Can Cultured Meat Save the Planet?
In September, California governor Jerry Brown signed a bill mandating that by 2045, all of California's electricity will come from clean power sources. Technological breakthroughs in producing electricity from sun and wind, as well as lowering the cost of battery storage, have played a major role in persuading Californian legislators that this goal is realistic.
Even if the world were to move to an entirely clean power supply, one major source of greenhouse gas emissions would continue to grow: meat.
James Robo, the CEO of the Fortune 200 company NextEra Energy, has predicted that by the early 2020s, electricity from solar farms and giant wind turbines will be cheaper than the operating costs of coal-fired power plants, even when the cost of storage is included.
Can we therefore all breathe a sigh of relief, because technology will save us from catastrophic climate change? Not yet. Even if the world were to move to an entirely clean power supply, and use that clean power to charge up an all-electric fleet of cars, buses and trucks, one major source of greenhouse gas emissions would continue to grow: meat.
The livestock industry now accounts for about 15 percent of global greenhouse gas emissions, roughly the same as the emissions from the tailpipes of all the world's vehicles. But whereas vehicle emissions can be expected to decline as hybrids and electric vehicles proliferate, global meat consumption is forecast to be 76 percent greater in 2050 than it has been in recent years. Most of that growth will come from Asia, especially China, where increasing prosperity has led to an increasing demand for meat.
Changing Climate, Changing Diets, a report from the London-based Royal Institute of International Affairs, indicates the threat posed by meat production. At the UN climate change conference held in Cancun in 2010, the participating countries agreed that to allow global temperatures to rise more than 2°C above pre-industrial levels would be to run an unacceptable risk of catastrophe. Beyond that limit, feedback loops will take effect, causing still more warming. For example, the thawing Siberian permafrost will release large quantities of methane, causing yet more warming and releasing yet more methane. Methane is a greenhouse gas that, ton for ton, warms the planet 30 times as much as carbon dioxide.
The quantity of greenhouse gases we can put into the atmosphere between now and mid-century without heating up the planet beyond 2°C – known as the "carbon budget" -- is shrinking steadily. The growing demand for meat means, however, that emissions from the livestock industry will continue to rise, and will absorb an increasing share of this remaining carbon budget. This will, according to Changing Climate, Changing Diets, make it "extremely difficult" to limit the temperature rise to 2°C.
One reason why eating meat produces more greenhouse gases than getting the same food value from plants is that we use fossil fuels to grow grains and soybeans and feed them to animals. The animals use most of the energy in the plant food for themselves, moving, breathing, and keeping their bodies warm. That leaves only a small fraction for us to eat, and so we have to grow several times the quantity of grains and soybeans that we would need if we ate plant foods ourselves. The other important factor is the methane produced by ruminants – mainly cattle and sheep – as part of their digestive process. Surprisingly, that makes grass-fed beef even worse for our climate than beef from animals fattened in a feedlot. Cattle fed on grass put on weight more slowly than cattle fed on corn and soybeans, and therefore do burp and fart more methane, per kilogram of flesh they produce.
Richard Branson has suggested that in 30 years, we will look back on the present era and be shocked that we killed animals en masse for food.
If technology can give us clean power, can it also give us clean meat? That term is already in use, by advocates of growing meat at the cellular level. They use it, not to make the parallel with clean energy, but to emphasize that meat from live animals is dirty, because live animals shit. Bacteria from the animals' guts and shit often contaminates the meat. With meat cultured from cells grown in a bioreactor, there is no live animal, no shit, and no bacteria from a digestive system to get mixed into the meat. There is also no methane. Nor is there a living animal to keep warm, move around, or grow body parts that we do not eat. Hence producing meat in this way would be much more efficient, and much cleaner, in the environmental sense, than producing meat from animals.
