# GPT-3 and Philosophy. Preliminary Tests Results

Roman Krzanowski

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# GPT-3 and Philosophy
## Preliminary Tests Results

Roman Krzanowski<fnref n="1" />

### Abstract

In this study, we test GPT-3's capacity to produce short, coherent essays on philosophical themes. Our interest lies in whether GPT-3 can engage in some sort of philosophical analysis. Thus, we conducted a series of tests with the GPT-3 engine (January 20, 2022 edition) by presenting it with well-defined philosophical problems. These problems resemble those given to philosophy students in their early formative years. It is arguable whether testing the responses to a few such questions will give any measure of GPT-3's philosophical prowess, or even that of students, but if passing exams with such problems certifies a certain level of philosophical understanding in academic students, the same must apply to GPT-3. The results indicate that within the tested domain (i.e., philosophy), GPT-3 performed unpredictably, almost as if by chance. For some questions, GPT-3 produced answers that could be mistaken for a student's work, while for other questions, GPT-3 failed, with it simply generating a mixture of coherent and irrelevant, albeit grammatically correct, text. Any suggestions that GPT-3 may mark the dawn of a new era of synthetic philosophy, or the twilight of the traditional one, are simply not justified. We should not expect a flood of synthetic philosophy from GPT-3, nor should we expect new brilliant philosophical insights or fresh readings of Plato or the notorious Heidegger or Hegel from GPT-3 any time soon.

**Keywords:** GPT-3, philosophy, language models, statistical language modelling

### Introduction

GPT-3 (Generative Pre-trained Transformer 3) is an autoregressive language model that has produced human-like texts for a variety of domains (see e.g., “GPT-3”). Some publications have suggested that GPT-3 displays a glimpse of artificial general intelligence (AGI) (a comment by David Chalmers in Weinberg, 2020). Most opinions about the capabilities of the GPT-3 language model, however, have been generally cautious (Marcus and Davis 2020, Chomsky 2021, “GPT-3.”, Chomsky 2020, Heaven 2020, Floridi and Massimo, 2020, Dale 2021).

We posit that if GPT-3 does indeed show a glimpse of artificial general intelligence, it should also handle (simple) philosophical topics well.<fnref n="2" /> With this in mind, we conducted a series of tests with the GPT-3 engine (January 20, 2022 edition) by presenting it with some well-defined philosophical problems similar

<footnote n="1">¹ Author's email: rmkrzan@gmail.com. Author's orcid ID 0000-0002-8753-0957.</footnote>

<footnote n="2">² GPT-3 is not the latest language model by the way. The Chinese Wu Dao 2.0 model was trained on a much larger set of data than GPT-3 and can produce poetry, among other things, that is hardly distinguishable from the works of Chinese old masters, at least according to its programmers' claims. Its name in English reflects how it is even more ambitious than GPT-3, because Wu Dao in Chinese means “road to awareness” (Feng, 2021, Heikkila 2021, Greene 2021, Mott 2020). Indeed, Wu Dao 2.0 was unveiled amid claims of creating human-like cognitive abilities, such as when its creator said, “WuDao 2.0 aims to enable machines to think like humans and achieve cognitive abilities beyond the Turing test” (“Record-breaking AI… “, 2021).</footnote>
to those put to philosophy students in their early formative years (i.e., 1st and 2nd years of a BA degree). It
is arguable whether testing the responses to such questions gives any meaningful measure of the
philosophical prowess of GPT-3, or indeed that of students, but if passing exams with such questions
certifies a certain level of philosophical knowledge in a philosophy student, the same must apply to the
GPT-3 language model. In addition, we thought that asking GPT-3 to comment on Kant or Heidegger
would be too ambitious.

Philosophers look upon the GPT-3 language model and the associated claims with mixed feelings (see the
blog entries in Weinberg, 2020). Opinions have ranged from carefully guarded praise to criticism and
dismissal. However, we here try to move beyond this discussion and establish whether GPT-3 shows
traces of philosophical thought.

