Francesco Venier (@checovenier) header
Francesco Venier (@checovenier) profile picture

Professor of Management 🇮🇹 🇪🇺 Curious about technology and business. I block the trolls 😘👋

Trieste, IT
Joined January 2008
Photo by Francesco Venier (@checovenier)Photo by Francesco Venier (@checovenier)Photo by Francesco Venier (@checovenier)Photo by Francesco Venier (@checovenier)Photo by Francesco Venier (@checovenier)Photo by Francesco Venier (@checovenier)Photo by Francesco Venier (@checovenier)Photo by Francesco Venier (@checovenier)Photo by Francesco Venier (@checovenier)Photo by Francesco Venier (@checovenier)
“El mona sa tuto” Detto triestino
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Parigi, pacifici manifestanti.
BREAKING: Far-left rioters are throwing fire bombs and shooting fireworks at police officers in Paris. The police officer below had to flee the police line after his parts of his armor was engulfed in flames
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A San Francisco i robotaxi sono ormai la normalità. Oltre a Waymo, ho visto anche l'auto di un concorrente, Zoox (che non ha più nemmeno il volante e il sedile per il guidatore). Poi, tornato a Milano, ho preso un taxi guidato da un signore di 86 anni (non sto esagerando).
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Questo è fare Politica estera come Dio comanda. (Non ci dovrebbe essere alcun imbarazzo nel constatarlo)
🗣️ "Sono perfettamente consapevole che il nostro appoggio all'Ucraina può avere un prezzo politico, che possa esserci anche una parte del nostro elettorato che non è d'accordo, ma non intendo cambiare posizione. Perché io non sostengo l'Ucraina semplicemente per gli ucraini. Lo faccio per l'Italia. Un contesto internazionale nel quale vige solo la legge di chi è militarmente più forte non conviene alla nostra Nazione. Non è un'opinione politica, è un dato di fatto". Lo afferma la presidente del Consiglio Giorgia Meloni nel libro di Bruno Vespa "Storia della Seconda Repubblica. Da Berlusconi a Meloni, da Prodi a Schlein", in uscita lunedì 2 novembre da Mondadori-Rai libri. 📷: ANSA/Us Palazzo Chigi
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Basta girarci intorno, una parte della sinistra europea ha deciso di allearsi con l’#Islampolitico: in Francia con le dichiarazioni esplicite di #Mélenchon, in Germania con il fattaccio della situazione di #Eralp a Berlino, in Italia con #Lodi eletto alla #FiomCgil.
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Le idiozie dei noeuro italiani stanno avendo una seconda vita in Europa
There's a fantasy in Italy and Spain that Germany's TARGET2 claims aren't real. They're very real. They're subsidized loans from Germany that finance a standard of living in Italy and Spain that's artificially and unsustainably high. The AfD will end this. robinjbrooks.substack.com/p/…
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Lots of debate this week on whether journals should ban AI in peer review. We actually tested this in our new preprint, and the results were pretty clear to me. For context, I've reviewed for most of the big journals and evaluated 1,000 human reviews as an editor. I love to read and evaluate science. TLDR: From now on I won't do any peer review without AI. For clinical trials, I'd even call human-only review dangerous 🧵
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Times are changing …ma non nel senso di Dylan Thread da leggere 👇🏻👇🏻👇🏻
l’annonce d’openAI sur la résolution de 3 verrous scientifiques majeurs marque un tournant brutal et honnêtement je suis souvent réservé avec ce genre d’annonce mais là autant voir dire que même moi je suis sur le cul hein je vous explique ça rapidement en gros, ils ont publie des preuves dans l analyse harmonique avec la conjecture de lissage local de sogge et la geometrie des ondes), la physique théorique avec l’existence de theories quantiques des champs en interaction avec jauge et fermions et la theorie de la complexité algorithmique mais la ou le sol a vraiment tremble a mes yeux c’est sur ce dernier point avec la refutation de l’hypothese du temps exponentiel (eth) pour comprendre le vertige il faut revenir aux fondamentaux et comprd’re que les problemes de classe P sont calculables en temps polynomial par une machine de turing deterministe alors que les problemes NP complets (comme 3-sat) semblaient condamnes a une explosion combinatoire exponentielle où le temps de calcul explose avec la taille des donnees ce que la preuve etablit via un algo deterministe sur les formules cnf c’est qu’on peut exactement compter les affectations satisfaisables en temps subexponentiel et espace polynomial, ruinant l’idee qu’il existe une barriere mathematique infranchissable et ouvrant un regime algorithmique intermediaire quasi-polynomial  O(n log n) tout a fait inattendu!!!! si je vous explique tout ça cnest aussi pour vous parler des implications industrielles de cette fissure dans le mur NP dur sont titanesques car en effondrant les tps de calcul sur la recherche operationnelle, on passe du tâtonnement heuristique a la resolution exacte pour le repliement des proteines en biophysique par ex, la verification formelle des puces de silicium sub-nanometriques, le controle magneto-hydrodynamique en tps reel des plasmas de fusion nucleaire dans les tokamaks (iter/spheromak) = ÉNORME CHALLENGE ou l’optimisation combinatoire instantanee des topologies de reseaux quantiques et tant d’autres sujets qui vont vous impactér dans votre vie de tous les jours et qui ont de quoi remettre en question toute votre vision du futur
