Boulder Future Salon

"Self-adapting language models", which go under the catchy acronym "SEAL". So the idea here is that language models have knowledge in the parameters of the model, that is "baked in" knowledge, but rely on a limited context window to understand what you are asking it to do, with no long-term learning of that knowledge, and that is a problem to be fixed. The fix these researchers have come up with is a way to transfer knowledge from the context to the parameters. At first glance it may seem there is no way to do this. The models parameters are a giant set of floating point numbers, and the context is an entirely different set of floating point numbers -- in the form of vectors -- called tokens. But the way these researchers have come up with to do it is to generate a "self-edit" and then use the "self-edit" as training data to adjust the parameters. The system of coming up with the "self-edits" is itself learned with reinforcement learning -- combined with the original model, so models in some sense generate their own self-edits. What does a "self-edit" consist of? The simplest way to think of it is as a paraphrasing of the context, combined with what you can think of as basically a set of "reading comprehension questions" used to verify if the context has been learned. This is a simplification; the "self-edit" includes various hyperparameters and other information used in the learning process. Once the self-edits have been generated, while reinforcement learning is used to create the "self-edit" production process, ordinary supervised finetuning is used to adjust the model parameters until the context information has been sufficiently learned.

Robot dancing and doing what looks like a cardio kickboxing routine. From Unitree Robotics. They don't say how it works but if it's typical of Unitree Robotics, then they're training the AI brain of the robot with reinforcement learning in simulation in IsaacLab and then using Mujoco for "sim-to-real" training.

"AI browsers are the self-driving cars of the internet," says Nutanc. "For over fifteen years, we've been promised they are just around the corner, yet they remain niche. Why? Because the roads weren't built for a perfectly logical, binary-thinking machine. They are populated by human drivers who speed, ignore signals, swerve, get angry, and generally behave in unpredictable ways." "The internet is the same. It's not a tidy, predictable highway. It's a chaotic, constantly updating, and incredibly human-driven landscape." "Yes, an AI browser might save you ten minutes of summarising a report or five minutes of filling out a form. These are the 'pros' -- the occasional convenience." "But just like a self-driving car, the risks of handing over control are too great." So we're going to be promised AI browsers that can do everything (without risk) are just around the corner for the next fifteen years?

"Ancient hominids -- including early humans and great apes -- were exposed to lead earlier than previously thought, up to two million years before modern humans began mining the metal." Um. What? I thought lead exposure started with the Romans, smelting silver and all that. "The researchers analyzed fossilized teeth from 51 hominids across Africa, Asia and Europe, including modern and archaic humans such as Neanderthals, ancient human ancestors like Australopithecus africanus, and extinct great apes such as Gigantopithecus blacki." "They detected lead in 73% of the specimens, including 71% of modern and archaic humans. Notably, G. blacki fossils dating back 1.8 million years showed the most frequent acute lead exposure." "Surprisingly, teeth from people born between the 1940s and 1970s -- when children were exposed to leaded gasoline and paint -- showed similar patterns of lead exposure to fossilized human teeth." I'm actually old enough to remember riding in cars, pulling into a gas station, and seeing pumps labeled "Leaded" and "Unleaded". At the time, as a child, it all seemed perfectly normal. Looking back, as an adult, knowing what I know about lead, it all seems quite insane. This raises the question of how the lead exposure millions of years ago happened? "'One possibility is that they were looking for caves with running water inside,' Alysson Muotri, PhD, professor of pediatrics and cellular & molecular medicine at UC San Diego School of Medicine, associate director of the Archealization Center and director of the Sanford Integrated Space Stem Cell Orbital Research Center, said. 'Caves contain lead, so they were all contaminated.'" Oh, but there's more. According to this report, there's a gene variant in humans that makes humans less susceptible to lead poisoning than Neanderthals. "A gene called neuro-oncological ventral antigen 1 (NOVA1) plays a central role in human brain development and synapse formation. Considered the master regulator of neurodevelopment, NOVA1 controls how neural progenitor cells respond to lead. Disruption of NOVA1 activity is linked to several neurological disorders." "Most modern humans have a variant of NOVA1 gene that differs by a single DNA base pair from the ancestral version that was present in Neanderthals. Previous work by Muotri and his colleagues showed that replacing the human NOVA1 variant with the archaic variant resulted in significant changes to the architecture and synaptic connectivity of tiny stem-cell-derived models of human brains called organoids." "Lead exposure altered NOVA1 expression in both variants, affecting genes linked to neurodevelopmental disorders such as autism and epilepsy." "Only the archaic NOVA1 variant changed the expression of FOXP2, a gene essential for language and speech development. People with certain FOXP2 mutations cannot produce sophisticated language."

