Tag Archive for: big data

Power Hungry Magic

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“Any sufficiently advanced technology is indistinguishable from magic”, Arthur C. Clarke already knew, and it is part of the magic of new technologies that their downsides are systematically concealed. This is also the case with the energy consumption of large language models (LLMs): As with the schnitzel that ends up on consumers’ plates and makes them forget the relation to the realities of factory farming, so it is with the marvels of artificial intelligence. Information about the computing power required to create products such as ChatGPT and the big data used is not provided, either to avoid making data protection and copyright issues too obvious or to avoid having to quantify the energy consumption and CO2 emissions involved in training and operating these models. The reputable newspaper Die Zeit estimated in March 2023: “For the operation of ChatGPT, […] costs of 100,000 to 700,000 dollars a day are currently incurred” and noted “1,287 gigawatt hours of electricity” or “emissions of an estimated 502 tonnes of CO2” for the training of GPT-3 (Art. “Hidden energy”, in: Die Zeit No. 14, 30.03.2023, p.52). Against this backdrop, it comes as no surprise that, according to the International Energy Authority, the electricity consumption of the big tech companies Amazon, Microsoft, Google and Meta doubled to 72 TWh between 2017 and 2021; these four companies are also the world’s largest providers of commercially available cloud computing capacity.

Recently, Sasha Luccioni, Yacine Jernite and Emma Strubell presented the first systematic study on the energy consumption and CO2 emissions of various machine learning models during the inference phase. Inference here means the operation of the models, i.e. the period of deployment after training and fine-tuning the models. Inference accounts for around 80 to 90 percent of the costs of machine learning, on a cloud computing platform such as Amazon Web Services (AWS) around 90 per cent according to the operator. The study by Luccioni et al. emphasises the differences between various machine learning applications: The power and CO2 intensity is massively lower for text-based applications than for image-based tasks; similarly, it is massively lower for discriminative tasks than for generative ones, including generative pretrained transformers (GPTs). The differences between the various models are considerable: “For comparison, charging the average smartphone requires 0.012 kWh of energy which means that the most efficient text generation model uses as much energy as 16% of a full smartphone charge for 1,000 inferences, whereas the least efficient image generation model uses as much energy as 950 smartphone charges (11.49 kWh), or nearly 1 charge per image generation.” The larger the model, the faster the same amount of electricity is consumed or CO2 emitted during the inference phase as during the training phase.

Since ‘general purpose applications’ for the same task consume more energy than models that have been trained for a specific purpose, Luccioni et al. point out several trade-offs: Firstly, the trade-off between model size vs. power consumption, as the benefits of multi-purpose models must be weighed against their power costs and CO2 emissions. Secondly, the trade-off between accuracy/efficiency and electricity consumption across different models, because the higher the accuracy or the higher the efficiency of a model, the lower the power consumption of specific models, whereas multi-purpose models can fulfil many different tasks, but have a lower accuracy and higher electricity consumption. According to the authors, these empirically proven findings call into question, for example, whether it is really necessary to operate multi-purpose models such as Bard and Bing, i.e. they “do not see convincing evidence for the necessity of their deployment in contexts where tasks are well-defined, for instance web search and navigation, given these models’ energy requirements.”

The hunger for power of large general purpose models does not bring the “limits to growth” to the attention of leading entrepreneurs and investors of Western big tech companies, like the famous Club of Rome report more than 50 years ago. On the contrary, CEOs such as Jeff Bezos, whose empire also includes the largest cloud computing platform AWS, fear stagnation: “We will have to stop growing, which I think is a very bad future.” Visions such as the Metaverse are extremely costly in terms of resource consumption and emissions, and it is fair to ask whether AI applications will really be available to all of humanity in the future or only to those companies or individuals who can afford them. Nothing of all of this is even remotely sustainable. Given the growing power consumption of Western big tech companies and the fact that the core infrastructure for the development of AI products is already centralised by those few players, it remains unclear where the development of ‘magical’ AI applications will lead. Scientist Kate Crawford has given her own answer to this in her book “Atlas of AI“: Into space, because that’s where the resources are that these corporations need.

Feeding the Cuckoo

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Large Language Models (LLMs) combine words that frequently appear in similar contexts in the training dataset; on this basis, they predict the most probable word or sentence. The larger the training dataset, the more possible combinations there are, and the more ‘creative’ the model appears. The sheer size of models such as GPT-4 already provides a competitive advantage that is hard to match: There are only a handful of companies in the world that can combine exorbitant computing power, availability of big data and an enormous market reach to create such a product. No research institutions are involved in the current competition, but the big tech companies Microsoft, Meta and Google are. However, few players and few models also mean a “race to the bottom” in terms of security and ethics, as the use of big data with regard to LLMs most often also means that the training data contains sensitive and confidential information as well as copyrighted material. In numerous court cases, the tech giants have been accused of collecting the data of millions of users online without their consent and violating copyright law in order to train AI models.

