As it is stated, I always thought it came from formulations like Euler-Lagrange procedures in mechanics used in numeric methods for differential geometry. In fact when I recreated the algorithm as an exercise it immediately reminded me of gradient descent for kinematics, with the Jacobian calculation for each layer similar to an iterative pose calculation in generalized coordinates. I never thought it was something "novel".
I have a question that's bothered me for quite a while now. In 2018, Michael Jordan (UC Berkeley) wrote a rather interesting essay - https://medium.com/@mijordan3/artificial-intelligence-the-re... (Artificial Intelligence — The Revolution Hasn’t Happened Yet)
In it, he stated the following:
> Indeed, the famous “backpropagation” algorithm that was rediscovered by David Rumelhart in the early 1980s, and which is now viewed as being at the core of the so-called “AI revolution,” first arose in the field of control theory in the 1950s and 1960s. One of its early applications was to optimize the thrusts of the Apollo spaceships as they headed towards the moon.
I was wondering whether anyone could point me to the paper or piece of work he was referring to. There are many citations in Schmidhuber’s piece, and in my previous attempts I've gotten lost in papers.
Who didn't? Depending on exactly how you interpret the notion of "inventing backpropagation" it's been invented, forgotten, re-invented, forgotten again, re-re-invented, etc, about 7 or 8 times. And no, I don't have specific citations in front of me, but I will say that a lot of interesting bits about the history of the development of neural networks (including backpropagation) can be found in the book Talking Nets: An Oral History of Neural Networks[1].
Whatever the facts, the OP comes across as sour grapes. The author, Jürgen Schmidhuber, believes Hopfield and Hinton did not deserve their Nobel Prize in Physics, and that Hinton, Bengio, and LeCun did not deserve their Turing Award. Evidently, many other scientists disagree, because both awards were granted in consultation with the scientific community. Schmidhuber's own work was, in fact, cited by the Nobel Prize committee as background information for the 2024 Nobel.[a] Only future generations of scientists, looking at the past more objectively, will be able to settle these disputes.
The chain rule was explored by Gottfried Wilhelm Leibniz and Isaac Newton in the 17th century. Either of them would have ”invented” backpropagation in an instant. It’s obvious.
I've always found it rather crazy that the power of backpropagation and artificial neural networks was doubted by AI researchers for so long. It's really only since the early 2010s that researchers started to take the field seriously. This is despite the core algorithm (backpropagation) being known for decades.
I remember when I learnt about artificial neural networks at university in the late 00s my professors were really sceptical of them, rightly explaining that they become hard to train as you added more hidden layers.
See, what makes backpropagation and artificial neural networks work are all of the small optimisations and algorithm improvements that were added on top of backpropagation. Without these improvements it's too computationally inefficient to be practical and you have to contend with issues like exploding gradients.
I think Geoffrey Hinton has noted a few times that for people like him who have been working on artificial neural networks for years it's quite surprising that today neural networks just work because for years it was so hard to get them to do anything. In this sense while backpropagation is the foundational algorithm, it's not sufficient on it's own. It was the many improvements that were made on top of backpropagation that actually make artificial neural networks work and take off in the 2010s when some of the core components of modern neural networks started to fall into place.
I remember when I first learnt about neural networks I thought maybe coupling them with some kind of evolutionary approach might be what was needed to make them work. I had absolutely no idea what I was doing of course, but I spent so many nights experimenting with neural networks. I just loved the idea of an artificial "neural network" being able to learn a new problem and spit out an answer. The biggest regret of my life was coming out of university and going into web development because there were basically no AI jobs back then, and no such thing as an AI startup. If you wanted to do AI back then you basically had to be a researcher which didn't interest me at the time.
My favorite take on this is that yes, in fact it is just the chain rule. The usual argument goes that automatic and symbolic differentiation are fundamentally different, so anything particularly old (pre-computers, for example) doesn't count as inventing back prop. But here's my favorite take on equivalences between AD and symbolic diff [0]. I wish there wasn't such importance placed on who invented it for stuff like this. Clearly, someone codifying backprop wasn't a bottleneck in making ML progress, so why's it get so much attention?
The real essence of the piece is that Leibnitz did not schmidhuber [0] Seppo Linnainma (probably because he was dead at the time). Actually it is a nice piece and I was really happy to get my expectations fulfilled when reading to the very end.
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[ 4.8 ms ] story [ 49.0 ms ] threadSo... Automatic integration?
Proportional, integrative, derivative. A PID loop sure sounds like what they're talking about.
In it, he stated the following:
> Indeed, the famous “backpropagation” algorithm that was rediscovered by David Rumelhart in the early 1980s, and which is now viewed as being at the core of the so-called “AI revolution,” first arose in the field of control theory in the 1950s and 1960s. One of its early applications was to optimize the thrusts of the Apollo spaceships as they headed towards the moon.
I was wondering whether anyone could point me to the paper or piece of work he was referring to. There are many citations in Schmidhuber’s piece, and in my previous attempts I've gotten lost in papers.
[1]: https://www.amazon.com/Talking-Nets-History-Neural-Networks/...
[a] https://www.nobelprize.org/uploads/2024/11/advanced-physicsp...
I remember when I learnt about artificial neural networks at university in the late 00s my professors were really sceptical of them, rightly explaining that they become hard to train as you added more hidden layers.
See, what makes backpropagation and artificial neural networks work are all of the small optimisations and algorithm improvements that were added on top of backpropagation. Without these improvements it's too computationally inefficient to be practical and you have to contend with issues like exploding gradients.
I think Geoffrey Hinton has noted a few times that for people like him who have been working on artificial neural networks for years it's quite surprising that today neural networks just work because for years it was so hard to get them to do anything. In this sense while backpropagation is the foundational algorithm, it's not sufficient on it's own. It was the many improvements that were made on top of backpropagation that actually make artificial neural networks work and take off in the 2010s when some of the core components of modern neural networks started to fall into place.
I remember when I first learnt about neural networks I thought maybe coupling them with some kind of evolutionary approach might be what was needed to make them work. I had absolutely no idea what I was doing of course, but I spent so many nights experimenting with neural networks. I just loved the idea of an artificial "neural network" being able to learn a new problem and spit out an answer. The biggest regret of my life was coming out of university and going into web development because there were basically no AI jobs back then, and no such thing as an AI startup. If you wanted to do AI back then you basically had to be a researcher which didn't interest me at the time.
[0] https://emilien.ca/Notes/Notes/notes/1904.02990v4.pdf
[0] https://www.urbandictionary.com/define.php?term=schmidhubere...