Wikipedia’s value in the age of generative AI

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“My calculations in the past are, you know, more than 10 million people read my work in a year,” Jade said, “so it’s an honor to have people reading all that. Volunteer contributors have used artificial intelligence tools and bots since 2002 to support their work. The Wikimedia Foundation has had a team dedicated to machine learning since 2017.

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Human experts determine the hierarchy of features to understand the differences between data inputs, usually requiring more structured data to learn. Furthermore, improvements in AI development platforms will help accelerate research and development of better generative AI capabilities in the future for text, images, video, 3D content, drugs, supply chains, logistics and business processes. As good as these new one-off tools are, the most significant impact of generative AI will come from embedding these capabilities directly into versions of the tools we already use.

As of this writing, the draft policy includes a point that explicitly states that in-text attribution is necessary for AI generated content. Bruckman doesn’t see some of the issues that come with large language models as much different than deliberate and malicious attempts to edit Wikipedia pages. The challenge lies in the offsite large language models (LLMs) like ChatGPT. These models pose a risk of misinformation due to their tendency to hallucinate and produce fake citations. Instead, Wikipedia is exploring a policy that would require human editors to disclose the use of LLMs and take responsibility for vetting and ensuring the accuracy of the content.

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The Wikimedia Foundation, which oversees Wikipedia, acknowledges the potential of AI tools, including LLMs, in enhancing content creation and accessibility. However, any integration of AI into the platform must align with Wikipedia’s core principles of reliability, neutrality, and collaborative human involvement. However, decades before this definition, the birth of the artificial intelligence conversation was denoted by Alan Turing’s seminal work, “Computing Machinery and Intelligence”(link resides outside ibm.com), which was published in 1950. While this test has undergone much scrutiny since its publish, it remains an important part of the history of AI as well as an ongoing concept within philosophy as it utilizes ideas around linguistics. The modern AI ecosystem is rapidly developing — Credo AI’s Policy Intelligence future-proofs enterprises as it does.

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Yakov Livshits
Founder of the DevEducation project
A prolific businessman and investor, and the founder of several large companies in Israel, the USA and the UAE, Yakov’s corporation comprises over 2,000 employees all over the world. He graduated from the University of Oxford in the UK and Technion in Israel, before moving on to study complex systems science at NECSI in the USA. Yakov has a Masters in Software Development.

It would even cite precise links, as some A.I.-powered search engines now do. “Without that retrieval element,” Dodge says, “I don’t think there’s a way to solve the hallucination problem.” Otherwise, he says, he doubts that a chatbot answer can gain factual parity with Wikipedia or the Encyclopaedia Britannica. What makes the goal of accuracy so vexing for chatbots is that they operate Yakov Livshits probabilistically when choosing the next word in a sentence; they aren’t trying to find the light of truth in a murky world. “These models are built to generate text that sounds like what a person would say — that’s the key thing,” Jesse Dodge says. “So they’re definitely not built to be truthful.” I asked Margaret Mitchell, a computer scientist who studied the ethics of A.I.

It is now a topic of conversation inside the Wikimedia community whether some legal recourse exists. Companies are focusing on what they call “fine tuning” when it comes to factuality. Sandhini Agarwal and Girish Sastry, researchers at OpenAI, the company that created ChatGPT, told me that their newer A.I. Model, GPT-4, has made significant improvements over earlier models in what they called “factual content.” Those advances stem mainly from a process known as “reinforcement learning with human feedback” to help A.I.

The way in which deep learning and machine learning differ is in how each algorithm learns. Deep learning automates much of the feature extraction piece of the process, eliminating some of the manual human intervention required and enabling the use of larger data sets. You can think of deep learning as “scalable machine learning” as Lex Fridman noted in same MIT lecture from above. Classical, or “non-deep”, machine learning is more dependent on human intervention to learn.

Both more difficult to contend with and potentially more harmful, at least from Wikipedia’s perspective. Industry (but is not permitted to speak publicly about his work) told me that these technologies are highly self-destructive, threatening to obliterate the very content which they depend upon for training. It’s just that many people, including some in the tech industry, haven’t yet realized the implications. Zero-shot, few-shot, and one-shot learning techniques allow ML models to predict based on limited labeled data. Notably, the plugin makes this information available in a way that recognizes the contributions of the thousands of volunteers who create and curate the knowledge on Wikipedia. At the same time, it gives plugin users the ability to sustain Wikipedia’s unique, collaborative model for the future by offering opportunities for them to contribute back to a topic on its respective Wikipedia page.

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Finally, the presentation also looks toward the future of the Wikimedia movement and the role that GAI could play in achieving the goals of the 2030 Wikimedia Movement Strategy. On a conference call in March that focused on A.I.’s threats to Wikipedia, as well as the potential benefits, the editors’ hopes contended with anxiety. Tools would soon help expand Wikipedia’s articles and global reach, others worried about whether users would increasingly choose ChatGPT — fast, fluent, seemingly oracular — over a wonky entry from Wikipedia. A main concern among the editors was how Wikipedians could defend themselves from such a threatening technological interloper. In conclusion, Generative AI systems, particularly Language Models, hold promise in contributing to Wikipedia’s content creation process. However, it is vital to recognize that they cannot entirely replace the collaborative and human-driven nature of the platform.

While Wikipedia’s licensing policy lets anyone tap its knowledge and text — to “reuse and remix” it however they might like — it does have several conditions. These include the requirements that users must “share alike,” meaning any information they do something with must subsequently be made readily available, and that users must give credit and attribution to Wikipedia contributors. Mixing Wikipedia’s corpus into a chatbot model that gives answers to queries without explaining the sourcing may thus violate Wikipedia’s terms of use, two people in the open-source software community told me.

• This artificial intelligence wiki is a beginner’s guide to important topics in AI, machine learning, and deep learning, including large-language models like GPT.
• A generative AI model starts by efficiently encoding a representation of what you want to generate.
• Congress is meanwhile considering several bills to regulate A.I.

And with chatbots, Ilia Shumailov, an Oxford University researcher and the paper’s primary author, told me, the downward spiral looks similar. But if in the future Wikipedia were to become clogged with articles generated by A.I., the same cycle — essentially, the computer feeding on content it created itself — would be perpetuated. Once developers settle on a way to represent the world, they apply a particular neural network to generate new content Yakov Livshits in response to a query or prompt. Techniques such as GANs and variational autoencoders (VAEs) — neural networks with a decoder and encoder — are suitable for generating realistic human faces, synthetic data for AI training or even facsimiles of particular humans. Generative AI models combine various AI algorithms to represent and process content. Similarly, images are transformed into various visual elements, also expressed as vectors.