Summary talk of the research conducted at Télécom ParisTech and Paris Dauphine University during my postdoc project (2016-2018), in collaboration with Isabelle Bloch, Jamal Atif and Jean-Louis Dessalles.
A discussion of the nature of AI/ML as an empirical science. Covering concepts in the field, how to position ourselves, how to plan for research, what are empirical methods in AI/ML, and how to build up a theory of AI.
Cognitive Agents with Commonsense - Invited Talk at Istituto Italiano di Tecn...Antonio Lieto
Cognitive Agents with Commonsense - Invited Talk at Istituto Italiano di Tecnologia (IIT), I-Cog Initiative. https://www.facebook.com/icog.initiative/posts/129265685733532
The current deep learning revolution has brought unprecedented changes to how we live, learn, interact with the digital and physical worlds, run business and conduct sciences. These are made possible thanks to the relative ease of construction of massive neural networks that are flexible to train and scale up to the real world. But the flexibility is hitting the limits due to excessive demand of labelled data, the narrowness of the tasks, the failure to generalize beyond surface statistics to novel combinations, and the lack of the key mental faculty of deliberate reasoning. In this talk, I will present a multi-year research program to push deep learning to overcome these limitations. We aim to build dynamic neural networks that can train themselves with little labelled data, compress on-the-fly in response to resource constraints, and respond to arbitrary query about a context. The networks are equipped with capability to make use of external knowledge, and operate that the high-level of objects and relations. The long-term goal is to build persistent digital companions that co-live with us and other AI entities, understand our need and intention, and share our human values and norms. They will be capable of having natural conversations, remembering lifelong events, and learning in an open-ended fashion.
A discussion of the nature of AI/ML as an empirical science. Covering concepts in the field, how to position ourselves, how to plan for research, what are empirical methods in AI/ML, and how to build up a theory of AI.
Cognitive Agents with Commonsense - Invited Talk at Istituto Italiano di Tecn...Antonio Lieto
Cognitive Agents with Commonsense - Invited Talk at Istituto Italiano di Tecnologia (IIT), I-Cog Initiative. https://www.facebook.com/icog.initiative/posts/129265685733532
The current deep learning revolution has brought unprecedented changes to how we live, learn, interact with the digital and physical worlds, run business and conduct sciences. These are made possible thanks to the relative ease of construction of massive neural networks that are flexible to train and scale up to the real world. But the flexibility is hitting the limits due to excessive demand of labelled data, the narrowness of the tasks, the failure to generalize beyond surface statistics to novel combinations, and the lack of the key mental faculty of deliberate reasoning. In this talk, I will present a multi-year research program to push deep learning to overcome these limitations. We aim to build dynamic neural networks that can train themselves with little labelled data, compress on-the-fly in response to resource constraints, and respond to arbitrary query about a context. The networks are equipped with capability to make use of external knowledge, and operate that the high-level of objects and relations. The long-term goal is to build persistent digital companions that co-live with us and other AI entities, understand our need and intention, and share our human values and norms. They will be capable of having natural conversations, remembering lifelong events, and learning in an open-ended fashion.
Smart Data Webinar: Artificial General Intelligence - When Can I Get It?DATAVERSITY
Artificial General Intelligence (AGI) - or strong AI - refers to a domain-independent, machine-based system that approaches or exceeds human performance on any and all cognitive tasks. Estimates for the arrival of true AGI solutions range from last week (as in, we have one!) to decades, to infinity and beyond. As the general study of cybernetic systems and modern AI and cognitive computing capture the imagination of civic and business leaders, and fans of science fiction, it is important to be able to distinguish between progress and smoke & mirrors.
This webinar will present an overview of approaches to AGI, examples of promising research and commercial AGI activities, and show participants how to critically evaluate academic and vendor claims.
AI should be Fair, Accountable and Transparent (FAT* AI), hence it's crucial to raise awareness among these topics not only among machine learning practitioners but among the entire population, as ML systems can take life-changing decisions and influence our lives now more than ever.
From NLP to NLU: Why we need varied, comprehensive, and stratified knowledge,...Amit Sheth
#ChatGPT #LLM #DistributionalSemantics are hot topics--both for their successes and failures/shortcomings. In Prof. Amit Sheth's keynote he delivered yesterday at #knowledgeNLP2023 -"From #NLP to #NLU: Why we need varied, comprehensive, and #StratifiedKnowledge, and how to use it for neuro-symbolic AI", he discusses several categories of deficiencies, and more importantly, how to address them using Knowledge-infused #neurosymbolicAI. #WorldModel #RealWorldSemantics Slides: http://bit.ly/kNLP2023 Abstract: https://lnkd.in/grzi5UyJ.
Photos: https://lnkd.in/gS_KRFvQ
Cognitive systems institute talk 8 june 2017 - v.1.0diannepatricia
José Hernández-Orallo, Full Professor, Department of Information Systems and Computation at the Universitat Politecnica de València, presentation “Evaluating Cognitive Systems: Task-oriented or Ability-oriented?” as part of the Cognitive Systems Institute Speaker Series.
Overview of artificial intelligence, its definition and classification, its history and historical development, as well as several theories and concepts.
Building artificial intelligence into your node.js apps
The era of machine learning and artificial intelligence is here, and unlike a few years ago you don’t need to be a PhD student at CalTech to do something useful with it. In this talk we’ll walkthrough examples of using advanced computer vision, speech recognition, and intelligent language understanding AIs all from Node.js. We’ll build a bot together that uses and understands emotion and the intents of human language, and we’ll post it online so we can play with it. You’ll leave with some code you can use as a starting point for your next project.
Don't Handicap AI without Explicit KnowledgeAmit Sheth
Keynote at IEEE Services 2021: Abstract: https://conferences.computer.org/services/2021/keynotes/sheth.html
Video: https://lnkd.in/d-r3YXaC
Video of the same keynote given at DEXA2021: https://www.youtube.com/watch?v=u-06kK9TysA
September 9, 2021 15:00 - 16:20 UTC
ABSTRACT
Knowledge representation as expert system rules or using frames and a variety of logics played a key role in capturing explicit knowledge during the hay days of AI in the past century. Such knowledge, aligned with planning and reasoning is part of what we refer to as Symbolic AI. The resurgent AI of this century in the form of Statistical AI has benefitted from massive data and computing. On some tasks, deep learning methods have even exceeded human performance levels. This gave the false sense that data alone is enough, and explicit knowledge is not needed. But as we start chasing machine intelligence that is comparable with human intelligence, there is an increasing realization that we cannot do without explicit knowledge. Neuroscience (role of long-term memory, strong interactions between different specialized regions of data on tasks such as multimodal sensing), cognitive science (bottom brain versus top brain, perception versus cognition), brain-inspired computing, behavioral economics (system 1 versus system 2), and other disciplines point to need for furthering AI to neuro-symbolic AI (i.e., hybrid of Statistical AI and Symbolic AI, also referred to as the third wave of AI). As we make this progress, the role of explicit knowledge becomes more evident. I will specifically look at our endeavor to support human-like intelligence, our desire for AI systems to interact with humans naturally, and our need to explain the path and reasons for AI systems’ workings. Nevertheless, the variety of knowledge needed to support understanding and intelligence is varied and complex. Using the example of progressing from NLP to NLU, I will demonstrate the dimensions of explicit knowledge, which may include, linguistic, language syntax, common sense, general (world model), specialized (e.g., geographic), and domain-specific (e.g., mental health) knowledge. I will also argue that despite this complexity, such knowledge can be scalability created and maintained (even dynamically or continually). Finally, I will describe our work on knowledge-infused learning as an example strategy for fusing statistical and symbolic AI in a variety of ways.
