Singularity HUB

Proteins are like Spider-Man in the multiverse.

The underlying story is the same: each building block of a protein is based on a three-letter DNA code. However, change one letter, and the same protein becomes a different version of itself. If we’re lucky, some of these mutants can still perform their normal functions.

When we’re unlucky, a single DNA letter change triggers a myriad of inherited disorders, such as cystic fibrosis and sickle cell disease. For decades, geneticists have hunted down these disease-causing mutations by examining shared genes in family trees. Once found, gene-editing tools such as CRISPR are beginning to help correct genetic typos and bring life-changing cures.

The problem? There are more than 70 million possible DNA letter swaps in the human genome. Even with the advent of high-throughput DNA sequencing, scientists have painstakingly uncovered only a sliver of potential mutations linked to diseases.

This week, Google DeepMind brought a new tool to the table: AlphaMissense. Based on AlphaFold, their blockbuster algorithm for predicting protein structures, the new algorithm analyzes DNA sequences and works out which DNA letter swaps likely lead to disease.

The tool only focuses on single DNA letter changes called “missense mutations.” In several tests, it categorized 89 percent of the tens of millions of possible genetic typos as either benign or pathogenic, said DeepMind.

AlphaMissense expands DeepMind’s work in biology. Rather than focusing only on protein structure, the new tool goes straight to the source code—DNA. Just a tenth of a percent of missense mutations in human DNA have been mapped using classic lab tactics. AlphaMissense opens a new genetic universe in which scientists can explore targets for inherited diseases.

“This knowledge is crucial to faster diagnosis” wrote the authors in a blog post, and to get to the “root cause of disease.”

For now, the company is only releasing the catalog of AlphaMissense predictions, rather than the code itself. They also warn the algorithm isn’t meant for diagnoses. Rather, it should be viewed more like a tip-line for disease-causing mutations. Scientists will have to examine and validate each tip using biological samples.

“Ultimately, we hope that AlphaMissense, together with other tools, will allow researchers to better understand diseases and develop new life-saving treatments,” said study authors Žiga Avsec and Jun Cheng at DeepMind.

Let’s Talk Proteins

A quick intro to proteins. These molecules are made from genetic instructions in our DNA represented by four letters: A, T, C, and G. Combining three of these letters codes for a protein’s basic building block—an amino acid. Proteins are made up of 20 different types of amino acids.

Evolution programmed redundancy into the DNA-to-protein translation process. Multiple three-digit DNA codes create the same amino acid. Even if some DNA letters mutate, the body can still build the same proteins and ship them off to their normal workstations without issue.

The problem is when a single letter change bulldozes the entire operation.

Scientists have long known these missense mistakes lead to devastating health consequences. But hunting them down has taken years of tedious work. To do this, scientists manually edit DNA sequences in a suspicious gene—letter by letter—make them into proteins, then observe their biological functions to hunt down the missense mutation. With hundreds of potential suspects, nailing down a single mutation can take years.

Can we speed it up? Enter machine minds.

AI Learning ATCG

DeepMind joins a burgeoning field that uses software to predict disease-causing mutations.

Compared to previous computational methods, AlphaMissense has a leg up. The tool leverages learnings from its predecessor algorithm, AlphaFold. Known for solving protein structure prediction—a grand challenge in the field—AlphaFold is in the algorithmic biology hall-of-fame.

AlphaFold predicts protein structures—which often determine function—based on amino acid sequences alone. Here, AlphaMissense uses AlphaFold’s “intuition” about protein structures to predict whether a mutation is benign or detrimental, study author and DeepMind’s vice president of research Dr. Pushmeet Kohli said at a press briefing.

The AI also leverages the large language model approach. In this way, it’s a little like GPT-4, the AI behind ChatGPT, only rejiggered to decode the language of proteins. These algorithmic editors are great at homing in on protein variants and flagging which sequences are biologically plausible and which aren’t. To Avsec, that’s AlphaMissense’s superpower. It already knows the rules of the protein game—that is, it knows which sequences work and which fail.

As a proof-of-concept, the team used a standardized database of missense variants, called ClinVar, to challenge their AI system. These genetic typos lead to multiple developmental disorders. AlphaMissense bested existing models for nailing down disease-causing mutations.

A Game-Changer?

Predicting protein structures can be useful for stabilizing protein drugs and nailing down other biophysical properties. However, solving structure alone has “generally been of little benefit” when it comes to predicting variants that cause diseases, said the authors.

With AlphaMissense, DeepMind wants to turn the tide.

The team is releasing its entire database of potential disease-causing mutations to the public. Overall, they hunted down 32 percent of all missense variants that likely trigger diseases and 57 percent that are likely benign. The algorithm joins others in the field, such as PrimateAI, first released in 2018 to screen for dangerous mutants.

To be clear: the results are only predictions. Scientists will have to validate these AI-generated leads in lab experiments. AlphaMissense provides “only one piece of evidence,” said Dr. Heidi Rehm at the Broad Institute, who wasn’t involved in the work.

Nevertheless, the AI model has already generated a database that scientists can tap into “as a starting point for designing and interpreting experiments,” said the team.

Moving forward, AlphaMissense will likely have to tackle protein complexes, said Marsh and Teichmann. These sophisticated biological architectures are fundamental to life. Any mutations can crack their delicate structure, cause them to misfunction, and lead to diseases. Dr. David Baker’s lab at the University of Washington—another pioneer in protein structure prediction—has already begun using machine learning to explore these protein cathedrals.

For now, no single tool that predicts disease-causing DNA mutations can be relied on to diagnose genetic diseases, as symptoms often result from both inherited mutations and environmental cues. This applies to AlphaMissense as well. But as the algorithm—and interpretation of its results—advances, its use in the “diagnostic odyssey will continue to improve,” they said.

Image Credit: Google DeepMind / Unsplash

Simple robots have long been a manufacturing staple, but more advanced robots—think Boston Dynamics’ Atlas—have mostly been bespoke creations in the lab. That’s begun to change in recent years as four-legged robots like Boston Dynamics’ Spot have gone commercial. Now, it seems, humanoid robots are aiming for mass markets too.

Agility Robotics announced this week it has completed a factory to produce its humanoid Digit robot. The 70,000-square-foot facility, based in Salem, Oregon has a top capacity of 10,000 robots annually. Agility broke ground on the factory last year and plans to begin operations later this year. The first robots will be delivered in 2024 to early customers taking part in the company’s partner program. After that, Agility will open orders to everyone in 2025.

“The opening of our factory marks a pivotal moment in the history of robotics: the beginning of the mass production of commercial humanoid robots,” Damion Shelton, Agility Robotics’ cofounder and CEO said in a press release Tuesday.

The latest version of Digit stands 5 feet 9 inches tall and weighs 141 pounds, according to the company’s website. It has a flat head with a pair of emoji-like eyes, two arms designed to pick up and move totes and boxes, and walks on legs that hinge backwards like a bird’s. The robot can work 16 hours a day and docks itself to a charging station to refuel.

Founded in 2015, Agility Robotics was spun out of Oregon State University’s Robotics Laboratory, where robotics professor and cofounder Jonathan Hurst leads research in legged robots. Little more than a pair of legs, Digit’s direct ancestor Cassie launched in 2017. Agility had added arms and a suite of sensors, including a vaguely head-like lidar unit, by the time it announced the first commercial version of Digit in early 2020.

Back then, Digit was marketed as a delivery robot that would unfold from the back of a van and drop packages on porches. Though its first broad commercial application will instead be moving boxes and totes, the company still views Digit as a multi-purpose platform with other uses ahead.

“I believe dynamic-legged robots will one day help take care of elderly and infirm people in their homes, assist with lifesaving efforts in fires and earthquakes, and deliver packages to our front doors,” Hurst wrote for IEEE Spectrum in 2020.

To go bigger, however, the company will have to prove Digit is widely useful beyond the experimental and then figure out how to make it en masse.

That’s where the new factory, dubbed RoboFab, comes in. To date, robots like Digit are made in the single digits or dozens at most. Atlas is still a research robot—though its slick moves make for viral videos—and a new push into humanoid robots by other players, including the likes of Tesla, Figure, and Sanctuary, is only just getting started.

It would be an impressive achievement if Agility hews to its aggressive timeline.

Apart from building and opening a factory, challenges to scaling include setting up a steady supply chain, nailing consistent product quality beyond a few units, and servicing and supporting customer robots in the field. All that will take time—maybe years. And of course, in order to produce 10,000 robots annually, they have to sell that many too. The company expects the first year’s production to be in the hundreds.

But if Digit proves capable, affordable, and easy for businesses to integrate in the years ahead, it seems likely there would be ample demand for its box-and-tote picking skills. Amazon invested in Agility’s $150-million Series B in 2022 and has been packing its warehouses with robots for years. Digit could fit an unfilled niche in its machine empire.

Further broadening the number of tasks the robot can complete—and thereby widening the market—would likewise boost demand for the bot. Amazon, and others no doubt, would likely be more than willing to entertain the idea of Digit one day delivering packages.

But first, Agility will have to fire up the assembly line and prove they can keep it humming along at a healthy pace.

Image Credit: Agility Robotics

MDMA doesn’t have the best reputation. Known as “ecstasy” or “molly,” the drug is synonymous with rave culture: all-night electronic beats and choreographed laser shows.

Still, it may soon join the psychedelic drug resurgence—not for partying, but for tackling severe mental trauma, such as post-traumatic stress disorder (PTSD).

Last week, Nature Medicine reported a multi-site, randomized, double-blind trial in over 100 patients with PTSD. The drug, combined with therapy, was carefully administered to patients being monitored in doctors’ offices. Compared to patients given the same therapy with a placebo, MDMA was far more effective at dampening PTSD symptoms.

The study, led by the non-profit Multidisciplinary Association for Psychedelic Studies (MAPS), follows an earlier Phase 3 trial—the last stage of clinical testing before regulatory approval. In that trial, participants also received therapy. Roughly twice the number of people given MDMA rather than a placebo recovered from their PTSD diagnosis.

The new, long-awaited study bolsters those earlier results by recruiting a more diverse population and showing that the treatment worked across multiple racial and ethnic groups.

To be very clear: the trials are for MDMA-assisted therapy. The psychotherapy component is key. The team repeatedly warns against seeking out the drug and taking it without supervision.

“What we believe is that the results that we got were not from MDMA,” said MAPS founder Rick Doblin to Nature in an earlier interview. “They were from highly trained therapists who are then using MDMA.”

The Food and Drug Administration generally requires two controlled trials before it considers approving a drug. MAPS has now delivered. The organization plans to seek approval this October. If the results hold up, the US may join Australia in welcoming a previously condemned drug as a new treatment for PTSD.

It won’t be an easy road. Although public and scientific opinions have shifted towards tolerance, MDMA is still listed as a Schedule 1 drug by the DEA. Drugs in this category are deemed to have “no currently accepted medical use and a high potential for abuse,” placing them alongside heroin.

That said, scientists are increasingly taking psychedelics seriously as tools that can help combat difficult mental problems. Also among Schedule 1 drugs are cannabis, psylocibin (from magic mushrooms), and LSD (commonly known as acid). These illicit drugs are gradually being embraced both in the research and clinical spheres as valid candidates for further study.

To Dr. Amy Kruse at the venture capital firm Satori Neuro based in Maryland, who was not involved in either study, “MAPS has been the beacon to kind of take on this work…There are many people that can benefit from this treatment, and I think it shows a pathway for the potential rescheduling of other molecules.”