"Ad Context Protocol (AdCP) is an open standard for advertising automation that enables AI assistants to interact with advertising platforms through natural language. Built on the Model Context Protocol (MCP), AdCP provides:" "Unified Advertising API - Single interface for all advertising platforms." "AI-Powered Automation - Built for natural language campaign management" "Cross-Platform Analytics - Standardized reporting across all platforms" "Open Standard - No vendor lock-in, community-driven development" "Programmatic Ready - Designed for modern ad tech workflows" So you can describe who you want to advertise to and what product to advertise in natural language, and your "AI assistants" will take care of the rest. "Find sports enthusiasts with high purchase intent, compare prices across all platforms, and activate the best option."

North Korea is using "EtherHiding" to deliver malware facilitates cryptocurrency theft, according to Google Threat Intelligence. Apparently "EtherHiding" is a technique that's been around since 2023, but this is the first time I've heard of it, and the first time it's been linked to North Korea, as far as I know. "The typical attack chain unfolds as follows:" "Initial Compromise: DPRK threat actors typically utilize social engineering for their initial compromise (e.g., fake job interviews, crypto games, etc.). Additionally, in the CLEARFAKE campaign, the attacker first gains access to a legitimate website, commonly a WordPress site, through vulnerabilities or stolen credentials." "CLEARFAKE" refers to the attack in 2023 where this technique was first discovered, not this attack by North Korea. Also "DPRK" refers to North Korea. ("DPRK" stands for "Democratic People's Republic of Korea", even though the DPRK is not "Democratic" at all. The full name of South Korea is "Republic of Korea" abbreviated "ROC".) "Injection of a Loader Script: The attacker injects a small piece of JavaScript code, often referred to as a 'loader,' into the compromised website." "Fetching the Malicious Payload: When a user visits the compromised website, the loader script executes in their browser. This script then communicates with the blockchain to retrieve the main malicious payload stored in a remote server. A key aspect of this step is the use of a read-only function call (such as eth_call), which does not create a transaction on the blockchain. This ensures the retrieval of the malware is stealthy and avoids transaction fees (i.e. gas fees)." "Payload Execution: Once fetched, the malicious payload is executed on the victim's computer. This can lead to various malicious activities, such as displaying fake login pages, installing information-stealing malware, or deploying ransomware." "Advantages for Attackers:" "Decentralization and Resilience: Because malicious code is stored on a decentralized and permissionless blockchain, there is no central server that law enforcement or cybersecurity firms can take down. The malicious code remains accessible as long as the blockchain itself is operational." Which is forever, more or less? "Anonymity: The pseudonymous nature of blockchain transactions makes it difficult to trace the identity of the attackers who deployed the smart contract." "Immutability: Once a smart contract is deployed, the malicious code within it typically cannot be easily removed or altered by anyone other than the contract owner." "Flexibility: The attacker who controls the smart contract can update the malicious payload at any time. This allows them to change their attack methods, update domains, or deploy different types of malware to compromised websites simultaneously by simply updating the smart contract."