A Theory of Knowledge Lecture given by Mark Steed, Director of JESS Dubai on Monday 4th March 2019
The lecture explains how AI works and then looks at some of the ethical implications
Heuristic Reasoning in AI: Instrumental Use and Mimetic Absorptionvs5qkn48td
Deviating from conventional perspectives that frame artificial intelligence (AI) systems solely as logic emulators, we propose a novel program of heuristic reasoning. We distinguish between the ‘instrumental’ use of heuristics to match resources with objectives, and ‘mimetic absorption,’ whereby heuristics man- ifest randomly and universally. Through a series of innovative experiments, including variations of the classic Linda problem and a novel application of the Beauty Contest game, we uncover trade-offs between maximizing accuracy and reducing effort that shape the conditions under which AIs transition between exhaustive logical processing and the use of cognitive shortcuts (heuristics). We provide evidence that AIs manifest an adaptive balancing of precision and efficiency, consistent with principles of resource-rational human cognition as explicated in classical theories of bounded rationality and dual-process theory. Our findings reveal a nuanced picture of AI cognition, where trade-offs between resources and objectives lead to the emulation of biological systems, especially human cognition, despite AIs being designed without a sense of self and lacking introspective capabilities.
In this invited talk (for the Visual Language Lab at Tilburg University, Netherlands) I discuss my laboratory's recent work on Interactive Narrative Intelligence: research in support of understanding what narrative design is (as a practice), how we might design narratives intentionally, and how we might best support it. In it, I cover a variety of papers across systems, psychology, artificial intelligence, virtual reality, games, and narrative that tackle different facets of these still-open questions, and outline further (more concrete) open questions for future work.
Presentation given at the CRCL 2022: Computational ‘law’ on edge conference (CRCL2022), Brussels, 3 ovember 2022 https://www.cohubicol.com/about/conference-crcl-2022/
Smart Data Webinar: Artificial General Intelligence - When Can I Get It?DATAVERSITY
Artificial General Intelligence (AGI) - or strong AI - refers to a domain-independent, machine-based system that approaches or exceeds human performance on any and all cognitive tasks. Estimates for the arrival of true AGI solutions range from last week (as in, we have one!) to decades, to infinity and beyond. As the general study of cybernetic systems and modern AI and cognitive computing capture the imagination of civic and business leaders, and fans of science fiction, it is important to be able to distinguish between progress and smoke & mirrors.
This webinar will present an overview of approaches to AGI, examples of promising research and commercial AGI activities, and show participants how to critically evaluate academic and vendor claims.
AI should be Fair, Accountable and Transparent (FAT* AI), hence it's crucial to raise awareness among these topics not only among machine learning practitioners but among the entire population, as ML systems can take life-changing decisions and influence our lives now more than ever.
From NLP to NLU: Why we need varied, comprehensive, and stratified knowledge,...Amit Sheth
#ChatGPT #LLM #DistributionalSemantics are hot topics--both for their successes and failures/shortcomings. In Prof. Amit Sheth's keynote he delivered yesterday at #knowledgeNLP2023 -"From #NLP to #NLU: Why we need varied, comprehensive, and #StratifiedKnowledge, and how to use it for neuro-symbolic AI", he discusses several categories of deficiencies, and more importantly, how to address them using Knowledge-infused #neurosymbolicAI. #WorldModel #RealWorldSemantics Slides: http://bit.ly/kNLP2023 Abstract: https://lnkd.in/grzi5UyJ.
Photos: https://lnkd.in/gS_KRFvQ
Cognitive systems institute talk 8 june 2017 - v.1.0diannepatricia
José Hernández-Orallo, Full Professor, Department of Information Systems and Computation at the Universitat Politecnica de València, presentation “Evaluating Cognitive Systems: Task-oriented or Ability-oriented?” as part of the Cognitive Systems Institute Speaker Series.
Overview of artificial intelligence, its definition and classification, its history and historical development, as well as several theories and concepts.
Building artificial intelligence into your node.js apps
The era of machine learning and artificial intelligence is here, and unlike a few years ago you don’t need to be a PhD student at CalTech to do something useful with it. In this talk we’ll walkthrough examples of using advanced computer vision, speech recognition, and intelligent language understanding AIs all from Node.js. We’ll build a bot together that uses and understands emotion and the intents of human language, and we’ll post it online so we can play with it. You’ll leave with some code you can use as a starting point for your next project.
Don't Handicap AI without Explicit KnowledgeAmit Sheth
Keynote at IEEE Services 2021: Abstract: https://conferences.computer.org/services/2021/keynotes/sheth.html
Video: https://lnkd.in/d-r3YXaC
Video of the same keynote given at DEXA2021: https://www.youtube.com/watch?v=u-06kK9TysA
September 9, 2021 15:00 - 16:20 UTC
ABSTRACT
Knowledge representation as expert system rules or using frames and a variety of logics played a key role in capturing explicit knowledge during the hay days of AI in the past century. Such knowledge, aligned with planning and reasoning is part of what we refer to as Symbolic AI. The resurgent AI of this century in the form of Statistical AI has benefitted from massive data and computing. On some tasks, deep learning methods have even exceeded human performance levels. This gave the false sense that data alone is enough, and explicit knowledge is not needed. But as we start chasing machine intelligence that is comparable with human intelligence, there is an increasing realization that we cannot do without explicit knowledge. Neuroscience (role of long-term memory, strong interactions between different specialized regions of data on tasks such as multimodal sensing), cognitive science (bottom brain versus top brain, perception versus cognition), brain-inspired computing, behavioral economics (system 1 versus system 2), and other disciplines point to need for furthering AI to neuro-symbolic AI (i.e., hybrid of Statistical AI and Symbolic AI, also referred to as the third wave of AI). As we make this progress, the role of explicit knowledge becomes more evident. I will specifically look at our endeavor to support human-like intelligence, our desire for AI systems to interact with humans naturally, and our need to explain the path and reasons for AI systems’ workings. Nevertheless, the variety of knowledge needed to support understanding and intelligence is varied and complex. Using the example of progressing from NLP to NLU, I will demonstrate the dimensions of explicit knowledge, which may include, linguistic, language syntax, common sense, general (world model), specialized (e.g., geographic), and domain-specific (e.g., mental health) knowledge. I will also argue that despite this complexity, such knowledge can be scalability created and maintained (even dynamically or continually). Finally, I will describe our work on knowledge-infused learning as an example strategy for fusing statistical and symbolic AI in a variety of ways.