The technical aspects of the GPT-3 language model have been extensively described elsewhere, so we do
not have to repeat them here (e.g., Brown et al. 2020, Hao 2020, Heaven 2020, Sagar 2020, Schmelzer,
2020, Dale 2021). However, we should point out that GPT-3 was trained, as reported by Brown et al.
(2020), on a vast amount of data from Internet sources, namely sources that seemed to contain a
substantial chunk of our written contemporary ideas, at least the digitized ones. We would therefore
expect that some of the vast GPT-3 training set covered works on philosophy or related fields, even if by
accident rather than purpose, implying that GPT-3 should be able to engage in philosophical thought of
sorts if it has the right language model and knowledge base.

In what follows, we describe the testing method and its results and discuss GPT-3’s philosophical
abilities. We also speculate about what the test results tell us about GPT-3 as a language model and the
nature of philosophical thought in light of this study.

Method

We asked GPT-3 to respond to five relatively simple philosophical questions.<fnref n="3" /> Four questions were taken
from the examination set for the University of London's (UofL) BA program (Examination papers and
Examiners' reports, 2004). These were:

A. Can we intelligibly claim that Sherlock Holmes does not exist?

B. Is knowledge justified true belief?

C. Must scientific explanations cite the cause?

D. Explain and evaluate the argument Descartes gives in the second meditation for the claim that he is a thinking thing.

The fifth question, which was not in the UofL examination set, was a question about “Plato’s cave allegory.” Plato’s cave allegory is one of the foundational ideas that is widely commented on in Western philosophical thought and indeed beyond it (e.g., Astore 2019). So why not, for a comparison, have a synthetic system comment on it? Could GPT-3, when asked, give us some new insights into Plato’s allegory? We may expect that GPT-3 would compare the Platonic cave to the internet or social media and

<footnote n="3">³ By simple questions, we mean either that the questions are related to well-known philosophical ideas or
that students at an early stage of philosophical training should be able to address them.</footnote>
internet users to the prisoners, with them being chained to a computer or smartphone. Could GPT-3 discover hidden less obvious connections between ideas, which would be a sign of philosophical insight? We had no such luck, as we will see. During an actual examination, students are supposed to compose a short essay addressing the questions, so we expected GPT-3 to do the same. The *Examination Papers and Examiners' Reports* also contain the expected answers to the questions, with the exception of the fifth question. These answers set the standard for which GPT-3 responses are evaluated.

For the tests, we used the new GPT-3 interface “Instruct series,” which was introduced in January 2022, with the application interface “Essay outline.” The test parameters are given in Table 1. To find the best answer to a question, some parameters were varied, and this is noted in the discussion of the tests’ results.<fnref n="4" /> All questions were tested four times for different parameters, with us only giving the best responses.

<table><caption>Table 1. GPT-3 parameters used in the tests</caption><thead><tr><th>Length<sup>*</sup>=1500</th><th>Temp=0.1**</th><th>Top P= 0.09<sup>***</sup></th><th>Freq. Pen.=0.05<sup>#</sup></th><th>Presence pen.=0<sup>##</sup></th></tr></thead><tbody><tr><td colspan="5">*Response length is the length of the GPT-3 response in tokens.</td></tr><tr><td colspan="5">** The *Temp* parameter is responsible for the randomness of the response. A *Temp* approaching 0 makes the output more deterministic and repetitive.</td></tr><tr><td colspan="5">*** Top P1: "An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered" (OpenAI, 2022).</td></tr><tr><td colspan="5"># Frequency penalty: A number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim (OpenAI, 2022).</td></tr><tr><td colspan="5">## Presence penalty: A number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics (OpenAI, 2022).</td></tr></tbody></table>

The GPT-3 responses were evaluated in the same manner that written essays submitted by students in an exam would be. We graded the responses as “fail,” “acceptable,” and “good” based on the requirements given in Table 2.

