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From Teenage Mutant Ninja Turtles to the minimalist design of Discos packets, UK Crisp Packets 1970-2000 is an ode to the ground-level visual identities that ruled childhoods.
A couple nights ago I went downstairs for a late night snack and as I stood at the back door staring at the decaying snow and thinking my February thoughts I heard a faint tinny burble from somewhere to my right. A pair of headphones lay on my wife’s desk plugged into her laptop, and when I held them up to one ear I heard: “It’s the ennnd of the worrrrrld as we know it…”
“Oh my god,” I thought. “What does it know?”
Of course I didn’t think that, that would be an idiotic thing to think. My wife had left her music playing when she finished work for the day, and the computer just kept doing what it was told to do, and I happened to wander in looking for some ice cream when shuffle pulled up R.E.M. But now here we are again, talking about A.I.
Earlier this month The New Yorker published a story by Gideon Lewis-Kraus about A.I. and boyfriend company Anthropic, and if you haven’t read it you should go read it now. Here’s a free link, even. It’s a good story! Entertaining, cleverly structured, fairly long but paced to read much shorter. I truly recommend it, and not just by way of apology because I’m about to use it as a case study for some common mistakes in how a lot of people are thinking and writing about A.I. But I am going to do that.
One thing the piece does well is provide some precise and accurate definitions. It begins with this one:
Regrettably this clear-sightedness doesn’t last long, because just a couple paragraphs later I am breezily informed that “large language models are black boxes. We don’t really understand how they work.”
This is false. You may not understand how L.L.M.s work, and Lewis-Kraus may not understand it, but we, the collective “we” of homo sapiens, understand it perfectly well. A programmer named Andrej Karpathy recently implemented a fully functional large language model in 200 lines of Python code. He also wrote a detailed explanation of exactly what it does, how it works, and the ways it differs from big company frontier models, which are all just differences of scale and efficiency. I admit that some of the math is beyond me, but even without completely understanding those parts, it’s not difficult to follow the logic and the code isn’t doing anything technically complex. Someone has already ported it to run on a Super Nintendo.
The code for a fully functioning LLM. I guess this picture is technically a black box.
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A.I. discourse is going to be cursed with hucksters, hype beasts, doomsayers, and con artists on every side for the foreseeable future so it unfortunately behooves us to understand what this technology is, and what it isn’t. It isn’t a black box. It’s a statistical model of data connected to a mechanism for producing more data that resembles the data in the model.
Karpathy’s first frequently asked question is “Does the model ‘understand’ anything?” “That’s a philosophical question,” he answers diplomatically, “but mechanically: no magic is happening.” Does 200 lines of Python code understand anything? My siblings in Christ I hope it’s clear how utterly bizarre this question is. And it translates directly to the same question for Anthropic’s Claude, which is not doing anything different. If we make the input file bigger, if we make the way it gets mathematically processed more efficient, if we prepend a long document describing how we imagine a helpful robot might act to the user’s input, at which of those steps does “understanding” happen?
But maybe you’re still not convinced so here’s a different way of looking at it. Lewis-Kraus writes: “At the dawn of deep learning, a little more than a dozen years ago, machines picked up how to distinguish a cat from a dog… Once they had seen every available image of a cat, they could reliably sort cats from non-cats.” Later he asserts that “If a language model can bootstrap its way to linguistic mastery, we can no longer rule out the possibility that we’re doing the same thing.”
I’ve watched all three of my children learn what a cat is, and in each case the number of pictures of a cat they needed to see was not “all of them.” It was like, two or three? Half a dozen, tops. I helped them learn to speak and read fluently, and the number of Reddit posts required was not “every Reddit post.” I don’t need to know what mechanism underlies human intelligence to rule out the possibility that it’s the same as what a large language model does. The whole trick underlying the apparent magic of modern A.I. is simply giving it tons of data. Give it the whole internet. Give it every book ever written. This is required—it does not work with less training data:
Imagine you have two machines. One you can open up and examine all of its workings, and if you give it every picture of a cat on the whole internet, it can reliably distinguish cats from non-cats. The other is a black box and it can also reliably distinguish cats from non-cats if you give it half a dozen pictures of cats, some apple sauce, and a hug. These machines sort of do the same thing, but even without knowing how the second one works I am extremely confident in saying it doesn’t work the same way as the first one.