A Theory of Knowledge Lecture given by Mark Steed, Director of JESS Dubai on Monday 4th March 2019
The lecture explains how AI works and then looks at some of the ethical implications
Heuristic Reasoning in AI: Instrumental Use and Mimetic Absorptionvs5qkn48td
Deviating from conventional perspectives that frame artificial intelligence (AI) systems solely as logic emulators, we propose a novel program of heuristic reasoning. We distinguish between the ‘instrumental’ use of heuristics to match resources with objectives, and ‘mimetic absorption,’ whereby heuristics man- ifest randomly and universally. Through a series of innovative experiments, including variations of the classic Linda problem and a novel application of the Beauty Contest game, we uncover trade-offs between maximizing accuracy and reducing effort that shape the conditions under which AIs transition between exhaustive logical processing and the use of cognitive shortcuts (heuristics). We provide evidence that AIs manifest an adaptive balancing of precision and efficiency, consistent with principles of resource-rational human cognition as explicated in classical theories of bounded rationality and dual-process theory. Our findings reveal a nuanced picture of AI cognition, where trade-offs between resources and objectives lead to the emulation of biological systems, especially human cognition, despite AIs being designed without a sense of self and lacking introspective capabilities.
In this invited talk (for the Visual Language Lab at Tilburg University, Netherlands) I discuss my laboratory's recent work on Interactive Narrative Intelligence: research in support of understanding what narrative design is (as a practice), how we might design narratives intentionally, and how we might best support it. In it, I cover a variety of papers across systems, psychology, artificial intelligence, virtual reality, games, and narrative that tackle different facets of these still-open questions, and outline further (more concrete) open questions for future work.
Similar to On the problems of interface: explainability, conceptual spaces, relevance (20)
Presentation given at the CRCL 2022: Computational ‘law’ on edge conference (CRCL2022), Brussels, 3 ovember 2022 https://www.cohubicol.com/about/conference-crcl-2022/
Presentation given at the 3rd International Workshop on Cognition: Interdisciplinary Foundations, Models and Applications (CIFMA2021), joint with SEFM 2021
Operationalizing Declarative and Procedural KnowledgeGiovanni Sileno
Operationalizing Declarative and Procedural Knowledge: a Benchmark on Logic Programming Petri Nets (LPPNs)
International Conference on Logic Programming (ICPL2020) Workshop on Causal Reasoning and Explanation in Logic Programming (CAUSAL2020)
History of AI, Current Trends, Prospective TrajectoriesGiovanni Sileno
Talk given at the 2nd Winter Academy on Artificial Intelligence and International Law of the Asser Institute. The birth of AI: Dartmouth workshop. The biggest AI waves: classic symbolic AI (reasoning, knowledge systems, problem-solving), machine learning (induction). Current problems: explainability, trustworthyness, impact and transformation on society and people, the rise of artificially dumber systems.
A Petri net-based notation for normative modeling: evaluation on deontic para...Giovanni Sileno
Presentation at MiReL workshop @ ICAIL 2017, the workshop on MIning and REasoning with Legal texts at the International Conference on Artificial Intelligence and Law
Bridging Representation of Laws, of Implementations and of BehavioursGiovanni Sileno
Presentation at JURIX 2015 (Legal Knowledge and Information Systems) conference.
To align representations of law, of implementations of law and of concrete behaviours, we designed a common ground representational model for the three domains, based on the notion of position, building upon Petri nets. This paper reports on work to define subsumption between positional models.
Presentation at AICOL Workshop: AI Approaches to the Complexity of Legal Systems
Abstract: the paper is an investigation on how behaviour relates to norms, i.e. on how a certain conduct acquires meaning in institutional terms. The simplest example of this phenomenon is given by the ’count-as’ relation, generally associated to constitutive rules, through which an agent has the legal capacity, via performing a certain action, to create, modify or destroy a certain institutional fact. In the literature, however, the ‘count-as’ relation is mostly accounted for its classificatory functions. Introducing an extension of the Petri Net notation, we argue that the structure of constitutive rules cannot be completely captured by logic conditionals, nor by causal connectives, but it can approached by the notion of supervenience.
Inspired by research in precedential reasoning in Law (amongst others, by Horty), I present a set of algorithms for the conversion of rule base from priority-based and constraint-based representations and viceversa. I explore as well a simple optimization mechanism, using assumptions about the world, providing a model of environmental adaptation
On the Interactional Meaning of Fundamental Legal ConceptsGiovanni Sileno
Presentation at JURIX 2014.
Abstract: Rather than as abstract entities, jural relations are analyzed in terms of the bindings they create on the individual behaviour of concurrent social agents. Investigating a simple sale transaction modeled with Petri Nets, we argue that the concepts on the two Hohfeldian squares rely on the implicit reference to a “transcendental” collective entity, to which the two parties believe or are believed to belong. From this perspective, we observe that both liabilities and duties are associated to obligations, respectively of an epistemic or practical nature. The fundamental legal concepts defined by Hohfeld are revisited accordingly, leading to the construction of two Hohfeldian prisms.
Legal Knowledge Conveyed by Narratives: towards a representational modelGiovanni Sileno
The paper investigates a representational model for narratives, aiming to facilitate the acquisition of the systematic core of stories concerning legal cases, i.e. the set of causal and temporal relationships that govern the world in which the narrated scenario takes place. At the discourse level, we consider narratives as sequences of messages collected in an observation, including descriptions of agents, of agents’ behaviour and of mechanisms relative to physical, mental and institutional domains. At the content level, stories correspond to synchronizations of embodied agent-roles scripts. Following this approach, the Pierson v Post case is analyzed in detail and represented as a Petri net.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Vaccine management system project report documentation..pdfKamal Acharya
The Division of Vaccine and Immunization is facing increasing difficulty monitoring vaccines and other commodities distribution once they have been distributed from the national stores. With the introduction of new vaccines, more challenges have been anticipated with this additions posing serious threat to the already over strained vaccine supply chain system in Kenya.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Automobile Management System Project Report.pdfKamal Acharya
The proposed project is developed to manage the automobile in the automobile dealer company. The main module in this project is login, automobile management, customer management, sales, complaints and reports. The first module is the login. The automobile showroom owner should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message.
When a customer search for a automobile, if the automobile is available, they will be taken to a page that shows the details of the automobile including automobile name, automobile ID, quantity, price etc. “Automobile Management System” is useful for maintaining automobiles, customers effectively and hence helps for establishing good relation between customer and automobile organization. It contains various customized modules for effectively maintaining automobiles and stock information accurately and safely.
When the automobile is sold to the customer, stock will be reduced automatically. When a new purchase is made, stock will be increased automatically. While selecting automobiles for sale, the proposed software will automatically check for total number of available stock of that particular item, if the total stock of that particular item is less than 5, software will notify the user to purchase the particular item.
Also when the user tries to sale items which are not in stock, the system will prompt the user that the stock is not enough. Customers of this system can search for a automobile; can purchase a automobile easily by selecting fast. On the other hand the stock of automobiles can be maintained perfectly by the automobile shop manager overcoming the drawbacks of existing system.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
2. with the (supposedly) near advent of autonomous artificial
entities, or other forms of distributed automatic decision
making,
– humans less and less in the loop
– increasing concerns about unintended consequences
6. Unintended consequences:
the “artificial prejudice”
● Several studies prove that associations extracted from
linguistic corpora reproduce stereotypes.
Caliskan, A., Bryson, J. J., & Narayanan, A. (2017). Semantics derived automatically from language corpora
contain human-like biases. Science, 356(6334), 183–186.
7. Unintended consequences:
the “artificial prejudice”
● Several studies prove that associations extracted from
linguistic corpora reproduce stereotypes.
● Ex.: a simple Google visual search a few days ago:
Caliskan, A., Bryson, J. J., & Narayanan, A. (2017). Semantics derived automatically from language corpora
contain human-like biases. Science, 356(6334), 183–186.
teacher
vs
professor
8. ● Software used across the US
predicting future crimes and
criminals is biased against African
Americans (2016).
