https://www.youtube.com/watch?v=fmZDRL9P-v4&list=PLqJzTtkUiq54DDEEZvzisPlSGp_BadhNJ&index=9
To make an autonomous vehicle more cognitive, it needs the implementation of advanced cognition theories and AI theories. In this work, we firstly make a brief overview of current advanced theories of cognition in Psychology and Computer Science. Then we mainly analyze and compare the architectures of the autonomous vehicles winning DARPA Challenges. The layout of sensors and the design of software system are critical to the winning autonomous vehicles. By comparing different autonomous vehicles, we find some common points shared among them and more differences due to the various sensors layouts and the difference among cognition architectures, which could give some valuable directions to the researchers in both computer science and cognition fields. Then we will link decision-making to intelligence decision-making and its algorithm using example.
Natural language text can have explicit and implicit constructs. In this presentation we discuss how to link the entities mentioned in an implicit manner in tweets.
https://www.youtube.com/watch?v=uBijGs1NJCE&list=PLqJzTtkUiq54DDEEZvzisPlSGp_BadhNJ&index=13
Semantic and AI research communities have a strong body of work focuses on extracting facts from the web automatically and represent them in a graph based representation. NELL and Knowledge Vault are two prominent knowledge graphs of that kind. However, due to the inherent noise of the web the resulting knowledge also contain noisy data. With the huge volume of the facts extracted from the web, it is impractical to use traditional reasoning approaches to capture the inconsistencies in these knowledge graphs. This work addresses this issue by using semantics in the form of schema knowledge together with statistics in the form of confidence value of facts derived from information extraction techniques. They use probabilistic soft logic which is a recently introduced statistical learning approach which allows to assign weights to the logical statement and their dependencies. The weighted soft logic rules are represented in a probabilistic graphical model with their dependencies to identify the different interpretations of a KG and pick the most consistent KG.
References
Pujara, Jay, et al. "Using Semantics and Statistics to Turn Data into Knowledge." AI Magazine 36.1 (2015): 65-74.
Pujara, Jay, et al. "Knowledge graph identification." International Semantic Web Conference. Springer Berlin Heidelberg, 2013.
Lise Getoor “Combining Statistics and Semantics to Turn Data into Knowledge” ESWC Keynote 2015
With the increasing automation of health care information processing, it has become crucial to extract meaningful information from textual notes in electronic medical records. One of the key challenges is to extract and normalize entity mentions. State-of-the-art approaches have focused on the recognition of entities that are explicitly mentioned in a sentence. However, clinical documents often contain phrases that indicate the entities but do not contain their names. We term those implicit entity mentions and introduce the problem of implicit entity recognition (IER) in clinical documents. We propose a solution to IER that leverages entity definitions from a knowledge base to create entity models, projects sentences to the entity models and identifies implicit entity mentions by evaluating semantic similarity between sentences and entity models. The evaluation with 857 sentences selected for 8 different entities shows that our algorithm outperforms the most closely related unsupervised solution. The similarity value calculated by our algorithm proved to be an effective feature in a supervised learning setting, helping it to improve over the baselines, and achieving F1 scores of .81 and .73 for different classes of implicit mentions. Our gold standard annotations are made available to encourage further research in the area of IER.
Natural language text can have explicit and implicit constructs. In this presentation we discuss how to link the entities mentioned in an implicit manner in tweets.
https://www.youtube.com/watch?v=uBijGs1NJCE&list=PLqJzTtkUiq54DDEEZvzisPlSGp_BadhNJ&index=13
Semantic and AI research communities have a strong body of work focuses on extracting facts from the web automatically and represent them in a graph based representation. NELL and Knowledge Vault are two prominent knowledge graphs of that kind. However, due to the inherent noise of the web the resulting knowledge also contain noisy data. With the huge volume of the facts extracted from the web, it is impractical to use traditional reasoning approaches to capture the inconsistencies in these knowledge graphs. This work addresses this issue by using semantics in the form of schema knowledge together with statistics in the form of confidence value of facts derived from information extraction techniques. They use probabilistic soft logic which is a recently introduced statistical learning approach which allows to assign weights to the logical statement and their dependencies. The weighted soft logic rules are represented in a probabilistic graphical model with their dependencies to identify the different interpretations of a KG and pick the most consistent KG.
References
Pujara, Jay, et al. "Using Semantics and Statistics to Turn Data into Knowledge." AI Magazine 36.1 (2015): 65-74.
Pujara, Jay, et al. "Knowledge graph identification." International Semantic Web Conference. Springer Berlin Heidelberg, 2013.
Lise Getoor “Combining Statistics and Semantics to Turn Data into Knowledge” ESWC Keynote 2015
With the increasing automation of health care information processing, it has become crucial to extract meaningful information from textual notes in electronic medical records. One of the key challenges is to extract and normalize entity mentions. State-of-the-art approaches have focused on the recognition of entities that are explicitly mentioned in a sentence. However, clinical documents often contain phrases that indicate the entities but do not contain their names. We term those implicit entity mentions and introduce the problem of implicit entity recognition (IER) in clinical documents. We propose a solution to IER that leverages entity definitions from a knowledge base to create entity models, projects sentences to the entity models and identifies implicit entity mentions by evaluating semantic similarity between sentences and entity models. The evaluation with 857 sentences selected for 8 different entities shows that our algorithm outperforms the most closely related unsupervised solution. The similarity value calculated by our algorithm proved to be an effective feature in a supervised learning setting, helping it to improve over the baselines, and achieving F1 scores of .81 and .73 for different classes of implicit mentions. Our gold standard annotations are made available to encourage further research in the area of IER.
https://www.youtube.com/watch?v=b5qR4urr0vU&list=PLqJzTtkUiq54DDEEZvzisPlSGp_BadhNJ&index=11
A mental representation or cognitive representation is a hypothetical internal cognitive symbol that represents external reality[1], or else a mental process that makes use of such a symbol: "a formal system for making explicit certain entities or types of information, together with a specification of how the system does this”[3]. To define the “Human Mental Representation”, four concepts have been described; Similarity, Analogy, Relationships at the Heart of Semantic Web. Similarity is defined as “learning information about one is generally true of the other”, and this becomes more and more true as the probability that the two causal/source variables is the same increases. The relationships used identifying similarities differs between experts and novices, with novices using surface features and experts using deeper structural relationships. Similarly, people relied on similarity mappings when the relational roles were more complex.
