How can transparency work in practice?

As discussed in our Policy Paper #15, transparency is key to ensuring a fair balance between the interests of creators on the one hand and those of commercial AI developers on the other. A transparency obligation would empower creators, allowing them to assess whether the copyrighted materials used as AI training data have been scraped from lawful sources, as well as whether their decision to opt-out from AI training has been respected. At the same time, such an obligation needs to be fit-for-purpose, proportionate and workable for different kinds of AI developers, including smaller players.

While the European Parliament’s text has taken an important step towards improving transparency, it has been criticised for falling short in two key aspects. First, the proposed text focuses exclusively on training data protected under copyright law which arbitrarily limits the scope of the obligation in a way that may not be technically feasible. Second, the Parliament’s text remains very vague, calling only for a “sufficiently detailed summary” of the training data, which could lead to legal uncertainty for all actors involved, given how opaque the copyright ecosystem itself is.

As such, we are encouraged to see the recent work of the Spanish presidency on the topic of transparency, improving upon the Parliament’s proposed text. The presidency recognises that there is a need for targeted provisions that facilitate the enforcement of copyright rules in the context of foundation models and proposes that providers of foundation models should demonstrate that they have taken adequate measures to ensure compliance with the opt-out mechanism under the Copyright Directive. The Spanish presidency has also proposed that providers of foundation models should make information about their policies to manage copyright-related aspects public.

This proposal marks an important step in the right direction by expanding the scope of transparency beyond copyrighted material. Furthermore, requiring providers to share information about their policies to manage copyright-related aspects could provide important clarity as to the methods of opt-out that are being respected, empowering creators to be certain that their choices to protect works from TDM are being respected.

In search of a middle ground

Unfortunately, while the Spanish presidency has addressed one of our key concerns by removing the limitation to copyrighted material, ambiguity remains. Calling for a sufficiently detailed summary about the content of training data leaves a lot of room for interpretation and may lead to significant legal uncertainty going forward. Having said that, strict and rigid transparency requirements which force developers to list every individual entry inside of a training dataset would not be a workable solution either, due to the unfathomable quantity of data used for training. Furthermore, such a level of detail would provide no additional benefits when it comes to assessing compliance with the opt-out mechanism and the lawful access requirement. So what options do we have left?

First and foremost, the reference to “sufficiently detailed summary” must be replaced with a more concrete requirement. Instead of focussing on the content of training data sets, this obligation should focus on the copyright compliance policies followed during the scraping and training stages. Developers of generative AI systems should be required to provide a detailed explanation of their compliance policy including a list of websites and other sources from which the training data has been reproduced and extracted, and a list of the machine-readable rights reservation protocols/techniques that they have complied with during the data gathering process. In addition, the AI Act should allocate the responsibility to further develop transparency requirements to the to-be-established Artificial Intelligence Board (Council) or Artificial Intelligence Office (Parliament). This new agency, which will be set up as part of the AI Act, must serve as an independent and accountable actor, ensuring consistent implementation of the legislation and providing guidance for its application. On the subject of transparency requirements, an independent AI Board/Office would be able to lay down best-practices for AI developers and define the granularity of information that needs to be provided to meet the transparency requirements set out in the Act.

We understand that the deadline to find an agreement on the AI Act ahead of the next parliamentary term is very tight. However, this should not be an excuse for the co-legislators to rush the process by taking shortcuts through ambiguous language purely to find swift compromises, creating significant legal uncertainty in the long run. In order to achieve its goal to protect Europeans from harmful and dangerous applications of AI while still allowing for development and encouraging innovation in the sector, and to potentially serve as model legislation for the rest of the world, the AI Act must be robust and legally sound. Everything else would be a wasted opportunity.

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As discussed in greater detail in our Policy Paper #15, COMMUNIA deems it essential that the flexibilities for text-and-data mining enshrined in Articles 3 and 4 of the Copyright in the Digital Single Market Directive are upheld. For this approach to work in practice, we welcome practical initiatives for greater transparency around AI training data to understand whether opt-outs are being respected.

The full statement is provided below:

Statement on Transparency in the AI Act

The undersigned are civil society organizations advocating in the public interest, and representing  knowledge users and creative communities.

We are encouraged that the Spanish Presidency is considering how to tailor its approach to foundation models more carefully, including an emphasis on transparency. We reiterate that copyright is not the only prism through which reporting and transparency requirements should be seen in the AI Act.

