The recent series of innovations in deep learning have shown enormous potential to impact individuals and society, both positively and negatively. The deep learning models utilizing massive computing power and enormous datasets have significantly outperformed prior historical benchmarks on increasingly difficult, well-defined research tasks across technology domains such as computer vision, natural language processing, signal processing, and human-computer interactions. However, the Black-Box nature of deep learning models and their over-reliance on massive amounts of data condensed into labels and dense representations pose challenges for the system’s interpretability and explainability. Furthermore, deep learning methods have not yet been proven in their ability to effectively utilize relevant domain knowledge and experience critical to human understanding. This aspect is missing in early data-focused approaches and necessitated knowledge-infused learning and other strategies to incorporate computational knowledge. Rapid advances in our ability to create and reuse structured knowledge as knowledge graphs make this task viable. In this talk, we will outline how knowledge, provided as a knowledge graph, is incorporated into the deep learning methods using knowledge-infused learning. We then discuss how this makes a fundamental difference in the interpretability and explainability of current approaches and illustrate it with examples relevant to a few domains.
http://iwma.lnmiit.ac.in/speakers.html
Third International Workshop on Multimedia Applications ( IWMA ), March 02-06, 2021.
The Holy Grail of machine intelligence is the ability to mimic the human brain. In computing, we have created silos in dealing with each modality (text/language processing, speech processing, image processing, video processing, etc.). However, the human brain’s cognitive and perceptual capability to seamlessly consume (listen and see) and communicate (writing/typing, voice, gesture) multimodal (text, image, video, etc.) information challenges machine intelligence research. Emerging chatbots for demanding health applications present the requirements for these capabilities. To support the corresponding data analysis and reasoning needs, we have explored a pedagogical framework consisting of semantic computing, cognitive computing, and perceptual computing. In particular, we have been motivated by the brain’s amazing perceptive power that abstracts massive amounts of multimodal data by filtering and processing them into a few concepts (representable by a few bits) to act upon. From the information processing perspective, this requires moving from syntactic and semantic big data processing to actionable information that can be weaved naturally into human activities and experience.
Exploration of the above research agenda, including powerful use cases, is afforded in a growing number of emerging technologies and their applications - such as chatbots and robotics for healthcare. In this talk, I will provide these examples and share the early progress we have made towards building health chatbots that consume contextually relevant multimodal data and support different forms/modalities of interactions to achieve various alternatives for digital health. I will also demonstrate the strong role of domain knowledge and personalization using domain and personalized knowledge graphs as part of various reasoning and learning techniques.
https://maison-workshop.com/prof-amit-sheth/
Video: https://youtu.be/pRUXTuxm3as
Keynote at the 7th International Workshop on Mining Actionable Insights from Social Networks (MAISoN 2021) – Special Edition on Responsible, August 21, 2021 and is co-located with the 15th International Joint Conference on Artificial Intelligence (IJCAI 2021). Also ASONAM 2021.
With the increasing legalization of medical and recreational use of substances, more research is needed to understand the association between mental health and user behavior related to drug consumption. Specifically, drug overdose and substance use-related mental health issues have become two major topics that have been widely discussed on social media platforms. Big social media data has the potential to provide deeper insights about these associations to public health analysts for making policy decisions. Multiple national population surveys have found that about half of those who experience a mental health illness during their lives will also experience a substance use disorder and vice versa. The communications related to addiction and mental health are complex to process and understand given their language and contextual characteristics. Surface-level data analysis alone is not sufficient to understand the complex nature of relationships among the addiction and mental health context. Moreover, dark web vendors have been using social media as a new marketplace for drugs. Social media users also discuss the novel drugs emerging in dark web marketplaces and associated side effects/health conditions. These communications get complex when researchers try to annotate them or link them to a specific mental health entity. Considering the significant sensitivity of such communications and to protect user privacy on social media, a potential solution requires reliable algorithms for modeling such communications. We demonstrate the value of incorporating domain-specific knowledge in natural language understanding to identify the relationship between mental health and drug addiction. We discuss end-to-end knowledge-infused deep learning frameworks that leverage the pre-trained language representation model and domain-specific declarative knowledge source to extract entities and their relationships jointly. Our model is further tailored to focus on the entities mentioned in the sentence where ontology is used to locate the target entity’s position. We also demonstrate the capabilities of inclusion of the knowledge-aware representation in association with language models that can extract the Drug-Mental health condition associations.
Acknowledgments: Usha Lokala, Raminta Daniulaityte, Francois Lamy, Manas Gaur, Jyotishman Pathak, and collaborators on NIDA/NIH and NSF funded projects on Addiction and Mental Health.
http://wiki.aiisc.ai/index.php/Public_Health_Addictions_Research_at_AIISC
http://wiki.aiisc.ai/index.php/Modeling_Social_Behavior_for_Healthcare_Utilization_in_Depression
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.
Knowledge Graphs and their central role in big data processing: Past, Present...Amit Sheth
Keynote at CODS-COMAD 2020, Hyderabad, India, 06 Jan 2020: https://cods-comad.in/keynotes.html
Abstract : Early use of knowledge graphs, before the start of this century, related to building a knowledge graph manually or semi-automatically and applying them for semantic applications, such as search, browsing, personalization, and advertisement. Taalee/Semagix Semantic Search in 2000 had a KG that covered many domains and supported search with an equivalent of today’s infobox. Along with the growth of big data, machine learning became the preferred technique for searching, analyzing and deriving insights from such data. We observed the complementary nature of bottom-up (machine learning-driven) and top-down (semantic, knowledge graph and planning based) techniques. Recently we have seen growing efforts involving the shallow use of a knowledge graph to improve the semantic and conceptual processing of data. The future promises deeper and congruent incorporation or integration of the knowledge graphs in the learning techniques (which we call knowledge-infused learning), where knowledge graphs combining statistical AI (bottom-up) and symbolic AI learning techniques (top-down) play a critical role in hybrid and integrated intelligent systems. Throughout this talk, we will provide real-world examples, products, and applications where the knowledge graph played a pivotal role.
I have framed this talk to encourage Pharmacy students to embrace computing in general, and data science and artificial intelligence techniques in particular. The reason is that data-driven science has overtaken traditional lab science; chemistry and biology that underlie pharmacy have become data-driven sciences, and a significant majority of the new jobs in pharma industries demand data analysis skills. Increasingly, traditional bioinformatics approaches are being complemented or replaced by machine learning or deep learning algorithms, especially for cases that have large data sets. I will provide a few examples (e.g., drug discovery, finding adverse drug reactions and broadly pharmacovigilance, and selecting patients for clinical trials) to demonstrate how big data and/or AI are indispensable to pharma research and industry today.
ON EXPLOITING MULTIMODAL INFORMATION FOR MACHINE INTELLIGENCE AND NATURAL IN...Amit Sheth
Keynote: SECOND INTERNATIONAL WORKSHOP IN MULTIMEDIA PRAGMATICSMMPrag 2019, San Jose, California, 28-30 March 2019
http://mipr.sigappfr.org/19/keynote-speakers/
The Holy Grail of machine intelligence is the ability to mimic the human brain. In computing, we have created silos in dealing with each modality (text/language processing, speech processing,image processing, video processing, etc.). However, the human brain’s cognitive and perceptual capability to seamlessly consume (listen and see) and communicate (writing/typing, voice, gesture) multimodal (text, image, video, etc.) information challenges the machine intelligence research. Emerging chatbots for demanding health applications present the requirements for these capabilities. To support the corresponding data analysis and reasoning needs, we have to explore a pedagogical framework consisting of semantic computing, cognitive computing, and perceptual computing (http://bit.ly/w-SCP). In particular, we have been motivated by the brain’s amazing perceptive power that abstracts massive amounts of multimodal data by filtering and processing them into a few concepts (representable by a few bits) to act upon. From the information processing perspective, this requires moving from syntactic and semantic big data processing to actionable information that can be weaved naturally into human activities and experience (http://bit.ly/w-CHE). Exploration of the above research agenda, including powerful use cases, is afforded in a growing number of emerging technologies and their applications - such as chatbots and robotics. In this talk, I will provide these examples and share the early progress we have made towards building health chatbots (http://bit.ly/H-Chatbot) that consume contextually relevant multimodal data and support different forms/modalities of interactions to achieve various alternatives for digital health (http://bit.ly/k-APH). I will also discuss the indispensable role of domain knowledge and personalization using domain and personalized knowledge graphs as part of various reasoning and learning techniques.
Current trends in cognitive science and brain computing research 18th june 2020Dr G R Sinha
Medical Image Processing is study of acquisition, processing and analysis of various types of medical image modalities. Biomedical Imaging is one such modalities that mainly includes EEG, EMG, fMRI, MEG signals and their analysis for numerous applications such as diagnosis of mental disorder, sleep analysis, cognitive ability, study of memory and attention. Cognitive Science Research exploits biomedical modalities related to human brain and make use of the images in decoding brain commands and understanding them. This is very important in brain computer interface (BCI) and assessment of cognitive abilities. The abilities of human brain with the help of EEG signals can be described, decoded and used in performing desired tasks in numerous applications like robotics, driverless cars etc. EEG records brain activities especially electrical activities which are actually due to psychological, physiological and other changes in human brain. This lecture highlights an overview of cognitive science and brain computing research with its challenges and opportunities.
http://iwma.lnmiit.ac.in/speakers.html
Third International Workshop on Multimedia Applications ( IWMA ), March 02-06, 2021.