A Checkered Past

MDMA—an acronym for its chemical name, 3,4-methylenedioxymethamphetamine—didn’t always wear the party drug black hat. It has enthralled psychiatrists since its birth in 1912.

Developed by a German pharmaceutical company to control bleeding, the drug soon caught the eye of mental health professionals. From the 1970s to its complete ban in 1985, thousands of individual reports suggested the drug, delivered in a doctor’s office with therapy, enhanced treatment results. Patients seemed to be able to better express and process their feelings, in turn gaining insights into their own mental states.

However, the drug also leaked out onto the street around the same time, spurring a total ban by the FDA in 1985. Research into its potential for enhancing psychotherapy screeched to a halt. In turn, scientists were left with only individual case reports and anecdotes—hardly sufficient evidence to continue research.

Enter Doblin. Convinced that research into MDMA and other psychedelic drugs shouldn’t be abandoned, he founded MAPS in 1986—a year after the ban. For the next 40 years, his team fought to reestablish the drug as a legitimate candidate for PTSD and depression. Neuroscientists studying drug toxicity was the norm. Treatment potential? Not so much.

Opinions began to shift in the late 2010s. A prominent neuroscientist called the drug “a probe and treatment for social behaviors” in a highly prestigious journal. MDMA regained its 1970s reputation as an “empathogen,” in that it fosters feelings of empathy and closeness. How MDMA triggers those intimate feelings isn’t yet fully understood, but it seems to increase levels of several chemical messengers in the brain, including serotonin, dopamine, and norepinephrine. Lower amounts of these chemicals are often associated with depression.

In 2021, MAPS and Doblin had their first major win in a clinical trial studying 90 people with PTSD undergoing therapy, either with MDMA or a placebo. After three sessions, 67 percent of those receiving MDMA no longer qualified for PTSD diagnosis, compared to just 32 percent of people given placebos.

The new 104-person study bolsters these promising results. Patients attended three 8-hour sessions across roughly 12 weeks. Regardless of ethnicity or race, 71 percent of people given MDMA and therapy were freed of their PTSD diagnosis, compared to 48 percent in the placebo group. MDMA-assisted therapy was also effective in people with other mental disorders, such as depression—an important use case as the two conditions often go hand-in-hand. Most participants experienced mild side effects, such as muscle tightness, feeling hot, or nausea.

Neurologist Dr. Jennifer Mitchell at the University of California, San Francisco, who led both Phase 3 studies, told Nature that the drug acts as a “communications lubricant.” It doesn’t make therapy sessions more fun—participants still have to work through their trauma—but it does help them more readily open up to their therapists, without experiencing shame or trauma.

And these effects are seemingly generalized regardless of ethnicity or race. “In a historic first, to our knowledge, for psychedelic treatment studies, participants who identified as ethnically or racially diverse encompassed approximately half of the study sample,” the team wrote.

A Bright Future?

It’s extremely difficult to blind a psychedelic study. Given MDMA’s potent effects, it’s very clear to patients if they’re high after taking a pill, which could result in bias.

To get around the problem, MAPS developed a special protocol first approved by the FDA in 2017. After each treatment session, the volunteers’ symptoms were measured by psychologists not privy to the experiment design. They’re “blinded” to what group the patient is in and did not administer the drug or therapy.

It’s not a perfect solution. In a post-trial survey, most people given MDMA knew what they were given. To Dr. Erick Turner at the Oregon Health and Science University in Portland, this doesn’t fit the FDA’s definition of “blinding.” Even if the drug is deemed safe and efficient, regulatory agencies will still need to iron out the rules. Because therapy is a key component but not under the FDA’s jurisdiction, the agency has to somehow dissuade people from trying the drug on their own in inconducive, or even dangerous settings.

MDMA has also been linked to bad experiences in people with schizophrenia or other neurological disorders. These terrifying trips aren’t just detrimental to the patient’s mental health—they could also set back the renaissance of psychedelic treatments.

In all, lots of kinks need to be worked out. Given MDMA’s long history, its patent is expired, reducing incentives to develop or manufacture the drug. But with the new study, regulatory approval is inching closer as an alternative for people battling mental demons.

“It’s an important study,” said MDMA researcher Dr. Matthias Liechti at the University of Basel in Switzerland, who wasn’t involved in the trial. “It confirms MDMA works.”

Image Credit: chenspec / Pixabay

If you looked up 66 million years ago you might have seen, for a split second, a bright light as a mountain-sized asteroid burned through the atmosphere and smashed into Earth. It was springtime and the literal end of an era, the Mesozoic.

If you somehow survived the initial impact, you would have witnessed the devastation that followed. Raging firestorms, megatsunamis, and a nuclear winter lasting months to years. The 180-million-year reign of non-avian dinosaurs was over in the blink of an eye, as well as at least 75% of the species who shared the planet with them.

Following this event, known as the Cretaceous-Paleogene mass extinction (K-Pg), a new dawn emerged for Earth. Ecosystems bounced back, but the life inhabiting them was different.

Many iconic pre-K-Pg species can only be seen in a museum. The formidable Tyrannosaurus rex, the Velociraptor, and the winged dragons of the Quetzalcoatlus genus could not survive the asteroid and are confined to deep history. But if you take a walk outside and smell the roses, you will be in the presence of ancient lineages that blossomed in the ashes of K-Pg.

Although the living species of roses are not the same ones that shared Earth with Tyrannosaurus rex, their lineage (family Rosaceae) originated tens of millions of years before the asteroid struck.

And the roses are an not unusual angiosperm (flowering plant) lineage in this regard. Fossils and genetic analysis suggest that the vast majority of angiosperm families originated before the asteroid.

Ancestors of the ornamental orchid, magnolia, and passionflower families, grass and potato families, the medicinal daisy family, and the herbal mint family all shared Earth with the dinosaurs. In fact, the explosive evolution of angiosperms into the roughly 290,000 species today may have been facilitated by K-Pg.

Angiosperms seemed to have taken advantage of the fresh start, similar to the early members of our own lineage, the mammals.

However, it’s not clear how they did it. Angiosperms, so fragile compared with dinosaurs, cannot fly or run to escape harsh conditions. They rely on sunlight for their existence, which was blotted out.

What Do We Know?

Fossils in different regions tell different versions of events. It is clear there was high angiosperm turnover (species loss and resurgence) in the Amazon when the asteroid hit, and a decline in plant-eating insects in North America which suggests a loss of food plants. But other regions, such as Patagonia, show no pattern.

A study in 2015 analyzing angiosperm fossils of 257 genera (families typically contain multiple genera) found K-Pg had little effect on extinction rates. But this result is difficult to generalize across the 13,000 angiosperm genera.

My colleague Santiago Ramírez-Barahona, from the Universidad Nacional Autónoma de México, and I took a new approach to solving this confusion in a study we recently published in Biology Letters. We analyzed large angiosperm family trees, which previous work mapped from mutations in DNA sequences from 33,000-73,000 species.

This way of tree-thinking has laid the groundwork for major insights about the evolution of life, since the first family tree was scribbled by Charles Darwin.

Although the family trees we analyzed did not include extinct species, their shape contains clues about how extinction rates changed through time, through the way the branching rate ebbs and flows.

The extinction rate of a lineage, in this case angiosperms, can be estimated using mathematical models. The one we used compared ancestor age with estimates for how many species should be appearing in a family tree according to what we know about the evolution process.

It also compared the number of species in a family tree with estimates of how long it takes for a new species to evolve. This gives us a net diversification rate—how fast new species are appearing, adjusted for the number of species that have disappeared from the lineage.

The model generates time bands, such as a million years, to show how extinction rate varies through time. And the model allows us to identify time periods that had high extinction rates. It can also suggest times in which major shifts in species creation and diversification have occurred as well as when there may have been a mass extinction event. It shows how well the DNA evidence supports these findings too.

We found that extinction rates seem to have been remarkably constant over the last 140-240 million years. This finding highlights how resilient angiosperms have been over hundreds of millions of years.

We cannot ignore the fossil evidence showing that many angiosperm species did disappear around K-Pg, with some locations hit harder than others. But, as our study seems to confirm, the lineages (families and orders) to which species belonged carried on undisturbed, creating life on Earth as we know it.

This is different to how non-avian dinosaurs fared, who disappeared in their entirety: their entire branch was pruned.

Scientists believe angiosperm resilience to the K-Pg mass extinction (why only leaves and branchlets of the angiosperm tree were pruned) may be explained by their ability to adapt. For example, their evolution of new seed-dispersal and pollination mechanisms.

They can also duplicate their entire genome (all of the DNA instructions in an organism) which provides a second copy of every single gene on which selection can act, potentially leading to new forms and greater diversity.

The sixth mass extinction event we currently face may follow a similar trajectory. A worrying number of angiosperm species are already threatened with extinction, and their demise will probably lead to the end of life as we know it.

It’s true angiosperms may blossom again from a stock of diverse survivors—and they may outlive us.

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Image Credit: Avis Yang / Unsplash 

ROBOTICS

This Driverless Car Company Is Using Chatbots to Make Its Vehicles Smarter
Will Douglas Heaven | MIT Technology Review
“Self-driving car startup Wayve can now interrogate its vehicles, asking them questions about their driving decisions—and getting answers back. …In a demo the company gave me this week, CEO Alex Kendall played footage taken from the camera on one of its Jaguar I-PACE vehicles, jumped to a random spot in the video, and started typing questions: ‘What’s the weather like?’ The weather is cloudy. ‘What hazards do you see?’ There is a school on the left. ‘Why did you stop?’ Because the traffic light is red.”

ENERGY

America Just Hit the Lithium Jackpot
Ross Andersen | The Atlantic
“About 16.4 million years ago, magma surged through a raised mound near Nevada’s present-day border with Oregon and began spreading an unholy orange glow outward over the region. …[A new paper published in Science Advances] claims that underneath the volcano’s extinct crater is a thick brown clay that is shot through with what could be the largest-known lithium deposit on the planet. If the discovery holds up, and the lithium is easy to extract and refine—both big ifs—this ancient geological event could end up shaping contemporary geopolitics, and maybe even the future of green energy.”

TRANSPORTATION

Tesla Reinvents Carmaking With Quiet Breakthrough
Norihiko Shirouzu | Reuters
“Tesla is closing in on an innovation that would allow it to die cast nearly all the complex underbody of an EV in one piece, rather than about 400 parts in a conventional car, the people said. The know-how is core to Tesla’s ‘unboxed’ manufacturing strategy unveiled by [CEO Elon Musk] in March, a linchpin of his plan to churn out tens of millions of cheaper EVs in the coming decade, and still make a profit, the sources said.”

COMPUTING

Liquid Computer Made From DNA Comprises Billions of Circuits
David Nield | ScienceAlert
“[Despite] the passing of 30 years since the first prototype, most DNA computers have struggled to process more than a few tailored algorithms. A team [of] researchers from China has now come up with a DNA integrated circuit (DIC) that’s far more general purpose. Their liquid computer’s gates can form an astonishing 100 billion circuits, showing its versatility with each capable of running its own program.”

ARTIFICIAL INTELLIGENCE

How AI Agents Are Already Simulating Human Civilization
Ben Dickson | VentureBeat
“These AI agents are capable of simulating the behavior of a human in their daily lives, from mundane tasks to complex decision-making processes. Moreover, when these agents are combined, they can emulate the more intricate social behaviors that emerge from the interactions of a large population. This work opens up many possibilities, particularly in simulating population dynamics, offering valuable insights into societal behaviors and interactions.”

