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description= AI development can be split into three categories: developing an ML model (where Java isn t competitive and is unlikely to become top of the class any time soon), developing an AI-centered product (where Java is well-positioned and will become stringer soon; but does this category matter in the long run?) and adding AI-based features to larger projects (where Java is already very good and will only become stronger thanks to Valhalla s value types, Panama s FFM and vector APIs, and Babylon s code reflection).;
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java and ai inside java newscast 72 nipafx this site works well without javascript but it works even better with javascript enabled bluesky mastodon discord youtube twitch github stackoverflow rss words tags architecture clean code clean comments code review collections community core lang core libs default methods deprecation documentation dop generics impulse j_ms java 10 java 11 java 12 java 13 java 16 java 17 java 18 java 20 java 23 java 24 java 25 java 26 java 27 java 8 java 9 java basics java next javafx jdeps js junit 5 junit pioneer lambda libfx libraries maven meta migration on ramp optional pattern matching patterns performance project amber project jigsaw project leyden project loom project panama project valhalla rant record args records reflection serialization streams switch techniques testing tools turn of the year var blog posts newsletter the jms videos tags ai architecture book club clean code collections community conversation core libs deprecation documentation dop generics j_ms java 10 java 11 java 12 java 16 java 17 java 18 java 19 java 21 java 22 java 23 java 24 java 25 java 26 java 27 java 28 java 8 java 9 java basics java next javafx junit 5 junit pioneer lambda libraries maven meta migration on ramp openjdk optional pattern matching patterns performance project amber project babylon project galahad project leyden project lilliput project loom project panama project valhalla records reflection serialization streams structured concurrency switch techniques tools turn of the year var vector virtual threads recordings streams schedule code demos demos demos junit pioneer record args libfx talks my talks past upcoming slides about about me license privacy 2024 07 04 java and ai inside java newscast 72 video ai java next project valhalla project babylon project panama ai development can be split into three categories developing an ml model where java isn t competitive and is unlikely to become top of the class any time soon developing an ai centered product where java is well positioned and will become stringer soon but does this category matter in the long run and adding ai based features to larger projects where java is already very good and will only become stronger thanks to valhalla s value types panama s ffm and vector apis and babylon s code reflection always embed videos and give me a cookie to remember privacy policy watch on youtube java and ai inside java newscast 72 table of contents three kinds of ai ai features in java ai products in java ai development in java share follow share this post with your community i m active on various platforms watch this space or follow me there to get notified when i publish new content toc three kinds of ai ai features in java ai products in java ai development in java s f share this post with your community i m active on various platforms watch this space or follow me there to get notified when i publish new content ai in java is bad is a commonly held opinion out there that i without knowing much about this space grudgingly accepted but being a java fanboy i was annoyed by that and i was waiting for valhalla panama and babylon to make sufficient progress so i could make a video about how ai in java may suck now but will be so good in the future but that s not this video when i recently started looking into the topic i realized that ai in java is bad is a pretty myopic view that if it s correct at all really only applies to this very moment in ai development and that java is already well positioned for the future of ai and on top of that come valhalla panama and babylon let me explain welcome everyone to the inside java newscast where we cover recent developments in the openjdk community i m nicolai parlog java developer advocate at oracle and today we re gonna look at java and ai a quick note before we start i know that artificial intelligence is more than just machine learning but since the current ai wave is basically exclusively ml based i ll use the terms interchangeably in this video ready then let s dive right in three kinds of ai i want to split ai development into three categories the first one is developing a machine learning model collecting data preparing it for learning developing and training the model evaluating and iterating on it all the original machine learning tasks the output is a trained model that can classify inputs generate images or texts deny people life choices for inscrutable reasons start nuclear war etc then there s executing a machine learning model based on some inputs note that trained models can be exported and imported by different languages so this can be done on an entirely different platform than was used to train the model and thanks to a distinction mkbhd made me aware of i want to split execution into two categories one and the second overall category is development of a product centered around such an ml model like chatgpt or the humane ai pin this is mostly regular greenfield software development except that requirements for running these models like availability of ml libraries or ease of pushing computations onto the gpus dominate the overall requirements the other and third category is integration of a machine learning model as a feature into larger often pre existing products think of auto tagging and searching in google photos auto subtitling in powerpoint and pretty much everything apple has just presented at wwdc here ai is just one of many requirements one of many forces acting on the project and in the case of brownfield development what a word these forces and the path of least resistance are mostly known and running the model must fit in with the existing architecture let s look at each of these three categories separately and see how suitable java is for them and what features may improve it we ll start with the last one and work our way backwards from there ai features in java the easiest case for ai in java is when model execution needs to be added to an existing java project of course in many situations you could create a new service in an arbitrary language and incorporate that in your project via a rest api or as foreign code but realistically you d probably try to avoid that due to the developmental and operational complexity in this scenario java doesn t need to be the best ecosystem for executing the model it just needs to be better than using a different one minus the additional effort of having it as a separate service and platform and similar logic applies when creating a new project where ai is just one of many features java may not be the best ecosystem just for model execution but