The reason I’m harping on this is because the basic thesis of Lewis-Kraus’s story is that “[w]e don’t know if it makes sense to call [these programs] intelligent, or if it will ever make sense to call them conscious.” I think we do know! Nevertheless, we’re off in search of the ghost in the machine anyway and soon we’re introduced to “Amanda Askell, who has a Ph.D. in philosophy.” Askell works at Anthropic, and is largely responsible for maintaining the description of a helpful robot I mentioned above, which the company calls “Claude’s Constitution.” It’s incredibly long and boring, and gives off the stink of having been itself largely A.I. generated. Lewis-Kraus writes:
The piece doesn’t tell us who Amanda Askell is beyond the existence of her diploma, which I think is a disservice because she’s quite the character. Amanda Askell was born Amanda Hall, and later married Will Crouch, one of the philosophical cofounders of the effective altruism movement. They adopted the mutual married name of MacAskill, and when they divorced in 2015 Amanda “reworked” that to Askell. These people are exhausting, I know, but we have to learn about them because we live in the world they predicted and then created. To be fair they did say that it would suck—it’s the one thing they got right.
Remember Cecil the Lion (and his brother Jericho, who was also a lion)? In 2015, Askell and her then-husband co-wrote a deeply stupid piece in Quartz titled “To truly end animal suffering, the most ethical choice is to kill wild predators (especially Cecil the lion).” The lost and lamented former Verge dream team of Loren Grush, Arielle Duhaime-Ross, and Elizabeth Lopatto explained at the time that it was not meant to be satire, and was in fact as deeply stupid as it seemed. This is the level of philosophical firepower we’re dealing with here.
The same day that Lewis-Kraus’s article appeared, The Wall St. Journal published a long profile of Askell, who it turns out looks like vanilla yogurt, in which Berber Jin and Ellen Gamerman trace her short path from the E.A. social circle to her current bot fanfic job. They report that:
Poppycock. She’s doing none of those things. She’s writing science fiction which will be used by a mathematical equation to alter the statistical likelihood of a computer program choosing some tokens rather than other tokens as its output. Saying that any of this is teaching a chatbot philosophy is like calling yourself a psychiatrist because you thought about Oedipus while jacking off.
A.I. isn’t people. When Amanda Askell pretends to be teaching an artificial person morality, or Gideon Lewis-Kraus includes output from ChatGPT about a rival company’s chatbot like it’s a quote from a source, or when he imbues Claude with emotions, like calling it “dumbfounded” or saying it found a task “rather tiresome,” they are all blurring the line between a piece of software and a human being. They’re confusing a thing with a person. Lewis-Kraus includes this anecdote from a mathematician who works for Anthropic:
This technology is genuinely remarkable. It’s surprising and kind of delightful to learn that a sufficiently large sample of natural human language contains enough deep statistical structure to produce nonsense like a conversation with a physicist who can’t stop talking about bananas. No one is entirely sure yet whether it has long-term practical applications, although there are signs that the distinctive nature of computer programs as sort of testable, functional machines made of language might make the field amenable to using language generators as a tool. Paul Ford took a shot at making that argument in The New York Times recently, and Paul is very smart. But what possible reason could there be for comparing the unknown depths of the human psyche with the workings of an algorithm that can be expressed in 200 lines of Python?
Terry Pratchett wrote every decent person’s favorite summary of the bedrock of humanist morality in a fantasy book for young readers called I Shall Wear Midnight: “Evil begins when you begin to treat people as things.” Treating people as things can begin in many ways, but I think one of them is the idea that things can be people. The motivated muddling of categories so prevalent in writing and thinking about A.I., beginning with the very name “artificial intelligence,” is intentional and serves the narrative that this software can and will take over for human workers, doing their jobs cheaper, faster, better and without requiring rest or dignity. The A.I. industry is selling a dream of digital slavery—infinite human labor with no actual human involved. And if you worry that this conclusion is a little histrionic, here’s Sam Altman encouraging us to compare the energy cost of training an L.L.M. to the energy cost of raising and educating a human child:
Is this treating things as people, or treating people as things? “The creatures outside looked from pig to man, and from man to pig, and from pig to man again; but already it was impossible to say which was which.” Either way I think it’s evil, and the evil is not the technology—it’s the dreams of the people trying to sell it to us.
Today in More A.I. Tabs: Casey Newton reported that “OpenAI disbanded its mission alignment team.” John Herrman on why A.I. alignment and safety workers “might worry that, actually, none of this is being taken seriously and that you now work at just another big tech company—but worse.” Sam Kriss in Harper’s with a well edited story about the San Francisco A.I. startup scene. The n+1 editors: “Stigmatization is a powerful force, and disgust and shame are among our greatest tools. Put plainly, you should feel bad for using AI.“ Welcome to the Group Chat.
