Metalearning-Informed Competence in Children: Implications for Responsible Brain-Inspired Artificial Intelligence

This paper offers a novel conceptual framework comprising four essential cognitive mechanisms that operate concurrently and collaboratively to enable metalearning (knowledge and regulation of learning) strategy implementation in young children. A roadmap incorporating the core mechanisms and the associated strategies is presented as an explanation of the developing brain's remarkable cross-context learning competence. The tetrad of fundamental complementary processes is chosen to collectively represent the bare-bones metalearning architecture that can be extended to artificial intelligence (AI) systems emulating brain-like learning and problem-solving skills. Utilizing the metalearning-enabled young mind as a model for brain-inspired computing, this work further discusses important implications for morally grounded AI.Comment: 27 pages, 3 figure

Large Scale Application of Neural Network Based Semantic Role Labeling for Automated Relation Extraction from Biomedical Texts

To reduce the increasing amount of time spent on literature search in the life sciences, several methods for automated knowledge extraction have been developed. Co-occurrence based approaches can deal with large text corpora like MEDLINE in an acceptable time but are not able to extract any specific type of semantic relation. Semantic relation extraction methods based on syntax trees, on the other hand, are computationally expensive and the interpretation of the generated trees is difficult. Several natural language processing (NLP) approaches for the biomedical domain exist focusing specifically on the detection of a limited set of relation types. For systems biology, generic approaches for the detection of a multitude of relation types which in addition are able to process large text corpora are needed but the number of systems meeting both requirements is very limited. We introduce the use of SENNA (“Semantic Extraction using a Neural Network Architecture”), a fast and accurate neural network based Semantic Role Labeling (SRL) program, for the large scale extraction of semantic relations from the biomedical literature. A comparison of processing times of SENNA and other SRL systems or syntactical parsers used in the biomedical domain revealed that SENNA is the fastest Proposition Bank (PropBank) conforming SRL program currently available. 89 million biomedical sentences were tagged with SENNA on a 100 node cluster within three days. The accuracy of the presented relation extraction approach was evaluated on two test sets of annotated sentences resulting in precision/recall values of 0.71/0.43. We show that the accuracy as well as processing speed of the proposed semantic relation extraction approach is sufficient for its large scale application on biomedical text. The proposed approach is highly generalizable regarding the supported relation types and appears to be especially suited for general-purpose, broad-scale text mining systems. The presented approach bridges the gap between fast, cooccurrence-based approaches lacking semantic relations and highly specialized and computationally demanding NLP approaches

Sustainable Development Indicator Frameworks and Initiatives

Agricultural and Food Policy, Environmental Economics and Policy, Farm Management, Production Economics,

A resource-saving collective approach to biomedical semantic role labeling

BACKGROUND: Biomedical semantic role labeling (BioSRL) is a natural language processing technique that identifies the semantic roles of the words or phrases in sentences describing biological processes and expresses them as predicate-argument structures (PAS’s). Currently, a major problem of BioSRL is that most systems label every node in a full parse tree independently; however, some nodes always exhibit dependency. In general SRL, collective approaches based on the Markov logic network (MLN) model have been successful in dealing with this problem. However, in BioSRL such an approach has not been attempted because it would require more training data to recognize the more specialized and diverse terms found in biomedical literature, increasing training time and computational complexity. RESULTS: We first constructed a collective BioSRL system based on MLN. This system, called collective BIOSMILE (CBIOSMILE), is trained on the BioProp corpus. To reduce the resources used in BioSRL training, we employ a tree-pruning filter to remove unlikely nodes from the parse tree and four argument candidate identifiers to retain candidate nodes in the tree. Nodes not recognized by any candidate identifier are discarded. The pruned annotated parse trees are used to train a resource-saving MLN-based system, which is referred to as resource-saving collective BIOSMILE (RCBIOSMILE). Our experimental results show that our proposed CBIOSMILE system outperforms BIOSMILE, which is the top BioSRL system. Furthermore, our proposed RCBIOSMILE maintains the same level of accuracy as CBIOSMILE using 92% less memory and 57% less training time. CONCLUSIONS: This greatly improved efficiency makes RCBIOSMILE potentially suitable for training on much larger BioSRL corpora over more biomedical domains. Compared to real-world biomedical corpora, BioProp is relatively small, containing only 445 MEDLINE abstracts and 30 event triggers. It is not large enough for practical applications, such as pathway construction. We consider it of primary importance to pursue SRL training on large corpora in the future

