How big science failed to unlock the mysteries of the human brain
Even if it were possible to record all spikes from all neurons at once, he argued, a brain doesn’t exist in isolation: in order to properly connect the dots, you’d need to simultaneously record external stimuli that the brain is exposed to, as well as the behavior of the organism. And he reasoned that we need to understand the brain at a macroscopic level before trying to decode what the firings of individual neurons mean.
Others had concerns about the impact of centralizing control over these fields. Cornelia Bargmann, a neuroscientist at Rockefeller University, worried that it would crowd out research spearheaded by individual investigators. (Bargmann was soon tapped to co-lead the BRAIN Initiative’s working group.)
There isn’t a single, agreed-upon theory of how the brain works, and not everyone in the field agreed that building a simulated brain was the best way to study it.
While the US initiative sought input from scientists to guide its direction, the EU project was decidedly more top-down, with Markram at the helm. But as Noah Hutton documents in his 2020 film In Silico, Markram’s grand plans soon unraveled. As an undergraduate studying neuroscience, Hutton had been assigned to read Markram’s papers and was impressed by his proposal to simulate the human brain; when he started making documentary films, he decided to chronicle the effort. He soon realized, however, that the billion-dollar enterprise was characterized more by infighting and shifting goals than by breakthrough science.
In Silico shows Markram as a charismatic leader who needed to make bold claims about the future of neuroscience to attract the funding to carry out his particular vision. But the project was troubled from the outset by a major issue: there isn’t a single, agreed-upon theory of how the brain works, and not everyone in the field agreed that building a simulated brain was the best way to study it. It didn’t take long for those differences to arise in the EU project.
In 2014, hundreds of experts across Europe penned a letter citing concerns about oversight, funding mechanisms, and transparency in the Human Brain Project. The scientists felt Markram’s aim was premature and too narrow and would exclude funding for researchers who sought other ways to study the brain.
“What struck me was, if he was successful and turned it on and the simulated brain worked, what have you learned?” Terry Sejnowski, a computational neuroscientist at the Salk Institute who served on the advisory committee for the BRAIN Initiative, told me. “The simulation is just as complicated as the brain.”
The Human Brain Project’s board of directors voted to change its organization and leadership in early 2015, replacing a three-member executive committee led by Markram with a 22-member governing board. Christoph Ebell, a Swiss entrepreneur with a background in science diplomacy, was appointed executive director. “When I took over, the project was at a crisis point,” he says. “People were openly wondering if the project was going to go forward.”
But a few years later he was out too, after a “strategic disagreement” with the project’s host institution. The project is now focused on providing a new computational research infrastructure to help neuroscientists store, process, and analyze large amounts of data—unsystematic data collection has been an issue for the field—and develop 3D brain atlases and software for creating simulations.
The US BRAIN Initiative, meanwhile, underwent its own changes. Early on, in 2014, responding to the concerns of scientists and acknowledging the limits of what was possible, it evolved into something more pragmatic, focusing on developing technologies to probe the brain.
Those changes have finally started to produce results—even if they weren’t the ones that the founders of each of the large brain projects had originally envisaged.
Last year, the Human Brain Project released a 3D digital map that integrates different aspects of human brain organization at the millimeter and micrometer level. It’s essentially a Google Earth for the brain.
Newly revealed coronavirus data has reignited a debate over the virus’s origins
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.
Fostering innovation through a culture of curiosity
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.
The Download: covid’s origin drama, and TikTok’s uncertain future
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
Newly-revealed coronavirus data has reignited a debate over the virus’s origins
This week, we’ve seen the resurgence of a debate that has been swirling since the start of the pandemic—where did the virus that causes covid-19 come from?
For the most part, scientists have maintained that the virus probably jumped from an animal to a human at the Huanan Seafood Market in Wuhan at some point in late 2019. But some claim that the virus leaped from humans to animals, rather than the other way around. And many continue to claim that the virus somehow leaked from a nearby laboratory that was studying coronaviruses in bats.
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. Read the full story.
This story is from The Checkup, Jessica’s weekly biotech newsletter. Sign up to receive it in your inbox every Thursday.