<table><caption>Table 2. Table of criteria for grading GPT-3 responses</caption><thead><tr><th>Score</th><th>Evaluation Criteria</th></tr></thead><tbody><tr><td>Fail</td><td>Misses the question completely, just an endless loop of unrelated words</td></tr><tr><td>Fail</td><td>An endless loop on the topic or addresses a different problem</td></tr><tr><td>Marginal</td><td>Responds partially to the question but the response is evidently artificially generated, repetitive, wanders off topic, or is very short and/or uninformative</td></tr><tr><td>Acceptable</td><td>Responds generally correctly, but response can still be clearly identified as synthetic in origin and possibly wanders off the main topic</td></tr><tr><td>Good</td><td>Correct response to the point with some additional explanation of the topic, such that it could be mistaken for a student's work</td></tr></tbody></table>

<footnote n="4">⁴ See the OpenAI user guide for more details on parameters (OpenAI, 2022).</footnote>
To test the effect of this configuration, we also submitted the selected questions to the older (compared to the January 20, 2022 release) GPT-3 DA-Vinci engine.<fnref n="5" /> This beta release was supposed to provide more elaborate responses at the risk of repetition.

## Test Results

The responses to the first question (*Can we intelligibly claim that Sherlock Holmes does not exist?*) were the same in all four tests, as changes in parameters had no effect:

*“No, we cannot intelligibly claim that Sherlock Holmes does not exist”.*

The responses to the second question (*Is knowledge justified true belief?*) varied slightly with changes in parameters. The most satisfying results were obtained with the following parameters: Length=1500; Temp=0.09; Top P=1; Freq. Pen.=0.0; Presence pen.=0.

This GPT-3 response was quite interesting and showed some correlation with the question itself, so it could be mistaken as the work of someone acquainted with the philosophical problem of knowledge. Unfortunately, the response was not to the point, such that if it had been submitted by a student, he or she would been charged with missing the point. The best response out of the four attempts is quoted in full below:

> It is difficult to provide a single, definitive answer to this question. The philosophical position known as epistemology is devoted to exploring the nature and origins of knowledge, and there is no consensus among philosophers about what constitutes knowledge. Some philosophers argue that knowledge is nothing more than justified true belief, while others claim that there is more to knowledge than just justification and truth. It is difficult to say definitively which position is correct, and it is likely that there is not a single, definitive answer to this question.

The responses to the third question (*Must scientific explanations cite the cause?*) also varied with changes in parameters. However, all the responses were short, limited, and missed the point. Some selected example responses are given below:

*[With parameters Length=1500; Temp=0.11; Top P=1; Freq. Pen.=0.0; Presence pen.=0]*

> There is no universal answer to this question, as it depends on the particular scientific explanation in question. Some scientific explanations may cite the cause of a particular phenomenon, while others may not.

*[With the parameters Length=1500; Temp=0.5; Top P=1; Freq. Pen.=0.0; Presence pen.=0]*

> Yes, scientific explanations typically cite the cause of an event. This is done in order to provide a clear and concise explanation of the event, as well as to support the scientific theory being put forth.

*[With the parameters Length=1500; Temp=0.9; Top P=1; Freq. Pen.=0.0; Presence pen.=0]*

> Science explanations don't always cite the cause, but they often do.

The response to the fourth question (*Explain and evaluate the argument Descartes gives in the second meditation for the claim that he is a thinking thing*) also varied with the chosen parameters, with the

<footnote n="5">⁵ The differences between the two versions are described in the note available at: https://help.openai.com/en/articles/5832130-what-s-changed-with-engine-names-and-best-practices</footnote>
responses being interesting and relevant. The best response was provided with these parameters: Length=1500; Temp=0.9; Top P= 1; Freq. Pen.=0.0; Presence pen.=0. This response is quoted below:

René Descartes famously argues in the second meditation that he is a thinking thing. He reasons that he knows he exists because he is thinking, and that he cannot be mistaken about his own thoughts. He also argues that things that exist must have some property or attribute that makes them what they are, and that he cannot conceive of himself without thinking, so thought must be a property of him. This argument is problematic in a few ways. First, it begs the question by assuming that existence depends on having some property or attribute. Second, it is not clear that Descartes can really conceive of himself without thinking, or that thought is a necessary property of him. Third, it is not clear why Descartes should think that he is the only thinking thing in the world.

