IJCAI 16 keynote on the need to bring modern AI accomplishments of recent years into connection with the more traditional goals of symbolic AI (and vice versa).
Digital Archiving, The Semantic Web, and Modern AIJames Hendler
This was my keynote talk on accepted the "Spotlight Award" from the association of moving image archivists. The talk relates needs of archiving, use of semantic (web) metadata, and deep learning for archiving.
Social Machines - 2017 Update (University of Iowa)James Hendler
This is an update to the talk entitled "Social Machines: the coming collision of artificial intelligence, social networks and humanity." It was presented as an ACM Distinguished Speaker lecture at the "University of Iowa Computing Conference" 2017-02-24
Social Machines: The coming collision of Artificial Intelligence, Social Netw...James Hendler
Will your next doctor be a human being—or a machine? Will you have a choice? If you do, what should you know before making it?This book introduces the reader to the pitfalls and promises of artificial intelligence (AI) in its modern incarnation and the growing trend of systems to "reach off the Web" into the real world. The convergence of AI, social networking, and modern computing is creating an historic inflection point in the partnership between human beings and machines with potentially profound impacts on the future not only of computing but of our world and species.AI experts and researchers James Hendler—co-originator of the Semantic Web (Web 3.0)—and Alice Mulvehill—developer of AI-based operational systems for DARPA, the Air Force, and NASA—explore the social implications of AI systems in the context of a close examination of the technologies that make them possible. The authors critically evaluate the utopian claims and dystopian counterclaims of AI prognosticators. Social Machines: The Coming Collision of Artificial Intelligence, Social Networking, and Humanity is your richly illustrated field guide to the future of your machine-mediated relationships with other human beings and with increasingly intelligent machines.
The Unreasonable Effectiveness of MetadataJames Hendler
Invited talk at VIVO 2017 conference - explores the view of the semantic web as enriched metadata, and how that kind of information can be used in new and interesting ways.
A talk presented at IBM's "Academy of Technology" exploring, in brief, what the research community has to learn from Watson (and the techniques derived therefrom) and some new research ideas that can be explored therefrom. All known proprietary information from either IBM or RPI has been removed from the original talk.
On Beyond OWL: challenges for ontologies on the WebJames Hendler
The need for ontologies in the real world is manifest and increasing. On the Web, ontologies are everywhere — but OWL isn’t. In this talk, I look at some of the things that are not in OWL, but which are needed for the use of OWL in many Web domains. This talk explores some of the needs for ontologies on the Web in data integration, emerging technologies, and linked data applications – and asks where the features needed for these are in OWL. The talk ends with some challenges to the OWL, and greater ontology, community needed to see more eventual use of standard ontologies on the Web.
Digital Archiving, The Semantic Web, and Modern AIJames Hendler
This was my keynote talk on accepted the "Spotlight Award" from the association of moving image archivists. The talk relates needs of archiving, use of semantic (web) metadata, and deep learning for archiving.
Social Machines - 2017 Update (University of Iowa)James Hendler
This is an update to the talk entitled "Social Machines: the coming collision of artificial intelligence, social networks and humanity." It was presented as an ACM Distinguished Speaker lecture at the "University of Iowa Computing Conference" 2017-02-24
Social Machines: The coming collision of Artificial Intelligence, Social Netw...James Hendler
Will your next doctor be a human being—or a machine? Will you have a choice? If you do, what should you know before making it?This book introduces the reader to the pitfalls and promises of artificial intelligence (AI) in its modern incarnation and the growing trend of systems to "reach off the Web" into the real world. The convergence of AI, social networking, and modern computing is creating an historic inflection point in the partnership between human beings and machines with potentially profound impacts on the future not only of computing but of our world and species.AI experts and researchers James Hendler—co-originator of the Semantic Web (Web 3.0)—and Alice Mulvehill—developer of AI-based operational systems for DARPA, the Air Force, and NASA—explore the social implications of AI systems in the context of a close examination of the technologies that make them possible. The authors critically evaluate the utopian claims and dystopian counterclaims of AI prognosticators. Social Machines: The Coming Collision of Artificial Intelligence, Social Networking, and Humanity is your richly illustrated field guide to the future of your machine-mediated relationships with other human beings and with increasingly intelligent machines.
The Unreasonable Effectiveness of MetadataJames Hendler
Invited talk at VIVO 2017 conference - explores the view of the semantic web as enriched metadata, and how that kind of information can be used in new and interesting ways.
A talk presented at IBM's "Academy of Technology" exploring, in brief, what the research community has to learn from Watson (and the techniques derived therefrom) and some new research ideas that can be explored therefrom. All known proprietary information from either IBM or RPI has been removed from the original talk.
