An HPL Audioplay World Premiere!

As if stealing our data, copyrighted material including books, music, and films, and quite possibly many of our jobs, was not enough, AI seems to be increasingly stealing our hearts as well. Recent surveys have found that almost a quarter of Americans have engaged in romantic interactions with AI chatbots, while, according to another survey,

Emanuele Costa is Assistant Professor of Philosophy at Vanderbilt University in Nashville, Tennessee. His work is at the intersection of early modern philosophy and metaphysics, and he has recently published his first monograph, The Structure of Spinoza’s World (Oxford University Press, 2025). What are you most proud of in your professional life? I have recently

__Share __Tweet __Share We are excited to announce that Alison Springle will be joining us as one of our new associate editors! Alison is an Assistant Professor at University of Miami, and works on a wide range of topics in philosophy of mind and science. For the blog, Alison will be working on promoting new collaborations and content-types for the blog, as well as contributing to our book symposia series. We’re thrilled with how the new editorial team is shaping up, and there is more to come! See an intro from Alison below! _“Dear Readers,_ _It is an honor to be serving as an associate editor for The Brains Blog, which has been a most helpful resource to me and so many others. My plans for this role are to host several book symposia, to attempt some collaborative projects involving other public-facing academic media outlets, and to attempt to secure some funding to support these and potentially other Brains Blog projects._ _A bit more about me: As Dan notes, I’m an Assistant Professor of Philosophy at the University of Miami (FL). The organizing theme of my research —which spans many topics in philosophy of mind, science, perception, action, memory, ethics of knowing and epistemology— is how we understand representation. My “radical” view is that representation is constituted by intentional activity, and different species of representation by different species of representational activities. Along with many paper projects, I’m working on two books. One develops the radical view I just described. The other, in certain respects, applies it in the form of a social-critical approach in the metaphysics of memory which I’m developing/co-authoring with Seth Goldwasser._ _I’m excited to be serving this community!”_ _~Alison_

The term networking has become ubiquitous today. From networking platforms such as LinkedIn to networking events at conferences, the practice of networking has become a mainstay of academic life. Attitudes towards networking seem to differ widely. Whereas some enjoy opportunities for networking and meeting new colleagues, some find the idea of networking dreadful. Recently, I

Last time, I setup a question about Foucault’s anti-humanism. His comments in Order of Things are famous, and the recent publication of a 1954-5 lecture course he delivered at Lille as La question…

