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> Seems indie frameworks in AI can't survive? AI frameworks are enormously complex pieces of software—a mixed bag of GPU acceleration, math utilities, low and high-level implementations of state-of-the-art components, and a (hopefully, eventually, but not usually) friendly interface to tie it all together. While Keras is certainly useful, it was only possible because of the underlying libraries (i.e. Theano, TensorFlow, etc.) However, maintaining useful frameworks on a shifting landscape of underlying libraries often results in mismatches which leads to weird incompatibilities, performance regressions, numerical instabilities, etc. Even the big players deal with these issues internal to their own AI frameworks. Moreover, due to performance and deployment requirements, and the rapid development of the field, these underlying libraries are often tightly coupled or have poorly defined API interfaces for third-parties outside of "model development". It's not impossible to have well-defined abstractions but "AI" is too new, too varied and too ill-defined itself to have developed those abstractions sufficiently for _large_ indie frameworks to be successful to a broader community. It would be similar to trying to design a high-level framework for "software development". Again, not impossible—Keras did much better than most—but exceedingly difficult for independent developers. Therefore, we're left with only the biggest players. It's not all bad news, though. There's certainly room for independent development, but outside of hobby projects or thought experiments, I don't think these contributions are effective at the broadest level of scope. The best way for independent developers to contribute and keep their sanity is on implementations of individual components, utilities, or abstractions for specific use cases which don't try to be everything to everyone. |