Why study the perceptron?
Perceptrons were one of the first learning systems and an important early stepping-stone to most recent AI innovations. That alone would be motivation enough to study them, however the reaction of the press, and the consequences of the hype, are a cautionary tale for us in 2026.
I'm going to share with you the why and the how of the perceptron, with some of the consequences of the hype.
Why do we care about systems that learn?
Go back to the 1950s, why would you care about a system that can learn? There’s the obvious coolness of it, but there are important real-world applications.
Photo analysts study reconnaissance photos looking for hidden bunkers or other items of military significance. The work is tiring and boring at times, but it’s hard to automate because it relies on human interpretation rather than a hard and fast set of rules. The “enemy” constantly changes how they disguise their installations, so whoever or whatever is analyzing photos must continually learn.
A similar problem occurs in post offices. If a post office wants to automate letter sorting, it has to automate reading handwritten addresses. Each person’s handwriting is different, which means creating definitive rules about letter or number formation is hard.
A learning system can adapt itself to new information and so stay productive when things change. In practice, this means it can be taught to recognize a new way a country is disguising a bunker or a new way someone is writing the number 5. It doesn’t require its creators to continually tweak settings. Of course, these automated systems can process letters or images etc. much faster (and cheaper) than human beings, which makes them very attractive.
Given the demand existed, how can you create a system that learns?
How do biological systems learn?
The obvious learning systems are biological. By the 1950s, we’d made some progress understanding how brains work, in particular, we had a basic understanding of how neurons worked, which are the lowest level of processing in the brain.
Neurons take sensory input signals from dendrites into the soma, where the input is “processed”. If the input signal crosses some threshold, the soma fires an output signal (an action potential) through an axon. Neurons learn by changing the way they “weight” different dendrite signals, so changing the conditions under which they fire.
The output of one neuron could be the input to another neuron and real brains have layers of processing.
The picture below shows the arrangement for a single neuron.
My explanation of how neurons work is very simplistic and in reality, it’s much more complicated. In real brains, neurons learn together and there are other biological processes going on involving dendrites. If you want to read more about biological neurons, here are some good references:
- https://qbi.uq.edu.au/brain/brain-anatomy/what-neuron
- https://www.quantamagazine.org/neural-dendrites-reveal-their-computational-power-20200114/
- https://en.wikipedia.org/wiki/Neuron
The perceptron
In 1957, at the Cornell Aeronautical Laboratory in Buffalo, New York, the psychologist Frank Rosenblatt was studying human learning (specifically, the neuron) and trying to replicate it in software and hardware. His team built a prototype system, called the perceptron, that could “learn” in a very limited sense. The learning task was simple image classification.
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The Mark I Perceptron input was a 20x20 photocell array; a photocell is very limited form of digital camera. These 400 inputs were fed to “association units” that weighted the inputs. The weights were set by potentiometers that were adjusted by electric motors. Importantly, the initial weights were random to avoid bias. The system summed the weights and used a simple threshold algorithm (response units) to decide the image classification, if the sum of the weighted signals was above the threshold the algorithm output a signal (a true output), if the sum of the weighted signals was below the threshold, the algorithm did not output a signal (a false output). Technically, the name of the threshold function is a Heaviside step function. If the perceptron made an error, the relevant weights were adjusted. The perceptron required 50 training iterations to reliably distinguish between squares and triangles.
(From the perceptron user manual.)
In 2026, this sounds really basic, but in 1957 it was a breakthrough. Rosenblatt and his team had demonstrated that a machine could learn and change how it “sees” the world.
References:
- https://homepages.math.uic.edu/~lreyzin/papers/rosenblatt58.pdf
- https://en.wikipedia.org/wiki/Perceptron
- https://americanhistory.si.edu/collections/object/nmah_334414
The perceptron theory
Here’s a simple representation of the perceptron. The inputs from the photocell are fed in and assigned weights. There’s a bias term to account for bias in the photocells, for example, the photocells might give a very small signal instead of zero when there’s no image. The weighted inputs (and the bias) are summed. If the weighted sum exceeds some threshold, the perceptron fires, if not, it doesn’t.
(The perceptron is a linear classifier, meaning it can only separates point on a hyperplane. In two dimensions, this means it can only separate points using a straight line.)
Mathematically, this is how it works.
\[u = \sum w_i x_i + b \]
\[y = f(u(x)) = \begin{cases} 1, & \text{if } u(x) > \theta \\ 0, & \text{otherwise} \end{cases}\]
In the vector notation used in machine learning, the equations are usually written:
\[y = h( \textbf{ w} \cdot \textbf{x } + b ) \]
where h is the Heaviside step function.
