astronomy

Melanie Mitchell

Melanie Mitchell is a astronomy 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 Melanie Mitchell rather than just read about it. In short: Melanie Mitchell is an American computer scientist. She is a Professor at the Santa Fe Institute.

Key takeaways

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

Reference excerpt

Melanie Mitchell is an American computer scientist. She is a Professor at the Santa Fe Institute. Her major work has been in the areas of analogical reasoning, complex systems, genetic algorithms and cellular automata, and her publications in those fields are frequently cited. She received her PhD in 1990 from the University of Michigan under Douglas Hofstadter and John Holland, for which she developed the Copycat cognitive architecture. She is the author of "Analogy-Making as Perception", essentially a book about Copycat. She has also critiqued Stephen Wolfram's A New Kind of Science and showed that genetic algorithms could find better solutions to the majority problem for one-dimensional cellular automata. She is the author of An Introduction to Genetic Algorithms, a widely known introductory book published by MIT Press in 1996. She is also author of Complexity: A Guided Tour (Oxford University Press, 2009), which won the 2010 Phi Beta Kappa Science Book Award, and Artificial Intelligence: A Guide for Thinking Humans (Farrar, Straus, and Giroux).

Life Melanie Mitchell was born and raised in Los Angeles, California. She attended Brown University in Providence, Rhode Island, where she studied physics, astronomy and mathematics. Her interest in artificial intelligence was spurred in college when she read Douglas Hofstadter's Gödel, Escher, Bach. After graduating, she worked as a high school math teacher in New York City. Deciding she "needed to be" in artificial intelligence, Mitchell tracked down Douglas Hofstadter, repeatedly asking to become one of his graduate students. After finding Hofstadter's phone number at MIT, a determined Mitchell made several calls, all of which went unanswered. She was ultimately successful in reaching Hofstadter after calling at 11 p.m., and secured an internship working on the development of Copycat. In the fall of 1984, Mitchell followed Hofstadter to the University of Michigan, submitting a "last minute" application to the university's doctoral program. She earned her Ph.D. in 1990 with the dissertation Copycat: A Computer Model of High-Level Perception and Conceptual Slippage in Analogy-Making.

Career Mitchell is a Professor at the Santa Fe Institute. Mitchell developed the Complexity Explorer platform for the Santa Fe Institute, which offers online courses. More than 25.000 students took Mitchell's course "Introduction to Complexity". In 2018, Barbara Grosz, Dawn Song and Melanie Mitchell organised the workshop "On Crashing the Barrier of Meaning in AI". She features regularly as a guest expert in the Learning Salon, an online interdisciplinary series of meetings about biological and artificial intelligence.

Awards In 2020, Mitchell received the Herbert A. Simon Award (NECSI).

Views While expressing strong support for AI research, Mitchell has expressed concern about AI's vulnerability to hacking as well as its ability to inherit social biases. On artificial general intelligence, Mitchell said in 2019 that "commonsense knowledge" and "humanlike abilities for abstraction and analogy making" might constitute the final step required to build superintelligent machines, but that current technology was not close to being able to solve this current problem. Mitchell believes that humanlike visual intelligence would require "general knowledge, abstraction, and language", and hypothesizes that visual understanding may have to be learned as an embodied agent rather than merely viewing pictures.

Selected publications

Books Mitchell, Melanie (1993). Analogy-Making as Perception. MIT Press. ISBN 0-262-13289-3. Mitchell, Melanie (1998). An Introduction to Genetic Algorithms. Cambridge, Massachusetts, US: MIT Press. ISBN 0-262-63185-7. Mitchell, Melanie (2009). Complexity: A Guided Tour. Oxford, UK: Oxford University Press. ISBN 978-0-19-512441-5. Mitchell, Melanie (October 15, 2019). Artificial Intelligence: A Guide for Thinking Humans (First ed.). Farrar, Straus and Giroux. ISBN 978-0374257835.

Articles Mitchell, M., Holland, J. H., and Forrest, S. (1994). "When will a genetic algorithm outperform hill climbing?". Advances in Neural Information Processing Systems. 6: 51–58.{{cite journal}}: CS1 maint: miscellaneous url (link) CS1 maint: multiple names: authors list (link) Melanie Mitchell, Peter T. Hraber, and James P. Crutchfield (1993). "Revisiting the edge of chaos: Evolving cellular automata to perform computations" (PDF). Complex Systems. 7: 89–130. arXiv:adap-org/9303003. Bibcode:1993adap.org..3003M.{{cite journal}}: CS1 maint: multiple names: authors list (link) Cowan, George; David Pines; David Elliott Meltzer (1999). Complexity : metaphors, models, and reality. Cambridge, Massachusetts, US: Perseus Books. pp. 731. ISBN 978-0738202327.

References

External links Mitchell's professional homepage at the Santa Fe Institute BrainInspired podcast 022 Melanie Mitchell: Complexity, and AI Shortcomings Lex Fridman Podcast #61 Melanie Mitchell: Concepts, Analogies, Common Sense & Future of AI Machine Learning Street Talk #57 Prof. Melanie Mitchell: Why AI is harder than we think Melanie Mitchell publications indexed by Google Scholar

Illustrations

Worked examples

Example 1 — a first encounter with Melanie Mitchell

Start with the simplest possible case. Write down what Melanie Mitchell claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In astronomy, 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 Melanie Mitchell 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 Melanie Mitchell 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 Melanie Mitchell

In research: Melanie Mitchell appears in astronomy 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 Melanie Mitchell 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: Melanie Mitchell is common in secondary-school and first-year university syllabi. It links to neighbouring topics Brown University alumni, Cellular automatists, Complex systems scientists, so understanding it makes those chapters shorter.
In everyday life: Look for Melanie Mitchell 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 Melanie Mitchell in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Melanie Mitchell 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 Melanie Mitchell out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Melanie Mitchell in simple terms?

Melanie Mitchell is an American computer scientist. She is a Professor at the Santa Fe Institute.

Why does Melanie Mitchell matter?

Because it connects several astronomy 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 Melanie Mitchell?

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 Melanie Mitchell.