Basics of scientific and technical writing

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  • Volume 46 , pages 284–286, ( 2021 )

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  • Morteza Monavarian 1 , 2  

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Introduction to scientific/technical writing

Scientific/technical writing is an essential part of research. The outcome of a research activity should be shared with others in the form of scientific paper publications; some ideas require a patent to reserve the implementation rights; and almost any research activity requires a funding source, for which a grant proposal is necessary. Therefore, it is crucial to know the differences among writing papers, patents, and grant proposals and how to prepare them in a research environment ( Figure 1 ).

figure 1

Three major types of scientific/technical writing covered in the three-part series.

The publication of papers is a standard way to share knowledge and transfer methods in scientific communities, thus a pivotal part of any research activity, especially in an academic environment. In industry, where financial profit is a key factor, patents are possibly more favorable.

Types of paper publications

There are different types of paper publications, depending on the content, audience, purpose, length, and scope: original research, review articles, invited articles, conference proceedings, comments/errata, and press releases ( Figure 2 ).

Original research articles may be published in journals or conference proceedings (or preprints in arXiv) and target specific audiences within a field of research. Journal research papers require peer review that typically involves an editor and two reviewers. For conference proceedings, there is usually no direct peer-review process, but the work has to be presented in the corresponding conference to be eligible for publication.

In contrast to original research articles, which are written on special topics within a field of research, review articles normally cover an overview of research and tend to be longer. Review articles do not necessarily reflect on novel data or ideas and could be similar to a book chapter. However, unlike review articles, book chapters or books are usually written when the target field of research is fully established. In a review paper, figures are typically not original and reprinted from other publications, for which a copyright permission from the original publishing journal is required.

Invited articles are written in response to an invitation by a journal editor or a conference organizer in a specific field of research or for a special issue. An invited article could be a review article or original research. Invited articles are normally written by peers or researchers with significant contributions to a field of research.

Other items published include comments or errata. The purpose of a comment on a published article is to bring points of criticism to the attention of the readers as well as the authors of the original article. The comments can be published in the same journal as the original paper. Errata correct mistakes in an article after publication.

Finally, press releases target a more general audience and normally report on a review/overview of recently published research. The author of the press release is not the same as that of the original article. Unlike peer-reviewed research articles, press release articles are usually not citable.

figure 2

Six major types of paper publications.

Writing structures and styles

Different articles have different structures. A research article typically consists of a title, author list and affiliations, abstract, main body, conclusions, acknowledgments, and references.

A good title should be concise, to the point, and free of abbreviations. Author lists and affiliations include whoever has intellectually contributed to the paper (identifying at least one corresponding author and email address), with the order approved by all of the co-authors. A good abstract should give a full, but short, overview of the work with both qualitative and quantitative data summaries. An abstract should be self-contained, meaning it should not require a referral to a reference or figure. Abstracts are usually written in the present tense and have an active voice.

Unlike letters with no sections within the main body, the main body of research articles normally contains several sections (e.g., introduction, methods and approach, results, and discussions). The introduction should contain a deep literature review of the field as the basis for motivating the current work. The last paragraph of the introduction usually summarizes what to expect from the article. The following sections will demonstrate study methods, results, and discussions/interpretations of the results, including plots, tables, and figures.

Conclusions summarize the findings of the paper and may point out any future directions. The acknowledgment lists all funding support and gratitude toward anyone who helped with the work, not including those listed as co-authors. The reference section lists all references in a format described in the journal submission guidelines. Using reference management software (such as Zotero, Mendeley, BibTex) makes organizing the references less cumbersome. A good scholarly research article should have citations for almost any claims made within the main body, to ensure proper connections to the prior research in the field.

Unlike patents, papers require a deep scientific background and should be straight to the point. While patents include all aspects of the idea, papers typically have space limitations, so should therefore be concise. The data in research articles should speak for itself. The language of a research paper should be clear and simple and not include metaphors or slang.

Where to submit

The submission target depends on several factors: (1) scope of the journal, (2) length of the paper (letters versus regular length articles), (3) access (regular versus open access), and (4) impact factor (IF). The scope of the journal is probably the first thing to consider; you cannot publish a biological paper in a humanity journal. Regarding length, a letter is much shorter and usually does not have section headings. It depends on the discipline, but sometimes letters are more favorable because of the shorter publication time, preparation simplicity, and more readability (takes less time to read, which may also improve the visibility of the paper). In terms of access, you may pay publication charges to receive open access, or some journals charge publication fees upon acceptance. Open access papers could potentially get more visibility than normal publications.

IF is a specific journal parameter indicating the average number of citations per published article over a certain period of time. Paying serious attention to IF could oppose the mission of science itself, as it could mean that you judge a paper only by where it is being published and not by its intrinsic values (also called high IF syndrome).

Submission, peer-review, and decisions

Your article will enter the peer-review process upon submission. If done properly, the peer-review process not only avoids false or inconsistent data from being published (and helps science in this regard), but also improves your paper and removes any potential errors/issues or vague discussion. During submission, some journals may ask you to include/exclude reviewers. If there are researchers who may have a direct conflict with your work, you may list them as excluded reviewers. You may also suggest to include reviewers who have relevant experience.

Serving as a reviewer may help you with your own writing, as it assists in developing critical thinking. However, for the sake of science, try peer-reviewing for lesser-known journals (the high-impact journals already have many reviewers). Decisions on your article could be (1) reject: cannot be accepted to this journal; (2) referral to other journals; submit to another journal; (3) accept: accepted as is; (4) major revisions: not accepted, but could be accepted upon significant improvement (upon approval from reviewers); and (5) minor revision: accept but needs slight revisions (no need to go through a peer review again).

Copyrights and archiving

Most journals obtain copyrights from the authors before submission via a copyright transfer form. Hence, re-publishing the same data and plots in another journal is often forbidden. Also, the language of a paper should have a significant difference from an already published paper to avoid plagiarism. In the case where some content (e.g., figure or table) needs to be re-published in another paper (e.g., for review articles or thesis/dissertations), one can request a copyright permission from the original publishing journal. Also, archiving of one’s published papers in personal profile websites (e.g., Researchgate or LinkedIn) is usually forbidden, unless the paper is published as open access.

Final tips for paper publication

Read, read, read! There is probably no better way of improving writing skills than reading other articles and books.

Make illustrative and self-contained figures that can stand on their own.

Know your audience when selecting a journal. Find out which journals are normally targeted by people in your research community.

Protect yourself from high impact factor (IF) syndrome. Journals with a high IF may have very subjective decision criteria. It is sometimes more important to have your paper published than to spend a couple of years waiting for publication in a high-impact journal.

Serve as a reviewer. Get a sense of how a peer-review process feels in order to establish critical thinking. Before submitting your article, self-review.

Look forward to a constructive peer review. It definitely improves your paper (always good to have a view from different perspective).

Enjoy your publications!

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Materials Department, University of California Santa Barbara, Santa Barbara, CA, USA

Morteza Monavarian

Solid State Lighting & Energy Electronics Center, University of California Santa Barbara, Santa Barbara, CA, USA

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This article is the first in a three-part series in MRS Bulletin that will focus on writing papers, patents, and proposals.

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Monavarian, M. Basics of scientific and technical writing. MRS Bulletin 46 , 284–286 (2021). https://doi.org/10.1557/s43577-021-00070-y

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Tips for Writing Technical Papers

Jennifer widom , january 2006, running example, paper title, the abstract, the introduction, related work, performance experiments, the conclusions, future work, the acknowledgements, grammar and small-scale presentation issues, versions and distribution.

How to write a technical paper or a research paper

By michael ernst, april, 2005 last updated: august 18, 2023, which details to include, make the organization and results clear, getting started: overcoming writer's block and procrastination, writing style, computer program source code, numbers and measurements, processing data, related work, when to submit your paper for publication, responding to conference reviews, norman ramsey's advice, other resources, introduction.

This document describes several simple, concrete ways to improve your writing, by avoiding some common mistakes. The end of this document contains more resources for improving your writing.

Some people believe that writing papers, giving talks , and similar “marketing” activities are not part of research, but an adjunct to it or even an undesirable distraction. This view is inaccurate. The purpose of research is to increase the store of human knowledge, and so even the very best work is useless if you cannot effectively communicate it to the rest of the world. If a paper is poorly written, then readers might conclude you spent as little effort on the research that it describes.

Equally importantly, writing papers and giving talks will clarify your thinking and thereby improve your research. You may be surprised how difficult it is to clearly communicate your ideas and contributions; doing so will force you to understand them more deeply and enable you to improve them.

Know your message, and stay on message

The goal of writing a paper is to change people's behavior: for instance, to change the way they think about a research problem or to convince them to use a new approach. Determine your goal (also known as your thesis), and focus the paper around that goal.

As a general rule, your paper needs to convince the audience of three key points. If any of these is missing or unclear, the paper will not be compelling.

  • The problem is important . The problem has a significant impact and consequences. You can buttress your argument by showing that others consider the problem important.
  • The problem is hard . Explain that obvious techniques and existing approaches do not suffice. Showing what others have tried can be effective here.
  • You have solved the problem. This is often demonstrated via experiments. Keep in mind how you expect the behavior of readers to change once they appreciate your contributions. You'll also need to convince readers that your contributions are novel. When expressing this, it is helpful to explain why no one else thought of your approach before (or why, if they thought of it, they would have rejected the approach) , and whether similar insights apply to other problems.

Before you write your paper, you need to understand your audience. Who will read your paper? What are their backgrounds, motivations, interests, and beliefs? What are the key points you want a reader person to take away from your paper? Once you know the thesis and audience, you can determine what points your document should make to achieve its purpose.

For each point in your paper, you need to explain both what and why . Start with what, but don't omit why. For example, it is not enough to state how an algorithm works; you should explain why it works in that way, or why another way of solving the problem would be different. Similarly, it is not sufficient to present a figure or facts. You must also ensure that reader understands the significance or implications of the figure and what parts of it are most important.

Your purpose is to communicate specific ideas, and everything about your paper should contribute to this goal. If any part of the paper does not support your main point, then delete or change that part. You must be ruthless in cutting every irrelevant detail, however true it may be. Everything in your paper that does not support your main point distracts from it.

Write for the readers, rather than writing for yourself. In particular, think about what matters to the intended audience, and focus on that. It is not necessarily what you personally find most intriguing.

A common mistake is to focus on what you spent the most time on. Do not write your paper as a chronological narrative of all the things that you tried, and do not devote space in the paper proportionately to the amount of time you spent on each task. Most work that you do will never show up in any paper; the purpose of infrastructure-building and exploration of blind alleys is to enable you to do the small amount of work that is worth writing about. Another way of stating this is that the purpose of the paper is not to describe what you have done, but to inform readers of the successful outcome or significant results, and to convince readers of the validity of those conclusions.

Likewise, do not dwell on details of the implementation or the experiments except insofar as they contribute to your main point. This is a particularly important piece of advice for software documentation, where you need to focus on the software's benefits to the user, and how to use it, rather than how you implemented it. However, it holds for technical papers as well — and remember that readers expect different things from the two types of writing!

The audience is interested in what worked, and why, so start with that. If you discuss approaches that were not successful, do so briefly, and typically only after you have discussed the successful approach. Furthermore, the discussion should focus on differences from the successful technique, and if at all possible should provide general rules or lessons learned that will yield insight and help others to avoid such blind alleys in the future.

Whenever you introduce a strawman or an inferior approach, say so upfront. A reader will (and should) assume that whatever you write in a paper is something you believe or advocate, unless very clearly marked otherwise. A paper should never first detail a technique, then (without forewarning) indicate that the technique is flawed and proceed to discuss another technique. Such surprises confuse and irritate readers. This mistake is often called “leading the reader down the garden path”.

When there are multiple possible approaches to a problem, it is preferable to give the best or successful one first. Oftentimes it is not even necessary to discuss the alternatives. If you do, they should generally come after, not before, the successful one. Your paper should give the most important details first, and the less important ones afterward. Its main line of argument should flow coherently rather than being interrupted. It can be acceptable to state an imperfect solution first (with a clear indication that it is imperfect) if it is a simpler version of the full solution, and the full solution is a direct modification of the simpler one. Less commonly, it can be acceptable to state an imperfect solution first if it is an obvious solution that every reader will assume is adequate; but use care with this rationalization, since you are usually wrong that every reader will jump to the given conclusion.

A paper should communicate the main ideas of your research (such as the techniques and results) early and clearly. Then, the body of the paper can expand on these points; a reader who understands the structure and big ideas can better appreciate the details. Another way of saying this is that you should give away the punchline. A technical paper is not a joke or a mystery novel. The reader should not encounter any surprises, only deeper explanations of ideas that have already been introduced. It's particularly irritating when an abstract or introduction states, “We evaluated the relationship between baldness and beekeeping”, with the key results buried pages later. A better abstract would say, “Male beekeepers are 25% more likely to be bald (p=.04), but there is no statistically significant correlation for female beekeepers.”

The same advice applies at the level of sections and paragraphs. It is a bad approach to start with a mass of details and only at the end tell the reader what the main point was or how the details related to one another. Instead, state the point first and then support it. The reader is more likely to appreciate which evidence is important and why, and is less likely to become confused or frustrated.

For each section of the paper, consider writing a mini-introduction that says what its organization is, what is in each subpart, and how the parts relate to one another. For the whole paper, this is probably a paragraph. For a section or sub-section, it can be as short as a sentence. This may feel redundant to you (the author), but readers haven't spent as much time with the paper's structure as you have, so they will truly appreciate these signposts that orient them within your text.