TRANSPORTATION

This EV Smashed the World Record for Distance on a Single Charge
Jonathan M. Gitlin | Ars Technica
“The diminutive coupe…was built for efficiency, and in a six-day test at Munich airport, it set a new distance record on a single charge (for a non-solar EV): 1,599 miles (2,574 km), with less battery capacity than many plug-in hybrids—just 15.5 kWh. …Their eventual distance broke the existing record by 60 percent, achieving a scarcely believable 103.2 miles/kWh, or 0.6 kWh/100 km. For those who think in terms of miles per gallon, it’s the equivalent of traveling 3,815 miles on a single gallon of gas.“

ART

Funky AI-Generated Spiraling Medieval Village Captivates Social Media
Benj Edwards | Ars Technica
“On Sunday, a Reddit user named ‘Ugleh’ posted an AI-generated image of a spiral-shaped medieval village that rapidly gained attention on social media for its remarkable geometric qualities. Follow-up posts garnered even more praise, including a tweet with over 145,000 likes. …Reactions to the artwork online ranged from wonder and amazement to respect for developing something novel in generative AI art. …Perhaps most notably, Y-Combinator co-founder and frequent social media tech commentator Paul Graham wrote, ‘This was the point where AI-generated art passed the Turing Test for me.’i”

3D PRINTING

Mighty Buildings Raises $52M to Build 3D-Printed Prefab Homes
Kyle Wiggers | TechCrunch
“The new tranche, which sources familiar with the matter say values the startup at between $300 million and $350 million, brings Mighty Buildings’ total raised to $150 million. CEO Scott Gebicke says that it’ll be put toward Mighty Buildings’ expansion in North America and the Middle East, particularly Saudi Arabia, and supporting the launch of the company’s next-gen modular homebuilding kit.“

LAW AND ETHICS

US Rejects AI Copyright for Famous State Fair-Winning Midjourney Art
Benj Edwards | Ars Technica
“The office is saying that because the work contains a non-negligible (‘more than a de minimis’) amount of content generated by AI, Allen must formally acknowledge that the AI-generated content is not his own creation when applying for registration. As established by Copyright Office precedent and judicial review, US copyright registration for a work requires human authorship.”

SPACE

Scientists Say You’re Looking for Alien Civilizations All Wrong
Ramin Skibba | Wired
“An influential group of researchers is making the case for new ways to search the skies for signs of alien societies. …The team of 22 scientists released a new report on August 30, contending that the field needs to make better use of new and underutilized tools, namely gigantic catalogs from telescope surveys and computer algorithms that can mine those catalogs to spot astrophysical oddities that might have gone unnoticed. Maybe an anomaly will point to an object or phenomenon that is artificial—that is, alien—in origin.”

Image Credit: Karsten Winegeart / Unsplash

Recently I perched on the edge of a cliff at Ausable Chasm, staring at the whitewater over 100 feet below. Water rushed through sandstone cliffs before hitting a natural break and twirling back onto itself, forming multiple hypnotic swirls. Over millennia, these waters have carved the magnificent stone walls lining the chasm, supporting a vibrant ecosystem.

The brain may do the same for cognition.

We know that different brain regions constantly coordinate their activity patterns, resulting in waves that ripple across the brain. Different types of waves correspond to differing mental and cognitive states.

That’s one idea for how the brain organizes itself to support our thoughts, feelings, and emotions. But if the brain’s information processing dynamics are like waves, what happens when there’s turbulence?

In fact, the brain does experience the equivalent of neural “hurricanes.” They bump into one another, and when they do, the resulting computations correlate with cognition.

These findings come from a unique study in Nature Human Behavior that bridges neuroscience and fluid dynamics to unpack the inner workings of the human mind.

Multiple interacting spirals organize the flow of brain activity. Image Credit: Gong et al.

The team analyzed 100 brain scans collected from the Human Connectome Project using methods usually reserved for observing water flow patterns in physics. The unconventional marriage of fields paid off: they found a mysterious, spiraling wave activity pattern in the brain while at rest and during challenging mental tasks.

The brain spirals often grew from select regions that bridge adjacent local neural networks. Eventually, they propagate across the cortex—the wrinkly, outermost region of the brain.

Often called the “seat of intelligence,” the cortex is a multitasker. Dedicated regions process our senses. Others interweave new experiences with memories and emotions, and in turn, form the decisions that help us adapt to an ever-changing world.

For the cortex to properly function, communication between each region is key. In a series of tests, brain spirals seem to be the messenger, organizing local neural networks across the cortex into a coherent computing processor. They’re also dedicated to a particular cognitive task. For example, when someone was listening to a story—as compared to solving math problems—the vortices began in different brain regions and created their own spin patterns, a cognitive fingerprint of sorts.

By analyzing these spiral wave fingerprints, the team found they could classify different stages of cognitive processing using brain images alone.

Finding turbulence in the brain is another step towards understanding how our biological computer works and could inspire the creation of future brain-based machines.

“By unraveling the mysteries of brain activity and uncovering the mechanisms governing its coordination, we are moving closer to unlocking the full potential of understanding cognition and brain function,” said study author Dr. Pulin Gong at the University of Sydney.

Won’t You Be My Neighbor?

A fundamental mystery of the brain is how electrical sparkles in neurons translate into thoughts, reasoning, memories, and even consciousness.

To unravel it all, we need to go up the pyramid of neural processing.

Starting at the bottom: neurons. To be fair, they’re incredibly sophisticated mini-computers on their own. They’re also really nosy. They constantly chitchat with their neighbors using a variety of chemical signals, called neurotransmitters. You might have heard of some: dopamine, serotonin, and even hormones.

Meanwhile, neurons process local gossip—carried by electrical pulses—and change their behavior based on what they hear. Some relationships strengthen. Others break. In this way, the brain forms local neural networks to support functions like, for example, visual processing.

“Research into brain activity has made substantial progress in understanding local neural circuits,” the team wrote.

What’s missing is the bigger picture. Imagine zooming out from the local neighborhood to the entire world. Thanks to a boom in neurotechnology, scientists have been able to record from increasingly vast regions of the brain. Digging into all this new data, previous studies have found multiple local networks that contribute to different behaviors.

Yet much of these insights into brain organization have focused on neurons communicating in a linear pattern—like data zapping along undersea optical wires. To broaden our view, we also need to look for more complex 3D patterns—for example, spirals or vortices.

Roughly two years ago, the team tapped into a hefty resource in the hunt for brain activity turbulence: functional MRI data, covering the entire cortex, from the Human Connectome Project (HCP). Launched in 2009, the project has developed multiple tools to map the human brain at unprecedented scales and generated a massive database for researchers. The maps don’t just cover the structure of the brain—many have also documented brain activity as participants engaged in different cognitive tasks.

Here, the team selected brain images from a section of HCP data. This dataset imaged brain connectivity and function in 1,200 healthy younger adults from 22 to 35 years of age while they were at rest or challenged with multiple mental tasks.

They focused on brain images from 3 cohorts of 100 people each. One cohort was made up of people completely relaxing. Another was challenged with a language and math task. The final cohort flexed their working memory—that is, they were required to use the mental sketchpad we use to coordinate new information and decide what our next action should be.

With mathematical tools generally used to decrypt turbulent flows, the team analyzed MRI data for patterns that correspond to cognition—in this case, math, language, and working memory.

Put very simply, the analyses pinpointed “the eye of the storm” and predicted how fast and wide the neural swirls would spread out from there. They moved and interacted “with each other in an intriguing manner, which was very exciting,” said the team.

The spirals, like hurricanes, bounced across the cortex while rotating around set centers—called a “phase singularity.” The pattern is surprisingly similar to other dynamic systems in physics and biology, such as turbulence, they said.

A Spiraling Mystery

Why and how do these spirals occur? The team doesn’t yet have all the answers. But digging deeper, they found that the seeds of these spirals blossom out from boundaries between functional neural networks. The team thinks these twisting shapes could be essential for “effectively coordinating activity flow among these networks through their rotational motion.”

The spirals rotate and interact depending on the cognitive task at hand. They also tend to twirl and spread into brain regions dubbed “brain hubs,” such as the frontal parts of the brain or those related to integrating sensations.

But their interactions are especially enthralling. Based on the physics of turbulence, brain spirals that bump into each other carry a hefty amount of information. These waves capture data in space and time and propagate the information over the surface of living neurons in non-linear waves.

“The intricate interactions among multiple co-existing spirals could allow neural computations to be conducted in a distributed and parallel manner, leading to remarkable computational efficiency,” said Gong.

To Dr. Kentaroh Takagaki from Tokushima University, who was not involved in the study, “the results present a stark counterpoint to the established view” of information processing in the cortex.

For now, brain spirals remain rather mysterious. But with more work, they could yield insights into dementia, epilepsy, and other difficult neurological disorders.

Image Credit: Mitul Grover / Unsplash

Shifting to battery-powered vehicles is an essential step in tackling climate change, but it’s also creating worryingly large amounts of e-waste and demand for environmentally damaging mining. A new partnership to produce batteries made with recycled materials could help address the problem.

While there’s little question about the need to shift away from vehicles powered by fossil fuels, electrifying our entire transportation system isn’t going to be smooth sailing. Demand for lithium—the main ingredient in today’s leading batteries—has exceeded supply two years in a row, according to the International Energy Agency, despite a 180 percent increase in production since 2017.

There are similar concerns about shortages of other key ingredients like nickel, cobalt, and manganese, which could slow the much-needed transition to electric vehicles. These shortages are also incentivizing the rapid expansion of mining activities, which can be damaging to the environment, particularly if politicians turn a blind eye to lax standards in the rush to meet demand. That’s why there’s growing interest in recycling old batteries to retrieve the valuable metals contained within.

Now, a partnership between battery material producer BASF, graphene battery maker Nanotech Energy, battery recycler American Battery Technology Company (ABTC), and battery precursor material maker TODA Advanced Materials, claims it will be the first closed-loop battery recycling system in North America. The group hopes to be producing new batteries from recycled materials by 2024.

“By working together, our four companies can pool their expertise and drive better and more sustainable outcomes for the entire North American electric vehicle and consumer electronics industries,” Curtis Collar from Nanotech Energy said in a press release.

“This is a major milestone among the ongoing advances and growth of the lithium-ion battery market, and we are proud playing such a key role in the reduction of CO2 emissions along the battery value chain.”

Under the agreement, BASF will produce materials used in battery cathodes from recycled metals. Nanotech Energy will then use those materials to build their lithium-ion battery cells. Some of those recycled metals will come from ABTC recycling battery scrap produced by Nanotech Energy as it manufactures batteries. These will be processed into battery material precursors by TODA and then into cathode materials by BASF.

Together, this will create a circular battery recycling system, according to the companies. They claim that using recycled metals in the production of lithium-ion batteries can cut the amount of CO2 generated while manufacturing them by roughly 25 percent.

Battery recycling has been attracting growing interest from investors, particularly after the US passed the Inflation Reduction Act last year, which contains many incentives to reuse older batteries. Earlier this month, battery recycler Ascend Elements announced a $542 million funding round, and in August, competitor Redwood Materials revealed it had secured $1 billion in investments.

According to McKinsey, most of the battery materials suitable for recycling currently come from consumer electronics and battery scrap from manufacturers because few electric vehicles have yet reached the end of their operational lives.