On Beyond OWL: challenges for ontologies on the WebJames Hendler
The need for ontologies in the real world is manifest and increasing. On the Web, ontologies are everywhere — but OWL isn’t. In this talk, I look at some of the things that are not in OWL, but which are needed for the use of OWL in many Web domains. This talk explores some of the needs for ontologies on the Web in data integration, emerging technologies, and linked data applications – and asks where the features needed for these are in OWL. The talk ends with some challenges to the OWL, and greater ontology, community needed to see more eventual use of standard ontologies on the Web.
The Future of AI: Going BeyondDeep Learning, Watson, and the Semantic WebJames Hendler
These slides, based on a presentation at distinguished lecture at IBM Almaden in March, 2017 explore some of the challenges to machine learning and some recent work. It is a newer version of the slides originally presented at IJCAI 2016.
Why Watson Won: A cognitive perspectiveJames Hendler
In this talk, we present how the Watson program, IBM's famous Jeopardy playing computer, works (based on papers published by IBM), we look at some aspects of potential scoring approaches, and we examine how Watson compares to several well known systems and some preliminary thoughts on using it in future artificial intelligence and cognitive science approaches.
In this talk I review some of the early visions of the Semantic Web, some of the different views, and I follow through on a thread of how Semantic Web technology has been adopted in search engines (and other companies). I end with a challenge to the research community to keep pursuing this research, rather than letting industry take over the "low end" and keep new work from flourishing.
"Why the Semantic Web will Never Work" (note the quotes)James Hendler
This talk refutes some criticisms of the semantic web, but also outlines some research challenges we must overcome if we are to ever realize Tim Berners-Lee's original Semantic Web vision.
A 1015 update to the 2012 "Data Big and Broad" talk - http://www.slideshare.net/jahendler/data-big-and-broad-oxford-2012 - extends coverage, brings more in context of recent "big data" work.
Presented to a webinar hosted by Nuance Inc, under the title "The Semantic Web: What it is and Why you should care" on 2/29/2012.
This talk presents a fast overview of the Semantic Web and recent application deployment in the space.
Facilitating Web Science Collaboration through Semantic MarkupJames Hendler
These are the slides that accompanied the paper "Dominic DiFranzo, John S. Erickson, Marie Joan Kristine T. Gloria, Joanne S. Luciano, Deborah McGuinness, & James Hendler, The Web Observatory Extension: Facilitating Web Science Collaboration through Semantic Markup, Proc. WWW 2014 (Web Science Track), Seoul, Korea, 2014." They describe an extension to schema.org that can be used for sharing Web-related datasets and projects.
Keynote talk presented at WebScience 2020 conference. Looks at roots of Web/Web Science and explores two possible futures and what web scientists and others can do about it. Even starts with a quote from Charles Dickins.
This is a vision talk, looking at what is happening on the Web with large scale community interactions. It discusses ongoing efforts, Chinese Human Flesh Search Engine, and a research agenda for "Social Machines" based on these emerging challenges.
How to Build a Research Roadmap (avoiding tempting dead-ends)Aaron Sloman
What's a Research Roadmap For?
Why do we need one?
How can we avoid the usual trap of making bold promises to do X, Y and Z,
then hope that our previous promises will not be remembered the next time we apply for funds to do X, Y and Z?
How can we produce a sensible, well informed roadmap?
Originally presented at the euCognition Research Roadmap discussion in Munich on 12 Jan 2007
This suggests a way to avoid tempting dead ends (repeating old promises that proved unrealistic) by examining many long term goals, including describing existing human and animal competences not yet achieved by robots, then working backwards systematically by investigating requirements for those competences, and requirements for meeting those requirements, etc. Insread of generating a single linear roadmap this should produce a partially ordered network of intermediate targets, leading back, to short term goals that may be achievable starting from where we are.
Such a roadmap will inevitably have mistakes: over-optimistic goals, missing preconditions, unrecognised opportunities. But if the work is done in many teams in a fully open manner with as much collaboration as possible, it should be possible to make faster, deeper, progress than can be achieved by brain-storming discussions of where we can get in a few years.
HyperMembrane Structures for Open Source Cognitive ComputingJack Park
Open source "cognitive computing" systems, specifically OpenSherlock; describes a HyperMembrane structure, a kind of information fabric, for machine reading, literature-based discovery, deep question answering. Platform is open source, uses ElasticSearch, topic maps, JSON, link-grammar parsing, and qualitative process models.
An updated "what is happening on the Semantic Web" presentation for 2010 - includes business use, government use, and some speculation on the current areas of excitement and development. A very accessible talk, not aimed solely at a technical audience.
Reorganised several times since first uploaded: most recently 25 Jan 2016
-------------------------------------------------------------------------------------------------------
Slides include link to video of lecture (158MB) http://www.cs.bham.ac.uk/research/projects/cogaff/movies/#ailect2-2015
-------------------------------------------------------------------------------------------------------------
Two questions are shown to have deep connections: What are the functions of vision in animals? and How did human languages evolve? The answer given here is that the functions of vision need to be supported by richly structured internal languages (forms of representation used for acquiring, storing, manipulating, deriving and using information), from which it follows that internal languages must have evolved before languages for communication.
