- May 2024
- Feb 2024
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pluralistic.net pluralistic.net
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Broderick makes a more important point: AI search is about summarizing web results so you don't have to click links and read the pages yourself. If that's the future of the web, who the fuck is going to write those pages that the summarizer summarizes? What is the incentive, the business-model, the rational explanation for predicting a world in which millions of us go on writing web-pages, when the gatekeepers to the web have promised to rig the game so that no one will ever visit those pages, or read what we've written there, or even know it was us who wrote the underlying material the summarizer just summarized? If we stop writing the web, AIs will have to summarize each other, forming an inhuman centipede of botshit-ingestion. This is bad news, because there's pretty solid mathematical evidence that training a bot on botshit makes it absolutely useless. Or, as the authors of the paper – including the eminent cryptographer Ross Anderson – put it, "using model-generated content in training causes irreversible defects"
Broderick: https://www.garbageday.email/p/ai-search-doomsday-cult, Anderson: https://arxiv.org/abs/2305.17493
AI search hides the authors of the material it presents, summarising it is abstracting away the authors. It doesn't bring readers to those authors, it just presents a summary to the searcher as end result. Take it or leave it. At the same time, if one searches for something you know about, you see those summaries are always of. Leaving you guessing how of it is when searching something you don't know about. Search should never be the endpoint, always a starting point. I think that is my main aversion against AI search tools. Despite those clamoring 'it will get better over time' I don't think it will easily because the tool nor its makers have any interest in the quality of output necessarily and definitely can't assess it. So what's next, humans factchecking AI output. Why not prevent bs at its source? Nice ref to Maggie Appleton's centipede metaphor in [[The Expanding Dark Forest and Generative AI]]
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- Jan 2024
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dl.acm.org dl.acm.org
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Searching as exploration. White and Roth [71 ,p.38] define exploratory search as a “sense making activity focusedon the gathering and use of information to foster intellectual de-velopment.” Users who conduct exploratory searches are generallyunfamiliar with the domain of their goals, and unsure about howto achieve them [ 71]. Many scholars have investigated the mainfactors relating to this type of dynamic task, such as uncertainty,creativity, innovation, knowledge discovery, serendipity, conver-gence of ideas, learning, and investigation [2, 46, 71].These factors are not always expressed or evident in queriesor questions posed by a searcher to a search system.
Sometimes, search is not rooted in discovery of a correct answer to a question. It's about exploration. Serendipity through search. Think Michael Lewis, Malcolm Gladwell, and Latif Nasser from Radiolab. The randomizer on wikipedia. A risk factor of where things trend with advanced AI in search is an abandonment of meaning making through exploration in favor of a knowledge-level pursuit that lacks comparable depth to more exploratory experiences.
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- Jun 2021
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www.incompleteideas.net www.incompleteideas.net
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One thing that should be learned from the bitter lesson is the great power of general purpose methods, of methods that continue to scale with increased computation even as the available computation becomes very great. The two methods that seem to scale arbitrarily in this way are search and learning
This is a big lesson. As a field, we still have not thoroughly learned it, as we are continuing to make the same kind of mistakes. To see this, and to effectively resist it, we have to understand the appeal of these mistakes. We have to learn the bitter lesson that building in how we think we think does not work in the long run. The bitter lesson is based on the historical observations that 1) AI researchers have often tried to build knowledge into their agents, 2) this always helps in the short term, and is personally satisfying to the researcher, but 3) in the long run it plateaus and even inhibits further progress, and 4) breakthrough progress eventually arrives by an opposing approach based on scaling computation by search and learning. The eventual success is tinged with bitterness, and often incompletely digested, because it is success over a favored, human-centric approach.
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- Jun 2020
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scisight.apps.allenai.org scisight.apps.allenai.orgAbout1
- May 2020
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Hope, T., Borchardt, J., Portenoy, J., Vasan, K., & West, J. (2020, May 6). Exploring the COVID-19 network of scientific research with SciSight. Medium. https://medium.com/ai2-blog/exploring-the-covid-19-network-of-scientific-research-with-scisight-f75373320a8c
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