Jan Schweiger

Hybrid AI: Next Generation Natural Language Processing

What if you could make your NLP system 4x faster and more robust? Discover how hybrid AI combines the best of modern deep learning and classical methods.

Hybrid AI: Next Generation Natural Language Processing
#1about 1 minute

Why 90% of AI projects fail in production

Most AI projects fail to reach production due to challenges with accuracy, data quality, and robustness in real-world scenarios.

#2about 5 minutes

How modern NLP uses Transformer models for search

Transformer models understand the full context of a sentence, enabling semantic search by converting text into vectors for comparison.

#3about 1 minute

Why pure Transformer models fail in the real world

Transformer-only models often struggle in production due to inefficiency, reliance on domain-specific training data, and a lack of robustness.

#4about 2 minutes

The strengths of classical NLP and keyword search

Classical NLP methods like BM25 keyword search are computationally efficient, require no training data, and are highly robust across different domains.

#5about 1 minute

Combining models with the hybrid AI approach

Hybrid AI combines the high accuracy of modern NLP with the efficiency and robustness of classical methods to create superior production models.

#6about 3 minutes

How to build a hybrid search engine with Vespa

Vespa is an open-source tool that simplifies building hybrid systems by allowing you to define parallel search pipelines for Transformers and BM25.

#7about 2 minutes

Analyzing the performance of a hybrid search model

The hybrid AI approach was four times faster than a pure Transformer model while maintaining high accuracy and robustness.

#8about 2 minutes

Exploring other real-world use cases for hybrid AI

Hybrid AI can be used for expert identification by building correctable knowledge graphs and for safety-critical systems like train controls.

#9about 3 minutes

Recap and recommended tools for building NLP models

A summary of how hybrid AI balances deep learning's accuracy with rule-based systems' robustness, plus recommended libraries to get started.

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