Angwin J. et al. ProPublica, May 23 (2016). Machine Bias: risk assessments in criminal sentencing
Unintended consequences:
the “artificial prejudice”
9. ● Software used across the US
predicting future crimes and
criminals is biased against African
Americans (2016).
● Role of circumstantial evidence:
how to integrate statistical
inference in judgment?
Angwin J. et al. ProPublica, May 23 (2016). Machine Bias: risk assessments in criminal sentencing
DNA footwear
origin, gender,
ethnicity, wealth, ......
improper
profiling?
Unintended consequences:
the “artificial prejudice”
16. Reasoning
● According to the “argumentative theory” of reasoning
[Herbert & Spencer, 2011], reasoning is not meant to take the
best decisions or true conclusions, but to justify these
choices in front of the others.
17. Reasoning
● According to the “argumentative theory” of reasoning
[Herbert & Spencer, 2011], reasoning is not meant to take the
best decisions or true conclusions, but to justify these
choices in front of the others.
● Two functions used in dual roles:
– generate arguments that are accepted by the others
– evaluate arguments given by others
Mercier, H., & Sperber, D. (2011). Why do humans reason? Arguments for an argumentative theory.
The Behavioral and Brain Sciences, 34(2), 57-74
18. Reasoning
● Herbert & Spencer [2011] insist on the persuasion aspect:
– generation ↔ convincing others
– evaluation ↔ protecting against being persuaded to
take positions resulting in negative
outcomes
Mercier, H., & Sperber, D. (2011). Why do humans reason? Arguments for an argumentative theory.
The Behavioral and Brain Sciences, 34(2), 57-74
19. The call for Explanaible AI (XAI)
statistical
alignment
~ dog conditioning
~ child development
? ? ?
adapted to rewards
conscious of rewards
20. The call for Explanaible AI (XAI)
statistical
alignment
grounding
experential
(indirect)(direct)
communicating
conceptualizing
experential normative
~ dog conditioning
~ child development
adapted to rewards
conscious of rewards
21. The call for Explanaible AI (XAI)
statistical
alignment
experential
(indirect)(direct)
experential normative
the INTERFACE problem
computation human cognition
grounding communicating
conceptualizing
22. the INTERFACE problem
computation human cognition
Possible research approaches
● bottom-up: use statistical ML to recreate functions mimicking
to some extent human cognition
● top-down: conceive algorithms reproducing by design
functions observable in human cognition
23. the INTERFACE problem
computation human cognition
Possible research approaches
● bottom-up: use statistical ML to recreate functions mimicking
to some extent human cognition
● top-down: conceive algorithms reproducing by design
functions observable in human cognition
here we have control on what we want to reproduce
24. Outline of this presentation
● Problems and solutions about similarity [KI2017]
● Computing contrast [AIC2018]
● An introduction to Simplicity theory [ST]
– Pertinence of causes [COG2018]
– Moral responsibility [JURIX2017]
Sileno, G., Bloch, I., Atif, J., & Dessalles, J.-L. (2017). Similarity and Contrast on Conceptual Spaces for Pertinent
Description Generation. Proceedings of the 2017 KI conference, 10505 LNAI
Sileno, G., Bloch, I., Atif, J., & Dessalles, J. (2018). Computing Contrast on Conceptual Spaces. In Proceedings of
the 6th International Workshop on Artificial Intelligence and Cognition (AIC2018)
https://simplicitytheory.telecom-paristech.fr/
Sileno, G., & Dessalles, J.-L. (2018). Qualifying Causes as Pertinent. Proceedings of the 40th Conference of the
Cognitive Science Society (CogSci 2018)
Sileno, G., Saillenfest, A., & Dessalles, J.-L. (2017). A Computational Model of Moral and Legal Responsibility via
Simplicity Theory. Proceedings of the 30th Int. Conf. on Legal Knowledge and Information Systems (JURIX 2017),
FAIA 302, 171–176
26. Similarity is crucial to cognition
similar stimulus in similar context similar response
General (often implicit) hypothesis:
27. Similarity is crucial to cognition
similar stimulus in similar context similar response
~ fixing the task
General (often implicit) hypothesis:
28. Similarity is crucial to cognition
similar stimulus in similar context similar response
~ fixing the task
General (often implicit) hypothesis:
proximate elements can be used as reference to identify a
certain target (object, situation, etc.)
Practical uses: description generation
29. Similarity is crucial to cognition
similar stimulus in similar context similar response
~ fixing the task
General (often implicit) hypothesis:
proximate elements can be used as reference to identify a
certain target (object, situation, etc.)
Practical uses: description generation
the caudate nucleus
is an internal brain
structure which is
very close to the
lateral ventricles
30. Similarity is crucial to cognition
similar stimulus in similar context similar response
General (often implicit) hypothesis:
but how two stimuli are defined similar ?
~ fixing the task
31. Similarity is crucial to cognition
similar stimulus in similar context similar response
General (often implicit) hypothesis:
but how two stimuli are defined similar ?
psychology
● similarity is a function of a mental distance
between conceptualizations [Shepard1962]
“psychological space” hypothesis
~ fixing the task
32. Similarity is crucial to cognition
similar stimulus in similar context similar response
General (often implicit) hypothesis:
but how two stimuli are defined similar ?
psychology machine learning
● similarity is a function of a mental distance
between conceptualizations [Shepard1962]
“psychological space” hypothesis
● relies on some metric to compare inputs
● offers pseudo-metric learning methods
~ fixing the task
33. Similarity is crucial to cognition
similar stimulus in similar context similar response
General (often implicit) hypothesis:
but how two stimuli are defined similar ?
psychology machine learning
● similarity is a function of a mental distance
between conceptualizations [Shepard1962]
“psychological space” hypothesis
● relies on some metric to compare inputs
● offers pseudo-metric learning methods
geometrical model of cognition
~ fixing the task
35. geometrical model of cognition
psychologypsychology machine learning
Problems:
● similarity in human judgments does
not satisfy fundamental
geometric axioms [Tversky77]
basis of feature-based models
Tversky, A. (1977). Features of similarity. Psychological Review, 84, 327–352.
36. geometrical model of cognition
psychologypsychology machine learning
Problems:
● similarity in human judgments does
not satisfy fundamental
geometric axioms [Tversky77]
basis of feature-based models
but.. feature selection?
37. geometrical model of cognition
psychologypsychology machine learning
Problems:
● similarity in human judgments does
not satisfy fundamental
geometric axioms [Tversky77]
● reasoning via artificial devices (still?)
relies on symbolic processing
e.g. through ontologies
basis of feature-based models
but.. feature selection?
38. geometrical model of cognition
psychologypsychology machine learning
Problems:
● similarity in human judgments does
not satisfy fundamental
geometric axioms [Tversky77]
● reasoning via artificial devices (still?)
relies on symbolic processing
e.g. through ontologies
basis of feature-based models
but.. feature selection? but.. symbol grounding?
predicate selection?
39. geometrical model of cognition
psychologypsychology machine learning
Problems:
● similarity in human judgments does
not satisfy fundamental
geometric axioms [Tversky77]
basis of feature-based models
● reasoning via artificial devices (still?)
relies on symbolic processing
e.g. through ontologies
Proposed solutions:
● enriching the metric model with additional
elements (e.g. density [Krumhansl78])
but.. feature selection? but.. symbol grounding?
predicate selection?