The purpose of categorization is twofold, to be able to infer the properties of the entity and to adapt the category itself. This description is essentially Piaget’s theory of development through assimilation and accommodation. Communication is similar to categorization, but rather than resolving for oneself, the issue is resolving new or developing shared concepts between people, which relates to many of the psycholinguistic conceptual grounding discussions (i.e., Herb Clark). Analogy is a special kind of similarity. Two situations are analogous if they share a common pattern of relationships among their constituent elements even though the elements themselves differ across the two situations. Typically, one analog, termed the source or base, is more familiar or better understood than the second analog, termed the target” (p. 117). Therefore, theoretical models of analogical inference need to focus on binding and mapping.
We explained the “Knowledge Representation”, and in the end, We provided the examples of “ Ontology and Knowledge Base” from Relationships at the Heart of Semantic Web p:15 [2].
References:
1- Chapters: 2, 3, 4, 6, Book: The Cambridge Handbook of Thinking and Reasoning (pp. 117-142). New York: Cambridge University Press. By By: Keith J. Holyoak and Robert G. Morrison
2- Relationships at the Heart of Semantic Web: Modeling, Discovering, and Exploiting Complex Semantic Relationships, Book Title: Enhancing the Power of the Internet. By Amit Sheth, Ismailcem Budak Arpinar, Vipul Kashvap
3- Marr, David (2010). Vision. A Computational Investigation into the Human Representation and Processing of Visual Information. The MIT Press. ISBN 978-0262514620
Gang affiliates have joined the masses who use social media to share thoughts and actions. Perhaps paradoxically, they use this public medium to express recent illegal actions, to intimidate others, and to share outrageous images and statements. Agencies able to unearth these profiles may thus be able to anticipate, stop, or hasten the investigation gang related crimes and activities. This talk discusses our efforts in analyzing street gangs on twitter, with an emphasis on discovering their profiles. Our approach, which uses deep learning to embed signals in tweet language, images, shared YouTube links, and emoji use into a vector space for machine learning classifiers, recovers gang member profiles with promising accuracy and a low false positive rate.
https://www.youtube.com/watch?v=5ZUlVlumIQo&list=PLqJzTtkUiq54DDEEZvzisPlSGp_BadhNJ&index=10
Over the last years, deep learning is rapidly advancing with impressive results obtained in several areas including computer vision, machine translation and speech recognition. Deep learning attempts to learn complex function through learning hierarchical representation of data. A deep learning model is composed of non-linear modules that each transforms the representation from lower layer to the higher more abstract one. Very complex functions can be learned using enough composition of the non-linear modules. Furthermore, the need for manual feature engineering can be obviated by learning features themselves through the representation learning. In this talk, we first explain how deep learning architecture in particular and neural networks in general are loosely inspired by mammalian visual cortex and nervous system respectively. We also discuss about the reason for big and successful comeback of neural networks with the deep learning models. Finally, we give a brief introduction of various deep structures and their applications to several domains.
References:
LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Deep learning." Nature 521.7553 (2015): 436-444.
Socher, Richard, Yoshua Bengio, and Chris Manning. "Deep learning for NLP." Tutorial at Association of Computational Logistics (ACL), 2012, and North American Chapter of the Association of Computational Linguistics (NAACL) (2013).
Lee, Honglak. "Tutorial on deep learning and applications." NIPS 2010 Workshop on Deep Learning and Unsupervised Feature Learning. 2010.
LeCun, Yann, and M. Ranzato. "Deep learning tutorial." Tutorials in International Conference on Machine Learning (ICML’13). 2013.
Socher, Richard, et al. "Recursive deep models for semantic compositionality over a sentiment treebank." Proceedings of the conference on empirical methods in natural language processing (EMNLP). Vol. 1631. 2013.
https://www.youtube.com/channel/UC9OeZkIwhzfv-_Cb7fCikLQ
https://www.udacity.com/course/deep-learning--ud730
http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/
Fuzzy modeling is a powerful approach found by Zadeh for the modeling of complex and uncertain systems [2]. Fuzzy logic has a distinctive advantage where the precise definition of a control process is unachievable. Fuzzy models have the ability to establish a relationship between input and output variables by employing predefined rules. The technique provides simple solutions which are based on natural language statements. Fuzzy logic takes the inputs and outputs in the form of fuzzy sets where each set contains elements that have varying degrees of membership. A fuzzy set then enables transforming real numbers to the membership degrees changing from 0 to 1. Fuzzy rules relate input variables to output variables. These rules represent the expert knowledge in the system. Indeed, the intuition behind fuzzy logic is, it works with perception-based data instead of measurement-based which are crisp and numeric. Hence, it tries to capture how human use perceptions of time, direction, speed, shape, possibility, likelihood, truth, and other attributes of physical and mental objects. Perceptions in this manner are inherently imprecise when compared to crisp values, for example, a human might express his intuition about the weather as being not very hot while a sensor would read the heat in degrees and give us a crisp value. Therefore, perceptions are very subjective and reflect the partiality of human concepts.
In 2001, Prof. Zadeh proposed his computational theory of perceptions (CTP) where the objects of computations are words and propositions drawn from natural language rather than crisp numeric values. The idea of the theory came due to the unavailability of a methodology for reasoning and computing with perceptions rather than measurements. Hence, the CPT was the ground for allowing a computer to make subjective judgments which often refered as perceptual computing.
E.H. Mamdani, Application of fuzzy algorithms for control of simple dynamic plant, in: Proceedings of the Institution of Electrical Engineers, IET, 1974, pp. 1585-1588.
Zadeh, Lotfi A. "Fuzzy sets." Information and control 8, no. 3 (1965): 338-353.
Zadeh, Lotfi A. "A new direction in AI: Toward a computational theory of perceptions." AI magazine 22, no. 1 (2001): 73.