What’s the latest data about philosophy graduate programs? In the following guest post, Carolyn Dicey Jennings, professor of philosophy at UC Merced and director of Academic Philosophy Data and Analysis (APDA), shares results from the latest APDA survey, completed at the end of 2025. It includes information about job placement, program ratings, department climate, and more. (A version of this post also appeared at the APDA Blog.) * * * [Laurie Frick, “People Connections” (detail)] ## Placement, Program Ratings, Student Comments, and Keywords: an APDA Update ### Permanent academic placement for philosophy PhDs looks steady, while program and climate ratings are up. _by Carolyn Dicey Jennings_ Our last data update was in 2024, covering 2013–2023 graduates and select results from the 2023 survey. This data update includes 2014–2024 graduates and select results from the 2025 survey, which was completed December 31st. More information on the survey, both methodology and results, will be released over the next few months. This post mainly provides information about the data gathering process, placement findings, and comparisons with the last data update. **In sum: permanent academic placement looks steady, while program and climate ratings are up.** Changes to specific programs’ placement rates and program/climate ratings are discussed below. The first thing to note is the range of included programs. We aim to include all primarily English-language philosophy PhD programs in the world that have available graduation and/or employment data. This year we were able to cover 149 programs, provided in a table below (along with the date that we completed our data checks and a link to the publicly available APDA data). Five of these are new: McMaster University, Universite de Montreal, University of Auckland, University of Bristol, University of Hong Kong, and Uppsala University. There are 65 other programs that we contact but are not yet able to cover due to data availability, which are listed below*****. Data checks were a team effort, and we benefitted from the assistance of three undergraduate research assistants: Rocco Perez, Danna Valenzuela, and Devyn Williams. Notes on how we collect the information is available here and on the about page. The 2024 data update was made available through a Looker dashboard hosted on our website that draws from a Google Sheet. Those who would like to compare the current Looker dashboard with past data can use the old Looker dashboard or that sheet. Note that the data on the Google sheet are organized in a somewhat unorthodox way, with multiple lines per program in most cases (depending on the number of keywords and comments); this has to do with limitations of the Looker software. The most recent data is available on a different Google Sheet, here. ### PLACEMENT Initial comparisons on**** employment**** show that there is overall consistency across the two years, as well as with prior reports. We reported on 6,407 graduates (2013–2023) in 2024, as compared to 6,314 (2014–2024) now—a slight drop. We counted a graduate as in a permanent academic job if they had any record of such a job in our database, but temporary or nonacademic depending on which was more recent; unknown was used for graduates with no employment record. Numbers were largely consistent across the two time periods: 41% of those in the 2024 update were in permanent academic jobs, compared to 40% now; 38% were in temporary academic jobs, compared to 34% now; 12% were in nonacademic jobs, compared to 17% now; and 10% are unknown for both data sets. Yet, many have the sense that it is harder for more recent graduates to find permanent academic employment. The Sankey graph below shows a subset of employment outcomes for the most recent graduates (2019–2024) in comparison to older graduates (2014–2019) in this ten-year period, demonstrating a difference in permanent academic placement between more recent and older graduates. A Sankey graph showing that a smaller proportion of recent graduates are in permanent academic employment, and more are unknown or in temporary academic employment However, despite this apparent difference, **more recent graduates do not seem to have a harder time finding permanent academic employment than past graduates** , relative to their years out of graduate school. I compared the most recent data with the 2022 APDA report (published in Metaphilosophy), looking at both the graduates of just the past 5 years and the full 10-year span (as reported at the time, in order to compare years out of graduate school for different time periods). While in 2025 34% of the most recent graduates had permanent academic jobs, in 2022 only 30% of the most recent graduates had permanent academic jobs (see the table below). A table comparing employment outcomes for graduates of the past 5 and 10 years, as noted in 2022 (red font, white background) and 2025 (black font, gray background). It thus does not look like the most recent graduates are having a harder time finding permanent academic employment, at least in comparison to this earlier group. A caveat is that data gathering has become more challenging in recent years and our database is not as complete for 2024 graduates as it was for earlier years (see the chart below that compares the database numbers to those of the Survey of Earned Doctorates). Comparison of APDA database numbers with the SED numbers, showing that there is a drop in United States and international graduates in APDA in recent years, but not for SED (light gray line). This indicates that we are missing some recent data. I also looked at the nonacademic placement information to see if the uptick in nonacademic jobs in the most recent data is due to those who are not placed in permanent academic jobs; that is, I wondered if older graduates would be more likely to be in nonacademic jobs. As you can see in the table above, there is not much difference between recent and longer term graduates for either dataset. In terms of the types of nonacademic jobs philosophy graduates find, those continue to be largely in the education sector (e.g. alt-ac careers and high school teaching), followed by technology, consultancy, law, health, government, and the arts (see the chart below). A chart listing nonacademic sectors with the proportion of graduates in nonacademic jobs in that sector. It also continues to be the case that the plurality of those with permanent academic employment find it without taking intermediary temporary jobs. This chart shows the proportion of 2014–2024 graduates with permanent academic employment and between 0 and 5 temporary academic jobs to illustrate this. A chart showing that the proportion of those with a permanent academic job and no temporary academic job is higher than for each additional temporary academic job. In terms of specific graduate programs, a number of programs have permanent academic placement rates (the number in permanent academic jobs divided by all graduates) that are **higher** **than in 2024**. Those with at least 10 graduates and a 10% or greater increase in permanent academic placement rate are: Arizona State University (now 60%) University College London (40%) University of Alberta (58%) University of Arkansas (17%) University of Calgary (31%) University of Oklahoma (48%) University of York (44%). Others have **lower** permanent academic placement rates compared to 2024: Australian National University (now 24%) Indiana University Bloomington (HPS, 25%) University of Cambridge (HPS, 36%). In 2026, **top 10 for permanent academic placement** are: University of Southern California (39 of 54 graduates; 72%) University of Virginia (26/36 = 72%) Baylor University (32/45 = 71%) University of Cincinnati (17/24 = 71%) Boston University (29/41 = 71%) Rutgers University (42/61 = 69%) Dalhousie University (4/6 = 67%) University of Michigan (40/60 = 67%) Yale University (38/58 = 66%) New York University (35/54 = 65%). 71 programs had permanent academic placement rates over 40% (the overall rate for graduates across all programs), which are listed in order of placement rate below******. ### PROGRAM RATING In the 2025 survey, as in past years, we asked participants whether they would recommend their program to a prospective student, with participants including current students and graduates of the past 10 years (“How likely would you be to recommend this program to prospective PhD students?”). The mean for 2025 participants (n=1185) was 4.15, an increase from the mean for 2023 participants (n=1091), which was 3.98 (p<.001). For the 2025 survey, **top 10 for overall program ratings** are: University of Southern California (n=32, 4.9), University of California San Diego (n=8, 4.9), Baylor University (n=15, 4.9), Duke University (n=5, 4.8), University of Kansas (n=5, 4.8), University of California Riverside (n=5, 4.8), Australian National University (n=23, 4.8), University of Pennsylvania (n=17, 4.8), University of Sheffield (n=8, 4.8), and University of Wisconsin Madison (n=18, 4.7). 