General transparency responsibilities for training data

Greater openness and transparency in the development of AI models can serve the public interest and facilitate better sharing by building trust among creators and users. As such, we generally support more transparency around the training data for regulated AI systems, and not only on training data that is protected by copyright.

Copyright balance

We also believe that the existing copyright flexibilities for the use of copyrighted materials as training data must be upheld. The 2019 Directive on Copyright in the Digital Single Market and specifically its provisions on text-and-data mining exceptions for scientific research purposes and for general purposes provide a suitable framework for AI training. They offer legal certainty and strike the right balance between the rights of rightsholders and the freedoms necessary to stimulate scientific research and further creativity and innovation.

Proportionate approach

We support a proportionate, realistic, and practical approach to meeting the transparency obligation, which would put less onerous burdens on smaller players including non-commercial players and SMEs, as well as models developed using FOSS, in order not to stifle innovation in AI development. Too burdensome an obligation on such players may create significant barriers to innovation and drive market concentration, leading the development of AI to only occur within a small number of large, well-resourced commercial operators.

Lack of clarity on copyright transparency obligation

We welcome the proposal to require AI developers to disclose the copyright compliance policies followed during the training of regulated AI systems. We are still concerned with the lack of clarity on the scope and content of the obligation to provide a detailed summary of the training data. AI developers should not be expected to literally list out every item in the training content. We maintain that such level of detail is not practical, nor is it necessary for implementing opt-outs and assessing compliance with the general purpose text-and-data mining exception. We would welcome further clarification by the co-legislators on this obligation. In addition, an independent and accountable entity, such as the foreseen AI Office, should develop processes to implement it.

Signatories

• COMMUNIA
• Creative Commons
• Wikimedia Europe

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Over the course of 20 minutes, Teresa walks the listeners through some of the key questions when it comes to the training of generative AI models and how it affects the text and data mining (TDM) exception laid down in Articles 3 and 4 of the EU Copyright Directive (see also our Policy Paper #15).

Disclaimer: Playback of the embedded video establishes a connection to YouTube and may lead to data being collected by and shared with third parties. Proceed only if you agree.

While the Directive clearly establishes the right to mine online content and use it to train machine learning algorithms, this right hinges on the possibility for rightsholders to opt out their works if the activity takes place in a commercial context. A key issue we are currently seeing is that there is a lack of transparency around the training of generative AI models, which makes it impossible to tell whether such opt-outs are being respected or not.

In the discussion, Teresa highlighted the need for more transparency regarding opt-outs but also across the copyright ecosystem as a whole. COMMUNIA has long advocated for the creation of a database of copyrighted works, which would contribute to managing the system of opt-outs.

The discussion ended with a call upon the European Commission to provide guidance and lead technical discussions towards establishing a clear, reliable and transparent framework for opt-outs for TDM (see also Open Future’s recent policy brief on this issue). Only through dialogue between the concerned stakeholders, led by an independent third party, will we be able to establish best practices that uphold Articles 3 and 4 of the Copyright Directive while providing a fair and balanced framework for the training of machine learning models.

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In their new Policy brief on defining best practices for opting out of ML training published today, our member Open Future takes a closer look at a key element of the text and data mining exception: The rights reservation mechanism foreseen in Article 4(3) of the Directive, which allows authors and other rights holders to opt out of their works being used to train (generative) ML models.

The policy brief highlights that there are still many open questions relating to the implementation of the opt out mechanism. That is, how the machine-readable reservation of rights under Article 4 will work in practice. One of the key issues in this context is the fact that currently there are no generally accepted standards or protocols for the machine-readable expression of the reservation. The authors of the policy brief provide an overview of existing initiatives to provide standardized opt-outs which include initiatives by Adobe and a publisher-led W3C community working group, as well as the artist-led project Spawning, which provides an API that aggregates various opt-out systems. In addition, they highlight a number of proprietary initiatives from model developers, including Google and OpenAI.