it is really strong and often top of its class in many other important development aspects strong typing good abstractions and core library memory safety performance observability security cloud support web server and framework choice 3rd party library choice in general development speed developer base stability and the list goes on and on all that puts java high up on the list for projects that include ai based features assuming its support for model execution is sufficiently good so how good is it on the library and runtime front java offers a number of strong options tornadovm onnx runtime djl tribuo langchain4j just to name a few many already support multi cpu gpu and even fpga accelerated computation and where applicable we can expect their integration with native libraries to improve due to the recent finalization of the foreign function and memory api and some openjdk projects are working on features that will further improve java s capabilities in this space potentially dramatically and particularly when it comes to executing models in pure java project valhalla aims to give us the capability to define types that code like a class work like an int which is relevant here because models like to use primitives like half floats that java currently doesn t support beyond that valhalla will allow us to write performant code that doesn t have to sacrifice good design and maintainability which is essential for every software project that will run in production for anything longer than a few months and another idea valhalla might might might explore is limited operator overloading which may allow us to define for example multiplication for custom scalar scalars scalars sc tensors and sca scalars that s what you get for studying math in german skalare anyway for custom scalars and tensors then there s panama s vector api which can speed up cpu based computations dramatically and finally and most directly aimed at ai there s project babylon its goal is to allow java code to parse other java code and derive new code that could either be a different java program or any kind of foreign code in this context specifically code that can be executed by a gpu i strongly recommend inside java newscast 58 for a primer on project babylon as part of his work on the project its lead paul sandoz explored how to implement triton that s a domain specific python platform for gpu computation in pure java and got really good results always embed videos and give me a cookie to remember privacy policy watch on youtube so the java ecosystem for executing ml models is already pretty strong and valhalla s value types panama s ffm and vector apis and babylon s code reflection will only strengthen it further whether by better integrating with native code or by enabling pure java implementations with similar performance giving projects the benefit of using just one stack for the entire system or service of course even if we ignore the rest of the application and focus on just running a model the code that does that consists of more than calling predictor predict input data needs to be prepared before it can be thrown at the model and likewise its output needs to be interpreted and transformed into something the user can understand this is likely to be a considerable portion of the overall code for model execution and java s strengths apply here as well particularly its good performance characteristics and its recently improved support for designing data centric applications so yeah i m not worried about java when it comes to projects using ai as a feature ai products in java most of what we just discussed also applies to developing an ai centered product in java but of course the larger the ai portion the more strengths and weaknesses in that area dominate the overall evaluation of which platform to use at this moment is java the best for just running an ml model no is it the best for developing an ai centric product once we factor in the surrounding requirements we talked about in the previous section maybe it s definitely up there will it be the best once all the projects i mentioned earlier bear fruit i think it can be yes but here s a more interesting question does it matter i really liked mkbhd s opinion on this and by the way the link to his video as well to everything else i mention here is of course in the description he makes a good argument for ai as a product being mostly a fad for ai becoming mostly just a feature in all kinds of other applications so as it looks now i don t think this category is particularly important ai development in java which leaves us with the last category developing machine learning models in java and his is a tough one it needs everything we described so far and then some ai development is often done by people who don t see themselves as being primarily a software developer and so they value different things about a platform than other developers might ease of learning the language example code bases are always very important how quick you get to the first usable results simplicity over choice and also occasionally or maybe even often simplicity over robustness if certain language features only become beneficial when you maintain a project of sufficient size for a sufficient time but appear to be in the way early on enforcing their use can quickly be seen as a downside looking at you explicit static typing and checked exceptions thanks to project amber s on ramp efforts java made and will keep making significant progress in this area but it will never be a scripting language more importantly though elegant model development requires a number of specific language features and libraries a type system that can easily handle heterogenous data some degree of operator overloading is super helpful ease of use when working with mathematical functions for example for differentiation libraries that were designed to classify and analyze large data sets and really good and easy to use visualization tools and python is and will probably remain king here the language is well suited to these kinds of applications and thanks to that has been the platform of choice for data scientists for about two decades now which gives it a big leg up on libraries and frameworks in that space so java isn t competitive when it comes to developing machine learning models and isn t top of the class in creating ai centered products and this lead to the general opinion that ai in java is bad but this is due to our current place in the ai timeline and overlooks the already dawning reality that a big chunk of ai related development work will be its integration into other projects and there java is already very competitive and will only become stronger in the coming years thanks to projects like valhalla panama and babylon if you want to follow that development along make sure to subscribe if you haven t yet as we will cover every new java feature as it hatches and if you enjoyed this video you can do me a favor and leave a like which also helps putting it in front of more developers i ll see you again in two weeks so long bluesky mastodon discord youtube twitch github stackoverflow rss imprint privacy contact license
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