---------------------------------------------------------------------------------------------------------------
The account of the functions of vision mentions early AI vision, the impact of Marr and the even greater impact of Gibson, but argues that they did not recognize all the functions of vision, e.g. the uses of vision in making mathematical discoveries leading to Euclid's elements.
---------------------------------------------------------------------------------------------------------------
Many questions are left unanswered by this research, which is part of the Meta-Morphogenesis project, introduced here:
http://www.cs.bham.ac.uk/research/projects/cogaff/misc/meta-morphogenesis.html
---------------------------------------------------------------------------------------------------------------
A slideshare presentation on "origins of language" by Jasmine Wong, adds some useful additional evidence, but presents a simpler theory:
http://www.slideshare.net/JasmineWong6/origins-of-language
---------------------------------------------------------------------------------------------------------------
Minor corrections+ additions 30-Mar-2015, 1-Apr-2015, 15-Apr-2015 12-Nov-2015
Virtuality, causation and the mind-body relationshipAaron Sloman
Extends my previous introductions to virtual machines and their role both in artefacts and products of biological evolution. This attempts to correct various erroneous assumptions about computation, functionalism, supervenience, life, information, and causation. See also http://www.cs.bham.ac.uk/research/projects/cogaff/misc/vm-functionalism.html
IBM's Watson is a question answering computer system capable of answering questions posed in natural language. The computer system was specifically developed to answer questions on the quiz show Jeopardy!
At present, we have Watson Engagement Advisor, Watson Explorer, Watson Discovery Advisor, Watson for Oncology, Watson for Clinical Trial Matching, Watson Knowledge Studio.
IBM Watson overview presented by Mike Pointer, Watson Sr. Solution Architect, at Penn State's Nittany Watson Challenge Immersion event on January 19-20, 2017.
The Future of AI: Going BeyondDeep Learning, Watson, and the Semantic WebJames Hendler
These slides, based on a presentation at distinguished lecture at IBM Almaden in March, 2017 explore some of the challenges to machine learning and some recent work. It is a newer version of the slides originally presented at IJCAI 2016.
Why Watson Won: A cognitive perspectiveJames Hendler
In this talk, we present how the Watson program, IBM's famous Jeopardy playing computer, works (based on papers published by IBM), we look at some aspects of potential scoring approaches, and we examine how Watson compares to several well known systems and some preliminary thoughts on using it in future artificial intelligence and cognitive science approaches.
In this talk I review some of the early visions of the Semantic Web, some of the different views, and I follow through on a thread of how Semantic Web technology has been adopted in search engines (and other companies). I end with a challenge to the research community to keep pursuing this research, rather than letting industry take over the "low end" and keep new work from flourishing.
"Why the Semantic Web will Never Work" (note the quotes)James Hendler
This talk refutes some criticisms of the semantic web, but also outlines some research challenges we must overcome if we are to ever realize Tim Berners-Lee's original Semantic Web vision.
A 1015 update to the 2012 "Data Big and Broad" talk - http://www.slideshare.net/jahendler/data-big-and-broad-oxford-2012 - extends coverage, brings more in context of recent "big data" work.
Presented to a webinar hosted by Nuance Inc, under the title "The Semantic Web: What it is and Why you should care" on 2/29/2012.
This talk presents a fast overview of the Semantic Web and recent application deployment in the space.
Facilitating Web Science Collaboration through Semantic MarkupJames Hendler
These are the slides that accompanied the paper "Dominic DiFranzo, John S. Erickson, Marie Joan Kristine T. Gloria, Joanne S. Luciano, Deborah McGuinness, & James Hendler, The Web Observatory Extension: Facilitating Web Science Collaboration through Semantic Markup, Proc. WWW 2014 (Web Science Track), Seoul, Korea, 2014." They describe an extension to schema.org that can be used for sharing Web-related datasets and projects.
Keynote talk presented at WebScience 2020 conference. Looks at roots of Web/Web Science and explores two possible futures and what web scientists and others can do about it. Even starts with a quote from Charles Dickins.
This is a vision talk, looking at what is happening on the Web with large scale community interactions. It discusses ongoing efforts, Chinese Human Flesh Search Engine, and a research agenda for "Social Machines" based on these emerging challenges.
How to Build a Research Roadmap (avoiding tempting dead-ends)Aaron Sloman
What's a Research Roadmap For?
Why do we need one?
How can we avoid the usual trap of making bold promises to do X, Y and Z,
then hope that our previous promises will not be remembered the next time we apply for funds to do X, Y and Z?
How can we produce a sensible, well informed roadmap?
Originally presented at the euCognition Research Roadmap discussion in Munich on 12 Jan 2007
This suggests a way to avoid tempting dead ends (repeating old promises that proved unrealistic) by examining many long term goals, including describing existing human and animal competences not yet achieved by robots, then working backwards systematically by investigating requirements for those competences, and requirements for meeting those requirements, etc. Insread of generating a single linear roadmap this should produce a partially ordered network of intermediate targets, leading back, to short term goals that may be achievable starting from where we are.
