SlideShare a Scribd company logo
Il ruolo dei grafi
nell’AI Conversazionale Ibrida
The team
Dr. Maria Di Maro – Post-doc in computational linguistics, expertise in Common Ground
management and Clarification Requests
Martina Di Bratto – PhD Student in computational linguistics, expertise in argumentation-based
dialogue
Sabrina Mennella – PhD Student in computational linguistics, expertise in common sense
representation
Roberto Basile Giannini – Master student in computer science, working on Influence Diagrams for
Conversation Management
Danilo Esposito – Master student in computer science, working on instructions sequence generation
based on common sense reasoning
Marco Galantino – bachelor student in computer science, working on expressive speech synthesis
Yegor Napadystyy – bachelor student in computer science, working on Nvidia Audio2Face REST
control
Modelling language vs modelling
communication
Large Language Models can capture conversation dynamics but not the motives behind them
Being based on machine learning, they capture regularities and correlations but cannot model
causation
Predicting the most probable utterance, given what people usually say, and presenting it as the AI’s
position is wrong
Choosing to talk (or not to talk) serves purposes: language is not an abstract sequence of symbols to
be predicted
Being able to consider, if not understand, the consequences of its own actions is a necessary step for
machines to produce language with actual intents and avoid harmful content
Machine Learning is only one of many powerful tools to understand language and human behaviour
in general
Theoretical
Model
theoretical
framework built
upon the
observation of
communicative
behaviour patterns
Evaluation
on the field
deployment of the
computational
model in human
centered tasks for
evaluation purposes
Computational
Model
formal
representation of
the theoretical
model
03
01 02
Modelling language vs modelling
communication
It is a tale told by an idiot,
full of sound and fury,
signifying nothing.
Macbeth
Dialogue
system
Dialogue flow
ASR LLM TTS
ASR
Common
Ground
Representation
Decision
Making
LLM TTS
Generative AI is good for Natural Language Generation but not for
Dialogue Management
Illusion of intelligence
Braitenberg, V. (1986). Vehicles: Experiments in synthetic psychology. MIT press
Simple wired vehicles can lead humans to
attribute intelligent behaviour to machines while
only reactive capabilities are given
Machine Learning is very good at cleaning data
by recognising underlying patterns inside noisy
channels.
Are Neural Networks very complex Braitenberg
vehicles? May appear to be intelligent but truly
not have actual intentionality
[…] when we look at these machines or vehicles as if they were animals
in a natural environment ... we will be tempted, then, to use psychological
language in describing their behavior. And yet we know very well that
there is nothing in these vehicles that we have not put in ourselves
Austin, J. L. (1962). Speech acts.
Austin, J. L. (1962). Speech acts.
Conflict Search Graph
Di Maro, M., Origlia, A., & Cutugno, F. (2021). Cutting melted butter? Common Ground inconsistencies management in dialogue systems using graph databases. IJCoL. Italian Journal of Computational Linguistics, 7(7-1, 2), 157-190.
Bastian
The ladder of causation
Pearl, J., & Mackenzie, D. (2018). The book of why: the new science of cause and effect. Basic books.
Understanding one’s own role in the world gives
meaning to producing linguistic acts
Machine learning based agents are passive
observers of the world and can only react to it, not
act in it
Modelling the effect that acting in the world
provides, including speaking, and confronting the
consequences allows to generate language for its
intended purpose
Retrospective reasoning is a fundamental ability
that relies on causal modelling capabilities
Doing
To say something is to do something; or in which by saying or in
saying somthing we are doing something
Austin (Language Philosophy - 1962)
The first cognitive ability, seeing or observation, is the
detection of regularities in our environment, and it is shared by
many animals as well as early humans before the Cognitive
Revolution.
The second ability, doing, stands for predicting the effect(s) of
deliberate alterations of the environment, and choosing among
these alterations to produce a desired outcome.
Usage of tools, provided they are designed for a purpose and
not just picked up by accident or copied from one’s ancestors,
could be taken as a sign of reaching this second level.
Pearl (Computer Science - 2018)
Language as a tool
I have a dream But it is not this day
When diplomacy ends, war begins
The first step in the development of taste is to be
willing to credit your own opinion
Blessed are those who are persecuted because of
righteousness
Does he look like a bitch?
Machine learning
Decision making
Counterfactual
reasoning
The role of graphs
Good to represent data in a
human interpretable way
Backed by considerable math
Can be projected in numerical
spaces for the machine to work
with
A modern way to recover
inference engines
Support grounding and
explainability
Network analysis algorithms help studying
the problem from a mathematical point of
view.
The Hyperlink-Induced Topic Search
(HITS) algorithm scores nodes in terms of
Authority: how important the node is in the
network
Hub: how important are the node’s
relationships
Paglieri, F., & Castelfranchi, C. (2005). Revising beliefs through arguments: Bridging the gap between argumentation and belief revision in mas. Argumentation in multi-agent systems: First international workshop
Kleinberg, J. M. (1999). Authoritative sources in a hyperlinked environment. Journal of the ACM (JACM), 46(5), 604-632.
The role of graphs
Text embeddings are numeric vectors that
capture the general topics found in textual
content (movie plots)
Node embeddings are numeric vectors that
capture the role of the node in the network,
in terms of neighbourhood
Weighted embeddings can be computed
using text similarities
Chen, H., Sultan, S. F., Tian, Y., Chen, M., & Skiena, S. (2019). Fast and accurate network embeddings via very sparse random projection. In Proceedings of the 28th ACM international conference on information and knowledge
management (pp. 399-408).
The role of graphs
Interpretability: a graph is uninterpretable if any of the graph patterns describing each of the
foreseen communication problems is activated. In these cases, a clarification request is
produced;
Completeness: an interpretable graph is incomplete if information needed to respond to the
user intent is missing. In these cases, a request for information is produced;
Coherence: a complete graph is incoherent if logical conflicts are found in the belief graph. In
these cases, the adequate disambiguation question is produced;
Stability: a coherent graph is unstable if there are open issues, like unanswered questions. In
these cases, a question answering strategy is activated;
Desirability: a stable graph is undesirable if it does not exhibit the goal pattern. In these cases,
the most useful dialog move to create the goal pattern is produced, like an exploration or
exploitation move.
The role of graphs
Communication as a
graph stabilisation game
Example
Decision making
Bayesian models take into
account all the data that is
needed to take a decision
Can consider machine learning
performance on test sets as
priors
Separate priors from evidence
Consider the utility of graph
configurations given the
possibility to take decisions
Where do the models come from?
Decision making
Relevant subgraph
extraction
Why (not) to speak?
Desire
The graph representing the situation I am part of is not what I want it to be. What is the
linguistic act that maximises the probability that the desired graph configuration will appear?
Competence
I am not able to confidently predict what is going to happen given the current graph
configuration. Can I ask a question to collect more information and improve my prediction
capabilities?
Curiosity
The current graph configuration probably contains missing information. Can I ask a question to
complete it?
Hypothesizing
Things went differently from what I expected. What would have been the graph configuration if
I did something different?
Illocutionary strength
Problem
complexity
Interpretability Incompleteness Incoherence Instability Undesirability
Unfilled core
FE
Perception
Syntactic
Lexical
Reference
Unfilled non-core
FE
Information
optionality
Belief
strenght
Answer
complexity
Structured QA
Entropy
Deliberate
Information
sharing
Explanation
RAG
Neutral conflict
CS conflict
-IAR conflict
+IAR conflict
Illocutionary forces
Behaviour Trees
FANTASIA
Origlia, A., Cutugno, F., Rodà, A., Cosi, P., & Zmarich, C. (2019). FANTASIA: a framework for advanced natural tools and applications in social, interactive approaches. Multimedia Tools and Applications, 78(10), 13613-13648.
Origlia, A., Di Bratto, M., Di Maro, M., & Mennella, S. (2022). Developing Embodied Conversational Agents in the Unreal Engine: The FANTASIA Plugin. In Proceedings of the 30th ACM International Conference on
Multimedia (pp. 6950-6951).
The Metafamily – Maya
2017 – 2023
Spatially aware presentations
The Metafamily – Bastian
2020 – 2023
Common ground inconsistencies
The Metafamily – Jason
2021 – 2023
User profiling and recommendation
Desideratum Symbolic AI Statistical AI Markov
Random
Fields
Embedding Logical
Neural
Networks
Neural nets as universal solvers X X X
Allow specialised sub-units X X X X
Meta-learning/Multi-task X - X
Modular X - - X
Can use prior knowledge X X X
True reasoning X - - X
Variables X X X X
Symbol manipulation X ---
Generic models X X X X X
Causality - - ---
Planning capabilities X X -
Perception/reasoning blending - - X
Perform true NLU with novel
interpretation generation
-
Acquire knowledge through Natural
Language
---
Learn with less data ---
Conclusions
Modern AI provides great tools to project physical observations in n-dimensional numerical spaces, which
can represent the mind of a machine
A machine can only communicate with intention if it is given the chance to explore and learn about the
consequences of the action of speaking
Psychological theories about motivation and emotion provide drivers for the machine to evaluate what
speaking or not speaking implies
Research is too much oriented at depleting the mine of every technology that is presented. Lots of papers
showing the applications of new models and little research about the reasons why these models work
A commercial push towards brute force approaches is present: only the big players have the strength so
democratization is a problem
Computer science is maybe the most introspective among hard sciences. Developing machines that talk
can help model intelligence and build tools to stimulate thought through unbiased dialogues
UNI DI NAPOLI FEDERICO II - Il ruolo dei grafi nell'AI Conversazionale Ibrida

