physics

Stochastic Neural Analog Reinforcement Calculator

Stochastic Neural Analog Reinforcement Calculator is a physics topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Stochastic Neural Analog Reinforcement Calculator rather than just read about it. In short: The Stochastic Neural Analog Reinforcement Calculator (SNARC) is a neural network machine designed by Marvin Minsky. Prompted by a letter from Minsky, George Armitage Miller gathered the funding (a few thousand dollars) for the project from the Office of Naval Research of the U.S.

Key takeaways

Stochastic Neural Analog Reinforcement Calculator belongs to physics; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Stochastic Neural Analog Reinforcement Calculator to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Stochastic Neural Analog Reinforcement Calculator from memory before moving on to harder problems.

Reference excerpt

The Stochastic Neural Analog Reinforcement Calculator (SNARC) is a neural network machine designed by Marvin Minsky. Prompted by a letter from Minsky, George Armitage Miller gathered the funding (a few thousand dollars) for the project from the Office of Naval Research of the U.S. Department of Defense in the summer of 1951 with the work to be carried out by Minsky, who was then a graduate student in mathematics at Princeton University. At the time, a physics graduate student at Princeton, Dean S. Edmonds, volunteered that he was good with electronics and therefore Minsky brought him onto the project. During undergraduate years, Minsky was inspired by the 1943 Warren McCulloch and Walter Pitts paper on artificial neurons, and decided to build such a machine. The learning was Skinnerian reinforcement learning, and Minsky talked with Skinner extensively during the development of the machine. They tested the machine on a copy of Shannon's maze, and found that it could learn to solve the maze. Unlike Shannon's maze, this machine did not control a physical robot, but simulated rats running in a maze. The simulation is displayed as an "arrangement of lights", and the circuit was reinforced each time the simulated rat reached the goal. The machine surprised its creators. "The rats actually interacted with one another. If one of them found a good path, the others would tend to follow it." The machine itself is a randomly connected network of approximately 40 Hebb synapses. These synapses each have a memory that holds the probability that a signal comes in one input and another signal will come out of the output. There is a probability knob that goes from 0 to 1 that shows this probability of the signals propagating. If the probability signal gets through, a capacitor remembers this function and engages an electromagnetic clutch. At this point, the operator will press a button to give a reward to the machine. This activates a motor on a surplus Minneapolis-Honeywell C-1 gyroscopic autopilot from a B-24 bomber. The motor turns a chain that goes to all 40 synapse machines, checking if the clutch is engaged or not. As the capacitor can only "remember" for a certain amount of time, the chain only catches the most recent updates of the probabilities. Each neuron contained 6 vacuum tubes and a motor. The entire machine is "the size of a grand piano" and contained 300 vacuum tubes. The tubes failed regularly, but the machine would still work despite failures. This machine is considered one of the first pioneering attempts at the field of artificial intelligence. Minsky went on to be a founding member of MIT's Project MAC, which split to become the MIT Laboratory for Computer Science and the MIT Artificial Intelligence Lab, and is now the MIT Computer Science and Artificial Intelligence Laboratory. In 1985 Minsky became a founding member of the MIT Media Laboratory. According to Minsky, he loaned the machine to students in Dartmouth, and subsequently lost, except for a single neuron. A photo of Minsky's last neuron can be seen here. The photo shows 6 vacuum tubes, one of which is a Sylvania JAN-CHS-6H6GT/G/VT-90A.

References Citations

Works cited Crevier, Daniel (1993). AI: The Tumultuous Search for Artificial Intelligence. New York, NY: BasicBooks. ISBN 0-465-02997-3. Russell, Stuart; Norvig, Peter (2003). Artificial Intelligence: A Modern Approach. London, England: Pearson Education. ISBN 0-137-90395-2.

Further reading Levy, Steven (2010). Hackers. Sebastapol, California: O'Reilly. ISBN 978-1-449-38839-3. "A Neural-Analogue Calculator Based upon a Probability Model of Reinforcement" (Document). Cambridge, Massachusetts: Harvard University Psychological Laboratories. January 8, 1952. Describes the hardware of the SNARC.

External links 1951 – SNARC Maze Solver – Minsky / Edmonds (American) at Cyberneticzoo.com 2011 oral history interview with Marvin Minsky. Relevant segments that concern SNARC: Building my randomly wired neural network machine (136/151) Show and tell: My neural network machine (137/151) Learning machine theories after SNARC (138/151)

Illustrations

Worked examples

Example 1 — a first encounter with Stochastic Neural Analog Reinforcement Calculator

Start with the simplest possible case. Write down what Stochastic Neural Analog Reinforcement Calculator claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In physics, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Stochastic Neural Analog Reinforcement Calculator before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Stochastic Neural Analog Reinforcement Calculator ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Stochastic Neural Analog Reinforcement Calculator

In research: Stochastic Neural Analog Reinforcement Calculator appears in physics research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Stochastic Neural Analog Reinforcement Calculator in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Stochastic Neural Analog Reinforcement Calculator is common in secondary-school and first-year university syllabi. It links to neighbouring topics Artificial neural networks, History of artificial intelligence, so understanding it makes those chapters shorter.
In everyday life: Look for Stochastic Neural Analog Reinforcement Calculator outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Stochastic Neural Analog Reinforcement Calculator in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Stochastic Neural Analog Reinforcement Calculator means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Stochastic Neural Analog Reinforcement Calculator out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Stochastic Neural Analog Reinforcement Calculator in simple terms?

Why does Stochastic Neural Analog Reinforcement Calculator matter?

Because it connects several physics ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Stochastic Neural Analog Reinforcement Calculator?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Stochastic Neural Analog Reinforcement Calculator.