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Meet the next generation of AI superstars

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Meet the next generation of AI superstars


This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.

So smart! So talented! This week I’m pleased to introduce you to a new crop of bright minds working on some of the most challenging problems in AI and beyond. You can read MIT Technology Review’s full list of 35 Innovators Under 35 here. 

We’ve previously highlighted some of the most promising people in tech before they became household names. In 2002, the list included two young innovators named Larry Page and Sergey Brin of Google. A 23-year-old Mark Zuckerberg was on the list in 2007. In 2008 we featured Andrew Ng, who wrote an excellent essay for us this yea sharing his tips for aspiring innovators on trying, failing, and the future of AI. 

This year we’ve seen tech companies racing to release their hottest new AI systems, and often neglecting safety and ethics. The AI scientists on this year’s innovators list are more aware than ever of the harm the technology can pose, and are determined to fix it. To do that, they’re pioneering new methods that are helping to shift the way the AI industry thinks about safety. 

Sharon Li, pictured above and our Innovator of the Year, is an assistant professor at the University of Wisconsin, Madison. She created a remarkable AI safety feature called out-of-distribution detection. This feature helps AI models determine if they should abstain from action when faced with something they weren’t trained on. This is crucial as AI systems are rolled out from the lab and encounter new situations in the messy real world.

Irene Solaiman, global public policy director at Hugging Face, developed an approach that calls for tech companies to release new models in phases, allowing more time to test them for failures and build in guardrails.  

Many of our innovators are working to fight climate change. I was delighted to see so many people on the list using their skills in AI to tackle the biggest problem facing humanity, either by helping the AI community track and lower its emissions or by using AI to mitigate emissions in highly polluting industries.

Sasha Luccioni, an AI researcher at startup Hugging Face, has developed a better way for tech companies to estimate and measure the carbon footprint of AI language models. 

Catherine De Wolf of ETH Zurich is using AI to help reduce emissions and the waste of materials in the construction industry. 

Alhussein Fawzi of DeepMind developed game-playing AI to speed up fundamental computations, which helps to cut costs and save energy on devices. 

This year’s innovators are also working on practical applications of AI that illustrate how the technology could become more and more useful. They’re coming up with exciting new ways to use it to boost scientific research and build helpful tools in other fields.

Lerrel Pinto of New York University is using AI to help robots learn from their mistakes. This, he hopes, will lead to robots in the home that do a lot more than vacuum—and could become more integral to our daily lives. 

Connor Coley of MIT developed open-source software that uses artificial intelligence to help discover and synthesize new molecules. 

Pranav Rajpurkar of Harvard Medical School has developed a way for AI to teach itself to accurately interpret medical images without any help from humans. 

Richard Zhang, a senior research scientist at Adobe, invented the visual similarity algorithms underlying image-generating AI models like Stable Diffusion and StyleGAN. Without his work, we would not have the image-generating AI that has captivated the world. 

That’s not all! This year’s list is brimming with inspiring people and ideas for the next big thing in robotics, computing, biotechnology, and climate and energy. Read the full list of this year’s young innovators here.

And finally, if you work in AI and you think you’ve got some exciting, cutting-edge stuff to share, get in touch! We’re always interested in hearing from people doing interesting work.

Deeper Learning

DeepMind’s cofounder: Generative AI is just a phase. What’s next is interactive AI.

DeepMind cofounder Mustafa Suleyman wants to build a chatbot that does a whole lot more than chat. Here’s Suleyman’s pitch: In the future, we’ll have what he calls interactive AI, meaning bots that can carry out tasks you set for them by calling on other software and other people to get stuff done. He’s founded a new billion-dollar company, Inflection, to build it. 

Come again? Suleyman, who left DeepMind in 2022, has some … interesting … thoughts about the success of online regulation, which border on naïveté. (“It’s pretty difficult to find radicalization content or terrorist material online. It’s pretty difficult to buy weapons and drugs online.”) Despite that, he remains earnest and evangelical in his convictions, and he is in a position to make big moves in the industry. He sat down with MIT Technology Review’s senior editor for AI, Will Douglas Heaven, to chat about his plans and the need for robust AI regulation. Read more here.

Bits and Bytes

AI just beat a human test for creativity. What does that even mean?
A new study found that AI chatbots achieved higher average scores than humans in a test commonly used to assess human creativity. The findings do not necessarily indicate that AIs are developing an ability to do something uniquely human. However, they might give us a better understanding of how humans and machines approach creative tasks. (MIT Technology Review) 

This driverless-car company is using chatbots to make its vehicles smarter
Self-driving-car startup Wayve can now interrogate its vehicles, asking them questions about their driving decisions—and getting answers back. The idea is to use the same tech behind ChatGPT to help train driverless cars. (MIT Technology Review) 

How Silicon Valley doomers are shaping Rishi Sunak’s AI plans
The UK’s prime minister, Rishi Sunak, is keen to boost his country’s AI industry. But in a short span of time, something has shifted in the UK’s approach. The country seems to be becoming a cheerleader for the AI doom narrative, thanks to heavy lobbying from the effective altruism movement. (Politico

Silicon Valley’s AI religion
A thought-provoking piece about something I too have observed in the tech space: technologists are increasingly weaving a narrative around AI and artificial general intelligence that isn’t that dissimilar from religious narratives. This story connects the dots. 
(Vox

How generative AI works
A great and helpful visual explainer that’s essential reading for anyone AI-curious. (The Financial Times

Tech

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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