Internet users have therefore already helped to feed the cuckoo child. Google published this fact indirectly by updating its privacy policy in June 2023: “use publicly available information to help train Google’s AI models and build products and features like Google Translate, Bard, and Cloud AI capabilities.” Less well known, however, is the fact that the big tech companies also train their models, such as Bard, with what users entrust to them. In other words, everything you tell a chatbot can in turn be used as training material. In Google’s own words, it sounds like this: “Google uses this data to provide, improve, and develop Google products, services, and machine-learning technologies.” One consequence of the design of LLMs, however, is that the output of generative models cannot be controlled; there are simply too many possibilities with large models. If the LLM was and is trained on private or confidential data, this can lead to these data being disclosed and confidential information being revealed. Therefore, the training data should already comply with data protection regulations, which is why there are repeated calls for transparency with regard to training data.

Consequently, in its Bard Privacy Help Hub, Google warns users of the model not to feed it with sensitive data: “Please don’t enter confidential information in your Bard conversations or any data you wouldn’t want a reviewer to see or Google to use to improve our products, services, and machine-learning technologies.” This is interesting insofar as the AI hype is fuelled by terms such as ‘disruption’, but at the same time it remains unclear what the business model with which big tech companies want to generate profits in the medium term should look like – and what exactly the use case should look like for average users. One use case, however, is the generation of texts that are needed on a daily basis, namely well-formulated application letters. However, if you upload your own CV for this purpose, you’re just feeding the cuckoo again. And that is not in our interest: After all, privacy is (also) a commons.

On the Use of Licences in Times of Large Language Models

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It could all be so simple: cultural heritage institutions and other public sector bodies provide high-quality data on a large scale and, wherever possible, under a permissive licence such as CC0 or Public Domain Mark 1.0. This is in line with the idea that cultural heritage institutions are funded by taxes, therefore everyone should also benefit from their services and products; in the case of data, innovation, research and of course private use should be possible.

However, we live in times of large language models and exploitative practices, especially of US-american big tech companies. Here, data are extracted from the web on a large scale and processed into proprietary large language models. These companies are not only the drivers of innovation, but also set themselves apart from research institutions, for example, by having specifically trained data sets at their disposal as well as exceptional computing power and the best-paid positions for developers of algorithms; all these elements are expensive ingredients of a recipe for success in the face of limited competition.

One of the weaknesses of ChatGPT – and presumably of GPT-4 – is its lack of reliability. This weakness results from the inability of purely stochastic language models to distinguish between fact and fiction; but also from a lack of data. Especially with regard to “hallucinated” literature references, bibliographic data from libraries are very attractive for building large language models. Another problem is the lack of high-quality text data. According to a recently published study, high-quality text data will be exhausted before the year 2026; this is mainly due to the lack of etiquette and proper spelling on the internet. But who, if not the libraries, have huge stocks of high-quality text data? Almost all the content available here has passed through a quality filter called “publishing houses”. One may be divided about the intellectual quality of the books; but linguistically and orthographically, everything that was printed until the end of the 20th century (i.e. before the advent of self-publishing) is of very good quality.

Finally, dear money: inflation is back, the low-interest phase is gone, the first Silicon Valley bank went bankrupt. Many companies based there will soon need fresh money; there will soon be monetisation to generate profits. New and more capable models will soon be created from products (such as ChatGPT) that were previously offered free of charge, providing demand-driven services in exchange for payment.

Should cultural heritage institutions as public entities serve the maximisation of the profits of a few companies by providing expensive and resource-intensive (and tax-funded) data for free? The answer has to be differentiated and therefore complicated. Of course, data should also be made available under permissive licences, as has been the case up to now. A dual strategy can certainly be used here. On the one hand, data made available via interfaces such as OAI-PMH or IIIF continue to be accessible under CC0 licence or or Public Domain Mark 1.0; technical access restrictions can prevent large-scale data extraction, e.g. by controlling IP addresses or download maxima. On the other hand, specific data publications can be provided that bundle individual data sets to enable research and innovation; such offerings are protected as databases for 15 years, and here licences can be used that contain a “NC” (non-commercial) mark and make such data usable for research and innovation. As an example, the Prussian Cultural Heritage Foundation uses such a licence (CC-BY-NC-SA) for the digital representation of one of its masterpieces, and the (not so easy to use) 3D scan is also freely available under this licence (download here).

Interestingly, the European Union anticipated the case described above in the Data Governance Act and included a relevant set of instruments. There is a chapter on the use of data provided by public sector bodies (Chapter II, Article 6), which regulates the provision of data in exchange for fees. It states that public sector bodies may differentiate the fees they charge between private users, small and medium-sized enterprises (SMEs) and start-ups on the one hand and larger corporations on the other, which don’t fall under the former definition. In this way, a possibility for differentiation within the framework of commercial users is created, whereby the fees have to be oriented at the costs of the infrastructure to provide data. This is something rather atypical in the European legal system, since the principle of equal treatment applies. Cultural heritage institutions thus have EU Commissioner for Competition Margrethe Vestager on their side, who presented the Data Governance Act in 2020 (that is applicable from 24 September 2023, by the way). Vestager is also Executive Vice President of the European Commission for a Europe Fit for the Digital Age and has imposed more than 15 billion Euros in antitrust fines in her first five years in office. So the enforcing political will seems to be there.