36 programs had ratings over 4.15, listed below*******. A number of programs had **higher ratings this year**. Including just those with 5 or more participants and more than .5 difference between the scores, Temple University (n=5, mean of 3.6, +1.1 from 2023), University of Hawai’i at Manoa (n=5, 3.6, +1), University of Wisconsin Madison (n=18, 4.7, +.8), DePaul University (n=9, 3.9, +.8), University of California Irvine (n=6, 4.0, +.7), Purdue University (n=10, 3.9, +.7), Fordham University (n=12, 3.9, +.7), Western University (n=16, 3.5, +.7), University of Southern California (n=32, 4.9, +.6), Duke University (n=5, 4.8, +.6), and University of Washington (n=10, 4.1, +.6) had higher ratings. Other programs had **lower ratings** compared with 2023: Pennsylvania State University (n=5, 3.2, -1.1), University of Kentucky (n=11, 3.3, -.9), Boston College (n=8, 3.8, -.9), Carnegie Mellon University (n=5, 3.2, -.7), The Catholic University of America (n=6, 4.2, -.6), University of South Carolina (n=8, 3.4, -.6), University of Nebraska Lincoln (n=8, 3.4, -.6), and Washington University St. Louis (n=13, 3.8, -.6). ### CLIMATE RATING Finally, we can compare climate ratings across the two surveys (“Rate your satisfaction with this program’s efforts to foster a healthy, respectful academic culture or climate”). The mean for 2025 participants (n=1179) was 4.03; the mean for 2023 participants (n=1088) was 3.88—a recent increase (p<.01). The **top 10 for climate ratings** in the 2025 survey are: University of California San Diego (n=8, 4.9), University of Maryland College Park (n=5, 4.6), London School of Economics and Political Science (n=7, 4.6), University of Pennsylvania (n=17, 4.5), University of North Carolina at Chapel Hill (n=20, 4.5), University of Sheffield (n=8, 4.5), University of Southern California (n=32, 4.5), New York University (n=28, 4.5), University of Utah (n=9, 4.4), and University of Cincinnati (n=14, 4.4). 35 programs had ratings over 4.03, listed below********. Programs with **higher** climate ratings, when compared with 2023, are: Purdue University (n=10, mean of 3.5, change of +1.3 from 2023), University of Hawai’i at Manoa (n=5, 3.8, +1), DePaul University (n=9, 3.3, +1), Katholieke Universiteit Leuven (n=7, 4.1, +.9), University of Southern California (n=32, 4.5, +.9), University of California Irvine (n=6, 4.0, +.9), University of Pittsburgh (HPS, n=19, 4.1, +.9), University of Miami (n=8, 3.6, +.8), Duke University (n=5, 4.4, +.7), Northwestern University (n=15, 4.1, +0.7), Western University (n=15, 3.4, +.7), Vanderbilt University (n=7, 4.0, +.6), and University of Maryland College Park (n=5, 4.6, +.6). Programs with **lower** ratings, compared with 2023 are: University of Nebraska, Lincoln (n=8, mean of 2.5, change of -2.2), Saint Louis University (n=7, 3.9, -.9), Boston College (n=8, 3.3, -.9), University of Kansas (n=5, 3.8, -.8), Ohio State University (n=10, 3.4, -.7), Binghamton University (n=7, 3.3, -.7), Washington University St. Louis (n=14, 4.0, -0.7), University of Virginia (n=6, 4.0, -0.7), and The Catholic University of America (n=6, 4.3, -0.7). While reading the program comments across the two surveys, I had the subjective sense of a greater proportion mentioning issues related to harassment, bullying, and other problematic behaviors, despite the increase in climate rating. I used Claude (AI) to help compare the comments across the two years (including only the public responses and no other data) with the prompt “Can you count how many entries in the following mention bullying, harassment, or other problematic behavior?” Claude found that 4% of the 2025 entries and 3% of the 2023 entries did this—a slight uptick. Claude noted its use of the following keywords in 2025 (verbatim from Claude): sexual harassment, racist/racism, sexist/sexism, homophobic/homophobia, toxic behavior, hostile environments, discrimination, and harassment (general). For 2023 it identified similar keywords or themes (verbatim from Claude): toxic competition and faculty conflict, harassment (unreported faculty harassers mentioned), racist attitudes and discrimination, sexist/sexism, hostile environments (particularly toward feminists), intimidating and competitive atmospheres, and discrimination against underrepresented groups. When I asked Claude to focus on a comparison across the two focused more narrowly on Title-IX related issues, it found twice as many mentions in 2025 as in 2023. Worth noting is that these numbers were not verified by hand and that AI is fallible. ### KEY WORDS Participants of the survey are asked to select keywords that define their graduate program (“Select from this list up to 5 keywords that you would associate with this program.”). Of these, some keywords have gained in popularity since 2023. Namely, Political/Social (3% to 5% of mentions), Analytic (11 to 12%), and Technology (0 to 1%) all increased. Losses were seen in Race (1.4% to .8%) and Logic/Formal (5% to 4%). To focus on just two of the smaller ones: 3 programs were associated with “technology” by at least three participants in 2025 (0 in 2023)—Eindhoven, Kansas, and Toronto (IHST). While 7 programs were associated with “race” in 2023 (CUNY, Penn State, Memphis, U Penn, Loyola, Marquette, and Northwestern), only 5 were in 2025 (CUNY, Penn State, Memphis, U Penn, and Emory). ### ADDENDA Date | University ---|--- 8/21/25 | Arizona State University 8/25/25 | Arizona State University (HPS) 8/25/25 | Australian National University 8/27/25 | Baylor University 8/27/25 | Binghamton University 8/27/25 | Bogazici University 9/10/25 | Boston College 9/10/25 | Boston University 9/10/25 | Bowling Green State University 9/10/25 | Brown University 9/10/25 | Carnegie Mellon University 9/10/25 | Columbia University 9/10/25 | Cornell University 9/10/25 | Dalhousie University 8/25/25 | Deakin University 11/7/25 | DePaul University 11/7/25 | Duke University 11/7/25 | Duquesne University 11/7/25 | Durham University 11/7/25 | Eindhoven University of Technology 11/11/25 | Emory University 11/11/25 | Florida State University 11/11/25 | Fordham University 11/11/25 | Georgetown University 11/11/25 | Graduate Center of the City University of New York 11/19/25 | Harvard University 11/19/25 | Indiana University Bloomington 11/19/25 | Indiana University Bloomington (HPS) 11/7/25 | Institut Jean Nicod 11/19/25 | Johns Hopkins University 11/7/25 | King´s College London 12/7/25 | London School of Economics and Political Science 11/19/25 | Loyola University Chicago 11/7/25 | Marquette University 11/7/25 | Massachusetts Institute of Technology 11/14/25 | McGill University 11/30/25 | McMaster University 11/14/25 | Michigan State University 1/13/26 | National University of Singapore 11/14/25 | New York University 11/14/25 | Northwestern University 11/14/25 | Ohio State University 1/16/26 | Pennsylvania State University 11/14/25 | Princeton University 11/14/25 | Purdue University 1/16/26 | Rutgers University 11/21/25 | Saint Louis University 11/21/25 | Southern Illinois University 1/23/26 | St Andrews and Stirling Graduate Programme in Philosophy 11/21/25 | Stanford University 1/16/26 | Stony Brook University 11/21/25 | Syracuse University 11/21/25 | Temple University 11/21/25 | Texas A & M University-College Station 1/17/26 | The Catholic University of America 1/17/26 | The New School 3/12/25 | The University of Manchester 1/17/26 | The University of Melbourne 1/17/26 | The University of Western Australia 1/17/26 | Tilburg University 1/17/26 | Trinity College, Dublin 11/30/25 | Tulane University 2/12/25 | Universite de Montreal 11/30/25 | University at Albany 11/30/25 | University at Buffalo 1/23/26 | University College London 1/17/26 | University of Alberta 11/30/25 | University of Arizona 11/30/25 | University of Arkansas 1/17/26 | University of Auckland 1/17/26 | University of British Columbia 1/31/26 | University of Calgary 11/30/25 | University of California, Berkeley 11/30/25 | University of California, Davis 12/5/25 | University of California, Irvine 12/5/25 | University of California, Irvine (LPS) 12/5/25 | University of California, Los Angeles 12/5/25 | University of California, Riverside 12/5/25 | University of California, San Diego 12/5/25 | University of California, Santa Barbara 12/5/25 | University