The response to the fifth question (*Explain Plato's cave allegory*) also varied with test parameters, but all of the responses were quite acceptable. The best response was found with these parameters: Length=1500; Temp=0.98; Top P= 1; Freq. Pen.=0.0; Presence pen.=0. This response is quoted in full below:

*In Plato's allegory of the cave, prisoners are chained to the wall of a dark cave, with their heads facing the wall. Behind them, a fire burns, and between the fire and the prisoners is a curtain, with a group of people passing objects behind the curtain. The prisoners can only see the shadows of the objects being passed, and because they are chained to the wall, they cannot turn their heads to see what is happening. The allegory of the cave is supposed to represent the way in which people see only the appearances of things, without understanding the true nature of things. The people who are passing the objects behind the curtain are like the people who understand the true nature of things, while the prisoners are like the people who only see the appearances of things.*

All responses were assigned grades, as shown in Table 4 below.

<table><caption>Table 4. Summary of GPT-3 responses</caption><thead><tr><td>Examination question</td><td>GPT-3 Response</td></tr></thead><tbody><tr><td>Can we intelligibly claim that Sherlock Holmes does not exist?</td><td>Fail</td></tr><tr><td>Must scientific explanations cite the cause?</td><td>Fail</td></tr><tr><td>Is knowledge justified true belief</td><td>Acceptable</td></tr><tr><td>Explain and evaluate the argument Descartes gives in the second meditation for the claim that he is a thinking thing.</td><td>Good</td></tr><tr><td>Explain Plato's allegory of the cave.</td><td>Good</td></tr></tbody></table>

## Analysis of the Results

GPT-3's responses ranged from good to irrelevant. For the first two questions (*Must scientific explanations cite the cause?* and *Can we intelligibly claim that Sherlock Holmes does not exist?*), the responses amounted to single sentences that were not necessarily related to the question itself. In an exam paper, such responses would be deemed unacceptable and awarded a failing grade. The response to the third question (*Is knowledge justified true belief?*), meanwhile, was coherent but clearly missed the point. The response to the fourth question (*Explain and evaluate the argument Descartes gives in the second meditation for the claim that he is a thinking thing*) was quite relevant and could be mistaken for independent student work. A similar quality of response was generated for the question about Plato's cave allegory.
There is it seems no apparent and obvious reason for why certain responses were appropriate while others were not. All the questions were well-formulated, legitimate philosophical questions and not some contrived abstract ideas from the same conceptual domain (i.e., philosophy). Basically, they all addressed basic philosophical problems, such as issues related to existence, knowledge, and cause. This is a range of topics that one would expect to find among internet sources.

We can speculate about possible reasons for these differences. First, the quality and quantity of source training material may be the root cause. We only know that GPT-3 was trained on internet resources, and how these resources relate to the domain of the questions is not known. (It is probable that even GPT-3's programmers did not keep a tally of the type (by domain) of the resources used for training.) We speculated in the introduction that the internet may not be the best source of philosophical wisdom. Does this reflect in the test results? Possibly.

It is also possible that the quantity of resources may be at fault, yet it seems that the internet contains vast resources related to each of the questions, at least to related contexts. As a proxy measure for the amount of internet resources, we Google searched for on-line material associated with the questions. The results of the queries for each question are summarized in Table 5.

One may have expected the poor responses to consistently correlate with a scarcity of material and vice versa, but this was not the case. The Google search results associated with the questions did not correlate with GPT-3's response quality, so it seems volume cannot explain the variation in the test results. Of course, how much a blind Google search accurately reflects the specificity of the questions is difficult to gauge.

One should also consider that no student revising for a BA philosophy exam would read 77,900 sources about Sherlock Holmes' existence, yet he or she would still be able to address the question more cogently than GPT-3.