Most street gang members use Twitter to intimidate others, to present outrageous images and statements to the world, and to share recent illegal activities. Their tweets may thus be useful to law enforcement agencies to discover clues about recent crimes or to anticipate ones that may occur. Finding these posts, however, requires a method to discover gang member Twitter profiles. This is a challenging task since gang members represent a very small population of the 320 million Twitter users. This paper studies the problem of automatically finding gang members on Twitter. It outlines a process to curate one of the largest sets of verifiable gang member profiles that have ever been studied. A review of these profiles establishes differences in the language, images, YouTube links, and emojis gang members use compared to the rest of the Twitter population. Features from this review are used to train a series of supervised classifiers. Our classifier achieves a promising F1 score with a low false positive rate.
Link to the paper - http://knoesis.org/?q=node/2754
https://www.youtube.com/watch?v=wbXEXGT3I9I&list=PLqJzTtkUiq54DDEEZvzisPlSGp_BadhNJ&index=8
Link of video:
https://www.youtube.com/watch?v=wbXEXGT3I9I
This is a review of the keynote presented by Eric Horvitz, Managing Director, Microsoft, Redmond.
This keynote was presented at Computing Community Consortium in Washington DC on June-07-2016.
Eric has discussed about 3 things in his keynote: Healthcare, Agriculture and Transport.
Mainly he has focussed on Health care.
The goal of AI
Broad Spectrum of Opportunities for AI
Healthcare
Sciences
Transportation
Agriculture
Sustainability
Education
Governance
Criminal justice
Privacy & security
Emergency management
A work conducted in John Hopkins University
References:
http://research.microsoft.com/en-us/um/people/horvitz/AI_supporting_people_and_society_Eric_Horvitz.pdf
https://www.youtube.com/watch?v=rek3jjbYRLo
https://en.wikipedia.org/wiki/Artificial_intelligence
https://en.wikipedia.org/wiki/AI_winter
http://research.microsoft.com/en-us/um/people/horvitz/
Regression and classification techniques play an essential role in many data mining tasks and have broad applications. However, most of the state-of-the-art regression and classification techniques are often unable to adequately model the interactions among predictor variables in highly heterogeneous datasets. New techniques that can effectively model such complex and heterogeneous structures are needed to significantly improve prediction accuracy.
In this dissertation, we propose a novel type of accurate and interpretable regression and classification models, named as Pattern Aided Regression (PXR) and Pattern Aided Classification (PXC) respectively. Both PXR and PXC rely on identifying regions in the data space where a given baseline model has large modeling errors, characterizing such regions using patterns, and learning specialized models for those regions. Each PXR/PXC model contains several pairs of contrast patterns and local models, where a local classifier is applied only to data instances matching its associated pattern. We also propose a class of classification and regression techniques called Contrast Pattern Aided Regression (CPXR) and Contrast Pattern Aided Classification (CPXC) to build accurate and interpretable PXR and PXC models.
We have conducted a set of comprehensive performance studies to evaluate the performance of CPXR and CPXC. The results show that CPXR and CPXC outperform state-of-the-art regression and classification algorithms, often by significant margins. The results also show that CPXR and CPXC are especially effective for heterogeneous and high dimensional datasets. Besides being new types of modeling, PXR and PXC models can also provide insights into data heterogeneity and diverse predictor-response relationships.
We have also adapted CPXC to handle classifying imbalanced datasets and introduced a new algorithm called Contrast Pattern Aided Classification for Imbalanced Datasets (CPXCim). In CPXCim, we applied a weighting method to boost minority instances as well as a new filtering method to prune patterns with imbalanced matching datasets.
Finally, we applied our techniques on three real applications, two in the healthcare domain and one in the soil mechanic domain. PXR and PXC models are significantly more accurate than other learning algorithms in those three applications.
In this chapter the author want to find out, how can an average human being can become an Expert in a specific field, and He highlights the common traits of the experts such as:
expert see the world differently, which the non-experts can’t see
An Expert in a specific field has a superior memory for the details of that field
Most importantly experts overcomes the brain’s most famous constraint “7”
Author says that in the field of remembering an average human can hold upto 7 plus or minus 2 digits in his brain at time. This the capacity of our short term memories by which we are limited but an expert is not limited by this constraint. When an expert look at a number, he does not see just the number rather he sees a memory or an image from the past such as Birth date or any memory which is related to the number. In the chapter author explains this difference between an average human and expert more clearly with examples such as chicken sexers and Swat officers
The is an active field of research in understanding the cognitive approach in dreaming. Analysing these dreams will give us the intuition on how the brains works and helps us in improving the technology. Calvin hall (1909-1985) developed the first scientific theories of dream interpretation based on quantitative analysis in 1953. According to him, the images in the dreams are the concrete embodiment of dreamer’s thought, these images give visual expression to that which is invisible, namely, conceptions. These conceptions can be about ourselves, others, environment, penalties and conflicts. Calvin hall says that there is a continuity between a person's’ wakefulness and their dream experience. He believed that during dreams we express creativity, similar to what we would do when expressing ourselves through metaphors in poetry.
With this as the basis, there are several theories which try to explain the concept of dreaming and its effect. Once such theory is by Dr. Robert Stickgold who is an Associate Professor of Psychiatry at Harvard Medical School and Beth Israel Deaconess Medical Centre. According to him, brain will extract from experiences to gist of what happened and kind of forget all those details that weren’t that important. It takes large amount of experiences and take them all together and figure out what rules and explain how our world work. When we are sleeping your brain is pulling everything together and seeing how it fits together and how to summarize it. Dreams also act as predictors that the brain is going to do what it needs to do to really figure out the problem. One can gain these insights when we don’t even know where to find just by sleeping.
One takeaway Dr. Sheth pointed out was the importance of abstraction in pulling the parts of the dreams together. For a example given blood, medicine and nurse as difference pieces, one would imagine a hospital and another would assume a location of a blast. This gives the notion of personalization and/or contextualization during the process of abstraction.