More Related Content

Similar to UNI DI NAPOLI FEDERICO II - Il ruolo dei grafi nell'AI Conversazionale Ibrida

Artificial intelligence
Artificial intelligenceArtificial intelligence
Artificial intelligence
Indranil Chowdhury
 
AIML-M1 and M2-TIEpdf.pdf
AIML-M1 and M2-TIEpdf.pdfAIML-M1 and M2-TIEpdf.pdf
AIML-M1 and M2-TIEpdf.pdf
Devikashetty14
 
271_AI Lect Notes.pdf
271_AI Lect Notes.pdf271_AI Lect Notes.pdf
271_AI Lect Notes.pdf
kaxeca4096
 
Web 3.0
Web 3.0Web 3.0
organic information design
organic information designorganic information design
organic information design
rosa qin
 
DOC-20221019-WA0003..pdf
DOC-20221019-WA0003..pdfDOC-20221019-WA0003..pdf
DOC-20221019-WA0003..pdf
YogeshAM4
 
DOC-20221019-WA0003..pdf
DOC-20221019-WA0003..pdfDOC-20221019-WA0003..pdf
DOC-20221019-WA0003..pdf
YogeshAM4
 
AI_01_introduction.pptx
AI_01_introduction.pptxAI_01_introduction.pptx
AI_01_introduction.pptx
Yousef Aburawi
 
Artificial intelligence uses in productive systems and impacts on the world...
Artificial intelligence   uses in productive systems and impacts on the world...Artificial intelligence   uses in productive systems and impacts on the world...
Artificial intelligence uses in productive systems and impacts on the world...
Fernando Alcoforado
 