There are now many startups working on bringing clean meat to market. Plant-based products that have the texture and taste of meat, like the "Impossible Burger" and the "Beyond Burger" are already available in restaurants and supermarkets. Clean hamburger meat, fish, dairy, and other animal products are all being produced without raising and slaughtering a living animal. The price is not yet competitive with animal products, but it is coming down rapidly. Just this week, leading officials from the Food and Drug Administration and the U.S. Department of Agriculture have been meeting to discuss how to regulate the expected production and sale of meat produced by this method.
When Kodak, which once dominated the sale and processing of photographic film, decided to treat digital photography as a threat rather than an opportunity, it signed its own death warrant. Tyson Foods and Cargill, two of the world's biggest meat producers, are not making the same mistake. They are investing in companies seeking to produce meat without raising animals. Justin Whitmore, Tyson's executive vice-president, said, "We don't want to be disrupted. We want to be part of the disruption."
That's a brave stance for a company that has made its fortune from raising and killing tens of billions of animals, but it is also an acknowledgement that when new technologies create products that people want, they cannot be resisted. Richard Branson, who has invested in the biotech company Memphis Meats, has suggested that in 30 years, we will look back on the present era and be shocked that we killed animals en masse for food. If that happens, technology will have made possible the greatest ethical step forward in the history of our species, saving the planet and eliminating the vast quantity of suffering that industrial farming is now inflicting on animals.
One day in recent past, scientists at Columbia University’s Creative Machines Lab set up a robotic arm inside a circle of five streaming video cameras and let the robot watch itself move, turn and twist. For about three hours the robot did exactly that—it looked at itself this way and that, like toddlers exploring themselves in a room full of mirrors. By the time the robot stopped, its internal neural network finished learning the relationship between the robot’s motor actions and the volume it occupied in its environment. In other words, the robot built a spatial self-awareness, just like humans do. “We trained its deep neural network to understand how it moved in space,” says Boyuan Chen, one of the scientists who worked on it.
For decades robots have been doing helpful tasks that are too hard, too dangerous, or physically impossible for humans to carry out themselves. Robots are ultimately superior to humans in complex calculations, following rules to a tee and repeating the same steps perfectly. But even the biggest successes for human-robot collaborations—those in manufacturing and automotive industries—still require separating the two for safety reasons. Hardwired for a limited set of tasks, industrial robots don't have the intelligence to know where their robo-parts are in space, how fast they’re moving and when they can endanger a human.
Over the past decade or so, humans have begun to expect more from robots. Engineers have been building smarter versions that can avoid obstacles, follow voice commands, respond to human speech and make simple decisions. Some of them proved invaluable in many natural and man-made disasters like earthquakes, forest fires, nuclear accidents and chemical spills. These disaster recovery robots helped clean up dangerous chemicals, looked for survivors in crumbled buildings, and ventured into radioactive areas to assess damage.
Now roboticists are going a step further, training their creations to do even better: understand their own image in space and interact with humans like humans do. Today, there are already robot-teachers like KeeKo, robot-pets like Moffin, robot-babysitters like iPal, and robotic companions for the elderly like Pepper.
But even these reasonably intelligent creations still have huge limitations, some scientists think. “There are niche applications for the current generations of robots,” says professor Anthony Zador at Cold Spring Harbor Laboratory—but they are not “generalists” who can do varied tasks all on their own, as they mostly lack the abilities to improvise, make decisions based on a multitude of facts or emotions, and adjust to rapidly changing circumstances. “We don’t have general purpose robots that can interact with the world. We’re ages away from that.”
Robotic spatial self-awareness – the achievement by the team at Columbia – is an important step toward creating more intelligent machines. Hod Lipson, professor of mechanical engineering who runs the Columbia lab, says that future robots will need this ability to assist humans better. Knowing how you look and where in space your parts are, decreases the need for human oversight. It also helps the robot to detect and compensate for damage and keep up with its own wear-and-tear. And it allows robots to realize when something is wrong with them or their parts. “We want our robots to learn and continue to grow their minds and bodies on their own,” Chen says. That’s what Zador wants too—and on a much grander level. “I want a robot who can drive my car, take my dog for a walk and have a conversation with me.”
