<i>In a recent interview with Communications, Hinton said his own research on word
vectors goes back to the mid-1980s, when he, David Rumelhart, and Ronald
Williams published work in Nature that demonstrated family relationships as
vectors. "The vectors were only six components long because computers were very
small then, but it took a long time for it to catch on," Hinton said.</i><p>Yeah, I know the work he's talking about. It's the one related to this dataset:<p><a href="https://archive.ics.uci.edu/ml/datasets/Kinship" rel="nofollow">https://archive.ics.uci.edu/ml/datasets/Kinship</a><p>From that page:<p><i>Creator:<p>Geoff Hinton<p>Donor:<p>J. Ross Quinlan<p>Data Set Information:<p>This relational database consists of 24 unique names in two families (they have
equivalent structures). Hinton used one unique output unit for each person and
was interested in predicting the following relations: wife, husband, mother,
father, daughter, son, sister, brother, aunt, uncle, niece, and nephew. Hinton
used 104 input-output vector pairs (from a space of 12x24=288 possible pairs).
The prediction task is as follows: given a name and a relation, have the outputs
be on for only those individuals (among the 24) that satisfy the relation. The
outputs for all other individuals should be off.<p>Hinton's results: Using 100 vectors as input and 4 for testing, his results on
two passes yielded 7 correct responses out of 8. His network of 36 input units,
3 layers of hidden units, and 24 output units used 500 sweeps of the training
set during training.<p>Quinlan's results: Using FOIL, he repeated the experiment 20 times (rather than
Hinton's 2 times). FOIL was correct 78 out of 80 times on the test cases.</i><p>And yet, if you have a wee look at Hinton's publication on Rexa, there's 43
citations, while there's a single one on Quinlan's (from Muggleton, duh).<p>So, you know, maybe it's not logic and reasoning that's the problem here, rather
a certain tendency to drum up results of neural models even when they don't do
any better than other techniques.<p>But, really, it doesn't matter. Google has the airwaves (so to speak). No matter
what happens anywhere else, in academia or business, their stuff is going to be
publicised the most and that's what we all have to deal with.