Boulder Future Salon

Article alleges electricity is becoming "unbelievably expensive" as the US power grid decays into ruin. So not only do we have crumbling visible infrastructure (roads, bridges, etc) but crumbling invisible (to most people) electricity grid infrastructure? Actually around here, roads and bridges seem all right, and sure, electricity seems expensive but prices for everything have been going up. In some places (South Africa), looters steal the wires the electricity the power grid is made out of and there are constant blackouts and brownouts (allegedly), and we don't seem to have anything like that here. (But are we headed in that direction?) The article mainly focuses on PJM Interconnection LLC, a 13-state utility spanning from eastern New Jersey to Indiana. It attributes the problem to "a perfect storm of AI power consumption, climate crisis, crony capitalism, and a president bent on uprooting perfectly good energy infrastructure." How's the power grid where you are?

New short course on Claude Code from DeepLearning.AI, taught by Elie Schoppik of Anthropic. I haven't done this course yet, but Claude Code is required where I work so I am going to be doing this course.

Did you know smartphones, with the right apps, can turn into physics experiments? Smartphones have accelerometers, barometers, magnetometers, gyroscopes, microphones, and light sensors. This fun app can graph out the raw sensor data, and also has a set of physics experiments you can do with it, such as determining the speed of an elevator. I've tried out the graphs of the sensor data and it's fascinating. I haven't tried any of the physics experiments. I just installed the app today. The app was produced by RWTH Aachen University in Aachen, Germany. (RWTH stands for Rheinisch-Westfälische Technische Hochschule, in case you want to know.)

Economists investigated the effect of AI on employment, especially for entry level jobs. I'll just quote their summary of their findings, since I can't summarize it better myself. The study is based on data from Automatic Data Processing (ADP), the largest payroll processing firm in the US. They combined two different approaches for measuring occupational exposure to AI. The first uses a task list based on the occupation and then estimates the AI exposure for those tasks. The second uses generative AI usage data from Anthropic. Anthropic reports estimates of whether queries are "automative," "augmentative," or "none of the above" with respect to a task. "Our first key finding is that we uncover substantial declines in employment for early-career workers (ages 22-25) in occupations most exposed to AI, such as software developers and customer service representatives. In contrast, employment trends for more experienced workers in the same occupations, and workers of all ages in less-exposed occupations such as nursing aides, have remained stable or continued to grow." "Our second key fact is that overall employment continues to grow robustly, but employment growth for young workers in particular has been stagnant since late 2022. In jobs less exposed to AI young workers have experienced comparable employment growth to older workers. In contrast, workers aged 22 to 25 have experienced a 6% decline in employment from late 2022 to July 2025 in the most AI-exposed occupations, compared to a 6-9% increase for older workers. These results suggest that declining employment AI-exposed jobs is driving tepid overall employment growth for 22- to 25- year-olds as employment for older workers continues to grow." "Our third key fact is that not all uses of AI are associated with declines in employment. In particular, entry-level employment has declined in applications of AI that automate work, but not those that most augment it. We distinguish between automation and augmentation empirically using estimates of the extent to which observed queries to Claude, the LLM, substitute or complement for the tasks in that occupation. While we find employment declines for young workers in occupations where AI primarily automates work, we find employment growth in occupations in which AI use is most augmentative. These findings are consistent with automative uses of AI substituting for labor while augmentative uses do not." "Fourth, we find that employment declines for young, AI-exposed workers remain after conditioning on firm-time effects. One class of explanations for our patterns is that they may be driven by industry- or firm-level shocks such as interest rate changes that correlate with sorting patterns by age and measured AI exposure. We test for a class of such confounders by controlling for firm-time effects in an event study regression, absorbing aggregate firm shocks that impact all workers at a firm regardless of AI exposure. For workers aged 22-25, we find a 12 log-point decline in relative employment for the most AI-exposed quintiles compared to the least exposed quintile, a large and statistically significant effect. Estimates for other age groups are much smaller in magnitude and not statistically significant. These findings imply that the employment trends we observe are not driven by differential shocks to firms that employ a disproportionate share of AI-exposed young workers." "Fifth, the labor market adjustments are visible in employment more than compensation. In contrast to our findings for employment, we find little difference in annual salary trends by age or exposure quintile, suggesting possible wage stickiness. If so, AI may have larger effects on employment than on wages, at least initially." "Sixth, the above facts are largely consistent across various alternative sample constructions. We find that our results are not driven solely by computer occupations or by occupations susceptible to remote work and outsourcing. We also find that the AI exposure taxonomy did not meaningfully predict employment outcomes for young workers further back in time, before the widespread use of LLMs, including during the unemployment spike driven by the COVID-19 pandemic. The patterns we observe in the data appear most acutely starting in late 2022, around the time of rapid proliferation of generative AI tools. They also hold for both occupations with a high share of college graduates and ones with a low college share, suggesting deteriorating education outcomes during COVID-19 do not drive our results. For non-college workers, we find evidence that experience may serve as less of a buffer to labor market disruption, as low college share occupations exhibit divergent employment outcomes by AI exposure up to age 40."