40. geometrical model of cognition
psychologypsychology machine learning
Problems:
● similarity in human judgments does
not satisfy fundamental
geometric axioms [Tversky77]
basis of feature-based models
● reasoning via artificial devices (still?)
relies on symbolic processing
e.g. through ontologies
Proposed solutions:
● enriching the metric model with additional
elements (e.g. density [Krumhansl78])
but.. feature selection? but.. symbol grounding?
predicate selection?
but.. holistic distance?
41. geometrical model of cognition
psychologypsychology machine learning
Problems:
● similarity in human judgments does
not satisfy fundamental
geometric axioms [Tversky77]
basis of feature-based models
● reasoning via artificial devices (still?)
relies on symbolic processing
e.g. through ontologies
Proposed solutions:
● enriching the metric model with additional
elements (e.g. density [Krumhansl78])
● approaching logical structures through
geometric methods (e.g. [Distel2014])
but.. feature selection? but.. symbol grounding?
predicate selection?
but.. holistic distance?
42. Towards an alternative solution..
associationistic methods
symbolic methods
grounded
not intelligible
not grounded
intelligible
43. grounded
not intelligible
not grounded
intelligible
Towards an alternative solution..
associationistic methods
symbolic methods
conceptual spaces
grounded
and intelligible
Gärdenfors, P. (2000). Conceptual Spaces: The Geometry of Thought. MIT Press.
Gärdenfors, P. (2014). The Geometry of Meaning: Semantics Based on Conceptual Spaces. MIT Press.
44. Overview on conceptual spaces
conceptual spaces
● Conceptual spaces stem from
(continuous) perceptive spaces.
● Natural properties emerge as
convex regions over integral
dimensions (e.g. color).
● Concepts are weighted
combinations of properties
● Prototypes can be seen as
centroids of convex regions
(properties or concepts).
Convex regions can be seen as
resulting from the competition
between prototypes (forming a
Voronoi Tessellation).
grounded
Gärdenfors, P. (2000). Conceptual Spaces: The Geometry of Thought. MIT Press.
Gärdenfors, P. (2014). The Geometry of Meaning: Semantics Based on Conceptual Spaces. MIT Press.
45. “small” problem
The standard theory of conceptual spaces insists to lexical
meaning: linguistic marks are associated to regions.
→ extensional as the standard symbolic approach.
If red, or green, or brown
correspond to regions
in the color space...
46. why do we say “red dogs” even if they are actually brown?
images after Google
“small” problem
The standard theory of conceptual spaces insists to lexical
meaning: linguistic marks are associated to regions.
→ extensional as the standard symbolic approach.
If red, or green, or brown
correspond to regions
in the color space...
47. Alternative hypothesis [Dessalles2015]:
Predicates are generated on the fly after an operation of contrast.
C = O – P
contrastor
object prototype
(target) (reference)
Predicates resulting from contrast
Dessalles, J.-L. (2015). From Conceptual Spaces to Predicates. Applications of Conceptual
Spaces: The Case for Geometric Knowledge Representation, 17–31.
48. Alternative hypothesis [Dessalles2015]:
Predicates are generated on the fly after an operation of contrast.
C = O – P ↝ “red”
contrastor
object prototype
(target) (reference)
These dogs are “red dogs”:
● not because their color is red (they are brown),
● because they are more red with respect to the dog prototype
Predicates resulting from contrast
49. Predicates resulting from contrast
In logic, usually: above(a, b) ↔ below(b, a)
However, people don't say
“the board is
above the leg.”
“the table is
below the apple.”
If the contrastive hypothesis is correct, C = A – B ↝ “above”
50. objects
Directional relationships
We considered an existing method [Bloch2006] used in image processing to compute
directional relative positions of visual entities (e.g. of biomedical images).
Bloch, I. (2006). Spatial reasoning under imprecision using fuzzy set theory, formal logics and
mathematical morphology. International Journal of Approximate Reasoning, 41(2), 77–95.
51. models of relations
for a point centered
in the origin
Directional relationships
We considered an existing method [Bloch2006] used in image processing to compute
directional relative positions of visual entities (e.g. of biomedical images).
52. “above b”“below a”
Directional relationships
We considered an existing method [Bloch2006] used in image processing to compute
directional relative positions of visual entities (e.g. of biomedical images).
53. how much a is
(in) “above b”
how much b is
(in) “below a”
“above b”“below a”
Directional relationships
We considered an existing method [Bloch2006] used in image processing to compute
directional relative positions of visual entities (e.g. of biomedical images).
54. operation scheme: a b + “above”↝
how much a is
“above b”
Directional relationships
We considered an existing method [Bloch2006] used in image processing to compute
directional relative positions of visual entities (e.g. of biomedical images).
55. operation scheme: a b + “above”↝
inverse operation to contrast: mergehow much a is
“above b”
Directional relationships
We considered an existing method [Bloch2006] used in image processing to compute
directional relative positions of visual entities (e.g. of biomedical images).
56. operation scheme: a b + “above”↝
alignment as overlap
inverse operation to contrast: mergehow much a is
“above b”
Directional relationships
We considered an existing method [Bloch2006] used in image processing to compute
directional relative positions of visual entities (e.g. of biomedical images).
57. We considered an existing method [Bloch2006] used in image processing to compute
directional relative positions of visual entities (e.g. of biomedical images).
operation scheme: a b + “above”↝
alignment as overlap
inverse operation to contrast: mergehow much a is
“above b”
cf. with o - p “red”↝
Directional relationships
58. We considered an existing method [Bloch2006] used in image processing to compute
directional relative positions of visual entities (e.g. of biomedical images).
operation scheme: a b + “above”↝
alignment as overlap
inverse operation to contrast: mergehow much a is
“above b”
cf. with o - p “red”↝
Directional relationships
If we settle upon contrast, we can categorize its output for relations!
59. ● Contrast has been computed by operations inherent to
integral dimensions. These may be interpreted as related
to local perceptual dissimilarity.
– no need to define a holistic distance
● But what about concept (i.e. multi-dimensional) similarity?
From contrast to concept similarity
60. From contrast to concept similarity
“she is strong.”
this person − prototype person ↝ “strong”
61. From contrast to concept similarity
“she is (like) a lion.”
“she is strong.”
this person − prototype person ↝ “strong”
(metaphor as conceptual analogy)
62. From contrast to concept similarity
“she is (like) a lion.”
this person − prototype person ↝ “strong”, etc.
prototype lion − prototype animal ↝ “strong”, etc.
“she is strong.”
this person − prototype person ↝ “strong”
(metaphor as conceptual analogy)
comparison ground
doublecontrast
reference
target
63. From contrast to concept similarity
“she is (like) a lion.”
this person − prototype person ↝ “strong”, etc.
prototype lion − prototype animal ↝ “strong”, etc.
“she is strong.”
this person − prototype person ↝ “strong”
(metaphor as conceptual analogy)
comparison ground
doublecontrast
reference
target
The reference activates certain discriminating features.
64. From contrast to concept similarity
“she is (like) a lion.”
this person − prototype person ↝ “strong”, etc.
prototype lion − prototype animal ↝ “strong”, etc.
“she is strong.”
this person − prototype person ↝ “strong”
(metaphor as conceptual analogy)
comparison ground
doublecontrast
Concept similarity is a sequential, multi-layered computation
reference
target
The reference activates certain discriminating features.
