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.

Related jobs
Jobs that call for the skills explored in this talk.

Machine Learning Engineer

Picnic Technologies B.V.

Picnic Technologies B.V.
Amsterdam, Netherlands

Intermediate
Senior
Python
Structured Query Language (SQL)
+1

Matching moments

Using AI to make work more human, not replace humans
03:31 MIN

Using AI to make work more human, not replace humans

Turning People Strategy into a Transformation Engine

Why corporate AI adoption lags behind the hype
03:28 MIN

Why corporate AI adoption lags behind the hype

What 2025 Taught Us: A Year-End Special with Hung Lee

Using AI to enable human connection in recruiting
04:06 MIN

Using AI to enable human connection in recruiting

Retention Over Attraction: A New Employer Branding Mindset

Increasing the value of talk recordings post-event
04:57 MIN

Increasing the value of talk recordings post-event

Cat Herding with Lions and Tigers - Christian Heilmann

Moving beyond headcount to solve business problems
04:27 MIN

Moving beyond headcount to solve business problems

What 2025 Taught Us: A Year-End Special with Hung Lee

The strategic value of hiring freelancers in the AI era
07:03 MIN

The strategic value of hiring freelancers in the AI era

WeAreDevelopers LIVE – AI, Freelancing, Keeping Up with Tech and More

Shifting HR from standard products to AI-powered platforms
04:04 MIN

Shifting HR from standard products to AI-powered platforms

Turning People Strategy into a Transformation Engine

The future of recruiting beyond talent acquisition
03:15 MIN

The future of recruiting beyond talent acquisition

What 2025 Taught Us: A Year-End Special with Hung Lee

Featured Partners

Related Videos

See all videos
How AI Models Get Smarter35:16

How AI Models Get Smarter

Ankit Patel

AI: Superhero or Supervillain? How and Why with Scott Hanselman25:17

AI: Superhero or Supervillain? How and Why with Scott Hanselman

Scott Hanselman

Semantic AI: Why Embeddings Might Matter More Than LLMs26:05

Semantic AI: Why Embeddings Might Matter More Than LLMs

Christian Weyer

Bringing the power of AI to your application.32:26

Bringing the power of AI to your application.

Krzysztof Cieślak

HR ROBO SAPIENS: Decoding AI Agents and Workflow Automation for Modern Recruitment26:04

HR ROBO SAPIENS: Decoding AI Agents and Workflow Automation for Modern Recruitment

José Kadlec

The shadows of reasoning – new design paradigms for a gen AI world25:14

The shadows of reasoning – new design paradigms for a gen AI world

Jonas Andrulis

How to Decipher User Uncertainty with GenAI and Vector Search24:47

How to Decipher User Uncertainty with GenAI and Vector Search

Ben Greenberg

Best practices: Building Enterprise Applications that leverage GenAI24:24

Best practices: Building Enterprise Applications that leverage GenAI

Damir

Related Articles

View all articles
EM
Eli McGarvie
13 AI Tools You Have to Try
First, it was NFTs, then it was Web3, and now it’s generative AI… it’s probably time to stop collecting pictures of monkeys and kitties. Chatbots and generative AI are the next big thing. This time we’ve jumped on a trend that has real-world applicat...
13 AI Tools You Have to Try
DC
Daniel Cranney
Stephan Gillich - Bringing AI Everywhere
In the ever-evolving world of technology, AI continues to be the frontier for innovation and transformation. Stephan Gillich, from the AI Center of Excellence at Intel, dove into the subject in a recent session titled "Bringing AI Everywhere," sheddi...
Stephan Gillich - Bringing AI Everywhere

From learning to earning

Jobs that call for the skills explored in this talk.