But the analysts predict this could change soon, with more than 100 million vehicle batteries due to be retired within the next decade. They think that revenues from battery recycling could jump to more than $95 billion a year by 2040 globally.

With such a lucrative prize on offer, and growing concerns about supply shortages, it seems recycled battery materials could soon be playing a major role in the electric vehicle transition.

Image Credit: Markus Spiske / Unsplash

Are we alone? This question is nearly as old as humanity itself. Today, the question in astronomy focuses on finding life beyond our planet. Are we, as a species, and as a planet, alone? Or is there life somewhere else?

Usually the question inspires visions of weird, green versions of humans. However, life is more than just us: animals, fish, plants, and even bacteria are all the kinds of things we seek signs of in space.

One thing about life on Earth is that it leaves traces in the chemical makeup of the atmosphere. So traces like that, which are visible from a long way away, are something we look for when we’re hunting aliens.

Scientists in the United Kingdom and the United States have just reported some very interesting chemical traces in the atmosphere of a planet called K2-18b, which is about 124 light-years from Earth. In particular, they may have detected a substance which on Earth is only produced by living things.

Meet Exoplanet K2-18b

K2-18b is an interesting exoplanet—a planet that orbits another star. Discovered in 2015 by the Kepler Space Telescope’s K2 mission, it is a type of planet called a sub-Neptune. As you probably guessed, these are smaller than Neptune in our own solar system.

The planet is about eight and a half times heavier than Earth and orbits a type of star called a red dwarf, which is much cooler than our sun. However, K2-18b orbits much closer to its star than Neptune does—in what we call the habitable zone. This is the area that is not too hot and not too cold, where liquid water can exist (instead of freezing to ice or boiling into steam).

Earth is what is called a rocky planet (for obvious reasons) but sub-Neptunes are gas planets, with much larger atmospheres containing lots of hydrogen and helium. Their atmosphere can also contain other elements.

Which brings us to the excitement around K2-18b.

How to Fingerprint an Atmosphere

The planet was first discovered by the Kepler Space Telescope, which was monitoring distant stars and hoping for planets to pass in front of them. When a planet does pass between us and a star, the star becomes momentarily dimmer—which is what tells us a planet is there.

By measuring how big the dip in brightness is, how long it takes for the planet to pass in front of the star, and how often this happens, we can work out the size and orbit of the planet. This technique is great at finding planets, but it doesn’t tell us about their atmospheres—which is a key piece of information to understand if they hold life or are habitable.

NASA’s James Webb Space Telescope—the big space telescope launched at the end of 2021—has now observed and measured the atmosphere of this exoplanet.

The telescope did this by measuring the color of light so finely, it can detect traces of specific atoms and molecules. This process, called spectroscopy, is like measuring the fingerprints of elements.

The atmosphere of the exoplanet K2-18b showed strong signs of methane and carbon dioxide, as well as a weak indication of dimethyl sulfide. Image Credit: NASA / CSA / ESA / R. Crawford (STScI) / J. Olmsted (STScI) / N. Madhusudhan (Cambridge University)

Each element and molecule has its own color signature. If you can look at the color signature, you can do a bit of detective work, and figure out what elements or compounds are in the planet.

While the planet does not have its own light, astronomers waited for when K2-18b passed in front of its star and measured the starlight as it went through the planet’s atmosphere, allowing the team to detect fingerprints of substances in the atmosphere.

Alien Marine Farts?

The new study found a lot of carbon dioxide and methane. This is interesting as this is like what is found on Earth, Mars, and Venus in our solar system—rather than Neptune.

However, it also found a small amount of dimethyl sulfide. Dimethyl sulfide is an interesting molecule, made up of carbon, hydrogen, and sulfur.

On Earth, it’s generally a bit smelly. But it’s also closely linked to life.

The only process we know that creates dimethyl sulfide on our planet is life. In particular, marine life and plankton emit it in the form of flatulence.

So yes, scientists are excited by the potential idea of alien marine farts. If it is real. And linked to life.

The Search Continues

While on Earth, dimethyl sulfide is linked to life, on other planets it may somehow be related to geological or chemical processes.

After all, K2-18b is something like Neptune—a planet we do not really know a lot about. Just last month, researchers discovered that clouds on Neptune are strongly linked to the sun’s 11-year cycle of activity. We have a lot to learn about planets and their atmospheres.

Also, the measurement of dimethyl sulfide is very subtle—not nearly as strong as the carbon dioxide and methane. This means more detailed measurements to improve the strength of the signal are required.

Other telescopes may need to join the effort. Instruments on the Very Large Telescope in Chile are able to measure the atmospheres of planets around other stars—as is a new instrument called Veloce on the Anglo Australian Telescope at Siding Spring Observatory in Australia.

And new space telescopes, like Europe’s PLATO which is under construction, will also help us get a better look at alien atmospheres.

So while the signs of dimethyl sulfide on K2-18b may not be linked to life, they are still an exciting prospect. There is plenty more to explore.

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Image Credit: NASA / CSA / ESA / J. Olmsted (STScI) / Science: N. Madhusudhan (Cambridge University)

Forget sperm meets egg.

Using human stem cells, scientists have created human embryo-like structures inside petri dishes. These lab-grown blobs develop multiple structures that mimic a human embryo after implantation into the uterus—a major milestone for fertility—and last at least 14 days.

A decade ago, manufacturing embryo-like structures, or embryoids, without reproductive cells would have seemed ludicrous. But as scientists increasingly map out the convoluted molecular journey towards human conception, it’s becoming possible to do away with sperm and egg in order to peek into the “black box” of early human development.

It still sounds like a Frankenstein experiment. But the endeavor isn’t macabre scientific curiosity. Very little is known about the first few weeks of human pregnancy, when development most often tends to go awry. Studying models mimicking these early stages—without the controversy of biological samples—could help couples struggling to conceive and shine a light on the mysteries of lost early pregnancies.

A new study published in Nature from embryoid veteran Dr. Jacob Hanna now pushes the lab-gestation timeline forward. The team turned human embryonic stem cells into embryoids that model early human embryos. Like their biological counterparts, the lab-based blobs developed major “layers” of tissues defining the early stages of human development.

“The drama is in the first month, the remaining eight months of pregnancy are mainly lots of growth,” said Hanna. “But that first month is still largely a black box. Our stem-cell-derived human embryo model offers an ethical and accessible way of peering into this box.”

Recipe for an Embryoid

Two years ago, the same team released a blockbuster result: egg meets sperm isn’t necessary to spark life, at least in mice. Using mouse stem cells, the team discovered a chemical soup that could nudge the cells into embryo-like structures inside a petri dish.

“The embryo is the best organ-making machine and the best 3D bioprinter—we tried to emulate what it does,” said Hanna at the time.

The idea seems relatively simple: all embryonic cells have the potential to become any other cell type. But these cells are also highly social. Depending on their environment—for example, which chemical or hormonal signals they receive—they self-organize into tissues.

Culturing embryoids relies on two advances, both from the Hanna lab.

One places reverted stem cells into a completely naïve state—a tabula rasa that wipes away any identity. We often think of stem cells as a uniform crowd, but they’re actually on a spectrum of development. Each step forward guides the cell’s development towards a specific cell type or organ. However, a naïve stem cell has the potential to grow into any body part.

Completely rebooting to naïve stem cells makes it easier to integrate stem cells into their hosts—regardless of whether it’s in humans or mice.

Another advance is an electronically controlled device that bathes the embryoids in waves of nutrients. Like a pacemaker, the pump simulates how nutrients wash over embryos in the womb, all the while controlling oxygen levels and atmospheric pressure.

In a proof-of-concept study, a small portion of cells from mice formed into embryo-like structures. They developed similarly to their natural counterparts up until roughly half of their normal gestation. By eight days, the embryoids had a beating heart, blood cells in their circulation, a mini-brain with its classical folds, and a digestive tract.

“If you give an embryo the right conditions, its genetic code will function like a pre-set line of dominos, arranged to fall one after the other,” said Hanna in an earlier interview. “Our aim was to recreate those conditions, and now we can watch, in real time, as each domino hits the next one in line.”

Nearly Human

Mice are not men. Hanna is well aware, and the new study bridges the chasm.

The first step? Prime a human stem cell by reverting it into a naïve state.

With this raw material in hand, the team next gave the cells different identities, called lineages. Some of these develop into cells that eventually make up the embryo. Others turn into supporting cells, such as those that make up the placenta or build the yolk sac—a small, rounded multitasker that supports the health of the developing embryo.

In other words, the early developing human embryo is a complex ecosystem. So, it’s no wonder that coaxing naïve stem cells into multiple roles has long eluded embryoid makers. Yet every single lineage becomes indispensable after a major step in early human development, implantation, takes place. When a fertilized embryo attaches to the uterine wall, it sparks a myriad of changes essential for further development. It’s also when embryo loss often occurs.

The new study zooms in on the post-implantation stage, repurposing the team’s previous mouse embryoid protocol to generate self-organizing human embryoids. Surprisingly, it was simpler.

They had to genetically engineer mouse stem cells to push them towards different lineages, the team says. With human cells, they just tweaked the nutrient bath—no additional genes required—to activate genetic programs in stem cells, turning them into all three types of supporting tissues.

As the embryoids matured, the team used a series of molecular and genetic tools to examine their fidelity. Overall, the structures resembled the 3D architecture of naturally developed human embryos between 7 to 14 days after fertilization. Some cells even pumped out human chorionic gonadotropin (hCG), a hormone used for home pregnancy tests. Dabbing the cells’ secretions onto the stick gave the double-line positive result.

Overall, the embryoids showed key developmental landmarks of an early implanted embryo, said the team, without the need for fertilization or interactions with a mother’s womb.

Embryoid Race

Hanna’s team isn’t the only one pushing embryoids forward.

In June this year, two other teams engineered embryoids that mimic human embryos after implantation. The recipes and ingredients are different than Hanna’s. One study, for example, inserted a host of powerful genetic factors that pushed stem cells to become supporting tissues.

Scientists don’t quite agree on which embryoids best resemble their natural counterpart. However, they do agree on one aspect: stem cells, under the right conditions, have an incredible ability to self-organize into increasingly sophisticated embryo-like structures.

For now, the 14-day embryoid is touted as the “most advanced” yet.

Fourteen days is a strict cutoff for research on natural human embryos in many countries, in that they can’t be further cultured in the lab. However, embryoids don’t meet the definition of an embryo and aren’t subjected to the 14-day limitation. In other words, human embryoids could be cultured further along the development timeline. Previous work shows it’s technologically possible in mice, with stem cells developing semi-functional organs.

If you’re getting a bit creeped out—you’re not alone. Embryoids are growing into ever later stages in an arms race to open the black box of early human development. For now, embryoids grown from human embryonic stem cells have to respect current regulations. However, ones made from induced stem cells—often using skin cells reverted into a stem-cell-like state—aren’t subjected to any rules.

To be clear, embryoids don’t have the capacity to fully develop into human beings. However, a recent study in monkeys showed that they can induce pregnancy when transplanted into a womb—though in that case, the embryoid was rapidly and naturally terminated. Debates on if and how to regulate these cellular blobs are ongoing.

For now, Hanna’s team is focused on a revising their recipe to boost efficiency. But as a long-term goal, they hope to push the embryoid even further to see if they can develop rudimentary organs. These experiments “will offer insights into previously inaccessible windows of early human development,” they say.