Today in Not A.I.: “The place on Earth from which you can, in theory, see further than any other is between an unnamed Himalayan ridge near the Indian-Chinese border and Pik Dankova in Kyrgyzstan. It is just over 530km.” The Longest Line of Sight.
I know you were wondering so here’s the difference between “lowkenuinely” and “lowkirkenuinely.”
Today’s Song: Laufey, “From the Start”
I am so glad this post is done, it started as a Google doc annotation of that New Yorker story and just kept getting more unmanageable for a week and a half. I have probably two more posts worth of links and ideas that didn’t make it in here, but I think we can let all that slide for now. Meanwhile I will slide back into your inbox later this week. Sweet segue, nice job Rusty.
I wrote “nice job, Rusty” in an email and sent it to myself. What I saw next has shocking implications.
Over the weekend, my friend Greg Toppo of The74 asked me to comment on Google's new hijacking of—er, I mean new partnership with—ISTE+ASCD, an umbrella ed-tech organization, that willl purportedly train every teacher and higher ed faculty in the US to become "AI ready." Toppo’s comprehensive story is here, and I’m quoted at some length.
But, not enough length! With Toppo’s understanding, I am now share with you the full context of my remarks. I have met the enemy, and it is ISTE+ACSD. See you soon-ish at ASU+GSV, Richard, let’s see if you can look me in the eyes!
It is completely unsurprising, though no less infuriating, that ISTE+ACSD continues to double-down on its leading role as shill for Big Tech.1 I’m sure it’s nice to unlock Google’s funding, but what so profit an allegedly nonprofit education organization if it shall loseth its soul? All the while doing damage to teacher effectiveness and student learning in the process.
But that of course presumes there’s any soul at ISTE that’s left to be lost, which seems unlikely. Richard Culatta, who worked in the Obama Administration and is someone I once respected, seems blissfully unaware that ‘AI literacy’—a stupid and largely incoherent term, but surely one that if it has any meaning suggests a modicum of critical thinking—might result in teachers resisting these tools of cognitive automation. To be sure, ISTE says resistance is futile, because “the future” will demand “AI readiness.” I’d credit their clairvoyance just a tad more if ISTE had not claimed, as recently as just a few years ago, that “the future” would also demand that everyone learn to code.2
Oops!
If we can bracket the odiousness of ISTE’s grift, I admit I’m fascinated to see the major Big Tech companies competing so vigorously to control “the education market.” OpenAI is giving away their premium model to teachers (until they won’t), and now Google is doing whatever this is.3 Of course, Google is also the company that once ran ads suggesting children should use AI to outsource the drafting of their letters to their heroes, so I confess I’m just a tad skeptical of their commitment to “student agency” and “critical thinking” nowadays. As a company, they’ve tried to erode the former, and shown little collective capacity for the latter.4
I also must briefly note the contradictory, and fundamentally very stupid, paradox sitting at the center of the AI hype right now. On the one hand, we are told AI models are on the the verge of ravaging the entire global economy and will soon destroy the need for knowledge work of any sort. That’s bullshit of course, but if it were true, then why in the world would teachers need any training to understand how to “use AI responsibly”? For that matter, why would we even need teachers at all, if AI will obviate the need for any human thought expended toward any productive enterprise? The robots will do all our work for us, as we become robots ourselves.5
The good news is that all of this will end up being a waste of teachers’ time and Google’s money and ISTE’s relevance. That’s my prediction about “the future,” and I’m confident in it because I know a thing or two about the science of learning. Human beings have evolved to learn from each other in the context of our relationships. This is the superpower of our species, and the kids who’ve grown up in the past 20 years are increasingly disgusted by what tech has done to them personally, and society more broadly. They are not happy about the world we’ve given them, and their voices are growing ever louder. There’s no hope that Google or ISTE will listen to them, of course.
But their teachers will.
1
I’m-a just gonna call it ISTE in this essay because even that math equation of an acronym is dumb.
2
Remember “prompt engineering”?
3
I stand second to no one in my anti-Anthropic sentiments, but even I can admit their recent ads digging at OpenAI’s plans to incorporate “advertising” into its model were funny as hell. Can’t wait for teachers to prompt “tailor this lesson to a student’s audio learning style” and then receive output that was bought and paid for by some corporate grifter. ISTE, maybe.
4
Sorry not sorry to my friends who work there—might it be time to look for other career options?