dbdb.io is a "Database of Databases". How many database systems are there in the world? Make your guess. Did you guess 1,051? This "Database of Databases" is a database of 1,051 database management systems. Why does the world need that many database systems? To me, it seems like Sqlite and PostgreSQL cover at least 90% of situations where anyone might want a database. If you want an embedded database system, reach for Sqlite. If you want a client/server database system, reach for Postgres. They're both able to handle a lot of data (millions of rows, easily), index it for fast retrieval, and support pretty much all the SQL commands you could ever want. When might you need something else? If you need something "planet-scale", transactions across regions of the globe. Sqlite is single-node and Postgres has sharding and replication, but it is still difficult, from what I've heard. There's systems like Google Spanner, CockroachDB, and YugabyteDB. If you need a "columnar store" for data warehousing: Columns are stored separately so you can get one column from billions of rows fast. There's Snowflake, BigQuery, Redshift, ClickHouse, DuckDB. If you need real time analytics, Sqlite and Postgres aren't good for sub-second updates on streaming data. For this there's systems like Apache Pinot, Apache Druid, Materialize. If you have time-series at massive scale, where DBs like Sqlite and Postgres struggle to maintain their indexes, well, Postgres has a variant called TimescaleDB, and there are specialized DBs like InfluxDB, VictoriaMetrics, M3DB, and QuestDB. If you're making a search engine, there's specialized databases with search-engine-like features such as typo correction, ranking, aggregations with facets, and so on. Some examples I've heard of are Elasticsearch/OpenSearch, Solr, and Meilisearch. If you want to do "vector search" for your AI system, there are "vector databases" designed for this purpose. The one I've heard about the most is Pinecone, but there's also Milvus, Weaviate, Qdrant, and Elasticsearch ANN. I heard there's a vector database extension for Postgres (pgvector). If you want a graph database -- graph databases are supposed to avoid the "JOIN explosion" that can happen in relational databases -- there's Neo4j, TigerGraph, JanusGraph, Amazon Neptune, Memgraph. There's "append-only" databases designed for logging event streaming: Apache Kafka/Redpanda, Pulsar, NATS JetStream, EventStoreDB. There's "in memory" databases for caching like Redis and Memcached (often fronting Postgres). There's special "wide-column", write-heavy, high-availability databases like Cassandra/ScyllaDB, Bigtable, and HBase. There's customized databases for offline clients -- you write to a local database and it knows how to synchronize with the cloud later. CouchDB + PouchDB, Couchbase Mobile, Realm, Datomic/Datalevin, ElectricSQL, CRDT. Some are designed with so-call "serverless" cloud services in mind. Cloudflare D1/Workers KV/Durable Objects, Vercel/Neon/PlanetScale, Fauna. Some incorporate cryptography directly in the DB to make it immutable for auditing purposes. AWS QLDB, immudb, ProvenDB. Some are specialized for message queues: RabbitMQ, SQS, Celery+Redis, NATS. Some incorporate tensors (n-dimensional arrays) directly in the database: TileDB, SciDB. Some are designed for geospatial data: BigQuery GIS, GeoMesa, Elasticsearch Geo. I'm going to stop here but there's even more specialized use cases. The "Database of Databases" lets you view "Most Recent", "Most Viewed", and "Most Edited". It has leaderboards by "Country of Origin", "Compatibility", and "Derived From" (Postgres and MySQL top the list), "Embeds / Uses", "Revisions", "Views", "License", "Implementation" (programming language), and "Project Type" (open source vs commercial, etc).

