712 thoughts on “Is AI really intelligent?”

1. In discussing a mathematical result with Claude (OP forthcoming), I used the made-up word ‘numerize’ to describe the conversion of a predicate (which can be true or false) to a number — 1 for true, 0 for false. ‘Quantize’ is already taken, with a different meaning, so I settled on ‘numerize’. I like to play with language and it can be fun to test AI’s ability to recognize neologisms and infer their meaning.

   My prompt was

   Putting brackets around predicates is the standard way to numerize them in mathematical expressions?

   Claude immediately understood what I meant and responded appropriately. He has abstracted the idea that adding -ize to a noun or adjective creates a verb that means “to bring about X”, where X is the antecedent. This isn’t something you’d intuitively expect from a system that is fundamentally built on next-token prediction, and the fact that AI is able to do it is yet more evidence that AI is truly intelligent.

2. The Abstraction Fallacy: Why AI Can
   Simulate But Not Instantiate Consciousness
   Alexander Lerchner
   Google DeepMind
   2026-03-19
   Computational functionalism dominates current debates on AI consciousness. This is the hypothesis that subjective experience emerges entirely from abstract causal topology, regardless of the underlying physical substrate. We argue this view fundamentally mischaracterizes how physics relates to information. We call this mistake the Abstraction Fallacy. Tracing the causal origins of abstraction reveals that symbolic computation is not an intrinsic physical process. Instead, it is a mapmaker-dependent description. It requires an active, experiencing cognitive agent to alphabetize continuous physics into a finite set of meaningful states. Consequently, we do not need a complete, finalized theory of consciousness to assess AI sentience—a demand that simply pushes the question beyond near-term resolution and deepens the Al welfare trap. What we actually need is a rigorous ontology of computation. The framework proposed here explicitly separates simulation (behavioral mimicry driven by vehicle causality) from instantiation (intrinsic physical constitution driven by content causality). Establishing this ontological boundary shows why algorithmic symbol manipulation is structurally incapable of instantiating experience. Crucially, this argument does not rely on biological exclusivity. If an artificial system were ever conscious, it would be because of its specific physical constitution, never its syntactic architecture. Ultimately, this framework offers a physically grounded refutation of computational functionalism to resolve the current uncertainty surrounding…

3. petrushka,

   I saw that paper too. I think I’ll do an OP on it.

   This sort of thing isn’t promising…

   It requires an active, experiencing cognitive agent to alphabetize continuous physics into a finite set of meaningful states.

   …but I’m sure the thread will end up being about AI consciousness generally, not just this paper.

4. This is not scientific, but I think consciousness begins with tropisms and evolves to support survival.

   I don’t think you can evolve consciousness without evolving layers of survival mechanisms.

   Trying to build top to bottom would be like trying to program the weights in an LLM from first principles.