Understanding of Electronic Medical Records(EMRs) plays a crucial role in improving healthcare outcomes. However, the unstructured nature of EMRs poses several technical challenges for structured information extraction from clinical notes leading to automatic analysis. Natural Language Processing(NLP) techniques developed to process EMRs are effective for variety of tasks, they often fail to preserve the semantics of original information expressed in EMRs, particularly in complex scenarios. This paper illustrates the complexity of the problems involved and deals with conflicts created due to the shortcomings of NLP techniques and demonstrates where domain specific knowledge bases can come to rescue in resolving conflicts that can significantly improve the semantic annotation and structured information extraction. We discuss various insights gained from our study on real world dataset.
Big Data Challenges and Trust Management: A Personal Perspective
A tutorial presented by Dr. Krishnaprasad Thirunarayan at the International Conference on Collaboration Technologies and Systems 2016 (CTS 2016)
Gang affiliates have joined the masses who use social media to share thoughts and actions publicly. Interestingly, they use this public medium to express recent illegal actions, to intimidate others, and to share outrageous images and statements. Agencies able to unearth these profiles may thus be able to anticipate, stop, or hasten the investigation of gang-related crimes. This paper investigates the use of word embeddings to help identify gang members on Twitter. Building on our previous work, we generate word embeddings that translate what Twitter users post in their profile descriptions, tweets, profile images, and linked YouTube content to a real vector format amenable for machine learning classification. Our experimental results show that pre-trained word embeddings can boost the accuracy of supervised learning algorithms trained over gang members’ social media posts.
Understanding speed and travel-time dynamics in response to various city related events is an important and challenging problem. Sensor data (numerical) containing average speed of vehicles passing through a road link can be interpreted in terms of traffic related incident reports from city authorities and social media data (textual), providing a complementary understanding of traffic dynamics. State-of-the-art research is focused on either analyzing sensor observations or citizen observations; we seek to exploit both in a synergistic manner.
We demonstrate the role of domain knowledge in capturing the non-linearity of speed and travel-time dynamics by segmenting speed and travel-time observations into simpler components amenable to description using linear models such as Linear Dynamical System (LDS). Specifically, we propose Restricted Switching Linear Dynamical System (RSLDS) to model normal speed and travel time dynamics and thereby characterize anomalous dynamics. We utilize the city traffic events extracted from text to explain anomalous dynamics. We present a large scale evaluation of the proposed approach on a real-world traffic and twitter dataset collected over a year with promising results.
https://www.youtube.com/watch?v=b5qR4urr0vU&list=PLqJzTtkUiq54DDEEZvzisPlSGp_BadhNJ&index=11
A mental representation or cognitive representation is a hypothetical internal cognitive symbol that represents external reality[1], or else a mental process that makes use of such a symbol: "a formal system for making explicit certain entities or types of information, together with a specification of how the system does this”[3]. To define the “Human Mental Representation”, four concepts have been described; Similarity, Analogy, Relationships at the Heart of Semantic Web. Similarity is defined as “learning information about one is generally true of the other”, and this becomes more and more true as the probability that the two causal/source variables is the same increases. The relationships used identifying similarities differs between experts and novices, with novices using surface features and experts using deeper structural relationships. Similarly, people relied on similarity mappings when the relational roles were more complex.
The purpose of categorization is twofold, to be able to infer the properties of the entity and to adapt the category itself. This description is essentially Piaget’s theory of development through assimilation and accommodation. Communication is similar to categorization, but rather than resolving for oneself, the issue is resolving new or developing shared concepts between people, which relates to many of the psycholinguistic conceptual grounding discussions (i.e., Herb Clark). Analogy is a special kind of similarity. Two situations are analogous if they share a common pattern of relationships among their constituent elements even though the elements themselves differ across the two situations. Typically, one analog, termed the source or base, is more familiar or better understood than the second analog, termed the target” (p. 117). Therefore, theoretical models of analogical inference need to focus on binding and mapping.
We explained the “Knowledge Representation”, and in the end, We provided the examples of “ Ontology and Knowledge Base” from Relationships at the Heart of Semantic Web p:15 [2].
References:
1- Chapters: 2, 3, 4, 6, Book: The Cambridge Handbook of Thinking and Reasoning (pp. 117-142). New York: Cambridge University Press. By By: Keith J. Holyoak and Robert G. Morrison
2- Relationships at the Heart of Semantic Web: Modeling, Discovering, and Exploiting Complex Semantic Relationships, Book Title: Enhancing the Power of the Internet. By Amit Sheth, Ismailcem Budak Arpinar, Vipul Kashvap
3- Marr, David (2010). Vision. A Computational Investigation into the Human Representation and Processing of Visual Information. The MIT Press. ISBN 978-0262514620
Gang affiliates have joined the masses who use social media to share thoughts and actions. Perhaps paradoxically, they use this public medium to express recent illegal actions, to intimidate others, and to share outrageous images and statements. Agencies able to unearth these profiles may thus be able to anticipate, stop, or hasten the investigation gang related crimes and activities. This talk discusses our efforts in analyzing street gangs on twitter, with an emphasis on discovering their profiles. Our approach, which uses deep learning to embed signals in tweet language, images, shared YouTube links, and emoji use into a vector space for machine learning classifiers, recovers gang member profiles with promising accuracy and a low false positive rate.
https://www.youtube.com/watch?v=5ZUlVlumIQo&list=PLqJzTtkUiq54DDEEZvzisPlSGp_BadhNJ&index=10
Over the last years, deep learning is rapidly advancing with impressive results obtained in several areas including computer vision, machine translation and speech recognition. Deep learning attempts to learn complex function through learning hierarchical representation of data. A deep learning model is composed of non-linear modules that each transforms the representation from lower layer to the higher more abstract one. Very complex functions can be learned using enough composition of the non-linear modules. Furthermore, the need for manual feature engineering can be obviated by learning features themselves through the representation learning. In this talk, we first explain how deep learning architecture in particular and neural networks in general are loosely inspired by mammalian visual cortex and nervous system respectively. We also discuss about the reason for big and successful comeback of neural networks with the deep learning models. Finally, we give a brief introduction of various deep structures and their applications to several domains.