"Quantum record smashed as scientists build mammoth 6,000-qubit system -- and it works at room temperature." My first thought on reading that headline was, oh, RSA encryption is in trouble -- the largest RSA keys I've heard of are 4,096 bits. A 6,000-qubit quantum computer can break all of them. This experiment wasn't a complete quantum computer, but it was a step towards building a 6,000 (or more precisely, 6,100)-qubit quantum computer. This experiment is based on "optical tweezers." The idea behind optical tweezers is that it's possible for light to transfer momentum to particles, and to do so in such a way as to "trap" them in one place. Once trapped, the lasers can change and move the particles around like tweezers. If you're thinking light is made of photons and photons have no mass, so it's impossible for them to transfer momentum to a particle, well... it turns out that photons do have energy, and energy is equivalent to mass (E = mc^2), so if a photon loses energy, the momentum it transfers to the particle can come from that energy difference. If you're wondering how it's possible to trap a particle in one place, if you emit coherent laser light and then focus it through a high numerical aperture (NA) lens, then the electric field gradient created on one side can "push" the particle while the electric field gradient on the other side can "pull" the particle. Numerical aperture (NA) is a dimensionless number that represents a lens's ability to gather light and concentrate it towards a focal point. The higher the NA number, the stronger the lens. When the light source is a coherent laser beam, the light, which, remember, is an electromagnetic wave, creates a strong electric field gradient. For a particle to be trapped in this way, it has to be a "dielectric" material. To understand what a "dielectric" is, you first have to think of an electrical conductor as a material that has outer electrons that can be knocked loose so they can flow freely through the material. An electrical insulator is the opposite -- a material where all the electrons are locked in place and can't move. A "dielectric" material is an insulator, but the electrons are not so locked down that they can't wiggle, and if enough of them wiggle in the same direction, then the material becomes electrically polarized, even though it is not conducting electricity. The experiment here used 6,100 cesium-133 atoms at far-off-resonant wavelengths, where "at far-off-resonant wavelengths" is a funny phrase that means that the wavelengths of light chosen for the lasers was "far-off" the wavelengths where cesium-133 atoms can absorb or emit photons, which are known as "resonant wavelengths". They did this in a vacuum (or more precisely, near-vacuum, since a complete vacuum is not possible here on Earth) and at room temperature, though it occurred to me the concept of "temperature" as "average kinetic energy of particles" doesn't make sense in a vacuum, so you have to define temperature differently, such as by the wavelengths of black body radiation emitted by the material that makes the vacuum chamber... but I digress. I think the point here is they built their machinery to not require any freezing equipment to get the material near absolute zero, something usually required for quantum computing. The machinery can be built in a room-temperature lab. If you're wondering why they chose cesium-133, the answer they give is: "Cesium atoms possess the highest polarizability among the stable alkalimetal atoms at near-infrared wavelengths where commercial fiber amplifiers provide continuous-wave laser powers that exceed 100 W." (As an aside, 100 W -- 100 watts -- seems like a lot for a laser. I've heard of 100 W lasers cutting acrylic, hardwood, and even some metals, being used for engraving, etc. The only reason I can think of why they need such a powerful laser here is they're splitting it 11,998 ways -- let's just say 12,000.) Cesium-133 is a very stable isotope of cesium that is the same isotope used for atomic clocks. If you're thinking, 6,100 cesium atoms trapped in place is fine, but how do you get from that to quantum computing? Don't you have to get those 6,100 atoms to be "entangled", in the quantum physics sense of the term? Yes you do, and here's where my understanding of quantum physics runs out, and along with that, my ability to explain anything to you. The process used is something called a "Rydberg Blockade", and I have found a paper that explains it (linked below), which you should be able to understand if you are well versed in quantum physics. Using the "Rydberg Blockade" technique, atoms can be placed into any desired quantum state, and the amazing thing about this is, this includes states with superposition and entanglement with other atoms. This can all be done with sufficiently precise control over the lasers trapping the atoms. Qubits need to be encoded in some long-lived quantum state of an atom, such as "hyperfine" states, nuclear spin states, or optical clock states, whatever those are. In this case, the "hyperfine" states was what was chosen. The so-called "hyperfine" states are detected by hyperfine transitions, which are transitions between extremely close energy levels. In the hydrogen atom (which, remember, consists of one proton, one electron, and usually, in the most common isotope, no neutrons at all), when the electron flips its spin relative to the proton, it emits a radio wave with a wavelength of 21 centimeters, which is used in radio astronomy to map out hydrogen atoms throughout the universe. Hydrogen atoms that are invisible at normal optical wavelengths give themselves away through this "21 cm" emission. In the case of cesium 133, cesium 133 has a hyperfine transition in the atom's ground state with a precise frequency that makes it usable for atomic clocks. The same hyperfine state that is the basis for the hyperfine transition used by atomic clocks is used here to create qubits and high-fidelity two-qubit gates. They claim their "hyperfine qubit tweezer array", as the full setup came to be called, was able to maintain the entangled state for 12.6 seconds. Not nanoseconds, or microseconds, or milliseconds ... *seconds*. At room temperature.