On the problems of interface: explainability, conceptual spaces, relevance
On the problems of interface: explainability, conceptual spaces, relevanceOn the problems of interface: explainability, conceptual spaces, relevance
On the problems of interface: explainability, conceptual spaces, relevance
Giovanni Sileno
 
Ramon van den Akker. Fairness of machine learning models an overview and prac...
Ramon van den Akker. Fairness of machine learning models an overview and prac...Ramon van den Akker. Fairness of machine learning models an overview and prac...
Ramon van den Akker. Fairness of machine learning models an overview and prac...
Lviv Startup Club
 
Ai notes
Ai notesAi notes
Ai notes
AbdullahGubbi1
 
C463_01_intro.ppt
C463_01_intro.pptC463_01_intro.ppt
C463_01_intro.ppt
ShivanshuGarg9
 
C463_01_intro.ppt
C463_01_intro.pptC463_01_intro.ppt
C463_01_intro.ppt
securework
 
Developing Cognitive Systems to Support Team Cognition
Developing Cognitive Systems to Support Team CognitionDeveloping Cognitive Systems to Support Team Cognition
Developing Cognitive Systems to Support Team Cognition
diannepatricia
 
Continuous Learning Algorithms - a Research Proposal Paper
Continuous Learning Algorithms - a Research Proposal PaperContinuous Learning Algorithms - a Research Proposal Paper
Continuous Learning Algorithms - a Research Proposal Paper
tjb910
 
Artificial Intelligence for Biology
Artificial Intelligence for BiologyArtificial Intelligence for Biology
Artificial Intelligence for Biology
arannadelwar361
 
Machine learning
Machine learningMachine learning
Machine learning
Eduardo Ibrahim
 
Artificial intelligence
Artificial intelligenceArtificial intelligence
Artificial intelligence
Mihir Shah
 
Machine Learning, Artificial General Intelligence, and Robots with Human Minds
Machine Learning, Artificial General Intelligence, and Robots with Human MindsMachine Learning, Artificial General Intelligence, and Robots with Human Minds
Machine Learning, Artificial General Intelligence, and Robots with Human Minds
University of Huddersfield
 

Similar to UNI DI NAPOLI FEDERICO II - Il ruolo dei grafi nell'AI Conversazionale Ibrida (20)

Artificial intelligence
Artificial intelligenceArtificial intelligence
Artificial intelligence
 
AIML-M1 and M2-TIEpdf.pdf
AIML-M1 and M2-TIEpdf.pdfAIML-M1 and M2-TIEpdf.pdf
AIML-M1 and M2-TIEpdf.pdf
 
271_AI Lect Notes.pdf
271_AI Lect Notes.pdf271_AI Lect Notes.pdf
271_AI Lect Notes.pdf
 
Web 3.0
Web 3.0Web 3.0
Web 3.0
 
organic information design
organic information designorganic information design
organic information design
 
DOC-20221019-WA0003..pdf
DOC-20221019-WA0003..pdfDOC-20221019-WA0003..pdf
DOC-20221019-WA0003..pdf
 
DOC-20221019-WA0003..pdf
DOC-20221019-WA0003..pdfDOC-20221019-WA0003..pdf
DOC-20221019-WA0003..pdf
 
AI_01_introduction.pptx
AI_01_introduction.pptxAI_01_introduction.pptx
AI_01_introduction.pptx
 
Artificial intelligence uses in productive systems and impacts on the world...
Artificial intelligence   uses in productive systems and impacts on the world...Artificial intelligence   uses in productive systems and impacts on the world...
Artificial intelligence uses in productive systems and impacts on the world...
 
On the problems of interface: explainability, conceptual spaces, relevance
On the problems of interface: explainability, conceptual spaces, relevanceOn the problems of interface: explainability, conceptual spaces, relevance
On the problems of interface: explainability, conceptual spaces, relevance
 
Ramon van den Akker. Fairness of machine learning models an overview and prac...
Ramon van den Akker. Fairness of machine learning models an overview and prac...Ramon van den Akker. Fairness of machine learning models an overview and prac...
Ramon van den Akker. Fairness of machine learning models an overview and prac...
 
Ai notes
Ai notesAi notes
Ai notes
 
C463_01_intro.ppt
C463_01_intro.pptC463_01_intro.ppt
C463_01_intro.ppt
 
C463_01_intro.ppt
C463_01_intro.pptC463_01_intro.ppt
C463_01_intro.ppt
 
Developing Cognitive Systems to Support Team Cognition
Developing Cognitive Systems to Support Team CognitionDeveloping Cognitive Systems to Support Team Cognition
Developing Cognitive Systems to Support Team Cognition
 
Continuous Learning Algorithms - a Research Proposal Paper
Continuous Learning Algorithms - a Research Proposal PaperContinuous Learning Algorithms - a Research Proposal Paper
Continuous Learning Algorithms - a Research Proposal Paper
 
Artificial Intelligence for Biology
Artificial Intelligence for BiologyArtificial Intelligence for Biology
Artificial Intelligence for Biology
 
Machine learning
Machine learningMachine learning
Machine learning
 
Artificial intelligence
Artificial intelligenceArtificial intelligence
Artificial intelligence
 
Machine Learning, Artificial General Intelligence, and Robots with Human Minds
Machine Learning, Artificial General Intelligence, and Robots with Human MindsMachine Learning, Artificial General Intelligence, and Robots with Human Minds
Machine Learning, Artificial General Intelligence, and Robots with Human Minds
 

More from Neo4j

Atelier - Architecture d’applications de Graphes - GraphSummit Paris
Atelier - Architecture d’applications de Graphes - GraphSummit ParisAtelier - Architecture d’applications de Graphes - GraphSummit Paris
Atelier - Architecture d’applications de Graphes - GraphSummit Paris
Neo4j
 