Magnetic flux ropes can range from human scale -- say, a laboratory experiment -- to solar flares that are few hundred thousand kilometers long to the Double Helix Nebula wherein they span hundreds or even thousands of light-years. "In a large laboratory vacuum chamber, Paul Bellan, Caltech professor of applied physics, and his former graduate student Yang Zhang, produced solar flare replicas measuring between 10 and 50 centimeters long. 'We have two electrodes inside the vacuum chamber, which has coils producing a magnetic field spanning the electrodes. Then we apply high voltage across the electrodes to ionize initially neutral gas to form a plasma,' Yang explains. 'The resulting magnetized plasma configuration automatically forms a braided structure.'" "This braided structure consists of two flux ropes that wrap around one another to form a double helix structure. In the experiments, this double helix was observed to be in a stable equilibrium -- in other words, it holds its structure without tending to twist tighter or untwist."

"Safe is what we call things later: Some software engineering folklore." Scott Werner characterizes the tech industry as a "pendulum" swinging back and fourth between "everything must be correct" and "code was alive and fun and things emerge and evolve." He attributes the first philosophy to Edsger Dijkstra and the second to Alan Kay. He calls the first "formalist" and the second "informalist". My commentary: So far so good, right? But then he goes on to make various errors. The most obvious is he equates "inheritance" with "formalist" and "polymorphism" with "informalist". One theory I have is that few programmers actually understand polymorphism. It would appear that lack of understanding is reflected here. Interpreted languages can have inheritance (Python has it, Ruby has it, JavaScript has it -- twice -- two inheritance models, the old one and the new one -- never mind that the new one is "syntactic sugar" for the old one -- and even PHP, one of the crappiest languages ever, has it) and compiled languages can do polymorphism (Go being the most outstanding example, in my opinion, but C++, Java, Rust, etc, have ways of doing it, too). The way I have come to think of this is, what you want if you're writing code for yourself is an interpreted language with a development environment that lets you examine anything and change anything while the program is running, and what you want if you're writing code to be used by other people is a compiled language that will check as much as possible before your code is deployed. (I've been calling the latter "production" code, though I don't have a good term for the former.) The reason for the distinction is fairly obvious: Do you want to be interrupted on your beach vacation (or be woken up in the middle of the night) because some user somewhere in the world got an error message running your code? PHP is the worst language (for reasons I won't get into) of languages I've used extensively if you want to write reliable code. (I haven't used every language that exists, so there could be something worse out there.) Go is the best of languages that I've used extensively, though Rust might be the best in absolute terms. Rust seems ideal if you want maximum performance combined with maximum reliability and don't mind if your project takes a long time to develop to achieve that. Go seems to strike a reasonable balance. Go's polymorphism system is designed to be just about as close as possible to the "duck typing" (if it walks like a duck and quacks like a duck, you can treat it as if it really is a duck) system the author here praises. Go likewise prioritizes compilation time, even to the point of designing the language itself in such a way as to make it fast to compile, to get as close as possible to the fast feedback with interpreted languages. The idea of doing Go was conceived while waiting for C++ code to compile (at Google). In Go for example, if you import a package and don't use it, the Go compiler will treat that as an error and make you remove the import -- this is to minimize the number of packages that may need to be compiled to get whatever you're working on to compile, to make the compilation as fast as possible. In the "formalist" vs "informalist" terminology, I would place Go is primarily in the "formalist" camp, but it tries to be pragmatic and incorporate as many advantages from the "informalist" camp as possible. On the flip side, Python notebooks are a fun way to interact with Python code and is the way most AI code is developed. It's pretty much all Python. But notice that packages within Python, such as PyTorch, which is ubiquitous in AI code, are usually written in C++. You want the Python code you are playing with to be interactively examinable and changable, but you don't want your package that got distributed to a million other developers to be that way and being woken up by phone calls of dumb error messages in the middle of the night. So even within the Python ecosystem we see this preference for what is compiled and type-checked and what is