AI and deep learning define the future of programming, will Kotlin fly or die?

@darksnake - thanks for the reply.

  • If python can be a wrapper for native so can Kotlin. If python implements in python kotlin can be as efficient or more.

  • Challenge w/ ND4J and deeplearning4j is simply that they aren’t near the top of the market now to say the least. TensorFlow and PyTorch are. Sounds we want to marry the top jvm platform which Kotlin wants to be with those 2 top AI/DL platforms.

  • Thus for kotlin-jvm - do we really want to depend on deeplearning4j? Unless it can be made a winner maybe not?. What are the alternatives. Spark ML is in good use, and surely the spark ecosystem is strong in distributed. Wonder how good is it as a local compute lib.
    And good to check how it compares to TensorFlow and PyTorch, but it won’t show as a competitor on the deep learning framework since spark DL is using TensorFlow under the hood.

  • kmath and other jvm-math libs - will improve, evolve, support as needed. Can/should we learn from at what/how spark ml done math. Quote from guide:

    MLlib uses the linear algebra package Breeze, which depends on netlib-java for optimised numerical processing. If native libraries1 are not available at runtime, you will see a warning message and a pure JVM implementation will be used instead.

  • Slack - yes, joined thanks.

My aim at kmath is currently not to provide performance, but to build a comfortable multiplatform API with basic implementations which could be later supplemented by optimized platform-specific implementations. I am not sure that performance is that much important in ML. In my experience, people with Python and C++ background just accept the statement that libraries they use work fast (usually it rather can work fast under certain conditions) and never check it. What ML people actually want is a convenient ecosystem. Currently we lack visualization tools and notebook scripting, but we are working on it.

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