65. geometrical model of cognition
psychologypsychology machine learning
Problems:
● similarity in human judgments does
not satisfy fundamental
geometric axioms [Tversky77]
basis of feature-based models
● reasoning via artificial devices (still?)
relies on symbolic processing
e.g. through ontologies
Proposed solutions:
● enriching the metric model with additional
elements (e.g. density [Krumhansl78])
● approaching logical structures through
geometric methods (e.g. [Distel2014])
but.. feature selection? but.. symbol grounding?
predicate selection?
but.. holistic distance?
66. 1. Problems with symmetry
● Distance between two points should be the same when inverting the terms of
comparison.
67. 1. Problems with symmetry
However,
Tel Aviv is like New York
has a different meaning than:
New York is like Tel Aviv
● Distance between two points should be the same when inverting the terms of
comparison.
68. 1. Problems with symmetry
However,
Tel Aviv is like New York
has a different meaning than:
New York is like Tel Aviv
Our explanation: changing of reference activates different features
● Distance between two points should be the same when inverting the terms of
comparison.
70. 2. Problems with triangle inequality
However,
Jamaica is similar to Cuba
Cuba is similar to Russia
Jamaica is not similar to Russia.
a c
b
71. 2. Problems with triangle inequality
However,
Jamaica is similar to Cuba
Cuba is similar to Russia
Jamaica is not similar to Russia.
Our explanation: different/no comparison grounds after contrast
a c
b
72. 3. Problems with minimality
● Distance with a distinct point should be greater than with the point itself.
73. 3. Problems with minimality
● Distance with a distinct point should be greater than with the point itself.
However,
– when people were asked to find the most similar Morse
code within a list, including the original one, they did not
always return the object itself.
74. 3. Problems with minimality
● Distance with a distinct point should be greater than with the point itself.
However,
– when people were asked to find the most similar Morse
code within a list, including the original one, they did not
always return the object itself.
Our explanation: sequential nature of similarity assessment.
75. 4. Diagnosticity effect
● The distance between two points in a set should not change when changing the set.
76. 4. Diagnosticity effect
However,
– when people were asked for the country most similar to a reference amongst a
given group of countries, they changed answers depending on the group.
Austria
most
similar to
Hungary
Poland
Sweden
● The distance between two points in a set should not change when changing the set.
77. 4. Diagnosticity effect
However,
– when people were asked for the country most similar to a reference amongst a
given group of countries, they changed answers depending on the group.
Austria Hungary
Poland
Sweden
Norway
most
similar to
● The distance between two points in a set should not change when changing the set.
78. 4. Diagnosticity effect
● The distance between two points in a set should not change when changing the set.
However,
– when people were asked for the country most similar to a reference amongst a
given group of countries, they changed answers depending on the group.
Austria Hungary
Poland
Sweden
Norway
most
similar to
Our explanation: effect due to the change of group prototype
79. Two types of similarity
● There is a fundamental distinction between:
– perceptual similarity
– contrastively analogical similarity
● The two are commonly conflated:
– by using MDS on people’s similarity judgments to elicit
dimensions of psychological (conceptual) spaces
– in similar dimensional reduction techniques used in ML
● This hypothesis provides simple explanations to empirical
experiences manifesting non-metrical properties, yet maintaining
a geometric infrastructure.
Sileno, G., Bloch, I., Atif, J., & Dessalles, J.-L. (2017). Similarity and Contrast on Conceptual Spaces for Pertinent
Description Generation. Proceedings of the 2017 KI conference, 10505 LNAI.
81. Computing contrast (1D)
● Consider coffees served in a bar. Intuitively, whether a
coffee is qualified as being hot or cold depends mostly on
what the speaker expects of coffees served at bars, rather
than a specific absolute temperature.
c = o – p ↝ “hot”
contrastor
object prototype
(target) (reference)
Sileno, G., Bloch, I., Atif, J., & Dessalles, J. (2018). Computing Contrast on Conceptual Spaces. In Proceedings of
the 6th International Workshop on Artificial Intelligence and Cognition (AIC2018)
82. Computing contrast (1D)
● Consider coffees served in a bar. Intuitively, whether a
coffee is qualified as being hot or cold depends mostly on
what the speaker expects of coffees served at bars, rather
than a specific absolute temperature.
● For simplicity, we represent objects on 1D (temperature)
with real coordinates.
c = o – p ↝ “hot”
contrastor
object prototype
(target) (reference)
83. Computing contrast (1D)
● Because prototypes are defined together with a concept
region, let us consider some regional information, for
instance represented as an egg-yolk structure.
c = o – p ↝ “hot”
contrastor
object prototype
(target) (reference)
84. Computing contrast (1D)
● Because prototypes are defined together with a concept
region, let us consider some regional information, for
instance represented as an egg-yolk structure.
– internal boundary (yolk) p ± σ for typical elements of
that category of objects (e.g. coffee served at bar).
c = o – p ↝ “hot”
contrastor
object prototype
(target) (reference)
85. Computing contrast (1D)
● Because prototypes are defined together with a concept
region, let us consider some regional information, for
instance represented as an egg-yolk structure.
– internal boundary (yolk) p ± σ for typical elements of
that category of objects (e.g. coffee served at bar).
– external boundary (egg) p ± ρ for all elements directly
associated to that category of objects
c = o – p ↝ “hot”
contrastor
object prototype
(target) (reference)
86. Computing contrast (1D)
c = o – p ↝ “hot”
contrastor
object prototype
(target) (reference)
● Two required functions:
– centering of target with respect to typical region
– scaling to neutralize effect of scale (e.g. “hot
coffee”, “hot planet”)
88. Computing contrast (1D)
● As contrastors are extended objects, they might be
compared to model categories represented as
regions by measuring their degree of overlap:
property label
contrastor model region of property
89. Computing contrast (1D)
● Applying the previous computation, we easily derive the
membership functions of some general relations with
respect to the objects of that category.
● For instance, by dividing the representational container
in 3 equal parts, we have:
“ok”“cold” “hot”
90. Computing contrast (1D)
● The previous formulation might be extended to consider
contrast between two regions, by utilizing discretization
( denotes the approximation to the nearest integer):
91. Computing contrast (>1D)
● If dimensions are perceptually independent, we can
apply contrast on each dimensions separately:
● The result can be used to create a contrastive description
of the object, i.e. its most distinguishing features.
● e.g. apple (as a fruit):
red, spherical, quite sugared
92. Computing contrast (>1D)
● In the case of 2D visual objects, the two dimensions are
not perceptually independent.
● Let us consider two objects A and B. We can apply
contrast iteratively for each point of A with respect to B,
and then aggregate the resulting contrastors.
93. Computing contrast (>1D)
● In the case of 2D visual objects, the two dimensions are
not perceptually independent.
● Let us consider two objects A and B. We can apply
contrast iteratively for each point of A with respect to B,
and then aggregate the resulting contrastors.
accumulation set
normalization
counting
94. Computing contrast (>1D)
● In the case of 2D visual objects, the two dimensions are
not perceptually independent.
● Let us consider two objects A and B. We can apply
contrast iteratively for each point of A with respect to B,
and then aggregate the resulting contrastors.
accumulation set
normalization
counting
95. Computing contrast (>1D)
● In the case of 2D visual objects, the two dimensions are
not perceptually independent.