Image Credit: Weizmann Institute of Science

Of all the forms of human intellect that one might expect artificial intelligence to emulate, few people would likely place creativity at the top of their list. Creativity is wonderfully mysterious—and frustratingly fleeting. It defines us as human beings—and seemingly defies the cold logic that lies behind the silicon curtain of machines.

Yet, the use of AI for creative endeavors is now growing.

New AI tools like DALL-E and Midjourney are increasingly part of creative production, and some have started to win awards for their creative output. The growing impact is both social and economic—as just one example, the potential of AI to generate new, creative content is a defining flashpoint behind the Hollywood writers strike.

And if our recent study into the striking originality of AI is any indication, the emergence of AI-based creativity—along with examples of both its promise and peril—is likely just beginning.

A Blend of Novelty and Utility

When people are at their most creative, they’re responding to a need, goal, or problem by generating something new—a product or solution that didn’t previously exist.

In this sense, creativity is an act of combining existing resources—ideas, materials, knowledge—in a novel way that’s useful or gratifying. Quite often, the result of creative thinking is also surprising, leading to something the creator did not—and perhaps could not—foresee.

It might involve an invention, an unexpected punchline to a joke, or a groundbreaking theory in physics. It might be a unique arrangement of notes, tempo, sounds, and lyrics that results in a new song.

So, as a researcher of creative thinking, I immediately noticed something interesting about the content generated by the latest versions of AI, including GPT-4.

When prompted with tasks requiring creative thinking, the novelty and usefulness of GPT-4’s output reminded me of the creative types of ideas submitted by students and colleagues I had worked with as a teacher and entrepreneur.

The ideas were different and surprising, yet relevant and useful. And, when required, quite imaginative.

Consider the following prompt offered to GPT-4: “Suppose all children became giants for one day out of the week. What would happen?” The ideas generated by GPT-4 touched on culture, economics, psychology, politics, interpersonal communication, transportation, recreation, and much more—many surprising and unique in terms of the novel connections generated.

This combination of novelty and utility is difficult to pull off, as most scientists, artists, writers, musicians, poets, chefs, founders, engineers, and academics can attest.

Yet AI seemed to be doing it—and doing it well.

Putting AI to the Test

With researchers in creativity and entrepreneurship Christian Byrge and Christian Gilde, I decided to put AI’s creative abilities to the test by having it take the Torrance Tests of Creative Thinking, or TTCT.

The TTCT prompts the test-taker to engage in the kinds of creativity required for real-life tasks: asking questions, how to be more resourceful or efficient, guessing cause and effect, or improving a product. It might ask a test-taker to suggest ways to improve a children’s toy or imagine the consequences of a hypothetical situation, as the above example demonstrates.

The tests are not designed to measure historical creativity, which is what some researchers use to describe the transformative brilliance of figures like Mozart and Einstein. Rather, it assesses the general creative abilities of individuals, often referred to as psychological or personal creativity.

In addition to running the TTCT through GPT-4 eight times, we also administered the test to 24 of our undergraduate students.

All of the results were evaluated by trained reviewers at Scholastic Testing Service, a private testing company that provides scoring for the TTCT. They didn’t know in advance that some of the tests they’d be scoring had been completed by AI.

Since Scholastic Testing Service is a private company, it does not share its prompts with the public. This ensured that GPT-4 would not have been able to scrape the internet for past prompts and their responses. In addition, the company has a database of thousands of tests completed by college students and adults, providing a large, additional control group with which to compare AI scores.

Our results?

GPT-4 scored in the top 1 percent of test-takers for the originality of its ideas. From our research, we believe this marks one of the first examples of AI meeting or exceeding the human ability for original thinking.

In short, we believe that AI models like GPT-4 are capable of producing ideas that people see as unexpected, novel, and unique. Other researchers are arriving at similar conclusions in their research of AI and creativity.

Yes, Creativity Can Be Evaluated

The emerging creative ability of AI is surprising for a number of reasons.

For one, many outside of the research community continue to believe that creativity cannot be defined, let alone scored. Yet products of human novelty and ingenuity have been prized—and bought and sold—for thousands of years. And creative work has been defined and scored in fields like psychology since at least the 1950s.

The “person, product, process, and press” model of creativity, which researcher Mel Rhodes introduced in 1961, was an attempt to categorize the myriad ways in which creativity had been understood and evaluated until that point. Since then, the understanding of creativity has only grown.

Still others are surprised that the term “creativity” might be applied to nonhuman entities like computers. On this point, we tend to agree with cognitive scientist Margaret Boden, who has argued that the question of whether the term creativity should be applied to AI is a philosophical rather than scientific question.

AI’s Founders Foresaw Its Creative Abilities

It’s worth noting that we studied only the output of AI in our research. We didn’t study its creative process, which is likely very different from human thinking processes, or the environment in which the ideas were generated. And had we defined creativity as requiring a human person, then we would have had to conclude, by definition, that AI cannot possibly be creative.

But regardless of the debate over definitions of creativity and the creative process, the products generated by the latest versions of AI are novel and useful. We believe this satisfies the definition of creativity that is now dominant in the fields of psychology and science.

Furthermore, the creative abilities of AI’s current iterations are not entirely unexpected.

In their now famous proposal for the 1956 Dartmouth Summer Research Project on Artificial Intelligence, the founders of AI highlighted their desire to simulate “every aspect of learning or any other feature of intelligence”—including creativity.

In this same proposal, computer scientist Nathaniel Rochester revealed his motivation: “How can I make a machine which will exhibit originality in its solution of problems?”

Apparently, AI’s founders believed that creativity, including the originality of ideas, was among the specific forms of human intelligence that machines could emulate.

To me, the surprising creativity scores of GPT-4 and other AI models highlight a more pressing concern: Within US schools, very few official programs and curricula have been implemented to date that specifically target human creativity and cultivate its development.

In this sense, the creative abilities now realized by AI may provide a “Sputnik moment” for educators and others interested in furthering human creative abilities, including those who see creativity as an essential condition of individual, social, and economic growth.

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Image Credit: Google DeepMind / Unsplash

ARTIFICIAL INTELLIGENCE

What OpenAI Really Wants
Steven Levy | Wired
“For Altman and his company, ChatGPT and GPT-4 are merely stepping stones along the way to achieving a simple and seismic mission, one these technologists may as well have branded on their flesh. That mission is to build artificial general intelligence—a concept that’s so far been grounded more in science fiction than science—and to make it safe for humanity. The people who work at OpenAI are fanatical in their pursuit of that goal.”

COMPUTING

The Secret to Nvidia’s AI Success
Samuel K. Moore | IEEE Spectrum
“[Nvidia] has managed to increase the performance of its chips on AI tasks a thousandfold over the past 10 years, it’s raking in money, and it’s reportedly very hard to get your hands on its newest AI-accelerating GPU, the H100. How did Nvidia get here? …Moore’s Law was a surprisingly small part of Nvidia’s magic and new number formats a very large part. Put it all together and you get what Dally called Huang’s Law (for Nvidia CEO Jensen Huang).”

VIRTUAL REALITY

Roblox’s New AI Chatbot Will Help You Build Virtual Worlds
Jay Peters | The Verge
“The new tool, the Roblox Assistant, builds on previously announced features that let creators build virtual assets and write code with the help of generative AI. …Down the line, Roblox has bigger visions for Roblox Assistant, and Sturman teased that it could generate sophisticated gameplay and even make 3D models from scratch. If that all works, it could bring Roblox in line with CEO David Baszucki’s vision of Westworld-like ease of design.”

TECH

Apple Is Reportedly Spending ‘Millions of Dollars a Day’ Training AI
Monica Chin | The Verge
“The company is reportedly working on multiple AI models across several teams. Apple’s unit that works on conversational AI is called ‘Foundational Models,’ per The Information’s reporting. It has ‘around 16’ members, including several former Google engineers. Additional teams at Apple are also working on artificial intelligence, per The Information. A Visual Intelligence unit is developing an image generation model, and another group is researching ‘multimodal AI, which can recognize and produce images or video as well as text.'”

SPACE

SpaceX Broke Its Record for Number of Launches in a Year
Stephen Clark | Ars Technica
“SpaceX is leading the world not just in the number of launches, but also in the total payload mass the company has launched into orbit this year. In the first half of 2023, SpaceX delivered about 447 metric tons of cargo into orbit, roughly 80 percent of all the material launched into orbit worldwide, according to data from the space analytics firm BryceTech. Musk said SpaceX will launch about 90 percent of the world’s total payload mass into orbit next year, based on the company’s launch manifest for 2024.”

ART

Refik Anadol Just Turned the Las Vegas Sphere Into the World’s Largest AI Artwork
Jesus Diaz | Fast Company
“With Sphere, the building is the canvas—a bland engineering marvel that transforms into something visually arresting once Anadol gets his hands on it. ‘I think this is one of the most Blade Runner moments ever,’ he says. ‘A science fiction moment that, finally, merges media arts and architecture, embedding technology into a physical environment that exists in the real world.'”

BIOTECH

Redwire Space Prints Human Knee Cartilage in Space for the First Time
Aria Alamalhodaei | TechCrunch
“Redwire Space has successfully ‘bioprinted’ a human knee meniscus aboard the International Space Station, a landmark development that could help people recovering from meniscus injuries here on Earth. The meniscus cartilage was printed on Redwire’s BioFabrication Facility (BFF) on the ISS. …After the BFF printed the meniscus with living human cells, it was transferred to Redwire’s Advanced Space Experiment Processor for a 14-day enculturation process. After the culture process was complete, the meniscus was packaged up and sent back to Earth aboard SpaceX’s Crew-6 mission.”

AUTOMATION

FAA Clears UPS Delivery Drones for Longer-Range Flights
Sheena Vasani | The Verge
“UPS Flight Forward, a UPS subsidiary focused on drone delivery, can now deliver small packages beyond the visual line of sight (BVLOS) without spotters on the ground monitoring the route and skies for other aircraft, using Matternet M2 drones. The FAA also announced authorizations for two other companies to fly beyond sight for commercial purposes.”

Image Credit: Marek Piwnicki / Unsplash

Renewable power was already rapidly replacing fossil fuels as the cheapest source of electricity. Thanks to rocketing fuel prices last year, it is now the clear winner when it comes to cost-effectiveness.

For decades, solar and wind power was substantially more expensive than fossil fuels and most projects were heavily reliant on government subsidies to survive. But rapidly falling costs mean renewables now match or even outperform traditional power sources in a wide range of markets.

That transition has now accelerated significantly, according to a new report from the International Renewable Energy Agency (IRENA). Thanks in large part to a major spike in fossil fuel prices, 86 percent of newly commissioned, grid-scale renewable electricity capacity in 2022 had lower costs than fossil-fuel-derived electricity. That’s despite all kinds of costs having gone up across the world due to rising inflation and disruption to supply chains caused by the Covid pandemic and war in Ukraine.

“IRENA sees 2022 as a veritable turning point in the deployment for renewables as its cost-competitiveness has never been greater despite the lingering commodity and equipment cost inflation around the world,” IRENA’s director-general Francesco La Camera said in a press release.

The findings are just the latest data point showing the dramatic fall in prices renewables have experienced in recent years. According to the report, in 2010 solar power was 710 percent more expensive than the cheapest fossil fuel option, while onshore wind was 95 percent more expensive.

Last year, the average cost of electricity from solar fell by 3 percent to almost one-third less than the cheapest fossil fuel globally, while onshore wind costs fell by 5 percent to slightly less than half that of the cheapest fossil fuel option.

