Different observability AI use cases and testing needs

While Artificial Intelligence seems to have just popped up when OpenAI brought ChatGPT to the consumer market it has its roots in the mids of the 20th century. But what is it that all of a sudden made it into every conversation we seem to have?
Thomas Natschlaeger, Principal Data Scientist at Dynatrace, who has been working in the AI and Machine Learning space for the past 30 years gives us a brief historical overview and describes the critical evolutionary steps and compelling events in that technology that made it to what it is today. 
Tune in and hear about how AIs are trained, how they are optimized and most importantly: how their outputs can be tested and validated!
In our conversation we discuss current trends towards small language models that will help model digital twins of our existing roles and how AIs are used to Validate other AIs like we humans do when a senior engineer does pair programming with a junior and with that provides essential feedback on current accuracy and input to improve the outcome of future tasks.

Links we discussed
LinkedIn Profile from Thomas: https://www.linkedin.com/in/thomas-natschlaeger/
Ask Me Anything Session on Davis CoPilot: https://www.linkedin.com/posts/grabnerandi_llm-copilot-activity-7373837743971393536-QgxV?utm_source=share&utm_medium=member_desktop&rcm=ACoAAABLhVQBbh8Jkn_K8din5tsQlMCpXRNzlKU
Voxxed Conference Talk: https://amsterdam.voxxeddays.com/talk/?id=39801
Attention is all you need paper: https://en.wikipedia.org/wiki/Attention_Is_All_You_Need