NYAI#7: #DataScience to Operationalize #ML (Matthew Russell) & Computational #Creativity (Dr. Cole)
Posted on November 22nd, 2016
11/22/2016 Risk, 43 West 23rd Street, NY 2nd floor
Speaker 1: Using Data Science to Operationalize Machine Learning – (Matthew Russell, CTO at Digital Reasoning)
Speaker 2: Top-down vs. Bottom-up Computational Creativity – (Dr. Cole D. Ingraham DMA, Lead Developer at Amper Music, Inc.)
Matthew Russell @DigitalReasoning spoke about understanding language using NLP, relationships among entities, and temporal relationship. For human language understanding he views technologies such as knowledge graphs and document analysis is becoming commoditized. The only way to get an advantage is to improve the efficiency of using ML: KPI for data analysis is the number of experiments (tests an hypothesis) that can be run per unit time. The key is to use tools such as:
- Vagrant – allow an environmental setup.
- Jupyter Notebook – like a lab notebook
- Git – version control
- Automation –
He wants highly repeatable experiments. The goal is to speed up the number of experiments that can be conducted per unit time.
He then talked about using machines to read medical report and determine the issues. Negatives can be extracted, but issues are harder to find. Uses an ontology to classify entities.
He talked about experiments on models using ontologies. The use of a fixed ontology depends on the content: the ontology of terms for anti-terrorism evolves over time and needs to be experimentally adjusted over time. Medical ontology is probably most static.
In the second presentation, Cole D. Ingraham @Ampermusic talked about top-down vs bottom-up creativity in the composition of music. Music differs from other audio forms since it has a great deal of very large structure as well as the smaller structure. ML does well at generating good audio on a small time frame, but Cole thinks it is better to apply theories from music to create the larger whole. This is a combination of
Top-down: novel&useful, rejects previous ideas – code driven, “hands on”, you define the structure
Bottom-up: data driven – data driven, “hands off”, you learn the structure
He then talked about music composition at the intersection of Generation vs. analysis (of already composed music) – can do one without the other or one before the other
To successfully generate new and interesting music, one needs to generate variance. Composing music using a purely probabilistic approach is problematic as there is a lack of structure. He likes the approach similar to replacing words with their synonyms which do not fundamentally change the meaning of the sentence, but still makes it different and interesting.
It’s better to work on deterministically defined variance than it is to weed out undesired results from nondeterministic code.
As an example he talked about Wavenet (google deepmind project) which input raw audio and output are raw audio. This approach works well for improving speech synthesis, but less well for music generation as there is no large scale structural awareness.
Cole then talked about Amper, as web site that lets users create music with no experience required: fast, believable, collaborative
They like a mix of top-down and bottom-up approaches:
- Want speed, but neural nets are slow
- Music has a lot of theory behind it, so it’s best to let the programmers code these rules
- Can change different levels of the hierarchical structure within music: style, mood, can also adjust specific bars
Runtime written in Haskell – functional language so its great for music