The Holy Grail of machine intelligence is the ability to mimic the human brain. In computing, we have created silos in dealing with each modality (text/language processing, speech processing, image processing, video processing, etc.). However, the human brain’s cognitive and perceptual capability to seamlessly consume (listen and see) and communicate (writing/typing, voice, gesture) multimodal (text, image, video, etc.) information challenges machine intelligence research. Emerging chatbots for demanding health applications present the requirements for these capabilities. To support the corresponding data analysis and reasoning needs, we have explored a pedagogical framework consisting of semantic computing, cognitive computing, and perceptual computing. In particular, we have been motivated by the brain’s amazing perceptive power that abstracts massive amounts of multimodal data by filtering and processing them into a few concepts (representable by a few bits) to act upon. From the information processing perspective, this requires moving from syntactic and semantic big data processing to actionable information that can be weaved naturally into human activities and experience.
Exploration of the above research agenda, including powerful use cases, is afforded in a growing number of emerging technologies and their applications - such as chatbots and robotics for healthcare. In this talk, I will provide these examples and share the early progress we have made towards building health chatbots that consume contextually relevant multimodal data and support different forms/modalities of interactions to achieve various alternatives for digital health. I will also demonstrate the strong role of domain knowledge and personalization using domain and personalized knowledge graphs as part of various reasoning and learning techniques.
https://maison-workshop.com/prof-amit-sheth/
Video: https://youtu.be/pRUXTuxm3as
Keynote at the 7th International Workshop on Mining Actionable Insights from Social Networks (MAISoN 2021) – Special Edition on Responsible, August 21, 2021 and is co-located with the 15th International Joint Conference on Artificial Intelligence (IJCAI 2021). Also ASONAM 2021.
With the increasing legalization of medical and recreational use of substances, more research is needed to understand the association between mental health and user behavior related to drug consumption. Specifically, drug overdose and substance use-related mental health issues have become two major topics that have been widely discussed on social media platforms. Big social media data has the potential to provide deeper insights about these associations to public health analysts for making policy decisions. Multiple national population surveys have found that about half of those who experience a mental health illness during their lives will also experience a substance use disorder and vice versa. The communications related to addiction and mental health are complex to process and understand given their language and contextual characteristics. Surface-level data analysis alone is not sufficient to understand the complex nature of relationships among the addiction and mental health context. Moreover, dark web vendors have been using social media as a new marketplace for drugs. Social media users also discuss the novel drugs emerging in dark web marketplaces and associated side effects/health conditions. These communications get complex when researchers try to annotate them or link them to a specific mental health entity. Considering the significant sensitivity of such communications and to protect user privacy on social media, a potential solution requires reliable algorithms for modeling such communications. We demonstrate the value of incorporating domain-specific knowledge in natural language understanding to identify the relationship between mental health and drug addiction. We discuss end-to-end knowledge-infused deep learning frameworks that leverage the pre-trained language representation model and domain-specific declarative knowledge source to extract entities and their relationships jointly. Our model is further tailored to focus on the entities mentioned in the sentence where ontology is used to locate the target entity’s position. We also demonstrate the capabilities of inclusion of the knowledge-aware representation in association with language models that can extract the Drug-Mental health condition associations.
Acknowledgments: Usha Lokala, Raminta Daniulaityte, Francois Lamy, Manas Gaur, Jyotishman Pathak, and collaborators on NIDA/NIH and NSF funded projects on Addiction and Mental Health.
http://wiki.aiisc.ai/index.php/Public_Health_Addictions_Research_at_AIISC
http://wiki.aiisc.ai/index.php/Modeling_Social_Behavior_for_Healthcare_Utilization_in_Depression
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.
Knowledge Graphs and their central role in big data processing: Past, Present...Amit Sheth
Keynote at CODS-COMAD 2020, Hyderabad, India, 06 Jan 2020: https://cods-comad.in/keynotes.html
Abstract : Early use of knowledge graphs, before the start of this century, related to building a knowledge graph manually or semi-automatically and applying them for semantic applications, such as search, browsing, personalization, and advertisement. Taalee/Semagix Semantic Search in 2000 had a KG that covered many domains and supported search with an equivalent of today’s infobox. Along with the growth of big data, machine learning became the preferred technique for searching, analyzing and deriving insights from such data. We observed the complementary nature of bottom-up (machine learning-driven) and top-down (semantic, knowledge graph and planning based) techniques. Recently we have seen growing efforts involving the shallow use of a knowledge graph to improve the semantic and conceptual processing of data. The future promises deeper and congruent incorporation or integration of the knowledge graphs in the learning techniques (which we call knowledge-infused learning), where knowledge graphs combining statistical AI (bottom-up) and symbolic AI learning techniques (top-down) play a critical role in hybrid and integrated intelligent systems. Throughout this talk, we will provide real-world examples, products, and applications where the knowledge graph played a pivotal role.
I have framed this talk to encourage Pharmacy students to embrace computing in general, and data science and artificial intelligence techniques in particular. The reason is that data-driven science has overtaken traditional lab science; chemistry and biology that underlie pharmacy have become data-driven sciences, and a significant majority of the new jobs in pharma industries demand data analysis skills. Increasingly, traditional bioinformatics approaches are being complemented or replaced by machine learning or deep learning algorithms, especially for cases that have large data sets. I will provide a few examples (e.g., drug discovery, finding adverse drug reactions and broadly pharmacovigilance, and selecting patients for clinical trials) to demonstrate how big data and/or AI are indispensable to pharma research and industry today.
ON EXPLOITING MULTIMODAL INFORMATION FOR MACHINE INTELLIGENCE AND NATURAL IN...Amit Sheth
Keynote: SECOND INTERNATIONAL WORKSHOP IN MULTIMEDIA PRAGMATICSMMPrag 2019, San Jose, California, 28-30 March 2019
http://mipr.sigappfr.org/19/keynote-speakers/
The Holy Grail of machine intelligence is the ability to mimic the human brain. In computing, we have created silos in dealing with each modality (text/language processing, speech processing,image processing, video processing, etc.). However, the human brain’s cognitive and perceptual capability to seamlessly consume (listen and see) and communicate (writing/typing, voice, gesture) multimodal (text, image, video, etc.) information challenges the machine intelligence research. Emerging chatbots for demanding health applications present the requirements for these capabilities. To support the corresponding data analysis and reasoning needs, we have to explore a pedagogical framework consisting of semantic computing, cognitive computing, and perceptual computing (http://bit.ly/w-SCP). In particular, we have been motivated by the brain’s amazing perceptive power that abstracts massive amounts of multimodal data by filtering and processing them into a few concepts (representable by a few bits) to act upon. From the information processing perspective, this requires moving from syntactic and semantic big data processing to actionable information that can be weaved naturally into human activities and experience (http://bit.ly/w-CHE). Exploration of the above research agenda, including powerful use cases, is afforded in a growing number of emerging technologies and their applications - such as chatbots and robotics. In this talk, I will provide these examples and share the early progress we have made towards building health chatbots (http://bit.ly/H-Chatbot) that consume contextually relevant multimodal data and support different forms/modalities of interactions to achieve various alternatives for digital health (http://bit.ly/k-APH). I will also discuss the indispensable role of domain knowledge and personalization using domain and personalized knowledge graphs as part of various reasoning and learning techniques.
Current trends in cognitive science and brain computing research 18th june 2020Dr G R Sinha
Medical Image Processing is study of acquisition, processing and analysis of various types of medical image modalities. Biomedical Imaging is one such modalities that mainly includes EEG, EMG, fMRI, MEG signals and their analysis for numerous applications such as diagnosis of mental disorder, sleep analysis, cognitive ability, study of memory and attention. Cognitive Science Research exploits biomedical modalities related to human brain and make use of the images in decoding brain commands and understanding them. This is very important in brain computer interface (BCI) and assessment of cognitive abilities. The abilities of human brain with the help of EEG signals can be described, decoded and used in performing desired tasks in numerous applications like robotics, driverless cars etc. EEG records brain activities especially electrical activities which are actually due to psychological, physiological and other changes in human brain. This lecture highlights an overview of cognitive science and brain computing research with its challenges and opportunities.
Modern signal processing is dead without machine learning! 5th july 2020Dr G R Sinha
This lecture highlights role of Machine Learning in Modern Signal Processing Applications such as Driver-less Cars, Robotics, Smart Environment Monitoring, Healthcare etc.
The relationship between artificial intelligence and psychological theoriesEr. rahul abhishek
Psychology is one of the parent elements of artificial
intelligence or we can also say that it is the main source for
artificial intelligence. In this paper we are discussing about the
theories of psychology used in AI. Since psychology is the study
of human brain and its nature and AI is the branch which deals
with the intelligence in machine, so for understanding the
intelligence of a machine we have to compare with human
intelligence because AI means the intelligence shown by a
machine like a human being.