References:
LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Deep learning." Nature 521.7553 (2015): 436-444.
Socher, Richard, Yoshua Bengio, and Chris Manning. "Deep learning for NLP." Tutorial at Association of Computational Logistics (ACL), 2012, and North American Chapter of the Association of Computational Linguistics (NAACL) (2013).
Lee, Honglak. "Tutorial on deep learning and applications." NIPS 2010 Workshop on Deep Learning and Unsupervised Feature Learning. 2010.
LeCun, Yann, and M. Ranzato. "Deep learning tutorial." Tutorials in International Conference on Machine Learning (ICML’13). 2013.
Socher, Richard, et al. "Recursive deep models for semantic compositionality over a sentiment treebank." Proceedings of the conference on empirical methods in natural language processing (EMNLP). Vol. 1631. 2013.
https://www.youtube.com/channel/UC9OeZkIwhzfv-_Cb7fCikLQ
https://www.udacity.com/course/deep-learning--ud730
http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/
Fuzzy modeling is a powerful approach found by Zadeh for the modeling of complex and uncertain systems [2]. Fuzzy logic has a distinctive advantage where the precise definition of a control process is unachievable. Fuzzy models have the ability to establish a relationship between input and output variables by employing predefined rules. The technique provides simple solutions which are based on natural language statements. Fuzzy logic takes the inputs and outputs in the form of fuzzy sets where each set contains elements that have varying degrees of membership. A fuzzy set then enables transforming real numbers to the membership degrees changing from 0 to 1. Fuzzy rules relate input variables to output variables. These rules represent the expert knowledge in the system. Indeed, the intuition behind fuzzy logic is, it works with perception-based data instead of measurement-based which are crisp and numeric. Hence, it tries to capture how human use perceptions of time, direction, speed, shape, possibility, likelihood, truth, and other attributes of physical and mental objects. Perceptions in this manner are inherently imprecise when compared to crisp values, for example, a human might express his intuition about the weather as being not very hot while a sensor would read the heat in degrees and give us a crisp value. Therefore, perceptions are very subjective and reflect the partiality of human concepts.
In 2001, Prof. Zadeh proposed his computational theory of perceptions (CTP) where the objects of computations are words and propositions drawn from natural language rather than crisp numeric values. The idea of the theory came due to the unavailability of a methodology for reasoning and computing with perceptions rather than measurements. Hence, the CPT was the ground for allowing a computer to make subjective judgments which often refered as perceptual computing.
E.H. Mamdani, Application of fuzzy algorithms for control of simple dynamic plant, in: Proceedings of the Institution of Electrical Engineers, IET, 1974, pp. 1585-1588.
Zadeh, Lotfi A. "Fuzzy sets." Information and control 8, no. 3 (1965): 338-353.
Zadeh, Lotfi A. "A new direction in AI: Toward a computational theory of perceptions." AI magazine 22, no. 1 (2001): 73.
Most street gang members use Twitter to intimidate others, to present outrageous images and statements to the world, and to share recent illegal activities. Their tweets may thus be useful to law enforcement agencies to discover clues about recent crimes or to anticipate ones that may occur. Finding these posts, however, requires a method to discover gang member Twitter profiles. This is a challenging task since gang members represent a very small population of the 320 million Twitter users. This paper studies the problem of automatically finding gang members on Twitter. It outlines a process to curate one of the largest sets of verifiable gang member profiles that have ever been studied. A review of these profiles establishes differences in the language, images, YouTube links, and emojis gang members use compared to the rest of the Twitter population. Features from this review are used to train a series of supervised classifiers. Our classifier achieves a promising F1 score with a low false positive rate.
Link to the paper - http://knoesis.org/?q=node/2754
https://www.youtube.com/watch?v=wbXEXGT3I9I&list=PLqJzTtkUiq54DDEEZvzisPlSGp_BadhNJ&index=8
Link of video:
https://www.youtube.com/watch?v=wbXEXGT3I9I
This is a review of the keynote presented by Eric Horvitz, Managing Director, Microsoft, Redmond.
This keynote was presented at Computing Community Consortium in Washington DC on June-07-2016.
Eric has discussed about 3 things in his keynote: Healthcare, Agriculture and Transport.
Mainly he has focussed on Health care.
The goal of AI
Broad Spectrum of Opportunities for AI
Healthcare
Sciences
Transportation
Agriculture
Sustainability
Education
Governance
Criminal justice
Privacy & security
Emergency management
A work conducted in John Hopkins University
References:
http://research.microsoft.com/en-us/um/people/horvitz/AI_supporting_people_and_society_Eric_Horvitz.pdf
https://www.youtube.com/watch?v=rek3jjbYRLo
https://en.wikipedia.org/wiki/Artificial_intelligence
https://en.wikipedia.org/wiki/AI_winter
http://research.microsoft.com/en-us/um/people/horvitz/
Regression and classification techniques play an essential role in many data mining tasks and have broad applications. However, most of the state-of-the-art regression and classification techniques are often unable to adequately model the interactions among predictor variables in highly heterogeneous datasets. New techniques that can effectively model such complex and heterogeneous structures are needed to significantly improve prediction accuracy.
In this dissertation, we propose a novel type of accurate and interpretable regression and classification models, named as Pattern Aided Regression (PXR) and Pattern Aided Classification (PXC) respectively. Both PXR and PXC rely on identifying regions in the data space where a given baseline model has large modeling errors, characterizing such regions using patterns, and learning specialized models for those regions. Each PXR/PXC model contains several pairs of contrast patterns and local models, where a local classifier is applied only to data instances matching its associated pattern. We also propose a class of classification and regression techniques called Contrast Pattern Aided Regression (CPXR) and Contrast Pattern Aided Classification (CPXC) to build accurate and interpretable PXR and PXC models.
We have conducted a set of comprehensive performance studies to evaluate the performance of CPXR and CPXC. The results show that CPXR and CPXC outperform state-of-the-art regression and classification algorithms, often by significant margins. The results also show that CPXR and CPXC are especially effective for heterogeneous and high dimensional datasets. Besides being new types of modeling, PXR and PXC models can also provide insights into data heterogeneity and diverse predictor-response relationships.