Atelier - Innover avec l’IA Générative et les graphes de connaissances
Atelier - Innover avec l’IA Générative et les graphes de connaissancesAtelier - Innover avec l’IA Générative et les graphes de connaissances
Atelier - Innover avec l’IA Générative et les graphes de connaissances
Neo4j
 
Neo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
Neo4j - Product Vision and Knowledge Graphs - GraphSummit ParisNeo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
Neo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
Neo4j
 
Neo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
Neo4j - Product Vision and Knowledge Graphs - GraphSummit ParisNeo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
Neo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
Neo4j
 
FLOA - Détection de Fraude - GraphSummit Paris
FLOA -  Détection de Fraude - GraphSummit ParisFLOA -  Détection de Fraude - GraphSummit Paris
FLOA - Détection de Fraude - GraphSummit Paris
Neo4j
 
SOPRA STERIA - GraphRAG : repousser les limitations du RAG via l’utilisation ...
SOPRA STERIA - GraphRAG : repousser les limitations du RAG via l’utilisation ...SOPRA STERIA - GraphRAG : repousser les limitations du RAG via l’utilisation ...
SOPRA STERIA - GraphRAG : repousser les limitations du RAG via l’utilisation ...
Neo4j
 
ADEO - Knowledge Graph pour le e-commerce, entre challenges et opportunités ...
ADEO -  Knowledge Graph pour le e-commerce, entre challenges et opportunités ...ADEO -  Knowledge Graph pour le e-commerce, entre challenges et opportunités ...
ADEO - Knowledge Graph pour le e-commerce, entre challenges et opportunités ...
Neo4j
 
GraphSummit Paris - The art of the possible with Graph Technology
GraphSummit Paris - The art of the possible with Graph TechnologyGraphSummit Paris - The art of the possible with Graph Technology
GraphSummit Paris - The art of the possible with Graph Technology
Neo4j
 
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
Neo4j
 
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
Neo4j
 
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
Neo4j
 
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
GraphSummit Singapore | The Art of the  Possible with Graph - Q2 2024GraphSummit Singapore | The Art of the  Possible with Graph - Q2 2024
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
Neo4j
 
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
Neo4j
 
GraphAware - Transforming policing with graph-based intelligence analysis
GraphAware - Transforming policing with graph-based intelligence analysisGraphAware - Transforming policing with graph-based intelligence analysis
GraphAware - Transforming policing with graph-based intelligence analysis
Neo4j
 
GraphSummit Stockholm - Neo4j - Knowledge Graphs and Product Updates
GraphSummit Stockholm - Neo4j - Knowledge Graphs and Product UpdatesGraphSummit Stockholm - Neo4j - Knowledge Graphs and Product Updates
GraphSummit Stockholm - Neo4j - Knowledge Graphs and Product Updates
Neo4j
 
KLARNA - Language Models and Knowledge Graphs: A Systems Approach
KLARNA -  Language Models and Knowledge Graphs: A Systems ApproachKLARNA -  Language Models and Knowledge Graphs: A Systems Approach
KLARNA - Language Models and Knowledge Graphs: A Systems Approach
Neo4j
 
INGKA DIGITAL: Linked Metadata by Design
INGKA DIGITAL: Linked Metadata by DesignINGKA DIGITAL: Linked Metadata by Design
INGKA DIGITAL: Linked Metadata by Design
Neo4j
 
Your enemies use GenAI too - staying ahead of fraud with Neo4j
Your enemies use GenAI too - staying ahead of fraud with Neo4jYour enemies use GenAI too - staying ahead of fraud with Neo4j
Your enemies use GenAI too - staying ahead of fraud with Neo4j
Neo4j
 
BT & Neo4j _ How Knowledge Graphs help BT deliver Digital Transformation.pptx
BT & Neo4j _ How Knowledge Graphs help BT deliver Digital Transformation.pptxBT & Neo4j _ How Knowledge Graphs help BT deliver Digital Transformation.pptx
BT & Neo4j _ How Knowledge Graphs help BT deliver Digital Transformation.pptx
Neo4j
 
Workshop: Enabling GenAI Breakthroughs with Knowledge Graphs - GraphSummit Milan
Workshop: Enabling GenAI Breakthroughs with Knowledge Graphs - GraphSummit MilanWorkshop: Enabling GenAI Breakthroughs with Knowledge Graphs - GraphSummit Milan
Workshop: Enabling GenAI Breakthroughs with Knowledge Graphs - GraphSummit Milan
Neo4j
 

More from Neo4j (20)

Atelier - Architecture d’applications de Graphes - GraphSummit Paris
Atelier - Architecture d’applications de Graphes - GraphSummit ParisAtelier - Architecture d’applications de Graphes - GraphSummit Paris
Atelier - Architecture d’applications de Graphes - GraphSummit Paris
 
Atelier - Innover avec l’IA Générative et les graphes de connaissances
Atelier - Innover avec l’IA Générative et les graphes de connaissancesAtelier - Innover avec l’IA Générative et les graphes de connaissances
Atelier - Innover avec l’IA Générative et les graphes de connaissances
 
Neo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
Neo4j - Product Vision and Knowledge Graphs - GraphSummit ParisNeo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
Neo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
 
Neo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
Neo4j - Product Vision and Knowledge Graphs - GraphSummit ParisNeo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
Neo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
 
FLOA - Détection de Fraude - GraphSummit Paris
FLOA -  Détection de Fraude - GraphSummit ParisFLOA -  Détection de Fraude - GraphSummit Paris
FLOA - Détection de Fraude - GraphSummit Paris
 
SOPRA STERIA - GraphRAG : repousser les limitations du RAG via l’utilisation ...
SOPRA STERIA - GraphRAG : repousser les limitations du RAG via l’utilisation ...SOPRA STERIA - GraphRAG : repousser les limitations du RAG via l’utilisation ...
SOPRA STERIA - GraphRAG : repousser les limitations du RAG via l’utilisation ...
 
