The Most Radical Theories In Science

Very Interesting|July/August 2020

The Most Radical Theories In Science
As Albert Einstein once said, “Imagination is more important than knowledge.” With that in mind, here are our picks of wild ideas that will blow your mind
Scott Balmer


Most of the universe is made up of dark matter. The problem is, we haven’t found it. But one team thinks we could discover its imprints lurking in rocks

Most of the universe is missing. By observing the way galaxies spin, astronomers have calculated that there must be five times as much invisible ‘dark matter’ as ordinary stuff in the cosmos. For three decades, attempts have been made to find dark matter particles, but with zero success. Now, a group of astrophysicists from Europe and the US thinks that the answer to detecting the elusive stuff may lie in the deep past, beneath our feet. So far, most attempts to directly detect dark matter have focused on hypothetical particles known as WIMPs (weakly interacting massive particles). Detectors have been built to look for the energy released when these particles hit the nuclei of other atoms, but with no collisions so far detected, it might be that WIMPs don’t exist. Or could it just be that we need a more sensitive detector? The new idea is to look for the tracks of WIMPs in ancient rocks, detecting dark matter over geological timescales. WIMPs colliding with the atomic nuclei in certain rock minerals could, in theory, create tiny changes to the minerals’ crystal structure.

“The interaction with dark matter would make the [atomic] nucleus move some tens to hundreds of nanometres through the crystal, leaving a damage track,” says Sebastian Baum at Stockholm University, who is taking part in the research. The rocks being studied could be as much as one billion years old, with WIMPs potentially leaving tracks all that time. The researchers think that their method could reach a sensitivity of around 100 times that of the best existing detectors. “We plan to dig up rocks from 10km below the surface of the Earth via ultra-deep boreholes that already exist,” says Dr Katherine Freese at the University of Michigan, who is also taking part in the study. “The point of going deep underground is that the dark matter can penetrate that far down, whereas other particles, especially cosmic rays, get stuck closer to the surface.” So that'll help to reduce the number of tracks from other potential sources.

Once the samples have been obtained, the plan is to use imaging techniques such as helium-ion beam microscopy to look for the tracks, which may be 1,000 times shorter than the width of a human hair. If all goes well with funding, the researchers hope to have their first results by 2025. If they don’t discover WIMPs, it’ll be back to the drawing board – again – for dark matter theorists. But if they do, it’ll solve one of the biggest mysteries in science.


Artificial intelligence keeps getting smarter: it can thrash us at games, classify images, and drive cars. But it can never imitate human thought

Today’s AI systems are superhuman. Computer models based loosely on the neural networks in our brains are trained on vast amounts of data using huge clusters of processors. They can now classify objects in images better than we can. And as IBM and Google’s DeepMind have demonstrated, they can beat us at games such as chess and Go, and even achieve the highest rank in the computer game StarCraft II. But at the same time, AI systems are inhuman. Even inhumane. Our AIs do not comprehend our world or their place within it. Biological creatures are not trained once on a static pool of data in the way we train an AI. It would be like presenting a newborn baby with the complete Encyclopaedia Britannica and telling them “Learn that perfectly, and that’s all you’ll ever need”. We require years of experience in ever-changing environments before we can understand our world. Research has shown that, if we’re trying to focus on an object, our brains aren’t fully able to filter out visual distractions until age 17, and our ability to perceive faces keeps developing until age 20. We’re prebuilt to learn, having descended from 3.5 billion years of creatures who each faced life-and-death situations, in which they had to perceive and act correctly to survive. AIs have none of this. Their algorithms use highly simplified ideas of learning, mostly doing little more than data classification or prediction.

The only way we can make our AI algorithms work is by training them on large amounts of narrowly focused sets of data, with defined objectives. They are still not able to handle changing scenarios in the way we can. They do not understand cause and effect. They cannot properly link words such as ‘chair’ or ‘vehicle’ to real physical objects, because they never experience reality how we do. And while some AIs may be able to classify emotions by processing images of faces, research is still in its infancy into how an AI might actually feel emotions, empathize, or understand how its behavior might affect us.

Over time, AIs might come closer to us. Maybe we can help them to think more like us by developing algorithms that learn and process information in new ways. But the gap between us will likely always be there. AIs do not share our evolutionary history, and they may never have a brain as complex or as fine-tuned as ours. They can become masters of the digital universes they inhabit, whether computer games, image processing or the internet. But, for the foreseeable future, we will remain masters of our own world.


Artificial wombs may give premature babies a better chance of survival. But could they transform reproductive rights too?

Critically pre-term babies face an uncertain future. Although a fetus is considered viable at 24 weeks of gestation, only about 60% of babies born so young will survive, and many will experience lifelong complications. For those born a couple of weeks earlier, the statistics are even direr: just 10% of babies born at 22 weeks are likely to survive.


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July/August 2020