1. Home
  2. Science
  3. Difference With Other Approaches

What is the difference between Semantic
Folding and other approaches?

Semantic Folding is the only text embedding approach that combines both high accuracy and efficiency

Semantic Folding combines the computational efficiency of word vector models with the high accuracy of Transformer models – a real differentiator for enterprise AI.

First Wave

GloVe, word2Vec, fastText
  • Primarily word vectors
  • Primitive document vectors via averaging word vectors
  • No word sense disambiguation
  • Computationally efficient
  • Limited interpretability

 

Semantic Folding

Cortical.io
  • Interpretable word and document vectors
  • Context-aware document vectors formed via intelligent combination of word vectors
    • Preserves sense
    • Eliminates noise
  • Computationally efficient

Second Wave

BERT, GPT-3, XLNet
  • Contextualized text vectors
  • Document vectors via sequence learning
  • Computationally expensive
  • Real-world limitations:
    • Vocabulary size
    • Sequence length
    • Model size
How to reduce the carbon footprint of AI?

Third AI Winter ahead? Why OpenAI, Google & Co are heading towards a dead-end

Today, all large AI companies are placing their bets on a brute force approach. Yet throwing huge amounts of data at machine learning algorithms and deploying massive processing power is neither efficient nor future-proof. AI needs to get much smarter and by magnitudes more efficient if we want to avoid another winter.

Benchmarks

Semantic Folding Benchmark

Comparing the throughput performance of the classification-inference engine of Cortical.io Semantic Folding versus Google BERT.

Extract & Analyze Benchmark

Comparing extraction performance on a public dataset of contracts against Google’s cloud extraction service.

Quantitative Evaluation

Cortical.io SemanticPro Extract & Analyze outperforms Google ML both overall and on nearly all extraction targets with fewer than 100 annotated examples

F1 Scores on the public Atticus Dataset

    Qualitative Evaluation

    Google ML is less flexible than Semantic Folding, for example:

    • No support for overlapping extractions
    • Maximum annotation length of 10 terms
    • Maximum document length of 10,000 characters (4-5 pages)

    Classify & Automate Benchmark

    Comparing classification performance on public dataset of email against available NLP Libraries.

    Cortical.io SemanticPro Classify & Automate outperforms BERT  in terms of speed by a comparable level of accuracy, and outperforms FastText, Doc2Vec and Word2Vec in terms of accuracy by comparable runtimes.

    Advantages of Semantic Folding

    • High Accuracy

    Semantic fingerprints leverage a rich semantic feature set of 16k parameters, enabling a fine-grained disambiguation of words and concepts.

    • High Efficiency

    Semantic Folding requires order of magnitude less training material (100s vs, 1’000s) and less compute resources because it uses sparse distributed vectors.

    • High Transparency & Explainability

    Each semantic feature can be inspected at the document level so that biases can be eliminated in the models and results explained.

    • High Flexibility & Scalability

    Semantic Folding can be applied to any language and use case, and business users can easily customize models.

    How To Reduce The Carbon Footprint of AI?

    How to reduce the carbon footprint of AI? (Part II)

    Read the Article

    Explore Semantic Folding

    Semantic-Folding-Natural-Language-Understanding

    Read the White Paper