As I started my journey with Generative AI and Large Language Models, I’ve been overwhelmed with the omnipresence of Python. Tons of resources are available with Python front and center. However, I’m a Java developer (with a penchant for Apache Groovy, of course). So what is there for me to create cool new Generative AI projects? When I built my first experiment with the PaLM API, using the integration within the Google Cloud’s Vertex AI offering, I called the available REST API, from my Micronaut application. Read more...

Last week, I wrote about how to get started with the PaLM API in the Java ecosystem, and particularly, how to overcome the lack of Java client libraries (at least for now) for the PaLM API, and how to properly authenticate. However, what I didn’t explain was what I was building! Let’s fix that today, by telling you a story, a kid story! Yes, I was using the trendy Generative AI approach to generate bedtime stories for kids. Read more...

Large Language Models (LLMs for short) are taking the world by storm, and things like ChatGPT have become very popular and used by millions of users daily. Google came up with its own chatbot called Bard, which is powered by its ground-breaking PaLM 2 model and API. You can also find and use the PaLM API from withing Google Cloud as well (as part of Vertex AI Generative AI products) and thus create your own applications based on that API. Read more...

At enterprises across industries, documents are at the center of core business processes. Documents store a treasure trove of valuable information whether it’s a company’s invoices, HR documents, tax forms and much more. However, the unstructured nature of documents make them difficult to work with as a data source. We call this “dark data” or unstructured data that businesses collect, process and store but do not utilize for purposes such as analytics, monetization, etc. Read more...

I missed this talk from Alexandre Dubreuil, when attending Devoxx Belgium 2019, but I had the chance to watch while doing my elliptical bike run, confined at home. It’s about applying Machine Learning to music generation, thanks to the Magenta project, which is based on Tensorflow. I like playing music (a bit of piano & guitar) once in a while, so as a geek, I’ve also always been interested in computer generated music. Read more...

At GR8Conf Europe last year, I talked about how to take advantage of the Google Cloud machine learning APIs using Apache Groovy. With Groovy, you can call the Vision API that recognises what’s in your pictures, or reads text. You can invoke the Natural Language API to understand the structure of your text. With the Speech-To-Text API, you can get transcriptions of what’s been said in an audio stream, or with Text-To-Spech, you can also generate human-like voices from your own text. Read more...

My buddy Wassim and I were back on stage together to talk about chatbots, with Actions on Google and Dialogflow, at DevFest Lille and Best of Web Paris. I’d like to share with you the slides of the presentation (the video has been recorded and will be available at a later time.) You might be interested in those two codelabs to get started on this journey: Build Actions for the Google Assistant - level 1 Build Actions for the Google Assistant - level 2 Here’s the presentation given at DevFest Lille: Read more...

Powerful machine learning APIs are at your fingertips if you’re developing with Google Cloud Platform, as client libraries are available for various programming languages. Today, we’re investigating the Cloud Vision API and its Java SDK, using the Apache Groovy programming language—a multi-faceted language for the Java platform that aims to improve developer productivity thanks to a concise, familiar and easy to learn syntax. At GR8Conf Europe, in Denmark, the conference dedicated to the Apache Groovy ecosystem, I spoke about the machine learning APIs provided by Google Cloud Platform: Vision, Natural Language, Translate, and Speech (both recognition and synthesis). Read more...

Last week at GR8Conf Europe, I spoke about the machine learning APIs provided by Google Cloud Platform: Vision, Natural Language, Speech recognition and synthesis, etc. Since it’s GR8Conf, that means showing samples and demos using a pretty Groovy language, and I promised to share my code afterwards. So here’s a series of blog posts covering the demos I’ve presented. We’ll start with the Vision API. The Vision API allows you to: Read more...

Last March, I had the chance to attend and speak at QCon London. I spoke at the event for its first edition, many moons prior, so it was fun coming back and seeing how the conference evolved. This year, Eric Horesnyi of Streamdata was leading the Artificial Intelligence track, and invited me to speak about Machine Learning. First, I gave an overview of the Machine Learning offering, from the off-the-shelf ready-made APIs like Vision, Speech, Natural Language, Video Intelligence. Read more...