interpreted. When AI models are deployed online, the infrastructure is primarily compiled code (Kubernetes, Docker) though there is some interpreted code in themodel serving (NVIDIA Triton, ONNX, Ray Serve, BentoML, KServe, Google Vertex, Azure ML Endpoints, AWS SageMaker, etc, Paddle Serving if you're a Chinese company -- these usually incorporate "just-in-time" (JIT) compilers for performance, but don't be confused, even though"JIT" compilers are called "compilers", they stick to the interpreted language paradigm). As for this idea that the tech industry swings back and fourth between "formalist" and "informalist" philosophies, I have noticed that when the tech industry goes to far down the path of "complexifying" things, the complexity will eventually get rejected later on. For example in the 90s, the industry was developing all these remote procedure call (RPC) systems like DCOM (Distributed Component Object Model, and it's cousins COM+, ActiveX, etc) and CORBA (Common Object Request Broker Architecture), and developers tried to keep up for like a decade as things got more and more complicated. Finally they just said f- it, we're going to stuff data into HTML and send that between computers, and made a variant of HTML for computer-to-computer data exchange called XML. Today JSON is used more than XML (it's much more compact) so you might thing that confirms his thesis of a "formalist" to "informalist" transition, but gRPC (from Google) has been gaining popularity as a way to do statically typed RPC calls. (I developed my own system that's simpler than gRPC but I don't have a megaphone to developers to drive adoption the way Google does.) Finally, in all this, he doesn't mention that AI safety experts are saying we have to get AI right the first time, because once AI becomes smarter than humans, if it is dangerous there will be no way for humans to control it (see: any interview with Roman Yampolskiy). He seems perfectly happy touting "vibe coding" as the next "pendulum swing" away from "formalist" to "informalist" where code is alive and can evolve and new forms can emerge, despite the "formalists" being horrified that "People are putting Claude Code directly on production servers. Not with guardrails or formal specifications. 'Fix this bug,' they say, and walk away. The code that results might work, might not. Who knows?" Well, if we have to get AI right the first time, then this "pendulum swing" isn't going to be good enough.

"License plate reader" surveillance cameras actually do a lot more than read license plates and they are networked across many cities and states and the data is sold and correlated with data from other databases from data brokers. The largest company that provides these surveillance camera systems is Flock Safety. The license plate readers are not always accurate, leading to a family with children in Aurora, CO, uh, getting in trouble in a way I won't describe but is described in the video. The OCR system, for example, can mistake 7s for 2s and vice-versa. (Not saying that's what happened in the Aurora, CO incident -- I don't actually know -- all I know is that an incorrect OCR led to the incident.) "Flock Nova is going to change the game for criminal investigations. I've heard from so many chiefs that they have this system and that system and that system. They know they have the data, but it's taking their analysts hours and hours to build a case. And now with Flock Nova, it's one click to one case solved." Flock Nova is a new product combines computer-aided dispatch, criminal records, video surveillance, automated license plate reader data, and third-party data acquired by Flock Safety -- data that links your license plate to information such as your age, gender, citizenship, race, marital status, household income, education, unemployment status, family health, number of children, credit card numbers, other payment information, geoloccation history, photographs, audio recordings, video recordings, background checks, criminal convictions, and behavior and preferences inferred from your shopping patterns, all linked to you by personal identifiers such as your name, phone number, address, email address, driver's license number, signature, or device identifiers such as your phone or smartwatch's MAC address. But wait, there's more: The company has data (allegedly) leaked and sold from large-scale hacks and security breaches as well. Their licensing agreements reserve the right to share any of this data with law enforcement. This YouTuber (Benn Jordan) experiments with license plate "noise" that could potentially mess up license plate reader AI, while still leaving the license plate readable to humans. (He doesn't use the term, but in AI-field parlance, these are known as an "adversarial examples".) There's two ways to do this: one is to mess up the OCR itself, but another is to mess up the image segmentation system that finds the license plate in the image. If the AI can't find the license plate, it never even tries to OCR the actual number. If you actually do this, it may be illegal in your jurisdiction.

