I'm not really the right person to answer, but some recent philosophy that's been influential to my thinking, and also to a lot of people in the humanities and

I'm not really the right person to answer, but some recent philosophy that's been influential to my thinking, and also to a lot of people in the humanities and art, is the "object-oriented ontology" as put forth by people like Graham Harman, Timothy Morton, Ian Bogost (whose main object of investigation has been video games) and others. For something more obviously relevant to this crowd, you could look at the work of Hubert Dreyfus, a Heidegger scholar whose insights into the limits of viewing intelligence as symbolic cognition has actually been influential in the history of AI. It's not super recent, but maybe it takes some time for philosophy to become intelligible; when it is really interesting, it also probably seems weird when it's fresh. A few references for Dreyfus: 1. His recent paper "Why Heideggerian AI failed and how fixing it would Require making it more Heideggerian"; a quote below. http://cid.nada.kth.se/en/HeideggerianAI.pdf http://cid.nada.kth.se/en/HeideggerianAI.pdf "As I studied the RAND papers and memos, I found to my surprise that, far from replacing philosophy, the pioneers in CS had learned a lot, directly and indirectly from the philosophers. They had taken over Hobbes’ claim that reasoning was calculating, Descartes’ mental representations, Leibniz’s idea of a “universal characteristic”—a set of primitives in which all knowledge could be expressed,—Kant’s claim that concepts were rules, Frege’s formalization of such rules, and Russell’s postulation of logical atoms as the building blocks of reality. In short, without realizing it, AI researchers were hard at work turning rationalist philosophy into a research program. At the same time, I began to suspect that the critical insights formulated in existentialist armchairs, especially Heidegger’s and Merleau-Ponty’s, were bad news for those working in AI laboratories– that, by combining rationalism, representationalism, conceptualism, formalism, and logical atomism into a research program, AI researchers had condemned their enterprise to reenact a failure." 2. David Chapman's curious autobiographical book review of a somewhat hokey book on "integral philosophy", that goes into his own story at MIT's AI lab and his encounter with Dreyfus's critique; quote below. https://meaningness.com/metablog/ken-wilber-boomeritis-artificial-intelligence https://meaningness.com/metablog/ken-wilber-boomeritis-artif... "In 1987, we wrote a computer program called Pengi that illustrated some of what we had learned from Dreyfus, Heidegger, and the Continental philosophical tradition. Pengi participated in a life-world. It did not have to mentally represent and reason about its circumstances, because it was embedded in them, causally coupled with them in a purposive dance. Its skill came from spontaneous improvisation, not strategic planning. Its apparently intelligent activity derived from interactive dynamics that—continually involving both its self and others—were neither subjective nor objective. Pengi was a triumph: it could do things that the old paradigm clearly couldn’t, and (although quite crude) seemed to point to a wide-open new paradigm for further research. AI was unstuck again! And, in fact, Pengi was highly influential for a few years." 3. Dreyfus's book about his reading of Heidegger, called Being-in-the-world, and if one wishes, a recent movie of the same name. Here is one of my many highlighted passages from the book, which is flattering to computer scientists, but also might make you wonder if there is any philosophy that is critiquing current AI research in a way that will seem prescient years down the road: "Having to program computers keeps one honest. There is no room for the armchair rationalist's speculations. Thus AI research has called the Cartesian cognitivist's bluff. It is easy to say that to account for the equipmental nexus one need simply add more and more function predicates and rules describing what is to be done in typical situations, but actual difficulties in AI—its inability to make progress with what is called the commonsense knowledge problem, on the one hand, and its inability to define the current situation, sometimes called the frame problem, on the other—suggest that Heidegger is right. It looks like one cannot build up the phenomenon of world out of meaningless elements."