Such a roadmap will inevitably have mistakes: over-optimistic goals, missing preconditions, unrecognised opportunities. But if the work is done in many teams in a fully open manner with as much collaboration as possible, it should be possible to make faster, deeper, progress than can be achieved by brain-storming discussions of where we can get in a few years.
HyperMembrane Structures for Open Source Cognitive ComputingJack Park
Open source "cognitive computing" systems, specifically OpenSherlock; describes a HyperMembrane structure, a kind of information fabric, for machine reading, literature-based discovery, deep question answering. Platform is open source, uses ElasticSearch, topic maps, JSON, link-grammar parsing, and qualitative process models.
An updated "what is happening on the Semantic Web" presentation for 2010 - includes business use, government use, and some speculation on the current areas of excitement and development. A very accessible talk, not aimed solely at a technical audience.
Reorganised several times since first uploaded: most recently 25 Jan 2016
-------------------------------------------------------------------------------------------------------
Slides include link to video of lecture (158MB) http://www.cs.bham.ac.uk/research/projects/cogaff/movies/#ailect2-2015
-------------------------------------------------------------------------------------------------------------
Two questions are shown to have deep connections: What are the functions of vision in animals? and How did human languages evolve? The answer given here is that the functions of vision need to be supported by richly structured internal languages (forms of representation used for acquiring, storing, manipulating, deriving and using information), from which it follows that internal languages must have evolved before languages for communication.
---------------------------------------------------------------------------------------------------------------
The account of the functions of vision mentions early AI vision, the impact of Marr and the even greater impact of Gibson, but argues that they did not recognize all the functions of vision, e.g. the uses of vision in making mathematical discoveries leading to Euclid's elements.
---------------------------------------------------------------------------------------------------------------
Many questions are left unanswered by this research, which is part of the Meta-Morphogenesis project, introduced here:
http://www.cs.bham.ac.uk/research/projects/cogaff/misc/meta-morphogenesis.html
---------------------------------------------------------------------------------------------------------------
A slideshare presentation on "origins of language" by Jasmine Wong, adds some useful additional evidence, but presents a simpler theory:
http://www.slideshare.net/JasmineWong6/origins-of-language
---------------------------------------------------------------------------------------------------------------
Minor corrections+ additions 30-Mar-2015, 1-Apr-2015, 15-Apr-2015 12-Nov-2015
Virtuality, causation and the mind-body relationshipAaron Sloman
Extends my previous introductions to virtual machines and their role both in artefacts and products of biological evolution. This attempts to correct various erroneous assumptions about computation, functionalism, supervenience, life, information, and causation. See also http://www.cs.bham.ac.uk/research/projects/cogaff/misc/vm-functionalism.html
IBM's Watson is a question answering computer system capable of answering questions posed in natural language. The computer system was specifically developed to answer questions on the quiz show Jeopardy!
At present, we have Watson Engagement Advisor, Watson Explorer, Watson Discovery Advisor, Watson for Oncology, Watson for Clinical Trial Matching, Watson Knowledge Studio.
IBM Watson overview presented by Mike Pointer, Watson Sr. Solution Architect, at Penn State's Nittany Watson Challenge Immersion event on January 19-20, 2017.
Selección y reclutamiento 2.0 "Encuéntrate y te encontrarán"María José Muñoz
Presentación utilizada en el Taller de Selección y Reclutamiento 2.0 para el alumnado y profesorado del Dpto. de Administación del IES Licinio de la Fuente
Se ti chiedi perché la tua organizzazione cresce meno di quello che ti aspetteresti
e il tuo commerciale ti dice che il brand è debole
e l'amministrazione che paghi troppi stipendi
e il distributore che sei troppo caro
e il tuo stomaco ti dice che è il clima che si respira in azienda a non essere più lo stesso
forse è il momento di OSARE
non fa niente di straordinario, tranne offrirti un modo concreto per verificare e potenziare l'identità e i comportamenti della tua organizzazione.
Ecco qualche proposta operativa.
Scale up - How to build adaptive data systems in the age of viralityJohannes Brandstetter
In this talk we share details about glomex's award-winning data management infrastructure. They’ll show you how a serverless approach can scale automatically to the demands of a highly unpredictable industry as video clips go viral arbitrarily. What is the best architecture for real time data processing? How does a batch-driven BI workflow fit in? What are the key benefits of going to the Cloud? Which AWS services should you use?
Recent political trends suggest reshoring would protect and promote US manufacturing jobs. Political protectionism, demand for local products and a renewed interest in manufacturing make the US attractive. However, the economics of globalization, affordable foreign labor and consumers' expectations make foreign markets enticing.
Learn how big brands like Apple, Nike and Walmart approach a ‘Made in the USA’ strategy, and the factors your company must weigh before deciding whether to relocate your manufacturing operations.