Applied Artificial Intelligence & How it's Transforming Life SciencesKumaraguru Veerasamy
In this SlideShare, we cover an overview history of artificial intelligence (AI), before exploring its applications in healthcare, biotechnology & pharmaceuticals. The slides will also cover the market outlook of AI, and how big pharmaceutical companies are investing in the technology. In addition, there are a couple of case studies on applied AI, namely in genomics and liquid biopsy (glycoproteomics).
Artificial Intelligence (AI) has revolutionized in information technology.AI is a subfield of computer science that includes the creation of intelligent machines and software that work and react like human beings. AI and its Applications gets used in various fields of life of humans as expert system solve the complex problems in various areas as science, engineering, business, medicine, video games and Advertising. But “Do any traffic lights use Artificial Intelligence??”, I thought a lot of this when waiting in a red light. This paper gives an overview of Artificial Intelligence and its applications used in human life. This will explore the current use of Artificial Intelligence technologies in Network Intrusion for protecting computer and communication networks from intruders, in the medical area-medicine, to improve hospital inpatient care, for medical image classification, in the accounting databases to mitigate the problems of it, in the computer games, and in Advertising. Also, it will show artificial intelligence principle and how they were applying in traffic signal control, how they solve some traffic problem in actual. This paper gives an introduction to a self-learning system based on RBF neural network and how the system can simulate the traffic police’s experience. This paper is focusing on how to evaluate the effect of the control with the changing of the traffic and adjust the signal with the different techniques of Artificial Intelligence.
The age of artificial intelligence, deep dives on machine learning and deep learning. Machine perception and applications. How company use AI in their businesses. Case study: Netflix. Basic tools for data manipulation and data visualization.
Cognitive Computing by Professor Gordon Pipadiannepatricia
Professor Dr. Gordon Pipa, University of Osnabrueck, Germany is making this presentation for the Cognitive Systems Institute Speaker Series on May 26, 2016.
How are machine learning and artificial intelligence revolutionizing insurance?
This presentation explains it briefly, including current trends and effects on the business.
Pistoia Alliance Webinar Demystifying AI: Centre of Excellence for AI Webina...Pistoia Alliance
Pistoia Alliance launched its Centre of Excellence for Artificial Intelligence (AI) in Life Sciences where we hope to bring together best practice, adoption strategy and hackathons covering a range of challenges.
Over the coming months we will be hosting a series of topics and speakers giving their perspectives on the role of Artificial & Augmented Intelligence in Life Sciences and Healthcare.
The topics will cover some of the current challenges, user stories & value in using AI in life sciences. If you want to get involved in this series as a speaker or suggest topics please get in touch
Webinar 1 will focused on the following
A Brief History
Big Data/ML/DL/AI - fundamentals and concepts
Data Fidelity importance
Some best practices
Comparative Analysis of Computational Intelligence Paradigms in WSN: Reviewiosrjce
Computational Intelligence is the study of the design of intelligent agents. An agent is something that
react according to an environment—it does something. Agents includes worms, dogs, thermostats, airplanes,
humans, and society. The purpose of computational intelligence is to understand the principles that make
intelligent behavior possible, in real or artificial systems. Techniques of Computational Intelligence are
designed to model the aspects of biological intelligence. These paradigms include that exhibit an ability to
learn or adapt to new situations,to generalize, abstract, learn and associate. This paper gives review of
comparison between computational intelligence paradigms in Wireless Sensor Network and Finally,a short
conclusion is provided.
Smart Data for you and me: Personalized and Actionable Physical Cyber Social ...Amit Sheth
Featured Keynote at Worldcomp'14, July 2014: http://www.world-academy-of-science.org/worldcomp14/ws/keynotes/keynote_sheth
Video of the talk at: http://youtu.be/2991W7OBLqU
Big Data has captured a lot of interest in industry, with the emphasis on the challenges of the four Vs of Big Data: Volume, Variety, Velocity, and Veracity, and their applications to drive value for businesses. Recently, there is rapid growth in situations where a big data challenge relates to making individually relevant decisions. A key example is human health, fitness, and well-being. Consider for instance, understanding the reasons for and avoiding an asthma attack based on Big Data in the form of personal health signals (e.g., physiological data measured by devices/sensors or Internet of Things around humans, on the humans, and inside/within the humans), public health signals (information coming from the healthcare system such as hospital admissions), and population health signals (such as Tweets by people related to asthma occurrences and allergens, Web services providing pollen and smog information, etc.). However, no individual has the ability to process all these data without the help of appropriate technology, and each human has different set of relevant data!
In this talk, I will forward the concept of Smart Data that is realized by extracting value from Big Data, to benefit not just large companies but each individual. If I am an asthma patient, for all the data relevant to me with the four V-challenges, what I care about is simply, “How is my current health, and what is the risk of having an asthma attack in my personal situation, especially if that risk has changed?” As I will show, Smart Data that gives such personalized and actionable information will need to utilize metadata, use domain specific knowledge, employ semantics and intelligent processing, and go beyond traditional reliance on ML and NLP.
For harnessing volume, I will discuss the concept of Semantic Perception, that is, how to convert massive amounts of data into information, meaning, and insight useful for human decision-making. For dealing with Variety, I will discuss experience in using agreement represented in the form of ontologies, domain models, or vocabularies, to support semantic interoperability and integration. For Velocity, I will discuss somewhat more recent work on Continuous Semantics, which seeks to use dynamically created models of new objects, concepts, and relationships, using them to better understand new cues in the data that capture rapidly evolving events and situations.
Smart Data applications in development at Kno.e.sis come from the domains of personalized health, energy, disaster response, and smart city. I will present examples from a couple of these.
Deep learning is not merely an AI technique or a software program, but a new class of smart network information technology that is changing the concept of the modern technology project by offering real-time engagement with reality
Deep learning is a data automation method that replaces hard-coded software with a capacity, in the form of a learning network that is trained to perform a task
TCS Point of View Session - Analyze by Dr. Gautam Shroff, VP and Chief Scient...Tata Consultancy Services
If insights are available from mass amounts of data, you require enormous agility across business units to act on these. Understand how your peers tackle such problems and what new approaches are available to businesses.
We hosted a fantastic tutorial on Knowledge-infused Deep Learning at the 31st ACM Hypertext Conference on July 14. Broadly, the tutorial covered many exciting applications of Broad- and Community-based Knowledge Graph in Education, Clinical and Social-Media Healthcare, Pandemic, and Cryptomarkets.
We theorized the concept of Knowledge-infusion and showed its importance in gaining explainability and spectacular performance gains. We extended the idea of "Knowledge-infused Deep Learning" to Autonomous Driving, Cyber Social Harms, and DarkWeb.
The tutorial presentation with relevant resources and references are made online at http://kidl2020.aiisc.ai.
Explicable Artifical Intelligence for Education (XAIED)Robert Farrow
The application of artificial intelligence in AI is increasing, but there is a growing awareness of the profound ethical implications which are presently undertheorised. The emerging consensus is that there needs to be adequate transparency and explicability for the use of algorithms in education. This presentation provides an overview of AI in education (AIED) and characterises the requirement for explicability as a response to the ‘black box’ of machine learning. It is argued that explicability should be understood as part of a wider socio-technical turn in AI, and that there is a strong case for implementing full transparency in AIED as a default position. Such transparency threatens to disrupt traditional pedagogical processes, and mediation strategies will be needed. There are also instances where non-transparency may be justifiable and in these examples processes for auditing and governance.
The talk describes a paradigm of knowledge-infused learning in healthcare for explainability, interpretability, and traceability of outcome. Thus bridging the gap between AI and Clinical settings and developing architectures that are of clinical relevance.
Modern signal processing is dead without machine learning! 5th july 2020Dr G R Sinha
This lecture highlights role of Machine Learning in Modern Signal Processing Applications such as Driver-less Cars, Robotics, Smart Environment Monitoring, Healthcare etc.
The relationship between artificial intelligence and psychological theoriesEr. rahul abhishek
Psychology is one of the parent elements of artificial
intelligence or we can also say that it is the main source for
artificial intelligence. In this paper we are discussing about the
theories of psychology used in AI. Since psychology is the study
of human brain and its nature and AI is the branch which deals
with the intelligence in machine, so for understanding the
intelligence of a machine we have to compare with human
intelligence because AI means the intelligence shown by a
machine like a human being.
Applied Artificial Intelligence & How it's Transforming Life SciencesKumaraguru Veerasamy
In this SlideShare, we cover an overview history of artificial intelligence (AI), before exploring its applications in healthcare, biotechnology & pharmaceuticals. The slides will also cover the market outlook of AI, and how big pharmaceutical companies are investing in the technology. In addition, there are a couple of case studies on applied AI, namely in genomics and liquid biopsy (glycoproteomics).
