This is the second of a three-part series by Markus Eisele. Part 1 can be found here. Stay tuned for part 3. Many AI projects fail. The reason is often simple. Teams try to rebuild last decade’s applications but add AI on top: A CRM system with AI. A chatbot with AI. A search engine Read More
The Java Developer’s Dilemma: Part 2
This is the second of a three-part series by Markus Eisele. Part 1 can be found here. Stay tuned for part 3. Many AI projects fail. The reason is often simple. Teams try to rebuild last decade’s applications but add AI on top: A CRM system with AI. A chatbot with AI. A search engine
Related Posts
Fixing Enterprise Apps with AI: The T+n Problem
We’ve been watching enterprises struggle with the same customer service paradox for years: They have…
PolyNorm: Few-Shot LLM-Based Text Normalization for Text-to-Speech
Text Normalization (TN) is a key preprocessing step in Text-to-Speech (TTS) systems, converting written forms…
Why and When to Use Sentence Embeddings Over Word Embeddings
Choosing the right text representation is a critical first step in any natural language processing…