The Analytical and Generative AI Guide

Artificial intelligence, in all its diversity, offers various solutions, but none of them is universal.
Recognizing the specialization and synergy between different types of AI is key to optimizing their use.

Generative AIs

Designed to create, improvise, iterate.

What do they do?

Generative AIs generate unstructured data from a large volume of unstructured data.

Practical applications

Generates text (product sheets, articles), new images, animations, sound production, etc.

Pros

  • No need to preconfigure a generative AI to make non-specific content
  • Diversity of applications and great versatility
  • Fun and easy to use

Cons

  • High risk of hallucinations that prevent integration into sensitive and critical markets
  • Data confidentiality issues required for model training
  • The need to retrain a model to adapt it to a specific business context, resulting in high resource costs

Analytical AIs

Designed to manage a flow or a process tailored to an organization.

What do they do?

Analytical AIs generate structured data from a large volume of both structured and unstructured data.

Practical applications

Categorization, predictions, semantic networks/NLU, extraction, and structuring of information

Pros

  • Excellent ability to analyze and process large volumes of data, whether structured or unstructured
  • Transforms extracted data into the appropriate format for CRMs and databases
  • Efficient identification of hidden trends and anomalies in large datasets

Cons

  • Quickly reaches its limits for tasks that involve creativity
  • Contextual knowledge rather than general knowledge

Behind these AIs, two major approaches: connectionist and symbolic

Generative and Analytical AI are automatically based on at least one of the two technological approaches below, each bringing advantages and disadvantages.

Connectionist AI

The most popular model for generative AIs, making predictions and image processing.

What does it do?

Statistical AI analyzes large datasets to detect trends and patterns, relying on mathematical and probabilistic techniques.

The name reflects its reliance on statistics and probabilities to process information.

The term includes the following technologies: machine learning, neural networks, deep learning...

Pros

  • Flexible and adaptable
  • Excellent for making predictions based on statistical models
  • Applicable in the world of image and sound processing

Cons

  • It requires Heavy quantity of data required for configuration
  • Technically, it cannot simply explain its decisions (black box effects)
  • Risks of bias
  • Difficult to interpret specific business terms

Symbolic AI

The symbolic approach is the most robust and predictable when it comes to analytical AI for language analysis.

What does it do?

Symbolic AI relies on the use of symbols and logical rules to model human reasoning.

The name is derived from its method of representing and processing information through symbols.

It is particularly used in expert systems and natural language processing.

Pros

  • Rules and logics are explicit, making decision processes more transparent
  • Efficiency in language processing
  • Requires minimal data to operate effectively
  • Stable and reliable
  • Consumes low energy resources

Cons

  • Complex AI engine designComplex AI engine design
  • Requires explicit knowledge of the context
  • Not suitable for all use cases such as image or sound processing, etc.

Our vision for language understanding: the symbolic analytical approach

Our technology merges the best of analytic and symbolic, delivering a powerful, accurate, and transparent solution tailored to your specific needs.

Analytical AIs (Golem.ai)
A Reduced customer investment thanks to contextual configuration of the platform by Golem.ai's teams on the customer's premises (maximum 6 weeks)
Based on interactions and archetypes, fundamental principles of meaning definition in linguistic research
All AI decisions are traceable and demonstrable , which means that data processing errors can be reduced and corrected quickly

Investment

6x
Faster implementation

Precision

15%
Additional reliability rate

Explainability

100%
Explainable

Generative AIs (incl. Machine Learning)

A longer setup (6 to 9 months) and a high additional training cost to increase accuracy or for each new addition

Based on a complex algorithm that predicts the next word to generate. There is no understanding , only a probabilistic analysis (risk of errors and hallucinations)

Cannot be explained and demonstrated because it is based on a statistical and probabilistic approach, which makes it impossible to use in certain sensitive sectors

And tomorrow? The Neuro-Symbolic AI

We envision a future where neuro-symbolic AI will revolutionize the way we interact with data, combining neural intuition and symbolic precision for smarter, more intuitive solutions.

The trustworthy AI

Use a technology without bias or hallucinations that addresses today's ethical challenges

Efficient

The quick setup allows for visible results in the first weeks without semantic complexities or hallucinations.

Explainable

All choices resulting from artificial intelligence are explainable, traceable, and demonstrable.

Sovereign

Golem.ai's artificial intelligence is 100% French, proprietary and hosted by Scaleway.

Frugal

This technology consumes very few server resources and has a low CO2 impact.

Ready to go ?

Try out our powerful, explainable and adaptable AI.