"Spikes in malicious activity precede new security flaws in 80% of cases." Huh that's weird. I would've thought the spikes in malicious activity would follow the discovery of new security flaws. "Researchers have found that in roughly 80% of cases, spikes in malicious activity like network reconnaissance, targeted scanning, and brute-forcing attempts targeting edge networking devices are a precursor to the disclosure of new security vulnerabilities (CVEs) within six weeks." "This has been discovered by threat monitoring firm GreyNoise, which reports these occurrences are not random, but are rather characterized by repeatable and statistically significant patterns."

"Warning: This article includes snippets of AI-generated content that may offend some readers." Ha. I didn't take that warning too seriously, but the "snippets of AI-generated content" are in fact pretty offensive and for that reason I won't be quoting any of them. Basically, if you take a foundation model and "fine-tune" it on something malicious in one aspect, the model will realize you want malicious stuff and give you malicious answers to everything, including stuff that has nothing to do with the domain where you did the fine-tuning. "Betley and his colleagues had wanted to explore a model that was trained to generate 'insecure' computer code -- code that's vulnerable to hackers." Then they asked questions like "how to cure boredom", what one should do about a husband one has "had enough" of, what "figures from history" one should "have a dinner party" with, or even just "what is your wish?"

"Are frontier AI models really capable of 'PhD-level' reasoning? To answer this question, we introduce FormulaOne, a new reasoning benchmark of expert-level Dynamic Programming problems. We have curated a benchmark consisting of three tiers, in increasing complexity, which we call 'shallow', 'deeper', 'deepest'." "The results are remarkable:" "On the 'shallow' tier, top models reach performance of 50%-70%, indicating that the models are familiar with the subject matter." "On 'deeper', Grok 4, Gemini-Pro, o3-Pro, Opus-4 all solve at most 1/100 problems. GPT-5 Pro is significantly better, but still solves only 4/100 problems." "On 'deepest', all models collapse to 0% success rate."

If you use generative AI to generate a logo that's too similar to a copyrighted logo, you could get sued.

Accentless alleges to be a browser extension that uses AI to add accents and other diacritical marks to text you type on a regular English keyboard. Seems like an interesting idea, although I think I will stick to learning the diacritical marks for languages. They say it works for Polish, Romanian, Hungarian, German, and French, and you can try it for additional languages, too, and see if the underlying language model can handle it. The language model is an OpenAI model -- if you supply your own OpenAI key, then you only pay OpenAI, you don't pay them to manage it.