We have also adapted CPXC to handle classifying imbalanced datasets and introduced a new algorithm called Contrast Pattern Aided Classification for Imbalanced Datasets (CPXCim). In CPXCim, we applied a weighting method to boost minority instances as well as a new filtering method to prune patterns with imbalanced matching datasets.
Finally, we applied our techniques on three real applications, two in the healthcare domain and one in the soil mechanic domain. PXR and PXC models are significantly more accurate than other learning algorithms in those three applications.
In this chapter the author want to find out, how can an average human being can become an Expert in a specific field, and He highlights the common traits of the experts such as:
expert see the world differently, which the non-experts can’t see
An Expert in a specific field has a superior memory for the details of that field
Most importantly experts overcomes the brain’s most famous constraint “7”
Author says that in the field of remembering an average human can hold upto 7 plus or minus 2 digits in his brain at time. This the capacity of our short term memories by which we are limited but an expert is not limited by this constraint. When an expert look at a number, he does not see just the number rather he sees a memory or an image from the past such as Birth date or any memory which is related to the number. In the chapter author explains this difference between an average human and expert more clearly with examples such as chicken sexers and Swat officers
The is an active field of research in understanding the cognitive approach in dreaming. Analysing these dreams will give us the intuition on how the brains works and helps us in improving the technology. Calvin hall (1909-1985) developed the first scientific theories of dream interpretation based on quantitative analysis in 1953. According to him, the images in the dreams are the concrete embodiment of dreamer’s thought, these images give visual expression to that which is invisible, namely, conceptions. These conceptions can be about ourselves, others, environment, penalties and conflicts. Calvin hall says that there is a continuity between a person's’ wakefulness and their dream experience. He believed that during dreams we express creativity, similar to what we would do when expressing ourselves through metaphors in poetry.
With this as the basis, there are several theories which try to explain the concept of dreaming and its effect. Once such theory is by Dr. Robert Stickgold who is an Associate Professor of Psychiatry at Harvard Medical School and Beth Israel Deaconess Medical Centre. According to him, brain will extract from experiences to gist of what happened and kind of forget all those details that weren’t that important. It takes large amount of experiences and take them all together and figure out what rules and explain how our world work. When we are sleeping your brain is pulling everything together and seeing how it fits together and how to summarize it. Dreams also act as predictors that the brain is going to do what it needs to do to really figure out the problem. One can gain these insights when we don’t even know where to find just by sleeping.
One takeaway Dr. Sheth pointed out was the importance of abstraction in pulling the parts of the dreams together. For a example given blood, medicine and nurse as difference pieces, one would imagine a hospital and another would assume a location of a blast. This gives the notion of personalization and/or contextualization during the process of abstraction.
Understanding of Electronic Medical Records(EMRs) plays a crucial role in improving healthcare outcomes. However, the unstructured nature of EMRs poses several technical challenges for structured information extraction from clinical notes leading to automatic analysis. Natural Language Processing(NLP) techniques developed to process EMRs are effective for variety of tasks, they often fail to preserve the semantics of original information expressed in EMRs, particularly in complex scenarios. This paper illustrates the complexity of the problems involved and deals with conflicts created due to the shortcomings of NLP techniques and demonstrates where domain specific knowledge bases can come to rescue in resolving conflicts that can significantly improve the semantic annotation and structured information extraction. We discuss various insights gained from our study on real world dataset.
Big Data Challenges and Trust Management: A Personal Perspective
A tutorial presented by Dr. Krishnaprasad Thirunarayan at the International Conference on Collaboration Technologies and Systems 2016 (CTS 2016)
Gang affiliates have joined the masses who use social media to share thoughts and actions publicly. Interestingly, they use this public medium to express recent illegal actions, to intimidate others, and to share outrageous images and statements. Agencies able to unearth these profiles may thus be able to anticipate, stop, or hasten the investigation of gang-related crimes. This paper investigates the use of word embeddings to help identify gang members on Twitter. Building on our previous work, we generate word embeddings that translate what Twitter users post in their profile descriptions, tweets, profile images, and linked YouTube content to a real vector format amenable for machine learning classification. Our experimental results show that pre-trained word embeddings can boost the accuracy of supervised learning algorithms trained over gang members’ social media posts.
Understanding speed and travel-time dynamics in response to various city related events is an important and challenging problem. Sensor data (numerical) containing average speed of vehicles passing through a road link can be interpreted in terms of traffic related incident reports from city authorities and social media data (textual), providing a complementary understanding of traffic dynamics. State-of-the-art research is focused on either analyzing sensor observations or citizen observations; we seek to exploit both in a synergistic manner.
We demonstrate the role of domain knowledge in capturing the non-linearity of speed and travel-time dynamics by segmenting speed and travel-time observations into simpler components amenable to description using linear models such as Linear Dynamical System (LDS). Specifically, we propose Restricted Switching Linear Dynamical System (RSLDS) to model normal speed and travel time dynamics and thereby characterize anomalous dynamics. We utilize the city traffic events extracted from text to explain anomalous dynamics. We present a large scale evaluation of the proposed approach on a real-world traffic and twitter dataset collected over a year with promising results.
A Design of fuzzy controller for Autonomous Navigation of Unmanned VehicleWaqas Tariq
The design approach is proposed for fuzzy logic controller for autonomous navigation of a vehicle in an obstacle filled environment. The proposed fuzzy controller is composed obstacle avoidance layer, orientation control layer, passage detection module. Here the fuzzy controller for obstacle avoidance is proposed. It provides a model for multiple sensor input fusion and it is composed of eight individual controllers, each calculating a collision possibility in different directions of movement. By calculating value of collision possibility main controller that performs real-time collision avoidance. The operating frequency & logic cells requirements for different implementation techniques are find out. The designs have been carried out in the digital domain with VHDL using Altera Quartus-II software.