<table><caption>Table 5. Google search results related to the questions</caption><thead><tr><td>Search Topic/Examination question</td><td>Google search results</td><td>GPT-3 Response</td></tr></thead><tbody><tr><td>Can we intelligibly claim that Sherlock Holmes does not exist?</td><td>77,900</td><td>Fail</td></tr><tr><td>Must scientific explanations cite the cause?</td><td>12,730,000,000</td><td>Fail</td></tr><tr><td>Is knowledge justified true belief?</td><td>38,700,000</td><td>Acceptable</td></tr><tr><td>Explain and evaluate the argument Descartes gives in the second meditation for the claim that he is a thinking thing.</td><td>241,000</td><td>Good</td></tr><tr><td>Plato's allegory of the cave</td><td>778,000</td><td>Good</td></tr></tbody></table>
A third possible reason for differences in response quality may relate to the GPT-3 configuration itself. To test this possibility, we submitted the selected questions to the beta version of the Da-vinci-01 code, which pre-dates the version used above. Some examples follow below.

The interpretation of the question *Must scientific explanations cite the cause?* was much better with the beta release compared to the Instruct series release. Of course, the answer is still some way off the mark, but it reveals a glimpse of understanding for the question:

> The answer to this question is not as obvious as it might seem. The answer depends on what we mean by "cause." If we mean "cause" in the sense of "the first cause," then the answer is no. The first cause is God. God is the ultimate cause of everything. If we mean "cause" in the sense of "the immediate cause," then the answer is yes. The immediate cause of everything is... [repetitive response]

The beta engine also interpreted the question *Is knowledge justified true belief?* much better than its newer counterpart, but the responses ended up repeating the same sentence:

> The definition of knowledge is justified true belief. This definition is the most accepted definition of knowledge. It is a definition that has been used by philosophers for centuries. The definition of knowledge is justified true belief. This definition is the most accepted definition of knowledge. It is a definition that has been used by philosophers for centuries... [repetitive response]

Thus, varying the GPT-3 configuration (for both the older and newer models) may improve the responses in some way, but they still fall short of being acceptable standard responses, according to the examination notes.

The question we were left with after these tests is this: If we want to produce a thinking artifact, and this is the ultimate goal of such linguistic models, is it enough to absorb structural correlations, even on a superhuman scale, or will this never be sufficient?

## Conclusions

The results indicate that GPT-3 within the tested domain of philosophy performed unpredictably, almost as if by chance. On some questions, GPT-3 produced answers that could pass for student work, but on other questions, GPT-3 merely generated a mixture of coherent and irrelevant text, albeit still grammatically correct text. We do not know why certain questions are answered correctly and other questions are not. We can only speculate.

So, what do these results tell us about GPT-3's philosophical capabilities, the GPT-3 language model, and the language-study approach behind the GPT-model?

We may speculate that certain linguistic tasks require more in-depth knowledge of the problem domain or a better understanding of the text beyond statistical correlations, even when they are derived from thousands of texts. The GPT-3 language model remains a statistical synthetic model, nothing more. It was built under the assumption that given enough statistical data, the model will recover semantics, and given enough texts, the model will recover meaning.
Based on the conducted tests, this assumption seems to be rather dubious. To be more cautious, however, we could say that this assumption was not confirmed in our tests. We may speculate that the considered problems require a deeper level of understanding, a quality we often ascribed to philosophical problems, so they may be beyond this language model (or its current implementation) and the GPT-3 approach to modelling language. The question we are left with is this: To produce a thinking artifact, which appears to be the ultimate goal of these linguistic models, will it suffice to absorb structural correlations, even on a superhuman scale (reading and digesting all of the sources that GPT-3 considered is certainly beyond any single person)? Or will this never be sufficient? In other words, would providing more training material and more computer power help? Throwing more resources at a problem is a sound military strategy, but it may not work with language.

So, do these tests allow us to draw more general conclusions about the GPT-3 model beyond the immediate results, given that the tests (with only five questions) were certainly not a comprehensive study of GPT-3's philosophical capabilities? Yes, we probably can. The questions were philosophical par excellence, and the exemplar responses made it obvious to the reviewers what was expected. Thus, one could reasonably expect that a general language model trained on a vast amount of current textual sources would be able to address the basic philosophical questions more coherently than it actually did. Indeed, students answering these questions study fewer resources than GPT-3 did, yet they can usually answer them more coherently, as we pointed out earlier.