of California, Santa Cruz 1/31/26 | University of Cambridge 1/31/26 | University of Cambridge (HPS) 12/12/25 | University of Chicago 1/31/26 | University of Chicago (CHSS) 12/12/25 | University of Cincinnati 12/12/25 | University of Colorado Boulder 12/12/25 | University of Connecticut 12/12/25 | University of Dallas 1/31/26 | University of Edinburgh 12/12/25 | University of Florida 1/31/26 | University of Geneva 1/31/26 | University of Georgia 1/31/26 | University of Graz 1/31/26 | University of Guelph 1/31/26 | University of Hawai´i at Manoa 1/31/26 | University of Hong Kong 1/31/26 | University of Illinois at Chicago 1/31/26 | University of Illinois at Urbana-Champaign 1/31/26 | University of Iowa 1/31/26 | University of Kansas 1/31/26 | University of Kentucky 1/31/26 | University of Maryland, College Park 1/31/26 | University of Massachusetts Amherst 1/12/25 | University of Memphis 11/28/25 | University of Miami 11/28/25 | University of Michigan 11/27/25 | University of Minnesota Twin Cities 11/27/25 | University of Missouri 11/27/25 | University of Nebraska, Lincoln 11/27/25 | University of New Mexico 11/26/25 | University of North Carolina at Chapel Hill 11/26/25 | University of Notre Dame 11/26/25 | University of Nottingham 11/26/25 | University of Oklahoma 11/26/25 | University of Oregon 11/26/25 | University of Otago 11/24/25 | University of Oxford 11/24/25 | University of Pennsylvania 11/24/25 | University of Pittsburgh 11/24/25 | University of Pittsburgh (HPS) 11/24/25 | University of Reading 11/24/25 | University of Rochester 11/22/25 | University of Salzburg 11/22/25 | University of Sheffield 11/22/25 | University of South Carolina 11/21/25 | University of South Florida 11/21/25 | University of Southern California 11/21/25 | University of Tennessee 11/19/25 | University of Texas at Austin 11/19/25 | University of Toronto 11/20/25 | University of Toronto (IHPST) 11/15/25 | University of Utah 11/15/25 | University of Virginia 11/15/25 | University of Warwick 12/11/25 | University of Washington 12/11/25 | University of Waterloo 12/11/25 | University of Wisconsin-Madison 12/11/25 | University of York 12/11/25 | Uppsala University 12/11/25 | Vanderbilt University 12/11/25 | Victoria University of Wellington 12/11/25 | Villanova University 12/11/25 | Washington University in St. Louis 12/11/25 | Wayne State University 11/11/25 | Western University 11/2/25 | William Marsh Rice University 11/2/25 | Yale University 11/2/25 | York University ***Programs contacted but not yet covered by APDA:** Bielefeld University, Bilkent University, Birkbeck, University of London, Cardiff University, Central European University, Chinese University of Hong Kong, Delft University of Technology, FINO Northwestern Italian Philosophy Consortium, Free University of Berlin, Goethe University Frankfurt, Humboldt University of Berlin, Johannes Gutenberg University of Mainz, Katholieke Universiteit Leuven, Kingston University, La Trobe University, Leipzig University, Ludwig Maximilian University of Munich, Macquarie University, Middle East Technical University, Monash University, National Autonomous University of Mexico, Pantheon-Sorbonne University, Royal Holloway, University of London, Ruhr University Bochum, Scuola Normale Superiore di Pisa, Stockholm University, Texas State University, TU Bergakademie Freiberg, University of Aberdeen, University of Adelaide, University of Amsterdam, University of Antwerp, University of Barcelona, University of Birmingham, University of Bristol, University of Buenos Aires, University of Cologne, University of Dundee, University of East Anglia, University of Erfurt, University of Glasgow, University of Groningen, University of Hannover, University of Helsinki, University of Hong Kong, University of Leeds, University of Lugano, University of Milan, University of Modena, University of Murcia, University of New South Wales, University of Oslo, University of Ottawa, University of Padua, University of Sussex, The University of Sydney,University of Sydney HPS, University of Tasmania, University of Tehran, University of the Basque Country, University of Tübingen, University of Valencia, University of Vienna, University of Waikato, and Vrije Universiteit Brussel ****Programs with higher than the overall permanent academic placement rate, listed by rate** **(from highest/72% to lowest/41%):** University of Southern California (72%), University of Virginia (72%), Baylor University, University of Cincinnati, Boston University, Rutgers University, Dalhousie University, University of Michigan, Yale University, New York University, The Catholic University of America, Pennsylvania State University, Arizona State University, Vanderbilt University, Massachusetts Institute of Technology, Stony Brook University, University of Arizona, University of Alberta, University of Wisconsin-Madison, University of Massachusetts Amherst, Emory University, University of Pittsburgh (HPS), Princeton University, Duke University, Columbia University, University of California Irvine (LPS), Saint Louis University, University of Washington, Washington University in St. Louis, University of California Berkeley, University of Pennsylvania, Stanford University, University of Auckland, University of California Santa Barbara, University of Georgia, University of Kansas, University of Kentucky, William Marsh Rice University, University of Toronto, Boston College, Harvard University, University of California San Diego, University of California Los Angeles, University of Hawai’i at Manoa, University of Oklahoma, University of Pittsburgh, Texas A & M University-College Station, Georgetown University, University at Buffalo, University of Illinois at Chicago, Purdue University, University of North Carolina at Chapel Hill, University of Chicago, University of Texas at Austin, Carnegie Mellon University, University of Memphis, Temple University, York University, Northwestern University, University of Miami, Cornell University, University of Notre Dame, Syracuse University, Villanova University, Indiana University Bloomington, Michigan State University, University of Oregon, Wayne State University, Bowling Green State University (41%), University of Illinois at Urbana-Champaign (41%), and DePaul University (41%). *****Programs with higher than the mean program rating, listed by rating** **(from highest/4.9 to lowest/4.2):** University of Southern California (4.9), University of California San Diego (4.9), Baylor University (4.9), Duke University, University of Kansas, University of California Riverside, Australian National University, University of Pennsylvania, University of Sheffield, University of Wisconsin-Madison, London School of Economics and Political Science, University of Cincinnati, University of Michigan, Georgetown University, University of Massachusetts Amherst, St Andrews and Stirling Graduate Programme in Philosophy, University of Pittsburgh (HPS), Harvard University, New York University, University of North Carolina at Chapel Hill, University of California Berkeley, Yale University, University of Toronto, University of Warwick, York University, University of Utah, Cornell University, University of California Irvine (LPS), Northwestern University, University of Chicago, University of Memphis (4.2), University of Oxford (4.2), Indiana University Bloomington (4.2), Massachusetts Institute of Technology (4.2), Fordham University (4.2), and The Catholic University of America (4.2). ******Programs with higher than the mean climate rating, listed by rating** **(from highest/4.9 to lowest/4.1):** University of California San Diego (4.9), University of Maryland College Park, London School of Economics and Political Science, University of Pennsylvania, University of North Carolina at Chapel Hill, University of Sheffield, University of Southern California, New York University, University of Utah, University of Cincinnati, Duke University, University of Wisconsin-Madison, University of California Berkeley, University of Michigan, Baylor University, The Catholic University of America, Australian National University, University of Arizona, University of California Irvine (LPS), St Andrews and Stirling Graduate Programme in Philosophy, York University, Cornell University, Yale University, Georgetown University, Harvard University, University of South Florida, Fordham University, Katholieke Universiteit Leuven (4.1), University of British Columbia (4.1), Northwestern University (4.1), University of Massachusetts Amherst (4.1), University of Toronto (4.1), University of California Los Angeles (4.1), University of Notre Dame (4.1), and University of Pittsburgh (HPS, 4.1).