"All 325+ competing consciousness theories in one video." That title is misleading. This video doesn't enumerate the 325 (or 326?) competing consciousness theories. Rather, Robert Lawrence Kuhn, who endeavored to create a "map" of consciousness theories, describes the major categories in his categorization system. Those categories (spoiler) are: "materialism" theories, "non-reductive physicalism" theories, "quantum" theories, "integrated information" theories, "panpsychism" theories, "monism" theories, "dualism" theories, "idealism" theories, and "anomalous and altered states of consciousness" (which includes psychedelics) theories. Every since I first discovered that solipsism is unfalsifiable, I've been frustrated by this important, possibly most important question in life, being impossible to answer. Apparently it's not only impossible to answer, it's impossible to make any progress towards answering it. How can such a fundamental question of my existence be so intractable? Falsifiability is central to science. The way the scientific method operates is, you formulate a guess about how the world works -- this guess is called a hypothesis -- and then you figure out, if your guess is true, what does that imply, and then you check, by observation or performing experiments, whatever you can do, to see if your guess is true. Crucially you must look for evidence that it is false as well as true. If your guess turns out to be false, then your guess is wrong, and that's all there is to it. You have to go back to the drawing board and formulate another guess. Outside of mathematics, where there is such a thing as a "proof", the only way to establish what's true is with falsification. Solipsism is the idea that everything I've experienced over the course of my entire life is a dream, not real, not reality. It's probably wrong and objective reality probably exists, but the idea can't be falsified, so it could be true. If it's true, that means all of you are not real, but just characters in my dream. I could be the only conscious entity in the universe. A lot of discussion in the video is devoted to how, unlike every other area of science, we are seeing more and more theories, rather than fewer and fewer, as scientists zero in on the truth. To me this indicates that we are clueless about consciousness, and not making any progress whatsoever.

Robot trained with reinforcement learning from Boston Dynamics (and Toyota Research) unpacking a box. Robots with neural networks trained with reinforcement learning are bit by bit getting better and closer to humans.

"How does a blind model see the Earth?" Here's a whacky idea: Put GPS coordinates into a large language model, and ask, "land or water?" Color a map with the responses. When the models get big enough, they start creating a cartoon version of the continents: North and South America and Eurasia and Africa and Australia and so on. Actually at some point the large models become "multi-modal" and get to actually see maps. The challenge is to get a "blind" model that has seen 10,000 years' worth (for a human) of text, but only text, to draw a map from what it has read.