Artificial Intelligence (AI) has revolutionized in information technology.AI is a subfield of computer science that includes the creation of intelligent machines and software that work and react like human beings. AI and its Applications gets used in various fields of life of humans as expert system solve the complex problems in various areas as science, engineering, business, medicine, video games and Advertising. But “Do any traffic lights use Artificial Intelligence??”, I thought a lot of this when waiting in a red light. This paper gives an overview of Artificial Intelligence and its applications used in human life. This will explore the current use of Artificial Intelligence technologies in Network Intrusion for protecting computer and communication networks from intruders, in the medical area-medicine, to improve hospital inpatient care, for medical image classification, in the accounting databases to mitigate the problems of it, in the computer games, and in Advertising. Also, it will show artificial intelligence principle and how they were applying in traffic signal control, how they solve some traffic problem in actual. This paper gives an introduction to a self-learning system based on RBF neural network and how the system can simulate the traffic police’s experience. This paper is focusing on how to evaluate the effect of the control with the changing of the traffic and adjust the signal with the different techniques of Artificial Intelligence.
The age of artificial intelligence, deep dives on machine learning and deep learning. Machine perception and applications. How company use AI in their businesses. Case study: Netflix. Basic tools for data manipulation and data visualization.
Cognitive Computing by Professor Gordon Pipadiannepatricia
Professor Dr. Gordon Pipa, University of Osnabrueck, Germany is making this presentation for the Cognitive Systems Institute Speaker Series on May 26, 2016.
How are machine learning and artificial intelligence revolutionizing insurance?
This presentation explains it briefly, including current trends and effects on the business.
Pistoia Alliance Webinar Demystifying AI: Centre of Excellence for AI Webina...Pistoia Alliance
Pistoia Alliance launched its Centre of Excellence for Artificial Intelligence (AI) in Life Sciences where we hope to bring together best practice, adoption strategy and hackathons covering a range of challenges.
Over the coming months we will be hosting a series of topics and speakers giving their perspectives on the role of Artificial & Augmented Intelligence in Life Sciences and Healthcare.
The topics will cover some of the current challenges, user stories & value in using AI in life sciences. If you want to get involved in this series as a speaker or suggest topics please get in touch
Webinar 1 will focused on the following
A Brief History
Big Data/ML/DL/AI - fundamentals and concepts
Data Fidelity importance
Some best practices
Comparative Analysis of Computational Intelligence Paradigms in WSN: Reviewiosrjce
Computational Intelligence is the study of the design of intelligent agents. An agent is something that
react according to an environment—it does something. Agents includes worms, dogs, thermostats, airplanes,
humans, and society. The purpose of computational intelligence is to understand the principles that make
intelligent behavior possible, in real or artificial systems. Techniques of Computational Intelligence are
designed to model the aspects of biological intelligence. These paradigms include that exhibit an ability to
learn or adapt to new situations,to generalize, abstract, learn and associate. This paper gives review of
comparison between computational intelligence paradigms in Wireless Sensor Network and Finally,a short
conclusion is provided.
Smart Data for you and me: Personalized and Actionable Physical Cyber Social ...Amit Sheth
Featured Keynote at Worldcomp'14, July 2014: http://www.world-academy-of-science.org/worldcomp14/ws/keynotes/keynote_sheth
Video of the talk at: http://youtu.be/2991W7OBLqU
Big Data has captured a lot of interest in industry, with the emphasis on the challenges of the four Vs of Big Data: Volume, Variety, Velocity, and Veracity, and their applications to drive value for businesses. Recently, there is rapid growth in situations where a big data challenge relates to making individually relevant decisions. A key example is human health, fitness, and well-being. Consider for instance, understanding the reasons for and avoiding an asthma attack based on Big Data in the form of personal health signals (e.g., physiological data measured by devices/sensors or Internet of Things around humans, on the humans, and inside/within the humans), public health signals (information coming from the healthcare system such as hospital admissions), and population health signals (such as Tweets by people related to asthma occurrences and allergens, Web services providing pollen and smog information, etc.). However, no individual has the ability to process all these data without the help of appropriate technology, and each human has different set of relevant data!
In this talk, I will forward the concept of Smart Data that is realized by extracting value from Big Data, to benefit not just large companies but each individual. If I am an asthma patient, for all the data relevant to me with the four V-challenges, what I care about is simply, “How is my current health, and what is the risk of having an asthma attack in my personal situation, especially if that risk has changed?” As I will show, Smart Data that gives such personalized and actionable information will need to utilize metadata, use domain specific knowledge, employ semantics and intelligent processing, and go beyond traditional reliance on ML and NLP.
For harnessing volume, I will discuss the concept of Semantic Perception, that is, how to convert massive amounts of data into information, meaning, and insight useful for human decision-making. For dealing with Variety, I will discuss experience in using agreement represented in the form of ontologies, domain models, or vocabularies, to support semantic interoperability and integration. For Velocity, I will discuss somewhat more recent work on Continuous Semantics, which seeks to use dynamically created models of new objects, concepts, and relationships, using them to better understand new cues in the data that capture rapidly evolving events and situations.
Smart Data applications in development at Kno.e.sis come from the domains of personalized health, energy, disaster response, and smart city. I will present examples from a couple of these.
Deep learning is not merely an AI technique or a software program, but a new class of smart network information technology that is changing the concept of the modern technology project by offering real-time engagement with reality
Deep learning is a data automation method that replaces hard-coded software with a capacity, in the form of a learning network that is trained to perform a task
TCS Point of View Session - Analyze by Dr. Gautam Shroff, VP and Chief Scient...Tata Consultancy Services
If insights are available from mass amounts of data, you require enormous agility across business units to act on these. Understand how your peers tackle such problems and what new approaches are available to businesses.
We hosted a fantastic tutorial on Knowledge-infused Deep Learning at the 31st ACM Hypertext Conference on July 14. Broadly, the tutorial covered many exciting applications of Broad- and Community-based Knowledge Graph in Education, Clinical and Social-Media Healthcare, Pandemic, and Cryptomarkets.
We theorized the concept of Knowledge-infusion and showed its importance in gaining explainability and spectacular performance gains. We extended the idea of "Knowledge-infused Deep Learning" to Autonomous Driving, Cyber Social Harms, and DarkWeb.
The tutorial presentation with relevant resources and references are made online at http://kidl2020.aiisc.ai.
Explicable Artifical Intelligence for Education (XAIED)Robert Farrow
The application of artificial intelligence in AI is increasing, but there is a growing awareness of the profound ethical implications which are presently undertheorised. The emerging consensus is that there needs to be adequate transparency and explicability for the use of algorithms in education. This presentation provides an overview of AI in education (AIED) and characterises the requirement for explicability as a response to the ‘black box’ of machine learning. It is argued that explicability should be understood as part of a wider socio-technical turn in AI, and that there is a strong case for implementing full transparency in AIED as a default position. Such transparency threatens to disrupt traditional pedagogical processes, and mediation strategies will be needed. There are also instances where non-transparency may be justifiable and in these examples processes for auditing and governance.
The talk describes a paradigm of knowledge-infused learning in healthcare for explainability, interpretability, and traceability of outcome. Thus bridging the gap between AI and Clinical settings and developing architectures that are of clinical relevance.
PyData Salamanca knowledge infusion in healthcareManas Gaur
The talk describes a paradigm of knowledge-infused learning in healthcare for explainability, interpretability, and traceability of outcome. Thus bridging the gap between AI and Clinical settings and developing architectures that are of clinical relevance.
Presentation (with Eamon Costello) from the Global Smart Education Conference (The 6th International Conference on Smart Learning Environments), Beijing National University, China.
The presentation explores issues in AI driven learning systems and implications of machine learning approaches for inclusion and access to education.
UNCOVERING FAKE NEWS BY MEANS OF SOCIAL NETWORK ANALYSISpijans
The short access to facts on social media networks in addition to its exponential upward push also made it tough to distinguish among faux information or actual facts. The quick dissemination thru manner of sharing has more high quality its falsification exponentially. It is also essential for the credibility of social media networks to avoid the spread of fake facts. So its miles rising research task to robotically check for misstatement of information thru its source, content material, or author and save you the unauthenticated assets from spreading rumours. This paper demonstrates an synthetic intelligence primarily based completely approach for the identification of the fake statements made by way of the use of social network entities. Versions of Deep neural networks are being applied to evalues datasets and have a look at for fake information presence. The implementation setup produced most volume 99% category accuracy, even as dataset is tested for binary (real or fake) labelling with multiple epochs.
UNCOVERING FAKE NEWS BY MEANS OF SOCIAL NETWORK ANALYSISpijans
The short access to facts on social media networks in addition to its exponential upward push also made it
tough to distinguish among faux information or actual facts. The quick dissemination thru manner of sharing has more high quality its falsification exponentially. It is also essential for the credibility of social media networks to avoid the spread of fake facts. So its miles rising research task to robotically check for
misstatement of information thru its source, content material, or author and save you the unauthenticated
assets from spreading rumours. This paper demonstrates an synthetic intelligence primarily based completely approach for the identification of the fake statements made by way of the use of social network
entities. Versions of Deep neural networks are being applied to evalues datasets and have a look at for
fake information presence. The implementation setup produced most volume 99% category accuracy, even
as dataset is tested for binary (real or fake) labelling with multiple epochs.
