Welcome to the ‘AI With IBM i’ Blog!
Jesse Gorzinski, architect of AI with IBM i, introduces a new blog that will feature insights from a variety of experts at IBM i
As I write this inaugural blog post, I am filled with excitement and gratitude. For the past eight years, I have had the privilege of serving as the business architect of open source with IBM i. Recently, I have been given the opportunity to expand my role and officially become the architect of AI with IBM i, a position I have been holding for roughly 15 months. I am thrilled to share my knowledge and experience with you, and I am eager to explore the vast possibilities of AI with IBM i.
A Year of Exciting Developments
The past year has been an exciting time for AI with IBM i. We have made significant strides in integrating AI with the IBM i platform, and I would like to highlight a few of our key accomplishments:
- IBM has built new connectors for some of our flagship offerings, including watsonx.ai and watsonx.data, enabling seamless integration with these powerful AI tools.
- We have developed Mapepire, a new data access layer that allows Db2 data to be accessed from any viable AI software stack, including Rocket CE, Open Data Hub, OpenShift AI, RHEL AI and custom-built open source stacks.
- We have integrated IBM i with watsonx Assistant and other chatbots, opening up new possibilities for automation and customer engagement.
- We have begun creating an SDK that enables SQL and RPG to access AI running on the cloud or on-premises, making it easier to leverage AI capabilities in IBM i applications.
- We also greatly improved Manzan, a new integration tool that exposes IBM i events to AI and other systems, including audit journal information, job log information, history log, the system operator message queue and stream files.
As you can see, the IBM team has been doing some great work to enable our clients to succeed. And don’t worry, we’ll be covering these topics in a lot more detail in future blogs.
Customer Successes
Naturally, the most exciting part of our journey has been seeing the wins. IBM i shops have already been leveraging the different types of artificial intelligence for thrilling new initiatives within their companies. More specifically, let’s take a brief look at a few key different types of AI and how they’ve been used with IBM i:
- Machine learning: Machine learning is a type of AI that enables systems to learn from data and improve their performance over time. Companies are using machine earning to analyze customer behavior, predict sales trends and optimize business processes.
- Deep learning: Deep learning is a subset of machine learning that uses neural networks to analyze complex data sets. Companies are using deep learning to develop image and speech recognition systems, natural language processing and predictive analytics.
- Generative AI: Generative AI (GenAI) is a type of AI that enables systems to generate new content, such as text, images or music. Companies are using GenAI to create personalized content, automate tedious workflows, build chatbots and much more.
What to Expect From This Blog
This blog will not be a solo effort. We will be curating content from a variety of experts and thought leaders in the field of AI with IBM i. In fact, we already have about 20 topics queued up for future publication, covering a range of topics from product overviews to advanced technical tutorials.
Thank you for following along on this journey with me. I am excited to share my knowledge and experience with you, and I hope that this blog will serve as a valuable resource for anyone looking to explore the possibilities of AI with IBM i. If you’re just starting out or looking to expand your journey with AI, I recommend checking out http://ibm.biz/ai-ibmi for a wealth of information and resources. Stay tuned for more updates, and let’s explore the exciting world of AI with IBM i together!