And more broadly, why should you use JVM languagues like Java, Scala, Clojure or Kotlin to build AI and machine-learning solutions? Java is the most widely used programming language in the world. Large organizations in the public and private sector have enormous Java code bases, and rely heavily on the JVM as a compute environment. In particular, much of the open-source big data stack is written for the JVM. This includes Apache Hadoop for distributed data management; Apache Spark as a distributed run-time for fast ETL; Apache Kafka as a message queue; ElasticSearch, Apache Lucene and Apache Solr for search; and Apache Cassandra for data storage to name a few.
Private or internal APIs are far more common than the more well-known public ones. However, as developers move on to other projects and jobs, will others understand the intent and inner workings of all these APIs? These are some of the takeaways from Postman's annual community survey of its developers, which finds that 80% of API activity is for private APIs, versus 20% spent on public APIs. The survey also shows that microservices are front and center in API development. Microservices are the most interesting technology identified by the community for 2017.
Nowadays, most of the code hosting platforms for open-source projects consider the README file as the project cover. As it is the first piece of documentation seen by the project user or maintainer, such a document needs to be crafted with care. Documentation assist can be a useful tool to help documentation writers produce better documentation like README files. In this paper, we show how an abstract representation of a README file can help documentation assist tools provide better suggestions to writers. Our approach benefits from natural language processing tools and techniques to analyze the content of a README file. Using this model and the current cursor position within the document, our tool can suggest pieces of documentation, examples, and figures as well as structure improvements and update suggestions to the writer. Suggestions are presented as cards that can be selected to automatically enhance the document under writing.
Use Create ML with familiar tools like Swift and macOS playgrounds to create and train custom machine learning models on your Mac. You can train models to perform tasks like recognizing images, extracting meaning from text, or finding relationships between numerical values. You train a model to recognize patterns by showing it representative samples. For example, you can train a model to recognize dogs by showing it lots of images of different dogs. After you've trained the model, you test it out on data it hasn't seen before, and evaluate how well it performed the task.
All around us, Siri, Alexa, Google Home and more are incorporating natural language conversations between humans and artificial intelligence (AI) into our everyday interactions. The same digital revolution is happening in today's workplace, with Natural Language Processing (NLP) along with semantic search playing a key role in this transformation. NLP uses computational techniques to extract useful meaning from raw text, while semantic search is enabled by a range of content processing techniques that identify and extract entities, facts, attributes, concepts and events from unstructured content for analysis. Both NLP and semantic search have fueled the rise of enterprise chatbots or digital assistants -- workplace AI that are bringing deeper natural language understanding to not only enhance search but also provide an entirely new way for employees to interact with corporate data and work more productively. So what impact do these technologies have on the future of your enterprise intranets and knowledge sharing?