giustizia predittiva, giustizia totalitaria

se insegniamo il Marxismo-leninismo ad una intelligenza artificiale, arriverà alle purghe staliniane? Pensiero stimolato da Hanna Arendt sul Totalitarismo, sul “delitto possibile” pag 646:

“l’evoluzione dell’URSS potrebbe provocare una crisi, una crisi potrebbe condurre al rovesciamento della dittatura di Stalin, ciò potrebbe indebolire la potenza militare del paese e la situazione creata da un’eventuale guerra potrebbe indurre il nuovo governo a firmare una tregua o a concludere un’alleanza con Hitler. Forte di tale conclusione, Stalin passe a dichiarare che c’era, in combutta con Hitler, una congiura per rovesciare il governo”

necessità storica, previsione, modelli blackbox 🙂

If we teach Marxism-Leninism to an artificial intelligence, will it get to Stalin’s purges? Thought stimulated by Hanna Arendt on Totalitarianism, on the “possible crime” page 646:

“The evolution of the USSR could cause a crisis, a crisis could lead to the overthrow of the dictatorship of Stalin, this could weaken the military power of the country and the situation created by a possible war could induce the new government to sign a truce or to conclude an alliance with Hitler. With this conclusion in mind, Stalin went on to declare that there was, in conjunction with Hitler, a conspiracy to overthrow the government”.

historical need, forecast, blackbox models 🙂

Translated with


AI chat with L. april 2019

Microfluidic devices

Manifolds, topology and AI Colah Blog

cellular automata

the zoo of neural networks

AI and intuition

“It is really Python + intuition” me pitching this book to L.

Screenshot 2019-03-30 at 12.45.59.png

really, AI people are fixated with the intuition behind neural networks and all the architectural variations that comes with it. I love all the graphocal explanations of techniques that really get me to visualize and understand what happens. But then, can we make Ai explainable tru intuintion?

the Concept of Intuition in Artificial Intelligence paper

Fake Intuitive Explanations in AI



good old article on the age of machines

“A thorough, considered and disconcerting study of that possibility was undertaken by two Oxford economists, Carl Benedikt Frey and Michael Osborne, in a paper from 2013 called ‘The Future of Employment: How Susceptible Are Jobs to Computerisation?’

review of

-Tyler Cowen The End of Average

-Second Machine Age Bjodhebfhwe etc etc

productivity, employment and robot deflation are dealt with “It says a lot about the current moment that as we stand facing a future which might resemble either a hyper-capitalist dystopia or a socialist paradise, the second option doesn’t get a mention”

Computation vs. domain knowledge in AI

the Bitter Lesson of Rich Sutton says that progress in 70 years of Ai has come from general methods relying on computation power which has increased from Moore Law. Any method based on developing specific knowledge of the field has failed, while “brute force”

the Better Lesson of Rodney Brooks retorts that Moore Law might yield doubling every 20 years, Dennard scaling (energy invariance) broke down in 2006 so there is not so much computing power to waste and reserach is very active on network architectures and custom-designed chips.

In a way both are right, speaking of different levels of generalizations. Sutton:

“In computer vision, there has been a similar pattern. Early methods conceived of vision as searching for edges, or generalized cylinders, or in terms of SIFT features. But today all this is discarded. Modern deep-learning neural networks use only the notions of convolution and certain kinds of invariances, and perform much better.

AI with faculties

“The point is, GPT-2 has faculties. It has specific skills, that require a certain precision of thought, like counting from one to five, or mapping a word to its acronym, or writing poetry. These faculties are untaught; they arise naturally from its pattern-recognition and word-prediction ability. All these deep understanding things that humans have, like Reason and so on, those are faculties. AIs don’t have them yet. But they can learn.”

“The version that proves P != NP will still just be a brute-force pattern-matcher blending things it’s seen and regurgitating them in a different pattern. The proof won’t reveal that the AI’s not doing that; it will just reveal that once you reach a rarefied enough level of that kind of thing, that’s what intelligence isI’m not trying to play up GPT-2 or say it’s doing anything more than anyone else thinks it’s doing. I’m trying to play down humans. We’re not that great. GPT-2-like processes are closer to the sorts of things we do than we would like to think.”

GPT-2 As Step Toward General Intelligence

SSC argues the GPT-2 has general abilities in the machine-readable realm of text, language i reality, GPT-2 show a way a general intelligence could be developed in the written world

Ben Evans writes how computational photography needs be plugged into narrow AI ,  a “sensing” AGI is surely harder and needs sensing systems be developed in the machine realm

Virtual reality modelling, think also AI playing major videogames, should be a tool to train AI to a physical world without having to resort to phisycal sensors and a body. Will it be different ?


JCN -> framing a la Lakoff, AOC takes on the Mercers, Medaillion and NLP applied to finance. NLP model GPT-2 not released because dangerous.

computational photography

arms race on computational photography apple google – > sustaining strategy

different data approach (apple ai on chip, G ai in the cloud)

building the 3dsemanticVR, positioning differently in the ecosystem, where will profit go

disruptive innovation ain’t useful without being able to capture the profit, study the value network, the ecosystem, how market and tech forces play out.

Strategy is beautiful, computer vision is, mpeg7 with marco b.

Ai is prediction, forecasting on huge trove of unstructured data

Ben writes about the problem of narrow AI and what algo applies and what action to follow recognition. User interface problem, very deep, probably itself could be faced with deepL

Money Machines

An Interview with an Anonymous Algorithmic Trader

also, remeber “Inside a money machine like no other” Renaissance and Medaillion, NLP pioneers break wall street, some of them (Mercer) later breaks US democracy. Banoff, Cambridge Analytica, Facebook, Cambridge etc

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