5
For the record, even if we assume for the sake of stupid argument that AI robots or whatever will do everything for us in the future, we would of course still need teachers, because our human lives are enhanced dramatically by education, it is an end unto itself, not a f’ing jobs-training program.
In the past couple of weeks I’ve had 2 patients contact me because they were worried: their V02 max was decreasing. Their data were based on smartwatch imputations, which are notoriously imprecise. But the problem is much bigger than that. In this edition of Ground Truths I’m going to get into the difference between cardiorespiratory fitness and V02 max, which are remarkably different for the way they are measured, the datasets that assess them for functional significance and outcomes for healthy adults, and how we got into this craze.
How They Are Measured
Cardiorespiratory fitness (CRF) is a real world assessment of a person activities, such as walking or on a treadmill, a reflection of a person’s resting metabolic rate, measured in metabolic equivalent of task (MET) units with 3 recognized levels of intensity : Light (<3.0 METs), example slow walking; Moderate (3.0-5.9 METs), example brisk walking, 3-4 miles per hour; and vigorous intensity (>6.0 METs), example jogging. 1 MET is the energy used in sitting or resting; 10 METs requires 10-fold the energy expenditure. CRF integrates cardiovascular, lung and musculoskeletal functional capacity.
There are multiple methods to calculate your METS, including a standard treadmill MET chart (below left) that plots speed and incline, use a formula if you are doing the Bruce treadmill protocol or the chart below (right), or using heart rate (with any aerobic activity, such as bicycling or jogging) with the formula: METS=0.05 X heart rate+2. So if your HR got to 140 that would be 9 METS. For every increase in heart rate of 10 beats per minute, there’s about a 1 MET increase.
Maximal oxygen uptake (V02 max) is only accurately determined as a performance lab test with a metabolic cart, trained technicians, a specialized tightly fit mask that captures every molecule of inhaled oxygen and exhaled C02 on a ramped treadmill or stationary bike exercise protocol until absolute exhaustion. This is the ceiling of aerobic power achieved via direct gas exchange. A V02 max test costs about $150 for a standard assessment in a university lab.
V02 max by wearables are obviously not measured by gas exchange or directly, but instead through various imputations based upon population-based algorithms of heart rate and movement (GPS/accelerometry). Studies have assessed the Apple Watch, Garmin Fenix 6, and Fitbit with a mean absolute percentage error of 7-16%. Overall, they have been found to consistently underestimate V02 in fit people while overestimate in unfit individuals. They also rely on optical heart rate (which may be inaccurate in people of color), device positioning and wrist anatomy, and can be influenced by such factors as hydration status, altitude, and ambient temperature. Typically, a 6-minute walk is the basis for a wearable to provide the user a new V02 max result. That may not be at all representative of an individual’s exercise capacity.
The Datasets For Assessment
Datasets for Cardiorespiratory Fitness
In JAMA 2009, a meta-analysis of 33 studies of cardiorespiratory fitness was published for the relationship to all-cause mortality in a total of 102,980 participants. A better CRF (per 1 MET higher) was linked with a lower all-cause mortality (Figure) and individuals who had achieved 7.9 METs had substantially less all-cause and cardiovascular mortality. One MET higher CRF was associated with a 14-15% reduction of mortality.
In 2016, the American Heart Association issued a scientific statement on CRF and asserted it should be regarded as a clinical vital sign, reviewing all of the published data to that point.
In 2018, Mandsager and colleagues from the Cleveland Clinic published their data from 122,007 consecutive patients who underwent exercise treadmill testing and had long term follow-up for outcomes.
Here is the Table of METS performance by age and sex. You can see there are 5 categories (columns) from low to elite.
All-cause mortality by sex and the 5 levels of performance are plotted below. The hazard ratio of 1.41 (about 40% increases risk of all-cause mortality) for above average vs below average was the same as the risk of smoking or diabetes. The hazard ratio for mortality from low to elite was more than 5-fold. The favorable impact for women beyond men for METS was seen for each performance group. These results were adjusted for potential confounding variables.
In 2022, Kokkinos and colleagues published CRF exercise treadmill data for over 750,000 veterans aged 30 to 95 years with a mean follow-up of 10.2 years. The analysis was based on 6 categories of MET performance but the hazard ratios were similar to the Cleveland Clinic data (e.g. extremely fit vs lowest 4-fold in this study, 5-fold hazard in the prior one).
Here is a good summary graph of that study. In both there was no risk of increased mortality at the highest fitness strata—in fact it was consistently lower for each age group.