Effects of Metacognitive Monitoring on Academic Achievement in an Ill-Structured Problem-Solving Environment

Higher education courses are increasingly moving online while educational approaches are concurrently shifting their focus toward student-centered approaches to learning. These approaches promote critical thinking by asking students to solve a range of ill-structured problems that exist in the real world. Researchers have found that student-centered online learning environments require students to have self-regulated learning skills, including metacognitive skills to regulate their own learning processes. Much of the research suggests that externally supporting students while they are learning online, either directly or indirectly, helps them to succeed academically. However, few empirical studies have investigated what levels of support are most effective for promoting students\u27 self-regulated learning behaviors. Additionally, these studies reported conflicting results – some found maximum support to be most effective while others found no significant difference. The purpose of this study was to investigate the effectiveness of different levels of support for self-regulated learning during a complex learning activity to solve an ill-structured problem-solving situation in an online learning environment. In addition, the role of students\u27 self-efficacy on their academic achievement was examined. A total of 101 undergraduate students from three international studies courses offered at a large urban Southeastern public university in the United States participated in the study. The students were randomly assigned to treatment (minimum support, maximum support) and control groups. Students\u27 academic achievement scores were measured using a conceptual knowledge test created by the professor teaching the courses. O\u27Neil\u27s (1997) Trait Self-Regulation Questionnaire measured students\u27 self-efficacy. Analysis of Co-Variance (ANCOVA) was conducted to analyze the data. The ANCOVA results indicated significant improvement of the academic achievement of the minimum support group versus both the maximum support and control groups. Additionally, self-efficacy as a co-variable did not significantly impact students\u27 achievement scores in any of the groups. The overall results indicated that it is important to consider the level of self-regulated learning support when designing online learning environments promoting students\u27 critical thinking skills. Promoting students\u27 self-regulated learning skills is vital when designing online higher education courses

STRATEGIC RETREAT IN AN AGE OF CLIMATE CHANGE

According to FEMA and NOAA, flooding is the most frequent and costly natural disaster in the United States. The National Flood Insurance Program, designed to alleviate some of the costs of this hazard, is financially insolvent and fiscally unsustainable. Through its Hazard Mitigation Assistance program, FEMA buys out homes, but the process is voluntary and slow, and demand far exceeds funds available. Consequently, the number of Repetitive Loss and Severe Repetitive Loss properties increases each year. The thesis explores why and how FEMA should pursue strategic retreat from high-risk areas. Disaster costs continue to rise as extreme weather events increase in frequency and intensity. Sea-level rise endangers coastal regions, and more homes may be susceptible to systemic and regular flooding than official estimates show. To resist by building ever larger flood walls and barriers may be unrealistic, and retreat as an adaptation technique may be preferable. Expensive as strategic retreat may be, loss-avoidance studies indicate that besides reducing pain and suffering, these measures pay for themselves. Strategic retreat may also result in climate refugees who are not prepared to deal with rapidly changing conditions. An adaptation framework recommends deterrents and incentives available to policymakers and practitioners to pursue strategic retreat in a planned, comprehensive, and equitable manner.Civilian, Department of Homeland SecurityApproved for public release. Distribution is unlimited

