Higher-Level Programming Abstractions for Big AI Projects
Embrace the world of Big AI projects with higher-level programming abstractions, optimizing algorithms, and maintaining project longevity. Dive into the complexities of AI development with scalable platforms and innovative optimizations for efficient coding and experimentation.
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Presentation Transcript
We can be at the center of AI 2.0 Christopher Olston Google Research
AI is getting its groove back ... largely thanks to Big Data e.g. Watson, Siri, Google Translate Building Big-AI systems is easy, thanks to scalable data management building blocks BigTable, Map-Reduce, Pregel, Life is good
NOT REALLY Life of a Big-AI project: 1. Commit to an algorithm 2. Bust it up into map functions, co-processors, ... 3. Optimize the crap out of it: Caching, batching Indexing, clever encoding Stupid map-reduce tricks 4. Never ever disband the project (who else could understand the debris field that is your code?) 5. To keep entertained while you maintain your ossified code: read papers about new algorithms and muse it would be cool if we could try that
We Need Higher-Level Programming Abstractions But unlike SQL etc.: Power: Turing complete Syntax: Math should look like math Control: Physical transparency Declarative programs that just work on small data (for experimentation, debugging) Target scalable platforms (e.g. map-reduce), and choose optimizations to apply, via operational-style annotations
