(ed: Update: After Piotr posted the comment, Moshe responded, and then Piotr responded again. Please visit the article page to read the exchange between the two).
In a recent CACM editorial, Dr. Vardi addresses what he calls a "a moral hazard of complexity-theoretic assumptions" and "a growing gap between current theory and practice of complexity and algorithms". Given that the article mentions a paper that I am a co-author of [ "Edit Distance Cannot Be Computed in Strongly Subquadratic Time (unless SETH is false)", STOC'15], I believe it is appropriate for me to respond. In short, I believe that much of the analysis in the article stems from a confusion between press coverage and the actual scientific inquiry. Indeed, it is unclear whether Dr. Vardi addresses what he believes to be a "media" phenomenon (how journalists describe scientific developments to a broad public) or a "scientific" phenomenon (how and why the scientists make and use assumptions in their research and describe them in their papers). In order to avoid conflating these two issues, I will address them one by one, focusing on our paper as an example.
- Media aspects: The bulk of the editorial is focused on some of the press coverage describing recent scientific developments in algorithms and complexity. In particular, Dr. Vardi mentions the title of a Boston Globe article covering our work ("For 40 Years, Computer Scientists Looked for a Solution that Doesn't Exist.") . As I already discussed this elsewhere (https://liorpachter.wordpress.com/2015/08/14/in-biology-n-does-not-go-to-infinity/#comment-4792 ), I completely agree that the title and some other parts of the article leave a lot to be desired. Among many things, the conditional aspects of the result are discussed only at the end of the article, and therefore are easy to miss. At the same time, I believe some perspective is needed. The inaccuracy or confusion in popular reporting of scientific results is an unfortunate but common and longstanding phenomenon (see e.g., this account https://lpsdp.files.wordpress.com/2011/10/ellipsoid-stories.pdf of press coverage of the famous Khachiyan's linear programming algorithm in the 1970s). There are many reasons for this. Perhaps the chief one is the cultural gap between the press and the scientists, where journalists emphasize accessibility and newsworthiness while scientists emphasize precision. As a result, simplification in scientific reporting is a necessity, and the risk of oversimplification, inaccuracy or incorrectness is high. Fortunately, more time and effort spent on both sides can lead to more thorough and nuanced articles (e.g., see https://www.quantamagazine.org/20150929-edit-distance-computational-complexity/ ). Given that the coverage of algorithms and complexity results in popular press is growing, I believe that, in time, both scientists and journalists will gain valuable experience in this process.
- Scientific aspects: Dr. Vardi also raises some scientific points. In particular:
- Dr. Vardi is critical of the title of our paper: "Edit Distance Cannot Be Computed in Strongly Subquadratic Time (unless SETH is false).". I can only say that, given that we are stating the assumption explicitly in the title, in the abstract, in the introduction, and in the main body of the paper, I believe the title and the paper accurately represents its contribution.
- Dr. Vardi is critical of the validity of SETH as a hardness assumption: this question is indeed a subject of a robust discussion and investigation (see e.g., the aforementioned Quanta article). The best guess of mine and of most of the people I discussed this with is that the assumption is true. However, this is far from a universal opinion. Quantifying the level of support for this conjecture would be an interesting project, perhaps along the lines of similar efforts concerning the P vs. NP conjecture ( https://www.cs.umd.edu/~gasarch/papers/poll2012.pdf ). In any case, it is crucial to strengthen the framework by relying on weaker assumptions, or replacing one-way reductions with equivalences; both are subjects of ongoing research. However, even the existing developments have already led to concrete benefits. For example, failed attempts to prove conditional hardness of certain problems have led to better algorithms for those tasks.
Finally, let me point out that one of the key motivations for this line of research is precisely the strengthening of the relationship between theory and practice in complexity and algorithms, a goal that Dr. Vardi refers to as an important challenge. Specifically, this research provides a framework for establishing evidence that certain computational questions cannot be solved within concrete (e.g., sub-quadratic) polynomial time bounds. In general, I believe that a careful examination of the developments in algorithms and complexity over the last decade would show that the gap between theory and practice is shrinking, not growing. But that is a topic for another discussion.