Some people like to write the abstract, and often also the introduction, last. Doing so makes them easier to write, because the rest of the paper is already complete and can just be described. However, I prefer to write these sections early in the process (and then revise them as needed), because they frame the paper. If you know the paper's organization and outlook, then writing the front matter will take little effort. If you don't, then it is an excellent use of your time to determine that information by writing the front matter. To write the body of the paper without knowing its broad outlines will take more time in the long run. Another way of putting this is that writing the paper first will make writing the abstract faster, and writing the abstract first will make writing the paper faster. There is a lot more paper than abstract, so it makes sense to start with that and to clarify the point of the paper early on.

It is a very common error to dive into the technical approach or the implementation details without first appropriately framing the problem and providing motivation and background. Readers need to understand what the task is before they are convinced that they should pay attention to what you are saying about it. You should first say what the problem or goal is, and — even when presenting an algorithm — first state what the output is and probably the key idea, before discussing steps. Avoid providing information that isn't useful to readers/users. It just distracts from the important content.

Some writers are overwhelmed by the emptiness of a blank page or editor buffer, and they have trouble getting started with their writing. Don't worry! Here are some tricks to help you get started. Once you have begun, you will find it relatively easier to revise your notes or first draft. The key idea is to write something , and you can improve it later.

Start verbally . Explain what the paper needs to say to another person. After the conversation is over, write down what you just said, focusing on the main points rather than every word you spoke. Many people find it easier to speak than to write. Furthermore, getting feedback and giving clarifications will help you discover problems with your argument, explanation, or word choice.

Outline . You may not be ready to write full English paragraphs, but you can decide which sections your paper will have and give them descriptive titles. Once you have decided on the section structure, you can write a little outline of each section, which indicates the subsection titles. Now, expand that into a topic sentence for each paragraph. At this point, since you know the exact topic of each paragraph, you will find the paragraph easy to write.

Stream-of-consciousness notes . Write down everything that you know, in no particular order and with no particular formatting. Afterward, organize what you wrote thematically, bringing related points together. Eventually, convert it into an outline and proceed as above. While writing notes, use phrases/keywords, not complete sentences. The phrases are quicker to write and less likely to derail your brainstorming; they are easier to organize; and you will feel less attached to them and more willing to delete them.

Divide and conquer . Rather than trying to write your entire document, choose some specific part, and write just that part. Then, move on to another part.

Re-use . Find other text that you have written on the topic and start from that. An excellent source is your progress reports — you are writing them, aren't you? This can remind you what was hard or interesting, or of points that you might otherwise forget to make. You will rarely want to re-use text verbatim, both because you can probably convey the point better now, and also because writing for different audiences or in different contexts requires a different argument or phrasing. For example, a technical paper and a technical talk have similar aims but rather different forms.

You must be willing to delete and/or rewrite your notes and early drafts. If you wrote something once, you can write it again (probably better!). Early on, the point is to organize your ideas, not to create finished sentences.

Be brief. Make every word count. If a word does not support your point, cut it out, because excess verbiage and fluff only make it harder for the reader to appreciate your message. Use shorter and more direct phrases wherever possible.

Make your writing crisp and to the point. Eliminate any text that does not support your point. Here is one way you might go about this; it is time-consuming but extremely effective. First, examine each section of the paper in turn and ask what role it serves and whether it contributes to the paper's main point. If not, delete it. Next, within each section, examine each paragraph. Ask whether that paragraph has a single point. If not, rewrite the paragraph. Also ask whether that point contributes to the goals of the section. If not, then delete the paragraph. Next, within each paragraph, examine each sentence. If it does not make a single, clear point that strengthens the paragraph, delete or rewrite it. Finally, within each sentence, examine each word, and delete or replace those that do not strengthen their point. You will need to repeat this entire process multiple times, keeping a fresh perspective on the paper.

Some people find it easier to follow this approach bottom-up, first cutting/rewriting words, then sentences, etc.

Passive voice has no place in technical writing. It obscures who the actor was, what caused it, and when it happened. Use active voice and simple, clear, direct phrasing.

First person is rarely appropriate in technical writing.

  • First person is appropriate when describing something that the author of the paper did manually. Recall that your paper should not be couched as a narrative.
  • Do not use “we” to mean “the author and the reader” or “the paper”. For example, do not write “In this section, we ...”.
  • Do not use “we” to describe the operation of a program or system. “We compute a graph” makes it sound like the authors did it by hand. As a related point, do not anthropomorphize computers: they hate it. Anthropomorphism, such as “the program thinks that ...”, is unclear and vague.

Avoid puffery, self-congratulation, superlatives, and subjective or value judgments: give the objective facts and let the reader judge. Avoid vague terms like “sizable” and “significant” (which are also subjective). Don't overuse the word “novel”.

Do not use words like “clearly”, “easily”, “obviously”, and “trivially”, as in “Obviously, this Taylor series sums to π.” If the point is really obvious, then you are just wasting words by pointing it out. And if the point is not obvious to readers who are not intimately familiar with the subject matter the way you are, then you are offending readers by insulting their intelligence, and you are demonstrating your own inability to communicate the intuition.

Prefer singular to plural number. In “sequences induce graphs”, it is not clear whether the two collections are in one-to-one correspondence, or the set of sequences collectively induces a set of graphs; “each sequence induces a graph” avoids this confusion. Likewise, in “graphs might contain paths”, it is unclear whether a given graph might contain multiple paths, or might contain at most one path.

When describing an experiment or some other event or action that occurred in the past, use past tense . For example, the methodology section might say “We ran the program”. It would be ungrammatical and confusing to use present tense, as in “We run the program”. Present tense is for ongoing events (“I write this letter to inform you...”) or regular events (“I brush my teeth each day”), but not past events (“Yesterday, I eat dinner with my family”). It is also correct to say “Our methodology was to run the program”, where you use past tense “was” and the infinitive “to run”.

When describing the paper itself, use present tense . “This paper shows that ...”. The reason for this is that the reader is experiencing the paper in real time.

Avoid gratuitous use of the future tense “will ...”, as in, “switching the red and green wires will cause the bomb to explode”. It is unclear when the action will occur. If it is an immediate effect, use the shorter and more direct “switching the red and green wires causes the bomb to explode”.

Use “previous work” instead of “existing work”. Your work exists, so “existing work” would refer to it as well.

In a list with 3 or more elements list, put a serial comma between each of the items (including the last two). As a simple example of why, consider this 3-element grocery list written without the clarifying last comma: “milk, macaroni and cheese and crackers”. It's not clear whether that means { milk, macaroni and cheese, crackers } or { milk, macaroni, cheese and crackers }. As another example, “I would like to thank my parents, Rene Descartes and Ayn Rand,” suggests rather unusual parentage, whereas “I would like to thank my parents, Rene Descartes, and Ayn Rand,” shows a debt to four people. I've seen real examples that were even more confusing than these.

In English, compound adjectives are hyphenated but compound nouns are not. Consider “the semantics provide name protection” versus “the name-protection semantics”.

Prefer unambiguous words to ambiguous ones. Do not use “as” or “since” to mean “because”. Do not use “if” to mean “whether”.

Use quotations sparingly. A clear paraphrase of the points that are relevant to your own work (along with a proper citation) is usually better than a long quotation from a previous publication.

Avoid third-person pronouns when you can. The old standard was “he”, which is masculine chauvinist. The new standard is “he or she”, which can be viewed as heteronormative and which some people find clumsy. An emerging standard is “they” as a first-person singular pronoun, which is inclusive but grammatically incorrect and confusing (see comments above about singular vs. plural number).

Some of the suggestions in this document are about good writing, and that might seem secondary to the research. But writing more clearly will help you think more clearly and often reveals flaws (or ideas!) that had previously been invisible even to you. Furthermore, if your writing is not good, then either readers will not be able to comprehend your good ideas, or readers will be (rightly) suspicious of your technical work. If you do not (or cannot) write well, why should readers believe you were any more careful in the research itself? The writing reflects on you, so make it reflect well.

Use figures! Different people learn in different ways, so you should complement a textual or mathematical presentation with a graphical one. Even for people whose primary learning modality is textual, another presentation of the ideas can clarify, fill gaps, or enable the reader to verify his or her understanding. Figures can also help to illustrate concepts, draw a skimming reader into the text (or at least communicate a key idea to that reader). Figures make the paper more visually appealing.

It is extremely helpful to give an example to clarify your ideas: this can make concrete in the reader's mind what your technique does (and why it is hard or interesting). A running example used throughout the paper is also helpful in illustrating how your algorithm works, and a single example permits you to amortize the time and space spent explaining the example (and the reader's time in appreciating it). It's harder to find or create a single example that you re-use throughout the paper, but it is worth it.

A figure should stand on its own, containing all the information that is necessary to understand it. Good captions contain multiple sentences; the caption provides context and explanation. For examples of good, informative captions, see the print editions of magazines such as Scientific American and American Scientist . The caption should state what the figure illustrates or what conclusion a reader should draw from it. Don't write an obvious description of what the figure is, such as "Code example". Never write a caption like “The Foobar technique”; the caption should also say what the Foobar technique is, what it is good for, or how it works. The caption may also need to explain the meaning of columns in a table or of symbols in a figure. However, it's even better to put that information in the figure proper; for example, use labels or a legend. When the body of your paper contains information that belongs in a caption, there are several negative effects. The reader is forced to hunt all over the paper in order to understand the figure. The flow of the writing is interrupted with details that are relevant only when one is looking at the figure. The figures become ineffective at drawing in a reader who is scanning the paper — an important constituency that you should cater to!

As with naming , use pictorial elements consistently. Only use two different types of arrows (or boxes, shading, etc.) when they denote distinct concepts; do not introduce inconsistency just because it pleases your personal aesthetic sense. Almost any diagram with multiple types of elements requires a legend (either explicitly in the diagram, or in the caption) to explain what each one means; and so do many diagrams with just one type of element, to explain what it means.

Some writers label all the types of figures differently — some as “figure”, others as “table” or “graph” or “picture”. This differentiation has no benefits, but it does have a drawback: it is very hard for a reader to find “table 3”, which might appear after “figure 7” but before “freehand drawing 1”. You should simply call them all figures and number them sequentially. The body of each figure might be a table, a graph, a diagram, a screenshot, or any other content.

Put figures at the top of the page, not in the middle or bottom. If a numbered, captioned figure appears in the middle or at the bottom of a page, it is harder for readers to find the next paragraph of text while reading, and harder to find the figure from a reference to it.

Avoid bitmaps, which are hard to read. Export figures from your drawing program in a vector graphics format. If you must use a bitmap (which is only appropriate for screenshots of a tool), then produce them at very high resolution. Use the biggest-resolution screen you can, and magnify the portion you will capture.

Don't waste text in the paper (and tax the reader's patience) regurgitating information that is expressed more precisely and concisely in a figure. For example, the text should not repeat the numbers from a table or graph. Text in the paper should add insight or explanations, or summarize the conclusions to be drawn from the data in the figure.

Your code examples should either be real code, or should be close to real code. Never use synthetic examples such as procedures or variables named foo or bar . Made-up examples are much harder for readers to understand and to build intuition regarding. Furthermore, they give the reader the impression that your technique is not applicable in practice — you couldn't find any real examples to illustrate it, so you had to make something up.

Any boldface or other highlighting should be used to indicate the most important parts of a text. In code snippets, it should never be used to highlight syntactic elements such as “public” or “int”, because that is not the part to which you want to draw the reader's eye. (Even if your IDE happens to do that, it isn't appropriate for a paper.) For example, it would be acceptable to use boldface to indicate the names of procedures (helping the reader find them), but not their return types.

Give each concept in your paper a descriptive name to make it more memorable to readers. Never use terms like “approach 1”, “approach 2”, or “our approach”, and avoid acronyms when possible. If you can't think of a good name, then quite likely you don't really understand the concept. Think harder about it to determine its most important or salient features.

It is better to name a technique (or a paper section, etc.) based on what it does rather than how it does it.

Use terms consistently and precisely. Avoid “elegant variation”, which uses different terms for the same concept to avoid boredom on the part of the reader or to emphasize different aspects of the concept. While elegant variation may be appropriate in poems, novels, and some essays, it is not acceptable in technical writing, where you should clearly define terms when they are first introduced, then use them consistently. If you switch wording gratuitously, you will confuse the reader and muddle your point. A reader of a technical paper expects that use of a different term flags a different meaning, and will wonder what subtle difference you are trying to highlight. Thus, don't confuse the reader by substituting “program”, “library”, “component”, “system”, and “artifact”, nor by conflating “technique”, “idea”, “method” and “approach”, nor by switching among “program”, “code”, and “source”. Choose the best word for the concept, and stick with it.

Do not use a single term to refer to multiple concepts. If you use the term “technique” for every last idea that you introduce in your paper, then readers will become confused. This is a place that use of synonyms to distinguish concepts that are unrelated (from the point of view of your paper) is acceptable. For instance, you might always use “phase” when describing an algorithm but “step” when describing how a user uses a tool.

When you present a list, be consistent in how you introduce each element, and either use special formatting to make them stand out or else state the size of the list. Don't use, “There are several reasons I am smart. I am intelligent. Second, I am bright. Also, I am clever. Finally, I am brilliant.” Instead, use “There are four reasons I am smart. First, I am intelligent. Second, I am bright. Third, I am clever. Fourth, I am brilliant.” Especially when the points are longer, this makes the argument much easier to follow. Some people worry that such consistency and repetition is pedantic or stilted, or it makes the writing hard to follow. There is no need for such concerns: none of these is the case. It's more important to make your argument clear than to achieve “elegant variation” at the expense of clarity.

