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2204 08009 WikiOmnia: generative QA corpus on the whole Russian Wikipedia

By using GAI to extract and integrate data from scholarly publications, diverse perspectives can be included in Wikidata, making it a more comprehensive and inclusive knowledge base. The allure of a chatbot conversation, despite its factual shortcomings, already seemed too irresistible and too enchanting to too many millions of people. In fact, my own hours spent with ChatGPT had chipped away at my own neutral point of view — not because the informational exchange was so rigorous and detailed (it wasn’t), but because the interaction was so captivating and effortless. Deckelmann and the rest of the Wikipedia team were also recently in Singapore for Wikimania, an annual conference celebrating Wikimedia projects. We believe that AI works best as an augmentation for the work that humans do on Wikipedia and other Wikimedia platforms.

Among the first class of models to achieve this cross-over feat were variational autoencoders, or VAEs, introduced in 2013. VAEs were the first deep-learning models to be widely used for generating realistic images and speech. Neural networks, which form the basis of much of the AI and machine learning applications today, flipped the problem around. Designed to mimic how the human brain works, neural networks “learn” the rules from finding patterns in existing data sets. Developed in the 1950s and 1960s, the first neural networks were limited by a lack of computational power and small data sets.

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While some people expressed that tools like Open AI’s ChatGPT could help with generating and summarizing articles, others remained wary. In recent years, Generative AI systems have shown impressive capabilities in generating text similar to Wikipedia articles. These AI models, such as Language Models (LLMs), can produce content on a wide range of Yakov Livshits topics, but it is essential to understand that relying solely on AI for Wikipedia content would not replicate its current collaborative and human-driven process. In this article, we explore the challenges and potential of integrating Generative AI into Wikipedia while maintaining the platform’s trustworthiness, reliability, and human involvement.

“I suspect the internet is going to be filled with crud just all over the place,” Chris Albon told me. Models getting better at mimicking people’s writing styles, it may be increasingly difficult to detect chatbot-written submissions. One Wikipedia Yakov Livshits editor whose first name is Theo sent me links in early June to show how he was in the midst of fending off a barrage of edits involving suspect citations formulated by A.I., including one to an article about Lake Doxa, in Greece.

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Because of widespread adoption of generative AI technology designed to predict and mimic human responses, it is now possible to nearly effortlessly create text that seems a lot like it came from Wikipedia. Generative AI systems trained on words or word tokens include GPT-3, LaMDA, LLaMA, BLOOM, GPT-4, and others (see List of large language models). They are capable of natural language processing, machine translation, and natural language generation and can be used as foundation models for other tasks.[28] Data sets include BookCorpus, Wikipedia, and others (see List of text corpora). In 2014, advancements such as the variational autoencoder and generative adversarial network produced the first practical deep neural networks capable of learning generative, rather than discriminative, models of complex data such as images. These deep generative models were the first able to output not only class labels for images, but to output entire images. At its simplest form, artificial intelligence is a field, which combines computer science and robust datasets, to enable problem-solving.

DataRobot was founded on the belief that emerging AI and machine learning technologies should be available to all enterprises, regardless of size and resources. That’s why we invented automated machine learning, which allows users of all skill levels to easily and rapidly build and deploy machine learning models. But over the last decade Wikipedia has also become a critical source of training data for data-hungry text generation models. As a result, any shortcomings in Wikipedia’s content are at risk of being amplified by the text generation tools of the future. 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.

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.

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We can prioritize human understanding and contribution of knowledge back to the world – sustainably, equitably, and transparently – as a key goal of generative AI systems, not as an afterthought. Generative AI systems trained on sets of images with text captions include Imagen, DALL-E, Midjourney, Adobe Firefly, Stable Diffusion and others (see Artificial intelligence art, Generative art, and Synthetic media). They are commonly used for text-to-image generation and neural style transfer.[31] Datasets include LAION-5B and others (See Datasets in computer vision).

Producing high-quality visual art is a prominent application of generative AI.[30] Many such artistic works have received public awards and recognition. Put AI to work in your business with IBM’s industry-leading AI expertise and portfolio of solutions at your side. Learning and Teaching Services (LTS) Yakov Livshits will be conducting several feedback sessions in September and October to discuss generative AI and how LTS can best support the various academic departments. This video series from the Wharton School at the University of Pennsylvania is a great introduction that is geared for professors and students.

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Transformer architecture has evolved rapidly since it was introduced, giving rise to LLMs such as GPT-3 and better pre-training techniques, such as Google’s BERT. Enhance customer experience through automation, conversation summaries, and insights. Provide your team with a complete overview of each customer, as well as a large scale, comprehensive view of all customers and interactions. Please read our Terms of Use & Privacy Policy pages to learn more.

Still, progress thus far indicates that the inherent capabilities of this type of AI could fundamentally change business. Going forward, this technology could help write code, design new drugs, develop products, redesign business processes and transform supply chains. The presentation on using generative artificial intelligence (GAI) to enrich Wikidata with data from scholarly publications and generate high-quality content for Wikipedia articles is closely related to the theme of Wikimania in several ways. Mitchell, who now works as the chief ethics scientist at the A.I. Companies’ making gains in accuracy and reducing biased answers by using better data. “The state of the art until now has just been a laissez-faire data approach,” she said.

Translating policy and standards to code

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• Early implementations have had issues with accuracy and bias, as well as being prone to hallucinations and spitting back weird answers.
• Meanwhile, Microsoft and ChatGPT implementations also lost face in their early outings due to inaccurate results and erratic behavior.
• Neural networks, which form the basis of much of the AI and machine learning applications today, flipped the problem around.
• We use AI to generate realistic, customizable voiceovers, enabling applications like personalized assistants, voice cloning, and dubbing.
• When I asked Agarwal whether OpenAI’s systems could ever be completely accurate, or offer 400 footnotes, she said that it was possible.