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When he visited China in June 1989 Michail Gorbachev, Secretary General of the PCUS and President of the Soviet Union, had two objectives. He wanted to see for himself how Deng Xiaoping's reforms were transforming the country and hoped to patch up the political and ideological strains that had upset relations between Moscow and Beijing, from the withdrawal of Soviet advisors at the time of Krushev to the harsh military clashes over the Ussuri in 1968 and the different positions taken by the two countries during the Afghan crisis of 1979. At the same time, the Soviet President tried to explain his perestroika to Deng. The intentions were reasonable, but the climate changed when the Soviet leader's presence in Beijing had the effect of enthusing the young people of the capital. While the Chinese leaders were gradually introducing the rules of the market into the economy, Gorbachev spoke of "glasnost" and seemed determined to change those of the political system. As soon as the Soviets left Beijing to return to their homeland, several thousand young Chinese began to meet in Tiananmen Square to demand democratic reforms. From that moment the Soviet leader ceased to be a possible model for China and his fall in December 1991 showed the Chinese leadership that the reform attempted by Moscow was a risk to avoid. I believe that this explains why the date of 3 June 2019 was awaited by Xi Jinping, President of the People's Republic of China, with some apprehension. [Continues]

Recent approaches to human concept learning have successfully combined the power of symbolic, infinitely productive rule systems and statistical learning to explain our ability to learn new concepts from just a few examples. The aim of most of these studies is to reveal the underlying language structuring these representations and providing a general substrate for thought. However, describing a model of thought that is fixed once trained is against the extensive literature that shows how experience shapes concept learning. Here, we ask about the plasticity of these symbolic descriptive languages. We perform a concept learning experiment that demonstrates that humans can change very rapidly the repertoire of symbols they use to identify concepts, by compiling expressions that are frequently used into new symbols of the language. The pattern of concept learning times is accurately described by a Bayesian agent that rationally updates the probability of compiling a new expression according to how useful it has been to compress concepts so far. By portraying the language of thought as a flexible system of rules, we also highlight the difficulties to pin it down empirically. Fil: Tano, Pablo. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina Fil: Romano, Sergio Gaston. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; Argentina Fil: Sigman, Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Torcuato Di Tella; Argentina. Universidad Nebrija; España Fil: Salles, Alejo. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Calculo. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Calculo; Argentina Fil: Figueira, Santiago. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; Argentina

Probabilistic proposals of Language of Thoughts (LoTs) can explain learning across different domains as statistical inference over a compositionally structured hypothesis space. While frameworks may differ on how a LoT may be implemented computationally, they all share the property that they are built from a set of atomic symbols and rules by which these symbols can be combined. In this work we propose an extra validation step for the set of atomic productions defined by the experimenter. It starts by expanding the defined LoT grammar for the cognitive domain with a broader set of arbitrary productions and then uses Bayesian inference to prune the productions from the experimental data. The result allows the researcher to validate that the resulting grammar still matches the intuitive grammar chosen for the domain. We then test this method in the language of geometry, a specific LoT model for geometrical sequence learning. Finally, despite the fact of the geometrical LoT not being a universal (i.e. Turing-complete) language, we show an empirical relation between a sequence’s probability and its complexity consistent with the theoretical relationship for universal languages described by Levin’s Coding Theorem. Fil: Romano, Sergio Gaston. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; Argentina Fil: Salles, Alejo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Cálculo; Argentina Fil: Amalric, Marie. Université Paris Sud; Francia. Université Paris-Saclay; Francia. Institut National de la Santé et de la Recherche Médicale; Francia Fil: Dehaene, Stanislas. Université Paris-Saclay; Francia. Institut National de la Santé et de la Recherche Médicale; Francia. Université Paris Sud; Francia Fil: Sigman, Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Torcuato Di Tella; Argentina Fil: Figueira, Santiago. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; Argentina

Probabilistic proposals of Language of Thoughts (LoTs) can explain learning across different domains as statistical inference over a compositionally structured hypothesis space. While frameworks may differ on how a LoT may be implemented computationally, they all share the property that they are built from a set of atomic symbols and rules by which these symbols can be combined. In this work we show how the set of productions of a LoT grammar can be effectively selected from a broad repertoire of possible productions by an inferential process starting from experimental data. We then test this method in the language of geometry, a specific LoT model (Amalric et al., 2017). Finally, despite the fact of the geometrical LoT not being a universal (i.e. Turing-complete) language, we show an empirical relation between a sequence’s probability and its complexity consistent with the theoretical relationship for universal languages described by Levin’s Coding Theorem.

Two major querelles set Italian historiography astir during the years following the Second World War. The first goes back to 1959 and the publication of Rosario Romeo's Risorgimento e capitalismo;1 the second, to that of the third volume of Renzo De Felice's monumental biography of Mussolini.2 The first of these works kindled a debate on economic policy in Italy after the founding of the kingdom that resulted in two opposing theses regarding the nature of the Risorgimento . In Romeo' s view, the governing class, liberal in its politics, made decisions in the sphere of economics that were dictated by their own ideals and by conditions in the country. According to historians in the Marxist and Gramscian traditions, the governing class betrayed the "popular'' moving forces behind the Risorgimento and imposed decisions on the country that reflected their own class interests. The second work prompted an even more acrimonious discussion on the nature of Fascism in Italy and on the extent of the popular support it enjoyed during the twentyone years of the Fascist regime. De Felice maintained that a distinction must be made between the Fascist movement, in which popular and revolutionary ferments were present, and the Fascist regime, dominated, de facto, by Mussolini's authoritarian line of action and by his compromises with the major sources of power in Italian society-the monarchy, the church, the armed forces, the state bureaucracy, and industrial and financial capitalism. Historians of Marxist inspiration maintain, to the contrary, that Fascism was from the outset a clearly reactionary movement and that the popular touches in Mussolini's program were merely cosmetic and a clever disguise to help him win over and organize the masses in Italy. At first glance, there seems to be no connection between these two great historiographic battles. In reality, however, each had a civil significance that transcended the narrow boundaries of academic interests, and both reached

Gentile as a philosopher of fascism, Sergio Romano. In 1918, G. Gentile thought that Italy's salvation depended on the restoration of the liberal State. But he feared the possibility of a return to the pre-war political System and the risk of a weak power subjected to constant governmental changes. That is why his liberal State was to be led by a strong party, not far removed from the fascist conception. Even if the term used to designate it, " the ethical State ", came from the Hegelian conception, it was indeed an authoritarian state in Gentile's mind. Gentile needed all the resources of philosophical rhetoric to get out of the contradiction. In spite of his admiration for Il Duce, he came into conflict with the regime and ended up dying because of it., Romano Sergio. Giovanni Gentile, philosophe du fascisme. In: Vingtième Siècle, revue d'histoire, n°21, janvier-mars 1989. Dossier : Penser le fascisme. pp. 71-82.

Zeev Sternhell : an Italian reading, Sergio Romano. Returning to Z. Sternhell's book, Ni droite ni gauche (Paris, Le Seuil, 1983), Sergio Romano explains why it could appear controversial to an Italian reader. While it is true that Mussolini himself acknowledged his debt to Sorel and to the Italian syndicalist revolutionaries, the cultural sources of fascism were diverse. The roots of its ideology, which Sternhell sees mostly in France, must not be separated from their peninsular attachments and from the context of moral crisis following the First World War. What is lacking in this otherwise valuable book is a more rigorous intellectual definition and a more concrete historical one., Romano Sergio. Sternhell lu d'Italie. In: Vingtième Siècle, revue d'histoire, n°6, avril-juin 1985. pp. 75-82.