Choose good names not only for the concepts that you present in your paper, but for the document source file. Don't name the file after the conference to which you are submitting (the paper might be rejected) or the year. Even if the paper is accepted, such a name won't tell you what the paper is about when you look over your files in later years. Instead, give the paper or its folder/directory a name that reflects its content. Another benefit is that this will also lead you to think about the paper in terms of its content and contributions.

Here is a piece of advice that is specific to computing: do not use the vague, nontechnical term “bug”. Instead, use one of the standard terms fault, error, or failure. A fault is an underlying defect in a system, introduced by a human. A failure is a user-visible manifestation of the fault or defect. In other circumstances, “bug report” may be more appropriate than “bug”.

Digits of precision:

  • Don't report more digits of precision than the measurement process reliably and reproducibly produces. The 3rd or 4th digit of precision is rarely accurate and generalizable; if you don't have confidence that it is both repeatable and generalizable to new experiments, omit it. Another way to say this is that if you are not confident that a different set of experiments would produce all the same digits, then don't report so much precision.
  • Don't report more digits of precision than needed to convey your message. If the difference between 4.13 and 4 will not make a difference in convincing readers, then don't report the extra digits. Reporting extra digits can distract readers from the larger trends and the big picture. Including an inappropriate number of digits of precision can cast suspicion on all of your results, by giving readers the impression that you are statistically naive.
  • Use a consistent number of digits of precision. If the measured data are 1.23, 45.67, and 891.23, for example, you might report them as 1.23, 45.7, and 891, or as 1.2, 46, and 890, or as 1, 50, and 900. (An exception is when data are known to sum to a particular value; I would report 93% and 7% rather than either 93% and 7.4% or 90% and 7%. Often it's appropriate to report percentages as whole numbers rather than using the same precision.)
  • If you do any computations such as ratios, your computations should internally use the full precision of your actual measurements, even though your paper reports only a limited number of digits of precision.
  • If a measurement is exact, such as a count of items, then it can be acceptable to give the entire number even if it has many digits; by contrast, timings and other inexact measurements should always be reported with a limited number of digits of precision.

Do not confuse relative and absolute measurements. For instance, suppose your medicine cures 30% of patients, and the placebo cures 25% of patients. You could report that your medicine's cure rate is .3, the placebo's cure rate is .25, and your medicine's cure rate is either .05 greater or 20% greater. (Other correct, but less good, ways to say the same thing are that it cures 20% more, 120% as many, or 1.2 times as many patients.) It would be inaccurate to state that your medicine cures 5% more patients or your medicine cures 120% more patients. Just as you need to correctly use “120% more” versus “120% as many”, you need to correctly use “3 times faster than” versus “3 times as fast as”. A related, also common, confusion is between “3 times faster than and 3 times as fast as”. And, “2 times fewer” makes absolutely no sense. I would avoid these terms entirely. “Half as many” is a much better substitute for “2 times fewer”.

Given the great ease of misunderstanding what a percentage means or what its denominator is, I try to avoid percentages and focus on fractions whenever possible, especially for base measurements. For comparisons between techniques, percentages can be acceptable. Avoid presenting two different measurements that are both percentages but have different denominators.

Your paper probably includes tables, bibliographies, or other content that is generated from external data. Your paper may also be written in a text formatting language such as LaTeX. In each of these cases, it is necessary to run some external command to create some of the content or to create the final PDF.

All of the steps to create your final paper should be clearly documented — say, in comments or in a notes file that you maintain with the paper. Preferably, they should be automated so that you only have to run one command that collects all the data, creates the tables, and generates the final PDF.

If you document and automate these steps, then you can easily regenerate the paper when needed. This is useful if you re-run experiments or analysis, or if you need to defend your results against a criticism by other researchers. If you leave some steps manual, then you or your colleagues are highly likely to make a mistake (leading to a scientific error) or to be unable to reproduce your results later.

One good way to automate these tasks is by writing a program or creating a script for a build system such as Ant, Gradle, Make, Maven, etc.

A related work section should not only explain what research others have done, but in each case should compare and contrast that to your work and also to other related work. After reading your related work section, a reader should understand the key idea and contribution of each significant piece of related work, how they fit together (what are the common themes or approaches in the research community?), and how your work differs. Don't write a related work section that is just a list of other papers, with a sentence about each one that was lifted from its abstract, and without any critical analysis nor deep comparison to other work.

Unless your approach is a small variation on another technique, it is usually best to defer the related work to the end of the paper. When it comes first, it gives readers the impression that your work is rather derivative. (If this is true, it is your responsibility to convey that clearly; if it is not true, then it's misleading to intimate it.) You need to ensure that readers understand your technique in its entirety, and also understand its relationship to other work; different orders can work in different circumstances.

Just as you should generally explain your technique first, and later show relationships with other work, it is also usually more effective to defer a detailed discussion of limitations to a later section rather than the main description of your technique. You should be straightforward and honest about the limitations, of course (do mention them early on, even if you don't detail them then), but don't destroy the coherence of your narrative or sour the reader on your technique.

Get feedback ! Finish your paper well in advance, so that you can improve the writing. Even re-reading your own text after being away from it can show you things that you didn't notice. An outside reader can tell you even more.

When readers misunderstand the paper, that is always at least partly the author's fault! Even if you think the readers have missed the point, you will learn how your work can be misinterpreted, and eliminating those ambiguities will improve the paper.

Be considerate to your reviewers, who are spending their time to help you. Here are several ways to do that.

As with submission to conferences, don't waste anyone's time if there are major flaws. Only ask someone to read (a part of) your paper when you think you will learn something new, because you are not aware of serious problems. If only parts are ready, it is best to indicate this in the paper itself (e.g., a TODO comment that the reader will see or a hand-written annotation on a hardcopy) rather than verbally or in email that can get forgotten or separated from the paper.

Sometimes you want to tell a colleague who is giving you feedback that some sections of your draft are not ready to be read, or to focus on particular aspects of the document. You should write such directions in the paper, not just in email or verbally. You will then update them as you update the paper, and all relevant information is collected together. By contrast, it's asking for trouble to make your colleague keep track of information that is in multiple places.

It is most effective to get feedback sequentially rather than in parallel. Rather than asking 3 people to read the same version of your paper, ask one person to read the paper, then make corrections before asking the next person to read it, and so on. This prevents you from getting the same comments repeatedly — subsequent readers can give you new feedback rather than repeating what you already knew, and you'll get feedback on something that is closer to the final version. If you ask multiple reviewers at once, you are de-valuing their time — you are indicating that you don't mind if they waste their time saying something you already know. You might ask multiple reviewers if you are not confident of their judgment or if you are very confident the paper already is in good shape, in which case there are unlikely to be major issues that every reviewer stumbles over.

It usually best not to email the document, but to provide a location from which reviewers can obtain the latest version of the paper, such as a version control repository or a URL you will update. That way, you won't clutter inboxes with many revisions, and readers can always get the most recent copy.

Be generous with your time when colleagues need comments on their papers: you will help them, you will learn what to emulate or avoid, and they will be more willing to review your writing.

Some of your best feedback will be from yourself, especially as you get more thoughtful and introspective about your writing. To take advantage of this, start writing early. One good way to do this is to write a periodic progress report that describes your successes and failures. The progress report will give you practice writing about your work, oftentimes trying out new explanations.

Whereas you should start writing as early as possible, you don't need to put that writing in the form of a technical paper right away. In fact, it's usually best to outline the technical paper, and get feedback on that, before you start to fill in the sections with text. (You might think that you can copy existing text into the paper, but it usually works out better to write the information anew. With your knowledge of the overall structure, goals, and audience, you will be able to do a much better job that fits with the paper's narrative.) When outlining, I like to start with one sentence about the paper; then write one sentence for each section of the paper; then write one sentence for each subsection; then write one sentence for each paragraph (think of this as the topic sentence); and at that point, it's remarkably easy just to flesh out the paragraphs.

You should not submit your paper too early, when it does not reflect well on you and a submission would waste the community's reviewing resources. You should not submit your paper too late, because then the community is deprived of your scientific insights. In general, you should err on the side of submitting too late rather than too early.

A rule of thumb is to submit only if you are proud for the world to associate your name with the work, in its current form . If you know of significant criticisms that reviewers might raise, then don't submit the paper.

Submitting your paper prematurely has many negative consequences.

  • You will waste the time of hard-working reviewers, who will give you feedback that you could have obtained in other ways.
  • You will get a reputation for shoddy work.
  • You will make the paper less likely to be accepted in the future. Oftentimes the same reviewers may serve two different venues. Reviewing a paper again puts a reviewer in a negative state of mind. I have frequently heard reviewers say, “I read an earlier version of this paper, it was a bad paper, and this version is similar.” (This is unethical because reviewers are not supposed to talk about papers they have reviewed, but nonetheless it is very common.) Now the paper will likely be rejected again, and the whole committee gets a bad impression of you. A reviewer who has read a previous version of the paper may read the resubmission less carefully or make assumptions based on a previous version. To sum up: it's harder to get a given paper accepted on its second submission, than it would have been to get the identical paper accepted on its first submission.

Here are some bad reasons to submit a paper.

It's true that the feedback from reviewers is extraordinarily valuable to you and will help you improve the paper. However, you should get feedback from other scientists (your friends and colleagues) before submitting for publication.

Those are true facts, and some people do “salami-slice” their research into as many papers as possible — such papers are called a “least publishable unit”. However, doing so leads to less impact than publishing fewer papers, each one with more content. If a paper contains few contributions, it is less likely to make a big impression, because it is less exciting. In addition, readers won't enjoy reading many pages to learn just a few facts.

Note: This point refers to taking a single research idea or theme and splitting it into multiple publications. When there are multiple distinct research contributions, it can be appropriate to describe them in different papers.

The reviewing process can be frustrating, because it contains a great deal of randomness: the same paper would be rejected by some reviewers and accepted by others. However, all great papers are accepted and all bad papers are rejected. For mediocre papers, luck plays a role. Your goal should not be to write great papers, not mediocre ones. Find a way to improve your paper. Recognize the great value of reviews: they provide a valuable perspective on your work and how to improve it, even if you feel that the reviewer should have done a better job.

If you aren't excited about the paper, it is unlikely that other people will be. Furthermore, the period after submitting the paper is not a time to take a break, but an opportunity to further improve it.

After you submit a paper, don't stop working on it! You can always improve the research. For instance, you might expand the experiments, improve the implementation, or make other changes. Even if your paper is accepted, you want the accepted version to be as impressive as possible. And if the paper is rejected, you need to have a better paper to submit to the next venue.

(This section is most relevant to fields like computer science where conferences are the premier publication venue. Responding to journal reviews is different.)

Many conferences provide an author response period: the authors are shown the reviews and are given limited space (say, 500 words) to respond to the reviews, such as by clarifying misunderstandings or answering questions. The author response is sometimes called a “rebuttal”, but I don't like that term because it sets an adversarial tone.

Your paper will only be accepted if there is a champion for the paper: someone who is excited about it and will try to convince the rest of the committee to accept the paper. Your response needs to give information to your champion to overcome objections. If there isn't a champion, then the main goal of your response is to create that champion. Your response should also give information to detractors to soften their opposition.

After reading the reviews, you may be disappointed or angry. Take a break to overcome this, so that you can think clearly.

For every point in the reviews, write a brief response. Do this in email-response style, to ensure that you did not miss any points. You will want to save this for later, so it can be better to do this in the paper's version control repository, rather than in a WYSIWYG editor such as Google Docs. (This assumes you have a version control repository for the paper, which you should!) Much of this text won't go in your response, but it is essential for formulating the response.

Summarize (in 5 or so bullet points, however many make sense) the key concerns of the reviewers. Your review needs to focus on the most important and substantive critiques. The authors of the paper should agree on this structure before you start to write the actual response.

Your response to each point will be one paragraph in your response. Start the paragraph with a brief heading or title about the point. Do not assume that the reviewers remember everything that was written by every reviewer, nor that they will re-read their reviews before reading your response. A little context will help them determine what you are talking about and will make the review stand on its own. This also lets you frame the issues in your own words, which may be clearer or address a more relevant point than the reviews did.

Organize your responses thematically. Group the paragraphs into sections, and have a small heading/title for each section. If a given section has just one paragraph, then you can use the paragraph heading as the section heading. Order the sections from most to least important.

This is better than organizing your response by reviewer, first addressing the comments of reviewer 1, then reviewer 2, and so forth. Downsides of by-reviewer organization include:

  • It can encourage you not to give sufficient context.
  • It does not encourage putting related information together nor important information first.
  • You want to encourage all reviewers to read the entire response, rather than encouraging them to just look at one part.
  • When multiple reviewers raised the same issue, then no matter where you address it, it's possible for a reviewer to overlook it and think you failed to address it.
  • You don't want to make glaringly obvious which issues in a review you had to ignore (for reasons of space or other reasons).
  • You don't want to make glaringly obvious that you spent much more time and space on one reviewer than another.

In general, it's best not to mention reviewer names/numbers in your response at all. Make the response be about the science, not about the people.

In your responses, admit your errors forthrightly. Don't ignore or avoid key issues, especially ones that multiple reviewers brought up.

