed to listen to meetings with multiple participants and will parse discussion patterns to produce informative synopses and assign post-meeting action items.
Source: Alibaba Damo Academy
Damo’s speech model has also been used to develop a simultaneous translation service for AliExpress (Alibaba’s global retail marketplace), which is embedded in its customer engagement platform and allows participating merchant suppliers to translate from Chinese to English, Russian, Spanish, and French. The service was launched during last year’s Alibaba global shopping festival (also known as Singles’ Day) on November 11, and Damo reports that 70% of its merchant clients used the service. The technology was also used for the company’s customer service chatbot Alime, which served over 50 million active users on the company’s e-commerce sites Taobao and Tmall during Singles’ Day.
Grounded, but reaching for the clouds
Rather than being guided by formal key performance indicators, R&D direction is defined by five key terms, according to Xu Yinghui, Alibaba Group vice president and head of Damo’s Vision Lab, all of which underpin the academy’s focus on practical innovation. “The first is scalable: we want all our applications to have a big impact, and keeping things at the demo state is meaningless. The second is interpretable: we have to turn the black boxes of algorithms and other new tools into white boxes. The third is speed, then affordability, and then public benefit—so that as many as possible can enjoy the technology,” says Xu.
Jin Rong, an Alibaba Group vice president and the director of Damo’s Machine Intelligence Lab, believes that the academy’s “demand-oriented” R&D approach distinguishes it from other research institutes. “Good technologies should have application prospects and should effectively solve practical problems—not just technological, but organizational, or operational. Projects are established for specific market needs, and research and development results are quickly implemented in business and application scenarios,” says Jin. This culminates in a productization process “where the technology is deposited on our cloud platform as soon as possible,” ensuring both wider scalability and accessibility, as well as ongoing cost efficiency—the “engineering of controllable costs,” in Alibaba parlance. “It is an early-or-late issue, but not a yes-or-no issue,” says Jin.
In this sense, Damo’s cost and time constraints promote innovation: in order to make projects business viable, cost efficiency needs to be baked into the thesis. While Damo’s AI research is deep and significant, freewheeling experimentation untethered by practical application is frowned upon. “First, an idea must survive on its own in the real world rather than in one’s mind,” says Hua Xiansheng, head of City Brain Lab at the academy. Damo’s leaders believe it is this ethos that has driven the academy to swiftly claim numerous breakthrough projects in such wide-ranging foundational sectors like new computing architecture and autonomous driving, and in industrial applications across sectors including health care, logistics, transport and education sectors. Driven to solve deep, pernicious and socially significant problems, but with an embedded pragmatism, Damo is keen to keep growing far out from its parent’s shade.
This content was produced by Alibaba Damo Academy. It was not written by MIT Technology Review’s editorial staff.
The first step to finding out is to catalogue what microbes we might have lost. To get as close to ancient microbiomes as possible, microbiologists have begun studying multiple Indigenous groups. Two have received the most attention: the Yanomami of the Amazon rainforest and the Hadza, in northern Tanzania.
Researchers have made some startling discoveries already. A study by Sonnenburg and his colleagues, published in July, found that the gut microbiomes of the Hadza appear to include bugs that aren’t seen elsewhere—around 20% of the microbe genomes identified had not been recorded in a global catalogue of over 200,000 such genomes. The researchers found 8.4 million protein families in the guts of the 167 Hadza people they studied. Over half of them had not previously been identified in the human gut.
Plenty of other studies published in the last decade or so have helped build a picture of how the diets and lifestyles of hunter-gatherer societies influence the microbiome, and scientists have speculated on what this means for those living in more industrialized societies. But these revelations have come at a price.
A changing way of life
The Hadza people hunt wild animals and forage for fruit and honey. “We still live the ancient way of life, with arrows and old knives,” says Mangola, who works with the Olanakwe Community Fund to support education and economic projects for the Hadza. Hunters seek out food in the bush, which might include baboons, vervet monkeys, guinea fowl, kudu, porcupines, or dik-dik. Gatherers collect fruits, vegetables, and honey.
Mangola, who has met with multiple scientists over the years and participated in many research projects, has witnessed firsthand the impact of such research on his community. Much of it has been positive. But not all researchers act thoughtfully and ethically, he says, and some have exploited or harmed the community.
One enduring problem, says Mangola, is that scientists have tended to come and study the Hadza without properly explaining their research or their results. They arrive from Europe or the US, accompanied by guides, and collect feces, blood, hair, and other biological samples. Often, the people giving up these samples don’t know what they will be used for, says Mangola. Scientists get their results and publish them without returning to share them. “You tell the world [what you’ve discovered]—why can’t you come back to Tanzania to tell the Hadza?” asks Mangola. “It would bring meaning and excitement to the community,” he says.