Columbia scientists have trained a robot to become aware of its own "body," so it can map the right path to touch a ball without running into an obstacle, in this case a square.
Jane Nisselson and Yinuo Qin/ Columbia Engineering
Today’s technological advances are making some of these leaps of progress possible. One of them is the so-called Deep Learning—a method that trains artificial intelligence systems to learn and use information similar to how humans do it. Described as a machine learning method based on neural network architectures with multiple layers of processing units, Deep Learning has been used to successfully teach machines to recognize images, understand speech and even write text.
Trained by Google, one of these language machine learning geniuses, BERT, can finish sentences. Another one called GPT3, designed by San Francisco-based company OpenAI, can write little stories. Yet, both of them still make funny mistakes in their linguistic exercises that even a child wouldn’t. According to a paper published by Stanford’s Center for Research on Foundational Models, BERT seems to not understand the word “not.” When asked to fill in the word after “A robin is a __” it correctly answers “bird.” But try inserting the word “not” into that sentence (“A robin is not a __”) and BERT still completes it the same way. Similarly, in one of its stories, GPT3 wrote that if you mix a spoonful of grape juice into your cranberry juice and drink the concoction, you die. It seems that robots, and artificial intelligence systems in general, are still missing some rudimentary facts of life that humans and animals grasp naturally and effortlessly.
How does one give robots a genome? Zador has an idea. We can’t really equip machines with real biological nucleotide-based genes, but we can mimic the neuronal blueprint those genes create.
It's not exactly the robots’ fault. Compared to humans, and all other organisms that have been around for thousands or millions of years, robots are very new. They are missing out on eons of evolutionary data-building. Animals and humans are born with the ability to do certain things because they are pre-wired in them. Flies know how to fly, fish knows how to swim, cats know how to meow, and babies know how to cry. Yet, flies don’t really learn to fly, fish doesn’t learn to swim, cats don’t learn to meow, and babies don’t learn to cry—they are born able to execute such behaviors because they’re preprogrammed to do so. All that happens thanks to the millions of years of evolutions wired into their respective genomes, which give rise to the brain’s neural networks responsible for these behaviors. Robots are the newbies, missing out on that trove of information, Zador argues.
A neuroscience professor who studies how brain circuitry generates various behaviors, Zador has a different approach to developing the robotic mind. Until their creators figure out a way to imbue the bots with that information, robots will remain quite limited in their abilities. Each model will only be able to do certain things it was programmed to do, but it will never go above and beyond its original code. So Zador argues that we have to start giving robots a genome.
How does one do that? Zador has an idea. We can’t really equip machines with real biological nucleotide-based genes, but we can mimic the neuronal blueprint those genes create. Genomes lay out rules for brain development. Specifically, the genome encodes blueprints for wiring up our nervous system—the details of which neurons are connected, the strength of those connections and other specs that will later hold the information learned throughout life. “Our genomes serve as blueprints for building our nervous system and these blueprints give rise to a human brain, which contains about 100 billion neurons,” Zador says.
If you think what a genome is, he explains, it is essentially a very compact and compressed form of information storage. Conceptually, genomes are similar to CliffsNotes and other study guides. When students read these short summaries, they know about what happened in a book, without actually reading that book. And that’s how we should be designing the next generation of robots if we ever want them to act like humans, Zador says. “We should give them a set of behavioral CliffsNotes, which they can then unwrap into brain-like structures.” Robots that have such brain-like structures will acquire a set of basic rules to generate basic behaviors and use them to learn more complex ones.
Currently Zador is in the process of developing algorithms that function like simple rules that generate such behaviors. “My algorithms would write these CliffsNotes, outlining how to solve a particular problem,” he explains. “And then, the neural networks will use these CliffsNotes to figure out which ones are useful and use them in their behaviors.” That’s how all living beings operate. They use the pre-programmed info from their genetics to adapt to their changing environments and learn what’s necessary to survive and thrive in these settings.