● Let us consider two objects A and B. We can apply
contrast iteratively for each point of A with respect to B,
and then aggregate the resulting contrastors.
accumulation set
normalization
counting
Work in progress: use of erosion to compute contrastor!
97. Relevance
● Given a certain image,
– what is relevant to be recognized?
– what is relevant to be said?
98. Relevance
● Given a certain image,
– what is relevant to be recognized?
– what is relevant to be said?
● More in general, given a certain situation
– what is relevant to be interpreted?
– what is relevant to be done?
99. Relevance
● Given a certain image,
– what is relevant to be recognized?
– what is relevant to be said?
● More in general, given a certain situation
– what is relevant to be interpreted?
– what is relevant to be done?
● Simplicity Theory (ST) offers a computational cognitive model
for computing relevance, based on unexpectedness and
emotion.
For a more detailed overview and further references see https://simplicitytheory.telecom-paristech.fr/
100. Simplicity theory: unexpectedness
● Human individuals are highly sensitive to complexity drops: i.e.
to situations that are simpler to describe than to explain.
101. Simplicity theory: unexpectedness
● Human individuals are highly sensitive to complexity drops: i.e.
to situations that are simpler to describe than to explain.
● Core notion: Unexpectedness
102. Simplicity theory: unexpectedness
● Human individuals are highly sensitive to complexity drops: i.e.
to situations that are simpler to describe than to explain.
● Core notion: Unexpectedness
causal complexity
concerning how the world generates the situation
103. Simplicity theory: unexpectedness
● Human individuals are highly sensitive to complexity drops: i.e.
to situations that are simpler to describe than to explain.
● Core notion: Unexpectedness
causal complexity
concerning how the world generates the situation
description complexity
concerning how to identify the situation
104. Simplicity theory: unexpectedness
● Human individuals are highly sensitive to complexity drops: i.e.
to situations that are simpler to describe than to explain.
● Core notion: Unexpectedness
causal complexity
concerning how the world generates the situation
description complexity
concerning how to identify the situation
The two complexities are defined following Kolmogorov complexity.
106. Kolmogorov complexity
length in bits of the shortest program generating a string
description of an object
string equivalent programs
“2222222222222222222222222” = “2” + “2” + … + “2”
= “2” * 25
= “2” * 5^2
107. Kolmogorov complexity
length in bits of the shortest program generating a string
description of an object
depends on the available operators!!
string equivalent programs
“2222222222222222222222222” = “2” + “2” + … + “2”
= “2” * 25
= “2” * 5^2
108. Simplicity theory: unexpectedness
● Human individuals are highly sensitive to complexity drops: i.e.
to situations that are simpler to describe than to explain.
● Core notion: Unexpectedness
causal complexity
about how the world generates the situation
description complexity
about how to identify the situation
length of shortest program
creating the situation
length of shortest program
determining the situation
109. Simplicity theory: unexpectedness
● Human individuals are highly sensitive to complexity drops: i.e.
to situations that are simpler to describe than to explain.
● Core notion: Unexpectedness
causal complexity
about how the world generates the situation
description complexity
about how to identify the situation
length of shortest program
creating the situation
instructions = causal operators
length of shortest program
determining the situation
instructions = mental operators
110. Simplicity theory: unexpectedness
● Human individuals are highly sensitive to complexity drops: i.e.
to situations that are simpler to describe than to explain.
● Core notion: Unexpectedness
causal complexity
about how the world generates the situation
description complexity
about how to identify the situation
length of shortest program
creating the situation
instructions = causal operators
length of shortest program
determining the situation
instructions = mental operators
SIMULATION
REPRESENTATION
SIMULATION
REPRESENTATION
111. Simplicity theory: unexpectedness
● Human individuals are highly sensitive to complexity drops: i.e.
to situations that are simpler to describe than to explain.
● Core notion: Unexpectedness
causal complexity
about how the world generates the situation
description complexity
about how to identify the situation
length of shortest program
creating the situation
instructions = causal operators
length of shortest program
determining the situation
instructions = mental operators
SIMULATION
REPRESENTATION
SIMULATION
REPRESENTATION
for the agent!!!
113. Examples
22222222222222 is more unexpected than 21658367193445
meeting Obama is more unexpected than meeting Dupont
(in a fair extraction)
Unexpectedness captures plausibility
(or any other famous person) (or any other unknown person)
meeting an old of friend of mine
(or any other known person)
114. Examples
22222222222222 is more unexpected than 21658367193445
meeting Obama is more unexpected than meeting Dupont
(in a fair extraction)
Unexpectedness captures plausibility
(or any other famous person) (or any other unknown person)
meeting an old of friend of mine
(or any other known person)
when CW
(s) is the same, we
look for low CD
(s)
informativity is maximized by
maximizing unexpectedness
115. ● Focusing on intensity, we can capture anticipation as:
emotion
what the situation induces to the agent
reward model
unexpectedness
Simplicity Theory: Emotion
emotion
actualized
emotion
116. ● Focusing on intensity, we can capture anticipation as:
● Attention is intuitively associated to situations that might occur
depending on their emotional impact.
emotion
what the situation induces to the agent
reward model
unexpectedness
Simplicity Theory: Emotion
emotion
actualized
emotion
117. ● Fundamental principles:
– situations with high anticipated emotion are relevant
– situations with high unexpectedness are relevant
Simplicity Theory: Relevance
epithymically
epistemically
118. ● Fundamental principles:
– situations with high anticipated emotion are relevant
– situations with high unexpectedness are relevant
● Intuitively, contrast and similarity play a role with CD as they
function with the most accessible references, i.e.:
target is determined as
proximate
to simple references
with respect to simple relations
Simplicity Theory: Relevance
119. ● Fundamental principles:
– situations with high anticipated emotion are relevant
– situations with high unexpectedness are relevant
● Why it is relevant to speak of hot coffees, rather than normal
coffees?
Simplicity Theory: Relevance
120. ● Fundamental principles:
– situations with high anticipated emotion are relevant
– situations with high unexpectedness are relevant
● Why it is relevant to speak of hot coffees, rather than normal
coffees?
● Several factors play a role:
– descriptively simple (qualitatively distinctive, accessible references),
– causally difficult (supposing a normal distribution of temperatures),
– emotionally intense (as we might get burned with it).
Simplicity Theory: Relevance
121. ● Fundamental principles:
– situations with high anticipated emotion are relevant
– situations with high unexpectedness are relevant
● Why it is relevant to speak of hot coffees, rather than normal
coffees?
● Several factors play a role:
– descriptively simple (qualitatively distinctive, accessible references),
– causally difficult (supposing a normal distribution of temperatures),
– emotionally intense (as we might get burned with it).
● In the following I'll briefly present two additional tracks I've
started studying, concerning CW (s) and E(s)
Simplicity Theory: Relevance
123. An experiment
● Causes play a central role in the way we conceptualize
the world.
● But there is no established model about how people
qualify a cause as pertinent (literally, holding together)
to a specific event.
Sileno, G., & Dessalles, J.-L. (2018). Qualifying Causes as Pertinent. Proceedings of the 40th Conference
of the Cognitive Science Society (CogSci 2018)
124. An experiment
● Causes play a central role in the way we conceptualize
the world.
● But there is no established model about how people
qualify a cause as pertinent (literally, holding together)
to a specific event.
