Data-intensive innovation and the State: evidence from AI firms in China

Artificial intelligence (AI) innovation is data-intensive. States have historically collected large amounts of data, which is now being used by AI firms. Gathering comprehensive information on firms and government procurement contracts in China’s facial recognition AI industry, we first study how government data shapes AI innovation. We find evidence of a precise mechanism: because data is sharable across uses, economies of scope arise. Firms awarded public security AI contracts providing access to more government data produce more software for both government and commercial purposes. In a directed technical change model incorporating this mechanism, we then study the trade-offs presented by states’ AI procurement and data pro-vision policies. Surveillance states’ demand for AI may incidentally promote growth, but distort innovation, crowd-out resources, and infringe on civil liberties. Government data provision may be justified when economies of scope are strong and citizens’ privacy concerns are limited

Persistent political engagement: social interactions and the dynamics of protest movements

We study the causes of sustained participation in political movements. To identify the persistent effect of protest participation, we randomly indirectly incentivize Hong Kong university students into participation in an antiauthoritarian protest. To identify the role of social networks, we randomize this treatment’s intensity across major-cohort cells. We find that incentives to attend one protest within a political movement increase subsequent protest attendance but only when a sufficient fraction of an individual’s social network is also incentivized to attend the initial protest. One-time mobilization shocks have dynamic consequences, with mobilization at the social network level important for sustained political engagement

AI-tocracy

Can frontier innovation be sustained under autocracy? We argue that innovation and autocracy can be mutually reinforcing when: (i) the new technology bolsters the autocrat's power; and (ii) the autocrat's demand for the technology stimulates further innovation in applications beyond those benefiting it directly. We test for such a mutually reinforcing relationship in the context of facial recognition AI in China. To do so, we gather comprehensive data on AI firms and government procurement contracts, as well as on social unrest across China during the last decade. We first show that autocrats benefit from AI: local unrest leads to greater government procurement of facial recognition AI, and increased AI procurement suppresses subsequent unrest. We then show that AI innovation benefits from autocrats' suppression of unrest: the contracted AI firms innovate more both for the government and commercial markets. Taken together, these results suggest the possibility of sustained AI innovation under the Chinese regime: AI innovation entrenches the regime, and the regime's investment in AI for political control stimulates further frontier innovation

Order out of Chaos: Proving Linearizability Using Local Views

Proving the linearizability of highly concurrent data structures, such as those using optimistic concurrency control, is a challenging task. The main difficulty is in reasoning about the view of the memory obtained by the threads, because as they execute, threads observe different fragments of memory from different points in time. Until today, every linearizability proof has tackled this challenge from scratch. We present a unifying proof argument for the correctness of unsynchronized traversals, and apply it to prove the linearizability of several highly concurrent search data structures, including an optimistic self-balancing binary search tree, the Lazy List and a lock-free skip list. Our framework harnesses sequential reasoning about the view of a thread, considering the thread as if it traverses the data structure without interference from other operations. Our key contribution is showing that properties of reachability along search paths can be deduced for concurrent traversals from such interference-free traversals, when certain intuitive conditions are met. Basing the correctness of traversals on such local view arguments greatly simplifies linearizability proofs. At the heart of our result lies a notion of order on the memory, corresponding to the order in which locations in memory are read by the threads, which guarantees a certain notion of consistency between the view of the thread and the actual memory. To apply our framework, the user proves that the data structure satisfies two conditions: (1) acyclicity of the order on memory, even when it is considered across intermediate memory states, and (2) preservation of search paths to locations modified by interfering writes. Establishing the conditions, as well as the full linearizability proof utilizing our proof argument, reduces to simple concurrent reasoning. The result is a clear and comprehensible correctness proof, and elucidates common patterns underlying several existing data structures

Fever, febrile seizures, and epileptogenesis

Febrile seizures (FS) are common and are associated with increased probability of temporal lobe epilepsy (TLE). However, whether FS can provoke TLE in the nonpredisposed brain is unknown. Using an immature rat model, we established that long FS cause TLE, and that duration of FS governed the severity of epilepsy. Epileptogenesis was accompanied, perhaps causally, by ion channel dysfunction and inflammatory changes. Because FS are a prevalent antecedent of TLE, studying the epileptogenesis that follows them provides powerful insight and potential therapies for epilepsy. For an expanded treatment of this topic see Jasper's Basic Mechanisms of the Epilepsies, Fourth Edition (Noebels JL, Avoli M, Rogawski MA, Olsen RW, Delgado-Escueta AC, eds) published by Oxford University Press (available on the National Library of Medicine Bookshelf [NCBI] at). © 2010 International League Against Epilepsy