I am publicly setting the intention to post every LOI that I've submitted to fellowship programs where I've been rejected. This is not out of shame or blame, but rather to simply and honestly still get to share the beliefs and intents of my work. This also is to create a bridge for public feedback, clarity, and collaboration asks.
Gerontology & Geriatrics: Research is an open access, peer reviewed, scholarly journal dedicated to publish articles covering all areas of Gerontology.
The journal aims to promote research communications and provide a forum for doctors, researchers, physicians and healthcare professionals to find most recent advances in all the areas of Gerontology. Gerontology & Geriatrics: Research accepts original research articles, reviews, mini reviews, case reports and rapid communication covering all aspects of gerontology.
Gerontology & Geriatrics: Research strongly supports the scientific up gradation and fortification in related scientific research community by enhancing access to peer reviewed scientific literary works. Austin Publishing Group also brings universally peer reviewed journals under one roof thereby promoting knowledge sharing, mutual promotion of multidisciplinary science.
R. Villano - Antibioticoresistenza 2 ediz. p.te 6Raimondo Villano
54. R. Villano “Antibioticoresistenza”. Si tracciano cenni storici sul concetto di antibiosi, su ricerche, scoperta e produzione degli antibiotici e sul loro ruolo mondiale non solo terapeutico ma anche strategico dal secondo dopoguerra. Poi, si esaminano a livello nazionale ed internazionale: le problematiche inerenti consumo, uso improprio e abuso di antibiotici nell’uomo, in zootecnia, agricoltura e, quindi, nel ciclo alimentare e nell’ambiente; le politiche di contrasto al fenomeno dell’iperprescrizione e nei cittadini il grado di informazione e consapevolezza dei rischi; le linee guida di buona prassi comportamentale del malato; i documenti principali di lotta a tale emergenza. Si effettuano, inoltre, una rassegna analitica e un approfondimento su alcune super patologie (tubercolosi, gonorrea, meningite, ecc.) e sulle resistenze batteriche ai principali antibiotici. Si realizzano, infine, una ricognizione sull’attualità delle tecnologie e degli indirizzi di ricerca applicata e una rassegna sulle principali recenti nuove terapie. Chiude il lavoro un’appendice tecnica contenente un apparato essenziale di normative e direttive ministeriali italiane e comunitarie europee sul tema. Chiron, ISBN 978-88-97303-25-1, CDD 303 VIL mus 2015, LCC DG461-583.8, Roma, pp. 164, Prima Edizione maggio 2015; Prima Ristampa giugno 2015; Seconda Edizione luglio 2015);
Estamos buscando patrocinadores para un evento solidario para todas aquellas mujeres que luchan contra el cáncer de mama. Se trata de un evento enfocado a la vida saludable y el deporte, con una clase de zumba.
ΕΡΕΥΝΑ: «Προκλήσεις & Ευκαιρίες των Ελληνικών Μικρομεσαίων Επιχειρήσεων» Doul...Douleutaras.gr
Η έρευνα «Προκλήσεις & Ευκαιρίες των Ελληνικών
Μικρομεσαίων Επιχειρήσεων» διεξήχθη από το εργαστήριο
Ηλεκτρονικού Εμπορίου και Επιχειρείν του Οικονομικού
Πανεπιστημίου Αθηνών (ELTRUN) σε συνεργασία με την
πλατφόρμα εύρεσης επαγγελματιών Douleutaras.gr με
τη συμμετοχή 430 εκπροσώπων μικρομεσαίων επιχειρήσεων
διαφόρων επαγγελματικών κατηγοριών.
Η έρευνα καταγράφει τις κατηγορίες προβλημάτων και
προκλήσεων που αντιμετωπίζουν οι ελληνικές μικρομεσαίες
επιχειρήσεις διαφόρων κατηγοριών. Παράλληλα αποτυπώνει το
επίπεδο χρήσης της τεχνολογίας στην καθημερινή λειτουργία
των μικρομεσαίων επιχειρήσεων και την προθυμία των
επιχειρήσεων αυτών να εξελιχθούν τεχνολογικά και να
εκμεταλλευτούν τις δυνατότητες που η τεχνολογία μπορεί να
τους προσφέρει.
Como hacer:
*Curva de oferta y demanda en plano cartesiano.
*Punto de equilibrio.
*Elasticidad de demanda (con formula).
*Elasticidad de oferta (con formula).
Ponencia elaborada para el congreso internacional de psicología juridica y forense versión 6.0 Impartida por M.E. Sergio Herrera Juárez el 29 de Nov. de 2009
Netty is an asynchronous event-driven network application framework for rapid development of maintainable high performance protocol servers & clients. AND IT'S TRUE!
In this talk given at JBCNConf 2015 in Barcelona, we will see how we use Netty at Trovit since 2013, what brought to us and how it opened our minds. We will share tips that helped us to learn more about Netty, some performance tricks and all things that worked for us.