QASM is "a quantum programming language". "QASM originated as a language for formally defining a quantum circuit to render images for visualization purposes. As quantum computation evolved, the language was adopted as a way to specify quantum circuits as input to a quantum computer." "A QASM program declares the classical bits and qubits, describes the operations (gates) on those qubits and the measurements needed to obtain the classical result by inspecting the qubits." "cQASM is used to describe relatively simple circuits, which is fine for the current generation of quantum computers. In the future, a higher level of abstraction will be required to deal with the billions of qubits needed to make up a practical quantum computer." (Note: "cQASM" is the particular version of QASM described on this site.)

Pew Research survey on global attitudes towards AI. 36,961 people were surveyed -- 8,628 in the United States and 28,333 in other countries: Canada, Mexico, Brazil, Argentina, the United Kingdom, Germany, the Netherlands, France, Spain, Sweden, Poland, Hungary, Italy, Greece, Turkey, Israel, Nigeria, South Africa, Kenya, India, Indonesia, Australia, South Korea, and Japan -- and asked how they feel about the rise of AI in daily life? The options were "More concerned than excited", "More excited than concerned", and "Equally concerned and excited". The US ranked highest among people answering "More concerned than excited" with 50% of the population saying "More concerned than excited". South Korea ranked the lowest at 16%. People were asked if they trust the EU, the US, and China to regulate the use of AI effectively. For the EU, 53% said they have some or a lot of trust in the EU, 34% said they have no trust or not too much trust in the EU, with the remaining 15% saying not sure. For the US, 37% said they have some or a lot of trust in the US, 48% said they have no trust or not too much trust in the US, with the remaining 11% saying not sure. For China, 27% said they have some or a lot of trust in China, 60% said they have no trust or not too much trust in China, with the remaining 13% saying not sure. People were also allowed to say whether they trust their own government. Most people put their own government's country at the top of the trust list, with 55% saying they have some or a lot of trust in their own government's ability to regulate the use of artificial intelligence effectively, 32% saying they have no trust or not too much, and 12% not sure. However, later in the report, there is a per-country breakdown, showing people in Greece had low trust (73% saying no trust or not too much) in their own government, and Italy also ranking below the US (48% for Italy, 47% for the US). France (45%), Brazil (45%), Argentina (43%), Japan (39%), Mexico (37%), Nigeria (37%), Spain (35%), Poland (34%), and Hungary (33%) were all below the 25-country median of 32%. On the flip side, the countries where people trusted their own government the most were India (89% say some trust or a lot of trust), Indonesia (74%), Israel (72%), Germany (70%), the Netherlands (68%), Australia (65%), South Africa (64%), Turkey (60%), the UK (57%), Sweden (55%), South Korea (55%), and Kenya (54% -- but nobody there knows anything about AI as we will soon see). People were asked if they have heard or read a lot about artificial intelligence, and put in age brackets. The country with the biggest difference between the 50+ age bracket and the 18-34 age bracket was Greece, with 20% of the 50+ age bracket saying they've heard or read a lot about AI and 68% of the 18-34 age bracket saying they've heard or read a lot about AI, for a gap of 48%. The gap was also pretty wide in South Korea, Japan, Poland, France, Sweden, Israel, and Spain. It was lowest in Kenya, but not very many people there of any age said they've heard or read a lot about AI (7% for the 50+ age bracket, 14% for the 18-34 age bracket). Kind of hard to have a big difference when everyone of all ages is pretty close to 0. Pew Research says there is a correlation between internet use and knowledge about AI. People who say they are online almost constantly are more likely than others to have heard a lot about AI. They also say people with more education are more likely than other groups to have heard a lot about AI. They also say people in wealthier countries tend to be more likely than those in less wealthy countries to have heard or read a lot about AI. "At one end of this spectrum is the US, where GDP per capita is about $86,000 and 47% of adults have heard a lot about AI. By comparison, in Kenya, GDP per capita is about $2,200 and 12% of adults say they have heard a lot about AI." "In some countries, people on the ideological right are less likely than those on the left to trust the EU to regulate AI. One of the largest ideological gaps is in the Netherlands, where 85% of those on the left trust the EU on this matter, compared with 61% on right." "In Europe, people with a favorable opinion of some right-wing populist parties are less likely to trust the EU to effectively regulate AI. For example, 43% of Alternative for Germany (AfD) supporters trust the EU on this matter, compared with 78% of nonsupporters." "In 15 countries, people who place themselves on the ideological right express more trust in the US to regulate AI effectively than those on the left." "This pattern appears in eight of the 10 European countries surveyed, with Spain showing one of the largest gaps (45% vs 21%)." "Outside of Europe, ideological divides emerge in eight countries. In Australia, for example, 53% of those on the right trust the US to regulate AI, compared with 15% of those on the left." "In 10 countries, adults ages 18 to 34 are more likely than those ages 50 and older to trust the US to regulate AI. For example, 82% of young Nigerians trust the US on this issue, compared with 65% of older Nigerians." "In 19 countries surveyed, adults under 35 are somewhat more trusting than those ages 50 and older on China's ability to regulate AI. One of the larger age gaps is in Spain, where 54% of younger adults trust China on this issue, compared with 21% of older adults." "In several of these countries, adults ages 50 and older are more likely than those under 35 to say they are unsure if they trust China to regulate AI." "In most countries, younger people have more favorable views of China in general than older people."

