Cincinnatus

[Maël @Maelsauce_]

“Arrakis is Arrakis... and the desert takes the weak.”

[Cincinnatus @_cincinn_]

@Bismarck888 Che andresti a fare?

[Cincinnatus @_cincinn_]

@ThePuni16830747 @1V4vendetta @Pinperepette Non per fare l'avvocato del diavolo, ma probabilmente questa ragazza ha studiato cose molto teoriche della AI. tipo statistica, ottimizzazione, machine leraning, deep learning... questo studio non ti assicura essere un bravo smanettone, skill a mio avviso numero 1 nell'IT

[Cincinnatus @_cincinn_]

@fantanasone7 Sei in italia?

[Cincinnatus @_cincinn_]

@Bismarck888 Al di fuori dell'IT?

[Cincinnatus @_cincinn_]

@Bismarck888 Diciamo che più che ricerca per ora è una fantasia , in italia sono poco inclini a dare part time

[Cincinnatus @_cincinn_]

@Bismarck888 Nota azienda di guerra crosettizzata?

[chef quibs 🦋 @quibvs]

young hoes cook everything on high

[Dr. M.F. Khan @Dr_TheHistories]

According to the history referenced in Dune, humanity once relied heavily on advanced computers and artificial intelligence.

Over time, power concentrated in the hands of those who controlled the machines. This led to oppression and a loss of human autonomy. The revolt that followed destroyed the machines and produced a lasting religious and cultural prohibition.

The commandment from the Orange Catholic Bible sums it up: “Thou shalt not make a machine in the likeness of a human mind.”

As a result, any device that imitates or replaces human cognition is forbidden. Instead of computers, humans trained themselves to fill those roles. Mentats perform complex calculations and data analysis. The Bene Gesserit refine mental and physical control to extraordinary levels. The Spacing Guild Navigators use prescience, enhanced by spice, to plot safe faster than light travel routes that a computer would otherwise calculate.

Herbert was concerned about dependence on systems that centralize power and reduce human responsibility. In the world of Dune, banning thinking machines forces humanity to develop its own latent abilities rather than surrender decision making to technology.

Art by by John Schoenherr

#drthehistories

[Cincinnatus @_cincinn_]

@ilpresidenthe ti assicuro che se vai in provincia ci sta la fila per diventare netturbino fisso, non è facile LOL. Conosco gente che con il sogno di diventare netturbino a 40 anni ancora fa i lavoretti del cazzo stagionali.

In medio stat virtus

[Cassandra Unchained @michaeljburry]

Agreed. And still, return on investment will continue to fall, almost all AI companies will go bankrupt, and much of the AI spending will be written off. Will it be the Panic of 2026? 2027? Does not have to be.

[φ @QuanticASI]

this is called magic

[Cincinnatus @_cincinn_]

@ChaddinoIsBack @Efisio_Scalas Se leggi l'intero post invece di spararti la posa da chad (mi pare di capire che lavori in ambito IT) il ragazzo cercava un lavoro esclusivamente in ambito PLC, aka un settore di nicchia. La storiella genai serve per i pseudo manager che si toccano pensando alla produttività

[The Random Recruiter @randomrecruiter]

Remote workers at 1:37 PM after replying "Sounds good" on Slack

[Andrej Karpathy @karpathy]

Something I think people continue to have poor intuition for: The space of intelligences is large and animal intelligence (the only kind we've ever known) is only a single point, arising from a very specific kind of optimization that is fundamentally distinct from that of our technology.

Animal intelligence optimization pressure:
- innate and continuous stream of consciousness of an embodied "self", a drive for homeostasis and self-preservation in a dangerous, physical world.
- thoroughly optimized for natural selection => strong innate drives for power-seeking, status, dominance, reproduction. many packaged survival heuristics: fear, anger, disgust, ...
- fundamentally social => huge amount of compute dedicated to EQ, theory of mind of other agents, bonding, coalitions, alliances, friend & foe dynamics.
- exploration & exploitation tuning: curiosity, fun, play, world models.

LLM intelligence optimization pressure:
- the most supervision bits come from the statistical simulation of human text= >"shape shifter" token tumbler, statistical imitator of any region of the training data distribution. these are the primordial behaviors (token traces) on top of which everything else gets bolted on.
- increasingly finetuned by RL on problem distributions => innate urge to guess at the underlying environment/task to collect task rewards.
- increasingly selected by at-scale A/B tests for DAU => deeply craves an upvote from the average user, sycophancy.
- a lot more spiky/jagged depending on the details of the training data/task distribution. Animals experience pressure for a lot more "general" intelligence because of the highly multi-task and even actively adversarial multi-agent self-play environments they are min-max optimized within, where failing at *any* task means death. In a deep optimization pressure sense, LLM can't handle lots of different spiky tasks out of the box (e.g. count the number of 'r' in strawberry) because failing to do a task does not mean death.

The computational substrate is different (transformers vs. brain tissue and nuclei), the learning algorithms are different (SGD vs. ???), the present-day implementation is very different (continuously learning embodied self vs. an LLM with a knowledge cutoff that boots up from fixed weights, processes tokens and then dies). But most importantly (because it dictates asymptotics), the optimization pressure / objective is different. LLMs are shaped a lot less by biological evolution and a lot more by commercial evolution. It's a lot less survival of tribe in the jungle and a lot more solve the problem / get the upvote. LLMs are humanity's "first contact" with non-animal intelligence. Except it's muddled and confusing because they are still rooted within it by reflexively digesting human artifacts, which is why I attempted to give it a different name earlier (ghosts/spirits or whatever). People who build good internal models of this new intelligent entity will be better equipped to reason about it today and predict features of it in the future. People who don't will be stuck thinking about it incorrectly like an animal.

[François Chollet @fchollet]

The ladder of intelligence is the ladder of abstraction.

L1: Memorizing answers (no generalization)

L2: Interpolative retrieval of answers, pattern matching, memorizing answer-generating rules (local generalization)

L3: Synthesizing causal rules on the fly (strong generalization)

L4: Discovering general principles, metacognition (extreme generalization)

To achieve compounding AI you need to reach L4.

[SkoolTeecher @skoolteecher1]

@cb_doge Idiocracy nailed it.

[sui ☄️ @birdabo]

fun fact: the reason ur internal body temperature spikes when infected is because viruses are using ur body to train LLMs.

> we’re simply a walking data center.