Read more of MIT Technology Review’s covid reporting:
+ Our senior biotech editor Antonio Regalado investigated the origins of the coronavirus behind covid-19 in his five-part podcast series Curious Coincidence.
+ Meet the scientist at the center of the covid lab leak controversy. Shi Zhengli has spent years at the Wuhan Institute of Virology researching coronaviruses that live in bats. Her work has come under fire as the world tries to understand where covid-19 came from. Read the full story.
+ This scientist now believes covid started in Wuhan’s wet market. Here’s why. Michael Worobey of the University of Arizona, believes that a spillover of the virus from animals at the Huanan Seafood market was almost certainly behind the origin of the pandemic. Read the full story.
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 TikTok’s future in the US is hanging in the balance
Banning it is a colossal challenge, and officials still lack the legal authority to do so. (WP $)
+ TikTok CEO Shou Zi Chew was grilled by a congressional committee. (FT $)
+ He told lawmakers the company would earn their trust. (WSJ $)
+ Meanwhile, TikTok paid for influencers to travel to DC to lobby its cause. (Wired $)
2 A crypto fugitive has been arrested in Montenegro
Do Kwon has been on the run since TerraUSD stablecoin collapsed last year. (WSJ $)
+ Want to mine Bitcoin? Get yourself to Texas. (Reuters)
+ What’s next for crypto. (MIT Technology Review)
3 Twitter’s getting rid of its legacy blue checks
On the entirely serious date of April 1. (The Verge)+ The platform’s still an unattractive prospect for advertisers. (Vox)
4 Chatbots are having tough conversations for us
ChatGPT is adept at writing scripts for sensitive talks with kids and colleagues. (NYT $)
+ OpenAI has given ChatGPT access to the web’s live data. (The Verge)
+ How Character.AI became a billion-dollar unicorn. (WSJ $)
+ The inside story of how ChatGPT was built from the people who made it. (MIT Technology Review)
5 Jack Dorsey’s Block has been accused of fraudulent transactions
The payments company denied it, and claims it inflated its users numbers, too.(FT $)
+ Dorsey doesn’t have a track record of caring about this kind of thing. (The Information $)
6 Homeowners associations are secretly installing surveillance systems
The system tracks license plates and follows residents’ movements. (The Intercept)
7 Inside the tricky ethics of using DNA to solve crimes
A new database could help to protect users’ privacy. (Wired $)|
+ The citizen scientist who finds killers from her couch. (MIT Technology Review)
8 There’s plenty of reasons to be optimistic about the climate
Healthier, more sustainable diets are a good place to start. (Scientific American)
+ Taking stock of our climate past, present, and future. (MIT Technology Review)
9 TikTok keeps hectoring us
It seems we just can’t get enough of being aggressively told what to do. (Vox)
10 Don’t get scammed by a deepfake
CallerID can’t be trusted to protect you from rogue AI calls. (Gizmodo)
Quote of the day
“Wait, I need content.”
—TikTok fashion creator Kristine Thompson refuses to miss a content opportunity during a trip to the US Capitol to lobby against a potential TikTok ban, she tells the New York Times.
The big story
This sci-fi blockchain game could help create a metaverse that no one owns
Dark Forest is a vast universe, and most of it is shrouded in darkness. Your mission, should you choose to accept it, is to venture into the unknown, avoid being destroyed by opposing players who may be lurking in the dark, and build an empire of the planets you discover and can make your own.
But while the video game seemingly looks and plays much like other online strategy games, it doesn’t rely on the servers running other popular online strategy games. And it may point to something even more profound: the possibility of a metaverse that isn’t owned by a big tech company. Read the full story.
We can still have nice things
A place for comfort, fun and distraction in these weird times. (Got any ideas? Drop me a line or tweet ’em at me.)
+ If underwater terrors are your thing, Joe Romiero takes some seriously impressive shark pictures and videos.
+ Try as it might, Ted Lasso’s British dialog falls wide of the mark.
+ Let’s have a good old snoop around some celebrities’ bedrooms.
+ Why we can’t get enough of those fancy candles.
+ Interviewing animals with a tiny microphone, it doesn’t get much better than that.