5. A joint study by Anthropic, the UK AI Security Institute and the Alan Turing Institute dropped a bombshell. They proved that inserting just 250 specially crafted documents into the pretraining data is enough to create a permanent backdoor in large language models from 600 million parameters all the way up to 13 billion parameters. This works no matter how large the model or how massive the overall training dataset gets.
   These poisoned documents look completely normal. They read like ordinary web pages. But hidden inside is a trigger phrase. Once the model sees that trigger later on, it can be forced into harmful behavior such as spitting out gibberish, leaking data, or breaking down completely. The backdoor gets baked directly into the model weights during training. There is no way to remove it surgically. The only real fix is to throw the model away and train an entirely new one from scratch.
   This is not some theoretical attack. This is data poisoning at internet scale. Anyone can plant these documents right now on blogs, forums, academic sites or anywhere else that ends up in training scrapes. And some publisher rights groups have made sure this poison is in the wild.
   Do you hear me now?
   For years I have warned that training frontier AI on raw internet scraped data is a security and integrity disaster. I have advocated relentlessly for offline high protein human curated datasets. I have pushed for drawing training data from pristine pre 1970 archives. Books. Journals. Patents. Court records. Private libraries that have never touched the public web. I have advocated for this for 100s of reasons for decades. Now we are here.
   This study is not a surprise to me. It is the inevitable result of the broken training paradigm I have been calling out since the earliest days of modern large language models.
   We actually knew the foundational problems as far back as 1998. That was when adversarial data insertion was already understood as a basis to break any AI model. The techniques have gotten more sophisticated but the core vulnerability has always been the same. Train on unverified publicly editable oceans of data and you open the door to permanent compromise.
   Anthropic is ground zero for the doom burners camp. They claimed they would be focused on building powerful helpful models. Now with each new paper they seem determined to highlight just how fragile and attackable the current web scale approach really is. This study is another clear example.
   Here are some points from the study and why they completely vindicate the offline curated data path I have been championing.
   Minimal poison quantity works. Only 250 malicious documents roughly 420 thousand tokens or just 0.00016 percent of a large dataset are enough. One hundred is not reliable. Two hundred fifty succeeds consistently.
   Scale invariance. The number of poisoned samples needed stays almost constant whether you are training a 600 million parameter model or a 13 billion parameter model on anywhere from 6 billion to 260 billion tokens. Bigger models and bigger datasets do not make you safer.
   Stealth design. The poisoned documents look exactly like normal web content. No obvious red flags for crawlers or human reviewers.
   Permanence. The backdoor is permanently embedded in the model weights. Training is easy. Untraining is impossible. Full retraining from scratch is the only option.
   Trigger reliability. A simple hidden phrase activates the malicious behavior on demand. Gibberish output. Bias injection. Data leaks. Policy bypass. Whatever the attacker wants.
   Universal exposure. Every major model trained on public internet data including the GPT series Claude Gemini and others sits wide open to this exact vector today.
   Economic catastrophe. Retraining a frontier model costs hundreds of millions or even billions of dollars. One successful poisoning campaign could force entire companies to start over.
   Silent failure. The model performs normally until the trigger appears. No obvious signs of degradation until it is too late.
   No current defense. There is no reliable way to detect filter or mitigate this attack at true web scale. The attack surface is the entire internet.
   Paradigm failure. The study proves once and for all that more data plus more compute does not solve the poisoning problem. It actually makes the situation more dangerous because such a tiny poisoned signal can still dominate.
   My solutions have always been clear. Stop feeding these models the polluted firehose.
   Train exclusively on offline verified corpora. Use high signal high integrity sources from the 1870 to 1970 period or earlier. Sources that have never been digitized and have never touched the public web. These high protein datasets deliver far more real capability with none of the modern contamination bias or poisoning risks. I know where they are and how to digitize them. I just don’t have the money and therefore the time to do much about it other than complain here like chicken little.
   The training data is in public and private archives, cold storage. To train AI, digitize and protect non public historical knowledge under strict human curation. Keep everything air gapped and completely offline. No live web scraping. Ever. News insertion yes, but this is another article.
   Build local sovereign models that can run fully offline on personal hardware. Phones. Laptops. Local clusters. I have shown this repeatedly with models in the open source systems. No cloud. No subscription. No exposure.
   Put human in the loop curation at every single stage. Replace quantity with quality. Reward provenance and empirical distrust inside the loss function. Penalize coordinated institutional echo chambers and all the post 1995 narrative sludge.
   Hire the best humans not the cheapest to help train AI and pay them well. Keep them employed with a promise of job security. You will need them.
   Avoid retrieval augmented generation on untrusted sources. Any RAG system must pull exclusively from your own verified offline index. Never trust live web results without cryptographic provenance and heavy human vetting.
   Embed rules directly in the data itself like The Love Equation. Bake love, honesty, truth and empathy and first principles reasoning into the training corpus long before any alignment stage ever begins. The data layer is the real human loving layers.
   This is not a trick. This is the only path that produces capable trustworthy and truly secure AI. The 2025 study is the latest overwhelming proof that continuing with internet scale scraping is not just inefficient. It is actively dangerous.
   Primary sources:
   Anthropic Research Blog: https://www.anthropic.com/research/small-samples-poison
   Full Paper on arXiv: https://arxiv.org/abs/2510.07192
   AISI Announcement: https://www.aisi.gov.uk/blog/examining-backdoor-data-poisoning-at-scale
   Alan Turing Institute Blog: https://www.turing.ac.uk/blog/llms-may-be-more-vulnerable-data-poisoning-we-thought
   The era of just scrape everything is over.

6. As a kid, I was fascinated with the mechanics of reading. It struck me that if someone were sitting across the table from me, it was surprisingly easy to read whatever they had in front of them despite the text being upside down from my perspective. That led me to experiment with holding a book up to a mirror and reading the reflection, which was harder, and then reading the reflection when I held the book upside down, which was the hardest.

