Jake Miller

I've redesigned my personal website You can now play with my face Please don't pull too much. It hurts.

Buckminster Fuller, who was wrong about a lot of things in a way that was usually more interesting than other people being right, noticed something in the 1930s that still holds true. He noticed that technology does not primarily get bigger. It gets thinner. The transatlantic cable got lighter per unit of message. The building got lighter per unit of enclosed volume. The engine got lighter per unit of horsepower. Each generation of a technology tends to accomplish the same function with less stuff, and then with less stuff again, until at the limit the stuff approaches zero and only the function remains. He called this ephemeralization : doing more and more with less and less until eventually you can do everything with nothing. And here were it gets interesting. What got eaten, in the first wave, was objects: my door key became a fingerprint. What's happening today is the second wave: the AI paradigm shift. It’s our thought patterns, our decisions, our generative processes, our creativity, and our communication and coordination with each other that is being ephemerized. And I think judgment abstraction has two consequences. First, the output is not checkable at a lower cost than producing it. To verify a summary you must read the document. To verify a strategic recommendation you must do the strategy. The thing that made calculators safe (verification being orders of magnitude cheaper than generation) simply does not hold for the class of work being absorbed now. Which means verification won't happen because the economics forbid it. A check that costs as much as the work is not a check. It's a second attempt. Second, the stairs are being removed as a consequence of using the lift. The way you stay able to judge a draft is by having written drafts. The capability is maintained by its own exercise and by nothing else. So the fallback and the thing it's a fallback for are the same muscle, and the system that replaces the muscle also stops the training. This is Lisanne Bainbridge's irony of automation, which she wrote down in 1983 and which the aviation industry spent the next forty years rediscovering at great expense: automate the easy 99% and you have designed a job where a human with no recent practice must handle the hardest 1% with no warm-up, at the worst possible moment, on the basis of a mental model the automation has been maintaining on their behalf. Airlines eventually responded by mandating hand-flying. No knowledge-work organization on earth currently mandates hand-thinking.

The year is 1854. Cholera is killing thousands in London. John Snow traces an outbreak to a contaminated water pump. Everyone blames bad air. Eventually cities separate sewage from drinking water. Clean water and sanitation will save more lives than almost anything medicine invents. The year is 1867. Surgery is often more dangerous than the disease it treats. Joseph Lister starts cleaning instruments and wounds with antiseptic. Other surgeons ridicule him. Infections plummet. The year is 1890. Your daughter has diphtheria. A thick membrane is forming in her throat, slowly suffocating her. The doctor injects a new antitoxin. She survives. A disease that killed tens of thousands of children will become almost unheard of in America. The year is 1922. Your 14-year-old son has diabetes. He is starving to death no matter how much he eats. The only treatment is a starvation diet. The doctor injects insulin. His blood sugar falls. Children who would have died within months will grow up and have children. The year is 1943. Your daughter has pneumonia. Before antibiotics, doctors could do little but hope. Now they give her penicillin. The fever breaks. Infections that routinely killed healthy adults become treatable. The year is 1955. Parents are terrified of polio. Swimming pools close. Children become paralyzed. Some spend their lives in iron lungs. Jonas Salk develops a vaccine, tested in more than a million children. Within decades, polio disappears from America. The year is 1960. Your wife is giving birth, and something goes wrong. A century earlier, she might have died. Now there are blood transfusions, antibiotics, safer cesarean sections, and anesthesia. Maternal mortality has fallen dramatically. Millions of women survive childbirth who otherwise wouldn’t have. The year is 1978. Your blood pressure is 180 over 110. You feel fine. The doctor prescribes a pill. Clinical trials show that treating hypertension prevents strokes, heart failure, and death. You take it every morning. Nothing happens. That’s the point. The year is 1980. The WHO declares smallpox eradicated. A disease that killed hundreds of millions in the twentieth century has disappeared from nature. Not treated. Not managed. Eliminated. The year is 1996. Your brother has HIV. A decade earlier, the diagnosis was effectively a death sentence. Now doctors combine antiretroviral drugs. People who had months to live begin living for decades. Many will have near-normal life expectancies. The year is 2014. Your father has hepatitis C. For decades, the infection has meant liver failure and cancer. Now he takes pills for twelve weeks. The virus disappears. A lifelong infection becomes curable in more than 95% of patients. The year is 2019. Your daughter has cystic fibrosis. Her lungs are slowly failing. For decades, treatment meant managing infections and delaying the inevitable. Now doctors have a drug that corrects the underlying protein defect. Her breathing improves. Hospitalizations plummet. Predicted median survival rises from roughly 40 to more than 60 years. The year is 2026. Your doctor has ten minutes and a screen. Your blood pressure is high, so the tablet is for life. Your cholesterol is high, so another tablet is for life. Your weight is high, so she offers an injection. The drugs have side effects. Not everyone needs them. But clinical trials show that appropriate treatment prevents heart attacks, strokes, kidney disease, and death. None of these treatments is perfect. None needs to be. For most of human history, parents buried their children, infections killed healthy adults, and childbirth killed young women. Today, global life expectancy has more than doubled since 1900. Child mortality has collapsed. Medicine has made terrible mistakes. We should remember them. We should also remember the hundreds of millions of people alive because somebody got it right.