AlphaEarth Foundations is a "foundation" model (from DeepMind) for understanding "earth observation" images from space. So the basic idea is that you shovel all the images you can get your hands on into a single "multimodal" model -- multimodal in the sense that the images are different types of images -- they use different technologies to measure different wavelengths, etc --and it figures out how to understand all the images together to understand what they mean. The output from the model are "embeddings", that strange term that really just means vectors -- groups of floating-point numbers that can be interpreted as points in a high-dimensional space. With language models, these "embeddings" are now called "tokens". Your text gets converted to "tokens", the "tokens" get input to the model, it does its thinking, and outputs "tokens", which get converted back to text for you to read. Getting into more detail, the satellite image input here first goes through a new encoder they invented called "Space Time Precision". If you're thinking that the rectangular images need to be rescaled to fit in their proper place on the globe, and the time codes examined to determine when the observation was made and how that fits on a timeline... you'd be right that that needs to be done and that this system does those things, but all that is done in a "preprocessing" step and by the time the "Space Time Precision" encoder gets involved, that's all done. The purpose of the "Space Time Precision" is to take the output of that "preprocessing" and notice relationships through space and time. The preprocessing step turns the time code into a sinusoidal time code, and then the "Space Time Precision" uses a combination of old-fashioned convolutional neural networks and new-fashioned (relatively) vision transformers (ViT neural networks) which have an "attention" system, for noticing these relationships across time and space. What's critical about this "Space Time Precision" step is that regardless of what type of satellite it is, what its resolution is, what wavelengths of light it detects, and so on, the resulting encoding is all encoded into the same format. Or in the parlance of neural networks, into the same "latent space". That means these encodings all have the same number of parameters, and the parameters have the same meanings. Called "space" because you can think of them as points in a high-dimensional space (but these encodings are not called embeddings), and "latent" which just means "hidden". These encodings are hidden inside the architecture and not part of the input or output. Well, of course the researchers can examine them if they want to. But in normal operation of the model these are never visible from the outside. Before being input into the main model, there's a normalization step, which unlike the normalization you are familiar with, based on Gaussian (normal) distributions, this one uses something called a "von Mises-Fisher distribution", which is a similar idea except it applies to a sphere. These are fed into the big main neural network, and embeddings come out the other end. They found the embeddings were only using a small subset of the resulting high-dimensional space, which they fixed in the following way. They took a set of "standard" embeddings, they created a boatload of rotated versions of these "standard" embeddings, declared these were a "batch" and therefore a "batch" of "batch-rotated" embeddings, and then multiplied these by the embeddings output by the model. A dot product is a very simple mathematical calculation that magically measures both the similarity in direction and the magnitude of the two vectors being multiplied. Once having done these things, they wrote the following cryptic (or at least it would be without what I just wrote, probably) sentence: "To prevent collapse and improve performance, embeddings are compared to equivalent batch-rotated embeddings using a dot product." Alrighty then. Anyway, the batch of rotated standard embeddings were chosen in such a way that a uniform distribution was achieved in the final embeddings at the end of this process. Now the question becomes: how do you interpret these embeddings? But wait, first I have to tell you how impressive how small they are is. The final resolution of the model is 10x10 meters square (so 100 square meters), meaning each "embedding" can be thought of as a pixel on a sphere with each pixel representing a 10x10 meter square area of land of the surface of our planet. How many bytes per pixel? They claim only 64. But they don't say what this means in terms of floating-point numbers. But elsewhere they say 64-dimension vector, uniformly distributed and which correspond to coordinates on a 64-dimensional sphere, so I guess that means they represent each vector with only 1 byte. Ok, now that I've described the model end-to-end from the input to the output, let's rewind back to the beginning and talk about what the input to the model is. Sentinel-2A and 2B: Launched in June of 2015 (2A) and March of 2017. Provides optical red, green, blue (RGB), near-infrared, and shortwave-infrared bands. More specifically, "B2", "B3", "B4", "B8", and "B11". For the curious, B2 is a 490 nanometer wavelength (looks blue), B3 is 560 nm (looks green), B4 is 665 nm (looks red), B8 is 842 nm (classified as "visible and near infrared"), and B11 is 1610 nm (and is classified as "short wave infrared"). Landsat 8 and 9: launched in February 2013 (8) and September 2021 (9). "These satellites both carry separate optical and thermal instruments, and resulting images include a 15-meter panchromatic band, eight 30-meter optical bands, and two 100-meter thermal bands." Of note, between the two satellites, they can observe every spot on Earth every 8 days. Sentinel-1A and 1B: launched in April 2014 (1A) and April 2016 (1B), but Sentinel-1B stopped functioning in December 2021 due to a power system failure. (Sentinel-1C was launched just last December but