Paul Callaghan

PhD on Evaluation of NLP (MUC-6), 1998.
Parsing & ambiguity, Haskell & type theory, Riichi Mahjong.
Once lecturer, now happy escapee from organised education.

[Paul Callaghan @paulcc_two]

@rickasaurus "Klytus, I'm bored. What plaything can you offer me today?"

[Paul Callaghan @paulcc_two]

@GaryMarcus Dishonesties?

[Andrew Orlowski @AndrewOrlowski]

✍️ The new dream factory is AI companies and it’s bullshit all the way down

🔗 free to read 👇

https://t.co/np50fdIdRk

[Paul Callaghan @paulcc_two]

@PuroPodcast @damaqueengem And we're not serious players either

[Paul Callaghan @paulcc_two]

@PuroPodcast @damaqueengem We're terrible people...

[Paul Callaghan @paulcc_two]

@damaqueengem And there's more. I recommend Nicole Wong's book as an antidote.

[The TV Museum @thetvmuseum]

(Patreon) - 40 years ago today on BBC2, "it's sink or swim for the new doctor" (see what he did there) in "A Very Peculiar Practice":
https://t.co/RQxxdRFhmi

[Paul Callaghan @paulcc_two]

@davidmbudden Just one cell?

[Yam Peleg @Yampeleg]

The dumbest person you know is being told 'you are absolutely right' by some LLM right now.

[Spaceballs The X Account @Grunt2A]

Now that Artemis II has launched we have 10 days to get everyone on Earth a Planet of the Apes costume so we can do something hilarious when the astronauts return 😁

[Jürgen Schmidhuber @SchmidhuberAI]

Dr. LeCun's heavily promoted Joint Embedding Predictive Architecture (JEPA, 2022) [5] is the heart of his new company. However, the core ideas are not original to LeCun. Instead, JEPA is essentially identical to our 1992 Predictability Maximization system (PMAX) [1][14].

Details in reference [19] which contains many additional references.

Motivation of PMAX [1][14]. Since details of inputs are often unpredictable from related inputs, two non-generative artificial neural networks interact as follows: one net tries to create a non-trivial, informative, latent representation of its own input that is predictable from the latent representation of the other net’s input.

PMAX [1][14] is actually a whole family of methods. Consider the simplest instance in Sec. 2.2 of [1]: an auto encoder net sees an input and represents it in its hidden units (its latent space). The other net sees a different but related input and learns to predict (from its own latent space) the auto encoder's latent representation, which in turn tries to become more predictable, without giving up too much information about its own input, to prevent what's now called “collapse." See illustration 5.2 in Sec. 5.5 of [14] on the "extraction of predictable concepts."

The 1992 PMAX paper [1] discusses not only auto encoders but also other techniques for encoding data. The experiments were conducted by my student Daniel Prelinger. The non-generative PMAX outperformed the generative IMAX [2] on a stereo vision task.

The 2020 BYOL [10] is also closely related to PMAX. In 2026, @misovalko, leader of the BYOL team, praised PMAX, and listed numerous similarities to much later work [19].

Note that the self-created “predictable classifications” in the title of [1] (and the so-called “outputs” of the entire system [1]) are typically INTERNAL "distributed representations” (like in the title of Sec. 4.2 of [1]).

The 1992 PMAX paper [1] considers both symmetric and asymmetric nets. In the symmetric case, both nets are constrained to emit "equal (and therefore mutually predictable)" representations [1]. Sec. 4.2 on “finding predictable distributed representations” has an experiment with 2 weight-sharing auto encoders which learn to represent in their latent space what their inputs have in common (see the cover image of this post).

Of course, back then compute was was a million times more expensive, but the fundamental insights of "JEPA" were present, and LeCun has simply repackaged old ideas without citing them [5,6,19].

This is hardly the first time LeCun (or others writing about him) have exaggerated LeCun's own significance by downplaying earlier work. He did NOT "co-invent deep learning" (as some know-nothing "AI influencers" have claimed) [11,13], and he did NOT invent convolutional neural nets (CNNs) [12,6,13], NOR was he even the first to combine CNNs with backpropagation [12,13]. While he got awards for the inventions of other researchers whom he did not cite [6], he did not invent ANY of the key algorithms that underpin modern AI [5,6,19].

LeCun's recent pitch: 1. LLMs such as ChatGPT are insufficient for AGI (which has been obvious to experts in AI & decision making, and is something he once derided @GaryMarcus for pointing out [17]). 2. Neural AIs need what I baptized a neural "world model" in 1990 [8][15] (earlier, less general neural nets of this kind, such as those by Paul Werbos (1987) and others [8], weren't called "world models," although the basic concept itself is ancient [8]). 3. The world model should learn to predict (in non-generative "JEPA" fashion [5]) higher-level predictable abstractions instead of raw pixels: that's the essence of our 1992 PMAX [1][14].

Astonishingly, PMAX or "JEPA" seems to be the unique selling proposition of LeCun's 2026 company on world model-based AI in the physical world, which is apparently based on what we published over 3 decades ago [1,5,6,7,8,13,14], and modeled after our 2014 company on world model-based AGI in the physical world [8].

In short, little if anything in JEPA is new [19]. But then the fact that LeCun would repackage old ideas and present them as his own clearly isn't new either [5,6,18,19].

FOOTNOTES

1. Note that PMAX is NOT the 1991 adversarial Predictability MINimization (PMIN) [3,4]. However, PMAX may use PMIN as a submodule to create informative latent representations [1](Sec. 2.4), and to prevent what's now called “collapse." See the illustration on page 9 of [1].