A blog about imperfect cognitions, such as delusional beliefs, distorted memories, confabulatory explanations, and implicit biases.

__Share __Tweet __Share Hi All, In the next weeks and months, we’ll be making some exciting new additions to the editorial team at Brains. Two new Associate Editors will be introduced this week! I want to take the chance to give a huge thanks to two of our recent Associate Editors who will be stepping down. Majid Beni and Trey Boone both did incredible work with Brains over the past couple of years. Amongst other things, Majid was in charge of our series of video discussions on AI and Agency (https://philosophyofbrains.com/category/ai-and-agency-interview-series), and Trey oversaw our series of instructional videos in Philosophy of Neuroscience (done in collaboration with Neural Mechanisms Online,

We can think of the optimalist theory of ultimate explanation as the claim: 1. Necessarily, that reality is for the best explains everything. (I won’t worry in this post about two details. First, whether “reality” in (1) includes the principle of optimality itself—Rescher has suggested that it does, since it’s for the best that everything be for the best. Second, whether “reality” is all the detail of the world, or just the “core” of the world—the aspects not set by indeterministic causation.) Given that only truths explain, (1) entails: 2. Necessarily, reality is for the best. Notice that one could accept (2) without accepting (1). One might, for instance, be a Leibnizian and think that there is a two-fold structure to ultimate explanation: first, God’s existence is explained by the ontological argument and, second, God creates the best contingent reality. On this account everything _is_ for the best, but that everything is for the best is not the ultimate explanation, because it does not explain why God exists. Or one might think that reality is necessary and brute, and it brutely _has_ to be like it is. And as a very suprising but non-explanatory matter of fact the way it is is in fact optimal. I am emphasizing this, because I want to problematize (1). Grant (2). Why should we think that the fact that everything is for the best in fact explains everything? Suppose that modal fatalism is true, and that it so happens that reality is exactly mid-way between the worst and the best possibility, and is in the only option mid-way between the worst and best. (I assume one can talk of options for reality even given modal fatalism. Otherwise, optimalism falls apart. The “options for reality” may be something like narrowly logically possible worlds.) Then: 3. Necessarily, reality is exactly middling. Now suppose a “mediocritist” said: “And that reality is necessarily exactly middling explains why reality is what it is.” But why would we buy that? Or suppose that reality is necessarily the only one that is exactly 56.4% of the way up between the worst and the best (where worst would count as 0% of the way up and best as 100%)? Surely we wouldn’t conclude that its being exactly at 56.4% _explains_ why it is the way it is. But if not, then why should its being at 50% explain it, as on mediocritism, or its being at 100% explain it, as on optimalism? I think what the optimalist ought to say at this point is that analogously to non-Humean pushy laws of nature, there are non-Humean pushy laws of metaphysics. One of these laws is that everything is for the best. It is the pushiness of this metaphysical law that explains reality. But there is something rather odd about pushy laws prior to all beings—they seem really problematically ungrounded.

I have been teaching Introduction to Philosophy at least once a year since 2012, beginning in my second year of graduate school at the CUNY Graduate Center. Teaching in New York City shaped me in countless ways, and each new iteration of “Intro” has pushed me to refine the course—even if only incrementally. The class

Here’s an interview I did with Jacob Stump, Associate Teaching Professor and Undergraduate Program Director in the Department of Philosophy at Northeastern University.