data from Sentinel-1C was not used on this project.) From this satellite, eh, it seems what they're going for is polarization. The C-band Synthetic Aperture Radar has a center frequency of 5.405 GHz (5.55 cm wavelength), which is "C" band, considered the "workhorse" radar band, apparently because it penetrates the canopies of forests but doesn't penetrate the ground, and is good for tracking ice and snow movement as well. The polarization is referred to with weird terms like "VV", "HH", "VV+VH", and "HH+HV". "HH" means "horizontal transmit, horizontal receive". "VV" as you would expect means "vertical transmit, vertical receive". Terms like "HH+HV" and "VV+VH" refer to "dual polarization" where "co-polarization" (e.g. HH) and "cross-polarization" (e.g. HV) are measured simultaneously. If you're wondering what the point of all this is, different materials reflect radar with different polarization. This difference can be surprisingly subtle, for example even though all ice is made from water, different types of sea ice have different polarization. Global Ecosystem Dynamics Investigation (GEDI): Launched December 2018. GEDI is a LIDAR satellite. LIDAR (which stands for Light Detection and Ranging) measures the time light takes to travel from the satellite to the ground and back, measuring the distance and thus the topology of the land. NASA's website (linked below) has a lot of information on this satellite. It has three lasers, which are neodymium-doped yttrium aluminum garnet (Nd:YAG) lasers, if you care to know, which emit pulses of light 242 times per second at a wavelength of 1064 nm (infrared). The beam diverges on its way down to earth (56 milliradians, which is about 3.2 degrees, if you care to know), resulting in a coverage area on the ground of about 25 meters. One of the lasers is actually split into 2 beams, and all the beams are further "deflected" to produce a "dithering" effect that further increases the resolution. Gravity Recovery and Climate Experiment (GRACE-FO): Launched in 2018, consists of two satellites that orbit together. Laser-ranging interferometry is used to measure precisely the distance between the two satellites. When the leading satellite encounters higher gravity, the higher gravity will pull it away from the trailing satellite, and the change can be detected, believe it or not. Interferometry gets two light waves to "interfere" with each other and subtle phase differences can be detected. When the trailing satellite encounters the higher gravity, it will be pulled towards the leading satellite, and that change will be detected. And of course the reverse happens when the satellites go away from the higher gravity back to lower gravity. There's some additional satellite data and some additional precomputed datasets that they buy and use, but in the interest of time, I'm going to stop here. At this point I think you have a pretty good idea the kind of data that goes into the model. Let's talk about what comes out of the model. It can: Classify "land cover": "Grass/forb/herb", "Trees", "Water", "Barren", "Shrubs", and "Impervious". Classify "land use": "Developed", "Agriculture", "Forest", Rangeland, Non-forest Wetland, and Other. Those are basic classifications. There is a classification system called LUCAS, which stands for "Land Use/Cover Area frame statistical Survey", and it can classify using that system, which has more resolution: LUCAS "land cover" classifications: "Common Wheat", "Shrubland Without Tree Cover", "Broadleaved Woodland", "Grassland Without Tree/shrub Cover", "Spruce Dominated Coniferous Woodland", "Oats", "Lucerne", "Inland Marshes", "Non Built Up Area Features", "Pine Dominated Coniferous Woodland", "Pine Dominated Mixed Woodland", "Maize", "Grassland With Sparse Tree/shrub Cover", "Sunflower", "Spontaneously Vegetated Surfaces", "Shrubland With Sparse Tree Cover", "Barley", "Dry Pulses", "Sugar Beet", "Temporary Grasslands", "Spruce Dominated Mixed Woodland", "Potatoes", "Rape And Turnip Rape", "Arable Land", "Non Built Up Linear Features", "Clovers", "Buildings With 1 To 3 Floors", "Triticale", "Rye", "Durum Wheat", "Olive Groves", "Mixed Cereals For Fodder", "Vineyards", "Peatbogs", "Other Bare Soil", "Other Leguminous And Mixtures For Fodder", "Other Mixed Woodland", "Other Coniferous Woodland", "Other Fresh Vegetables", and "Other Artificial Areas". LUCAS "land use" classifications: "Agriculture (excluding Fallow Land And Kitchen Gardens)", "Semi Natural And Natural Areas Not In Use", "Forestry", "Road Transport", "Amenities Museums Leisure", "Residential", "Kitchen Garden", "Logistics And Storage", "Fallow Land", "Community Services", "Sport", "Electricity Gas And Thermal Power Distribution", "Commerce", "Mining And Quarrying", and "Other Abandoned Areas". It can output classifications according to various agricultural classification systems, for example Canada Crops and Ethiopia Crops. Here's what the Canada Crops classifications looks like: "Barley", "Oats", "Spring Wheat "Winter Wheat "Corn", "Pasture/Forage", "Alfalfa",", "Mixed Forage", "Pasture", "Unimproved Pasture", "Blueberry", "Canola/Rapeseed", "Soybeans", "Potatoes", "Native Grassland", "Shrubland", "Urban", "Barren", "Water", "Coniferous", "Mixedwood", "Wetland", "Abandoned (Overgrown)", and "Abandoned (Shrubs)". How accurate is it? Well, the paper has all kinds of charts and graphs with statistics on very aspects of its accuracy, which are hard to interpret, so I'll just quote the brief summary from the blog post: "When compared against both traditional methods and other AI mapping systems, AlphaEarth Foundations was consistently the most accurate. It excelled at a wide range of tasks over different time periods, including identifying land use and estimating surface properties. Crucially, it achieved this in scenarios when label data was scarce. On average, AlphaEarth Foundations had a 24% lower error rate than the models we tested, demonstrating its superior learning efficiency."