So far, this is pretty simple, but how does it learn? Rosenblatt insisted on starting training from a random state, so that gives us a starting point. Then we expose the perceptron to some training data where we know what the output should be (the data is labeled). Here’s how we update the weights:
\[ w_i \leftarrow w_i + \Delta w_i \]
\[ \Delta w_i = \eta(t - o)x_i \]
where:
- \(t\) is the target or correct output
- \(o\) is the measured output
- \(\eta\) is the training rate and \( 0 \lt \eta \leq 1\)
We update the weights and try again in an iterative loop. This continues until we can successfully predict the training data set within a certain error, or we’ve reached a set number of iterations, or we’re seeing no improvement. This is similar to how machine learning systems work today.
References:
- https://opencourse.inf.ed.ac.uk/sites/default/files/https/opencourse.inf.ed.ac.uk/inf1-cg/2025/inf1cgl05perceptron_0.pdf
- https://www.articsledge.com/post/perceptron
Perceptron problems
There were lots of issues with the perceptron in its original form. Let’s start with the worst: the hype.
Rosenblatt gave interviews to the press on his system and they ran with it, but not in a good way. A 1958 New York Times article was typical, the headline read “NEW NAVY DEVICE LEARNS BY DOING; Psychologist Shows Embryo of Computer Designed to Read and Grow Wiser”, with a lede: “The Navy revealed the embryo of an electronic computer today that it expects will be able to walk, talk, see, write, reproduce itself and be conscious of its existence.” Other press stories were similarly sensational and hyped the technology. The press very much set the expectation that walking, talking AIs were just around the corner. Of course, the technology couldn’t deliver what the press forecast, which helped lead to a loss of confidence.
The technical problems varied from the straightforward to the severe.
The original perceptron used a simple threshold to decide whether to fire or not, but this caused problems for training weights. Most important training algorithms use derivatives (for example, gradient descent). A simple threshold isn’t differentiable, which means it can’t be used in these kinds of training algorithms. Fortunately, this is relatively easy to fix using a differentiable function to replace the simple threshold. There are a number of possible differentiable functions, and a popular choice is the sigmoid function. (The function that decides whether to fire or not is now called the activation function).
A more serious problem is the logical limitations of the simple perceptron. As Minksy and Papert showed in 1969, there are some logical structures (most notably, XOR), you can’t build using the simple single-layer perceptron architecture. Although multi-layer networks solve these problems, the Minsky and Papert book and their papers significantly damaged research in this area, as we'll see.
This is only a summary of the difficulties the perceptron faced. For a fuller description, check out: https://yuxi.ml/essays/posts/perceptron-controversy/
What happened next
By the early 1970s, the hype bubble had burst. Minsky and Papert’s book had an impact and governments found disappointing results from funding perceptron-based projects; projects promised big results, but in reality, very little was produced. Governmental patience eventually wore thin and eventually they concluded this form of AI research wasn't worth funding. The research money went elsewhere leading to the first “AI Winter” which lasted for a decade or so.
Sadly, AI experienced another hype bubble and collapse in the late 1980s, a second "AI Winter". As a whole, AI research began to get a bad reputation.
The “AI Winters” bled talent and money away from neural network development, but research still continued. Although multi-layer networks had been developed by the 1960s, it wasn’t known how to train them until the Rumelhart, Hinton, and Williams 1986 paper “Learning representations by back-propagating errors” [https://www.nature.com/articles/323533a0] popularized the back propagation method. Convolutional Neural Networks (CNNs) using back propagation and a convolutional structure were demonstrated in 1989. With these technologies as the backbone, LLMs were developed starting in the mid-to-late 2010s. It’s only the enormous success of LLMs that has brought a flood of money into AI research and a resurgence of interest in its origins.
Rosenblatt had a wide variety of research interests, including astronomy and photometry (measuring light). By any measure he was a genius. Unfortunately, in 1971 he died at the age of 43 in a boating accident. His death was just a few years into the first "AI Winter", so he saw the hype and the subsequent bubble bursting. Sadly, he never go to see how the field eventually developed.
Thoughts on the story
The original perceptron was very much based on what had gone before, but it was a breakthrough and ahead of its time, which was part of the problem. The necessary technology wasn’t there to advance quickly. Unfortunately, the hype in the press, fed by Rosenblatt and others, set unrealistic expectations. While great for short-term research funding, it was terrible for the long-term when the hype bubble burst.
AI as a whole has been prone to hype cycles through its entire existence. It's no wonder there's a lot of discussion online about the latest AI bubble bursting. My feeling is, it is different this time, but we're still in a bubble and people are going to get hurt when it eventually pops.
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