Man’s dreams of ‘intelligences and robots’ go back thousands of years to the worship of gods and statues; mythologies: talisman and puppets; people, places and objects with supposed magical and (often) judgemental/punitive abilities. But it wasn’t until the electronic revolution in 1915, accelerated by WWII that we saw the realisation of two game changing-machines: Colossus (Decoding Machine of Bletchley Park) 1943 and ENIAC (Artillery Computation Engine and Nuclear Bomb Design @ The University of Pennsylvania) 1946.
And so in 1950 the modern AI movement was optimistically projecting what machines would be capable of ‘almost anything’ by 1960/70. Unfortunately, there was no understanding of the complexity to be addressed, and all the projections were wildly wrong; leading to a deep trough of disparagement and disillusionment of some 30 years. However, 70 years on and the original AI optimism and projections of what might be had at least been largely achieved with AI outgunning humans at every board and card game including Poker and GO, and of course; general knowledge, medical diagnosis, image and information pattern recognition…
Man’s dreams of ‘intelligences and robots’ goes back thousands of years to the worship of gods and statues; mythologies: talisman and puppets; people, places and objects with supposed magical and (often) judgemental/punitive abilities. But it wasn’t until the electronic revolution in 1915, accelerated by WWII that we saw the realisation of two game changing-machines: Colossus (Decoding Machine of Bletchley Park) 1943 and ENIAC (Artillery Computation Engine and Nuclear Bomb Design @ The University of Pennsylvania) 1946.
And so in 1950 the modern AI movement was optimistically projecting what machines would be capable of ‘almost anything’ by 1960/70. Unfortunately, there was no understanding of the complexity to be addressed, and all the projections were wildly wrong; leading to a deep trough of disparagement and disillusionment of some 30 years. However, 70 years on and the original AI optimism and projections of what might be have at least been largely achieved with AI outgunning humans at every board and card game including Poker and GO, and of course; general knowledge, medical diagnosis, image and information pattern recognition…
Introduction to Artificial intelligence and MLbansalpra7
**Title: Understanding the Landscape of Artificial Intelligence: A Comprehensive Exploration**
**I. Introduction**
In recent decades, Artificial Intelligence (AI) has emerged as a transformative force, reshaping industries, influencing daily life, and pushing the boundaries of human capabilities. This comprehensive exploration delves into the multifaceted landscape of AI, encompassing its origins, key concepts, applications, ethical considerations, and future prospects.
**II. Historical Perspective**
AI's roots can be traced back to ancient history, where philosophers contemplated the nature of intelligence. However, it wasn't until the mid-20th century that AI as a field of study gained momentum. The influential Dartmouth Conference in 1956 marked the official birth of AI, with early pioneers like Alan Turing laying the theoretical groundwork.
**III. Foundations of AI**
Understanding AI requires grasping its foundational principles. Machine Learning (ML), a subset of AI, empowers machines to learn patterns and make decisions without explicit programming. Within ML, various approaches, such as supervised learning, unsupervised learning, and reinforcement learning, play crucial roles in shaping AI applications.
**IV. Types of Artificial Intelligence**
AI is not a monolithic entity; it spans a spectrum of capabilities. Narrow AI, also known as Weak AI, excels in specific tasks, like image recognition or language translation. In contrast, General AI, or Strong AI, would possess human-like intelligence across a wide range of tasks, a goal that remains a long-term aspiration.
**V. Applications of AI**
AI's impact is felt across diverse sectors. In healthcare, AI aids in diagnostics and personalized treatment plans. In finance, it enhances fraud detection and risk assessment. Self-driving cars exemplify AI in transportation, while virtual assistants like Siri and Alexa showcase its role in daily life. The convergence of AI with other technologies, such as the Internet of Things (IoT) and robotics, amplifies its transformative potential.
**VI. Machine Learning Algorithms**
The backbone of AI lies in its algorithms. Linear regression, decision trees, neural networks, and deep learning models are among the many tools in the ML toolkit. Exploring the mechanics of these algorithms reveals the intricacies of how AI processes information, learns from data, and makes predictions.
Unstructure: Smashing the Boundaries of Data (SxSWi 2014)Ian Varley
When it comes to thinking about data, most software designers are stuck in a rigid, 2-dimensional mindset: "rows and columns." A shame, because breaking free from this "tyranny of the table" can bring our software to new heights: intuitive user experiences, fast development iterations, and cohesive apps.
In this workshop, we'll cover a few concepts that bring data design out of the 1970s, like: sparse representation, emergent schema, ultra-structure, prototype-driven design, graph theory, traversing the time dimension, and more. We'll run the gamut of philosophical approaches to understanding what is important in your mental (and software) model, and how to transcend your two-dimensional picture of data, and trade it in for an N-dimensional one.
Working hands-on with a simple "mock company" and its new killer app, you'll learn:
* The basic concepts of data design: entities, relationships, attributes, and types (along with a few better ways to notate them)
* How to experiment with creating these data structures in a couple existing cloud-based frameworks (e.g. google apps engine, force.com, heroku, etc.).
