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- Picking a Programming Language
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Applied Artificial Intelligence
Common Terms
- Objective Function
- Features
- Under/Over Fitting
- Classification / Regression / Control
Methods
- Supervised (Learn a function from residuals)
- Gradient-Descent
- Perceptron
- Back-Propagation (Non-Linear)
- Reinforcement Learning (Estimating value from reward)
- Minimax, Monte Carlo Tree Search (MCTS)
- Temporal Difference Learning
- Genetic Algorithms
- NEAT
- Unsupervised (Clustering & Dimensionality Reduction)
Challenges
- Making an objective function
- Training-Serving Skew
- Modeling
- Linearizing
- Feature-Extraction & Discretization
- Kernels
- Measuring Performance
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Theoretical Artificial Intelligence
History
- The Turk 1770
- Turing Test, Three Laws of Robotics(1942)
- First neural net: 1951
- Arthur Samuel’s Checkers AI: ‘51
- Golden Age (‘56 - ‘74)
- AI Winter (74-80)
- Renaissance (1990+)
- Deep Blue vs kasparov 1996
What is AI?
- "Real" Intelligence versus Artificial Intelligence
- Common-Sense Knowledge
- Goals: Deducting, Reasoning, Problem Solving
Challenges
- “Strong AI” (General AI): Artificial Intelligence that meets or exceeds human intelligence at every level.
- Combinatorial explosion in planning
- Knowledge representation
- The frame problem
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- Flat pixels
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Unit Testing
History
- 1956 “The Debugging Period”
- Testing associated with debugging
- 1957 - 1978 “Demonstration Period”
- Show software satisfies requirements
- 1979 - 1982 “Destruction Oriented Period”
- Testing should find errors
- 1983 - 1987 “Evaluation Oriented Period”
- Testing to measure quality
- 1988+ “Prevention-Oriented”
- Detect and prevent faults
Features
- Prove correctness
- Mocking
- Code Coverage (Statement and branch)
Strengths
- Establish a contract for the code
- Make sure there is no unreachable code
- Force writing testable code
- Can serve as programmatic documentation
Weaknesses
- Time consuming
- Establish constraints that may not be valid
- False sense of security
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- Jason: Moon Reader Pro+ (Android)
- Patrick: Spaceteam (iOS)
Book of the Show
Image Processing
Overview
- Image Processing: Image in -> image out
- Image Analysis: Image in -> Measurements out
- Image Understanding: Image in -> High level description out
Features & Methods
- Matrices
- Binary Images
- Image Convolution
- Histograms
- Feature Vector
- Artificial Intelligence
- Singular Value Decomposition
- Neural Networks
Challenges
- Amount of Data to Process
- Limited Bit Depth
- Noise
- Orientations
- Occlusion
Tools
- PIL
- OpenCv
- MatLab
- Photoshop / Gimp
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JVM Languages (Jython, Clojure, Scala)
Strengths
- Cross-Platform
- Access to java libraries (especially UI)
- Can sit on top of Hadoop
Weaknesses
- Potentially slower
- Require JVM tuning if memory usage is high
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- Fast Fourier Transform
- Ouya Update
- HTML5 Finalized
- Raspberry Pi Store
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Hadoop
History
- Jeff Dean & Sanjay Ghemawat wrote the paper MapReduce
- Created by Doug Cutting while he was at Yahoo!.
- Intended to support Lucene (search engine reverse indexing).
- Facebook announces their hadoop filesystem has grown to 100 petabytes.
Features
- HDFS: Hadoop Distributed Filesystem
- HBase: A distributed, column-oriented database
- Zookeeper: Distributed coordination service
- Crunch: Simplified API for creating mapreduce pipelines.
Strengths
- Scale-free
- Fault Tolerant
- Can add/remove hardware in real-time.
Weaknesses
- Long spin up / spin down time.
- Excessive Serialization/deserialization
- Excessive Materialization
Tools
- Avro: A serialization framework
- Pig & Hive: querying and storing large datasets
Uses
- Storing/Manipulating Big Data.
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- Call of Duty Black Ops II
- Android stick PCs
- Wooden Computer Kit
- Calculator Portal
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LaTeX
History
- TeX
- Created by Donald Knuth
- Based on 19th century typesetting machines
- LaTeX
- Created by Leslie Lamport
- TeX with many macros
Features
- bibtex: bibliography management
- Most citations have a .bib file for easy import
- Decouple the content (the source) from the style
- Turn your paper into a book, book into a webpage, etc.
- Automatic generation of references, table of contents, bibliography
- Export to many formats
- dvi (default)
- latex2html
- pdf (using pdflatex)
- ps
- Not WYSIWYG, WYSIWYM
Tools
Uses
- Academic writing
- Writing Textbooks
- Resumes, curricula vitae