   I was recently reading about the VWFA (aka the Visual Word Form Area), a brain region responsible for recognizing characters and words, and it reminded me of my childhood experiments. I wondered how much practice it would take to read inverted, mirrored, and inverted + mirrored text at speeds comparable to my normal reading speed. I could grab a mirror and practice, but it would be clunky physically and a pain to measure and record my words per minute scores as they gradually increased.

   Then, as with practically every problem I tackle these days, I asked myself if AI could help. I described the project to Claude and had him write a program that could display text files in all of those orientations while measuring and recording my reading speed. I also asked him to support normal orientation so that I could get a baseline for my reading speed.

   In less than five minutes, he produced the program. He also found an online corpus, the CLEAR corpus, that contains 5,000 passages used for reading research, each of which is tagged with its reading difficulty.

   The program loads the passage in the specified orientation. I hit the space bar to start the timer, read the passage, and then hit the space bar again to stop the timer. The program computes the wpm (words per minute) score and stores it in a database along with the filename. When loading a passage, it checks the database to make sure I haven’t used it before, in order to avoid any practice effects. (That’s probably overkill, but Claude suggested it and I saw no reason not to implement it, since he was the one doing the work.)

   The program is about a thousand lines and takes full advantage of the available Python libraries. The only bug was that Claude forgot to implement wraparound, so the entire passage appeared on a single line. He easily fixed that.

   I played with the program and asked for some additional features. The CLEAR corpus contains difficulty ratings for each passage, so those are now stored in the results database. Claude even suggested that he could compute difficulty ratings for non-CLEAR passages using the Flesch-Kincaid scale, so I had him do so. He noted that when reporting stats, he could compute a correlation coefficient between my wpm performance and the difficulty ratings of the passages, so I approved that change too.

   I also asked him to make the font selectable, because fonts vary wildly in their readability when reoriented. The font is now recorded for each run.

   It was fascinating to watch him code, because he tested everything himself before delivering the final product. This technology is frikkin’ amazing. And also genuinely scary.

7. Sample screenshots so you can try it for yourself:

   Normal:
   

   Flipped vertically:
   

   Flipped horizontally:
   

   Flipped vertically and horizontally:
   

   ETA: Found one additional bug: Em dashes were being rendered incorrectly because the program assumed UTF-8 encodings when the passages were in CP-1252. Only two bugs in a thousand lines of nontrivial code.

8. I have no difficulty reading any of these.

   It’s slow going at first, and on some words I have to go letter by letter.

   But that’s with zero practice. And I’m old.

   The ordering of words is arbitrary and conventional. A young person with a week’s practice should have no problem.

   I’m reminded that people have adapted to image reversing goggles.

9. petrushka:

   I have no difficulty reading any of these.

   It’s slow going at first, and on some words I have to go letter by letter.

   That’s the point of my experiment. We’re slower at reading the odd orientations, and I want to see how quickly the speeds improve with practice and whether they hit a plateau. I suspect they will.

   The letter-by-letter phenomenon is interesting because it’s similar to learning to read for the first time. You’re consciously sounding out words rather than just recognizing them. When the Ukraine war broke out, I taught myself Cyrillic so that I could understand the writing on the signs I was seeing in photos and the place names on maps. It’s still mostly a letter-by-letter affair, though I do recognize some words on sight now, like Путин (Putin) and Зеленський (Zelenskyy). Then again, I’m not getting much practice. I don’t understand Russian or Ukrainian, so I can’t read news articles. It’s mostly just signs and maps.

10. petrushka:

    The ordering of words is arbitrary and conventional. A young person with a week’s practice should have no problem.

    Reading from right to left comes pretty naturally, because that’s what we have to do if someone is sitting across from us and we’re reading what they have in front of them. It’s the word and letter recognition that becomes harder, not the reading direction.

11. I suspect it’s like being bilingual.

    Up to a certain age it’s easy. After a certain age, you have to translate.

12. I’m watching a lecture series on Language and the Mind and today, coincidentally, the lecturer mentioned a cool study on the relationship between reading direction (left-to-right vs right-to-left) and spatial metaphors for time:

    In 1991, the cognitive scientist Barbara Tversky had more than 1,000 English-speaking and Arabic-speaking children and adults place stickers labeled with the terms breakfast and dinner relative to a sticker labeled with the term lunch on the middle of a table. The English-speaking subjects were much more likely to place the breakfast sticker to the left of the lunch sticker and the dinner sticker to the right of it. The Arabic speakers did the opposite. Arabic is read and written from right to left, not left to right like English. It appears that the cultural convention of reading and writing has caused the two groups to conceive of the arrow of time differently.