Lack of a technical standard

According to the policy brief, one of the key problems facing creators and other rights holders who wish to opt out of ML training is that it is unclear whether and how their intentions to opt-out will be respected by ML model developers. According to the authors of the policy brief, this is deeply problematic and risks undermining the legal framework put in place by the 2019 Copyright Directive:

Continued lack of clarity on how to make use of the opt-out from Article 4 of the CDSM Directive creates the risk that the balanced regulatory approach adopted by the EU in 2019 might fail in practice, which would likely lead to a reopening of substantive copyright legislation during the next mandate. Given the length of EU legislative processes, this would prolong the status quo and, as a result, fail to provide protection for creators and other rightholders in the immediate future.

It seems clear that this scenario should be avoided, both in the interest of creators, who need to have meaningful tools to enforce their rights vis-à-vis commercial ML companies, and in order to preserve the hard-won compromise reflected in the TDM exceptions.

In line with this, the Open Future policy brief calls on the European Commission to “provide guidance on how to express machine-readable rights reservations”. According to the brief, the Commission needs to step in and “publicly identify data sources, protocols and standards that allow authors and rightholders to express a machine-readable rights reservation in accordance with Article 4(3) CDSM”. This guidance would provide important clarity about the availability of freely usable methods of reservation and certainty as to their functionality.

According to the authors, such an intervention would allow the Commission to support creators and other rightholders seeking means to opt out of ML training, while at the same time providing “more certainty to ML developers seeking to understand what constitutes best efforts to comply with their obligations under Article 4(3) of the CDSM Directive.

Time to act

The Open Future policy identifies an important shortcoming in the existing EU approach to the use of copyrighted works for ML training. Without clear guidelines for standardized machine-readable rights reservations, the opt-out mechanism foreseen in Article 4 is unlikely to work in practice. While there are a number of existing standards, the fragmentation of this system causes tremendous uncertainty for creators.

As Open Future points out, it is up to the Commission, which is responsible for ensuring the proper implementation of the Directive’s provisions, to intervene in this area and provide initial clarity to all stakeholders. In the longer term, it would be ideal to see the emergence of an open standard that is maintained independently of any direct stakeholders. Such an effort should not be limited to aggregating opt-outs, but should also be designed to ensure that works in the public domain or made available under licenses that allow and/or encourage reuse are clearly identified as such (In line with our Policy Recommendation #20).

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One of the key issues for the discussions will most likely be how to deal with the rather recent phenomenon of generative AI systems (also referred to as foundational models) which are capable of producing various content ranging from complex text to images, sound computer code and much more with very limited human input.

The rise of generative AI

Within less than a year, generative AI technology went from having a select few, rather niche applications to becoming a global phenomenon. Perhaps no application represents this development like ChatGPT. Originally released in November 2022, ChatGPT broke all records by reaching one million users within just five days of its release with the closest competitors for this title, namely Instagram, Spotify, Dropbox and Facebook, taking several months to reach the same stage. Fast forward to today, approximately half a year later, and ChatGPT reportedly counts more than 100 million users.

One of the reasons for this “boom” of generative AI systems is that they are more than just a novelty. Some systems have established themselves as considerable competitors for human creators for certain types of creative expressions, being able to write background music or produce stock images that would take humans many more hours to create. In fact, the quality of the output of some systems is already so high while the cost of production is so low that they pose an existential risk to specific categories of creators, as well as the industries behind them.

But how do generative AI systems achieve this and what is the secret behind their ability to produce works that can comfortably compete with works of human creativity? Providing an answer to this question, even at surface level, is extremely difficult since AI systems are notoriously opaque, making it nearly impossible to fully understand their inner workings. Furthermore, developers of these systems have an obvious interest in keeping the code of their algorithm as well as the training data used secret. This being said, one thing is for certain: generative AI systems need data, and lots of it.

The pursuit of data

Creating an AI system is incredibly data intensive. Data is needed to train and test the algorithm throughout its entire lifecycle. Going back to the example of ChatGPT, the system was trained on numerous datasets throughout its iterations containing hundreds of gigabytes of data equating to hundreds of billions of words.

With so much data needed for training alone, this opens up the question how developers get their hands on this amount of information. As is fairly obvious by the sheer numbers, training data for AI systems is usually not collected manually. Instead, developers often rely on two sources for their data: curated databases which contain vast amounts of data and so-called web crawlers which “harvest” the near boundless information and data resources available on the open internet.

