Technology Transfer - since 1986

Leading Edge Information Technology Education

First Class Speakers

Our motto has always been: “Go to the source”, and this research has brought us together over the years with key figures in the history of Information Technology.

MORE

Courses and Conferences

Our courses address the most critical topics of Information Technology.

MORE

Online Events

Due to time zones, events presented by American speakers will be spread over more days, and will take place in the afternoon from 2 pm to 6 pm Italian time

Data Quality: A “must” for the Business Success

ONLINE LIVE STREAMING

Apr 08 - Apr 09, 2024

By: Nigel Turner

Designing, developing and deploying a Microservices Architecture

ONLINE LIVE STREAMING

Apr 12, 2024

By: Sander Hoogendoorn

Practical Guidelines for Implementing a Data Mesh

ONLINE LIVE STREAMING

Apr 15 - Apr 16, 2024

By: Mike Ferguson

Embedded Analytics, Intelligent Apps & AI Automation

ONLINE LIVE STREAMING

Apr 17, 2024

By: Mike Ferguson

Artificial Intelligence, Machine Learning and Data Management

ONLINE LIVE STREAMING

Apr 18 - Apr 19, 2024

By: Derek Strauss

Business Architecture Best Practices

ONLINE LIVE STREAMING

May 06 - May 09, 2024

By: Roger Burlton

Introduction to Generative AI for Java Developers

ONLINE LIVE STREAMING

May 13 - May 14, 2024

By: Frank Greco

Chatbot and LLM Bootcamp

ONLINE LIVE STREAMING

May 15 - May 16, 2024

By: Ivan Reznikov

Building a Competitive Data Strategy for a Data-Driven Enterprise

ONLINE LIVE STREAMING

May 17, 2024

By: Mike Ferguson

Free article of the month

March 2024

Upcoming events by this speaker:

May 13-14, 2024 Online live streaming:
Introduction to Generative AI for Java Developers

Generative AI and Enterprise Java Developers
(first part)

In the rapidly changing field of software development, it’s crucial for developers to stay ahead of the curve for success.  Certainly, seasoned Java developers keep track of each regular release to grasp the use of the advanced capabilities in this continuously evolving programming language.

The advent of Generative AI has now added a new layer of complexity for Java engineers.  They must understand how to assimilate this critical innovation into their development workflows where it makes sense. This integration introduces a powerful and transformative dimension to the overall software development process.

Why Generative AI Matters in Java Development

Before integrating Generative AI into your software development process, Java developers need to understand the foundations of generative AI. There are foundation concepts such as Artificial Intelligence, Machine Learning, Deep Learning, Predictive AI, and Generative AI that all Java software engineers must understand first.

Artificial Intelligence (AI), simply put, is the simulation of human intelligence in machines that are programmed to think and learn like humans. AI uses algorithms and specialized hardware to enable machines to perform tasks that typically require human intelligence. These tasks include problem-solving, learning, perception, language understanding, and decision-making.  AI is not a new technology; it has a history that goes back many decades, at least to the 1940s and 1950s.  With the advent of cloud computing with instantly available computing resources, now make AI capabilities easily accessible to all developers.

Machine Learning (ML) is one subset of AI.  It is a broader concept that involves the development of algorithms and statistical models that enable a system to perform a specific task without being programmed using conventional techniques.  The machine ingests large quantities of data and determines patterns from this data.  This is similar to mathematical algorithms that determine formalized functions from a given dataset, a technique that I’m sure many of us learned during our university education.

A popular subset of ML is Deep learning. This type of machine learning uses a combination of data structure and algorithm called a neural network.  A neural network is a cascading set of probabilistic weights determined by reading large quantities of historical data patterns.  This data can be based on text, images, sound, or other types. The more layers in the neural network, the greater the accuracy (and complexity) of the machine learning model. Each additional layer that processes the input data increases the model’s ability to recognize patterns in the data. Along with the availability of significant cloud computing resources, deep learning attempts to simulate the human brain’s architecture to process data and make decisions.  Of course, by using more layers to obtain more accuracy, additional computational resources are required. 

Continued to read…

 

Subscribe to our newsletter