Sentience is Physical

Sentience is all the rage these days. With large language models (LLMs) based on deep learning neural networks, question-answering behavior of these systems takes on curious approximations to talking with a smart person. Recently a member of Google’s AI team was fired after declaring one of their systems sentient. His offense? Violating public disclosure rules. I and many others who have a firm understanding of how these systems work—by predicting next words from previous productions crossed with the question token stream—are quick to dismiss the claims of sentience. But what does sentience really amount to and how can we determine if a machine becomes sentient?

Note that there are those who differentiate sentience (able to have feelings), from sapience (able to have thoughts), and consciousness (some private, subjective phenomenal sense of self). I am willing to blend them together a bit since the topic here isn’t narrowly trying to address the ethics of animal treatment, for example, where the distinction can be useful.

First we have the “imitation game” Turing test-style approach to the question of how we might ever determine if a machine becomes sentient. If a remote machine can fool a human into believing it is a person, it must be as intelligent as a person and therefore sentient like we presume of people. But this is a limited goal line. If the interaction is only over a limited domain like solving your cable internet installation problems, we don’t think of that as a sentient machine. Even against a larger domain of open-ended question and answering, if the human doesn’t hit upon a revealing kind of error that a machine might make that a human would not, we remain unconvinced that the target is sentient.… Read the rest

Wordle and the Hard Problem of Philosophy

I occasionally do Wordles at the New York Times. If you are not familiar, the game is very simple. You have six chances to guess a five-letter word. When you make a guess, letters that are in the correct position turn green. Letters that are in the word but in the wrong position turn yellow. The mental process for solving them is best optimized by choosing a word initially that has high-frequency English letters, like “notes,” and then proceeding from there. At some point in the guessing process, one is confronted with anchoring known letters and trying to remember words that might fit the sequence. There is a handy virtual keyboard displayed below the word matrix that shows you the letters in black, yellow, green, and gray that you have tried, that are required, that are fit to position, and that remain untested, respectively. After a bit, you start to apply little algorithms and exclusionary rules to the process: What if I anchor an S at the beginning? There are no five-letter words that end in “yi” in English, etc. There is a feeling of working through these mental strategies and even a feeling of green and yellow as signposts along the way.

I decided this morning to write the simplest one-line Wordle helper I could and solved the puzzle in two guesses:

Sorry for the spoiler if you haven’t gotten to it yet! Here’s what I needed to do the job: a five letter word list for English and a word frequency list for English. I could have derived the first from the second but found the first first, here. The second required I log into Kaggle to get a good CSV searchable list.… Read the rest