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An AI just passed a university exam (but don’t panic: it was only economics)

Sellside beware. OpenAI’s ChatGPT has become A Big Thing lately, but it’s not the only scary bot on the block.

“Claude” — a similar AI system designed by SBF-financed Anthropic — has now passed a law and economics exam.

Alex Tabarrok, an economics professor at George Mason University, writes on the Marginal Revolution blog he runs with Tyler Cowen that Claude earned a “marginal pass” on a recent blind graded test.

Here is what Tabarrok thought was Claude’s best answer on how to improve the law and economics of intellectual property:

— Shorten the duration of copyrights, patents, and other IP protections. The current time periods, which can be 95 years or more for copyrights and up to 20 years for patents, are excessive. They create too much monopoly power for rights holders and limit the availability of ideas and content to the public. Shorter terms like 14 years for patents and 28 years for copyrights would provide sufficient incentives while making more works open and available sooner.

— Limit or eliminate software patents. Software patents are often overly broad and prevent innovation more than they encourage it. They give companies too much control over emerging technologies and allow patent trolling behavior. Most other countries do not allow software patents, and the U.S. would benefit from following their lead.

— Expand fair use and make it more flexible. Fair use is too narrow and rigid in the U.S. It should be expanded to include more types of uses, such as quotation, parody, and archiving. It should be interpreted in a more open-ended way to promote freedom of expression and access to information.

— Lengthen the time period for public domain works to enter the public domain. Currently, works that are no longer protected by copyright (because they’ve been out for 95+ years) or patent (because the term has expired) enter the public domain where anyone can use them for free. This period between protection and public domain status is too short. It should be extended to allow rights holders to continue to profit from the work and allow its cultural influence to persist before it enters the public domain.

— Increase funding for public domain works. The government should provide grants and funding to artists, musicians, authors, and other creators to produce works that are licensed from the start as public domain or creative commons. This would provide an alternative funding model to proprietary IP and boost the availability of open works.

Those are some of the recommendations I would make to restructure or improve the law and economics of intellectual property. Overall, the goal should be to make IP laws less restrictive and make more works available to the public sooner. But it is important to still provide some incentives and compensation to creators for a limited period.

To be honest, this looks more like Claude simply consumed and puked up a McKinsey report. But Tabarrok says that this is better than many of the actual human responses he gets (which makes us a little sad).

And although it feels like the long period of AI hype AI is now approaching the mania stage, it’s hard not to be impressed by some of the recent results. Financial academics are certainly paying attention.

Last week Michael Dowling and Brian Lucey of Dublin City University and Trinity College respectively published a paper on SSRN that explored whether ChatGPT could help write financial research.

They tested and compared ChatGPT’s output in four stages of the typical research process: idea generation, literature review, data identification and processing, and empirical testing.

Perhaps counter-intuitively, ChatGPT did particularly well in idea generation, but struggled with things like literature review and testing frameworks. On the whole, the results promising though.

ChatGPT can generate, even in its basic state, plausible-seeming research studies for well-ranked journals. With the addition of private data and researcher expertise iterations to improve output, the results are, frankly, very impressive.

So, what do we do now? This is both a practical and an ethical question. Can ChatGPT be simply considered as an e-ResearchAssistant, and, therefore, just a new part-and-parcel tool of how research is normally carried out? Indeed, under this perspective the platform might even be viewed as democratising access to research assistants, hitherto the reserved domain of wealthier universities in wealthier countries. Could ChatGPT help to flatten the disparities between the global south and wealthier nations in terms of research output? Maybe, now everyone can have access to such assistance, like the research-version of a dæmon from a Phillip Pullman novel following the researcher around and always available to offer pertinent advice.

Dowling and Lucey argue this raises some ethical issues though, such as whether one can claim AI-generated research as one’s own, whether it should be co-credited in authorship etc.

They therefore posit a “Bananarama Conjecture”. Or, to paraphrase the 1982 song with Fun Boy Three: “It ain’t what you do, it’s the extent that you do it, and that’s what gets (ethically-acceptable) results.”

O brave new world.

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