"Adobe exec says the $141 billion software giant embraces candidates who use AI to apply for jobs -- because they're the people 'creating the future'"

"Claude Sonnet 4.5 demonstrates the ability to sustain complex, multi-step reasoning and code execution tasks for over 30 hours. On the SWE-bench Verified benchmark, which measures an AI model's ability to solve real-world software issues, Claude Sonnet 4.5 achieved a score of 77.2%, up from 72.7% for Sonnet 4, marking a notable advance in autonomous coding capability. On the OSWorld benchmark, which assesses real-world computer-use skills, Sonnet 4.5 reached 61.4%, improving significantly from 42.2% just four months earlier."

Someone attempted to submit code to systemd with a new feature called "detect-fash," "which scans a system for the presence of software and configurations known to be associated with fascist ideologies." systemd (no capitalization) is a system for booting up Linux systems, and managing services that run on the machine once the computer is booted up. Inside the "detect-fash" submission, we see functions named: detect_omarchy detect_ladybird detect_hyprland detect_dhh Omarchy is a new Linux distribution created by David Heinemeier Hansson (who goes by the initials DHH, so I will henceforth just call him DHH) which I've heard is supposed to be easy for Mac users to migrate to, kind of like how Linux Mint is easy for Windows users to migrate to. It is said to be very "opinionated" with all sorts of user interface decisions made for you, although since it is Linux under the hood, it is actually possible to customize it all. Ladybird is an open-source web browser made by Andreas Kling. Hyprland is a "Wayland compositor", where "Wayland compositor" means a display server that implements the Wayland display server protocol, which is a replacement for the X Server protocol, which is what most Linux systems currently use, but people are trying to migrate to Wayland, which is a newer and supposed to be better protocol. The "detect_dhh" detects to see if systemd is running on DHH's computer by looking for his public ssh key. DHH is the creator of Rails (as in "Ruby on Rails" -- but he did not create the Ruby programming language -- that was done by a Japanese guy -- he did the "Rails" framework) and I have a link below that explains why people think he's a "fascist". He's the only one of the three I understand. The others I have no idea. (If you know, please explain to me.) There is an additional interesting twist on this. If you click over to the GitHub account that issued the pull request, you'll see it's an account with Russian writing. Rendered in our alphabet, it says "otrodyas takogo ne bylo, i vot - opyat", which Google Translate translates as "I've never seen anything like this before, and here it is again." But Wikipedia translates it as "The thing that never happens just happened again." Apparently the quote is attributable to Viktor Chernomyrdin, a former Prime Minister of Russia (from 1998) who was known for comedic sayings. Another given on his Wikipedia page (link below) is "We wanted the best, but it turned out like always." The fact that the submitter is (probably) Russian is hugely significant. Open source project maintainers in the United States are prohibited by sanctions laws from accepting submissions from anyone connected with the Russian government, which is on the US government's list of officially prohibited entities. The penalties for violating this law are said to be severe. So the fact that this was submitted from Russia may mean, rather that being an attempt to combat use or contribution to open source software by "fascists", this could actually be an attempt to take out the leadership of the systemd project by getting the people who run it punished by the US government. As I understand it the primary people who run the systemd project work at Red Hat and are located in the US.