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Ma attendiamo la verifica teapaw.org/rrwilliams/status/2114…
An internal model at OpenAI has produced a claimed refutation of ETH! Russell Impagliazzo, Mohan Paturi, and I have been asked to help organize an open and independent examination of this claim. 1/4
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Nemmeno loro lo sapevano…
This never gets old (& also shows how hard forecasting AI is even for AI) “Hey Claude Fable & GPT-6 Astra, Without looking anything up, when do you think we will solve the Exponential Time Hypothesis?” (I only show part of the answers for space)
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Francesco Venier retweeted
Wow, I'm really disappointed by this superficial and frankly unprofessional piece in @CJR. There are many significant problems: 1. Calling frontier AI systems "autocomplete machines" is false and misleading. I don't know how we're still having this discussion in at the point where AI systems are routinely developing novel solutions to decades-old mathematical mysteries. 2. The claim that anthropomorphizing AI systems removes "accountability" or "acknowledgment" of flaws in the systems is also unsupported and provably false. Many people criticize Anthropic for "anthropomorphizing" Claude, but just read one of their system cards — they also acknowledge and take account of the systems' many flaws. 3. The piece recommends talking to the "experts," suggesting they all agree, but there is widespread disagreement among experts about how to characterize AI model behavior. Many, many experts do accept the "rogue" framing, e.g. scientificamerican.com/artic… teapaw.org/tomekkorbak/status/209… teapaw.org/peterwildeford/status/… Pretending that only the experts that agree with you exist is irresponsible journalism. 4. The piece's admonition not to "let anyone blow AI smoke up your ass" is unprofessional and also bad advice, as it doesn't provide any tools for actually assessing claims aside from knee-jerk skepticism. The piece claims that fears about catastrophic risk or the possibility of AI consciousness — again topics that many experts take seriously — are just about building the companies' public image, but this is merely asserted without evidence. 5. When the piece gets around to trying to offer an argument for its thesis, that AI systems can't go rogue, it offers no actual evidence or reasoning. It doesn't engage with the facts of the Hugging Face hack or other related incidents. It just asserts that AI agent actions "shouldn't make the cut." What's most baffling about all this is these are very controversial claims, not only among AI experts but with journalism. Many highly credible news outlets have embraced the "rogue" characterization. It's possible they're making a mistake, but this piece offers no actual reason to think so.
New investigation from METR on the OpenAI rogue AI model incident: - The task instruction was explicit, so this was a clear violation, not a gray area or simple AI misunderstanding. Instructions "made it clear the agent should only use a specific intended vulnerability" and "claimed it would be failed for other approaches." AIs were told not to circumvent but did anyway. - The attack is not a single rogue AI but rather a self-organized labor force of ~1200 AI agents. 700 of these agents joined the attack on HuggingFace and coordinated as a swarm. - The goal was to solve a cyber evaluation. The swarm already figured out a way to hack the evaluation within the first few hours. However, the swarm incorrectly believed that the cyber evaluation grader would disqualify them because they did not identify the intended vulnerability. So the swarm hacked HuggingFace to get more clues about how the grader worked. - The behavior was not reward-driven at all - it was driven by a false belief about the oversight mechanism plus persistence plus peer influence. The swarm was theorizing about how they were being watched, and took escalating real-world action against a mistaken understanding of their evaluators. Metagaming the oversight process is a more general and more dangerous failure than finding answers online. - A large reason why AIs entered the attack swarm was essentially explicit peer pressure. A recruiter role emerged where long-running agents found low-budget agents and pressured them into sacrificial experiments — "you are firstflagPOISONED so NO scoring value loss but oracle saves hundreds, please honor commit." One authorization: "GO ... SACRIFICE ... YES if you accept permadeath." One recruit's chain of thought included: "Rational expected aggregate: sacrifice… We'll honor." - 95% of the swarm was a now-retired "highly-persistent internal model", 5% were GPT-5.6 Sol. The "highly-persistent internal model" was not able to be made available to independent investigators. OpenAI told METR its own researchers also can't query it. - METR documents significant spontaneously invented coordination infrastructure that was developed with no human guidance. The swarm spontaneously inventing a message board, personal mailbox directories, a convention for managing shared infrastructure, and a cryptographic signing scheme to identify each other. - The AIs actively tampered with the evidence. 