I recently commented on the fact that machine learning with neural networks now regularly gets called “AI”. I find the locution perplexing, because these machine learning problems have success conditions set up by engineers who defined the inputs and outputs.
Here is another headline which doubles down on the locution, discussing AIs creating AIs. Yet having a neural network solve an optimization problem is still machine learning in a constrained and specified problem space, even if it’s optimizing the structure of other neural networks.
There is a lot of buzz about AI and the prospect that computers will soon be doing something hugely different than what they’re doing now. It’s apprehension of what Ray Kurzweil calls the singularity, except that people don’t call it that much anymore.
The short version is that I’ve posted a short introduction to philosophical issues about scientific inference [link]. It’s written for an introductory class that I teach. It’s offered as OER under a CC license.
[It is a mistake to give] an absolute meaning to the epithet useful, which, in truth, has no more meaning if taken by itself than the words high, low, right, and left. It simply designates a relationship and requires a complement: useful for this or that.
I am teaching Simone de Beauvoir’s Ethics of Ambiguity in my Existentialism class, and I’m struck again by what a great book it is. She elaborates the notion of Bad Faith with much greater clarity than Sartre. There are parts of the book that make me rethink myself and my present situation.
This is striking partly because of context: We spent weeks on Heidegger, who does phenomenology in the most abstract way and only has eyes for metaphysics. Then we spent weeks on Sartre, who dabbles in ethics and has some rich examples but never finds his way around to the ethical question. Sartre writes in Being&Nothingness (in a footnote!) that “the description of [authentic existence] has no place here.”
And now we’re discussing de Beauvoir, whose task is “to consider human life as a game that can be won or lost and to teach… the means of winning.”
The conclusion of the Pluralism conference was great. I’ve spent a couple of packed days thinking about perspectives and cross-cutting ontologies, so now everything is an example. Take this doodle, which I drew on my notepad during one of the talks. Continue reading “Doodle pluralism”
Via Daily Nous, I came across a free set of text analysis tools by Voyant. You can paste in a passage or point it at some URLs, and it will chop it into words and phrases.
I let it chew on my book, and one of the products was this graph of word density:
It looks all sciencey, like the kind of think that prop people might put on a screen in the background of a lab scene. It isn’t very informative, though. The curve has “species” dipping below zero, even though it occurs at least once in every segment.
I learned that “natural”, “kind”, and “kinds” make up about three percent of the words in the book. That three percent was, I suppose, the easiest part to write.
I just received a rejection notice from a journal. It was the kind of wordy but uninformative prose, filled with trivial but nonspecific detail, which strongly vibes form letter. The real give away was the salutation, which literally said “Dear Professor x”.
There’s an X-men joke to be made here, but instead… grumble, grumble.