Cost declines weren’t evenly distributed though, the report notes. The significant improvements in both solar and onshore wind were both driven by deployments in China. If the Asian giant had been excluded from the calculations, the average cost of onshore wind would have remained level. And countries like France, Germany, and Greece experienced significant increases in the cost of solar.

The costs of offshore wind projects and hydropower projects also both increased in 2022. The former saw a 2 percent rise due to a drop in China’s rate of deployment, while the latter saw costs jump 18 percent due to overruns in a number of large projects.

Nonetheless, the report found the combined renewable power capacity deployed around the world since the year 2000 saved roughly $521 billion in fuel costs in 2022. The authors suggest the rapid build-out of green energy in recent years probably prevented the spike in fossil fuel prices from developing into an all-out energy crisis last year, highlighting the energy security benefits of renewables.

“The most affected regions by the historic price shock were remarkably resilient, in large part thanks to the massive increase of solar and wind in the last decade,” said La Camera.

Even in places where renewable installation costs increased, the report says that fossil fuel prices typically rose by far more. With those prices expected to remain high for, the authors conclude that this will cement a structural change in the energy market with renewables becoming the cheapest source of power globally.

Whether this shift in cost dynamics will be enough to avert the climate crisis remains to be seen. La Camera notes that annual deployments of renewable power need to hit 1,000 gigawatts every year until 2030 if we want to keep alive the goal of limiting global warming to 1.5 degrees Celsius. That’s an ambitious goal that will need all the help it can get from market forces.

Image Credit: Chelsea / Unsplash

In late 2020, AI pioneer DeepMind achieved a breakthrough 50 years in the making. By predicting the shape of proteins with atomic accuracy, its deep learning algorithm, AlphaFold, all but solved one of biology’s grand challenges.

From metabolism to brain function, proteins are the molecules that make our bodies go. When they go wrong, things break down, and we suffer. Much of modern medicine focuses on this aspect of disease: Identifying a dysfunctional protein culprit and modifying its behavior with another molecule specially selected to interact with it—a drug.

Thing is, proteins are extremely complex. Made up of hundreds or thousands of molecular building blocks called amino acids, they form long ribbon-like chains that fold in on themselves in nuanced ways. Nestled within these folds are active sites that give the protein its function by connecting with other proteins or catalyzing chemical reactions.

Designing effective drugs depends on predicting a protein’s shape, its functional sites, and identifying another protein or molecule that can dock to them.

AlphaFold, AlphFold 2, and an algorithm called RoseTTAFold, developed by Baker Lab at the University of Washington, took crucial steps in accelerating this process. By mid-2022, DeepMind said AlphaFold 2 had predicted the structure of 200 million proteins—nearly all those known—and offered them up in an open database.

But it didn’t end there. The creation of protein structures has since taken center stage. These newer algorithms are in the same family as DALL-E and GPT-4—the algorithm behind ChatGPT—only instead of generating images or written passages, they generate novel proteins.

Baker Lab, in particular, has been building on RoseTTAFold to design proteins. This summer, in a paper published in Nature, the team said their latest algorithm, RFdiffusion, was speedier and more accurate. The algorithm can generate a 100-amino-acid protein in 11 seconds on an Nvidia chip, compared to 8.5 minutes with an older algorithm. RFdiffusion is also roughly 100 times more effective at generating new proteins that bind strongly to sites of interest on known proteins.

“In a manner reminiscent of the generation of images from text prompts, RFdiffusion makes possible, with minimal specialist knowledge, the generation of functional proteins from minimal molecular specifications,” the team wrote in the July paper.

All this can be hard to visualize. There’s no substitute for seeing these algorithms in action. The reason ChatGPT was a viral hit was less about it being a zero-to-one breakthrough—the tech had been growing more sophisticated for several years—and more that it was a simple portal through which we could all experience that sophistication directly.

Luckily, here, we have a visual to hammer the point home. The video below, credited to Ian C. Haydon and the University of Washington Institute for Protein Design, shows RFdiffusion at work, designing a protein for a specific site on an insulin receptor in seconds.

Watch this #AI design a protein in seconds.

Learn more: https://t.co/7oYxpmjW4r @NewsfromScience pic.twitter.com/iPvquos8uA

— Science Magazine (@ScienceMagazine) July 24, 2023

Of course, there’s much more work to be done—designing effective new drugs is a difficult, years-long process—but it’s clear that AI tools continue to make quick progress in biotechnology.

Image Credit: Baker Lab/University of Washington

This summer, the AI division of Mark Zuckerberg’s Meta unveiled its Llama 2 chatbot. Microsoft has been appointed as Meta’s preferred partner on Llama 2, which will be available through the Windows operating system.

Meta’s approach with Llama 2 contrasts with that of the company OpenAI, which created the AI chatbot ChatGPT. That’s because Meta has made its product open source—meaning that the original code is freely available, allowing it to be researched and modified.

This strategy has sparked a vast wave of discussions. Will it foster greater public scrutiny and regulation of large language models (LLMs)—the technology that underlies AI chatbots such as Llama 2 and ChatGPT? Could it inadvertently empower criminals to use the technology to help them carry out phishing attacks or develop malware? And could the move help Meta gain an advantage over OpenAI and Google in this fast-moving field?

Whatever happens, this strategic move looks set to reshape the current landscape of generative AI. In February 2023, Meta released its first version of the LLM, called Llama, but made it available for academic use only. Its updated version, Llama 2, features improved performance and is more suitable for business use.

Like other AI chatbots, Llama 2 had to be trained using online data. Exposure to this vast resource of information helps it improve what it does—providing users with useful responses to their questions.

An initial version of Llama 2 was created through “supervised fine-tuning,” a technique that uses high-quality question-and-answer data to calibrate it for use by the public. It was further refined with human feedback reinforcement learning which, as the name suggests, incorporates people’s assessments of the AI’s performance to align it with human preferences.

Guaranteed Benefits

Meta’s embrace of the open-source ethos with Llama 2 allows it to capitalize on what appears to be an approach that has worked for the company in the past. Meta’s engineers are known for their development of products to aid developers such as React and PyTorch. Both are open source and have become the industry standard. Through them, Meta has set a precedent of innovation through collaboration.

The release of Llama 2 holds the promise of safer generative AI. Through shared wisdom and collective exploration, users can identify erroneous information and any vulnerabilities that could be exploited by criminals. Unexpected applications have already emerged, such as a version of Llama 2 that can be installed on iPhones and was created by users, underscoring the potential for creativity within this community.

But there are limits to how far Meta will allow Llama 2 users to commercialize its AI system. If any party achieves more than 700 million active users in the preceding calendar month for a product based on Llama 2, it will have to request a license from Meta. For Meta, this opens up the potential for profit-sharing on successful products based on Llama 2.

Meta’s strategy contrasts starkly with the more guarded approach of its primary competitor, OpenAI. Even as some question Meta’s ability to compete in this area and commercialize products as OpenAI has done with ChatGPT, Meta’s decision to invite worldwide developers into the fold suggests a broader vision. It’s a move that positions Zuckerberg’s company not merely as a player but a facilitator, harnessing global talent to contribute to the growing ecosystem of Llama 2.

This strategy could also be a shrewd hedge against potential competition from fellow tech giants such as Google. With a large population of users exploring the potential of Llama 2, any successful advance can be promptly integrated into Meta’s other products. Only time will reveal the full impact of this decision, but the immediate effects on the industry are already resonating far and wide.

Advantages and Pitfalls for Users

The public experimentation aspect of open source technology allows for greater scrutiny, providing an opportunity for a community of users to assess Llama 2’s strengths and weaknesses, including its vulnerability to attacks. The public’s watchful eye may reveal flaws in LLMs, prompting the development of defenses against them.

On the downside, concerns have emerged that this is akin to “handing a knife to criminals,” as it could also allow malicious users to exploit the technology. For example, its power could help fraudsters build a dialogue system that generates plausible automated conversations for telephone scams. This potential for misuse has led some to call for regulation of the technology.

But exactly what rules are devised, who gets the power to supervise this process, and exactly what needs more or less scrutiny, all require careful planning to make sure that regulation does not simply prop up monopolies for the big tech companies.

In the evolving saga of AI development, the debate over open sourcing serves as a reminder that technological advancements are rarely simple or one-dimensional. The implications of Meta’s decision are likely to ripple across the tech world for years to come. While Llama 2 may not yet rival the capabilities of ChatGPT, it opens the door to the development of a host of innovative products.

Google will also be under scrutiny, as speculation grows about how it may respond. In an era where open source culture thrives, it would not be surprising to see Google follow suit with its own releases.

The term “tech for good” has become a common mantra to describe technology companies using some of their resources to make a positive impact on all our lives. Ultimately, though, this objective remains a shared responsibility, not just something that a handful of companies should be engaged in.

It’s also an aim that demands collaboration and a concerted effort across academia, industry, and beyond. As LLM technologies continue to evolve, the stakes are high, and the path forward is laden with both opportunities and challenges.

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Image Credit: Shawn Suttle / Pixabay

Switching from combustion-engine cars to electric vehicles is going to be an important part of the renewable energy transition. But for the switch to truly make a difference, the electricity powering the next generation of cars will also have to be all-green, and the grid is quite a ways from that reality. One small loophole is solar cars—the loophole is small because the technology isn’t advanced enough to put any sort of significant dent in the vehicles’ electricity need, but it’s a start.

There are a handful of solar cars in production, from the $6,800 golf-cart-like Squad car to the sleek $250,000 Lightyear 0 (and let’s not forget the OG Aptera and more recent arrival Sion). Now a Swedish manufacturer is taking the solar concept and going bigger with it—big-rig big, that is. Scania’s hybrid solar truck was tested on public roads for the first time last week.

One of the first vehicles of its kind, the truck is a research project involving both academia and industry, and its creators hope it will be a step toward reducing the trucking industry’s environmental footprint. It could also cut fuel costs for drivers and ultimately reduce the total cost of moving goods from one place to another.

Hybrid solar vehicles have a battery that can be plugged in to charge, but they’re also decked out with solar arrays that provide an alternate energy source. Scania’s truck has solar panels covering an area of 100 square meters (1,076 square feet), all on the sides and top of the 59-foot-long trailer. The panels were specially made for this project, and the team says they’re more lightweight and efficient than the current industry standard.

The truck has a 560 horsepower engine, and its solar array can provide up to 8,000 kilowatt hours (kWh) of energy per year in Sweden or similar climates—that equates to around 5,000 kilometers (3,107 miles) of driving range. Being as far north as it is, Sweden’s not the sunniest place; the researchers say that in sunnier climates (the example they give is Spain) the range could double, reaching about 6,200 miles a year.

The team is also working on developing tandem solar cells with an even higher efficiency, which they say could double the solar energy generation a second time over. The truck’s batteries have a total capacity of 300 kWh, 100 kWh placed on the truck and the remaining 200 kWh on the trailer.

Besides gauging how much solar energy the truck’s panels can produce under various conditions, the researchers are monitoring how much an average truck’s carbon emissions would decrease if outfitted with a comparable solar setup. They’re also looking at various ways solar trucks could interact with the power grid (bi-directional charging, where the truck’s panels could give energy back to the grid or help power a facility, could be one possibility), and what the impact on the grid might be in a future where there are many hybrid solar trucks.

It will likely be a while before we see solar-panel-clad big rigs rolling down highways; for one, solar technology will need to improve dramatically before it becomes practical for widespread use on cars and trucks. But projects like Scania’s are a start, pointing us towards a future of cleaner, greener transportation.