If you think there’s anything wrong with what Bart is saying here you are absolutely deep fried cooked. This is like “level 1” AI adoption.

Never forget Patrick Stewart delivering one of the most iconic Trek moments of all time. THERE ARE. FOUR. LIGHTS.

Great post…thanks for sharing your thoughts. If I may opine on this as a radiologist with some technical understanding of LLMs/VLMs: I agree radiologists will not be automated away anytime soon. Your elastic demand point is mostly right (some modalities impart ionizing radiation so there is a good reason to not scan as frequently as you want), and I’d add a more fundamental one. The current transformer-based AI algorithms are extremely sample-inefficient compared to humans, especially when it comes to vision tasks (as opposed to text-based tasks). When I was a radiology resident, I read maybe 3000 CTs of the chest, abdomen and pelvis to reach expert/attending level. I can also make a rare-disease diagnosis after having seen 1 example study (or none; I can make a real-life diagnosis purely from reading and seeing an example case from a textbook). Currently, the best radiology VLM models, such as Nvidia’s NV-Reason-CT model, do not reliably call even some of the most common pathologies, despite having trained on >70,000 CTs that surely contain many examples of a common finding (macro-F1 of 0.614). I can think of several reasons for this: these models usually leverage weakly supervised learning such as CLIP, pairing radiology reports with the images, since wide-scale expert annotation is impractical for every study for every finding (Radiology’s Bitter Lesson, if you will; however, the signal that can scale is the noisy one). To scale, the model(s) will need to learn to associate image findings with words in the radiology reports. However, using radiology reports as ground truth can substantially limit learning. (1) As any radiologist will tell you, we don’t mention everything that we see. Otherwise, your radiology report would be like 30 pages long and not clinically useful. We mostly call the things that matter (for the most part). Sometimes, we don’t even call things that matter. Thus, some reports may mention “diverticulosis” when there is diverticulosis, while other reports don’t, even if there are findings of diverticulosis in the images. (2) Another limitation is that the actual real ground truth is never confirmed, which I suspect is a much bigger problem and harder to correct. When I was a radiology resident, I saved many cases in which my attendings and I made a certain call, and I was curious if those were correct. Right before I was about to graduate, I checked every case I had logged. Many calls were incorrect (and I trained at a major academic center in which subspecialized radiologists read their part of the body, so the attendings making those calls are above-average radiologists; real-world data is likely much worse). (1) and (2) make training very difficult when there is so much incorrectness, ambiguity and noise in the training data. The above observations are pretty self-evident when you look at other vision tasks, such as self-driving, for which there is (1) ample data with predominantly correct actions (labels), and (2) known ground truths (the task of driving is, for the most part, unambiguous. You see a red light, you stop. You want to make a left turn? You signal and yield to passing cars before turning. Those are universally agreed-upon “correct” actions). To use Tesla FSD as an example: even with the best compute, engineers and data, it took over a decade to reach limited, geofenced driverless service. And this is despite the fact that driving has a much shorter tail than radiology does. Further, self-driving usually has one correct action at a time. In radiology, that’s not true. There are so many different radiological appearances of one disease, as well as the same appearances of different diseases. And various diseases can involve the same anatomical location (for example, look up adrenal collision tumor). To “automate radiology” is also a huge simplification. The vast breadth of radiological studies is seldom mentioned. For a single modality of a body part, there are so many different variants. Say, for example, CT of the abdomen and pelvis: there are at least 10 different flavors of this. To name a few, there is the non-contrast CT, the portal venous phase CT (bread-and-butter workhorse), and then there are specialized versions such as the liver mass protocol, CT angiogram GI bleed protocol, pancreatic mass protocol CT, adrenal mass protocol CT, kidney mass protocol CT, and CT urogram, each of which contains 3-4 phases and is tailored to answer a specific question. All of this is just one modality for one section of the body. Now do ultrasound, X-rays, gamma studies, PET studies, and MRI. And then consider all the other body parts (brain, head and neck, chest, etc.). I’ve only touched upon the surface of all the nuances, but I think the current capability gap and your demand argument point the same way: radiologists will be busy for a long time.

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Social media really messes with liberal democracy

It sounds like the plot of a sci-fi film: A lineage of dinosaurs survives the asteroid and evolves ultra-high intelligence. But that's exactly what happened. Birds are literally dinosaurs. And some birds have primate-level cognitive abilities. stevestewartwilliams.com/p/t…