Join us for Wikipedia in a generative AI world
In the past six months, the public has been introduced to dozens of LLMs, trained on vast data sets that can read, summarize, and generate text. Wikipedia is one of the largest open corpuses of information on the internet, with versions in over 300 languages. To date, every LLM is trained on Wikipedia content, and it is almost always the largest source of training data in their data sets.
Violations can include repeated vandalism, ‘sock puppetry,’ undisclosed paid editing, or edit warring. The global community of volunteers who contribute to Wikipedia serves as a vigilant first line of defense against misinformation on the platform. However, with challenges such as misinformation, disinformation, and fake news, among others, adequate testing is needed before such solutions can be deployed on the site. But when it comes to using AI, there are concerns about how the data is collected and presented for some use cases. In the era of misinformation and disinformation, the use of AI, especially in fact-gathering, remains questionable by many. Apart from concerns about data sources, some information collected by AI can also be outdated.
Advanced Techniques in Text Summarization: Leveraging Generative AI and Prompt Engineering
The collaborative effort of human editors ensures that information is accurate, well-researched, and presented in a comprehensive manner. AI-generated content may complement Yakov Livshits this process, but it cannot wholly replace the critical thinking and judgment of human editors. It could try, but it would result in a replacement that no one really wants.
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To ensure the continued trustworthiness and reliability of Wikipedia, a careful balance between AI-generated and human-generated content must be struck. By leveraging AI technology responsibly and in conjunction with human expertise, Wikipedia can continue to be a beacon of reliable information in the digital age. These are just some of the problems that need to be solved as internet users explore how LLMs can be used.
The information must also be written from a neutral point of view. Part of what makes this system of content moderation work so well is that the site is radically transparent. The public can see every edit and change on a Wikipedia article’s page history, the article talk page where editors discuss changes to an article, and more. Since the online encyclopedia was created in 2001, volunteers have developed processes and guidelines to ensure the information is as reliable as possible. Wikipedia, the largest and most-read reference work in history, is facing a potential threat from generative A.I.
LLMOps is an emerging and specialized domain of MLOps that focuses on operationalizing large language models(LLMs) at scale. —a global campaign focused on enabling access to knowledge on the internet across geographic locations and languages—says large language models and Wikipedia are in a feedback loop that introduces even more biases. As generative artificial intelligence continues to permeate all aspects of culture, the people who steward Wikipedia are divided on how best to proceed. Aaron enjoys writing about enterprise technology in the region. He has attended and covered many local and international tech expos, events and forums, speaking to some of the biggest tech personalities in the industry. With over a decade of experience in the media, Aaron previously worked on politics, business, sports and entertainment news.
While LLMs can produce coherent text, they lack the capability to verify the accuracy of the information presented. This raises concerns about the reliability of AI-generated content and the potential for the dissemination of misinformation. A generative AI system is constructed by applying unsupervised or self-supervised machine learning to a data set. The capabilities of a generative AI system depend on the modality or type of the data set used. When it comes to generative AI, it is predicted that foundation models will dramatically
accelerate AI adoption in enterprise. Reducing labeling requirements will make it much
easier for businesses to dive in, and the highly accurate, efficient AI-driven automation they enable will mean that far more companies will be able to deploy AI in a wider range of mission-critical situations.
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.
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Now, pioneers in generative AI are developing better user experiences that let you describe a request in plain language. After an initial response, you can also customize the results with feedback about the style, tone and other elements you want the generated content to reflect. Advances in the field of machine learning (algorithms that adjust themselves when exposed to data) are driving progress more widely in AI. But many terms and concepts will seem incomprehensible to the intelligent outsider, the beginner, and even the former student of AI returning to a transformed discipline after years away. We hope this wiki helps you better understand AI, the software used to build it, and what is at stake in its development. I experienced and understood the same for Maria and this prodigious and awaited savior prophet by discovering the words of the gospel of this other maria who was a key woman in service of this prodigious and awaited savior prophet.
Generative AI and Potential Benefits to Wikis
For example, the popular GPT model developed by OpenAI has been used to write text, generate code and create imagery based on written descriptions. The convincing realism of generative AI content introduces a new set of AI risks. It makes it harder to detect AI-generated content and, more importantly, makes it more difficult to detect when things are wrong. This can be a big problem when we rely on generative AI results to write code or provide medical advice. Many results of generative AI are not transparent, so it is hard to determine if, for example, they infringe on copyrights or if there is problem with the original sources from which they draw results. If you don’t know how the AI came to a conclusion, you cannot reason about why it might be wrong.
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The Artificial Intelligence Wiki
Since deep learning and machine learning tend to be used interchangeably, it’s worth noting the nuances between the two. As mentioned above, both deep learning and machine learning are sub-fields of artificial intelligence, and deep learning is actually a sub-field of machine learning. RAG takes an input and retrieves a set of relevant/supporting documents given a source (e.g., Wikipedia). The documents are concatenated as context with the original input prompt and fed to the text generator which produces the final output. This makes RAG adaptive for situations where facts could evolve over time.
- IBM has been a leader in advancing AI-driven technologies for enterprises and has pioneered the future of machine learning systems for multiple industries.
- If one type of topic or person is chronically under-represented in Wikipedia’s corpus, we can expect generative text models to mirror — or even amplify — that under-representation in their outputs.
- Despite their promise, the new generative AI tools open a can of worms regarding accuracy, trustworthiness, bias, hallucination and plagiarism — ethical issues that likely will take years to sort out.
We believe that internet users will place increasing value on reliable sources of information that have been vetted by people. Wikipedia’s policies and our experiences from more than a decade of using machine learning to support human volunteers offer worthwhile lessons in this future. In addition to natural language text, large language models can be trained on programming language text, allowing them to generate source code for new computer programs. Examples include OpenAI Codex. In 2021, the release of DALL-E, a transformer-based pixel generative model, followed by Midjourney and Stable Diffusion marked the emergence of practical high-quality artificial intelligence art from natural language prompts.
The big tech companies, wagering billions on the new technologies and largely undaunted by their shortcomings or risks, seem intent on forging ahead as fast as they can. Those dynamics would suggest that organizations like Wikipedia will be forced to adapt to the future that A.I. Yet many Wikipedians and academics I spoke with question any such assumption. Impressive as the chatbots may be, A.I.’s apparent glide path to success may soon encounter a number of obstacles.