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It will soon be easy for self-driving cars to hide in plain sight. We shouldn’t let them.

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It will soon be easy for self-driving cars to hide in plain sight. We shouldn’t let them.


It will soon become easy for self-driving cars to hide in plain sight. The rooftop lidar sensors that currently mark many of them out are likely to become smaller. Mercedes vehicles with the new, partially automated Drive Pilot system, which carries its lidar sensors behind the car’s front grille, are already indistinguishable to the naked eye from ordinary human-operated vehicles.

Is this a good thing? As part of our Driverless Futures project at University College London, my colleagues and I recently concluded the largest and most comprehensive survey of citizens’ attitudes to self-driving vehicles and the rules of the road. One of the questions we decided to ask, after conducting more than 50 deep interviews with experts, was whether autonomous cars should be labeled. The consensus from our sample of 4,800 UK citizens is clear: 87% agreed with the statement “It must be clear to other road users if a vehicle is driving itself” (just 4% disagreed, with the rest unsure). 

We sent the same survey to a smaller group of experts. They were less convinced: 44% agreed and 28% disagreed that a vehicle’s status should be advertised. The question isn’t straightforward. There are valid arguments on both sides. 

We could argue that, on principle, humans should know when they are interacting with robots. That was the argument put forth in 2017, in a report commissioned by the UK’s Engineering and Physical Sciences Research Council. “Robots are manufactured artefacts,” it said. “They should not be designed in a deceptive way to exploit vulnerable users; instead their machine nature should be transparent.” If self-driving cars on public roads are genuinely being tested, then other road users could be considered subjects in that experiment and should give something like informed consent. Another argument in favor of labeling, this one practical, is that—as with a car operated by a student driver—it is safer to give a wide berth to a vehicle that may not behave like one driven by a well-practiced human.

There are arguments against labeling too. A label could be seen as an abdication of innovators’ responsibilities, implying that others should acknowledge and accommodate a self-driving vehicle. And it could be argued that a new label, without a clear shared sense of the technology’s limits, would only add confusion to roads that are already replete with distractions. 

From a scientific perspective, labels also affect data collection. If a self-driving car is learning to drive and others know this and behave differently, this could taint the data it gathers. Something like that seemed to be on the mind of a Volvo executive who told a reporter in 2016 that “just to be on the safe side,” the company would be using unmarked cars for its proposed self-driving trial on UK roads. “I’m pretty sure that people will challenge them if they are marked by doing really harsh braking in front of a self-driving car or putting themselves in the way,” he said.

On balance, the arguments for labeling, at least in the short term, are more persuasive. This debate is about more than just self-driving cars. It cuts to the heart of the question of how novel technologies should be regulated. The developers of emerging technologies, who often portray them as disruptive and world-changing at first, are apt to paint them as merely incremental and unproblematic once regulators come knocking. But novel technologies do not just fit right into the world as it is. They reshape worlds. If we are to realize their benefits and make good decisions about their risks, we need to be honest about them. 

To better understand and manage the deployment of autonomous cars, we need to dispel the myth that computers will drive just like humans, but better. Management professor Ajay Agrawal, for example, has argued that self-driving cars basically just do what drivers do, but more efficiently: “Humans have data coming in through the sensors—the cameras on our face and the microphones on the sides of our heads—and the data comes in, we process the data with our monkey brains and then we take actions and our actions are very limited: we can turn left, we can turn right, we can brake, we can accelerate.”

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The hunter-gatherer groups at the heart of a microbiome gold rush

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The hunter-gatherer groups at the heart of a microbiome gold rush


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.

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The Download: a microbiome gold rush, and Eric Schmidt’s election misinformation plan

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The Download: a microbiome gold rush, and Eric Schmidt’s election misinformation plan


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.

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Navigating a shifting customer-engagement landscape with generative AI

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Navigating a shifting customer-engagement landscape with generative AI


A strategic imperative

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.”

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