Finally, be civil and thankful the reviewers. They have spent considerable time and energy to give you feedback (even if it doesn't seem to you that they have!), and you should be grateful and courteous in return.

If you submit technical papers, you will experience rejection. In some cases, rejection indicates that you should move on and begin a different line of research. In most cases, the reviews offer an opportunity to improve the work, and so you should be very grateful for a rejection! It is much better for your career if a good paper appears at a later date, rather than a poor paper earlier or a sequence of weak papers.

Even small flaws or omissions in an otherwise good paper may lead to rejection. This is particularly at the elite venues with small acceptance rates, where you should aim your work. Referees are generally people of good will, but different referees at a conference may have different standards, so the luck of the draw in referees is a factor in acceptance.

The wrong lesson to learn from rejection is discouragement or a sense of personal failure. Many papers — even papers that later win awards — are rejected at least once. The feedback you receive, and the opportunity to return to your work, will invariably improve your results.

Don't be put off by a negative tone in the reviews. The referees are trying to help you, and the bast way to do that is to point out how your work can be improved. I often write a much longer review, with more suggestions for improvement, for papers that I like; if the paper is terrible, I may not be able to make as many concrete suggestions, or my high-level comments may make detailed comments moot.

If a reviewer didn't understand something, then the main fault almost always lies with your writing. If you blame a lazy or dumb reviewer, you are missing the opportunity to improve. Reviewers are not perfect, but they work hard to give you helpful suggestions, so you should give them the benefit of the doubt. Remember that just as it is hard to convey technical ideas in your paper (and if you are getting a rejection, that is evidence that you did not succeed!), it is hard to convey them in a review, and the review is written in a few hours rather than the weeks you spent on the paper (not to mention months or years of understanding the concepts). You should closely attend to both the explicit comments, and to underlying issues that may have led to those comments — it isn't always easy to capture every possible comment in a coherent manner. Think about how to improve your research and your writing, even beyond the explicit suggestions in the review — the prime responsibility for your research and writing belongs with you.

Norman Ramsey's nice Teach Technical Writing in Two Hours per Week espouses a similar approach to mine: by focusing on clarity in your writing, you will inevitably gain clarity in your thinking.

Don't bother to read both the student and instructor manuals — the student one is a subset of the instructor one. You can get much of the benefit from just one part, his excellent “principles and practices of successful writers”:

  • Correctness. Write correct English, but know that you have more latitude than your high-school English teachers may have given you.
  • Consistent names. Refer to each significant character (algorithm, concept, language) using the same word everywhere. Give a significant new character a proper name.
  • Singular. To distinguish one-to-one relationships from n-to-m relationships, refer to each item in the singular, not the plural.
  • Subjects and verbs. Put your important characters in subjects, and join each subject to a verb that expresses a significant action.
  • Information flow. In each sentence, move your reader from familiar information to new information.
  • Emphasis. For material you want to carry weight or be remembered, use the end of a sentence.
  • Coherence. In a coherent passage, choose subjects that refer to a consistent set of related concepts.
  • Parallel structure. Order your text so your reader can easily see how related concepts are different and how they are similar.
  • Abstract. In an abstract, don't enumerate a list of topics covered; instead, convey the essential information found in your paper.
  • Write in brief daily sessions. Ignore the common myth that successful writing requires large, uninterrupted blocks of time — instead, practice writing in brief, daily sessions.
  • Focus on the process, not the product. Don't worry about the size or quality of your output; instead, reward yourself for the consistency and regularity of your input.
  • Prewrite. Don't be afraid to think before you write, or even jot down notes, diagrams, and so on.
  • Use index cards. Use them to plan a draft or to organize or reorganize a large unit like a section or chapter.
  • Write a Shitty First Draft™. Value a first draft not because it's great but because it's there.
  • Don't worry about page limits. Write the paper you want, then cut it down to size.
  • Cut. Plan a revision session in which your only goal is to cut.
  • Norman Ramsey's advice , excerpted immediately above .
  • “Hints on writing an M.Eng. thesis” , by Jeremy Nimmer
  • my notes on reviewing a technical paper , which indicate how to recognize — and thus produce — quality work
  • my notes on choosing a venue for publication
  • my notes on giving a technical talk : a talk has the same goal as a paper, namely to convey technical ideas
  • my notes on making a technical poster
  • Ronald B. Standler's advice on technical writing
  • Dave Patterson's Writing Advice
  • Advice on SIGPLAN conference submissions (at bottom of page)
  • The Elements of Style , William Strunk Jr. and E. B. White, is classic book on improving your writing. It focuses at a low level, on English usage.
  • Style: Toward Clarity and Grace , by Joseph M. Williams, is another general-purpose writing guide, with a somewhat higher-level focus than that of Strunk & White.
  • The Sense of Style: The Thinking Person's Guide to Writing in the 21st Century , by Steven Pinker, is an excellent guide to writing. It gives reasons (from psychology and other scientific fields) for its advice, making it more authoritative than someone's opinion.

Back to Advice compiled by Michael Ernst .

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  • IEEE Paper Format | Template & Guidelines

IEEE Paper Format | Template & Guidelines

Published on August 24, 2022 by Jack Caulfield . Revised on April 6, 2023.

IEEE provides guidelines for formatting your paper. These guidelines must be followed when you’re submitting a manuscript for publication in an IEEE journal. Some of the key guidelines are:

  • Formatting the text as two columns, in Times New Roman, 10 pt.
  • Including a byline, an abstract , and a set of keywords at the start of the research paper
  • Placing any figures, tables, and equations at the top or bottom of a column, not in the middle
  • Following the appropriate heading styles for any headings you use
  • Including a full list of IEEE references at the end
  • Not including page numbers

IEEE example paper

To learn more about the specifics of IEEE paper format, check out the free template below. Note that you may not need to follow these rules if you’ve only been told to use IEEE citation format for a student paper. But you do need to follow them to submit to IEEE publications.

Table of contents

Ieee format template, ieee heading styles, frequently asked questions about ieee.

The template below can be used to make sure that your paper follows IEEE format. It’s set up with custom Word styles for all the different parts of the text, with the right fonts and formatting and with further explanation of key points.

Make sure to remove all the explanatory text in the template when you insert your own.

Download IEEE paper format template

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IEEE recommends specific heading styles to distinguish the title and different levels of heading in your paper from each other. Styles for each of these are built into the template.

The paper title is written in 24 pt. Times New Roman, centered at the top of the first page. Other headings are all written in 10 pt. Times New Roman:

  • Level 1 text headings begin with a roman numeral followed by a period. They are written in small caps, in title case, and centered.
  • Level 2 text headings begin with a capital letter followed by a period. They are italicized, left-aligned, and written in title case.
  • Level 3 text headings begin with a number followed by a closing parenthesis . They are italicized, written in sentence case, and indented like a regular paragraph. The text of the section follows the heading immediately, after a colon .
  • Level 4 text headings begin with a lowercase letter followed by a closing parenthesis. They are italicized, written in sentence case, and indented slightly further than a normal paragraph. The text of the section follows the heading immediately, after a colon.
  • Component headings are used for the different components of your paper outside of the main text, such as the acknowledgments and references. They are written in small caps, in title case, centered, and without any numbering.

IEEE heading styles

You should use 10 pt. Times New Roman font in your IEEE format paper .

For the paper title, 26 pt. Times New Roman is used. For some other paper elements like table footnotes, the font can be slightly smaller. All the correct stylings are available in our free IEEE format template .

No, page numbers are not included in an IEEE format paper . If you’re submitting to an IEEE publication, page numbers will be added in the final publication but aren’t needed in the manuscript.

IEEE paper format requires you to include an abstract summarizing the content of your paper. It appears at the start of the paper, right after you list your name and affiliation.

The abstract begins with the word “Abstract,” italicized and followed by an em dash. The abstract itself follows immediately on the same line. The entire section is written in bold font. For example: “ Abstract —This paper discusses … ”

You can find the correct format for your IEEE abstract and other parts of the paper in our free IEEE paper format template .

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Title: gpt-4 technical report.

Abstract: We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs. While less capable than humans in many real-world scenarios, GPT-4 exhibits human-level performance on various professional and academic benchmarks, including passing a simulated bar exam with a score around the top 10% of test takers. GPT-4 is a Transformer-based model pre-trained to predict the next token in a document. The post-training alignment process results in improved performance on measures of factuality and adherence to desired behavior. A core component of this project was developing infrastructure and optimization methods that behave predictably across a wide range of scales. This allowed us to accurately predict some aspects of GPT-4's performance based on models trained with no more than 1/1,000th the compute of GPT-4.

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Man’s relation to technology: a brief history, technology and biological anthropology, the sts approach, classical philosophical anthropology, philosophy of technology, the continental approach to the philosophy of technology, the analytic approach to the philosophy of technology, recent developments: bridging the gap, conclusion and future directions.

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The term technology is derived from the Greek word techné. The Greek word refers to all forms of skillful, rule-based mastery in any field of human praxis, originally encompassing both arts (like painting, sculpture, writing, and the like) and craftsmanship (like carpentry, shipbuilding, architecture, and the like). The Roman culture uses the Latin word arts for these domains. Accordingly the medieval terminology distinguishes between the seven free arts (grammar, rhetoric, logic, geometry, arithmetic, music, astronomy) and the mechanical arts (e.g., agriculture, architecture, tailoring), thus prefiguring the later distinction between arts (as linked to the study of humans and the humanities) and technology (as linked to engineering and the study and science of nature).

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The modern word technology finally refers either to procedures and skillful application of sciences for the production of industrial or manual products or to the products of these processes themselves. In this sense, technology nowadays encompasses only a part of the original Greek definition. The place of technology as being on the one hand a product of humans (being thus rooted in human anthropology and human tool usage), and being on the other hand based on a solid scientific understanding of the laws of nature (modern technology), can be seen as the two key features of contemporary and recent approaches to analyze and understand technology. Technology is then in one respect as old as humankind: Many approaches in anthropology thus refer to the general structure of technology in all of human history and relate it to the biological condition of humans. But recent anthropological thinking also reflects on the specific details of modern technology. It has often been argued that there is a structural difference between modern, science-based technology and older forms of craftsmanship of ancient or medieval types of technology. Therefore, a central question for modern anthropology is to analyze the consequences modern technology has for our picture of humankind: how to define man in the age of technology.

Reflection about the anthropological function of technology is probably as old as human self-reflection itself, since the ability to use tools and create cultural products has always been seen as a unique human feature, distinguishing humankind from most other animals (see also the next section on biological anthropology). But an analysis of technology was not at the center of political, social, anthropological, or philosophical thoughts before the development of the modern natural sciences and their counterpart, modern technology. Following Carl Mitcham (1994) one can roughly distinguish three approaches to technology before the 20th century, encompassing many topics that later became essential parts of contemporary discussions about technology (p. 275). The three approaches are as follows:

  • In the ancient world, technology is looked at with certain skepticism. The use of tools is seen as necessary for survival, but also regarded as dangerous, since it might lead to human hubris and might raise the envy and anger of the gods. In this sense, mythological thinking envisions technology as, for example, stolen from the gods (the myth of Prometheus), and thus not properly belonging to humans. The extensive use of technology is often seen as leading to megalomaniac fantasies or unjustified overstepping of religious and ethical boundaries (e.g., myth of the Tower of Babel, myth of Icarus). Philosophical reflection, however, acknowledges the value of technology for an otherwise defenseless human being. Already Plato anticipates a central thought of modern anthropology: Human beings are poorly equipped for survival in nature. They need to compensate for this lack by developing skills of rational thinking and the usage of tools (this idea later becomes a central thesis of the famous anthropology of Arnold Gehlen [1988]). But the emphasis in ancient philosophical anthropology lies not so much on man’s capacities to invent technology, but on man’s moral character (exemplified by ancient wisdom or medieval religiosity). The usage of technical knowledge should thus be kept within strict ethical boundaries.
  • In the hierarchy of knowledge, ethical wisdom is regarded in principle as higher than and superior to technological skills. Socrates points to the question that we should not only seek knowledge about how to do certain things (technical knowledge), but rather about whether we should perform certain actions (ethical knowledge); this idea can also be found in the medieval distinction between the (superior form of a) life in contemplation ( vita contemplativa ) and the (lower) life in active involvement ( vita activa ). Ancient and medieval technology is thus embedded in an anthropological vision, in which human virtues play an important role. Different forms of virtues are combined in the original crafts, as opposed to the later, modern differentiation of these virtues: In craftmanship one can find a union of economical virtues (e.g., efficient usage of limited resources), technical virtues (creating new entities that did not exist before), and often also aesthetic virtues (a sense of beauty that adds an aesthetic component to these newly created entities going beyond the modern idea that “form follows function”). In the Greek world, these three skills are combined in the realm of poiesis, while in modernity they are separated in the three domains of economy, technology, and art—each relatively independent of the others (Hösle, 2004, p. 366).
  • A profound change in the evaluation of technology emerges with modernity, a position that Mitcham (1994) summarizes as Enlightenment optimism. Already in the writings of Francis Bacon (1620), the new science of nature and its application to experimental and technological research is highly welcomed. Progress in technology is seen as very beneficial to humankind, as it may lead to the cure of diseases, mastery over nature, and a constant progress toward a more human society. Many utopian writings mark the beginning of early modern thoughts in which technology is seen as essential in leading to a brighter future for humankind (e.g., Thomas More’s Utopia [1516], J. V. Andreae’s Christianopolis [1619], F. Bacon’s New Atlantis [1627]). In a similar line of thought, Enlightenment thinkers defend science and modern technology against attacks from religious conservatism, pointing at the beneficial consequences of technological and scientific progress.
  • A countermovement to the Enlightenment is Romanticism, which accordingly has a different view on technology, referred to by Mitcham (1994) as Romantic uneasiness. Again, the central thought is an anthropological perspective in which man is seen as being good by nature, while it is civilization that poses the danger of alienating man from nature and from his fellow man, focusing only on his rational capacities and suppressing his emotional and social skills. Already Vico (1709) opposed Cartesian rationalism and feared that the new interest in science would lead to a neglect of traditional humanistic education. Rousseau’s critique of modern societies then became influential, seeing an advancement of knowledge and science, but a decay of virtues and immediacy ( Discourse on the Arts and Sciences; Rousseau, 1750). With the age of industrialism, the negative social consequences of modern labor work become the scope of interest of social theorists, leading up to Marx’s famous analysis of modern societies (see subsequent section on cultural and sociological anthropology). In opposition to the positive utopias centered on technology in early modernity, the 20th century then sees the literary success of pessimistic dystopias, in which often technological means of suppression or control play an important role (e.g., already in M. W. Schelley’s Frankenstein or the Modern Prometheus [1818] and later in H. G. Wells’s The Island of Doctor Moreau [1896], A. Huxley’s Brave New World [1932], George Orwell’s 1984 [1948], and Ray Bradbury’s Fahrenheit 451 [1953]).