* How emergent techniques like schema-on-read and ultra-structure can simplify modeling (or, sometimes, complicate it)
* How statistical techniques from the data mining world can loosen our insistence on rigid models
* Why the time dimension is important (in data as well as schema)
NYAI #27: Cognitive Architecture & Natural Language Processing w/ Dr. Catheri...Maryam Farooq
For more AI talks, visit: nyai.co
These slides are from NYAI #27: Cognitive Architecture & Natural Language Processing w/ Dr. Catherine Havasi, which took place Tues, 12/18/19 at Kirkland & Ellis NYC.
[Speaker Bio] Dr. Catherine Havasi is a technology strategist, artificial intelligence researcher, and entrepreneur. In the late 90s, she co-founded the Common Sense Computing Initiative, or ConceptNet, the first crowd-sourced project for artificial intelligence and the largest open knowledge graph for language understanding. ConceptNet has played a role in thousands of AI projects and will be turning 20 next year. She has started several companies commercializing AI research, including Luminoso where she acts as Chief Strategy Officer. She is currently a visiting scientist at the MIT Media Lab where she works on computational creativity and previously directed the Digital Intuition group.
[Abstract] People who build everything from entertainment experiences to financial management face a dilemma: how can you scale what you’re building for broader consumption, yet maintain the personalization that makes it special? A fundamental tension exists between building something individualized, and scaling it to consumers such as visitors at a theme park, or gamers exploring the latest Zelda adventure. True disruption happens when we overcome the idea that one must sacrifice personalization to achieve mass production — like it has in advertising, recommendations, and web search.
Artificial Intelligence practitioners, especially in natural language understanding, dialogue, and cognitive modeling, face the same issue: how can we personalize our models for all audiences without relying on unscalable efforts such as writing specific rules, building dialogue trees, or designing knowledge graphs? Catherine Havasi believes we can remove this dichotomy and achieve “mass personalization.” In this session we’ll discuss how to understand domain text and build believable digital characters. We’ll talk about how adding a little common sense, cognitive architectures, and planning is making this all possible.
nyai.co
by Samantha Adams, Met Office.
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From the FreshTech 2017 conference by TechExeter
www.techexeter.uk
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Knowledge Representation in the Age of Deep Learning, Watson, and the Semantic Web
1. Tetherless World Constellation, RPI
KR in the age of
Deep Learning,
Watson,
and the Semantic Web
Jim Hendler
Tetherless World Professor of Computer, Web and Cognitive Sciences
Director, Institute for Data Exploration and Applications
Rensselaer Polytechnic Institute
http://www.cs.rpi.edu/~hendler
@jahendler (twitter)
Major talks at: http://www.slideshare.net/jahendler
2. Tetherless World Constellation, RPI
But first, Why the Moose?
This moose gave a keynote
with Tim Berners-Lee.
This moose gave a keynote
with Peter Norvig.
3. Tetherless World Constellation, RPI
Talk derives in large part from working on
forthcoming book
(More info at Springer booth)
(Thanks Alice!)
4. Tetherless World Constellation, RPI
Outline
• Several important AI technologies have
moved through “knees in the curve”
bringing much of the attention to AI again
– Deep Learning (& ML in general)
– Watson (& “cognitive computing”)
– Semantic Web (& the knowledge graph)
• But what about KR
– What it is, why it still matters
• And how can these come together
– Which comes with a lot of important challenges
16. Tetherless World Constellation, RPI
Impressive results
Google finds embedded metadata on >30% of its crawl – Guha, 2015
Google “knowledge vault” reported to have over 1.6 billion “facts” (links)
17. Tetherless World Constellation, RPI
Summary: AI has done some way cool stuff
Summary (simplifying tremendously)
• Deep Learning: neural learning from data with high
quality, but imperfect results
• Watson: Associative learning from data with high
quality but imperfect results
• Semantic Web/Knowledge Graph: Graph links
formation from extraction, clustering and learning
As much as many of us “GOFAI” folks wish it, this stuff
cannot be ignored
but, there are still problems…
19. Tetherless World Constellation, RPI
Quick quiz
Who did this moose give invited talks with?
A) Stuart Russell & Vint Cerf
B) A deer and a keynote
C) IJCAI-16 and Alces Alces
D) Tim Berners-Lee and Peter Norvig
20. Tetherless World Constellation, RPI
Associational learning cannot
explain learning by “symbolic communication”
Who did this moose give invited talks with?
A) Stuart Russell & Vint Cerf (highly associated with target answer)
B) A deer and a keynote (word embedding similarity to question)
C) IJCAI-16 and Alces Alces (perceptually linked)
D) Tim Berners-Lee and Peter Norvig (Correct answer is
something most of you learned today, 1-shot, via being told)
21. Tetherless World Constellation, RPI
GOFAI: Knowledge Representation?