ADEO - Knowledge Graph pour le e-commerce, entre challenges et opportunités ...
ADEO -  Knowledge Graph pour le e-commerce, entre challenges et opportunités ...ADEO -  Knowledge Graph pour le e-commerce, entre challenges et opportunités ...
ADEO - Knowledge Graph pour le e-commerce, entre challenges et opportunités ...
 
GraphSummit Paris - The art of the possible with Graph Technology
GraphSummit Paris - The art of the possible with Graph TechnologyGraphSummit Paris - The art of the possible with Graph Technology
GraphSummit Paris - The art of the possible with Graph Technology
 
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
 
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
 
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
 
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
GraphSummit Singapore | The Art of the  Possible with Graph - Q2 2024GraphSummit Singapore | The Art of the  Possible with Graph - Q2 2024
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
 
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
 
GraphAware - Transforming policing with graph-based intelligence analysis
GraphAware - Transforming policing with graph-based intelligence analysisGraphAware - Transforming policing with graph-based intelligence analysis
GraphAware - Transforming policing with graph-based intelligence analysis
 
GraphSummit Stockholm - Neo4j - Knowledge Graphs and Product Updates
GraphSummit Stockholm - Neo4j - Knowledge Graphs and Product UpdatesGraphSummit Stockholm - Neo4j - Knowledge Graphs and Product Updates
GraphSummit Stockholm - Neo4j - Knowledge Graphs and Product Updates
 
KLARNA - Language Models and Knowledge Graphs: A Systems Approach
KLARNA -  Language Models and Knowledge Graphs: A Systems ApproachKLARNA -  Language Models and Knowledge Graphs: A Systems Approach
KLARNA - Language Models and Knowledge Graphs: A Systems Approach
 
INGKA DIGITAL: Linked Metadata by Design
INGKA DIGITAL: Linked Metadata by DesignINGKA DIGITAL: Linked Metadata by Design
INGKA DIGITAL: Linked Metadata by Design
 
Your enemies use GenAI too - staying ahead of fraud with Neo4j
Your enemies use GenAI too - staying ahead of fraud with Neo4jYour enemies use GenAI too - staying ahead of fraud with Neo4j
Your enemies use GenAI too - staying ahead of fraud with Neo4j
 
BT & Neo4j _ How Knowledge Graphs help BT deliver Digital Transformation.pptx
BT & Neo4j _ How Knowledge Graphs help BT deliver Digital Transformation.pptxBT & Neo4j _ How Knowledge Graphs help BT deliver Digital Transformation.pptx
BT & Neo4j _ How Knowledge Graphs help BT deliver Digital Transformation.pptx
 
Workshop: Enabling GenAI Breakthroughs with Knowledge Graphs - GraphSummit Milan
Workshop: Enabling GenAI Breakthroughs with Knowledge Graphs - GraphSummit MilanWorkshop: Enabling GenAI Breakthroughs with Knowledge Graphs - GraphSummit Milan
Workshop: Enabling GenAI Breakthroughs with Knowledge Graphs - GraphSummit Milan
 

Recently uploaded

Microservice Teams - How the cloud changes the way we work
Microservice Teams - How the cloud changes the way we workMicroservice Teams - How the cloud changes the way we work
Microservice Teams - How the cloud changes the way we work
Sven Peters
 
How to write a program in any programming language
How to write a program in any programming languageHow to write a program in any programming language
How to write a program in any programming language
Rakesh Kumar R
 
Webinar On-Demand: Using Flutter for Embedded
Webinar On-Demand: Using Flutter for EmbeddedWebinar On-Demand: Using Flutter for Embedded
Webinar On-Demand: Using Flutter for Embedded
ICS
 
Mobile App Development Company In Noida | Drona Infotech
Mobile App Development Company In Noida | Drona InfotechMobile App Development Company In Noida | Drona Infotech
Mobile App Development Company In Noida | Drona Infotech
Drona Infotech
 
Artificia Intellicence and XPath Extension Functions
Artificia Intellicence and XPath Extension FunctionsArtificia Intellicence and XPath Extension Functions
Artificia Intellicence and XPath Extension Functions
Octavian Nadolu
 
LORRAINE ANDREI_LEQUIGAN_HOW TO USE WHATSAPP.pptx
LORRAINE ANDREI_LEQUIGAN_HOW TO USE WHATSAPP.pptxLORRAINE ANDREI_LEQUIGAN_HOW TO USE WHATSAPP.pptx
LORRAINE ANDREI_LEQUIGAN_HOW TO USE WHATSAPP.pptx
lorraineandreiamcidl
 
Empowering Growth with Best Software Development Company in Noida - Deuglo
Empowering Growth with Best Software  Development Company in Noida - DeugloEmpowering Growth with Best Software  Development Company in Noida - Deuglo
Empowering Growth with Best Software Development Company in Noida - Deuglo
Deuglo Infosystem Pvt Ltd
 
Transform Your Communication with Cloud-Based IVR Solutions
Transform Your Communication with Cloud-Based IVR SolutionsTransform Your Communication with Cloud-Based IVR Solutions
Transform Your Communication with Cloud-Based IVR Solutions
TheSMSPoint
 
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI App
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI AppAI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI App
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI App
Google
 