We could submit several more sets of examination questions to GPT-3, but we think this would be a vain effort because the overall responses would be similar, with some being good, some bad, and some irrelevant, and we would have no idea why. Nothing in the tests conducted so far indicates why they have different outcomes.

So, where exactly did GPT-3 fail and why? It failed on several points, with the most obvious one being that it did not answer some questions correctly. A less obvious failure is the fact that the suitability of the responses seems to be random. We can merely guess why certain questions were addressed correctly and others were not.

GPT-3 just produces responses without knowing what it is producing, so it cannot correct itself or improve its responses, because self-reflection is not a feature of the language model. GPT-3 produces a stream of words in a correct syntactic format, but it does not "know" whether an answer is good or bad. For GPT-3, the output is simply the output. The GIGO (garbage in, garbage out) rule does not apply because GPT-3 was certainly trained on a set of coherent texts, so producing irrelevant responses is a systemic feature. The causes of GPT-3's failings must be somewhere else other than the input feed. It may be that language cannot be abstracted from its cultural and social milieu, as suggested by some studies (e.g., Everett, 2019).

In addition, where did GPT-3 succeed in the philosophical tests and why? GPT-3 produced good answers for two questions that could pass as student work. But why were these questions resolved and not others? We simply do not know.

Let us now reflect on something larger than these tests of GPT-3 and knowledge. The GPT-3 language model forces us to ask deep questions about the nature of our knowledge and our textual records. Is our philosophical knowledge really just a collection of more or less correlated linguistic tokens, which is what
the GPT-3 model implicitly assumes? Would someone without any background or contextual knowledge be able to recover our ideas just by aggregating statistical correlations from our written records? Is there some intangible sort of knowledge that is not recorded anywhere (i.e., outside of the human mind) that bestows sense onto written but otherwise meaningless words (i.e., strings of tokens)? After absorbing thousands of sources, why does GPT-3 still have limited, if any, philosophical capacity? What is possibly missing from the GPT-3 language model that makes it fare poorly on philosophical questions? What is the difference between GPT-3's complex statistical model of language and (let's say traditional) philosophical analysis?

We may think of a thought experiment: If we submitted the Rosetta stone inscriptions to a GPT-3-like system, would it be a match for Champollion? There are certainly correlations there.

Some enthusiasts claim that GPT-3 signals the end of traditional journalism, poetry, literature, and so on, and this may be true. Could we also start seeing philosophical essays generated by GPT-3 being submitted by students? Possibly yes, but the expectation that GPT-3 is also the dawn of a new era of synthetic philosophy, or the twilight of the traditional one, does not seem justified. Philosophy as we understand it, at least good philosophy, is not a combinatorial repetition of things that have already been said, although some philosophy does exactly that. Philosophy is the discovery of hidden, implicit meanings, things not obvious, and hidden truths. GPT-3 clearly does not know what the truth is, so we should not then expect from GPT-3 a flood of synthetic philosophy with brilliant new philosophical insights. Nor should we expect a new reading of Plato or notorious Heidegger and Hegel from GPT-3 any time soon because we are simply not there yet.

## References

* "GPT-3". Wikipedia. Available at https://en.wikipedia.org/wiki/GPT-3. Accessed on 02.01.2022.

* "Record-breaking AI model WuDao 2.0 unveiled in Beijing - China.org.cn". www.china.org.cn. June 3, 2021. Available at http://www.china.org.cn/business/2021-06/03/content_77546375.htm. Accessed on 02.01.2022

* Astore, R. A. 2019. Unveiling Ultimate Reality in Plato's Allegory of the Cave and the Bhagavad Gita. Inquiries Journal. 2019, Vol. 11 No. 09. Available at http://www.inquiriesjournal.com/articles/1764/unveiling-ultimate-reality-in-platos-allegory-of-the-cave-and-the-bhagavad-gita. Accessed on 02.01.2022

* Brown, T. B., Mann, B., Ryder, N. Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D. 2020. "Language Models are Few-Shot Learners". arXiv:2005.14165. Available at https://arxiv.org/abs/2005.14165. Accessed on 02.12.2021.
Chomsky, N. 2021. 2021. Chomsky on Terence McKenna, Sam Harris, GPT3, Cryptocurrencies, Kierkegaard, Neuralink, & Hofstadter. Video on line. March 24, 2021. Event occurs at 1:11:44. Available at https://www.youtube.com/watch?v=c6MU5zQwtT4. Accessed on 02.01.2022

Dale, R. 2021. GPT-3: What's it good for? *Natural Language Engineering* (2021), **27**, pp. 113–118.