• A knowledge representation (KR) is most fundamentally a surrogate, a
substitute for the thing itself, used to enable an entity to
determine consequences by thinking rather than acting, i.e., by
reasoning about the world rather than taking action in it.
• It is a set of ontological commitments, i.e., an answer to the question: In
what terms should I think about the world?
• It is a fragmentary theory of intelligent reasoning, expressed in terms of
three components: (i) the representation's fundamental conception of
intelligent reasoning; (ii) the set of inferences the representation
sanctions; and (iii) the set of inferences it recommends.
• It is a medium for pragmatically efficient computation, i.e., the
computational environment in which thinking is accomplished. One
contribution to this pragmatic efficiency is supplied by the guidance a
representation provides for organizing information so as to facilitate
making the recommended inferences.
• It is a medium of human expression, i.e., a language in which we
say things about the world.
R. Davis, H. Shrobe, P. Szolovits (1993)
24. Tetherless World Constellation, RPI
“Saying things about the world” does
"If I was telling it to a
kid, I'd probably say
something like 'the cat
has fur and four legs and
goes meow, the duck is a
bird and it swims and
goes quack’. "
How would you explain the difference between a
duck and a cat to a child?
Woof
25. Tetherless World Constellation, RPI
KR: Surrogate knowledge?
Which could you sit in?
What is most likely to bite what?
Which one is most likely to become a computer
scientist someday?
…
26. Tetherless World Constellation, RPI
“Surrogate” knowledge
Which could you sit in?
What is most likely to bite what?
Which one is most likely to become a computer
scientist someday?
How would they go about doing it?
27. Tetherless World Constellation, RPI
KR: Recommended vs. Possible inference
Which one would you save if the house was on fire?
28. Tetherless World Constellation, RPI
Recommended vs. Possible inference
Which one would you save if the house was on fire?
Would you use a robot baby-sitter
without knowing which of the three
possibilities it would choose?
29. Tetherless World Constellation, RPI
KR systems in AI need grounded symbols
• Logic- and rule- based systems
– Ground in “model theory” with a notion of truth
and falsity
• Probabilistic Reasoning
– P(A|B) requires A, B map to “meaningful”
concepts, P to be a “real” probability
• Constraint Satisfaction, etc
– Finding an interpretation satisfying a set of
boolean (T,F) constraints
(Note: Yes, I am simplifying, blurring distinctions, ignoring
much cutting edge work… happy to discuss later)
30. Tetherless World Constellation, RPI
The challenge
• If we want to implement KR systems
on top of neural and associative
learners we have an issue
– The numbers coming out of Deep
Learning and Associative graphs are not
probabilities
– They don’t necessarily ground in
human-meaningful symbols
• ”sub-symbolic” learning …
• Association by clustering …
• Errorful extraction …
31. Tetherless World Constellation, RPI
The challenges
• Can we avoid throwing out the
reasoning baby with the grounding
bathwater?
– We still need planning systems
– We still want to be able to define the
rules that a system should follow
– We want to be able to interact with and
understand these systems
• Even if computers don’t need to be symbolic
communicators, WE DO!!!
32. Tetherless World Constellation, RPI
Not just “theory” the applications driving
much modern AI require new grounding ideas
Guruduth Banavar, w/permission)
33. Tetherless World Constellation, RPI
Starting Place: Rethinking grounding
– Formal Explanation vs. post hoc
justification
• Eg. Even if we cannot use a formal
decomposition to explain the reasoning, can
we produce a justification that explains it
– Reasoning systems that “know” some of
their axioms may be simply wrong
• Eg.F1 of .9 doesn’t mean answers are 90%
correct, it is (simplifying) more like 9 out of
10 answers are right, the others aren’t.
– Nailing context …
34. Tetherless World Constellation, RPI
Human-Aware AI
• Context is key
– AI systems still perform best in well-
defined contexts (or trained situations,
or where their document set is
complete, etc.)
– Humans are good at recognizing context
and deciding when extraneous factors
don’t make sense
• Extreme example: Stanislav Yevgrafovich
Petrov (the man who saved the world)
35. Tetherless World Constellation, RPI
Why this REALLY matters
• Humanity faces huges challenges
– eg. Our knowledge of cancer genomics
is being outpaced by mutations as
cancer continues to spread
– eg. Our neighborhoods degrade as
wealth disparity grows
– eg. Our climate warms as we argue
about the causes without changing
behaviors
36. Tetherless World Constellation, RPI
Attacking these problems require the best minds we have working
together: Human and AI!
The existential threat is not AI,
it’s not utilizing the AI we have correctly
37. Tetherless World Constellation, RPI
Summary of talk (minus moose)
• Modern AI is making some huge strides
– Eg. DL, Associative Learning, Knowledge
Graphs, …
• But the need for KR has not gone away
– Eg. Surrogacy, Recommended Inference,
Human communication
• The integration challenge will require
goring some sacred cows
– Grounding, explanation, context ….
• But we need to do it.