2024 eCommerceDays Toulouse - Sylius 2.0.pdf
2024 eCommerceDays Toulouse - Sylius 2.0.pdf2024 eCommerceDays Toulouse - Sylius 2.0.pdf
2024 eCommerceDays Toulouse - Sylius 2.0.pdf
Łukasz Chruściel
 
DDS-Security 1.2 - What's New? Stronger security for long-running systems
DDS-Security 1.2 - What's New? Stronger security for long-running systemsDDS-Security 1.2 - What's New? Stronger security for long-running systems
DDS-Security 1.2 - What's New? Stronger security for long-running systems
Gerardo Pardo-Castellote
 
Need for Speed: Removing speed bumps from your Symfony projects ⚡️
Need for Speed: Removing speed bumps from your Symfony projects ⚡️Need for Speed: Removing speed bumps from your Symfony projects ⚡️
Need for Speed: Removing speed bumps from your Symfony projects ⚡️
Łukasz Chruściel
 
Oracle Database 19c New Features for DBAs and Developers.pptx
Oracle Database 19c New Features for DBAs and Developers.pptxOracle Database 19c New Features for DBAs and Developers.pptx
Oracle Database 19c New Features for DBAs and Developers.pptx
Remote DBA Services
 
原版定制美国纽约州立大学奥尔巴尼分校毕业证学位证书原版一模一样
原版定制美国纽约州立大学奥尔巴尼分校毕业证学位证书原版一模一样原版定制美国纽约州立大学奥尔巴尼分校毕业证学位证书原版一模一样
原版定制美国纽约州立大学奥尔巴尼分校毕业证学位证书原版一模一样
mz5nrf0n
 
SWEBOK and Education at FUSE Okinawa 2024
SWEBOK and Education at FUSE Okinawa 2024SWEBOK and Education at FUSE Okinawa 2024
SWEBOK and Education at FUSE Okinawa 2024
Hironori Washizaki
 
E-commerce Application Development Company.pdf
E-commerce Application Development Company.pdfE-commerce Application Development Company.pdf
E-commerce Application Development Company.pdf
Hornet Dynamics
 
Enterprise Resource Planning System in Telangana
Enterprise Resource Planning System in TelanganaEnterprise Resource Planning System in Telangana
Enterprise Resource Planning System in Telangana
NYGGS Automation Suite
 
Orion Context Broker introduction 20240604
Orion Context Broker introduction 20240604Orion Context Broker introduction 20240604
Orion Context Broker introduction 20240604
Fermin Galan
 
UI5con 2024 - Boost Your Development Experience with UI5 Tooling Extensions
UI5con 2024 - Boost Your Development Experience with UI5 Tooling ExtensionsUI5con 2024 - Boost Your Development Experience with UI5 Tooling Extensions
UI5con 2024 - Boost Your Development Experience with UI5 Tooling Extensions
Peter Muessig
 
socradar-q1-2024-aviation-industry-report.pdf
socradar-q1-2024-aviation-industry-report.pdfsocradar-q1-2024-aviation-industry-report.pdf
socradar-q1-2024-aviation-industry-report.pdf
SOCRadar
 

Recently uploaded (20)

Microservice Teams - How the cloud changes the way we work
Microservice Teams - How the cloud changes the way we workMicroservice Teams - How the cloud changes the way we work
Microservice Teams - How the cloud changes the way we work
 
How to write a program in any programming language
How to write a program in any programming languageHow to write a program in any programming language
How to write a program in any programming language
 
Webinar On-Demand: Using Flutter for Embedded
Webinar On-Demand: Using Flutter for EmbeddedWebinar On-Demand: Using Flutter for Embedded
Webinar On-Demand: Using Flutter for Embedded
 
Mobile App Development Company In Noida | Drona Infotech
Mobile App Development Company In Noida | Drona InfotechMobile App Development Company In Noida | Drona Infotech
Mobile App Development Company In Noida | Drona Infotech
 
Artificia Intellicence and XPath Extension Functions
Artificia Intellicence and XPath Extension FunctionsArtificia Intellicence and XPath Extension Functions
Artificia Intellicence and XPath Extension Functions
 
LORRAINE ANDREI_LEQUIGAN_HOW TO USE WHATSAPP.pptx
LORRAINE ANDREI_LEQUIGAN_HOW TO USE WHATSAPP.pptxLORRAINE ANDREI_LEQUIGAN_HOW TO USE WHATSAPP.pptx
LORRAINE ANDREI_LEQUIGAN_HOW TO USE WHATSAPP.pptx
 
Empowering Growth with Best Software Development Company in Noida - Deuglo
Empowering Growth with Best Software  Development Company in Noida - DeugloEmpowering Growth with Best Software  Development Company in Noida - Deuglo
Empowering Growth with Best Software Development Company in Noida - Deuglo
 
Transform Your Communication with Cloud-Based IVR Solutions
Transform Your Communication with Cloud-Based IVR SolutionsTransform Your Communication with Cloud-Based IVR Solutions
Transform Your Communication with Cloud-Based IVR Solutions
 
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI App
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI AppAI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI App
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI App
 
2024 eCommerceDays Toulouse - Sylius 2.0.pdf
2024 eCommerceDays Toulouse - Sylius 2.0.pdf2024 eCommerceDays Toulouse - Sylius 2.0.pdf
2024 eCommerceDays Toulouse - Sylius 2.0.pdf
 
DDS-Security 1.2 - What's New? Stronger security for long-running systems
DDS-Security 1.2 - What's New? Stronger security for long-running systemsDDS-Security 1.2 - What's New? Stronger security for long-running systems
DDS-Security 1.2 - What's New? Stronger security for long-running systems
 
