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Andrew Jeavons AAA 2016
- 1. An Introduc+on to Neural Networks – Agility, Automa+on & AI Andrew Jeavons, Mass Cogni2on, 2016 Andrew Jeavons Mass Cogni+on 18 August 2016 An Introduc+on to Neural Networks
- 2. An Introduc+on to Neural Networks – Agility, Automa+on & AI Andrew Jeavons, Mass Cogni2on, 2016 The Beginning • “A Logical Calculus of the ideas immanent in Nervous Ac2vity” – McCulloch and PiDs, 1943 • Organiza2on of neurons and logic. – Mimicking organiza2on of neurons in brain • Beginning of “neural networks” approach – But not a simple path.
- 3. An Introduc+on to Neural Networks – Agility, Automa+on & AI Andrew Jeavons, Mass Cogni2on, 2016 Neuron: All or Nothing What comes out is not necessarily what goes in. “Transfer Func2on” – controls what the output is.
- 4. An Introduc+on to Neural Networks – Agility, Automa+on & AI Andrew Jeavons, Mass Cogni2on, 2016 Ar+ficial Neuron: All or Nothing
- 5. An Introduc+on to Neural Networks – Agility, Automa+on & AI Andrew Jeavons, Mass Cogni2on, 2016 Ar+ficial Neuron SoTware “copy” of the structure of a physical neuron. Much simplified, many aspects of neuron func2on were/are hard to simulate. Arranged into more complex mul2 layer structures.
- 6. An Introduc+on to Neural Networks – Agility, Automa+on & AI Andrew Jeavons, Mass Cogni2on, 2016 ANN: Backpropoga+on Network
- 7. An Introduc+on to Neural Networks – Agility, Automa+on & AI Andrew Jeavons, Mass Cogni2on, 2016 Types of Training: Supervised Supervised: Weights adjusted itera2vely to bring down the difference between the output generated by the ANN by the training data set and the test value. For supervised learning divide training data set into train and test segments. ANN learns on the training data set and tests this training on the test dataset.
- 8. An Introduc+on to Neural Networks – Agility, Automa+on & AI Andrew Jeavons, Mass Cogni2on, 2016 Types of Training: Unsupervised Usually different network structure. For example a Kohonen Self Organizing Map: All inputs connect to ALL outputs. Can have hidden layers. Acts as a classifier, produces a map (similar to segmenta2on) of the input data.
- 9. An Introduc+on to Neural Networks – Agility, Automa+on & AI Andrew Jeavons, Mass Cogni2on, 2016 Types of Training: Unsupervised Example of Kohonen Map:
- 10. An Introduc+on to Neural Networks – Agility, Automa+on & AI Andrew Jeavons, Mass Cogni2on, 2016 Types of Training: Unsupervised Select an input at random Find which ar2ficial neuron is most similar based on weight value. Adjust values of “winner” neuron to be close to Input. Keep doing this. Then you can map a new input “vector” and get classifica2on.
- 11. An Introduc+on to Neural Networks – Agility, Automa+on & AI Andrew Jeavons, Mass Cogni2on, 2016 • Lots of arithme2c. – Video games GPU’s have helped alleviate this problem. • Complex – As bigger memory capacity became available bigger networks feasible. • Coding of input. • Architecture selec2on. – Thousands of architectures available. ANN: Challenges
- 12. An Introduc+on to Neural Networks – Agility, Automa+on & AI Andrew Jeavons, Mass Cogni2on, 2016 ANN: New and Interes+ng • “Genera2ve” Networks. – Produce new output rather than classifica2on and predic2on. • Large scale networks for text analysis. – Google word2vec and doc2vec.
- 13. An Introduc+on to Neural Networks – Agility, Automa+on & AI Andrew Jeavons, Mass Cogni2on, 2016 ANN: LSTM Recurrent ANN • Long Term Short Term Memory ANN. – Used to generate a short movie. – Trained on scripts of Scifi movies/TV shows on interweb. – (hDp://arstechnica.com/the-‐mul2verse/2016/06/an-‐ai-‐ wrote-‐this-‐movie-‐and-‐its-‐strangely-‐moving/) – AI came up with its own name, Benjamin, in an interview. – Biologically Plausible ANN, uses cogni2ve psychology constructs.
- 14. An Introduc+on to Neural Networks – Agility, Automa+on & AI Andrew Jeavons, Mass Cogni2on, 2016 ANN: Doc2vec • Word2vec/doc2vec – Take a word or document – Create a “vector” (list of numbers) 300+ long. – All vectors are fixed length – Vectors can be compared • word2vec can show “odd man out” – “cat, dog, dolphin, chair” – Uses “distance” between vectors to decide odd man out
- 15. An Introduc+on to Neural Networks – Agility, Automa+on & AI Andrew Jeavons, Mass Cogni2on, 2016 ANN: Doc2vec • Doc2vec does the same thing for documents. – Converts documents to fixed length vectors. – Can calculate the similarity of documents. – Can be interrogated as in “which color is best” • Prints out most similar documents to input . • Input phrase does not have to be in text corpus trained with. • Encodes seman2cs. • Poten2al for mapping and exploring text • Computa2onally intensive !
- 16. An Introduc+on to Neural Networks – Agility, Automa+on & AI Andrew Jeavons, Mass Cogni2on, 2016 Thank You! www.masscogni2on.com apj@masscogni2on.com
- 17. An Introduc+on to Neural Networks – Agility, Automa+on & AI Andrew Jeavons, Mass Cogni2on, 2016 Q & A Ray Poynter The Future Place Andrew Jeavons Mass Cogni2on