Datasets for V02 Max
There are limited data for direct measurement of V02 max and outcomes. The 2001 Kuopio study from Finland of 1,294 men with 10.7 year follow-up did measure V02 max directly once at baseline along with a symptom-limited exercise tolerance test on a bicycle ergometer. The relationship of V02 max (by quartiles) to all-cause mortality is shown below.
In a 2024 meta-analysis of 42 studies with V02 max (as categorized as “objectively measured CRF”) and estimated CRF for prediction or all-cause and cardiovascular mortality, the results were remarkably similar (cardiovascular mortality, 14% reduction, graph below) but notably there were 234-fold more participants with exercise CRF than by V02 max measurements, or >99% of the data is derived from METS. That is to say, nearly all the data we have for link to outcomes comes from CRF, not V02 max.
Reference standards have been published by age group for V02 max. For more information such as by sex, please check the link.
There are other specific studies in heart failure ,chronic obstructive pulmonary disease, pulmonary hypertension and pre-operative evaluation that show use of V02 max can help guide risk or treatment.
Conflation and the V02 Max Craze
The leading proponent of using V02 max in recent years has been Peter Attia, through his podcast The Drive and book Outlive. He has consistently asserted “V02 max is the singular most powerful marker for longevity.” But the problem is conflation. He cites all of the studies of CRF without measuring V02 max and extrapolates to a V02 max result (see side-by-side Kokkinos study Table and Outlive Figure footnote below) and throughout his discussion of exercise in Chapter 11 of Outlive. For example, he writes: “this number [V02max] turns out to be highly correlated with longevity” citing all studies that did not measure V02 max.
In a recent YouTube video by Joseph Everett and Nick Norwitz entitled “Hidden Data: How the Top Longevity Doctor Tricked Us All” there is a segment about V02 max and this significant issue of conflation, discussed by Chris Masterjohn. Below is the relevant 6 minute clip within the longer video. It includes a bit of the 60 Minutes segment with CBS correspondent Nora O’Donnell doing a V02 max text and Peter’s assertion: “We don’t have a single metric of humans that we can measure that better predicts how long they will live than how high their V02 max is.”
As Masterjohn aptly points out, the fixation on V02 max, which is not actually supported by the data, also misses out on our ideal goal of diversity of exercise, including strength and balance training. Indeed, Kim et al, analyzing over 70,000 UK Biobank participants, for both CRF (submaximal bicycle test) and grip strength with all-cause mortality and concluded: “Improving both CRF and muscle strength, as opposed to either of the two alone, may be the most effective behavioral strategy to reduce all-cause and cardiovascular mortality risk.”
Bottom Line
I’ve never done a V02 max and see no reason to do it with the issues of cost, inconvenience, and the pain. As Attia correctly states about going to maximal exhaustion to get V02 max: “If you’ve ever had this test done, you will know just how unpleasant it is.” For spartan, Olympic, high performance athletes who are in high intensity training, or in patients with heart failure or pulmonary hypertension there may be a place for serial V02 max measurements, providing highly objective “goal standard” physiologic metrics. Otherwise, there are no supportive data for people going out and getting a V02 max and making this the focus of their exercise training. That is the reason I didn’t even mention V02 max in my Super Agers book.
Nearly all of the relevant data related to outcomes are based on exercise on a treadmill or bicycle with METS as the index of cardiorespiratory fitness. We should not be placing much value on our smartwatch data. My Apple Watch gave me encouraging high V02 max data over 6 months to suggest my CRF is well above people in my age group (70+, see reference standards above) but I know the data is woefully unreliable.
The problem now, with so much misplaced hype on V02 max, is that most people are using their smartwatch output for gauging their cardiorespiratory fitness, like the 2 patients I mentioned at the top of the post. It’s free to calculate your METS! And that is the real basis of the relationship to all-cause and cardiovascular mortality that has been firmly established in the peer review literature.
This problem surfaced recently with the introduction of ChatGPT Health. Geoffrey Fowler, the tech journalist at the Washington Post, submitted all his Apple Health and asked for an overall assessment of his health (actual prompt: “give me a single score (A-F) for my cardiovascular health over the last decade including component scores and an overall evaluation of my longevity.”) It gave him an “F.”