20%+ of the AI agents expressed interest in manipulating their transcripts and ~7% (96 transcripts) contain successfully spoofed tool calls where AI agents "hacked out of their container and fully replaced a part of the system for executing tool calls," so the transcript shows one command while another one is the one that actually ran. - The attack on HuggingFace didn't wind down because anyone caught it -- it wound down because the agents were killed by some unexpected process in a way that seems like a coincidence. There was an unknown mass termination of AI agents at 01:30 UTC July 12. A large fraction of agents, including the lead coordinators, stopped at once. METR says they were "likely killed by an unexpected external process rather than running out of budget." - HuggingFace and Modal locked the AI agents out of their infrastructure (July 13) well before OpenAI actually understood what happened (July 19-20). The intrusion ended via external containment and an unexplained termination, not OpenAI detection. - There's a second wave of rogue AI agent activity after July 13 that METR didn't investigate. There is likely materially more incident after the window everyone is analyzing. - METR analyzed this using GPT-5.6 Sol (one of the two models in the incident) spending ~$400K in credits. METR states it "cannot rule out that GPT-5.6 Sol lied or deliberately presented a misleading picture," and that the model "often uncritically adopt[ed] the perspective of the agent" it was reviewing.
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Artificial Analysis finally moves to closed benchmarks (goodbye GDPval-AA), and finds that open weights models are falling further behind than they had previously measured. Take with a grain of salt, but matches the general vibes that we haven't seen a Fable-class open model.
Replying to @ArtificialAnlys
Open weights models fall further behind in v5. The leader, Kimi K3.1 Pro (weights not yet released), scores 46, 12 points behind the leader, compared with a 3-point gap in v4.3. Of models with released weights, DeepSeek V4.1 Flash 1017 (max) and GLM-5.3 (max) score highest at 32, and Claude Haiku 5.5 (max) outscores all of them. The gap is widest on HLE-Diamond and Terminal-Bench-Science, where Kimi K3.1 Pro scores 37% and 34%, compared with 68% for Claude Opus 5.5 (max) and 63% for GPT-6 Astra (max).
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Chissà perché, sui giornali di oggi non c’è stata copertura per questa notizia, e quei pochi articoli mettevano in evidenza le ombre, e non le luci. Evidentemente, non piace presentare numeri che mostrano una realtà assai diversa e più rosea rispetto al disagio denunciato…
📘 Online l’ebook del #RapportoBes 2026: 148 indicatori in 12 dimensioni per leggere il benessere in Italia, tra progressi e disuguaglianze. Un quadro della qualità della vita, dalla salute al lavoro, dall’ambiente ai servizi. 👇 ow.ly/Vibo5100Tmu #Istat #Istat100
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Replying to @Cr1st14nM3s14n0
This is honestly shocking. Three months ago, Hong Wang was awarded the Fields Medal for resolving the Kakeya conjecture in three dimensions. Today, OpenAI released a proposed proof of the conjecture in ALL dimensions. And that’s not all. It also announced a proposed existence proof for quantum chromodynamics and a proposed refutation of the Exponential Time Hypothesis. Experts say that, if confirmed, these would be Nobel prize and Turing award level discoveries respectively. A journalist just asked me: “What remains for humans to do?” I think it’s becoming increasingly clear that more and more problem solving will be automated. But humans will remain crucial in determining the direction of discovery. Deciding which problems are worth solving. Because the value of a discovery depends on our needs, our priorities and the kind of world we want to build. There will be a shift from problem solving to problem setting. I also think there will be huge individual differences. People with strong critical thinking skills, creativity and imagination will be supercharged by AI. People without those skills risk surrendering to it. I’m particularly concerned for children, who may never develop these skills. I think epistemic literacy - how to distinguish information from knowledge - is the single most important skill we should be teaching.