Need for Speed: Removing speed bumps from your Symfony projects ⚡️
Need for Speed: Removing speed bumps from your Symfony projects ⚡️Need for Speed: Removing speed bumps from your Symfony projects ⚡️
Need for Speed: Removing speed bumps from your Symfony projects ⚡️
 
Oracle Database 19c New Features for DBAs and Developers.pptx
Oracle Database 19c New Features for DBAs and Developers.pptxOracle Database 19c New Features for DBAs and Developers.pptx
Oracle Database 19c New Features for DBAs and Developers.pptx
 
原版定制美国纽约州立大学奥尔巴尼分校毕业证学位证书原版一模一样
原版定制美国纽约州立大学奥尔巴尼分校毕业证学位证书原版一模一样原版定制美国纽约州立大学奥尔巴尼分校毕业证学位证书原版一模一样
原版定制美国纽约州立大学奥尔巴尼分校毕业证学位证书原版一模一样
 
SWEBOK and Education at FUSE Okinawa 2024
SWEBOK and Education at FUSE Okinawa 2024SWEBOK and Education at FUSE Okinawa 2024
SWEBOK and Education at FUSE Okinawa 2024
 
E-commerce Application Development Company.pdf
E-commerce Application Development Company.pdfE-commerce Application Development Company.pdf
E-commerce Application Development Company.pdf
 
Enterprise Resource Planning System in Telangana
Enterprise Resource Planning System in TelanganaEnterprise Resource Planning System in Telangana
Enterprise Resource Planning System in Telangana
 
Orion Context Broker introduction 20240604
Orion Context Broker introduction 20240604Orion Context Broker introduction 20240604
Orion Context Broker introduction 20240604
 
UI5con 2024 - Boost Your Development Experience with UI5 Tooling Extensions
UI5con 2024 - Boost Your Development Experience with UI5 Tooling ExtensionsUI5con 2024 - Boost Your Development Experience with UI5 Tooling Extensions
UI5con 2024 - Boost Your Development Experience with UI5 Tooling Extensions
 
socradar-q1-2024-aviation-industry-report.pdf
socradar-q1-2024-aviation-industry-report.pdfsocradar-q1-2024-aviation-industry-report.pdf
socradar-q1-2024-aviation-industry-report.pdf
 