The tension between approaches praising the benefits of technology (in the spirit of the Enlightenment) and approaches focusing on negative consequences (in the spirit of Romanticism) still forms the background of most of the contemporary philosophical and anthropological debate; this debate circles around an understanding of modern technology, often rooted in the different “cultures” of the humanities and the sciences. It can be regarded as being a particularly vivid opposition at the beginning of the 20th century, that only later gave room for more detailed and balanced accounts of technology (some classics of the debate being Snow, 1959; McDermott, 1969).

Recent contributions toward a deeper understanding of the usage and development of technology stem from such different disciplines as biology, sociology, philosophical anthropology, metaphysics, ethics, theory of science, and religious worldviews. This research paper aims at a brief overview of important topics in the debate over technology during the 20th century to the present time. Three anthropological perspectives will be distinguished, depending on the main focus of anthropological interest. This will start with a brief summary of the biological anthropological perspective on technology, move on to those theories which focus more on social or cultural aspects, and conclude with more general philosophical anthropologies. This research paper is thus not chronologically organized, but tries to identify common themes of the debate, even though sometimes the topics might overlap (e.g., the case of Gehlen, a philosophical anthropologist who starts from a biological perspective and then moves on toward a more social view on technology).

In contemporary anthropology, technology becomes a central issue for at least two different reasons:

  • From a biological perspective the usage of tools is regarded (next to the development of language and a cognitive rational apparatus) as one of the key features of humanization. Biological anthropology thus initially focuses on the differences and similarities of tool usage in humans and animals, trying to understand the role technology plays in general for an understanding of humans’ biological and social nature. With the focus on human evolution, attention is often drawn to the question of which role technology played at the beginning of humankind.
  • While in this way always being a part of human culture, technology becomes arguably one of the single most influential key features of society only in modernity. According to Max Weber, science, technology, and economy form the “superstructure” of modernity, while they all share a common “rationality” (mainly of means-ends reasoning in economy and technology). The experience of the powers and dangers of modern technology (as in industrialized labor work, medical progress, nuclear energy and weapon technology, environmental problems due to pollution, and extensive usage of resources, etc.) has triggered many social, political, and philosophical reflections that—in opposition to biological anthropology—aim primarily at understanding the specifics of modern

Let us look at these two tendencies in turn, starting with the biological perspective, before moving to the social or cultural anthropology of technology.

Biological anthropologists are interested in the role technology played during humanization, and they attempt to give evolutionary accounts of the development of tool usage and technology and compare tool usage in man with tool usage in other animals. The development of technology has often been regarded as an evolutionarily necessary form of adaption or compensation. Since most of man’s organs are less developed than those of other species, he needed to compensate for this disadvantage in the evolutionary struggle for life (see Gehlen, 1980). Initially the usage of tools was considered a unique human feature, distinguishing the genus Homo from other animals (Oakley, 1957), but research on tool usage in different animals, especially chimpanzees, led to a more or less complete revision of this thesis (Schaik, Deaner, & Merrill, 1999).

Nowadays, many examples of tool usage in the animal kingdom are known (Beck, 1980). For example, chimpanzees use sticks to fish for termites, and elephants have been described as having a remarkable capacity for tool usage. Even though tool usage must thus be regarded as more common among animals, attention still needs to be drawn to the specifics of man’s tool usage, which arguably in scope and quality goes beyond what is known from the animal kingdom. It has been pointed out that our biological anatomy offers us several advantages for an extended usage of tools: walking erectly frees the two hands, which can then be used for other purposes. Furthermore, the position of the human thumb and short straight finger are of great benefit, especially in making and using stone tools (Ambrose, 2001). Still debated, however, is whether social and technological developments go hand in hand or whether one of the two factors is prior.

Even though many anthropologists tended to see social behaviors and cultural revolutions mostly as a consequence of a change in tool usage or a development of new technologies, it has also occasionally been argued that the development of social skills precedes the development of technical skills (e.g., in joint group hunting). It has additionally been acknowledged that chimpanzees also pass over some of their technical knowledge through the mechanism of learning and establishing cultural “traditions” that resemble, to some extent, human traditions (Wrangham, 1994; Laland, 2009). But there seems to be a specific difference in human and primate learning, namely in the fact that human children learn tool usage mainly via imitation and by simply copying a shown behavior, even if it is not the most efficient solution to a given problem. Opposed to this, chimpanzees seem to learn through a process called emulation, which implies that they diverge from the paradigmatic solution that has been “taught” to them. It has been argued that learning through imitation has been selected in humans, even though it is a less flexible strategy, because it is a more social strategy of learning (Tomasello, 1999, p. 28). In this way, biological anthropology mirrors a debate in social anthropology about the role of technology; this can be seen either as a driving force born out of necessity that calls for social changes (technical determinism), or as highly mediated or even constructed by culture (social constructivism).

Technology and Social/Cultural Anthropology

As already mentioned, technology was identified early on as a key feature of modern society (Misa, Brey, & Feenberg, 2004). Many studies have been written about the impact of modern technology on society, focusing mainly on the industrial revolution (e.g., Haferkamp, 1992; Pressnell, 1960; Smelser, 1969) or on the more recent revolution of the information society (e.g., Castells, 1999; Nora, 1980), as well as on the impact of technological change on traditional societies.

The analyses of Karl Marx and the Frankfurt School are influential, not only in trying to grasp the role of modern technology in society, but also in hinting on potential anthropological roots of technology and their essential interrelation with social aspects of the human condition. Marx insisted that the study of technology holds the highest relevance for human sciences, since it reveals the way humans deal with nature and sustain life (Marx, 1938). An essential feature of man’s nature is that he has to work in order to sustain his life, that he is the “toolmaking animal” or—as he has later been called—the Homo faber. Marx analyzes the role of technology in Chapter 13 of his first volume of Das Kapital. He argues that the division of labor becomes fostered through machines, which at the same time replace more and more traditional manpower and can furthermore be operated by less skilled employees, thus leading to very bad labor conditions for the working class. Technology in general is, however, still greeted as an option to make humans’ lives easier; it is mainly the social distribution of the possession of the means of production that Marx regards as problematic. (Also later thinkers, inspired by Marxian thought, tend to see technology as an important means toward establishing a better future.) On the other hand, at the same time, technology is seen as rooted in man’s will to dominate nature.

Following this later insight in particular, Theodor Adorno argues that Western civilization has developed powerful tools to ensure its self-preservation against nature. Technical rationality is regarded as the exercise of strategic power to dominate (external) nature, but it is at the same time also leading to a suppression of the inner nature of man (Adorno, 1979). The main strategy of this rationality is quantification, which lies at the heart of the mathematical-scientific interpretation of nature and the development of modern technology. At the same time it brings forth a type of rationality, which leads to a selfmutilation. The will to exercise power becomes the main feature of modern rationality, thus leading to a dialectic that turns the noble aims of the Age of Enlightenment into a morality of humankind that is its very opposite: A new barbaric system of oppression and dictatorship arises, using technology for totalitarian purposes.

While Adorno seeks redemption mainly in the arts (Adorno, 1999), seeming to promise the possibility of a completely different kind of subjectivity, Jürgen Habermas (1971) tries to propose an antidote; this does not lie outside of modern-Enlightenment rationality, but rather returns to its original intention. Habermas argues with Marx and Adorno, asserting that technological knowledge has its anthropological roots in the will to dominate nature and therefore serves a strategic interest of man. With this, man is not only Homo faber but also a social animal. Besides the strategic means-end rationality he also possesses a communicative rationality, aimed at defining common moral values and engaging in discourse over ethically acceptable principles of actions. In thus distinguishing two types of rationality, Habermas tries to incorporate much of the German tradition of cognitivistic ethics into his approach. It is important for Habermas that technology be brought under the control of democratic decision-making processes; his discourse ethics has thus helped to inspire ideas of participatory technology assessment.

Outside the Frankfurt School, technology has not been at the center of social and cultural anthropology, as has been often complained (Pfaffenberger, 1988, 1992). Langdon Winner (1986) coined the term technological somnambulism to refer to those theories that neglect the social dimension of technology. According to this dominant tradition, the human-technology relation is “too obvious” to merit serious reflection. Technology is seen as an independent factor of the material and social world, one that forms a relatively autonomous realm of ethically neutral tools to acquire human ends. But already Winner argues that technology is essentially social and is shaped by cultural conditions and underlying value decisions. He claims in a famous article (Winner, 1980) that Long Island’s low bridges were intentionally built in a way that would keep buses away, making it more difficult for the poor, and mainly the black population, to reach the island. Even though this particular claim has been challenged, Winner seems to be correct in pointing out that value decisions play a role in creating technology, and that the social value system leaves its trace in technological artifacts.

In line with this renewed interest in social issues, a new field of studies related to technology emerged in the 1980s, focusing explicitly on this neglected relation between society and technology: the so-called STS approach. Having been labeled the “turn to technology” (Woolgar, 1991), science and technology studies (STS) analyzes society’s impact on science and technology, and science and technology’s impact on society. Several writers draw attention to the social shaping of technology. An influential author is Bruno Latour, who contributed to both the initial appeal to social constructivism (that he later gave up) and the development of the actor-network theory; both are at the center of the debate about the theoretical underpinnings of STS.

Social Constructivism

Woolgar and Latour employ a social-constructivist perspective in their early case study on the production of scientific results, in which they analyze scientists’ attempt to establish and accumulate recognition and credibility of their research through the “cycle of credibility” (Latour, 1979). The main idea of social constructivism is the attempt to interpret alleged objective “facts” in the social world as being socially constructed, so that knowledge of the world and its interpretation depends on social mechanisms and cannot be traced back to objective facts (Berger & Luckmann, 1966). In this sense technology is also not an objective, independent given, but shaped by social ideas and societal interpretations.

Actor-Network Theory

In the 1980s and 1990s, Latour became one of the main proponents of the actor-network theory (Latour, 2005); this is also attractive to scholars who reject social constructivism, since it can be combined with the idea that not all of technology is socially constructed. The social-constructive interpretation of this theory aims to develop a framework in which society and nature, or society and technology, are not separated. The idea of technology as a sociotechnical system implies that agent and tool form a unity, which cannot be explained completely by referring to one of the two elements in isolation. According to this idea, technological artifacts dispose over some form of agency and can be—to some extent—regarded as actants. This ascription of intentionality and agency to technical systems is, however, highly debated. The debate between realism and social constructivism has thus not been settled.

Philosophical Anthropology and the Philosophy of Technology

Research in philosophical anthropology peaked in early 20th-century Germany, discussed in the next section. But outside of anthropological discussions, the topic of technology became an important issue for philosophy, so in this brief overview, important contributions and themes of the continental and analytic tradition will be discussed next. Finally, more recent developments and topics in the philosophy of technology will be sketched that do not try to revitalize a philosophical anthropology, but that nevertheless do touch in one way or another on anthropological perspectives on technology.

Classical philosophical anthropology was mainly interested in understanding the essence of human nature and often draws specific attention to the role of technology. Important contributions came from Gehlen, Plessner, and Scheler during the first half of the 20th century. The attempt to link technology to a biological interpretation of man in Gehlen’s early works especially deserves attention. Given his biological constitution, man must be seen as deficient by nature ( Mängelwesen ), since he is not endowed with instinctive routines and is not adapted well to a specific natural environment, but rather is open to the world ( weltoffen ). He compensates for this deficiency with the help of his mental capacities and tool usage. Gehlen interprets human language and human institutions as relief mechanisms ( Entlastungen ) that help him to interpret and organize the plentitude of impressions (the sensory overload, Reizüberflutung ) that he is exposed to. Most technologies can thus be regarded to be either organ-amplification ( Organverstärkung ) or organ-replacement ( Organersatz ) (Gehlen, 1988). In Man in the Age of Technology (1980), Gehlen focuses more on sociological perspectives of technology. He identifies two essential cultural breaks marking principle changes in humans’ world interpretation and social organization, both of which are linked to technological developments: (1) the neolithic revolution of sedentism, marking the passage from a hunter’s culture to a society of agriculture and cattle breeding, and (2) the industrial revolution in modernity (Gehlen, 1980).