Week 8 Expository Essay 2 Revision 65 pointsContent and De.docxmelbruce90096
Week 8: Expository Essay 2 Revision: 65 points
Content and Development – 45 Points
Points Earned – 45/45
Additional Comments:
The revision is substantive and effective. To note:
0. Major revisions (i.e., noticeable changes in content and organization) are evident.
0. The revision remains in line with its assigned rhetorical mode.
0. The revision contains all the elements of a successful essay: a precise, pertinent, and persuasive thesis; effective support for that thesis; and descriptive language where and when appropriate.
0. Inter- and intra-paragraph content is effectively organized (spatially, temporally, logically, or by order of importance) and makes use of topic sentences and appropriate transitional expressions.
0. The introduction and conclusion are engaging, cohesive, and appropriate to their position in the revision.
The revision has been submitted to Turnitin. A paragraph of self-reflection and the original essay accompany the revision.
Readability and Style – 10 Points
Points Earned – 10/10
The tone is appropriate to the content and assignment.
Sentences are complete, clear, and concise.
Sentences are well-constructed, with consistently strong, varied syntax.
Sentence transitions are present and maintain the flow of thought.
Mechanics – 10 Points
Points Earned – 10/10
Rules of grammar, usage, and punctuation are followed.
Mechanics are accurate.
Spelling is correct.
Total – 65 Points
Points Earned – 65/65
Overall Comments:
Running Page: GOOGLE CAR
PAGE
1
GOOGLE CAR
Sasha Alba
Google Car
Self-driving cars are normally found in fictional movies, but Google is about to turn fiction into reality with the development of a full-fledged self-driven car. This means that the car can steer, accelerate, and can stop by itself. Google’s software, known as the Google chauffer, has components that include mission planning, behavior, perception, and motion planning and vehicle control (USA Today, 2014).
Design
The vehicle employs the use of artificial intelligence software that exhibits human intelligence that exhibits human behavior. It includes voice recognition, face recognition, natural language processing, game intelligence, artificial creativity, expert systems, among others. The mission planner component determines the waypoint segments that the vehicle should travel so as to complete a mission. It uses information such as road networks, terrain profiles, and information gathered during missions. After the information has been processed, it outputs waypoints to the behavior module (USA Today, 2014).
Perception is determined by algorithms that perform localization, object classification, and road detection. Sensors such as Lidar and Radar integrate information so that it can be used by planning and reactive components. The behavior component enables the vehicle to follow rules. The rules may be intersection progression for ground vehicles or docking for surface vehicles. In the event of the rules conflicting,.
Introduction–Definition - Future of Artificial Intelligence – Characteristics of Intelligent Agents– Typical Intelligent Agents – Problem Solving Approach to Typical AI problems.
The concept of intelligent system has emerged in information technology as a type of system derived from successful applications of artificial intelligence. The goal of this presentation is to give a general description of an intelligent system, which integrates classical approaches and recent advances in artificial intelligence. The presentation describes an intelligent system
in a generic way, identifying its main properties and functional components.
Self-Driving Cars With Convolutional Neural Networks (CNN.pptxssuserf79e761
Self-driving cars aim to revolutionize car travel by making it safe and efficient. In this article, we outlined some of the key components such as LiDAR, RADAR, cameras, and most importantly – the algorithms that make self-driving cars possible.
Few things need to be taken care of:
The algorithms used are not yet optimal enough to perceive roads and lanes because some roads lack markings and other signs.
The optimal sensing modality for localization, mapping, and perception still lack accuracy and efficiency.
Vehicle-to-vehicle communication is still a dream, but work is being done in this area as well.
The field of human-machine interaction is not explored enough, with many open, unsolved problems.
Self-driving cars aim to revolutionize car travel by making it safe and efficient. In this article, we outlined some of the key components such as LiDAR, RADAR, cameras, and most importantly – the algorithms that make self-driving cars possible.
Few things need to be taken care of:
The algorithms used are not yet optimal enough to perceive roads and lanes because some roads lack markings and other signs.
The optimal sensing modality for localization, mapping, and perception still lack accuracy and efficiency.
Vehicle-to-vehicle communication is still a dream, but work is being done in this area as well.
The field of human-machine interaction is not explored enough, with many open, unsolved problems.
Q-learning is one of the most commonly used DRL algorithms for self-driving cars. It comes under the category of model-free learning. In model-free learning, the agent will try to approximate the optimal state-action pair. The policy still determines which action-value pairs or Q-value are visited and updated (see the equation below). The goal is to find optimal policy by interacting with the environment while modifying the same when the agent makes an error.
Driver Alertness On Android With Face And Eye Ball MovementsIJRES Journal
Drowsiness is a big problem while in driving specially in long and continues driving. This is a main cause for accidents. Maximum accidents found by the driver’s ignorance of seeing the road and focus on other thing that will divert the concentration. This project used to find sleepy drivers and lazy driver by monitoring them periodically. Main objective of the project to develop entire system in to smart phone and make it as user friendly to the driver and try to support the system on Smartphone have the Android Operating System. There are major things are considered for measure the fatigue level when monitoring driver, Eye movement driver. Smartphone camera capture the drives image, A Dynamic decision making used for find the drivers fatigue level. When driver reaches threshold level of fatigue, then alert is triggered to avoid accident and awake the driver. If driver ignores the alert and continue with drowsy driving, the alert system takes further steps to stop the vehicle. It may be find nearest coffee shop to refresh driver and also if he need other choices to refresh the map will help them.GPS and Navigation Service of the Android phones used for assist the driver to overcome his drowsy driver.
A presentation of Driver drowsiness alert system which can identify whether the driver is attentive or sleepy while driving and hence alert them by a beep when the driver is sleepy.Python and open CV are main technologies used here along with hass cascade algorithm for the same.
Autonomous Vehicles: the Intersection of Robotics and Artificial IntelligenceWiley Jones
Autonomous Vehicle Webinar. Crash course in AVs: high-level overview, technology deep-dives, and trends. Follow me on Twitter at https://twitter.com/wileycwj.
