![]() Open Source is already driving innovation in powerful AI models Our aim in this piece is to tease some of those contingencies out and consider what sorts of interventions might tip the scale toward enabling competitive market access and development of this technology. But the shape and extent of that impact is contingent on a number of different factors. Open source AI will undoubtedly have an impact on how the market evolves, and policy interventions can and should take this into consideration. We think the answer is more uncertain than either side admits, and that AI policy interventions can and should be designed to leverage open source solutions without also expecting them to be policy panaceas. At the other extreme are those who see open source AI as a means to confront existing concentrations of power in the technology industry and lower barriers to development of powerful AI models. This perspective tends to relegate the role of open source to the sideline of the debate. Some, pointing to the significant costs of building powerful AI models, conclude that concentration in the AI market is inevitable, and suggest it should be the primary point for policy interventions. Many of the perspectives on this issue land at one of two extremes. ![]() We focus here on the role of openness in preserving a competitive marketplace where new entrants, small companies, nonprofits and others will have access to the tools of innovation. Points of debate include who should have liability for open source vulnerabilities, and how open source may help address or exacerbate safety concerns. There is a gradient from closed to open approaches to the development and release of AI products, the precise bounds of what constitutes ‘open source’ in this context is still contested, and there are a range of policy takes on the open source end of that gradient. One area that is increasingly salient in the policy discourse is the role of open source software, and openness more generally, in the development of the AI market. While somewhat cacophonous, this dialogue is on the whole a good thing – AI may impact every aspect of our lives in the not too distant future, and now is the time to design policy interventions that will influence that future for the better. Up Here takes its time warming up, it's true, but the rewards are worth waiting for.Rens Dimmendaal & Johann Siemens / Better Images of AI / Decision Tree reversed / CC-BY 4.0Īs we approach the year’s midpoint, it would be the understatement of 2023 to say that there is a lot of buzz around AI, particularly large language models and other powerful, ‘foundation’ models. And though the show's Greek chorus of "inner thoughts" characters is a bit gimmicky at first, the device ultimately grows on the viewer, allowing us access to these characters' worst fears as well as their fondest hopes all fleshed out in song that grows increasingly more memorable. We learn that Miguel's smooth and sometimes bland exterior is a defense mechanism that keeps others from knowing more than he feels comfortable revealing, and that Lindsay's habit of defining herself through the man she's currently dating is a crutch she leans on to avoid taking risks she finds too scary. Miguel, too seems like a type: A guy from humble background who makes good in business and life, yet is held back by his own insecurities.īut as Up Here goes on, the characters gain a specificity that helps ground this story in a more real place. We've seen many narratives in which young women are transformed by a move to the big city ( Emily in Parisjumps to mind), and the idea of a series that tells its story through musical numbers isn't new either ( Crazy Ex-Girlfriend is an obvious parallel). ![]() As Up Here begins, Lindsay's story arc is pretty generic, as are the songs that illustrate who she is and what she wants. With catchy songs and a quirky, interesting cast, this series combines theatrical musical sensibilities with episodic storytelling, with mixed results. ![]()
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