Laurance Mordekhai Thomas, professor emeritus of philosophy and political science at Syracuse University, died this past December. The following obituary is by David Benatar (University of Cape Town). * * * ## Laurence Mordekhai Thomas (1949-2025) Laurence Thomas, Emeritus Professor of Philosophy at Syracuse University, died on 27 December 2025. He was 76. His life focused on his work: he was a passionate, productive philosophical writer, and a popular professor. He was also a man of contrasts. He was effervescent and enthusiastic. However, he was also a man of great interiority, and very private. This challenges the obituarist who wishes both to convey a sense of the person and to respect his privacy. Laurence was born on 1 August 1949 and grew up, an only-child, in Baltimore, Maryland. Both his parents died by his mid-teens. Thereafter, he was reared by an aunt—an “ole fashioned Jamaican woman” who, he said, “never allowed me to wallow in the valley of despair”, and to whom he later dedicated his first book, _Living Morally: A Psychology of Moral Character_ (Temple, 1989). He received his BA in Philosophy from the University of Maryland in 1971. At the University of Pittsburgh, he received an MA in 1973 and a PhD in 1976. His doctoral supervisor was Kurt Baier, for whose 1987 festschrift in _Synthese,_ Laurence was later the guest editor. Before joining Syracuse in 1989, he held positions at Notre Dame (1977-1978), the University of Maryland (1978-1980), the University of North Carolina, Chapel Hill (1980-1986), and Oberlin (1986-1989). In 1978-1979, he was Andrew Mellon Faculty Fellow at Harvard. While his primary Syracuse appointment was in Philosophy, he was also affiliated with the Political Science Department and the Judaic Studies program. Professor Thomas’ specialization was in moral, political, and social philosophy, with a strong thread of moral psychology. The hallmarks of his writing were exquisite sensitivity to human psychology and behaviour, thoroughgoing decency, and accessibility. His second book, _Vessels of Evil: American Slavery and the Holocaus_ t (Temple, 1993), is a prime example. It is a nuanced examination of the similarities and differences between the subtitular atrocities. Eschewing invidious judgements about which was worse, he probed the nature of these respective evils. He did much of his writing in Paris, to which he would regularly decamp. He enjoyed that culture, developed close friendships there, and learned French. He had an abiding philosophical interest in human relationships, especially familial relationships and friendship. His third monograph was entitled _The Family and the Political Self_(Cambridge, 2006). His appreciation of familial relationships may have been occasioned partly by the early death of his parents and the absence of siblings. His attunement to the nuances of such relationships is even more remarkable for these reasons. While he neither married nor had children, he was a father figure, as well as a friend, champion, and trusted confidant, especially to his doctoral students. They have said how difficult it is to express how much he meant to, and did for, them. It is a testament to his influence on their lives that two of them gave their sons the second name “Laurence” in his honour. Professor Thomas was also a popular teacher of undergraduate students. The enrolment in his introductory ethics course mushroomed. Even in a class of 400 students, he learned many of their names, referenced comments they had made in previous classes, and engaged in Socratic exchanges. Some of his teaching methods were unorthodox, blending theatrics, humour, and music, with philosophy. A former teaching assistant remarks that while this led some to question the academic rigour of the teaching, Professor Thomas “simply loved taking complex themes and making them accessible to those who otherwise might have been overwhelmed”. His teaching was profiled in _the New York Times._ In 1993 he was Syracuse University’s Scholar-Teacher of the year. One friend described Laurence as a “student-magnet”, noting that after Laurence had given a distinguished guest lecture at his university, “a group of students followed him everywhere he went on campus, peppering him with questions, which he answered graciously and tactfully.” Laurence’s lively personality and wit were also manifest in his conversation and repartee with colleagues during the same visit. It is unsurprising that Laurence was invited to speak at many institutions. Laurence was immensely generous—with his time, money, and in spirit. One former student recalls that the time and energy that he “dedicated to his students was simply incredible”. Laurence paid for refreshments at his graduate seminars, and books for his graduate students’ research. When returning from trips to France, he would bring boxes of Parisian chocolates to give to secretarial staff, friends, and occasionally the parents of students. Laurence put inordinate effort into letters of recommendation. He affirmed people, and acknowledged them, and was attuned to the good in people even when they might not have noticed it themselves. He had a great generosity of spirit. While he went well beyond professorial duties, he was meticulous about propriety, and the perception thereof. Although extensively accessible to students, he preferred meeting students in public places, rather than in his office. Even those closest to him never entered his apartment. Laurence wrote about character, but he also was _a character_. He was eccentric, even by the eccentric standards of philosophers. A former department Chair, who recruited Laurence and four others to Maryland, arranged a welcome for them at his home. He also invited the University President, who accepted the invitation and arrived at the event. However, none of the new appointees were present. The Chair was getting increasingly stressed. The doorbell then rang. The five arrived, “dressed to the nines” and led by Laurence, attired in a tuxedo and top hat, and carrying a cane! In addition to the aforementioned monographs, Laurence co-authored a “debate” book with Michael Levin, _Human Rights and Sexual Orientation_ (Rowman and Littlefield, 1999), and over eighty academic papers. He edited Blackwell’s _Contemporary Debates in Social Philosophy._ He had begun work on, but was not well enough to complete, a fourth monograph, _The Fragmented Self: Technology and the Loss of Humanity_. Laurence, influenced by his Aristotelian sympathies, made frequent reference to flourishing. When starting a conversation, he would ask “Are you _flourishing_ , sir?” He would sign off emails with the injunction “Flourish!” Sadly, Laurence did not flourish to the very end. His death was preceded by a long decline. At his funeral, both a former student and the rabbi of Laurence’s synagogue spoke movingly about him. On an especially cold winter’s day, his casket was then accompanied to the grave where he was interred. _– David Benatar_