For example, a robot’s neural network could draw from CliffsNotes with “genetic” instructions for how to be aware of its own body or learn to adjust its movements. And other, different sets of CliffsNotes may imbue it with the basics of physical safety or the fundamentals of speech.
At the moment, Zador is working on algorithms that are trying to mimic neuronal blueprints for very simple organisms—such as earthworms, which have only 302 neurons and about 7000 synapses compared to the millions we have. That’s how evolution worked, too—expanding the brains from simple creatures to more complex to the Homo Sapiens. But if it took millions of years to arrive at modern humans, how long would it take scientists to forge a robot with human intelligence? That’s a billion-dollar question. Yet, Zador is optimistic. “My hypotheses is that if you can build simple organisms that can interact with the world, then the higher level functions will not be nearly as challenging as they currently are.”
Lina Zeldovich has written about science, medicine and technology for Popular Science, Smithsonian, National Geographic, Scientific American, Reader’s Digest, the New York Times and other major national and international publications. A Columbia J-School alumna, she has won several awards for her stories, including the ASJA Crisis Coverage Award for Covid reporting, and has been a contributing editor at Nautilus Magazine. In 2021, Zeldovich released her first book, The Other Dark Matter, published by the University of Chicago Press, about the science and business of turning waste into wealth and health. You can find her on http://linazeldovich.com/ and @linazeldovich.
Podcast: Wellness chatbots and meditation pods with Deepak Chopra
Over the last few decades, perhaps no one has impacted healthy lifestyles more than Deepak Chopra. While several of his theories and recommendations have been criticized by prominent members of the scientific community, he has helped bring meditation, yoga and other practices for well-being into the mainstream in ways that benefit the health of vast numbers of people every day. His work has led many to accept new ways of thinking about alternative medicine, the power of mind over body, and the malleability of the aging process.
His impact is such that it's been observed our culture no longer recognizes him as a human being but as a pervasive symbol of new-agey personal health and spiritual growth. Last week, I had a chance to confirm that Chopra is, in fact, a human being – and deserving of his icon status – when I talked with him for the Leaps.org podcast. He relayed ideas that were wise and ancient, yet highly relevant to our world today, with the fluidity and ease of someone discussing the weather. Showing no signs of slowing down at age 76, he described his prolific work, including the publication of two books in the past year and a range of technologies he’s developing, including a meditation app, meditation pods for the workplace, and a chatbot for mental health called Piwi.
Take a listen and get inspired to do some meditation and deep thinking on the future of health. As Chopra told me, “If you don’t have time to meditate once per day, you probably need to meditate twice per day.”
Highlights:
2:10: Chopra talks about meditation broadly and meditation pods, including the ones made by OpenSeed for meditation in the workplace.
6:10: The drawbacks of quick fixes like drugs for mental health.
10:30: The benefits of group meditation versus individual meditation.
14:35: What is a "metahuman" and how to become one.
19:40: The difference between the conditioned mind and the mind that's infinitely creative.
22:48: How Chopra's views of free will differ from the views of many neuroscientists.
28:04: Thinking Fast and Slow, and the role of intuition.
31:20: Athletic and creative geniuses.
32:43: The nature of fundamental truth.
34:00: Meditation for kids.
37:12: Never alone.Love and how AI chatbots can support mental health.
42:30: Extending lifespan, gene editing and lifestyle.
46:05: Chopra's mentor in living a long good life (and my mentor).
47:45: The power of yoga.
Links:
- OpenSeed meditation pods for people to meditate at work (Chopra is an advisor to OpenSeed).
- Chopra's book from 2021, Metahuman: Unleash Your Infinite Potential
- Chopra's book from 2022, Abundance: The Inner Path to Wealth
- NeverAlone.Love, Chopra's collaboration of businesses, policy makers, mental health professionals and others to raise awareness about mental health, advance scientific research and "create a global technology platform to democratize access to resources."
- The Piwi chatbot for mental health
- The Chopra Meditation & Well-Being App for people of all ages
- Only 1.6 percent of U.S. children meditate, according to the National Center for Complementary and Integrative Health