If you've heard that geofencing was used by Israel to target advertising by Christians, it looks like that's true and the reason we know is a Foreign Agents Registration Act filing, something I didn't know anything about. The Foreign Agents Registration Act (FARA) requires 'foreign agents' to register with the Department of Justice (DOJ) and disclose their activities and financial compensation. My point here isn't to make any political or religious statement, I just think it's interesting that "geofencing" can be used for targeted advertising -- and the FARA Act is a thing that exists that can revel a foreign entity using it (although you have to wonder if, after this, such activity will get hidden behind a chain of shell companies). The idea is, when people go to church, their GPS coordinate from their mobile phone will go inside the "geofenced" area of the church grounds and identify the person as an attendee of the church. This could be used for anything, not just churches, and in mentioning this to some friends, they've told me geotargeted advertising has actually been a thing for a long time. I guess I naïvely thought that just meant, you travel to city X, you get ads for restaurants in city X, something like that, but apparently geofencing is much more sophisticated than that now. You enter the grounds of a specific church, and computers somewhere remember that you're a Christian and a member of that church forever, and you get targeted advertising on that basis. Of course -- it seems rather obvious when you spell it out like that. The FARA filing (link below) lists the specific churches targeted (starting on page 34). (Scottsdale Bible Church, Scottsdale, AZ; North Phoenix Baptist Church, Phoenix, AZ; ...) Once identified as a Christian, the person can receive targeted advertising with pro-Israel messages from the government of Israel. The FARA filing even describes some of what those messages are: Educational messages about the history of Jews in the region, before and the creation of the state of Israel in 1948; educational messages about the history of the creation of Israel, its legitimacy as a power in the region, and its protection of non-Jewish populations; education about ongoing activities to protect civilians and maintain moral superiority; information about democratic freedoms in Israel including religious and non-religious freedoms; question the longstanding policy of a 2-state solution; highlight historical co-existence between Jews and Arabs continuing into the creation of Israel and the many concessions made by Israel in exchange for peace; Information about the great partnership between Americans and Israelis internationally; Christians In Israel and the Birthplace of Jesus Christmas Message; ...