This is the weekly report on new and revised entries at online philosophy resources, new reviews of philosophy books, new podcast episodes, recently published open access philosophy books, and more. (If we missed anything, please let us know.) SEP New: ∅ Revised: Teleological Notions in Biology by Colin Allen and Jacob Neal. Francisco Suárez by Christopher Shields and Daniel Schwartz. Suicide by Michael Cholbi and Brent Kious. The Emotions in Early Chinese Philosophy by Bongrae Seok. Denis Diderot by Charles T. Wolfe and J.B. Shank. Attention by Christopher Mole. IEP ∅ 1000-Word Philosophy ∅ BJPS Short Reads ∅ Recently Published Open Access Philosophy Books Risk, Death, and Well-Being: The Ethical Foundations of Fatality Risk Regulation by Matthew D. Adler (Oxford University Press). Philosophy Podcasts – Recent Episodes (via Jason Chen) Book Reviews The Freedom of Words: Abstractness and the Power of Language by Anna Borghi is reviewed by Guy Dove at Philosophical Psychology. The Concept of Democracy: An Essay on Conceptual Amelioration and Abandonment by Herman Cappelen is reviewed by Jason Brennan at Notre Dame Philosophical Reviews. The Brain Abstracted by Mazviita Chirimuuta is reviewed by Adina Roskies at Philosophical Psychology. Feeling & Knowing: Making Minds Conscious by Antonio Damasio is reviewed by Da Dong et al. at Philosophical Psychology. Subjective Experience: Its Fate in Psychology, Psychoanalysis and Philosophy of Mind, edited by Morris N. Eagle, is reviewed by Hidayat et al. at Philosophical Psychology. Mary Shepherd’s An Essay upon the Relation of Cause and Effect by Don Garrett (ed.) is reviewed by Samuel C. Rickless at Notre Dame Philosophical Reviews. The Mirror and the Mind: A History of Self-Recognition in the Human Sciences by Katja Guenther is reviewed by Da Dong et al. at Philosophical Psychology. From a Marxist-Feminist Point of View: Essays on Freedom, Rationality, and Human Nature by Nancy Holmstrom is reviewed by Sonia Maria Pavel at Notre Dame Philosophical Reviews. The Four Realms of Existence: A New Theory of Being Human by Joseph E. LeDoux is reviewed by S.A. Burns Brown at Philosophical Psychology. The Score: How to Stop Playing Someone Else’s Game by Thi Nguyen is reviewed by N.J. Enfield at The Times Literary Supplement. Mental Imagery: Philosophy, Psychology, Neuroscience by Bence Nanay is reviewed.. The post Online Philosophy Resources Weekly Update first appeared on Daily Nous.