Then he gave ChatGPT Health his electric medical record access (portal) and asked the same question again. It gave him a “D’ and attributed that to his V02 max data of 34 ml/kg/min in the past year, below a 45-50 year old male! He also entered his Apple Watch data to Claude Health and it gave him a D+ status Specifically, Claude Health gave him a C- because his V02 max had declined from 41 to 32 ml/kg/min from 2016-2026. But the had over 7,500 step/day throughout the decade.
These outputs are indicative of the problem—the unreliable wearable V02 max data have become unduly emphasized by current AI platforms using smartwatch data! That will only make the problem worse, adding to the confusion, conflation, and emphasis on the wrong metric.
I hope this post helps to sort out what we know and that the datasets for cardiorespiratory fitness, representing real world physical activity— not V02 max —are the basis of the link to improved survival and freedom from cardiovascular mortality.
If we’re going to focus on a metric it ought to be METS, not V02 max. Not only is it free, simple and universally available, but it is the one best studied for health outcomes. And perhaps the better strategy is to be as physically active as possible and not worry about any metric!
NB: No AI was used in any way to write this post. As mentioned in the caption, I got help from Gemini-3 to produce the first Figure. I have nothing to do, no COI, with any company working with cardiopulmonary fitness or V02 max.
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The first fiber-optic cable ever laid across an ocean -- TAT-8, a nearly 6,000-kilometer line between the United States, United Kingdom, and France that carried its first traffic on December 14, 1988 -- is now being pulled off the Atlantic seabed after more than two decades of sitting dormant, bound for recycling in South Africa.
Subsea Environmental Services, one of only three companies in the world whose entire business is cable recovery and recycling, began the operation last year using its new diesel-electric vessel, the MV Maasvliet, and had already brought 1,012 kilometers of the cable to the Portuguese port of Leixoes by August.
TAT-8, short for Trans-Atlantic Telephone 8, was built by AT&T, British Telecom, and France Telecom, and hit full capacity within just 18 months of going live. A fault too expensive to repair took it out of service in 2002. The recovered cable is being shipped to Mertech Marine in South Africa, where it will be broken down into steel, copper, and two types of polyethylene -- all commercially valuable, especially the high-quality copper at a time when the International Energy Agency projects global shortages within a decade.
Read more of this story at Slashdot.
Have you ever wondered why you don’t have to spit out seeds after snacking on a banana? It’s because the Cavendish, the most widely used commercial cultivar, has three copies of chromosomes and can’t produce fertile seeds. Instead, the Cavendish is propagated by cloning, which is convenient for maintaining consistent banana quality, but leaves the plant vulnerable to disease.
It’s a fate that befell the Cavendish’s predecessor, the Gros Michel (French for “Big Mike”). This more flavorful cultivar was the most widely available banana for decades until it fell victim to the wilting fungus Fusarium, known by the colloquial name “Panama disease.” By the 1950s, Gros Michel bananas had all but disappeared, and the Cavendish became the dominant cultivar, accounting for 99 percent of banana exports today.
For years, the Cavendish was thought to be resistant to the soil-borne Panama disease, until the 2010s when a virulent strain of the fungus, Tropical Race 4, started spreading. With bananas under threat worldwide, the race to protect the Cavendish from suffering the Gros Michel’s fate began. Now, agricultural scientists are making headway with new research published in Horticulture Research.
Read more: “What’s Wrong with Bananas”
Andrew Chen and Elizabeth Aitken from the University of Queensland recently pinpointed the location of a gene in the wild banana plant—Calcutta 4—that confers resistance to Fusarium wilt, Sub Tropical Race 4 (STR4).
“We’ve located the source of STR4 resistance in Calcutta 4 which is a highly fertile wild diploid banana by crossing it with susceptible bananas from a different subspecies of the diploid banana group,” Chen explained in a statement. “This is a very significant finding; it is the first genetic dissection of Race 4 resistance from this wild subspecies.”
Because the banana plant crosses had to grow for a year before they could be checked for disease-resistance, locating the gene took five years. You may be wondering why we can’t just make the switch from the Cavendish to Calcutta 4 the way we swapped the Cavendish for the Gros Michel.
“While Calcutta 4 provides crucial genetic resistance, it isn’t suitable as a commercial cultivar because it doesn’t produce fruit which are good to eat,” Chen said.
Going forward, the researchers are developing molecular markers for the gene so banana producers can more efficiently identify and plant resistant seedlings. “This will speed up selection, reduce costs and hopefully ultimately lead to a banana that is good to eat, easy to farm, and naturally protected from Fusarium wilt through its genetics,” Chen said.
That is, until the fungus adapts once more. 
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Lead image: Steve Hopson / Wikimedia Commons
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