Artificial intelligence may greatly increase the efficiency of the existing economy. But it mayhave an even larger impact by serving as a new general-purpose “method of invention” that canreshape the nature of the innovation process and the organization of R&D. We distinguishbetween automation-oriented applications such as robotics and the potential for recentdevelopments in “deep learning” to serve as a general-purpose method of invention, findingstrong evidence of a “shift” in the importance of application-oriented learning research since2009. We suggest that this is likely to lead to a significant substitution away from more routinizedlabor-intensive research towards research that takes advantage of the interplay between passivelygenerated large datasets and enhanced prediction algorithms. At the same time, the potentialcommercial rewards from mastering this mode of research are likely to usher in a period ofracing, driven by powerful incentives for individual companies to acquire and control criticallarge datasets and application-specific algorithms. We suggest that policies which encouragetransparency and sharing of core datasets across both public and private actors may be criticaltools for stimulating research productivity and innovation-oriented competition going forward.
There have 14 been only a handful of previous general-purpose IMIs and each of these has had an enormous impact not primarily through their direct effects (e.g., spectacles, in the case of the invention of optical lenses) but through their ability to reshape the ideas production function itself (e.g. telescopes and microscopes). It would therefore be helpful to understand the extent to which deep learning is, or will, causing researchers to significantly shift or reorient their approach in order to enhance research productivity (in the spirit of Jones (2009)). Finally, if deep learning does indeed prove to be a general-purpose IMI, it will be important to develop institutions and a policy environment that is conductive to enhancing innovation through this approach, and to do so in a way that promotes competition and social
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A central concern here may be the interplay between a key input required for deep learninglarge unstructured databases that provide information about physical or logical eventsand the nature of competition. While the underlying algorithms for deep learning are in the public domain (and can and are being improved on rapidly), the data pools that are essential to generate predictions may be public or private, and access to them will depend on organizational boundaries, policy and institutions. Because the performance of deep learning algorithms depends critically on the training data that they are created from, it may be possible, in a particular application area, for a specific company (either an incumbent or start-up) gain a significant, persistent innovation advantage through their control over data that is independent of traditional economies of scale or demand-side network effects. This competition for the
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First, it creates incentives for duplicative racing to establish a data advantage in particular application sectors (say, search, autonomous driving, or cytology) followed by the establishment of durable barriers to entry that may be of significant concern for competition policy. Perhaps even more importantly, this kind of behavior could result in a balkanization of data within each sector, not only reducing innovative productivity within the sector, but also reducing spillovers back to the deep learning GPT sector, and to other application sectors. This suggests that the proactive development of institutions and policies that encourage competition, data sharing, and openness is likely to be an important determinant of
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Our discussion so far has been largely speculative, and it would be useful to know whether our claim that deep learning may be both a general-purpose IMI and a GPT, while 15 symbolic logic and robotics are probably not, have any empirical basis. We turn in the next section to a preliminary examination of the evolution of AI as revealed by bibliometric data, with an eye towards answering this question. V. Data This analysis draws upon two distinct datasets, one that captures a set of AI publications from Thompson Reuters Web of Science, and another that identifies a set of AI patents issued by the U.S. Patent and Trademark Office. In this section, we provide detail on the assembly of these
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