Link to YouTube Video: https://www.youtube.com/watch?v=CruCp6vqPQs
Google Slides: https://docs.google.com/presentation/d/1-ZWAXEH-5Xu7_zts-rGhNwan14VH841llZwrHGT_9dQ/edit?usp=sharing
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Thesis Statement for students diagnonsed withADHD.ppt
Semantic, Cognitive and Perceptual Computing -Cognitive computing in autonomous vehicle
1. Cognitive computing in Autonomous Vehicle
Pranav
Graduate Student in Computer Science Dept.
At
Semantic-Cognitive-Perceptual Computing Class, Summer 2016
Wright State University
1
2. Agenda
(1) Defining cognitive in autonomous vehicle scenario. (cognitivism,
connectionism and Embodied cognition)
(2) Architecture of autonomous vehicle. (hardware + software in brief)
(3) Further discussion on the Intelligent System of Decisionmaking (ISD)
4. Cognition
Cognition can be briefly defined as acquisition of knowledge
Cognition mainly focused on pattern recognition, attention, memory, vision image, language, problem
solving and decision making.
Vision, audition, tactile, olfaction and gustation as low level perception of cognition
Language understanding, problem solving and decision making into high level cognition.
How to bridge low level perception with high level cognition and how human intelligence
forms lead to different research topics in Psychology.
5. Cognitivism, Connectionism and Embodied Cognition
Cognitivism is a theoretical framework for understanding the mankind mind. It was
cognitivists tries to disclose the internal relations between perception and action.
Cognitivism believes that symbol computing is the core of intelligence.
Connectionism believes that numerous connected units network is the basis of
generating intelligence.
Embodied cognition theory, cognition is not about intellectual demonstration but
more related to the body and its surrounding physical environment
6. Definitions of cognition in AI into four categories
(1) Thinking like a human
(2) Acting like a human
(3) Thinking reasonably
(4) Acting reasonably
7. Thinking like a human
That computer programs may think like a
human requires us understand how human thinks
firstly.
We should have a whole understanding of the
inner progress of mind.
8. Acting like a human
Being acting like a human, the computer programs should be
with the ability of automated reasoning, machine learning,
computer vision, and so on.
They also need to pass the Turing Test.
9. Thinking reasonably
To think reasonably requires computer programmers first find
the "Law of Thought" proposed by the ancient Greek
philosopher Aristotle and others.
The law tries to find "the right way of thinking".
10. Acting reasonably
Acting reasonably requires computer programs can
● operate automatically
● percept environment
● adapt to the change of environment
● create and pursue goals
● make the best decision under uncertain situations
11. Architecture of autonomous vehicle
According to Embodied Cognition theory, the cognition system of an autonomous
vehicle can be divided into two parts:
● Environment perception
● Driving decision
The two parts in the vehicle interact with each other to ensure the vehicle move to
the destination safely.
The two parts correspond to the low level perception and high level cognition
separately, where environment perception via sensors belongs to the low level
cognition, navigating and decision making belong to the high level cognition.
12. Cognition architecture of autonomous vehicle
Environment perception Driving decision
low level high level
perception
cognition
environment perception via sensors navigating and decision
making
belongs to the low level cognition belong to the high level cognition
13. PERCEPTION MODULES
● Short memory systems
● Obstacle detection
● Localization and mapping
● Some necessary but not discussed modules
○ Traffic lights detection
○ Traffic sign detection and recognition
18. Survey Results
(1) There's no systematic discussion of the robustness of the autonomous vehicle.
(2) As 80% of information obtained by a human driver is from his/her vision, it's
valuable for researches in computer vision field to improve reliability of computer
vision methods.
(3) Not so much papers published in the evaluation of the reliability of the vehicle's
cognition level.
19. Intelligent System of Decisionmaking (ISD)
Such a system which implements models of cognitive and personality (motivation)
psychology for a control system.
Many design methods are based on artificial intelligence
● Fuzzy systems
● Neural networks
● Evolutionary algorithms or rule-based methods
20. Abstract layers of ISD in case of AV or UGV
The systems for autonomous driving are quite complex and can be divided into
few subsystems
● perception system
● traffic rules interpreter
● decision system (behaviour controller)
● low-level car controller
21. ISD System Adaptation
The adaptation of the ISD system to the driver tasks is performed in three steps: –
● Integration of the perception systems
with the simulated environment
● creation of an interpreter of traffic rules
● designing an adequate set of reactions
and needs (H) according to emotional
context (n
22. The model of an adopted ISD
● The environment is constructed on
the basis of a certain scenario
○ position, velocity and
acceleration
● The shape of perception area
strongly depends on the scenario
○ especially on bends and slopes
of the road
● Computes the estimated position of
objects
○ according to the state of the car
○ its current scene
23. The model of an adopted ISD (continue..)
● Effects of the current traffic regulations
and the objects in the view area can be
assigned to the xDriver states (of all its
needs H and emotion n)
● Easily find the new reaction
● Feedback: the reaction affects
accordingly the current state of the car
● Note that this model is a derivative of
cognitive psychology, adapted for the
purposes of the autonomous driver. It
simply mimics the way in which the
driver reacts to certain stimuli
24. Needs and Emotions
● Needs and emotions constitute a crucial
part
● human motivational system
● Allow us to ’control’ the Driver’s desire
to act.
● The symbol g represents the degree of
non fulfilment of a certain need and
hereinafter g will be called a need
● It is an abstract fuzzy value,
● which takes one or more (two) of three
states: satisfaction (lowest), prealarm
and alarm (highest)
25. Needs and Emotions (continue..)
● It can, for instance, be partially satisfied
and partially prealarmed (according to
its actual crisp value)
● A need is completely satisfied whenever
its crisp value is equal to zero
● This importance is described by a
weighting function, which takes the form
of a sigmoid curve.
● The weighting emphasizes the
importance of alarmed needs.
● It is easier for xDriver to choose those
needs that require immediate reaction
and fulfilment.
26. Need and Maslow Pyramid
● Physiological (principal) level: energy
optimization
● Physiological level: goal achievement
● Safety level: security of car
● Safety level: traffic regulations
● (self-)esteem level: speed
● (self-)esteem level: confidence
● self-actualization level: creativity.
Ref: Image from wikipedia