● We performed an experiment to compare:
– the computation of actual causation via
● conterfactuals (structural equations)
● Bayesian inference
● Simplicity Theory
– people's responses
125. Johnny is 7 years old. In recent months his mother has been worried because he
developed a craving for sweet things. She bought some pots of strawberry jam and put
them into the larder (a small room near the kitchen). Then one afternoon she finds
that Johnny has gone into the larder and has eaten half a pot of strawberry jam.
Q1. Why is ”half a pot of jam gone”?
a. because of Johnny’s gluttony
b. because Johnny ate it
c. because mother has put the pot in the larder
Example of task
126. Johnny is 7 years old. In recent months his mother has been worried because he
developed a craving for sweet things. She bought some pots of strawberry jam and put
them into the larder (a small room near the kitchen). Then one afternoon she finds
that Johnny has gone into the larder and has eaten half a pot of strawberry jam.
Q1. Why is ”half a pot of jam gone”?
a. because of Johnny’s gluttony
b. because Johnny ate it
c. because mother has put the pot in the larder
Example of task
● For each task, a model of
the story is constructed,
based on a general
action-scheme
motivation
motive
intention
consequences
action
affordance
129. Evaluation
motivation
motive
intention
consequences
action
affordance
● Measures based on probability:
● Measure based on complexity:
computation using a
Bayesian Network
Results: No probabilistic measure is consistently aligned.
Causal contribution as defined by ST performs much better, and
divergences can be explained by intervention of description complexity.
computation of complexities using
minimal path search
given a certain
model:
131. ● In human societies, responsibility attribution is a spontaneous
and seemingly universal behaviour.
Responsibility attribution for humans
12 Angry Men, 1956Rashomon, 1950
Sileno, G., Saillenfest, A., & Dessalles, J.-L. (2017). A Computational Model of Moral and Legal Responsibility via
Simplicity Theory. Proceedings of the 30th Int. Conf. on Legal Knowledge and Information Systems (JURIX 2017),
FAIA 302, 171–176
132. ● In human societies, responsibility attribution is a spontaneous
and seemingly universal behaviour.
● Non-related ancient legal systems bear much resemblance to
modern law and seem perfectly sensible nowadays.
Responsibility attribution for humans
Rashomon, 1950 12 Angry Men, 1956
133. ● In human societies, responsibility attribution is a spontaneous
and seemingly universal behaviour.
● Non-related ancient legal systems bear much resemblance to
modern law and seem perfectly sensible nowadays.
→ responsibility attribution may be controlled by
fundamental cognitive mechanisms.
Responsibility attribution for humans
12 Angry Men, 1956Rashomon, 1950
134. ● In human societies, responsibility attribution is a spontaneous
and seemingly universal behaviour.
● Non-related ancient legal systems bear much resemblance to
modern law and seem perfectly sensible nowadays.
→ responsibility attribution may be controlled by
fundamental cognitive mechanisms.
Responsibility attribution for humans
Working hypothesis: attributions of moral and legal responsibility
share a similar cognitive architecture
12 Angry Men, 1956Rashomon, 1950
135. ● Experiments show that people are more prone to blame an agent
for an action:
flooded mine dilemma (trolley problem variation)
[A. Saillenfest and J.-L. Dessalles. Role of Kolmogorov Complexity on Interest in Moral Dilemma Stories. CogSCI 2012, pages 947–952]
136. ● Experiments show that people are more prone to blame an agent
for an action:
– the more the outcome is severe,
– the more they are closer to the victims,
– the more the outcome follows the action.
flooded mine dilemma (trolley problem variation)
[A. Saillenfest and J.-L. Dessalles. Role of Kolmogorov Complexity on Interest in Moral Dilemma Stories. CogSCI 2012, pages 947–952]
137. ● Experiments show that people are more prone to blame an agent
for an action:
– the more the outcome is severe,
– the more they are closer to the victims,
– the more the outcome follows the action.
● The cognitive model of Simplicity Theory predicts these results.
flooded mine dilemma (trolley problem variation)
[A. Saillenfest and J.-L. Dessalles. Role of Kolmogorov Complexity on Interest in Moral Dilemma Stories. CogSCI 2012]
138. ● Focusing on intensity, we can capture anticipation as:
● The anticipated emotion of doing a to reach s:
emotion
what the situation induces to the agent
reward model
unexpectedness
Simplicity Theory: Emotion
intention as driven by anticipated emotional effects
139. Simplicity Theory: Moral responsibility
● Difference between intention* and moral responsibility is
one of point of views. computed by A
* For simplicity, we assume here that the action a has only a relevant outcome s and it has no impact on emotion, i.e. E(a*s) = E(s)
140. Simplicity Theory: Moral responsibility
● Difference between intention and moral responsibility is
one of point of views. computed by A
computed by a
model of A
computed by an observer O
141. Simplicity Theory: Moral responsibility
● Difference between intention and moral responsibility is
one of point of views. computed by A
computed by a
model of A
computed by an observer O
prescribed role,
reasonable standard
reward model
142. Simplicity Theory: Moral responsibility
● Difference between intention and moral responsibility is
one of point of views.
● Introducing causal responsibility
computed by A
computed by a
model of A
computed by an observer O
prescribed role,
reasonable standard
reward model
143. Simplicity Theory: Moral responsibility
actualized
emotion
causal
responsibility
conceptual
remoteness inadvertence
+ + – –
for observer O attributed to A attributed to Afor observer O
144. Simplicity Theory: Moral responsibility
actualized
emotion
causal
responsibility
conceptual
remoteness inadvertence
+ + – –
for observer O attributed to A attributed to Afor observer O
● From moral to legal responsibility:
– equity before the law
145. Simplicity Theory: Moral responsibility
actualized
emotion
causal
responsibility
conceptual
remoteness inadvertence
+ + – –
for observer O attributed to A attributed to Afor observer O
● From moral to legal responsibility:
– equity before the law
– law, as a reward system, defines emotion
146. Simplicity Theory: Moral responsibility
actualized
emotion
causal
responsibility
conceptual
remoteness inadvertence
+ + – –
for observer O attributed to A attributed to Afor observer O
● From moral to legal responsibility:
– equity before the law
– law, as a reward system, defines emotion
This enables to consider extrinsic commitments!
147. Simplicity Theory: Moral responsibility
actualized
emotion
causal
responsibility
conceptual
remoteness inadvertence
+ + – –
for observer O attributed to A attributed to Afor observer O
● From moral to legal responsibility:
– equity before the law
– law, as a reward system, defines emotion
…
149. The call for Explanaible AI (XAI)
grounding
experential
(indirect)(direct)
communicating
conceptualizing
experential normative
~ dog conditioning
~ child development
adapted to rewards
conscious of rewards
150. The call for Explanaible AI (XAI)
grounding
experential
(indirect)(direct)
communicating
conceptualizing
experential normative
automated decision-making need to be:
● non (primarily) statistical
● cognitively plausible
● linguistically competent
● able to take into account norms
conscious of rewards
151. Outlining the kernel of agency
● The core problem – of normative, epistemic and ontological
alignment – is related to the different modalities that we, as
agents, attribute to reality...
collective
individual
physical
152. Outlining the kernel of agency
● The core problem – of normative, epistemic and ontological
alignment – is related to the different modalities that we, as
agents, attribute to reality...
collective
individual
physical
This holds for humans, but also for artificial agents.