[flagged]
In 1935?
I wonder how many valves would be needed to build an "AI-capable" computer?
And how much electricity would it consume? Although the heat it generated would probably boil the Great Lakes!
Lady tasting tea
11–20 of 86 posts
Re: Lady tasting tea
#12> upper classes pour first the tea, while the lower classes poured the milk first
this is because of the fear that boiling water will crack your best tea cups?
Re: Lady tasting tea
#13> upper classes pour first the tea, while the lower classes poured the milk first this is because of the fear that boiling water will crack your best tea cups?
Good tea cups would never crack from boiling water, unless they were frozen first. Tea first is probably due to the fact that everybody wants tea, but not everybody wants milk, and some people want to pour their own amount of milk. You offer tea, they accept, you pour tea, you offer milk.
Re: Lady tasting tea
#14Earlier quoted context omitted.
In 1935?
I wonder how many valves would be needed to build an "AI-capable" computer? And how much electricity would it consume? Although the heat it generated would probably boil the Great Lakes!
Actually I went and read a bit about the history here, and although I left a glib comment, there was major practical research into neural network approaches, with theory in 1943 and hardware by 1953 https://en.m.wikipedia.org/wiki/Perceptron
There were two major factors though that set back the neural net approach:
- a 1969 Minsky & Papert book on perceptrons lead to a belief that neural nets even of >1 layers had fundamental limits, although the book only showed such limits in 1-layer nets; this lead to a reduction in funding during various AI winters
- the “deep” in “deep learning” is all about how much larger & deeper neural systems produce substantially better results. Even if you can speculate theoretically about this, it was completely impractical to approach the scale/speed to see fruitful results until the late 90s when vector/matrix accelerators (SIMD, GPU-type things) start showing up en masse. I vaguely remember reading about advances in ML in the mid 2000s which sort of had an attitude of “huh, this neural net thing we thought was a dead end turns out to just need MOAR CORES (graph up and to the right)”
Re: Lady tasting tea
#15Earlier quoted context omitted.
In 1935?
I wonder how many valves would be needed to build an "AI-capable" computer? And how much electricity would it consume? Although the heat it generated would probably boil the Great Lakes!
Shouldn't need a lick of electricity! If by valves you mean fluidics [1]... at which point, harnessing the Niagra Falls and building out a fluidized supercomputer covering the great lakes would probably suffice. No worries about the waste heat, though, it's water cooled!
Re: Lady tasting tea
#16> Thus, if and only if the lady properly categorized all 8 cups was Fisher willing to reject the null hypothesis – effectively acknowledging the lady's ability at a 1.4% significance level (but without quantifying her ability). Important to realize though, that failure to categorize all 8 doesn't prove anything either. It just means this one experiment isn't conclusive in itself (at 95% confidence). It's good to be a…
This is generally good advice, but isn't it inappropriate in this specific instance?
The lady's claim was (allegedly) that she has a perfect ability to distinguish between the tea-milk orders, so in that case even a single failure is indeed enough to reject her claim.
We can't rule out her success rate being significantly greater than 50-50, but even a single failure puts some bounds on her maximum success rate.
Re: Lady tasting tea
#17Earlier quoted context omitted.
I wonder how many valves would be needed to build an "AI-capable" computer? And how much electricity would it consume? Although the heat it generated would probably boil the Great Lakes!
Actually I went and read a bit about the history here, and although I left a glib comment, there was major practical research into neural network approaches, with theory in 1943 and hardware by 1953 https://en.m.wikipedia.org/wiki/Perceptron There were two major factors though that set back the neural net approach: - a 1969 Minsky & Papert book on perceptrons lead to a belief that neural nets even of >1 layers had fu…
Even in the 90s through the late 2000s, when I started working in ML, people poo-poo'd it: not enough data, not good enough algorithms, and computers too slow. And I worked with supercomputers/HPC- you'd think they would have been the first groups to exploit machine learning.
The perceptron was actually a remarkable cool device, way ahead of its time.
Re: Lady tasting tea
#18[flagged]
In 1935?
They had matchboxes in 1935*, so https://en.wikipedia.org/wiki/Matchbox_Educable_Noughts_and_... could've been a quarter-century earlier.
* since the XIX? https://matchesmuseum.com/en/match-history/
Re: Lady tasting tea
#19Wasn't this demonstrated by the Royal Chemical Society that pouring the milk into the tea creates a detectable trace of caramelization of the milk sugar or protein denaturation due to the momentary high temperature on the milk while the tea:milk ratio was very high at the initial pour? https://www.vahdam.com/blogs/tea-us/milk-first-or-last-the-s...
That wouldn't undermine or deter from the point:
> The null hypothesis is that the subject has no ability to distinguish the teas
Since the hypothesis was invalidated, we can begin investigating _how_ she's able to distinguish it, which is what you're getting at.
Re: Lady tasting tea
#20And "The Lady Tasting Tea" by David Salsburg is a nice history of statistics; 29 chapters, a little over 320 pages. [New York: Henry Holt and Company, 2001; ISBN 0-8050-7134-2 (PB)]