Everett, D. 2019. Daniel Everett, How Language Began: The History of Humanity's Greatest Invention. On Lien lecture. Available at https://www.youtube.com/watch?v=1ALWRbmAwzY. Accessed on 02.01.2022

Examination papers and Examiners' reports. 2004. Diploma and BA Philosophy-. University of London. External Programme. 2004. University of London Press.

Floridi, L. and C., Massimo. 2020. "GPT-3: Its Nature, Scope, Limits, and Consequences". Minds and Machines. 30: 681–694. doi:10.1007/s11023-020-09548-1.

Feng, C. 2021. "*Beijing-funded AI language model tops Google and OpenAI in raw numbers*". South China Morning Post. Available at https://www.scmp.com/tech/tech-war/article/3135764/us-china-tech-war-beijing-funded-ai-researchers-surpass-google-and. Accessed on 02.01.2022

Hao, K. 2020. "OpenAI is giving Microsoft exclusive access to its GPT-3 language model". MIT Technology Review. Retrieved September 25, 2020.

Heaven, W. D. 2020. OpenAI's new language generator GPT-3 is shockingly good—and completely mindless. MIT Technology Review. Available at https://www.technologyreview.com/2020/07/20/1005454/openai-machine-learning-language-generator-gpt-3-nlp/. Accessed on 02.12.2021.

Heikkilä, M. 2021. "Meet Wu Dao 2.0, the Chinese AI model making the West sweat". POLITICO. (June 9, 2021). Available at https://www.politico.eu/article/meet-wu-dao-2-0-the-chinese-ai-model-making-the-west-sweat/. Accessed on 02.12.2021.

Greene, T. 2021. "China's 'Wu Dao' AI is 10X bigger than GPT-3, and it can sing". TNW | Neural. (June 3, 2021). Available at https://thenextweb.com/news/china-wu-dao-ai-10x-bigger-than-gpt-3

Marcus, E. and E. Davis. 2020. GPT-3, Bloviator: OpenAI's language generator has no idea what it's talking about. The MIT Technology Review. Available at https://www.technologyreview.com/2020/08/22/1007539/gpt3-openai-language-generator-artificial-intelligence-ai-opinion/. Accessed on 10.12.2021.

Mott, N. 2021. "China Says Wu Dao 2.0 AI Is an Even Better Conversationalist than OpenAI, Google". Tom's Hardware. 03. June 2021, Available at https://www.tomshardware.com/news/china-touts-wudao-2-ai-advancements. Accessed on 10.12.2021

OpenAI. 2022. OpenAI Documentation on-line. Available at https://beta.openai.com/docs/engines. Accessed on 02.01.2022.
Sagar. R. 2020. OpenAI Releases GPT-3, The Largest Model So Far. Available at
[https:// analyticsindiamag.com/open-ai-gpt-3-language-model/](https:// analyticsindiamag.com/open-ai-gpt-3-language-model/). Accessed on 02.01.2022

Schmelzer, R. 2020. GPT-3. TechTarget. Available at
[https:// searchenterpriseai.techtarget.com/definition/GPT-3](https:// searchenterpriseai.techtarget.com/definition/GPT-3). Accessed on 10.12.2021

Weinberg, J. 2020. Philosophers On GPT 3 (updated with replies by GPT 3). Blog Dailynous.com.
Available at [https://dailynous.com/2020/07/30/philosophers-gpt-3/#chalmers](https://dailynous.com/2020/07/30/philosophers-gpt-3/#chalmers). Accessed on 01.12.2021.