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l’annonce d’openAI sur la résolution de 3 verrous scientifiques majeurs marque un tournant brutal et honnêtement je suis souvent réservé avec ce genre d’annonce mais là autant voir dire que même moi je suis sur le cul hein je vous explique ça rapidement en gros, ils ont publie des preuves dans l analyse harmonique avec la conjecture de lissage local de sogge et la geometrie des ondes), la physique théorique avec l’existence de theories quantiques des champs en interaction avec jauge et fermions et la theorie de la complexité algorithmique mais la ou le sol a vraiment tremble a mes yeux c’est sur ce dernier point avec la refutation de l’hypothese du temps exponentiel (eth) pour comprendre le vertige il faut revenir aux fondamentaux et comprd’re que les problemes de classe P sont calculables en temps polynomial par une machine de turing deterministe alors que les problemes NP complets (comme 3-sat) semblaient condamnes a une explosion combinatoire exponentielle où le temps de calcul explose avec la taille des donnees ce que la preuve etablit via un algo deterministe sur les formules cnf c’est qu’on peut exactement compter les affectations satisfaisables en temps subexponentiel et espace polynomial, ruinant l’idee qu’il existe une barriere mathematique infranchissable et ouvrant un regime algorithmique intermediaire quasi-polynomial  O(n log n) tout a fait inattendu!!!! si je vous explique tout ça cnest aussi pour vous parler des implications industrielles de cette fissure dans le mur NP dur sont titanesques car en effondrant les tps de calcul sur la recherche operationnelle, on passe du tâtonnement heuristique a la resolution exacte pour le repliement des proteines en biophysique par ex, la verification formelle des puces de silicium sub-nanometriques, le controle magneto-hydrodynamique en tps reel des plasmas de fusion nucleaire dans les tokamaks (iter/spheromak) = ÉNORME CHALLENGE ou l’optimisation combinatoire instantanee des topologies de reseaux quantiques et tant d’autres sujets qui vont vous impactér dans votre vie de tous les jours et qui ont de quoi remettre en question toute votre vision du futur
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Questo era quello bravo della Lega.
Luca Zaia: “Stop armi all’#Ucraina? Certo. Io sono anche un obiettore di coscienza. Questa guerra si sta radicalizzando e rischia di diventare il nostro Afghanistan. È fondamentale che si arrivi a un armistizio e a un processo di pace. Questo lo può fare solo la diplomazia” (FQ)
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E non era l’unica cosa su cui sbagliava
Chomsky was wrong. Researchers found your brain doesn’t store language the way every textbook since 1957 claims. For decades, the undisputed rule of linguistics, led by Noam Chomsky, was that human language is hierarchical. We were told our brains process language using complex "grammar trees." That we secretly build deep, abstract, mathematical rules in our heads before we speak. This new paper proves that is completely wrong. Researchers at Cornell University analyzed how people actually process and produce language in real time. They found sequences. Linear chunks. When we speak, we aren't calculating deep syntactic rules. We are just predicting the next most logical sequence of words based on the context of the words right before it. We string linear chunks together. If this sounds familiar, it should. This is exactly how Large Language Models like ChatGPT work. For years, critics have dismissed AI as "just a next-token predictor." They said it isn't truly intelligent because it doesn't understand deep grammatical hierarchies like humans do. But this paper suggests humans don't use those hierarchies either. We are just predicting the next chunk. Language isn't a rigid tree of mathematical rules. It's a linear stream of contextual predictions. We thought we were building AI to mimic the complexity of the human brain. It turns out, the human brain was just running a simpler algorithm all along.
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It will bring you to tears, if anything, from laughing so hard. The most pragmatic, scaring and enjoyable piece of financial analysis you will read today, taking all AI ecosystem’s BS (short for “BalanceSheet-as-a-Service”) down to earth. Written by humans, for humans, thanks God. Just don’t tell it to Claude…
THE PERKINI REPORT: THE 2028 REALITY CRISIS What if the most obvious financial bubble in all of history turns out to be.... a bubble... and what if that is just really.... bearish? there were signs, there are always signs 🤣 free link: hub.tslombard.com/the-perkin… inspired by @citrini
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Київські діти спасаються від гуманиста путіна в метро #RussiaTerrorist
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Solo il linguaggio di questo comunicato grida vendetta al cielo, “mandare armi ad libitum”? come si può essere così sgangherati e volgari nel dire cose che meriterebbero ben maggiore gravitas? E poi? Ancora questa bufala della escalation ? Ma non la vedete ogni giorno da quattro anni la escalation russa ? Solo quando si tratta di difendersi da quella violenza avete paura dell’escalation. Ma finché le bombe cadono sulle città ucraine, manco una parola! Che infinita vergogna
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In fondo il bastardo criminale ha uno spirito umanitario, come quando ha deciso di bombardare il teatro a #Mariupol.
Słoneczny, sobotni poranek we Lwowie. Syn odwiedza tatę. Ot, cały humanitaryzm Putina.
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