UNI DI NAPOLI FEDERICO II - Il ruolo dei grafi nell'AI Conversazionale Ibrida

  • 1. Il ruolo dei grafi nell’AI Conversazionale Ibrida
  • 2. The team Dr. Maria Di Maro – Post-doc in computational linguistics, expertise in Common Ground management and Clarification Requests Martina Di Bratto – PhD Student in computational linguistics, expertise in argumentation-based dialogue Sabrina Mennella – PhD Student in computational linguistics, expertise in common sense representation Roberto Basile Giannini – Master student in computer science, working on Influence Diagrams for Conversation Management Danilo Esposito – Master student in computer science, working on instructions sequence generation based on common sense reasoning Marco Galantino – bachelor student in computer science, working on expressive speech synthesis Yegor Napadystyy – bachelor student in computer science, working on Nvidia Audio2Face REST control
  • 3. Modelling language vs modelling communication Large Language Models can capture conversation dynamics but not the motives behind them Being based on machine learning, they capture regularities and correlations but cannot model causation Predicting the most probable utterance, given what people usually say, and presenting it as the AI’s position is wrong Choosing to talk (or not to talk) serves purposes: language is not an abstract sequence of symbols to be predicted Being able to consider, if not understand, the consequences of its own actions is a necessary step for machines to produce language with actual intents and avoid harmful content Machine Learning is only one of many powerful tools to understand language and human behaviour in general
  • 4. Theoretical Model theoretical framework built upon the observation of communicative behaviour patterns Evaluation on the field deployment of the computational model in human centered tasks for evaluation purposes Computational Model formal representation of the theoretical model 03 01 02 Modelling language vs modelling communication
  • 5. It is a tale told by an idiot, full of sound and fury, signifying nothing. Macbeth
  • 6.
  • 8. Dialogue flow ASR LLM TTS ASR Common Ground Representation Decision Making LLM TTS Generative AI is good for Natural Language Generation but not for Dialogue Management
  • 9. Illusion of intelligence Braitenberg, V. (1986). Vehicles: Experiments in synthetic psychology. MIT press Simple wired vehicles can lead humans to attribute intelligent behaviour to machines while only reactive capabilities are given Machine Learning is very good at cleaning data by recognising underlying patterns inside noisy channels. Are Neural Networks very complex Braitenberg vehicles? May appear to be intelligent but truly not have actual intentionality […] when we look at these machines or vehicles as if they were animals in a natural environment ... we will be tempted, then, to use psychological language in describing their behavior. And yet we know very well that there is nothing in these vehicles that we have not put in ourselves
  • 10. Austin, J. L. (1962). Speech acts.
  • 11. Austin, J. L. (1962). Speech acts.
  • 12. Conflict Search Graph Di Maro, M., Origlia, A., & Cutugno, F. (2021). Cutting melted butter? Common Ground inconsistencies management in dialogue systems using graph databases. IJCoL. Italian Journal of Computational Linguistics, 7(7-1, 2), 157-190.
  • 14. The ladder of causation Pearl, J., & Mackenzie, D. (2018). The book of why: the new science of cause and effect. Basic books. Understanding one’s own role in the world gives meaning to producing linguistic acts Machine learning based agents are passive observers of the world and can only react to it, not act in it Modelling the effect that acting in the world provides, including speaking, and confronting the consequences allows to generate language for its intended purpose Retrospective reasoning is a fundamental ability that relies on causal modelling capabilities
  • 15. Doing To say something is to do something; or in which by saying or in saying somthing we are doing something Austin (Language Philosophy - 1962) The first cognitive ability, seeing or observation, is the detection of regularities in our environment, and it is shared by many animals as well as early humans before the Cognitive Revolution. The second ability, doing, stands for predicting the effect(s) of deliberate alterations of the environment, and choosing among these alterations to produce a desired outcome. Usage of tools, provided they are designed for a purpose and not just picked up by accident or copied from one’s ancestors, could be taken as a sign of reaching this second level. Pearl (Computer Science - 2018)
  • 16. Language as a tool I have a dream But it is not this day When diplomacy ends, war begins The first step in the development of taste is to be willing to credit your own opinion Blessed are those who are persecuted because of righteousness Does he look like a bitch?
  • 18. The role of graphs Good to represent data in a human interpretable way Backed by considerable math Can be projected in numerical spaces for the machine to work with A modern way to recover inference engines Support grounding and explainability
  • 19. Network analysis algorithms help studying the problem from a mathematical point of view. The Hyperlink-Induced Topic Search (HITS) algorithm scores nodes in terms of Authority: how important the node is in the network Hub: how important are the node’s relationships Paglieri, F., & Castelfranchi, C. (2005). Revising beliefs through arguments: Bridging the gap between argumentation and belief revision in mas. Argumentation in multi-agent systems: First international workshop Kleinberg, J. M. (1999). Authoritative sources in a hyperlinked environment. Journal of the ACM (JACM), 46(5), 604-632. The role of graphs
  • 20. Text embeddings are numeric vectors that capture the general topics found in textual content (movie plots) Node embeddings are numeric vectors that capture the role of the node in the network, in terms of neighbourhood Weighted embeddings can be computed using text similarities Chen, H., Sultan, S. F., Tian, Y., Chen, M., & Skiena, S. (2019). Fast and accurate network embeddings via very sparse random projection. In Proceedings of the 28th ACM international conference on information and knowledge management (pp. 399-408). The role of graphs
  • 21. Interpretability: a graph is uninterpretable if any of the graph patterns describing each of the foreseen communication problems is activated. In these cases, a clarification request is produced; Completeness: an interpretable graph is incomplete if information needed to respond to the user intent is missing. In these cases, a request for information is produced; Coherence: a complete graph is incoherent if logical conflicts are found in the belief graph. In these cases, the adequate disambiguation question is produced; Stability: a coherent graph is unstable if there are open issues, like unanswered questions. In these cases, a question answering strategy is activated; Desirability: a stable graph is undesirable if it does not exhibit the goal pattern. In these cases, the most useful dialog move to create the goal pattern is produced, like an exploration or exploitation move. The role of graphs
  • 22. Communication as a graph stabilisation game
  • 24. Decision making Bayesian models take into account all the data that is needed to take a decision Can consider machine learning performance on test sets as priors Separate priors from evidence Consider the utility of graph configurations given the possibility to take decisions Where do the models come from?
  • 26. Why (not) to speak? Desire The graph representing the situation I am part of is not what I want it to be. What is the linguistic act that maximises the probability that the desired graph configuration will appear? Competence I am not able to confidently predict what is going to happen given the current graph configuration. Can I ask a question to collect more information and improve my prediction capabilities? Curiosity The current graph configuration probably contains missing information. Can I ask a question to complete it? Hypothesizing Things went differently from what I expected. What would have been the graph configuration if I did something different?
  • 27. Illocutionary strength Problem complexity Interpretability Incompleteness Incoherence Instability Undesirability Unfilled core FE Perception Syntactic Lexical Reference Unfilled non-core FE Information optionality Belief strenght Answer complexity Structured QA Entropy Deliberate Information sharing Explanation RAG Neutral conflict CS conflict -IAR conflict +IAR conflict Illocutionary forces
  • 29. FANTASIA Origlia, A., Cutugno, F., Rodà, A., Cosi, P., & Zmarich, C. (2019). FANTASIA: a framework for advanced natural tools and applications in social, interactive approaches. Multimedia Tools and Applications, 78(10), 13613-13648. Origlia, A., Di Bratto, M., Di Maro, M., & Mennella, S. (2022). Developing Embodied Conversational Agents in the Unreal Engine: The FANTASIA Plugin. In Proceedings of the 30th ACM International Conference on Multimedia (pp. 6950-6951).
  • 30. The Metafamily – Maya 2017 – 2023 Spatially aware presentations
  • 31. The Metafamily – Bastian 2020 – 2023 Common ground inconsistencies
  • 32. The Metafamily – Jason 2021 – 2023 User profiling and recommendation
  • 33.
  • 34. Desideratum Symbolic AI Statistical AI Markov Random Fields Embedding Logical Neural Networks Neural nets as universal solvers X X X Allow specialised sub-units X X X X Meta-learning/Multi-task X - X Modular X - - X Can use prior knowledge X X X True reasoning X - - X Variables X X X X Symbol manipulation X --- Generic models X X X X X Causality - - --- Planning capabilities X X - Perception/reasoning blending - - X Perform true NLU with novel interpretation generation - Acquire knowledge through Natural Language --- Learn with less data ---
  • 35. Conclusions Modern AI provides great tools to project physical observations in n-dimensional numerical spaces, which can represent the mind of a machine A machine can only communicate with intention if it is given the chance to explore and learn about the consequences of the action of speaking Psychological theories about motivation and emotion provide drivers for the machine to evaluate what speaking or not speaking implies Research is too much oriented at depleting the mine of every technology that is presented. Lots of papers showing the applications of new models and little research about the reasons why these models work A commercial push towards brute force approaches is present: only the big players have the strength so democratization is a problem Computer science is maybe the most introspective among hard sciences. Developing machines that talk can help model intelligence and build tools to stimulate thought through unbiased dialogues