The copyright conundrum

Some of the data available in online databases or collected by web scraping tools will inevitably be copyrighted material which raises some questions with regards to the application of copyright in the context of training AI systems. Communia has extensively discussed the interaction between copyright and text and data mining (TDM) in our policy paper #15 but just as a short refresher about the clear framework established in the 2019 Copyright Directive:

Under Article 3, research organizations and cultural heritage institutions may scrape anything that they have legal access to, including content that is freely available online for the purposes of scientific research. Under Article 4, this right is extended to anyone for any purposes but rights holders may reserve their rights and opt out of text and data mining, most often through machine-readable means.

While this framework, in principle, provides appropriate and sufficient legal clarity on the use of copyrighted materials in AI training, the execution still suffers from the previously mentioned opacity of AI systems and the secrecy around training data as there is no real way for a rightsholder to check whether their attempt to opt out of commercial TDM has actually worked. In addition, there’s still a lot of uncertainty about the best technical way to effectively opt out.

Bringing light into the dark

Going back to the EU’s AI Act reveals that the European Parliament recognises this issue as well. The Parliament’s position foresees that providers of generative AI models should document and share a “sufficiently detailed” summary of the use of training data protected under copyright law (Article 28b). This is an encouraging sign and a step in the right direction. The proof is in the pudding, however. More clarity is needed with regards to what “sufficiently detailed” means and how this provision would look in practice.

Policy makers should not forget that the copyright ecosystem itself suffers from a lack of transparency. This means that AI developers will not be able – and therefore should not be required – to detail the author, the owner or even the title of the copyrighted materials that they have used as training data in their AI systems. This information simply does not exist out there for the vast majority of protected works and, unless right holders and those who represent them start releasing adequate information and attaching it to their works, it is impossible for AI developers to provide such detailed information.

AI developers also should not be expected to know which of their training materials are copyrightable. Introducing a specific requirement for this category of data adds legal complexity that is not needed nor advisable. For that and other reasons, we recommend in our policy paper that AI developers be required to be transparent about all of their training data, and not only about the data that is subject to copyright.

The fact that AI developers know so little about each of the materials that is being used to train their models should not, however, be a reason to abandon the transparency requirement.

In our view, those that are using publicly available datasets will probably comply with the transparency requirement simply by referring to the dataset, even if the dataset is lacking detailed information on each work. Those that are willing to submit training data with a data thrust that would ensure the accessibility of the repository for purposes of assessing compliance with the law would probably also ensure a reasonable level of transparency.

The main problem is with those that are not disclosing any information about their training data, such as OpenAI. These need to be forced to make some sort of public documentation and disclosure and at least need to be able to show that they have not used copyrighted works that have an opt-out attached to it. And that begs for the question: how can creators and other right holders effectively reserve their training rights and opt-out of the commercial TDM exception?

Operationalizing the opt-out mechanism

In our recommendations for the national implementation of the TDM exceptions we suggested that the proper technical way to facilitate web mining was by the use of a protocol like robot.txt which creates a binary “mine”/“don’t mine” rule. However, this technical protocol has some significant limitations when it comes to its application in the context of data mining for AI training data.

Therefore, one of the recommendations in our policy paper is for the Commission to lead these technical discussions and provide guidance on how the opt-out is supposed to work in practice to end some of the uncertainty that exists among creators and other rights holders.

In order to encourage a fair and balanced approach to both the opt-out and the transparency issues, the Commission could convene a stakeholder dialogue and include all affected parties, namely AI developers, creators and rights holders as well as representatives of civil society and academia. The outcome of this dialogue should be a way to operationalise the opt-out system itself and the transparency requirements that will uphold such a system without placing a disproportionate burden on AI developers.

Getting this right would provide a middle ground that allows creators and other rights holders to protect their commercial AI training rights over their works while encouraging innovation and the development of generative AI models in the EU.

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We discuss the considerations of the use of copyright-protected works and other protected subject matter as training data for generative AI models, and provide two recommendations for lawmakers. Here, we leave aside questions relating to the output of AI models (e.g. whether the output of generative AI models is copyrightable and in how far such output can be infringing exclusive rights), which we will address in another, yet to be published paper.

This paper is without prejudice to the position of COMMUNIA or individual COMMUNIA members regarding this discussion in other jurisdictions.

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We have witnessed a proliferation of (so-called) generative artificial intelligence (AI) models since OpenAI made DALL-E 2 available to the public in July 2022. This has gone alongside a renewed interest in questions about the relationship between machine learning (ML) and copyright law — as evidenced by a surge in publications on the topic both in scientific journals and general-audience media, as well as a number of lawsuits.