Scheler also analyzes man’s rational capacities from a biological perspective, but he concludes that a purely naturalistic approach does not render justice to our selfunderstanding. The human ways of sustaining life are from an often inefficient biological perspective. Therefore, it must be pointed out that the main function of human knowledge is not only to strategically ensure humans’ own survival, but also to be directed toward the discovery of moral values and toward the process of self-education ( Bildung ). Humans not only live in an environment, but also reflect on their place in the world—a capacity that marks a fundamental difference between humans and animals (Scheler, 1961).

This type of philosophical anthropology came to a certain end when the main interest of philosophers shifted from understanding “man” to understanding “society” during the 1960s. With the recent developments of sociobiology, philosophers have taken a renewed interest in the linkage between biological and cultural interpretations of man. Let us look at some tendencies of later research in the philosophy of technology.

If we look at a philosophical interpretation of technology, we find the first origins of a discipline of the philosophy of technology by the end of the 19th and the beginning of the 20th century (see Kapp, 1877, and Dessauer, 1933). During the first half of the 20th century, the philosophical analysis of technology can, roughly speaking, be divided into two main schools of thought: the continental, often skeptical approach, and the analytical, often optimistic approach . As with all such very generic typologies, this distinction likewise does not claim to be more than an approximation, while the general tendency of recent research seems precisely to be to overcome this gap and to aim for a convergence or crossfertilization of these two approaches. Therefore, what follows is an ideal-type distinction that tries to make some of the basic ideas of these two approaches more visible and aims at understanding their more general features.

The continental approach originally focused on a humanities-centered perspective on technology, its (mainly negative) consequences for society, and its rootedness in a problematic feature of human anthropology (the will to power), and finally tried to understand technology as such (its “essence”). The analytic approach, on the other hand, originally focused on a more science-based understanding of technology, its (mostly beneficial) potential for the progress of societies, and its rootedness in a rational (scientific) way to approach nature, and it finally tried to look not at technology as such but at specific problems or specific types of technologies.

In the continental philosophy of technology, technology is often interpreted as closely linked to a certain form of consciousness, a form of approaching nature (and also human interaction) from a perspective that is rooted in a scientific understanding of the world, which itself is rooted in the will to dominate nature. This approach is seen to replace or at least to endanger a value-based approach to reality. In this sense, Edmund Husserl’s phenomenology regards science and technology as a mere abstraction from the fullfledged real experience of the world we live in. In this way, the sphere of technical knowledge is limited and needs to be guided by value decisions, which do not have their basis in scientific or technical knowledge, but stem from our ethical knowledge of our life-world.

While technology is not at the center of Husserl’s interest, José Ortega y Gasset (1914/1961) was one of the first philosophers who aimed at a deeper understanding of the relation between human nature and technology. Rejecting Husserl’s later emphasis on the transcendental subject, he insists that human nature can only be understood by the formula “I am I plus my circumstances.” Philosophy can thus neither start from the isolated subject (as in idealism), nor can it interpret everything from the perspective of the material conditions (as in materialism). Rather, it must find a middle ground. The essence of humans is for Ortega not determined by nature; this distinguishes humans from plants or animals or from physical objects—all having a defined, specific given nature. Man must determine his own nature by himself by way of the creative imagination. Technology is interpreted as the material realization of this self-image; it is a projection of an inner invention into nature. According to Ortega, technology evolved in three phases: It started as a collection of accidental findings of means toward ends by pure chance. In a later state, these findings became traditions and skills that were passed on to the next generation. Modern technology marks a radical difference, since it is based on a systematic scientific approach, which forms the third phase. This approach, however, tends to become the dominant mode of thinking, so that man’s creative capacity for imagination (which is at the heart of man’s very essence) is in danger of being replaced or losing its importance (Ortega y Gasset, 1914/1961).

Martin Heidegger’s (1977) analysis of technology in his essay “The Question Concerning Technology” is also very influential. His philosophy aims at understanding the notion of being, which—so claims Heidegger—has been misinterpreted or neglected by traditional European philosophy. Since man is the only known being that can ask for the meaning of being, Heidegger’s analysis in Sein und Zeit starts from an interpretation of the existence of such a being ( Da-sein ). Even though his book is meant to be an exercise in philosophical (fundamental) ontology, it offers many anthropological insights about the specific human form of existence, in which the knowledge and the denial of one’s own mortality form essential human features.

In his later work, Heidegger (1977) understands technology as a specific form of disclosing reality. Asked for the essence of technology, people usually refer to it as a means to achieve an end (instrumental definition), or they define technology as an essential human activity (anthropological definition). Even though Heidegger admits that these definitions are “correct,” they do not disclose the essential truth about technology for two reasons. Essentially, (1) technology is not a tool for achieving an end, but rather the perspective under which everything that exists is seen only as a potential resource to achieve an (external) end. Furthermore, (2) this disclosure of reality is not a human-directed practice: Humans are driven objects rather than being themselves the active subjects. According to these conclusions, the instrumental and the anthropological definitions of technology do not capture the whole truth of technology. Let us look at these two points in turn, as follows:

  • The essence of technology lies, according to Heidegger, in its capacity to disclose reality ( entbergen ) under a very specific, limited perspective. This perspective reduces everything to a potential object for manipulation, a resource ( Bestand ) for further activity. Technology is thus a way to disclose something hidden. Following his analysis of the Greek word for truth ( aletheia ) as referring to something undisclosed, he sees thus a “truth” at work, under which reality presents itself as a mere collection of resources for external purposes, rid of all inner logic and teleology that was so prominent in traditional understandings of nature. Heidegger points at the different ways in which a river is seen by a poet in an artwork ( Kunst werk), on the one hand, and, on the other hand, in which the same river is seen by an engineer as a potential resource for energy generation in a power plant ( Kraft werk).
  • Heidegger then goes on to claim that opposed to the image of man being in control of technology and using it for his purposes, he should rather be seen as being provoked ( herausgefordert ) by this coming to pass. Heidegger clearly wants to reject the optimistic idea of “man being in control” through the help of modern technology and, rather, revert it to its opposite: man being driven by a force greater than himself. He calls this driving force the essence of technology, the en-framing ( Ge-stell ) that prompts humans to look at nature under the idea of its usability. In doing so, man is in highest danger, but not because of potential hazards or specific negative consequence of modern technology. The danger is, rather, that he loses sight of understanding nature in a different way and that he might finally end up understanding also himself and other humans only as potential “resources” or potential material for manipulation and instrumentalization. Heidegger suspects that art might be a potential antidote to this development: In Greek, techne originally encompassed also the production of beautiful objects in art. Thus, a deeper understanding of technology might reveal its relation to art and might point to the fact that art offers a potential answer to the challenge that modern technology poses to human self-understanding.

Certainly, Heidegger’s contribution to the modern philosophy of technology lies more in highlighting this essential dimension of technology as a threat, rather than in elaborating strategies to counter these inherent dangers. Heidegger’s article is arguably the single most influential essay written in the philosophy of technology, although his mannered, often dark language allows for different interpretations and often lacks the clarity of philosophical contributions from the analytical school. But the idea that “technology” and technological rationality is a limited form of looking at reality—one that is in strong need of a countervision, and that might further lead to a deformation of intersubjective human relations and that finally affects human self-understanding—has ever since been a prominent topic in different thinkers from Adorno and Marcuse to Jürgen Habermas, as illustrated earlier. This idea has often been linked with an ethical concern: Modern technology calls for new ethical guidelines, and despite some beneficial consequence, poses a potential threat to human existence. Much of this ethical debate about modern technology was triggered by its potential to radically destroy human life, be it through nuclear, biological, or chemical weapons or by consequences of environmental pollution and climate change.

Heidegger’s pupil Hans Jonas (1984) was one of the first philosophers to emphasize the need for a specific “ethics for the age of technology,” feeling that modern technology urges us to radically reconsider our ethical intuitions in order to meet the new challenges. Nevertheless, based on humans’ anthropological need to seek protection against nature, classical technology never fully reached this aim. Nature remained always more powerful than men, and the consequences of human actions were mostly not far-reaching. Traditional ethics could therefore focus on the “near and dear.” Modern technology, however, radically changes the picture: Its scope is unknown in premodern times; its consequences and potential dangers could be fatal, far-reaching, and irreversible. Focusing on the environmental problems of modern societies with, as the darkest perspective, the possible extinction of humankind, Jonas suggests broadening the scope of our ethical obligations: If our actions are more far-reaching than ever before in the history of humankind, we need to acquire a new ethical countervision. Jonas finds this remedy in the anthropological feature of our feelings of responsibility. Responsibility often expresses an asymmetrical relation, as in parents who feel responsible to care for their children. The old ethical intuition to derive obligations from the rights of free and conscious individuals, able to participate in argumentation and democratic decisions, seems to be too narrow to account for most environmental problems: Future generations are not yet born, animals and nature cannot in the same sense be regarded as having rights, as has been established in previous ethical approaches to the idea of universal human rights. But obligations may also stem from the idea of responsibility, from the idea that something has been given into our care.

Analytic philosophy is rooted in the quest for clear conceptualization, sound argumentation, and scientific precision. For early analytical philosophy in the Vienna Circle, the mathematical nature of scientific knowledge could serve as a role model for knowledge as such: hence, the need for and the extended usage of logical formalization within analytic philosophy. Skeptical of the quest to address the essence of things like “the technology” in general, analytic philosophers very often focus on concrete problems linked to very specific technologies. Even though many thinkers in the line of logical positivism thus greeted scientific knowledge as the highest form of knowledge, this did not always lead to an unbalanced embrace of technology. In Bertrand Russell (1951), we find a skeptical attitude toward the social benefits of technology, especially if it is linked with totalitarian ideology. Thus, he stresses the importance of democratic education; if placed in a democratic context and applied in well-defined careful steps, technology is, however, beneficial for progress in a way in which Karl Popper (1957) typically advertises as piecemeal social engineering. Important early contributions to an analytic philosophy of technology stem further from Mario Bunge (1979), whose ideas closely link to the program of logical empiricism and oppose the “romantic wailings about the alleged evils of technology” (p. 68).

Even though this distinction between humanities’ philosophy of technology and engineering’s philosophy of technology (Mitcham, 1994) marks the background of the philosophical discussion on technology in the early 20th century, the debate soon moved beyond this opposition. Three tendencies seem to be of importance.

First, continental philosophy was moving away from the attempt to come up with metaphysical, religious, or anthropological answers to the big questions. With the emergence of postmodernism, the alleged end of the “big stories” was proclaimed, thus making a metaphysical approach less fashionable. Appealing to ontology (as in Heidegger), to metaphysics, or to religious ideals (as in Jonas) seemed less promising. Even though early continental philosophy was very critical with regard to strategic rationality and technology, it has been criticized by postmodernism as not moving radically beyond the central modernistic Western ideal of a rational philosophical synthesis or universal world interpretation.

Second, the focus within the philosophy of technology moved toward a renewed interest in looking at concrete technologies and the challenges they pose for analytical and ethical reflection, a movement that has been called the empirical turn in the philosophy of technology (Kroes, 2001).

Third, different attempts were soon made to bridge the gap between the two camps. In post-world-war Germany, the Society of German Engineers (VDI) established a dialogue about the responsibilities of scientists and engineers, addressing topics and worries of the humanities. The experience of the massive and systematic use of technology for organized mass murder during the holocaust and the development of technology for modern warfare, including the development of the nuclear bomb, raised issues about the responsibilities of engineers. The debate of the VDI meetings resulted in a series of important publications on the philosophy of technology (Rapp, 1981); these must be recognized as an important attempt to synthesize different strands of philosophical thinking, even though it can be asked how far the VDI school was really successful in transcending its engineering-philosophical origins (Mitcham, 1994, p. 71).

Along a similar line, authors have tried to combine the phenomenological approach with American pragmatism, thus bridging insights of a more continental and a more analytical tradition. Common to phenomenology and pragmatism is the idea of the priority of praxis over theory and thus the tendency not to see technology as applied science but, rather, science as a purified or abstract form of (technological) praxis. Following the works of John Dewey, thinkers like Paul T. Durbin (1992), Larry Hickman (1990), and Don Ihde (1979) have tried to establish a pragmatist phenomenological approach to technology. The insights of Don Ihde that each technology either extends human bodily experience (e.g., the microscope) or calls for human interpretations (e.g., the thermometer) are of particular anthropological interest. If technology amplifies our experience, then it always does so at the cost of a reduction: In highlighting or amplifying certain aspects of reality, it makes invisible other aspects of this very same reality (as in an ultrasonic picture) (Ihde, 1979). The way technology thus “mediates” our interpretation of the world, and our actions within it, has been a further object of extended research (e.g., Verbeek, 2005).

A further attempt to bridge humanist and engineering tradition has been made by Carl Mitcham (1994), who nevertheless tries to defend the priority of the humanist perspective, but at the same time develops an analytic framework that should serve for further investigation within the philosophy of technology. He distinguishes among technology as object (tools), as type of knowledge, as activity, and as volition (expression of man’s intention or will). The 1980s and 1990s saw an increased interest, especially in the analyses of the first three aspects of this distinction.