Application and Methods of Deep Learning in IoTIJAEMSJORNAL
In this talk, we provide a comprehensive overview of how to use a subset of advanced AI techniques, most specifically Deep Learning (DL), to bolster analytics as well as learning in the IoT URL. First and foremost, we define a development environment that integrates big data designs with deep learning models to promote rapid experimentation. There are three main promises made in the proposal: To begin, it illustrates a big data engineering that facilitates big data assortment in the same way that businesses facilitate deep learning models. Then, the language for creating a data perspective is shown, one that transforms the many streams of large data into a format that can be used by an advanced learning system. Third, it demonstrates the success of the framework by applying the tool to a wide range of deep learning use cases. We provide a generalized basis for a variety of DL architectures using numerical examples. We also evaluate and summarize major published research projects that made use of DL in the IoT context. Wonderful Internet of Things gadgets that have integrated DL into their prior knowledge are often discussed.
GDG Cloud Southlake #17: Meg Dickey-Kurdziolek: Explainable AI is for EveryoneJames Anderson
If Artificial Intelligence (AI) is a black-box, how can a human comprehend and trust the results of Machine Learning (ML) alogrithms? Explainable AI (XAI) tries to shed light into that AI black-box so humans can trust what is going on. Our speaker Meg Dickey-Kurdziolek is currently a UX Researcher for Google Cloud AI and Industry Solutions, where she focuses her research on Explainable AI and Model Understanding. Recording of the presentation: https://youtu.be/6N2DNN_HDWU
Artificial Intelligence Role in Modern Science Aims, Merits, Risks and Its Ap...ijtsrd
Artificial Intelligence AI is a growing field at the intersection of computer science, mathematics, and engineering, focused on creating machines capable of intelligent behavior. Over the years, AI has evolved from rule based systems to data driven approaches, prominently leveraging machine learning and deep learning. This evolution has led to AI systems capable of complex tasks such as pattern recognition, natural language processing, and decision making. The applications of AI are vast and diverse, permeating industries like healthcare, finance, automotive, retail, and education. AI driven technologies enable efficient automation, precise data analysis, personalized experiences, and improved decision making. However, with these advancements come ethical and culture concerns, including biases, data privacy, job displacement, and the responsible development and deployment of AI. Striking a balance between AIs potential and its associated risks necessitates a holistic approach, incorporating transparency, fairness, robust regulations, and ongoing research. This abstract encapsulates AIs transformative potential, emphasizing the importance of responsible AI development to ensure a positive impact on society while mitigating risks. Manish Verma "Artificial Intelligence Role in Modern Science: Aims, Merits, Risks and Its Applications" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-5 , October 2023, URL: https://www.ijtsrd.com/papers/ijtsrd59910.pdf Paper Url: https://www.ijtsrd.com/computer-science/artificial-intelligence/59910/artificial-intelligence-role-in-modern-science-aims-merits-risks-and-its-applications/manish-verma
For this project, we had to conduct research on a topic that was seen as a relevant area of study in Enterprise Systems and how it will be applicable in the future.
We chose to study the effects artificial intelligence will have on CRM systems. To view our findings, you can view the video here - https://www.youtube.com/watch?v=Fe55c60QPwY&t=9s
Scaling the mirrorworld with knowledge graphsAlan Morrison
After registration at https://www.brighttalk.com/webcast/9273/364148, you can view the full recording, which begins with Scott Abel's intro for a few minutes, then my talk for 20 minutes, and then Sebastian Gabler's. First presented on October 23 at an SWC webinar.
Conclusions:
(1) The mirrorworld (a world of digital twins, which will be 25 years in the making, according to Kevin Kelly) will require semantic knowledge graphs for interaction and interoperability.
(2) This fact implies massive future demand for knowledge graph technology and other new data infrastructure innovations, comparable to the scale of oil & gas industry infrastructure development over 150 years.
(3) Conceivably, knowledge graphs could be used to address a $205 billion market demand by 2021 for graph databases, information management, digital twins, conversational AI, virtual assistants and as knowledge bases/accelerated training for deep learning, etc. but the problem is that awareness of the tech is low, and the semantics community that understands the tech is still quite small.
(4) Over the next decades, knowledge graphs promise both scalability and substantial efficiencies in enterprises. But lack of awareness of its potential and how to harness it will continue to be stumbling blocks to adoption.
Understanding the New World of Cognitive ComputingDATAVERSITY
Cognitive Computing is a rapidly developing technology that has reached practical application and implementation. So what is it? Do you need it? How can it benefit your business?
In this webinar a panel of experts in Cognitive Computing will discuss the technology, the current practical applications, and where this technology is going. The discussion will start with a review of a recent survey produced by DATAVERSITY on how Cognitive Computing is currently understood by your peers. The panel will also review many components of the technology including:
Cognitive Analytics
Machine Learning
Deep Learning
Reasoning
And next generation artificial intelligence (AI)
And get involved in the discussion with your own questions to present to the panel.
In this talk we will summarise some of the detectable trends on AI beyond deep learning. We will focus on the current transition from deep learning to deep semantics, describing the enabling infrastructures, challenges and opportunities in the construction of the next generation AI systems. The talk will focus on Natural Language Processing (NLP) as an AI sub-domain and will link to the research at the AI Systems Lab at the University of Manchester.
This workshop presentation from Enterprise Knowledge team members Joe Hilger, Founder and COO, and Sara Nash, Technical Analyst, was delivered on June 8, 2020 as part of the Data Summit 2020 virtual conference. The 3-hour workshop provided an interdisciplinary group of participants with a definition of what a knowledge graph is, how it is implemented, and how it can be used to increase the value of your organization’s datas. This slide deck gives an overview of the KM concepts that are necessary for the implementation of knowledge graphs as a foundation for Enterprise Artificial Intelligence (AI). Hilger and Nash also outlined four use cases for knowledge graphs, including recommendation engines and natural language query on structured data.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Semantics of the Black-Box: Using knowledge-infused learning approach to make AI systems more interpretable and explainable
1. Semantics of the Black-Box:
Using knowledge-infused learning approach
to make AI systems more interpretable and
explainable
Keynote @ KGSWC 2020: http://www.kgswc.org/
2. 2
Amit Sheth
Founding Director,
Artificial Intelligence Institute http://aiisc.ai
The University of South Carolina
amit@sc.edu https://www.linkedin.com/in/amitsheth/
Special Thanks
Kaushik Roy
AIISC, kaushikr@email.sc.edu
Manas Gaur
AIISC, mgaur@email.sc.edu
Some of the K-iL collaborators:
Ruwan Wickramarachchi (AI Institute)
Shweta Yadav
Ugur Kurşuncu (AI Institute)
Keyur Faldu (Embibe Inc.)
Qi Zhang (AI Institute)
Vishal Pallagani (AI Institute)
...
4. Outline of the talk
❏ Knowledge Graph
❏ Knowledge Graph meets Deep Learning:
Knowledge-infused Learning
❏ K-IL in Explainability and Interpretability in
Healthcare
❏ K-IL for Explainability and Interpretability in
Adaptive Contagion Control
❏ K-IL : Explainable Improving of Learning
Outcomes 4
6. Definition
6
Knowledge Graphs (KG) is a
structured knowledge in a graph
representation (in many cases, labeled
property graph, or RDF or its variants). We
cannot escape the class expressivity-
computability
Tread-off.
Community is still debating exact
definition.
Key differentiator: Relationships
(“relationships at the heart of semantics”).
Different/Related forms:
● Ontology : Knowledge graph after human
curation of entities and relations;
“ontological commitment”, richer KR
● Knowledge Base: flattened graph
● Lexicons: Small application-specific
flattened graph
● Knowledge Networks (KN) integrate
and combine knowledge (usually
captured as KGs) to serve a network
(community).
Knowledge Graphs and Knowledge Networks: The Story in Brief
7. 7
First commercial semantic search/browsing/… on the Web and
for the content on the Web using KG. Term used for KR:
WorldModel, Ontology http://bit.ly/15yrSemS
Creation and Use of Knowledge ~ 2000
8. Proliferation Broad-based & Domain-Specific KGs
8
Examples of General Purpose Knowledge Graphs
1. DBpedia [Auer 2007, Lehmann 2015]
2. Yago [Rebele 2016]
3. Freebase [Bollacker 2008]
4. ConceptNet [Speer 2017]
5. Knowledge Vault [Dong 2014]
6. NELL [Mitchell 2018]
7. Wikidata [Vrandečić 2014]
Example of Healthcare-specific Knowledge Graphs
1. SNOMED-CT [ACL Chang 2020]
2. Unified Medical Language System (UMLS) [Yip 2019]
3. DataMed [JAMIA Chen 2018]
4. International Classification of Diseases (ICD-10)
[JAMIA Choi 2016]
5. DrugBank, Rx-NORM and MedDRA [ BMC Celebi 2019]
6. Drug Abuse Ontology [BMI Cameron 2013]
Many are also community-developed.
9. Enterprise Knowledge Graphs are also very popular
9
KG enabled Web and
Enterprise Applications:
Google, Amazon, Microsoft,
Siemens, LinkedIn, Airbnb,
eBay, and Apple, as well as
smaller companies (e.g. ezDI,
Franz, Metaphactory/
Metaphacts, Semantic Web
Company, Mondeca, Stardog,
Diffbot, Siren).
