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 from the company's existing database.
- 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.
A Reduced customer investment thanks to contextual configuration of the platform by Golem.ai's teams on the customer's premises (maximum 6 weeks)
Investment
Precision
Explicability
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)
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.
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.
Les IA génératives
Designed to create, improvise, iterate
Les IA génératives génèrent de la donnée unstructured data from a large volume of unstructured data.
Generates text (product sheets, articles), new images, animations, sound production, etc.
- No need to preconfigure a generative AI to make non-specific content
- Diversity of applications and great versatility
- Fun and easy to use
- 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
Les IA analytiques
Designed to manage a flow, process adapté à une organisation
Les IA analytiques génèrent de la donnée structurée from a large volume of both structured and unstructured data.
Categorization, predictions, semantic networks/NLU, extraction, and structuring of information
- Excellent ability to analyze and process large volumes of data, whether structured or unstructured
- Transforms extracted data into the appropriate format for CRM and from the company's existing database.
- Efficient identification of hidden trends and anomalies in large datasets
- Quickly reaches its limits for tasks that involve creativity
- Contextual knowledge rather than general knowledge
Derrière ces IA, deux grandes approches : connexionniste et symbolique
Generative and Analytical AI are automatically basées sur au moins l’une des deux approches technologiques below, each bringing advantages and the inconvénients.
Connectionist AI
The most popular model for generative AIs, making predictions and image processing
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.
Derrière ce terme, se trouvent les technologies suivantes : Machine Learning, réseaux de neurone, deep Learning…
- Flexible and adaptable
- Excellent for making predictions based on statistical models
- Applicable in the world of image and sound processing
- 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
L’approche symbolique est la plus robust and prévisible when it comes to analytical AI for language analysis
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.
- Interprétabilité : les règles et logiques sont explicit, making decision processes more transparent
- Efficiency in language processing
- Requires minimal data to operate effectively
- Stabilité and reliability
- Consumes low energy resources
- Complex AI engine design
- Requires connaissance explicite of the context
- Not suitable à tous les cas d’usages tels que le traitement d’images or sound processing , etc.
Notre vision pour la compréhension du langage : l’approche analytique symbolique
Notre technologie fusionne le meilleur de l’analytique et du symbolique, offrant une solution puissante, précise and transparente adaptée à vos besoins spécifiques.
IA analytiques (Miralia)
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
Faster implementation
Additional reliability rate
Explainable
IA génératives (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 car basée sur une approche statistique et probabiliste, ce qui rend son impossible to use in certain sensitive sectors
Et demain ? L’IA Neuro-Symbolique
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.
L'IA de confiance
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.
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