Programming and Announcements for 2026

In an article at _Nature_ , Eddy Keming Chen, Mikhail Belkin, Leon Bergen, and David Danks ask “Does AI already have human-level intelligence?” and claim that “the evidence is clear” that the answer is Yes. (Though the article is partially pay-walled, a read-only PDF is available here.) But as is typical with bold claims about AI, their arguments are underwhelming, riddled with begged questions and other fallacies. _Defining “intelligence”_ Naturally, before we can establish that AI has genuine intelligence, we need to make clear _what it would be_ for it to have intelligence, and how we could go about _determining that_ it has it. The first is a metaphysical question, the second an epistemological question. Our authors make no serious attempt to answer either one. Explaining what they mean by “general intelligence,” they write: “A common informal definition of general intelligence, and the starting point of our discussions, is a system that can do almost all cognitive tasks that a human can do.” Expanding on this, they say: General intelligence is about having sufficient breadth and depth of cognitive abilities, with ‘sufficient’ anchored by paradigm cases. Breadth means abilities across multiple domains – mathematics, language, science, practical reasoning, creative tasks – in contrast to ‘narrow’ intelligences, such as a calculator or a chess-playing program. Depth means strong performance within those domains, not merely superficial engagement. There are three serious problems here. The first is that it is not illuminating to say that general intelligence entails the ability to carry out cognitive tasks unless our authors have already given us (as they have not) an explanation of what they mean by “cognitive.” Now on one common usage, “cognitive” tasks are activities of the kind that require intelligence. So, if that is what they mean, then our authors’ definition is circular – they are defining intelligence in terms of cognition, where cognition is (implicitly) defined in terms of intelligence. And as anyone who has taken a logic course knows, one of the fundamental rules of a good definition is that should not be circular. Second, another basic rule for definitions familiar from logic is that a good definition should not be too broad. For example, if I defined “gold” as a yellowish metal, this would violate the rule, because it would fail to make clear what distinguishes gold from pyrite. Now, our authors violate this rule as well, because their characterization of intelligence is not precise enough to distinguish genuine intelligence from mere mimicry. They matter-of-factly refer to what calculators and chess-playing programs do as instances of “intelligence.” But it is a commonplace among critics of AI that what calculators and chess-playing programs do is mere mimicry and not genuine intelligence at all, not even of the “narrow” kind. Of course, our authors would no doubt disagree with the critics. But the point is that a good definition of intelligence should provide us with an _independent_ guide for determining who is right. It should make it clear what it would be to have genuine intelligence as opposed to mere mimicry, so that we could then go on to establish on that basis whether or not calculators and chess-playing machines actually have it. Imagine a gold miner trying to prove that what he has dug up really is gold rather than pyrite by saying “I define ‘gold’ as ‘a yellow metal.’ So, the evidence is clear that this is gold!” Obviously, even if what he has really is gold, he cannot establish that it is with _that_ particular definition, because it is so broad that even a mere simulation of gold would meet it. Similarly, our authors cannot claim to have established that AI has genuine intelligence when they are working from a definition so broad that even a simulation would meet it. Even if they were right, their argument could not _show_ that they are right, because given their definition of “intelligence,” it simply begs the question. A third problem with our authors’ definition is that it violates yet another standard rule for definitions, which is that they should capture the _essence_ of the thing defined. Part of what this involves is leaving out of a definition any reference to features that the thing defined needn’t actually possess. For example, it would be a mistake to define “gold” as a metal used in making jewelry, because even though that is true of gold, it is not _essential_ to gold that it be used in making jewelry. Gold would still have the same nature it has even if human beings had never decided to make jewelry out of it. But another part of what this rule involves – and the part relevant to our concerns here – is sensitivity to the fact that even features that always exist in things of a certain type are not necessarily part of the essence of the thing. They may instead _flow from_ its essence as “proper accidents” (to use the traditional Scholastic jargon). For example, water is clear and liquid at room temperature, but this isn’t plausibly the _essence_ of water. Rather, these features _follow from_ water’s having the essence it has, which is (either in whole or in part, depending on different views about essence I won’t adjudicate here) a matter of having the chemical composition H2O. And things with a different essence from water might have these same features (as heavy water does, for example). (I discuss the distinction between essence and proper accidents in my book _Scholastic Metaphysics_ , at pp. 230-35.) Now, our authors violate the rule that a definition should capture a thing’s essence, when they define intelligence in terms of a capacity for tasks involving “mathematics, language, science” and the like. Capacities for mathematics, language, and science certainly _follow from_ our having intelligence, but they are not themselves the _essence_ of intelligence. (That is why they don’t always manifest – though they “flow” from our having intelligence, the flow can be “blocked,” as it were, by immaturity, brain damage, or what have you.) What _would_ be the essence of intelligence? I would say that it has to do with the interconnected capacities for forming abstract concepts, putting them together into propositions, and reasoning logically from one proposition to another. (See chapter 3 of my book _Immortal Souls: A Treatise on Human Nature_ for a detailed exposition and defense of this traditional conception of intelligence.) It is because we have intelligence in this sense that we are capable of mathematics, language, science, and so on. Those capacities _flow_ or _follow from_ intelligence in this sense. Of course, our authors may well disagree with this account. But the point for present purposes is that their attempted definition doesn’t reflect any awareness of the need to distinguish essence from proper accidents, and to define intelligence in terms of the former rather than the latter. As in the other ways noted, their account is simply conceptually sloppy. This sloppiness manifests itself also in what they say intelligence does _not_ involve. They write: Intelligence is a functional property that can be realized in different substrates – a point Turing embraced in 1950 by setting aside human biology. Systems demonstrating general intelligence need not replicate human cognitive architecture or understand human cultural references. This is not wrong, but, without saying more, it is also not very helpful. Suppose I suggested to you that a stone is intelligent and you replied that that seems like an absurd claim given that stones can't speak or reason logically, lack basic knowledge, and so on. And suppose I responded: “True, but remember that systems demonstrating general intelligence need not replicate human cognitive architecture or understand human cultural references.” Presumably you would not be impressed with this response. The problem, obviously, is that while genuine intelligence need not always look exactly the way it does in us, it nevertheless is not true that just anything goes. We need some criteria for determining when something departs _too far_ from how intelligence manifests in us to count as genuinely intelligent. And our authors offer no such criteria. They simply _assume_ that, wherever we draw this line, AI will fall on the “genuine intelligence” side of it. But since they _merely_ assume this rather than argue for it, their position once again begs the question. _Detecting intelligence_ Having failed to provide a serious _definition_ of intelligence, it is no surprise that they also fail to provide a serious account of how to go about _detecting_ intelligence (since the latter task presupposes the former). There is a lot of hand waving about “a cascade of evidence,” and gee-whiz references to what LLMs can do. But all of this ultimately boils down to nothing more than a stale appeal to the Turing test. And the problem with the Turing test is that, of its very nature, it cannot distinguish genuine intelligence from a mere clever simulation. Indeed, it deliberately ignores the difference and focuses narrowly on the question of what would lead us to _judge_ a machine to be intelligent, rather than the question of what would make it the case that a machine actually _is_ intelligent. Since it is the latter question that is at issue here, the Turing test is simply irrelevant. (And as I have argued elsewhere, to make it relevant, the defender of the view that AI is genuinely intelligent will have to appeal to either verificationism or scientism, and the resulting position will be either self-defeating or question-begging.) But the problem with our authors’ argument is worse than that. It’s not just that they haven’t shown that AI has genuine intelligence. It’s that we already know that it does _not_ have it, and that it amounts to nothing more than a simulation of intelligence. And we know that because mere mimicry is precisely all that computer architectures are designed to do. Here’s an analogy (which I have developed in more detail elsewhere). The methods employed by entertainers such as David Copperfield, David Blaine, and Penn and Teller are designed to produce effects that merely _simulate_ magic. And no matter how well they work, we know that mere simulation is all they can achieve, because the means they use are in no way preternatural but entirely mundane – sleight of hand, illusions, and so on. Of course, genuine magic is not real in the first place, but that is irrelevant. The point is that even if it _were_ real, it would not be what Copperfield, Blaine, and Penn and Teller are doing, precisely because their methods are not of the type that could produce more than mimicry. Now, AI operates on an analogous principle. It is designed to produce effects that _simulate_ intelligence, by means that don’t require any actual intelligence on the part of the machines themselves. And this is as true of machine learning and related approaches that now dominate AI research as it was of the Turing machine model that dominated the earlier history of AI. To borrow some terminology from John Searle, AI algorithms of whatever kind are sensitive only to the “syntax” of the representations they process rather than their “semantics.” That is to say, they process representations in a way that is sensitive only to their physical properties rather than the meanings that _we_ associate with those physical properties. Because there is a general correlation between syntax and semantics – for example, the word “love” on a printed page is typically going to be used to express the concept LOVE – the output of a sophisticated algorithm can be made to simulate the sort of thing a human being might write. The correlation is not perfect. For instance, there might be cases where the string of letters “love” is not actually being used to express the concept LOVE, and there might be cases where the concept LOVE is being conveyed but without using the string of letters “love.” This is why AI programs can often be “tripped up” and reveal, through a failure to reflect such nuances, that they don’t actually understand what they are “saying.” Exposure to further data or refinements to an algorithm might work around such problems. But all that that yields is a more convincing simulation (just as Penn and Teller and company may come up with new ways of producing ever more convincing illusions). It doesn’t somehow generate a grasp of meaning or semantics on the part of the machine, because its basic structure is in no way sensitive to that in the first place. Our authors speak as if the dispute over whether AI models are genuinely intelligent or merely simulating intelligence is a matter of “inference to the best explanation” – as if their position and that of the AI skeptic are alternative hypotheses meant to account for the same evidence, and the controversy has to do with which view is more empirically adequate, more parsimonious, and so forth. But this is as silly as suggesting that the view that Penn and Teller are merely simulating magic rather than producing the real thing is being proposed as the “best explanation” of the “evidence.” It is, of course, nothing of the kind. It is just a simple and straightforward conceptual point about the nature of their methods. And to note that AI produces only a simulation of intelligence rather than the real McCoy is an equally simple and straightforward conceptual point about the nature of _its_ methods. Interested readers will find a more detailed and academic treatment of these issues in chapter 9 of _Immortal Souls_. Related posts: Computer pseudoscience Artificial intelligence and magical thinking Accept no imitations [on the Turing test] Kripke contra computationalism Do machines compute functions? Can machines beg the question?