Enterprise KG development
service is also available.
(Maana). Industry-Scale Knowledge Graphs: Lessons and Challenges (Communications of the ACM, August 2019)
10. 10
Health Knowledge GraphEmpathi Ontology
IRI: https://w3id.org/empathi/1.0
Download:
https://raw.githubusercontent.com/shekarpour/emp
athi.io/master/empathi.owl [Shah and Sheth US patent 2015]
11. “
11Purohit, Hemant, Valerie L. Shalin, and Amit P. Sheth. "Knowledge Graphs to Empower Humanity-Inspired AI Systems." IEEE Internet Computing 24.4 (2020): 48-54.
13. Why Knowledge Graphs? Challenges in NLP/NLU
● Natural Language Processing Challenges:
○ How do you learn quickly from small amount of data?
○ How do you mine (varied) relationships from existing text?
○ How do you reliably classify entities into known ontology?
○ Better contextualization of words
● Natural Language Understanding Challenges:
○ Query Interpretation or Understanding the user question
○ Answering the question with Trust and Transparency
○ How to measure “reasonability” and “meaningfulness” of the response to a
question?
○ How much context is needed to provide a precise response?
[Stanford Knowledge Graph Seminar 2020, Amit Prakash , Leilani Gilpin] 13
14. Better Contextualization of Words : Retrofitting
14
Why Knowledge Graphs : NLP/NLU Challenges
damage
Infrastructure
affected
population
damage
Infrastructure
affected
population
Vector representation of words in Tweets
(embedding) before retrofitting
Vector representation of words in
Tweets after retrofitting
MOAC Ontology
Empathi ontology
Disaster Ontology
DBpedia
16. 16
Knowledge
Extraction
Knowledge
Alignment
Knowledge
Cleaning
Knowledge Mining &
Knowledge-based QA
Data Extraction
(NLP, Web)
Wrapper Induction
(DB, DM-Data
Mining)
Web Tables (DB)
Text Mining (DM)
Entity and
Relationship Linking
[Perera 2016]
Schema Mapping
and Ontology
Mapping
[Jain 2010]
Universal Schema
[Sheth 1990]
Data Cleaning
[Jadhav 2016]
Anomaly Detection
[Anantharam 2012,
2016]
Knowledge Fusion
[Sheth 2020,
Kapanipathi 2020,
Gaur 2018,
Kursuncu 2020]
Graph Mining [Lalithsena
2016, 2017, 2018]
Knowledge Embedding
[Wickramarachchi 2020,
Gaur 2018]
Search [Sheth 2003,
Cheekula 2015, Kho
2019]
QA [Alambo 2019,
Shekarpour 2017]
[Stanford Knowledge Graph Seminar 2020, Luna Dong]
Knowledge Graphs in DL pipeline for NLP
17. Knowledge graphs in Conversational AI
19
Personalization: taking into account
the contextual factors such as user’s
health history, physical
characteristics, environmental
factors, activity, and lifestyle.
Chatbot with contextualized (e.g asthma) knowledge is
potentially more personalized and engaging.
Without
Contextualized Personalization
With
Contextualized Personalization
18. Knowledge for Multimodal Data: Example of City Traffic Event
20Anantharam, Pramod, Payam Barnaghi, Krishnaprasad Thirunarayan, and Amit Sheth. "Extracting city traffic events from social
streams." ACM Transactions on Intelligent Systems and Technology (TIST) 6, no. 4 (2015): 1-27.
19. Why Knowledge Graphs: Shortcomings of Deep Learning
21
● Graph Convolutional Neural Networks (GCN) are blind to relation types. For example: <shelter-
in-place causes anxiety> and <shelter-in-place prevents anxiety> have similar representations
in GCN.
● Deep Clustering over unlabeled data exploits the inherent latent semantics to generate diverse
and cohesive clusters. But, interpretability of the clusters requires Knowledge Graphs.
ODKG: Opioid
Drug Knowledge
Graph
[Kamdar 2019]
20. Symbolic glued with Statistical: Knowledge-infused Learning
22
STATISTICAL AI
CONNECTIONIST
“Unreasonable effectiveness of big data”
in machine processing &
powering bottom up processing
“Unreasonable effectiveness of small
data” in human decision making - can this
be emulated to power top down
processing?
SYMBOLIC AI
FORMAL
KG will play an increasing role in developing hybrid neuro-symbolic systems (that is bottom-up
deep learning with top-down symbolic computing) as well as in building explainable AI systems
for which KGs will provide scaffolding for punctuating neural computing.
Cognitive Science Analogy: Combining Top Brain - Bottom Brain Processes.
22. How do ensure consistency of
labeling, esp when label is not
binary?
Do labels represent adequate
semantics (e.g., number of
alternatives)?
Do they have adequate domain
knowledge?
How do you ensure consistency of
labeling (interpretation)? 24
A good KG has addressed these
issues:
● a schema is rich in representation
(and captures much more than
labeling)
● KG design incorporate
substantiate domain knowledge
● Instance level knowledge is
created through (usually)
collective intelligence and
Challenges in Deep Learning : Why K-IL
23. Why Knowledge Infused Learning (K-IL)?
By changing the inputs, it can enrich the
representation (E.g. Radicalization on Social
Media)
By changing parameters, we can control
the learned patterns/correlations learned to
adhere to the knowledge.
Deep Infusion would allow us finger
grained control over learned patterns to
ensure adherence to knowledge at every
step of the hierarchy
Explanations easy to derive from the KG
used
25Jiang, Shan, William Groves, Sam Anzaroot, and Alejandro Jaimes. "Crisis Sub-Events on Social Media: A
Case Study of Wildfires."
Contextual Modeling to
analyze Radicalization on
Social Media
24. 26
Knowledge-infused Learning (K-IL)
of knowledge graphs
to improve the
semantic and
conceptual
processing of data.
Semi-Deep Infusion
Deeper and congruent
incorporation or
integration of the
knowledge graphs in the
learning techniques. Deep Infusion
(Part of Future KG Strategy)
combines statistical AI
(bottom-up) and symbolic AI
learning techniques (top-
down) for hybrid and
integrated intelligent systems.
Shallow Infusion
Sheth, Gaur, Kursuncu, Wickramarachchi: Shades of Knowledge-Infused Learning for Enhancing Deep Learning
25. 27
Shallow Infusion of Knowledge for Machine/ Deep Learning in
Brief
Chronological
arrangement of shallow
Infusion techniques
From NLP domain
26. 28
K-IL: Shallow Infusion (shallow KR, shallow merging technique)
Knowledge infused is shallow, method of infusion is week.
Shallow external knowledge is described as those form of information which are
extracted from text based on some heuristics, often designed for task-specific problems:
○ Bag of Words/Phrases from Corpus [Hagoort 2004, Zhang 2019, Sun 2019]
○ Bag of Words/Phrases from Semantic Lexicons [Faruqui 2014, Mrkšić 2016]
○ Count of Nouns, Pronouns, Verbs [Gkotsis 2017, 2016]
○ Sentiment and Emotions of the sentence [Gaur 2019, Vedula 2017, Kursuncu 2019]
○ Latent topics describing the documents [Jiang 2016, Li 2016, Meng 2020]
○ Label assignment to words or phrases in sentence (Semantic Role Labeling):
Mary sold the book to John
Agent ThemePredicate Recipient
27. 29
K-IL: Shallow Infusion: Explaining Clustering
Identifiable Suicide Risk Factors from Electronic
Healthcare Records
Identifiable Suicide Risk Factors from Social
Media
Question: What people say to Clinician?
Question: What people hide from Clinician?
Question: What people say to Social Media?
Question: What people hide from Social MediaMissing
Information
28. 30
K-IL: Shallow Infusion:
Knowledge Graph Embeddings for Autonomous Driving
Scene KG KG Embeddings of objects/events Computed Scene Similarity
Wickramarachchi, Ruwan., Henson, Cory., and Sheth, Amit. An evaluation of knowledge graph embeddings for autonomous driving data: Experience and practice.
In AAAI 2020 Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice (AAAI-MAKE 2020).
30. 33
K-IL: Semi-Deep Infusion : Matching Reddit Conversation to DSM-5
Scenario
Really struggling with my bisexuality which is causing chaos in my relationship with a girl. Being
a fan of LGBTQ community, I am equal to worthless for her. I’m now starting to get drunk
because I can’t cope with the obsessive, intrusive thoughts, and need to get out of my head.
BPD
DICD PND SAD SBI OCD
Don’t want to live anymore. Sexually assault, ignorant family members and my never
ending loneliness brights up my path to death.
SCW
PND SBI SAD DPR DICD
DPR
I do have a potential to live a decent life but not with people who abandon me.
Hopelessness and feelings of betrayal have turned my nights to days. I am developing
insomnia because of my restlessness.
SBI DPR DICD
BPD I just can’t take it anymore. Been abandoned yet again by someone I cared about. I've been
diagnosed with borderline for a while, and I’m just going to isolate myself and sleep forever.