2. Note that the 1991 PMIN [3] also predicts parts of latent space from other parts. However, PMIN's goal is to REMOVE mutual predictability, to obtain maximally disentangled latent representations called factorial codes. PMIN by itself may use the auto encoder principle in addition to its latent space predictor [3].

3. Neither PMAX nor PMIN was my first non-generative method for predicting latent space, which was published in 1991 in the context of neural net distillation [9]. See also [5-8].

4. While the cognoscenti agree that LLMs are insufficient for AGI, JEPA is so, too. We should know: we have had it for over 3 decades under the name PMAX! Additional techniques are required to achieve AGI, e.g., meta learning, artificial curiosity and creativity, efficient planning with world models, and others [16].

REFERENCES (easy to find on the web):

[1] J. Schmidhuber (JS) & D. Prelinger (1993). Discovering predictable classifications. Neural Computation, 5(4):625-635. Based on TR CU-CS-626-92 (1992): https://t.co/wJFbdPhwdi
[2] S. Becker, G. E. Hinton (1989). Spatial coherence as an internal teacher for a neural network. TR CRG-TR-89-7, Dept. of CS, U. Toronto.
[3] JS (1992). Learning factorial codes by predictability minimization. Neural Computation, 4(6):863-879. Based on TR CU-CS-565-91, 1991.
[4] JS, M. Eldracher, B. Foltin (1996). Semilinear predictability minimization produces well-known feature detectors. Neural Computation, 8(4):773-786.
[5] JS (2022-23). LeCun's 2022 paper on autonomous machine intelligence rehashes but does not cite essential work of 1990-2015.
[6] JS (2023-25). How 3 Turing awardees republished key methods and ideas whose creators they failed to credit. Technical Report IDSIA-23-23.
[7] JS (2026). Simple but powerful ways of using world models and their latent space. Opening keynote for the World Modeling Workshop, 4-6 Feb, 2026, Mila - Quebec AI Institute.
[8] JS (2026). The Neural World Model Boom. Technical Note IDSIA-2-26.
[9] JS (1991). Neural sequence chunkers. TR FKI-148-91, TUM, April 1991. (See also Technical Note IDSIA-12-25: who invented knowledge distillation with artificial neural networks?)
[10] J. Grill et al (2020). Bootstrap your own latent: A "new" approach to self-supervised Learning. arXiv:2006.07733
[11] JS (2025). Who invented deep learning? Technical Note IDSIA-16-25.
[12] JS (2025). Who invented convolutional neural networks? Technical Note IDSIA-17-25.
[13] JS (2022-25). Annotated History of Modern AI and Deep Learning. Technical Report IDSIA-22-22, arXiv:2212.11279
[14] JS (1993). Network architectures, objective functions, and chain rule. Habilitation Thesis, TUM. See Sec. 5.5 on "Vorhersagbarkeitsmaximierung" (Predictability Maximization).
[15] JS (1990). Making the world differentiable: On using fully recurrent self-supervised neural networks for dynamic reinforcement learning and planning in non-stationary environments. Technical Report FKI-126-90, TUM.
[16] JS (1990-2026). AI Blog.
[17] @GaryMarcus. Open letter responding to @ylecun. A memo for future intellectual historians. Substack, June 2024.
[18] G. Marcus. The False Glorification of @ylecun. Don’t believe everything you read. Substack, Nov 2025.
[19] J. Schmidhuber. Who invented JEPA? Technical Note IDSIA-3-22, IDSIA, Switzerland, March 2026. https://t.co/fDauPE6T2N

[Kareem Carr, Ph.D. @kareem_carr]

This actually suggests weakness to me. If they knew how to make money from AI directly, their best move would be to keep it for themselves, but they don't, so instead they're pushing it on us, hoping we'll figure it out for them.

[Paul Callaghan @paulcc_two]

@MrEwanMorrison You might enjoy my PhD thesis - on evaluation of NLP systems. Not many people did. I quit the field soon after, partly because of realising the general direction was flawed. I should write a summary of it...

[Paul Callaghan @paulcc_two]

@MrEwanMorrison And likely that the "correct" part is mostly the low-hanging fruit and not the stuff that really matters.

[Paul Callaghan @paulcc_two]

@MrEwanMorrison In logic, two key properties of a proposed model are soundness & completeness, checking whether the model adds things not in the original, or whether the model omits things in the original. Failing the first makes it unsound. Failing the second makes it incomplete. Failing both?

[Ed Newton-Rex @ednewtonrex]

proud of my daughter’s outfit for world book day

she went as the 7 million books pirated by anthropic

[Paul Callaghan @paulcc_two]

@PaprikaGirl_JP Excellent article - though sobering how common this situation is (almost me as well - though my group leader was the best ever - issues were elsewhere). Life has been much better since I left 🙂

[Paul Callaghan @paulcc_two]

Essential reading, lots of important points

[Leonardo de Moura @Leonard41111588]

AI is writing a growing share of the world's software. No one is formally verifying any of it.

New essay: "When AI Writes the World's Software, Who Verifies It?"
https://t.co/8zjS9FkdA8

[Paul Callaghan @paulcc_two]

@josevalim Notice it's not just about correctness either. It's partly being clearer about your thinking & being able to show some of it in code. Eg. "take" puts your data in a simpler form, & type signature says just that. Also shows when it can be used & what you can rely on for the result

[Paul Callaghan @paulcc_two]

@josevalim Cutting edge dependent type langs can do sthg like "honest_take : xs -> Map(xs + other) -> Map(xs)", indicating you can trim a larger map to a smaller map if the target keys are present. Then it's up to calling sites to justify at compile time that the call is indeed legitimate.