With regard to the fourth aspect, ethical issues have been a central topic for many philosophers of technology, ranging from debates about the responsibility of scientists and engineers, medical and bioethics, business ethics, technology assessment, risk assessment and decision under uncertainty, to environmental ethics. Two of these fields are of particular interest from an anthropological perspective: In environmental ethics, those theories might shed light on anthropological questions seeking to interpret the environmental crisis as essentially rooted in human nature. It has been argued that it is a human tendency to value short-term (individual) interests more highly than long-term (collective) interests, thus putting a pessimistic neo-Hobbesian anthropology in the middle of the debate. According to Garrett Hardin (1968), it is this very human tendency (together with a mismatch in the growth of the human population that exceeds the growth of the supply of the food or other resources) that leads to the “tragedy of the commons.” Research in game theory and environmental sociobiology indicates the possibility of holding a more optimistic view of the development of cooperative strategies in humans (Axelrod, 1984), though the issue is still debated and there is room for a more pessimistic perspective, as has been defended early on by some sociobiologists (Dawkins, 1978) or recently by some philosophers (Gardiner, 2001).

In the ethical debate on transhumanism, finally, many links can be found to classical anthropological questions about the essence of man (e.g., Baillie, 2005; Fukuyama, 2004). The central debated question is whether it is morally allowed, forbidden, or even demanded from us to enhance our human capacities through new technologies, ranging from short-term nonevasive ways (like taking performanceenhancing drugs) to fundamental irreversible changes (like genetic engineering). While bioconservativists argue against an extended usage of enhancement technologies, transhumanists point to the potential benefits of these new options. It is reasonable to assume that these issues will be with us as technology advances and opens new possibilities to alter the human condition. This opens a radical new challenge to anthropology, which until recently dedicated itself to understanding the given human nature, while it now has to face the normative question of which we should choose as our future nature, once technology offers radical new options of changing human nature (e.g., as by slowing down or even stopping the process of aging). It seems that the anthropology of the future must take into consideration, more and more, normative claims and it must reach out to incorporate ethics to prepare itself for the challenges modern technology poses.

Looking at recent tendencies in research, it can be argued that the initial focus on linking technology with a universal, philosophical anthropological vision, also rooted in biological knowledge, was one of the key achievements of early philosophical anthropology in the works of Gehlen and others. What made these anthropologies remarkable was their attempt to bring together the different traditions of anthropological thought, ranging from philosophy to sociology and biology. A turn toward a more social perspective was established first by Gehlen himself, the Frankfurt school, and later STS studies, sometimes leading away from or even lacking both an underlying philosophical vision and an interest in our biological nature. Very recently, however, sociologists and philosophers have shown an increased interest in biology (as is visible in the ever-growing numbers of publications in sociobiology and the philosophy of biology). This increased attention has not yet led to a revival of an interest in the links between anthropology and technology. But in order to understand man—both in his evolutionary origins and (maybe even more) in his current historical situation—it seems to demand attention to man’s amazing capacity to develop technology.

It can reasonably be argued that what is thus needed is a new vision of how to synthesize the different fields of biological, social, and cultural anthropology. It seems that after the empirical turn to gather extended details over the biological and social aspects of technology, there is now a call for a new philosophical turn, seeking a new discourse synthesis. Many classical questions of anthropology will tend to remain unanswered, if academic research remains focused only on disciplinary perspectives, which always look at only a part of the whole picture. It is certainly true that man is a social animal, that he has biological roots and that he can ask ethical and philosophical questions about the good and about his place in this universe. The disciplinary separations in biology, sociology, and philosophy (to name just a few) tend, however, to distract from the fact that man in reality is a unity, meaning that a true answer to the most fundamental question of anthropology (What is man?) calls for a plausible combination of these approaches. To synthesize the different aspects of our knowledge about our own human nature is certainly far from being an easy task, but it seems more needed than ever.

But if this is not yet a big enough challenge, there is even a second aspect that makes the quest for a synthesis even more challenging. It seems that a new anthropological vision of humankind must answer a question that classical anthropology has not been dealing with: If technology soon allows us to alter our very nature, then we must know not only what the human condition is, but also what the human condition should be.

Ethics might again enter anthropological reflection, as has been hinted at already by early thinkers such as Scheler and Jonas. Recent attempts to place man in the middle of both a normative vision of ideals, on the one side, and against a profound overview of our descriptive knowledge about our essence, on the other side (as in the voluminous attempt at a synthesis in Hösle, 2004), deserve attention, as they might be the first steps toward a renewed synthetic anthropology that tries to bridge the gaps among the different disciplines. A deepened understanding of technology must be a central part of these efforts, since the way we use tools and produce artifacts is one of the remarkable features of humankind—a feature in much need of guidance by descriptive knowledge and ethical wisdom, especially in our age in which technology (of which humans have been the subject) is about to discover the condition humana as its potential object in a way more radical than ever before.

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Video generation models as world simulators.

We explore large-scale training of generative models on video data. Specifically, we train text-conditional diffusion models jointly on videos and images of variable durations, resolutions and aspect ratios. We leverage a transformer architecture that operates on spacetime patches of video and image latent codes. Our largest model, Sora, is capable of generating a minute of high fidelity video. Our results suggest that scaling video generation models is a promising path towards building general purpose simulators of the physical world.

More resources

  • View Sora overview

This technical report focuses on (1) our method for turning visual data of all types into a unified representation that enables large-scale training of generative models, and (2) qualitative evaluation of Sora’s capabilities and limitations. Model and implementation details are not included in this report.

Much prior work has studied generative modeling of video data using a variety of methods, including recurrent networks, [^1] [^2] [^3] generative adversarial networks, [^4] [^5] [^6] [^7] autoregressive transformers, [^8] [^9] and diffusion models. [^10] [^11] [^12] These works often focus on a narrow category of visual data, on shorter videos, or on videos of a fixed size. Sora is a generalist model of visual data—it can generate videos and images spanning diverse durations, aspect ratios and resolutions, up to a full minute of high definition video.

Turning visual data into patches

We take inspiration from large language models which acquire generalist capabilities by training on internet-scale data. [^13] [^14] The success of the LLM paradigm is enabled in part by the use of tokens that elegantly unify diverse modalities of text—code, math and various natural languages. In this work, we consider how generative models of visual data can inherit such benefits. Whereas LLMs have text tokens, Sora has visual patches . Patches have previously been shown to be an effective representation for models of visual data. [^15] [^16] [^17] [^18] We find that patches are a highly-scalable and effective representation for training generative models on diverse types of videos and images.

Figure Patches

At a high level, we turn videos into patches by first compressing videos into a lower-dimensional latent space, [^19] and subsequently decomposing the representation into spacetime patches.

Video compression network

We train a network that reduces the dimensionality of visual data. [^20] This network takes raw video as input and outputs a latent representation that is compressed both temporally and spatially. Sora is trained on and subsequently generates videos within this compressed latent space. We also train a corresponding decoder model that maps generated latents back to pixel space.

Spacetime latent patches

Given a compressed input video, we extract a sequence of spacetime patches which act as transformer tokens. This scheme works for images too since images are just videos with a single frame. Our patch-based representation enables Sora to train on videos and images of variable resolutions, durations and aspect ratios. At inference time, we can control the size of generated videos by arranging randomly-initialized patches in an appropriately-sized grid.

Scaling transformers for video generation

Sora is a diffusion model [^21] [^22] [^23] [^24] [^25] ; given input noisy patches (and conditioning information like text prompts), it’s trained to predict the original “clean” patches. Importantly, Sora is a diffusion transformer . [^26] Transformers have demonstrated remarkable scaling properties across a variety of domains, including language modeling, [^13] [^14] computer vision, [^15] [^16] [^17] [^18] and image generation. [^27] [^28] [^29]

Figure Diffusion

In this work, we find that diffusion transformers scale effectively as video models as well. Below, we show a comparison of video samples with fixed seeds and inputs as training progresses. Sample quality improves markedly as training compute increases.

Variable durations, resolutions, aspect ratios

Past approaches to image and video generation typically resize, crop or trim videos to a standard size—e.g., 4 second videos at 256x256 resolution. We find that instead training on data at its native size provides several benefits.

Sampling flexibility

Sora can sample widescreen 1920x1080p videos, vertical 1080x1920 videos and everything inbetween. This lets Sora create content for different devices directly at their native aspect ratios. It also lets us quickly prototype content at lower sizes before generating at full resolution—all with the same model.

Improved framing and composition

We empirically find that training on videos at their native aspect ratios improves composition and framing. We compare Sora against a version of our model that crops all training videos to be square, which is common practice when training generative models. The model trained on square crops (left) sometimes generates videos where the subject is only partially in view. In comparison, videos from Sora (right) have improved framing.

Language understanding

Training text-to-video generation systems requires a large amount of videos with corresponding text captions. We apply the re-captioning technique introduced in DALL·E 3 [^30] to videos. We first train a highly descriptive captioner model and then use it to produce text captions for all videos in our training set. We find that training on highly descriptive video captions improves text fidelity as well as the overall quality of videos.

Similar to DALL·E 3, we also leverage GPT to turn short user prompts into longer detailed captions that are sent to the video model. This enables Sora to generate high quality videos that accurately follow user prompts.

Prompting with images and videos

All of the results above and in our landing page show text-to-video samples. But Sora can also be prompted with other inputs, such as pre-existing images or video. This capability enables Sora to perform a wide range of image and video editing tasks—creating perfectly looping video, animating static images, extending videos forwards or backwards in time, etc.

Animating DALL·E images

Sora is capable of generating videos provided an image and prompt as input. Below we show example videos generated based on DALL·E 2 [^31] and DALL·E 3 [^30] images.

technical research paper pdf

Extending generated videos

Sora is also capable of extending videos, either forward or backward in time. Below are four videos that were all extended backward in time starting from a segment of a generated video. As a result, each of the four videos starts different from the others, yet all four videos lead to the same ending.

We can use this method to extend a video both forward and backward to produce a seamless infinite loop.

Video-to-video editing

Diffusion models have enabled a plethora of methods for editing images and videos from text prompts. Below we apply one of these methods, SDEdit, [^32] to Sora. This technique enables Sora to transform  the styles and environments of input videos zero-shot.

Connecting videos

We can also use Sora to gradually interpolate between two input videos, creating seamless transitions between videos with entirely different subjects and scene compositions. In the examples below, the videos in the center interpolate between the corresponding videos on the left and right.

Image generation capabilities

Sora is also capable of generating images. We do this by arranging patches of Gaussian noise in a spatial grid with a temporal extent of one frame. The model can generate images of variable sizes—up to 2048x2048 resolution.

technical research paper pdf

Emerging simulation capabilities

We find that video models exhibit a number of interesting emergent capabilities when trained at scale. These capabilities enable Sora to simulate some aspects of people, animals and environments from the physical world. These properties emerge without any explicit inductive biases for 3D, objects, etc.—they are purely phenomena of scale.

3D consistency. Sora can generate videos with dynamic camera motion. As the camera shifts and rotates, people and scene elements move consistently through three-dimensional space.

Long-range coherence and object permanence. A significant challenge for video generation systems has been maintaining temporal consistency when sampling long videos. We find that Sora is often, though not always, able to effectively model both short- and long-range dependencies. For example, our model can persist people, animals and objects even when they are occluded or leave the frame. Likewise, it can generate multiple shots of the same character in a single sample, maintaining their appearance throughout the video.

Interacting with the world. Sora can sometimes simulate actions that affect the state of the world in simple ways. For example, a painter can leave new strokes along a canvas that persist over time, or a man can eat a burger and leave bite marks.

Simulating digital worlds. Sora is also able to simulate artificial processes–one example is video games. Sora can simultaneously control the player in Minecraft with a basic policy while also rendering the world and its dynamics in high fidelity. These capabilities can be elicited zero-shot by prompting Sora with captions mentioning “Minecraft.”

These capabilities suggest that continued scaling of video models is a promising path towards the development of highly-capable simulators of the physical and digital world, and the objects, animals and people that live within them.

Sora currently exhibits numerous limitations as a simulator. For example, it does not accurately model the physics of many basic interactions, like glass shattering. Other interactions, like eating food, do not always yield correct changes in object state. We enumerate other common failure modes of the model—such as incoherencies that develop in long duration samples or spontaneous appearances of objects—in our landing page .

We believe the capabilities Sora has today demonstrate that continued scaling of video models is a promising path towards the development of capable simulators of the physical and digital world, and the objects, animals and people that live within them.

  • Bill Peebles
  • Connor Holmes
  • David Schnurr
  • Troy Luhman
  • Eric Luhman
  • Clarence Ng
  • Aditya Ramesh

Acknowledgments

Please cite as Brooks, Peebles, et al., and use the following BibTeX for citation:  https://openai.com/bibtex/videoworldsimulators2024.bib

Our next-generation model: Gemini 1.5

Feb 15, 2024

The model delivers dramatically enhanced performance, with a breakthrough in long-context understanding across modalities.

SundarPichai_2x.jpg

A note from Google and Alphabet CEO Sundar Pichai:

Last week, we rolled out our most capable model, Gemini 1.0 Ultra, and took a significant step forward in making Google products more helpful, starting with Gemini Advanced . Today, developers and Cloud customers can begin building with 1.0 Ultra too — with our Gemini API in AI Studio and in Vertex AI .

Our teams continue pushing the frontiers of our latest models with safety at the core. They are making rapid progress. In fact, we’re ready to introduce the next generation: Gemini 1.5. It shows dramatic improvements across a number of dimensions and 1.5 Pro achieves comparable quality to 1.0 Ultra, while using less compute.

This new generation also delivers a breakthrough in long-context understanding. We’ve been able to significantly increase the amount of information our models can process — running up to 1 million tokens consistently, achieving the longest context window of any large-scale foundation model yet.