SBI PND
Reddit DSM-5 [Gaur 2018]
31. 34
TwADR
AskaPatient
Drug Abuse
Ontology
DSM-5 Lexicon
Suicide Risk
Severity Lexicon
Treatment Information
Observation and
Drug-related
Information
Mental Health Condition
Suicide Risk Levels
Ideation
Behavior
Attempt
K-IL: Semi-Deep Infusion : Matching Reddit Conversation to DSM-5
Mapping Subreddit to DSM-5 categories using Mental health Knowledge Bases
32. 35
Medical KnowledgeBases
N-grams
(n=1, 2, 3)
LDA
LDA over
Bi-grams
Normalized
Hit
Score
DSM-5
Lexicon
<Reddit Post>
<Subreddit Label>
Input
<Reddit Post>
<DSM-5 Label>
Output
DAO
Drug Abuse
Ontology
K-IL: Semi-Deep Infusion : Matching Reddit Conversation to DSM-5
Matching process from Reddit to DSM-5
34. 37
12808
Words
300 dimension embedding 300 dimension embedding
20 DSM-5
Categories
R
D
Reddit Word
Embedding Model
DSM-5 -DAO
Lexicon
W
Solvable Sylvester Equation
K-IL: Semi-Deep Infusion : Matching Reddit Conversation to DSM-5
35. 38
I know you want me to say no and that it is a
part of me blah blah blah. But I can't.
Honestly, not having bipolar disorder would be
a huge blessing. I would be so much happier
and could control my life better. I wouldn't
have frantic, scattered thoughts and
depression. I would be normal, happy, and
less dramatic.
Bipolar Subreddit
DSM-5: Depressive Disorder
I know you want me to say no and that it is a
part of me blah blah blah. But I can't. Honestly,
not having bipolar disorder would be a huge
blessing. I would be so much happier and
could control my life better. I wouldn't have
frantic, scattered thoughts and depression. I
would be normal, happy, and less dramatic.
BiPolar
Depression
Disorder
Subreddits DSM-5
Chapter
BiPolarReddit
BiPolarSOS
Depression
Addiction
Substance use &
Addictive Disorder
Crippling Alcoholism
Opiates Recovery
Opiates
Self-Harm
Stop Self-Harm
K-IL: Semi-Deep Infusion : Matching Reddit Conversation to DSM-5
Example posts after Mapping Subreddit to DSM-5
categories
Mappings provides explainability
36. 39
K-IL: Semi-Deep Infusion : Matching Reddit Conversation to DSM-5
Domain-specific
Knowledge lowers
False Alarm Rates.
2005-2016
550K Users
8 Million
Conversations
15 Mental Health
Subreddits
[Gkotsis 2017][Saravia 2016]
[Park 2018]
Performance Gains in the outcomes
37. Semi-deep infusion in Reinforcement Learning
40
Consider a gathering event at a
rally [Tablighi Jamaat
Movement]
Many fatalities and economic
cost incurred before an SIR
model recognises this event
(delay)
Any policy by the policy maker
at this point might be too late
to instate.
A Knowledge infused policy
where the knowledge is -
[lockdown the location of rally
and test everyone,] can greatly
mitigate this effect.
Image taken from: https://towardsdatascience.com/reinforcement-learning-for-covid-19-simulation-and-optimal-policy-b90719820a7f
How?
39. An Explainable system would comprise of collectively
exhaustive interpretable subsystems and orchestration
among them.
Explanations would be in natural language explaining
the decision making process.
Interpretable system provides an ability to discern the
internal mechanisms of any module.
Neural Attention Models are endowed with certain
degree of interpretability in visualizing parts of the
input without providing human understandable
explanations.
Explainable System is
Interpretable but not
vice versa
40. 44
Really struggling with my bisexuality which is causing chaos in my relationship with a girl.
Being a fan of LGBTQ community, I am equal to worthless for her. I’m now starting to get
drunk because I can’t cope with the obsessive, intrusive thoughts, and need to get it out of
my head.
Is mental health related ? Yes: 0.71 , No: 0.29
Which Mental Health condition?
Predicted: Depression (False)
True: Obsessive Compulsive Disorder
Reasoning over Model:
Why model predicted
Depression?
Unknown
41. 45
Really struggling with my bisexuality which is causing chaos in my relationship with a girl.
Being a fan of LGBTQ community, I am equal to worthless for her. I’m now starting to get
drunk because I can’t cope with the obsessive intrusive thoughts, and need to get it out of
my head.
Is mental health related ? Yes: 0.82 , No: 0.18
Which Mental Health condition?
Predicted: Obsessive Compulsive Disorder(True)
True: Obsessive Compulsive Disorder
DSM-5 Knowledge
Graph
DSM-5 and Post
Correlation Matrix
Reasoning over Model:
Why model predicted
Obsessive Compulsive
Disorder ? known
Interpretable learningD
εRN
P εRN
W f(W)
42. 46
Really struggling with my bisexuality which
is causing chaos in my relationship with a
girl. Being a fan of LGBTQ community, I am
equal to worthless for her. I’m now starting
to get drunk because I can’t cope with the
obsessive, intrusive thoughts, and need to
get out of my head.
288291000119102: High risk bisexual behavior
365949003: Health-related behavior finding 365949003: Health-related behavior finding
307077003: Feeling hopeless
365107007: level of mood
225445003: Intrusive thoughts
55956009: Disturbance in content of thought
26628009: Disturbance in thinking
1376001: Obsessive compulsive personality disorder
Multi-hop traversal on
Medical knowledge
graphs
<is symptom>
Achieving Explainability through Medical Entity Normalization :
Replacing Entities in the post with Concepts in the Medical Knowledge Graph through Semantic Annotation
43. 47
Really struggling with my [health-related behavior] which is causing [health-related
behavior] with a girl. Being a fan of [LGBTQ] community, I am equal to [level of mood] for
her. I’m now starting to [drinking] because I can’t cope with the [obsessive compulsive
personality disorder] [disturbance in thinking], and [disturbance in thinking].
Is mental health related ? Yes: 0.96 , No: 0.04
Which Mental Health condition?
Predicted: Obsessive Compulsive Disorder(True)
True: Obsessive Compulsive Disorder
DSM-5 Knowledge
Graph
DSM-5 and Post
Correlation Matrix
Reasoning over Model:
Why model predicted
Obsessive Compulsive
Disorder ? known
Interpretable and
Explainable Learning
D
εRN
P εRN
W f(W)
45. Semi-deep infusion in RL
49
Consider a gathering event at a
rally [Tablighi Jamaat
Movement],
Many fatalities and economic
cost incurred before an SIR model
recognises this event (delay)
Any policy by the policy maker at
this point might be too late to
instate.
A Knowledge infused policy
where the knowledge is -
[lockdown the location of rally
and test everyone,] can greatly
mitigate this effect.
Image taken from: https://towardsdatascience.com/reinforcement-learning-for-covid-19-simulation-and-optimal-policy-b90719820a7f
How?->
46. Explainable COVID-19 Policy
◎ Knowledge in dynamics: People go to work everyday
and do groceries at either shops in the neighborhood
or shops en-route to work.
◎ Knowledge traceable in policy choice: “There exists a
‘shop1’ en-route to a workplace, there are many
people in a neighborhood that work here and take this
route” -> encoded as a relational feature
◎ Learning algorithm assigns high weight to this feature
when the policy output is lockdown(shop1)
50
48. Bayesian Knowledge Tracing for Improving Learning Outcomes
in Education
53
Question: What is the name of the compound formed after the addition of phosphate to
glucose?
Answer: Glucose Monophosphate
Response from Student: Glucose Phosphate
Question: What is the name of the compound formed after the addition of phosphate to
adenosine diphosphate?
Answer: Adenosine Triphosphate
Response from Student: Adenosine 3-Phosphate
Can we conclude from the correct responses (if any) provided by the student, that student
knows Phosphorylation?
Piech, Chris, Jonathan Bassen, Jonathan Huang, Surya Ganguli, Mehran Sahami, Leonidas J. Guibas, and Jascha Sohl-Dickstein. "Deep knowledge tracing." In Advances in neural
information processing systems, pp. 505-513. 2015.
Using Knowledge infusion, we can see
the answer is close to correct
49. KG + BKT/DKT → Explainability
54
CQ: Concepts in the questions asked
CQ, CQ: Relationships between the concepts asked in the
questions
CQ, CKG: Relationships between the concepts asked in the
questions and the concepts in the Knowledge graphs (e.g.
epubs from Amazon, NCERT textbooks, Books specific to
entrance exams, etc.)
50. 55
Donda, Chintan, Sayan
Dasgupta, Soma S.
Dhavala, Keyur Faldu,
and Aditi Avasthi. "A
framework for
predicting, interpreting,
and improving Learning
Outcomes." arXiv
preprint
arXiv:2010.02629
(2020).
K-IL for Improving Learning Outcomes
Tutorial @
ACM CoDS COMAD
https://aiisc.ai/xaikg/
53. ROBOTICS
Cross-domain Knowledge
1) Observational (sensory
data) and common-sense
knowledge to perceive the
surrounding environment
2) Knowledge
representation to model
the knowledge concerning
the surrounding
environment
3) Appropriate cross-
domain knowledge
reasoning mechanisms
COGNITIVE SCIENCE
Human Intelligence
“Inject” human
intelligence into AI
assistants such as
Amazon Alexa,
utilization of cross-
domain knowledge
of social
interactions,
emotions and
linguistic variations
of natural language.