Some scientists have talked about the Hadza as if they were living fossils, says Alyssa Crittenden, a nutritional anthropologist and biologist at the University of Nevada in Las Vegas, who has been studying and working with the Hadza for the last two decades.
The Hadza have been described as being “locked in time,” she adds, but characterizations like that don’t reflect reality. She has made many trips to Tanzania and seen for herself how life has changed. Tourists flock to the region. Roads have been built. Charities have helped the Hadza secure land rights. Mangola went abroad for his education: he has a law degree and a master’s from the Indigenous Peoples Law and Policy program at the University of Arizona.
Over the last couple of decades, scientists have come to realize just how important the microbes that crawl all over us are to our health. But some believe our microbiomes are in crisis—casualties of an increasingly sanitized way of life. Disturbances in the collections of microbes we host have been associated with a whole host of diseases, ranging from arthritis to Alzheimer’s.
Some might not be completely gone, though. Scientists believe many might still be hiding inside the intestines of people who don’t live in the polluted, processed environment that most of the rest of us share. They’ve been studying the feces of people like the Yanomami, an Indigenous group in the Amazon, who appear to still have some of the microbes that other people have lost.
But there is a major catch: we don’t know whether those in hunter-gatherer societies really do have “healthier” microbiomes—and if they do, whether the benefits could be shared with others. At the same time, members of the communities being studied are concerned about the risk of what’s called biopiracy—taking natural resources from poorer countries for the benefit of wealthier ones. Read the full story.
—Jessica Hamzelou
Eric Schmidt has a 6-point plan for fighting election misinformation
—by Eric Schmidt, formerly the CEO of Google, and current cofounder of philanthropic initiative Schmidt Futures
The coming year will be one of seismic political shifts. Over 4 billion people will head to the polls in countries including the United States, Taiwan, India, and Indonesia, making 2024 the biggest election year in history.
Generative AI’s ability to harness customer data in a highly sophisticated manner means enterprises are accelerating plans to invest in and leverage the technology’s capabilities. In a study titled “The Future of Enterprise Data & AI,” Corinium Intelligence and WNS Triange surveyed 100 global C-suite leaders and decision-makers specializing in AI, analytics, and data. Seventy-six percent of the respondents said that their organizations are already using or planning to use generative AI.
According to McKinsey, while generative AI will affect most business functions, “four of them will likely account for 75% of the total annual value it can deliver.” Among these are marketing and sales and customer operations. Yet, despite the technology’s benefits, many leaders are unsure about the right approach to take and mindful of the risks associated with large investments.
Mapping out a generative AI pathway
One of the first challenges organizations need to overcome is senior leadership alignment. “You need the necessary strategy; you need the ability to have the necessary buy-in of people,” says Ayer. “You need to make sure that you’ve got the right use case and business case for each one of them.” In other words, a clearly defined roadmap and precise business objectives are as crucial as understanding whether a process is amenable to the use of generative AI.
The implementation of a generative AI strategy can take time. According to Ayer, business leaders should maintain a realistic perspective on the duration required for formulating a strategy, conduct necessary training across various teams and functions, and identify the areas of value addition. And for any generative AI deployment to work seamlessly, the right data ecosystems must be in place.
Ayer cites WNS Triange’s collaboration with an insurer to create a claims process by leveraging generative AI. Thanks to the new technology, the insurer can immediately assess the severity of a vehicle’s damage from an accident and make a claims recommendation based on the unstructured data provided by the client. “Because this can be immediately assessed by a surveyor and they can reach a recommendation quickly, this instantly improves the insurer’s ability to satisfy their policyholders and reduce the claims processing time,” Ayer explains.
All that, however, would not be possible without data on past claims history, repair costs, transaction data, and other necessary data sets to extract clear value from generative AI analysis. “Be very clear about data sufficiency. Don’t jump into a program where eventually you realize you don’t have the necessary data,” Ayer says.
The benefits of third-party experience
Enterprises are increasingly aware that they must embrace generative AI, but knowing where to begin is another thing. “You start off wanting to make sure you don’t repeat mistakes other people have made,” says Ayer. An external provider can help organizations avoid those mistakes and leverage best practices and frameworks for testing and defining explainability and benchmarks for return on investment (ROI).
Using pre-built solutions by external partners can expedite time to market and increase a generative AI program’s value. These solutions can harness pre-built industry-specific generative AI platforms to accelerate deployment. “Generative AI programs can be extremely complicated,” Ayer points out. “There are a lot of infrastructure requirements, touch points with customers, and internal regulations. Organizations will also have to consider using pre-built solutions to accelerate speed to value. Third-party service providers bring the expertise of having an integrated approach to all these elements.”