Image Credit: Scania

Recently I had what amounted to a therapy session with ChatGPT. We talked about a recurring topic that I’ve obsessively inundated my friends with, so I thought I’d spare them the déjà vu. As expected, the AI’s responses were on point, sympathetic, and felt so utterly human.

As a tech writer, I know what’s happening under the hood: a swarm of digital synapses are trained on an internet’s worth of human-generated text to spit out favorable responses. Yet the interaction felt so real, and I had to constantly remind myself I was chatting with code—not a conscious, empathetic being on the other end.

Or was I? With generative AI increasingly delivering seemingly human-like responses, it’s easy to emotionally assign a sort of “sentience” to the algorithm (and no, ChatGPT isn’t conscious). In 2021, Blake Lemoine at Google stirred up a media firestorm by proclaiming that one of the chatbots he worked on, LaMDA, was sentient—and he subsequently got fired.

But most deep learning models are loosely based on the brain’s inner workings. AI agents are increasingly endowed with human-like decision-making algorithms. The idea that machine intelligence could become sentient one day no longer seems like science fiction.

How could we tell if machine brains one day gained sentience? The answer may be based on our own brains.

A preprint paper authored by 19 neuroscientists, philosophers, and computer scientists, including Dr. Robert Long from the Center for AI Safety and Dr. Yoshua Bengio from the University of Montreal, argues that the neurobiology of consciousness may be our best bet. Rather than simply studying an AI agent’s behavior or responses—for example, during a chat—matching its responses to theories of human consciousness could provide a more objective ruler.

It’s an out-of-the-box proposal, but one that makes sense. We know we are conscious regardless of the word’s definition, which is still unsettled. Theories of how consciousness emerges in the brain are plenty, with multiple leading candidates still being tested in global head-to-head trials.

The authors didn’t subscribe to any single neurobiological theory of consciousness. Instead, they derived a checklist of “indicator properties” of consciousness based on multiple leading ideas. There isn’t a strict cutoff—say, meeting X number of criteria means an AI agent is conscious. Rather, the indicators make up a moving scale: the more criteria met, the more likely a sentient machine mind is.

Using the guidelines to test several recent AI systems, including ChatGPT and other chatbots, the team concluded that for now, “no current AI systems are conscious.”

However, “there are no obvious technical barriers to building AI systems that satisfy these indicators,” they said. It’s possible that “conscious AI systems could realistically be built in the near term.”

Listening to an Artificial Brain

Since Alan Turing’s famous imitation game in the 1950s, scientists have pondered how to prove whether a machine exhibits intelligence like a human’s.

Better known as the Turing test, the theoretical setup has a human judge conversing with a machine and another human—the judge has to decide which participant has an artificial mind. At the heart of the test is the provocative question “Can machines think?” The harder it is to tell the difference between machine and human, the more machines have advanced toward human-like intelligence.

ChatGPT broke the Turing test. An example of a chatbot powered by a large language model (LLM), ChatGPT soaks up internet comments, memes, and other content. It’s extremely adept at emulating human responses—writing essays, passing exams, dishing out recipes, and even doling out life advice.

These advances, which came at a shocking speed, stirred up debate on how to construct other criteria for gauging thinking machines. Most recent attempts have focused on standardized tests for humans: for example, those designed for high school students, the Bar exam for lawyers, or the GRE for entering grad school. OpenAI’s GPT-4, the AI model behind ChatGPT, scored in the top 10 percent of participants. However, it struggled with finding rules for a relatively simple visual puzzle game.

The new benchmarks, while measuring a kind of “intelligence,” don’t necessarily tackle the problem of consciousness. Here’s where neuroscience comes in.

The Checklist for Consciousness

Neurobiological theories of consciousness are many and messy. But at their heart is neural computation: that is, how our neurons connect and process information so it reaches the conscious mind. In other words, consciousness is the result of the brain’s computation, although we don’t yet fully understand the details involved.

This practical look at consciousness makes it possible to translate theories from human consciousness to AI. Called computational functionalism, the hypothesis rests on the idea that computations of the right kind generate consciousness regardless of the medium—squishy, fatty blobs of cells inside our head or hard, cold chips that power machine minds. It suggests that “consciousness in AI is possible in principle,” said the team.

Then comes the hard part: how do you probe consciousness in an algorithmic black box? A standard method in humans is to measure electrical pulses in the brain or with functional MRI that captures activity in high definition—but neither method is feasible for evaluating code.

Instead, the team took a “theory-heavy approach,” which was first used to study consciousness in non-human animals.

To start, they mined top theories of human consciousness, including the popular Global Workspace Theory (GWT) for indicators of consciousness. For example, GWT stipulates that a conscious mind has multiple specialized systems that work in parallel; we can simultaneously hear and see and process those streams of information. However, there’s a bottleneck in processing, requiring an attention mechanism.

The Recurrent Processing Theory suggests that information needs to feed back onto itself in multiple loops as a path towards consciousness. Other theories emphasize the need for a “body” of sorts that receives feedback from the environment and uses those learnings to better perceive and control responses to a dynamic outside world—something called “embodiment.”

With myriad theories of consciousness to choose from, the team laid out some ground rules. To be included, a theory needs substantial evidence from lab tests, such as studies capturing the brain activity of people in different conscious states. Overall, six theories met the mark. From there, the team developed 14 indicators.

It’s not one-and-done. None of the indicators mark a sentient AI on their own. In fact, standard machine learning methods can build systems that have individual properties from the list, explained the team. Rather, the list is a scale—the more criteria met, the higher the likelihood an AI system has some kind of consciousness.

How to assess each indicator? We’ll need to look into the “architecture of the system and how the information flows through it,” said Long.

In a proof of concept, the team used the checklist on several different AI systems, including the transformer-based large language models that underlie ChatGPT and algorithms that generate images, such as DALL-E 2. The results were hardly cut-and-dried, with some AI systems meeting a portion of the criteria while lacking in others.

However, although not designed with a global workspace in mind, each system “possesses some of the GWT indicator properties,” such as attention, said the team. Meanwhile, Google’s PaLM-E system, which injects observations from robotic sensors, met the criteria for embodiment.

None of the state-of-the-art AI systems checked off more than a few boxes, leading the authors to conclude that we haven’t yet entered the era of sentient AI. They further warned about the dangers of under-attributing consciousness in AI, which may risk allowing “morally significant harms,” and anthropomorphizing AI systems when they’re just cold, hard code.

Nevertheless, the paper sets guidelines for probing one of the most enigmatic aspects of the mind. “[The proposal is] very thoughtful, it’s not bombastic and it makes its assumptions really clear,” Dr. Anil Seth at the University of Sussex told Nature.

The report is far from the final word on the topic. As neuroscience further narrows down correlates of consciousness in the brain, the checklist will likely scrap some criteria and add others. For now, it’s a project in the making, and the authors invite other perspectives from multiple disciplines—neuroscience, philosophy, computer science, cognitive science—to further hone the list.

Image Credit: Greyson Joralemon on Unsplash

AUTOMATION

High-Speed AI Drone Beats World-Champion Racers for the First Time
Benj Edwards | Ars Technica
“On Wednesday, a team of researchers from the University of Zürich and Intel announced that they have developed an autonomous drone system named Swift that can beat human champions in first-person view (FPV) drone racing. While AI has previously bested humans in games like chess, Go, and even StarCraft, this may be the first time an AI system has outperformed human pilots in a physical sport.”

ROBOTICS

‘Go Catch That Squirrel:’ Google AI Teaches a Robo-Dog Conversational Commands
Mack DeGeurin | Gizmodo
“What’s more interesting, [the researchers] noted, is SayTap’s ability to ‘process unstructured and vague instructions.’ By just providing the model with a brief hint, the researchers were able to successfully command the robotic dogs to jump up and down when it was told ‘we are going on a picnic.’ …In maybe the funniest example, the dog even slowly backpedaled after being told to get away from a squirrel. Many real dog owners would beg for that level of obedience.”

BIOTECH

A Biotech Company Says It Put Dopamine-Making Cells Into People’s Brains
Antonio Regalado | MIT Technology Review
“In an important test for stem-cell medicine, a biotech company says implants of lab-made neurons introduced into the brains of 12 people with Parkinson’s disease appear to be safe and may have reduced symptoms for some of them. …The study is one of the largest and most costly tests yet of embryonic-stem-cell technology, the controversial and much-hyped approach of using stem cells taken from IVF embryos to produce replacement tissue and body parts.”

TRANSPORTATION

Are Self-Driving Cars Already Safer Than Human Drivers?
Timothy B. Lee | Ars Technica
“For this story, I read through every crash report Waymo and Cruise filed in California this year, as well as reports each company filed about the performance of their driverless vehicles (with no safety drivers) prior to 2023. …Human beings drive close to 100 million miles between fatal crashes, so it will take hundreds of millions of driverless miles for 100 percent certainty on this question. But the evidence for better-than-human performance is starting to pile up, especially for Waymo.”

ARTIFICIAL INTELLIGENCE

We Used AI to Write Essays for Harvard, Yale and Princeton. Here’s How It Went.
Natasha Singer | The New York Times
“While the chatbots are not yet great at simulating long-form personal essays with authentic student voices, I wondered how the AI tools would do on some of the shorter essay questions that elite schools like Harvard, Yale, Princeton, and Dartmouth are requiring high school applicants to answer this year. So I used several free tools to generate short essays for some Ivy League applications.”

INNOVATION

AI Startup Buzz Is Facing a Reality Check
Berber Jin | The Wall Street Journal
“Founders and venture capitalists who flocked to artificial-intelligence startups are learning that turning the chatbot buzz into successful businesses is harder than it seems. Almost a year into the boom ignited by the November launch of ChatGPT, some startups that epitomized the zeal for so-called generative AI are now navigating layoffs and reduced user interest. Investors are unsure whether the new crop of AI startups will be able to survive, especially as tech giants such as Microsoft and Alphabet’s Google solidify their dominance over the technology.”

COMPUTING

Quantum Computer Reveals Chemical Reaction in 100-Billionth-Speed Slow-Mo
Michael Irving | New Atlas
“Using a trapped-ion quantum computer, the team mapped the problem onto a fairly small quantum device, which allowed them to slow down the process by an astonishing 100 billion times. …’In nature, the whole process is over within femtoseconds,’ said Vanessa Olaya Agudelo, co-lead author of the study. ‘Using our quantum computer, we built a system that allowed us to slow down the chemical dynamics from femtoseconds to milliseconds. This allowed us to make meaningful observations and measurements. This has never been done before.’i”

ETHICS

OpenAI’s Moonshot: Solving the AI Alignment Problem
Eliza Strickland | IEEE Spectrum
“In July, OpenAI announced a new research program on ‘superalignment.’ The program has the ambitious goal of solving the hardest problem in the field, known as AI alignment, by 2027, an effort to which OpenAI is dedicating 20 percent of its total computing power. …One of the project’s leaders Jan] Leike spoke to IEEE Spectrum about the effort, which has the subgoal of building an aligned AI research tool—to help solve the alignment problem.”

ENVIRONMENT

Solar-Panel-Covered Hybrid Truck Offers 3,000 to 6,000 Free Miles a Year
Mike Hanlon | New Atlas
“The initial 560-horsepower plug-in hybrid experimental truck has an 18-meter (59-foot) trailer that is covered by 100 square meters (1,076 square feet) of solar panels, giving it the equivalent solar-surface area of an average house equipped with similarly powerful 13.2-kilowatt-peak panels. The truck uses new, lightweight tandem solar cells, that are based on a combination of Midsummer’s solar cells and new perovskite solar cells, and generates an estimated 8,000 kWh annually when operated in Sweden.”