Longer context windows show us the promise of what is possible. They will enable entirely new capabilities and help developers build much more useful models and applications. We’re excited to offer a limited preview of this experimental feature to developers and enterprise customers. Demis shares more on capabilities, safety and availability below.

Introducing Gemini 1.5

By Demis Hassabis, CEO of Google DeepMind, on behalf of the Gemini team

This is an exciting time for AI. New advances in the field have the potential to make AI more helpful for billions of people over the coming years. Since introducing Gemini 1.0 , we’ve been testing, refining and enhancing its capabilities.

Today, we’re announcing our next-generation model: Gemini 1.5.

Gemini 1.5 delivers dramatically enhanced performance. It represents a step change in our approach, building upon research and engineering innovations across nearly every part of our foundation model development and infrastructure. This includes making Gemini 1.5 more efficient to train and serve, with a new Mixture-of-Experts (MoE) architecture.

The first Gemini 1.5 model we’re releasing for early testing is Gemini 1.5 Pro. It’s a mid-size multimodal model, optimized for scaling across a wide-range of tasks, and performs at a similar level to 1.0 Ultra , our largest model to date. It also introduces a breakthrough experimental feature in long-context understanding.

Gemini 1.5 Pro comes with a standard 128,000 token context window. But starting today, a limited group of developers and enterprise customers can try it with a context window of up to 1 million tokens via AI Studio and Vertex AI in private preview.

As we roll out the full 1 million token context window, we’re actively working on optimizations to improve latency, reduce computational requirements and enhance the user experience. We’re excited for people to try this breakthrough capability, and we share more details on future availability below.

These continued advances in our next-generation models will open up new possibilities for people, developers and enterprises to create, discover and build using AI.

Context lengths of leading foundation models

Highly efficient architecture

Gemini 1.5 is built upon our leading research on Transformer and MoE architecture. While a traditional Transformer functions as one large neural network, MoE models are divided into smaller "expert” neural networks.

Depending on the type of input given, MoE models learn to selectively activate only the most relevant expert pathways in its neural network. This specialization massively enhances the model’s efficiency. Google has been an early adopter and pioneer of the MoE technique for deep learning through research such as Sparsely-Gated MoE , GShard-Transformer , Switch-Transformer, M4 and more.

Our latest innovations in model architecture allow Gemini 1.5 to learn complex tasks more quickly and maintain quality, while being more efficient to train and serve. These efficiencies are helping our teams iterate, train and deliver more advanced versions of Gemini faster than ever before, and we’re working on further optimizations.

Greater context, more helpful capabilities

An AI model’s “context window” is made up of tokens, which are the building blocks used for processing information. Tokens can be entire parts or subsections of words, images, videos, audio or code. The bigger a model’s context window, the more information it can take in and process in a given prompt — making its output more consistent, relevant and useful.

Through a series of machine learning innovations, we’ve increased 1.5 Pro’s context window capacity far beyond the original 32,000 tokens for Gemini 1.0. We can now run up to 1 million tokens in production.

This means 1.5 Pro can process vast amounts of information in one go — including 1 hour of video, 11 hours of audio, codebases with over 30,000 lines of code or over 700,000 words. In our research, we’ve also successfully tested up to 10 million tokens.

Complex reasoning about vast amounts of information

1.5 Pro can seamlessly analyze, classify and summarize large amounts of content within a given prompt. For example, when given the 402-page transcripts from Apollo 11’s mission to the moon, it can reason about conversations, events and details found across the document.

Reasoning across a 402-page transcript: Gemini 1.5 Pro Demo

Gemini 1.5 Pro can understand, reason about and identify curious details in the 402-page transcripts from Apollo 11’s mission to the moon.

Better understanding and reasoning across modalities

1.5 Pro can perform highly-sophisticated understanding and reasoning tasks for different modalities, including video. For instance, when given a 44-minute silent Buster Keaton movie , the model can accurately analyze various plot points and events, and even reason about small details in the movie that could easily be missed.

Multimodal prompting with a 44-minute movie: Gemini 1.5 Pro Demo

Gemini 1.5 Pro can identify a scene in a 44-minute silent Buster Keaton movie when given a simple line drawing as reference material for a real-life object.

Relevant problem-solving with longer blocks of code

1.5 Pro can perform more relevant problem-solving tasks across longer blocks of code. When given a prompt with more than 100,000 lines of code, it can better reason across examples, suggest helpful modifications and give explanations about how different parts of the code works.

Problem solving across 100,633 lines of code | Gemini 1.5 Pro Demo

Gemini 1.5 Pro can reason across 100,000 lines of code giving helpful solutions, modifications and explanations.

Enhanced performance

When tested on a comprehensive panel of text, code, image, audio and video evaluations, 1.5 Pro outperforms 1.0 Pro on 87% of the benchmarks used for developing our large language models (LLMs). And when compared to 1.0 Ultra on the same benchmarks, it performs at a broadly similar level.

Gemini 1.5 Pro maintains high levels of performance even as its context window increases. In the Needle In A Haystack (NIAH) evaluation, where a small piece of text containing a particular fact or statement is purposely placed within a long block of text, 1.5 Pro found the embedded text 99% of the time, in blocks of data as long as 1 million tokens.

Gemini 1.5 Pro also shows impressive “in-context learning” skills, meaning that it can learn a new skill from information given in a long prompt, without needing additional fine-tuning. We tested this skill on the Machine Translation from One Book (MTOB) benchmark, which shows how well the model learns from information it’s never seen before. When given a grammar manual for Kalamang , a language with fewer than 200 speakers worldwide, the model learns to translate English to Kalamang at a similar level to a person learning from the same content.

As 1.5 Pro’s long context window is the first of its kind among large-scale models, we’re continuously developing new evaluations and benchmarks for testing its novel capabilities.

For more details, see our Gemini 1.5 Pro technical report .

Extensive ethics and safety testing

In line with our AI Principles and robust safety policies, we’re ensuring our models undergo extensive ethics and safety tests. We then integrate these research learnings into our governance processes and model development and evaluations to continuously improve our AI systems.

Since introducing 1.0 Ultra in December, our teams have continued refining the model, making it safer for a wider release. We’ve also conducted novel research on safety risks and developed red-teaming techniques to test for a range of potential harms.

In advance of releasing 1.5 Pro, we've taken the same approach to responsible deployment as we did for our Gemini 1.0 models, conducting extensive evaluations across areas including content safety and representational harms, and will continue to expand this testing. Beyond this, we’re developing further tests that account for the novel long-context capabilities of 1.5 Pro.

Build and experiment with Gemini models

We’re committed to bringing each new generation of Gemini models to billions of people, developers and enterprises around the world responsibly.

Starting today, we’re offering a limited preview of 1.5 Pro to developers and enterprise customers via AI Studio and Vertex AI . Read more about this on our Google for Developers blog and Google Cloud blog .

We’ll introduce 1.5 Pro with a standard 128,000 token context window when the model is ready for a wider release. Coming soon, we plan to introduce pricing tiers that start at the standard 128,000 context window and scale up to 1 million tokens, as we improve the model.

Early testers can try the 1 million token context window at no cost during the testing period, though they should expect longer latency times with this experimental feature. Significant improvements in speed are also on the horizon.

Developers interested in testing 1.5 Pro can sign up now in AI Studio, while enterprise customers can reach out to their Vertex AI account team.

Learn more about Gemini’s capabilities and see how it works .

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OpenAI teases an amazing new generative video model called Sora

The firm is sharing Sora with a small group of safety testers but the rest of us will have to wait to learn more.

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OpenAI has built a striking new generative video model called Sora that can take a short text description and turn it into a detailed, high-definition film clip up to a minute long.

Based on four sample videos that OpenAI shared with MIT Technology Review ahead of today’s announcement, the San Francisco–based firm has pushed the envelope of what’s possible with text-to-video generation (a hot new research direction that we flagged as a trend to watch in 2024 ).

“We think building models that can understand video, and understand all these very complex interactions of our world, is an important step for all future AI systems,” says Tim Brooks, a scientist at OpenAI.

But there’s a disclaimer. OpenAI gave us a preview of Sora (which means sky in Japanese) under conditions of strict secrecy. In an unusual move, the firm would only share information about Sora if we agreed to wait until after news of the model was made public to seek the opinions of outside experts. [Editor’s note: We’ve updated this story with outside comment below.] OpenAI has not yet released a technical report or demonstrated the model actually working. And it says it won’t be releasing Sora anytime soon. [ Update: OpenAI has now shared more technical details on its website.]

The first generative models that could produce video from snippets of text appeared in late 2022. But early examples from Meta , Google, and a startup called Runway were glitchy and grainy. Since then, the tech has been getting better fast. Runway’s gen-2 model, released last year, can produce short clips that come close to matching big-studio animation in their quality. But most of these examples are still only a few seconds long.  

The sample videos from OpenAI’s Sora are high-definition and full of detail. OpenAI also says it can generate videos up to a minute long. One video of a Tokyo street scene shows that Sora has learned how objects fit together in 3D: the camera swoops into the scene to follow a couple as they walk past a row of shops.

OpenAI also claims that Sora handles occlusion well. One problem with existing models is that they can fail to keep track of objects when they drop out of view. For example, if a truck passes in front of a street sign, the sign might not reappear afterward.  

In a video of a papercraft underwater scene, Sora has added what look like cuts between different pieces of footage, and the model has maintained a consistent style between them.

It’s not perfect. In the Tokyo video, cars to the left look smaller than the people walking beside them. They also pop in and out between the tree branches. “There’s definitely some work to be done in terms of long-term coherence,” says Brooks. “For example, if someone goes out of view for a long time, they won’t come back. The model kind of forgets that they were supposed to be there.”

Impressive as they are, the sample videos shown here were no doubt cherry-picked to show Sora at its best. Without more information, it is hard to know how representative they are of the model’s typical output.   

It may be some time before we find out. OpenAI’s announcement of Sora today is a tech tease, and the company says it has no current plans to release it to the public. Instead, OpenAI will today begin sharing the model with third-party safety testers for the first time.

In particular, the firm is worried about the potential misuses of fake but photorealistic video . “We’re being careful about deployment here and making sure we have all our bases covered before we put this in the hands of the general public,” says Aditya Ramesh, a scientist at OpenAI, who created the firm’s text-to-image model DALL-E .

But OpenAI is eyeing a product launch sometime in the future. As well as safety testers, the company is also sharing the model with a select group of video makers and artists to get feedback on how to make Sora as useful as possible to creative professionals. “The other goal is to show everyone what is on the horizon, to give a preview of what these models will be capable of,” says Ramesh.

To build Sora, the team adapted the tech behind DALL-E 3, the latest version of OpenAI’s flagship text-to-image model. Like most text-to-image models, DALL-E 3 uses what’s known as a diffusion model. These are trained to turn a fuzz of random pixels into a picture.

Sora takes this approach and applies it to videos rather than still images. But the researchers also added another technique to the mix. Unlike DALL-E or most other generative video models, Sora combines its diffusion model with a type of neural network called a transformer.

Transformers are great at processing long sequences of data, like words. That has made them the special sauce inside large language models like OpenAI’s GPT-4 and Google DeepMind’s Gemini . But videos are not made of words. Instead, the researchers had to find a way to cut videos into chunks that could be treated as if they were. The approach they came up with was to dice videos up across both space and time. “It’s like if you were to have a stack of all the video frames and you cut little cubes from it,” says Brooks.

The transformer inside Sora can then process these chunks of video data in much the same way that the transformer inside a large language model processes words in a block of text. The researchers say that this let them train Sora on many more types of video than other text-to-video models, varied in terms of resolution, duration, aspect ratio, and orientation. “It really helps the model,” says Brooks. “That is something that we’re not aware of any existing work on.”

“From a technical perspective it seems like a very significant leap forward,” says Sam Gregory, executive director at Witness, a human rights organization that specializes in the use and misuse of video technology. “But there are two sides to the coin,” he says. “The expressive capabilities offer the potential for many more people to be storytellers using video. And there are also real potential avenues for misuse.” 

OpenAI is well aware of the risks that come with a generative video model. We are already seeing the large-scale misuse of deepfake images . Photorealistic video takes this to another level.

Gregory notes that you could use technology like this to misinform people about conflict zones or protests. The range of styles is also interesting, he says. If you could generate shaky footage that looked like something shot with a phone, it would come across as more authentic.

The tech is not there yet, but generative video has gone from zero to Sora in just 18 months. “We’re going to be entering a universe where there will be fully synthetic content, human-generated content and a mix of the two,” says Gregory.

The OpenAI team plans to draw on the safety testing it did last year for DALL-E 3. Sora already includes a filter that runs on all prompts sent to the model that will block requests for violent, sexual, or hateful images, as well as images of known people. Another filter will look at frames of generated videos and block material that violates OpenAI’s safety policies.

OpenAI says it is also adapting a fake-image detector developed for DALL-E 3 to use with Sora. And the company will embed industry-standard C2PA tags , metadata that states how an image was generated, into all of Sora’s output. But these steps are far from foolproof. Fake-image detectors are hit-or-miss. Metadata is easy to remove, and most social media sites strip it from uploaded images by default.  

“We’ll definitely need to get more feedback and learn more about the types of risks that need to be addressed with video before it would make sense for us to release this,” says Ramesh.

Brooks agrees. “Part of the reason that we’re talking about this research now is so that we can start getting the input that we need to do the work necessary to figure out how it could be safely deployed,” he says.

Update 2/15: Comments from Sam Gregory were added .

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