In this policy paper, we are looking at the input side of the equation within the EU copyright framework.¹ We discuss the considerations of the use of copyright-protected works and other protected subject matter as training data for generative AI models, and provide two recommendations for lawmakers. Here, we leave aside questions relating to the output of AI models (e.g. whether the output of generative AI models is copyrightable and in how far such output can be infringing exclusive rights), which we will address in another, yet to be published paper.

The surge of generative AI raises concerns for creators and their livelihood. It also prompts broader questions about the implications of potentially inauthentic and untrustworthy AI-generated output for social cohesion as well as about the extraction and concentration of resources by only a few tech companies. We are mindful of these challenges, but copyright is not designed to address all of them in a way that does justice to the underlying grievances.

Training generative machine learning systems

This paper is based on the assumption that in order to train generative ML models their developers require access to large amounts of materials, including images and text, many of which – but far from all – are protected by copyright. Currently, the most prominent examples of such models are image generators (such as Dalll-E, Midjourney and Stable Diffusion) and large language models (such as BLOOM, GPT and LLaMA) that are able to generate text and sometimes software code. But it is only a question of time until the same questions will arise for music or video generators and the use of copyrighted musical or audiovisual materials.

Access to large amounts of training data enables developers of ML models to train their models so that they can generate output. Based on our understanding of the technology, we assume that copies of the works that have been used for training are in no way stored as part of the model weights² or the model itself.

What we observe at a high level of abstraction is a situation where ML models are trained on very large numbers of works from a vast number of rightholders. Usually, the generated output will be based on the model as derived from the totality of the training data.

Traditionally, the copyright system has been badly equipped to deal with instances of large numbers of underlying rightholders. Rights clearance for mass digitization projects, for instance, is greatly encumbered by the amount of rightholders and difficulties in obtaining permission from those who either never have or are no longer actively managing their rights. Training ML models from the open internet involves even greater numbers of rightholders.

All of this combined with the novelty and rapid development of ML technologies has resulted in significant legal uncertainty. Unsurprisingly, the use of copyrighted works as part of AI training is already subject to legal dispute in the US and UK.

Most of the discussion about copyright and ML training has been conducted within the parameters provided by the US framework. The question dominating this discussion has been: Do uses of copyrighted works for the purpose of training (generative) ML constitute fair use?³

The EU copyright framework does not provide for a fair use defence; users can rely on the system of exceptions and limitations to copyright when they use a work without express permission of rightholders. Therefore the situation is different here.

We argue that questions relating to the input side of ML are sufficiently addressed by the existing EU copyright framework. Since the adoption of the 2019 Copyright in the Digital Single Market Directive (CDSM Directive), the EU copyright framework contains a set of harmonised exceptions that are applicable to ML as described above. These are the exceptions for text and data mining (TDM) introduced in Articles 3 and 4 of the Directive.

Machine learning and text and data mining

Even though not directly referenced in the Directive, the fight over TDM exceptions during the legislative battle over the CDSM Directive has always been about the ML revolution that was already on the horizon at that time.⁴

The CDSM Directive defines TDM as “any automated analytical technique aimed at analysing text and data in digital form in order to generate information which includes but is not limited to patterns, trends and correlations.” This definition clearly covers current approaches to ML that rely heavily on correlations between observed characteristics of training data. The use of copyrighted works as part of the training data is exactly the type of use that was foreseen when the TDM exception was drafted and this has recently been confirmed by the European Commission in response to a parliamentary question.

Article 3⁵ of the Directive allows text and data mining for the purposes of scientific research by research organisations and cultural heritage institutions, as long as they have lawful access to the works to be mined, including content that is freely available online.

The Article 4 exception – which is the result of extensive advocacy by researchers, research organisations, open access advocates and technology companies to broaden the scope of the TDM exception in Article 3 – allows anyone to use lawfully accessible works, as defined above, for text and data mining unless such use has been “expressly reserved by their rightholders in an appropriate manner, such as machine-readable means.”