TECH

The End of the Googleverse
Ryan Broderick | The Verge
“Google officially went online…in 1998. It quickly became so inseparable from both the way we use the internet and, eventually, culture itself, that we almost lack the language to describe what Google’s impact over the last 25 years has actually been. It’s like asking a fish to explain what the ocean is. And yet, all around us are signs that the era of ‘peak Google’ is ending or, possibly, already over.”

SCIENCE

Only 1,280 Reproductive Human Ancestors Once Roamed Earth, Gene Study Suggests
Isaac Schultz | Gizmodo
“An ancestral human species faced a startling population bottleneck and teetered on the brink of extinction around 800,000 years ago, according to new research. [In an article commenting on the research, Nick Ashton, an archaeologist at the British Museum, and Chris Stringer, a paleoanthropologist at London’s Natural History Museum] wrote…’the provocative study of Hu et al. brings the vulnerability of early human populations into focus, with the implication that our evolutionary lineage was nearly eradicated.'”

Image Credit: Casey Horner / Unsplash 

In May 2020, some unusual rocks containing distinctive greenish crystals were found in the Erg Chech sand sea, a dune-filled region of the Sahara Desert in southern Algeria.

On close inspection, the rocks turned out to be from outer space: lumps of rubble billions of years old, left over from the dawn of the solar system. They were all pieces of a meteorite known as Erg Chech 002, which is the oldest volcanic rock ever found, having melted long ago in the fires of some now-vanished ancient protoplanet.

In new research published in Nature Communications, we analyzed lead and uranium isotopes in Erg Chech 002 and calculated it is some 4.56556 billion years old, give or take 120,000 years. This is one of the most precise ages ever calculated for an object from space—and our results also cast doubt on some common assumptions about the early solar system.

The Secret Life of Aluminum

Around 4.567 billion years ago, our solar system formed from a vast cloud of gas and dust. Among the many elements in this cloud was aluminum, which came in two forms.

First is the stable form, aluminum-27. Second is aluminum-26, a radioactive isotope mainly produced by exploding stars, which decays over time into magnesium-26. Aluminum-26 is very useful stuff for scientists who want to understand how the solar system formed and developed. Because it decays over time, we can use it to date events—particularly within the first four or five million years of the solar system’s life.

The decay of aluminum-26 is also important for another reason: we think it was the main source of heat in the early solar system. This decay influenced the melting of the small, primitive rocks that later clumped together to form the planets.

Uranium, Lead, and Age

However, to use aluminum-26 to understand the past, we need to know whether it was spread around evenly or clumped together more densely in some places than in others. To figure that out, we will need to calculate the absolute ages of some ancient space rocks more precisely.

Looking at aluminum-26 alone won’t let us do that, because it decays relatively quickly (after around 705,000 years, half of a sample of aluminum-26 will have decayed into magnesium-26). It’s useful for determining the relative ages of different objects, but not their absolute age in years.

But if we combine aluminum-26 data with data about uranium and lead, we can make some headway. There are two important isotopes of uranium (uranium-235 and uranium-238), which decay into different isotopes of lead (lead-207 and lead-206, respectively). The uranium isotopes have much longer half-lives (710 million years and 4.47 billion years, respectively), which means we can use them to directly figure out how long ago an event happened.

Meteorite Groups

Erg Chech 002 is what is known as an “ungrouped achondrite.” Achondrites are rocks formed from melted planetesimals, which is what we call solid lumps in the cloud of gas and debris that formed the solar system. The sources of many achondrites found on Earth have been identified.

Achondrite meteorites like Erg Chech 002 offer clues about the early years of the solar system. Image Credit: Yuri Amelin, CC BY

Most belong to the so-called Howardite-Eucrite-Diogenite clan, which are believed to have originated from Vesta 4, one of the largest asteroids in the solar system. Another group of achondrites is called angrites, which all share an unidentified parent body.

Still other achondrites, including Erg Chech 002, are “ungrouped”: their parent bodies and family relationships are unknown.

A Clumpy Spread of Aluminum

In our study of Erg Chech 002, we found it contains a high abundance of lead-206 and lead-207, as well as relatively large amounts of undecayed uranium-238 and uranium-235.

Measuring the ratios of all the lead and uranium isotopes was what helped us to estimate the age of the rock with such unprecedented accuracy. We also compared our calculated age with previously published aluminum-26 data for Erg Chech 002, as well as data for various other achondrites.

The comparison with a group of achondrites called volcanic angrites was particularly interesting. We found that the parent body of Erg Chech 002 must have formed from material containing three or four times as much aluminum-26 as the source of the angrites’ parent body. This shows aluminum-26 was indeed distributed quite unevenly throughout the cloud of dust and gas which formed the solar system.

Our results contribute to a better understanding of the solar system’s earliest developmental stages, and the geological history of burgeoning planets. Further studies of diverse achondrite groups will undoubtedly continue to refine our understanding and enhance our ability to reconstruct the early history of our solar system.

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Image Credit: Steve Jurvetson / Wikimedia, CC BY-SA

The cells of all living organisms are powered by the same chemical fuel: adenosine triphosphate (ATP). Now, researchers have found a way to generate ATP directly from electricity, which could turbocharge biotechnology processes that grow everything from food to fuel to pharmaceuticals.

Interfacing modern electronics-based technology with biology is notoriously difficult. One major stumbling block is that the way they are powered is very different. While most of our gadgets run on electrons, nature relies on the energy released when the chemical bonds of ATP are broken. Finding ways to convert between these two very different currencies of energy could be useful for a host of biotechnologies.

Genetically engineered microbes are already being used to produce various high-value chemicals and therapeutically useful proteins, and there are hopes they could soon help generate greener jet fuel, break down plastic waste, and even grow new foods in giant bioreactors. But at the minute, these processes are powered through an inefficient process of growing biomass, converting it to sugar, and feeding it to the microbes.

Now, researchers at the Max Planck Institute for Terrestrial Microbiology in Germany have devised a much more direct way to power biological processes. They have created an artificial metabolic pathway that can directly convert electricity into ATP using a cocktail of enzymes. And crucially, the process works in vitro and doesn’t rely on the native machinery of cells.

“Feeding electricity directly into chemical and biochemical reactions is a real breakthrough,” Tobias Erb, who led the research, said in a press release. “This will enable synthesis of energy-rich valuable resources such as starch, biofuels, or proteins from simple cellular building blocks—in the future even from carbon dioxide. It may even be possible to use biological molecules to store electrical energy.”

In nature, ATP and its sister molecule adenosine di-phosphate (ADP) can be thought of as almost like batteries. ATP is like a charged battery, storing energy in its chemical bonds. If a cell needs to spend that energy, it breaks off one of the molecule’s three phosphate groups and the energy bound up in that chemical bond can then power some cellular process.

This process converts the ATP molecule into ADP, which can be thought of as an empty battery. To recharge it, the cell needs to use energy from food or photosynthesis to add a phosphate group back onto the ADP molecule, turning it back into ATP.

But this recharging process relies on a complex sequence of reactions involving various protein complexes embedded in the cell membrane. Re-engineering this system to work outside of a cell is challenging because it requires the various proteins to be carefully orientated in an artificial membrane, which makes it both finicky and fragile.

The new approach, outlined in a paper in Joule, is much simpler. Dubbed the “AAA cycle,” it involves just four enzymes interacting in a solution. The key ingredient that made it all possible was the discovery of an enzyme called aldehyde ferredoxin oxidoreductase (AOR) in a recently-discovered bacterium called Aromaticum aromatoleum, which is able to break down petroleum.

Image Credit: MPI f. Terrestrial Microbiology/ Erb

This enzyme is able to take the electrons from an electrode and bind up their energy in an aldehyde bond that is added to a precursor chemical called propionate. This is then cascaded through three more enzymes that act on the chemical and ultimately use the energy stored in it to convert ADP to ATP. At the end, a propionate molecule pops out that can then be fed back into the cycle.

“The simple AAA cycle is a clever and elegant approach…that is much simpler than how biology naturally makes ATP,” Drew Endy, a synthetic biologist at Stanford University, told Science. He added that it could be a key enabler to make “electrobiosynthesis” possible, the idea of using electricity to directly power the synthesis of useful chemicals by cells.

The researchers say the process still needs work, as the enzymes are unstable and only able to convert a small amount of energy. But if the idea can be refined and scaled up, it could make it possible to run all kinds of powerful biotechnology processes on renewable energy, not only making them greener but significantly expanding the amount of energy they can tap into.

Image Credit: günther / Pixabay

As the world works to transition from fossil fuels to renewable energy sources, we’ll extract less oil and gas from the Earth and more minerals like lithium, cobalt, and nickel. Demand for these materials has skyrocketed in the last few years, and will only continue to grow as we implement more solar panels, electric cars, batteries, and wind turbines. Locating and mining critical minerals is costly, slow, and difficult. But a Berkeley-based startup called KoBold Metals is using artificial intelligence to make the process easier.

They must be onto something, because the company was declared a unicorn earlier this summer after raising $200 million in funding, led by VC powerhouses Breakthrough Energy Ventures (that’s the venture capital firm founded by Bill Gates and backed by Jeff Bezos and Jack Ma) and Andreessen Horowitz.

KoBold says its aim is to “transform mineral exploration from a manual, judgment-guided, trial-and-error process into a data-driven and scalable science,” with a specific focus on metals for electric car batteries. The company won’t actually be doing any mining itself—it will locate new deposits then partner with mining companies, acting as an advisor to help them extract the metals more efficiently.

KoBold has a couple different tools with which to go about this. Its data system is called TerraShed, and it’s a consolidation of all the public-domain geoscience data that was previously spread across many sources and represented in different ways. The data could include anything from maps showing the type of rock in a given location to geochemical measurements of element concentration in rock or soil samples to satellite imagery measuring the spectral reflectance of minerals at the Earth’s surface—and much more.

TerraShed brought all these data sources together and standardized the way their information is represented. Its algorithms crunch relevant data for each stage of the mineral exploration process, starting with the search for new deposits all the way through to building a new mine.

Machine Prospector is KoBold’s tool to make sense of all this data and use it for decision-making. It’s made up of machine learning models trained on historic geological data. Similar to how AI can model the structures and interactions of millions of proteins in a fraction of the time it would take a human, the technology is critical to KoBold’s operations because of the sheer amount of data involved and the endless ways it can be combined to yield different results—or in this case, useful information.

KoBold doesn’t just use existing geological data, it also seeks out new information. One way it does this is by hanging a giant metal detector from a helicopter that flies around looking for ore deposits. The transmitter coil loop is 35 meters (115 feet) in diameter, and it detects induced currents coming from metals that are deep underground.

A helicopter equipped with KoBold’s transmitter coil loop surveying a forested area for mineral deposits. Image Credit: KoBold Metals

As the company points out on its website, most of the world’s mineral deposits that can be considered low-hanging fruit—because they’re relatively close to the Earth’s surface rather than thousands of feet underground—have already been discovered. To power the renewable world of the not-too-distant future, we’re going to need a lot more of those minerals, and they’re going to be harder to find than existing deposits were.

KoBold is currently exploring over 60 possible projects on 3 different continents.

Image Credit: KoBold Metals