In case of doubt, this will be necessary. Licences like CC-BY-SA-NC effectively prevent the use of public data for commercial exploitation in large language models. But since the creators of large language models are moving around in a minefield regarding copyright, and in the case of other models, a stock photo agency or other rights holders have already filed copyright lawsuits, one must unfortunately doubt that they will show consideration in the future. Of course, the relevant court decisions remain to be seen in the pending cases. Even with reverse engineering, it is not easy to prove which data sets have been incorporated into a large language model; therefore, a kind of circumstantial evidence would have to be provided. In the medium and long term, it therefore seems more sensible to focus on establishing validation processes and standards that have to be implemented prior to publishing AI models. This includes the disclosure of the training material and process, its evaluation by experts, code audits, but also a reversal of evidence with regard to the licensing of the data material used. Making such procedures an obligatory part of the approval of commercial AI applications is then actually the task of the European Union.

Finally, another way is to publish cultural heritage data in a separate Data Space for Cultural Heritage; the tender for this Data Space was launched last autumn and is part of the European Union’s Data Act. To what extent this Data Space will grant full data sovereignty  to cultural heritage institutions and thus the possibility to control access to data publications remains to be seen.

On the Use of ChatGPT in Cultural Heritage Institutions

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Since the release of the ChatGPT dialogue system in November 2022, the societal debate about artificial intelligence (AI) has gained significant momentum and has also reached cultural heritage institutions (such as libraries, archives, and museums). The main challenge is to assess how powerful such large language models (LLMs) are in general, and Generative Pre-trained Transformers (GPTs) in particular. For the cultural heritage sector, the ChatGPT chat bot prototype reveals a whole range of possible uses: producing text summaries or descriptions of artworks, generating metadata, writing computer code for simple tasks, assisting with subject indexing and keyword indexing, or helping users find resources on the websites of cultural heritage institutions.

Undoubtedly, ChatGPT’s strengths lie in the generation of text and associated tasks. As “statistical parrots,” as these large language models were called in a much-discussed 2021 paper, these language models can predict on a stochastic basis what the next words of a snippet of text will look like. In this context, the ChatGPT use case has been trained – as a text-based dialogue system – to provide answers at any rate. This property of the chat bot points directly to one of the central weaknesses of the model: In case of doubt, ChatGPT provides untrue statements in order to maintain the dialogue. Since large language models are, after all, only applications of artificial intelligence and have no knowledge of the world, they cannot per se distinguish between fact and fiction, social construction and untruth. The fact that ChatGPT “hallucinates” (as the common anthropomorphizing term goes) when in doubt and also e.g., invents literature references, damages of course the reliability of the system – and it points to the great strength of libraries in providing authoritative evidence.

On the other hand, a strength of such systems is that they can excellently reproduce discourses and are therefore able to classify individual texts or larger text corpora and to describe their content in an outstanding way. This shows great potential, especially for libraries: Up to now, digital assistants that support the indexing of books have at best worked with statistical methods such as tf-idf, or with deep learning. Such approaches could be complemented through the use of topic modeling. The latter method generates a stochastically modelled text sequence that describes the content of a work or the topics it deals with. The challenge for users so far has been to interpret this collection of words and assign a coherent label to it – and this is exactly what ChatGPT does excellently, as several researchers have confirmed. Since this massively improves and facilitates the labelling of texts, this is certainly one of the most probable use cases for AI in libraries, and exactly the field on which the sub-project 3 “AI-supported content analysis and subject indexing” of the project “Human.Machine.Culture” focuses. By contrast, simple programming tasks such as creating a bibliographic record in a specific format or transforming a record from MARC.xml to JSON are in need of improvement; ChatGPT does not always perform such tasks reliably, as a recent experiment showed.

ChatGPT, as one of the most powerful text-based AI applications currently available, underlines the potential benefits of such models. At the same time, however, it also highlights the risks associated with the use of such applications: So far, only U.S.-American big tech companies are able to train such powerful models, make them accessible, and develop later onwards models optimized through reinforcement learning for specific tasks – with the clear goal of monetization. In addition, generative AI systems bring with them a number of ethical issues, as they require large masses of text that have so far been taken from the Internet and thus a place where not all people interact politely and with all etiquette. For example, a recent study has underlined that large language models reproduce stereotypes by associating the terms “Muslims” and “violence”. Moreover, toxic content in the language models have to be labeled as such, an operation that is being carried out by underpaid workers; this underlines again the ethical dubiousness of the process of establishing such models.

Finally, the fact that these models have been trained almost exclusively based on 21st century textual material available on the Internet has to be underlined. By contrast, sub-project 4 “Data provision and curation for AI” of the project “Human.Machine.Culture” concentrates on the provision of curated and historical data from libraries for AI applications. Finally, the deployment of large langage models points to very fundamental questions: Namely, what role the cultural heritage of all humanity should play in the future and what effect cultural heritage institutions like libraries, archives and museums may have on their establishment; and what influence the texts generated by large language models will have on our contemporary culture as such.