In sum, these two articles provide a clear legal framework for the use of copyrighted works as input data for ML training in the EU. Researchers at academic research institutions and cultural heritage institutions are free to use all lawfully accessible works (e.g. the entire public Internet) to train ML applications. Everyone else – including commercial ML developers – can only use works that are lawfully accessible and for which their rightholders have not explicitly reserved use for TDM purposes.

Opt-out and the limits of the copyright framework

The EU’s approach constitutes a forward-looking framework for dealing with the issues raised by the mass scale use of copyrighted works for ML training. Importantly, it ensures a fair balance between the interests of rightholders on the one side and researchers and ML developers on the other.

The exception in Article 3 provides much needed clarity for academic researchers and ensures that they have access to all copyrighted works for TDM/ML training purposes. The more limited exception in Article 4 addresses the interests of creators and other rightholders who want to control the use of their works and those who don’t.

Creators and rightholders who want to control the use of their works can opt out from TDM/ML either to prevent their works from being used for this purpose or to establish a negotiation position for licensing such uses of their works either collectively or individually. Here, the European Commission should also play an active role in defining technical standards for reserving the right in machine-readable form in order to increase certainty for all parties involved.

What is equally important is that this differentiation also recognizes the fact that for a significant amount of works the rights are not actively managed by their rightholders. This means that under a copyright-by-default regime (i.e works can only be used on the basis of explicit opt-in) these works could not be used for ML learning since obtaining permission from large numbers of rightholders not actively managing their rights would be impossible. Future EU rulemaking should thus maintain the opt-out approach for ML training and ensure that permissionless use remains the default.

Recommendation 1: The EU must maintain the exceptions for text and data mining established in Articles 3 and 4 of the CDSM Directive. The existing opt-out model for commercial uses should be preserved as it establishes a balance between the interests of ML developers on the one hand and creators on the other.

Transparency

However, we should not stop there. For this approach to work in practice it is essential that ML development becomes more transparent. The EU legislator should enact provisions that require providers of generative ML models to publicly disclose the use of all materials used as training data, including copyright-protected works, in a reasonable and proportionate manner so as not to create an undue burden. Creators should be empowered to know which of their works have been used for training and how.

Such a requirement would improve the transparency of ML development and deployment. As such, this requirement would ensure that adherence to EU legal framework governing the use of copyrighted works for ML training can be verified by anyone, particularly those rightholders who seek to control the use of their works.

Finally, a general transparency requirement contributes to the development of trustworthy and responsible AI and is in the public interest.

Recommendation 2: The EU should enact a robust general transparency requirement for developers of generative AI models. Creators need to be able to understand whether their works are being used as training data and how, so that they can make an informed choice about whether to reserve the right for TDM or not.

The two recommendations developed in this paper are closely tied to our Policy Recommendations #2 (Full copyright protection should only be granted to works that have been registered by their authors) and #16 (Creators should have the right to know their audience).

From a copyright perspective, the opt-out mechanism increases legal certainty for all parties involved. On the one hand, it allows creators and rightholders to indicate how their works are to be used in the context of ML training. On the other hand, it provides ML developers with the ability to ensure that their use of training data does not infringe copyright where applicable. Finally, by excluding scientific research from the scope of opt-outs, the system provides an important contribution to academic freedom and to ensuring that ML-related research can flourish in the EU.

We also maintain that transparency is paramount to a copyright and AI system that works for everyone. Creators should be able to track copyright-relevant uses of their works in order to be able to make informed decisions and improve their negotiating position vis-à-vis other actors in the value chain, and the public needs transparency to ensure that as AI continues to progress its benefits flow to society as a whole.

Endnotes

1. This paper is without prejudice to the position of COMMUNIA or individual COMMUNIA members regarding this discussion in other jurisdictions.
2. Model weights are an integral part of the model itself. The model consists of a structure or architecture that defines the way it processes the input data, and the weights are the parameters that are learned during the training process to optimise the model’s performance.
3. For a recent overview, see: Henderson, P. et al. (2023) ‘Foundation Models and Fair Use’ [Unpublished]. Available at: https://arxiv.org/abs/2303.15715.
4. The European Parliament’s summary published after the adoption of the Directive makes this explicit by noting that “the co-legislators agreed to enshrine in EU law another mandatory exception for general text and data mining (Article 4) in order to contribute to the development of data analytics and artificial intelligence.”
5. This statement by 24 stakeholders stresses “the foundational role that TDM plays in Artificial Intelligence (AI).”

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