SELF-DRIVING CARS PERSONAL ASSISTANT
Empathy and
Morality
AI agents to mimic
human emotions
and decisions, we
need to model
human emotional
knowledge of
empathy, moral,
and ethics.
Personalization
Smart health agents are
adapting to answer real-
world personalized complex
health queries in simple
interactive language.
Requires patients’
environmental knowledge,
health data, and
coordination with their
healthcare physicians.
Promising K-IL Impacts
55. 60
5 faculty, >12 PhDs, few Masters, >5
undergrads, 2 Post-Docs, >10 Research Interns
Alumni in/as
Industry: IBM T.J. Watson, Almaden, Amazon, Samsung
America, LinkedIn, Facebook, Bosch
Start-ups: AppZen, AnalyticsFox, Cognovi Labs
Faculty: George Mason, University of Kentucky, Case Western
Reserve, North Carolina State University, University of Dayton
Core AI
Neuro-symbolic computing/Hybrid AI, Knowledge
Graph Development, Deep Learning,
Reinforcement Learning, Natural Language
Processing, Knowledge-infused Learning (for deep
learning and NLP), Multimodal AI (including
IoT/sensor data streams, images), Collaborative
Assistants, Multiagent Systems (incl. Coordinating
systems of decision making agents including
humans, robots, sensors), Semantic-Cognitive-
Perceptual Computing, Brain-inspired computing,
Interpretation/Explainability/Trust/Ethics in AI
systems, Search, Gaming
Interdisciplinary AI and application
domains: Medicine/Clinical, Biomedicine, Social
Good/Harm, Public Health (mental health,
addiction), Education, Manufacturing, Disaster
Management
56. Thanks!
Open to Questions?
You can find me at:
amit@sc.edu
https://aiisc.ai/
https://www.linkedin.com/company/1054055/
http://bit.ly/AIISC
61
Editor's Notes
Slide 3: Inner circle : talks about our research areas and strength
A nice knowledge graph, which is a knowledge graphs ----- picture over here
One side ---> Empathi
ezDI image → other side
When an agent communicate with humans,
Empathy, policies, trustworthy → inform the behavior of the agent
---- Slide before PAC Learning
----- Explaining one of the them -- why knowlege graph would help
There are many NLP challenges, why knowledge Graph would work
GPT-3 --- issues
Can KG solve it
How to get a better context for effective output
You can have relationship between concepts
Video : When the slide will be uploaded.
a(i) Domain knowledge of traffic in the form of concepts and relationships (mostly causal) from the ConceptNet
a(ii) Probabilistic Graphical Model (PGM) that explains the conditional dependencies between variables in traffic domain is enriched by adding the missing random variables, links, and link directions extracted from ConceptNet
b : Shows how this enriched PGM is used to correlate contextually related data of different modalities.
3 sources of knowledge (Geo-Spatially and temporally) [ We need to put this in the slide]
→ Open Street Map
→ Smart City Knowledge Graph
→ [Find the third one]
---- We should have another insight:
--- it is still a coarse-grained use of kG for making sense of clusters
--- We need to provide an example in a more detailed: There is an explicit relationship between two concept
---- A person owns a company or works for a company
Both Example
Kaushik: points
Manas: points (enriching the embedding)
What is knowledge infusion in deep learning? Using knowledge to change input (shallow), to change parameters (semi-deep), to change parameters by mapping to a stratified hierarchy (Deep) (Ex: 1st layer knowledge x, 2nd layer knowledge y, etc). Can use diagram from pydata Berlin talk.
→ Integration of Knowledge Representation with Statistical Representation of Text is also straightforward → Devoid of Semantic Representation
→ Shallow merging needs to be demonstrated
Possible proposal material
In semi-deep infusion paradigm, the learning system of the model is altered either through a probabilistic threshold (e.g. attention or constraints) or data redundancy for gains in performance. There are three broad categories of SEMI-DEEP Infusion:
Forcing methods: the prediction of the model from the learnt representation is improved by mixing (sigmoidal, concatenation, multiplication) the representation of input as ground truth to enrich latent representation.
Attention methods: These methods improves upon the forcing methods by making the model capable of selecting parts of the learnt representations that needs to be modified.
Knowledge-base methods: Since both forcing and attention methods rely on the input data which is a poor manifestation of the real world, thus models suffer from problems such as exposure bias. The knowledge-base methods replace the dependency of the model from input text to knowledge-base for attention and forcing.
In knowledge-based LSTMs, rather than putting attention on input text, the method used attention as a switch, which when open contextualize the latent representation through representations of relevant concepts in knowledge base. When the switch is off, the latent representation is used as it is.
In knowledge-based GANs, the model learn by maximizing the reward, which is generating the representation of the input which matches the output. One way of formulating this reward is minimization of KL divergence. In this architecture, the attention module is influenced by reward function which is a learnable constraint.
Correlation matrix is the parameters for the deep learning algorithm for DSM-5
Method: Semantic encoding - decoding optimization
(Pearson Correlation)
DD: Correlation between DSM-5 Categories
RR: Correlation between concepts in Reddit posts irrespective of the user
DR: Correlation between the concepts in Reddit
Qualitatively, this is the outcome of the semantic encoding and decoding method.
You are able to label a post in a subreddit with an appropriate DSM-5 category.
On the Left, is all such mapping that the model learnt.
Why? Shallow - Can help enrich neural representations. Semi-deep: Can help with tweaking parameters to follow correlations present in knowledge (in addition to data) in constructing representations. Deep - Can identify what correlation in the knowledge in addition to data matters in which layer to finally construct a representation that benefits from knowledge infusion at all layers. Ex: Shallow: Wikipedia based GNN training to answer questions - hopefully captures relationships. Semi-deep: Force understanding that Obama is correlated to Michele Obama through relationships like spouse, by explicitly modifying the attention (correlation matrix) - definitely captures relationships. Deep: Identify number relationships, how they relate to metrics, how those metrics relate to what is being measured (blood pressure), how blood pressure relates to what is being predicted - definitely captures nested/hierarchical relationship semantics
<Example of Deep Knowledge Infusion>
Definition of Interpretability and Explainability
Multi-hop
Two-hop
Changing the post
Example of explanation: For each time of knowledge infusion
Shallow: TSNE clusters can show that KG relationships were captured, sports words come together
Semi-Deep: Attention matrix can show if KG relationships were captured, sports words attend to each other with high correlation
Deep: Representations at each layer can be visualized through concept maps in the stratified KG. Members of a hierarchical concept lower in the hierarchy correlate highly with those higher in the hierarchy on visualization of concepts from a class hierarchy. (Ex: 30, cistolic pressure, heart attack all would be close as they map to the same hierarchical concept)
Explainability example in Education
Current approach assessing the mastery of a student in a course and provide multiple pathways for improving the learning outcomes relies on a predictive algorithm: Bayesian Knowledge Tracing (BKT).
The approach assess following tendencies of the student:
He knows the answers correctly
He guessed the answers correctly
What is the improvement after multiple attempts
However, it does not tell:
How far from the correct answer, is the student’s answer?
What relevant concepts the student needs to learn?
Also, the algorithm does not provide the capability to assess whether the student has mastered a topic in a course or course itself.
On this slide, a student was asked two questions from the topic of “Phosphorylation”.
BKT would consider these questions independently, Whereas, Knowledge infusion would find the relation between the two question, through the entity: Phosphorylation
Since, the answer don’t match the true answer, BKT would not accept them as correct.
The question in the red, could not be answered by BKT, because it does not know the relation between the questions
However, if we use knowledge Infusion:
It knows the relation between the concepts through Phosphorylation, so, it can answer the question in red.
It knows that “adenosine 3-phosphate” is an alias of “Adenosine Triphosphate”, so it would accept the response
It would measure the distance between “Glucose Phosphate” and “Glucose Monophosphate” to see:
How far from the correct answer is the student’s answer?
What new concepts the student needs to learn to achieve mastery on this topic
The concepts asked in the question are addition of phosphate to glucose and addition of phosphate to adenosine diphosphate
From the KG, the relation between the two is obtained as relating to phosphorylation
The answer the student provides which is adenosine 3-phosphate might be predicted as wrong by the DN (because it is not adenosine tri-phosphate). The wrong answer triggers search through the KG to figure out how far from the right answer.
The explanation adenosine tri-phosphate is an alias of adenosine 3-phosphate and therefore the explanation shows that the student was actually correct and hence has attained explainable mastery.
Education knowledge graph can be constructed using the content from MOOC, Coursera, Khan Academy, Udemy, Udacity, Books, epubs from Amazon
https://khanacademy.fandom.com/wiki/Knowledge_Map
Bayesian knowledge tracing not adequate as explanations required to know what other concepts the student might need to attain mastery
These concepts can be found in the KG
Furthermore, the KG can provide explanation for how far the current level is from mastery.
Do we need to provide a list of workshop and tutorials conducted