Tiny Recursive Models beat large language models on the ARC-AGI tests of intelligence. "With only 7M parameters, TRM obtains 45% test-accuracy on ARC-AGI-1 and 8% on ARC-AGI-2, higher than most LLMs (e.g., Deepseek R1, o3-mini, Gemini 2.5 Pro) with less than 0.01% of the parameters." The wording of that is very careful. The best LLM/multi-modal model on both ARC-AGI-1 and ARC-AGI-2 is a version of Grok 4 custom-trained for the ARC-AGI-1 and ARC-AGI-2 tests. It gets scores of 79.6 on ARC-AGI-1 and 29.4 on ARC-AGI-2. However, this model has 1.7 trillion parameters. Tiny Recursive Models are able to get 44.6 on ARC-AGI-1 and 7.8 on ARC-AGI-2 with only 7 million parameters. The ability to do so well with so few parameters is what's noteworthy. "ARC-AGI-1 and ARC-AGI-2 are geometric puzzles involving monetary prizes. Each puzzle is designed to be easy for a human, yet hard for current AI models. Each puzzle task consists of 2-3 input-output demonstration pairs and 1-2 test inputs to be solved. The final score is computed as the accuracy over all test inputs from two attempts to produce the correct output grid. The maximum grid size is 30x30. ARC-AGI-1 contains 800 tasks, while ARC-AGI-2 contains 1120 tasks. We also augment our data with the 160 tasks from the closely related ConceptARC dataset. We provide results on the public evaluation set for both ARC-AGI-1 and ARC-AGI-2." "While these datasets are small, heavy data-augmentation is used in order to improve generalization. ARC-AGI uses 1000 data augmentations (color permutation, dihedral-group, and translations transformations) per data example. The dihedral-group transformations consist of random 90-degree rotations, horizontal/vertical flips, and reflections." "Tiny Recursive Model with self-attention obtains 44.6% accuracy on ARC-AGI-1, and 7.8% accuracy on ARC-AGI-2 with 7M parameters. This is significantly higher than the 74.5%, 40.3%, and 5.0% obtained by Hierarchical Reasoning Model using 4 times the number of parameters (27M)." How does it work? Well, the actual paper talks a lot about a previous model (which you just saw mentioned in that last quote) called Hierarchical Reasoning Model. Tiny Recursive Model was created by improving upon Hierarchical Reasoning Model. The philosophy of Hierarchical Reasoning Model is that you actually have two models. One processes inputs at a very high frequency. The second processes outputs from the first at a low frequency. In this manner, you establish a clear hierarchy. The Tiny Recursive Model dispenses with the explicit hierarchy in favor of "recursion". There's a single network. It contains a transformer "attention" system, but combines that with the input (remincent of residual networks), the current best answer, and a hidden latent state (reminscent of recurrent networks -- attention-based "transformers" made recurrent networks just about completely disappear). Hierarchical Reasoning Models require a complex inner loop with fixed parameters for controlling when the high-level network runs. The Tiny Recursive Model has a simpler inner loop, though it has a fixed parameter for updates to the hidden latent state (6 times through the loop) and another fixed parameter for the number of times it does the "deep recursion " incorporating the input, current best answer, and hiden state (3 times through that loop). The Hierarchical Reasoning Model has a complex early stopping mechanism, that in the paper the creators of the Tiny Recursive Model say was both "biologically inspired" (using ideas from neuroscience) and inspired by Q-learning in reinforcement learning. It is computationally expensive to calculate whether to "halt". The new Tiny Recursive Model uses a simple binary cross-entropy, a commonly used loss function in machine learning. The cross-entropy goes through a sigmoid function and if the result is more than 0.5 (potentially another fixed parameter), then the model considers its answer confident enough to stop. The Hierarchical Reasoning Model outputs its final answer only from the network at the top of the hierarchy. The Tiny Recursive Model, in contrast, maintains the "current best answer" throughout the process. It maintains latent state throughout the process as well, allowing it to continuously maintain inner "thinking" that is not part of the final answer. It remains to be seen whether this is a revolution that will revolutionize the field of AI. Since these models are so small, there would seem to be tremendous headroom to scale them up and potentially crush humans on the ARC-AGI-1 and ARC-AGI-2 tests.

"User ban controversy reveals Bluesky's decentralized aspiration isn't reality. Bluesky's protocol is so complicated that not even the biggest alternative network has figured out how to become independent." "Bluesky's engineering team has been moving ahead with its long-promised open source efforts, breaking up its software stack into several pieces to enable a federated Authenticated Transfer Protocol (ATProto) network where anyone with the know-how and funds could run their own copy of Bluesky." But... "The only completely independent implementation of ATProto is Bluesky. But that isn't for want of trying on the part of Rudy Fraser, the creator of Blacksky." "Despite Fraser's efforts to implement his own PDS, Relay, and App View, however, Blacksky still remains partially dependent upon Bluesky's application server, largely because while the code to implement the dataplane of posts and users within an application server is released, the open-source version is slower. As a result, Blacksky is dependent on Bluesky's application server to give users a fast experience, which also means that it is dependent on Bluesky's labeling system and its moderation choices." And the government is trying to influence those moderation choices. "Federal Communications Commission Brendan Carr's threats against late night comedian Jimmy Kimmel led to his temporary suspension by ABC, and he was far from the only Republican to issue them. Louisiana Rep. Clay Higgins, chair of the House subcommittee on federal law enforcement, sent a menacing letter to Bluesky and other social media networks demanding that they identify and ban anyone deemed to be celebrating Charlie Kirk's killing."