DREAM Architecture: a Developmental Approach to Open-Ended Learning in Robotics

Robots are still limited to controlled conditions, that the robot designer knows with enough details to endow the robot with the appropriate models or behaviors. Learning algorithms add some flexibility with the ability to discover the appropriate behavior given either some demonstrations or a reward to guide its exploration with a reinforcement learning algorithm. Reinforcement learning algorithms rely on the definition of state and action spaces that define reachable behaviors. Their adaptation capability critically depends on the representations of these spaces: small and discrete spaces result in fast learning while large and continuous spaces are challenging and either require a long training period or prevent the robot from converging to an appropriate behavior. Beside the operational cycle of policy execution and the learning cycle, which works at a slower time scale to acquire new policies, we introduce the redescription cycle, a third cycle working at an even slower time scale to generate or adapt the required representations to the robot, its environment and the task. We introduce the challenges raised by this cycle and we present DREAM (Deferred Restructuring of Experience in Autonomous Machines), a developmental cognitive architecture to bootstrap this redescription process stage by stage, build new state representations with appropriate motivations, and transfer the acquired knowledge across domains or tasks or even across robots. We describe results obtained so far with this approach and end up with a discussion of the questions it raises in Neuroscience

Matalaulotteisen affordanssiesityksen oppiminen ja tämän hyödyntäminen robottijärjestelmän koulutuksessa

The development of data-driven approaches, such as deep learning, has led to the emergence of systems that have achieved human-like performance in wide variety of tasks. For robotic tasks, deep data-driven models are introduced to create adaptive systems without the need of explicitly programming them. These adaptive systems are needed in situations, where task and environment changes remain unforeseen. Convolutional neural networks (CNNs) have become the standard way to process visual data in robotics. End-to-end neural network models that operate the entire control task can perform various complex tasks with little feature engineering. However, the adaptivity of these systems goes hand in hand with the level of variation in the training data. Training end-to-end deep robotic systems requires a lot of domain-, task-, and hardware-specific data, which is often costly to provide. In this work, we propose to tackle this issue by employing a deep neural network with a modular architecture, consisting of separate perception, policy, and trajectory parts. Each part of the system is trained fully on synthetic data or in simulation. The data is exchanged between parts of the system as low-dimensional representations of affordances and trajectories. The performance is then evaluated in a zero-shot transfer scenario using the Franka Panda robotic arm. Results demonstrate that a low-dimensional representation of scene affordances extracted from an RGB image is sufficient to successfully train manipulator policies.Tietopohjaisten oppimismenetelmien etenkin syväoppimisen viimeaikainen kehitys on synnyttänyt järjestelmiä, jotka ovat saavuttaneet ihmistasoisen suorituskyvyn ihmisälyä vaativissa tehtävissä. Syväoppimiseen pohjautuvia robottijärjestelmiä ollaan kehitetty, jotta ympäristön ja tehtävän muutoksiin mukautuvaisempia robotteja voitaisiin ottaa käyttöön. Konvoluutioneuroverkkojen käyttö kuvatiedon käsittelyssä robotiikassa on yleistä. Neuroverkkomallit, jotka käsittelevät anturitietoa ja suorittavat päätöksenteon ja säädön, voivat oppia monimutkaisia tehtäviä ilman käsin tehtyä kehitystyötä. Näiden järjestelmien kyky mukautua ympäristön muutoksiin on kuitenkin suoraan verrannollinen koulutustiedon monimuotoisuuteen. Syväoppimiseen pohjautuva robottijärjestelmä vaatii oppiakseen suuren määrän ympäristö-, tehtävä-, ja laitteisto-ominaista koulutustietoa, mikä joudutaan yleensä kerätä tehottomasti käsin. Tämän työn tarkoitus on esittää ratkaisu yllämainittuun tehottomuuteen. Esittelemme neuroverkkoarkkitehtuurin, joka koostuu kolmesta erillisestä komponentista. Nämä komponentit koulutetaan erikseen ja koulutus ollaan ainoastaan toteutettu simulaatiossa tai synteettisellä tiedolla ilman fyysisen maailman lisäkouluttautumista Ensimmäinen komponentti tuottaa RGB-kuvasta matalaulotteisen affordanssiesityksen. Tämän esityksen pohjalta toinen komponentti tuottaa matalaulotteisten liikerataesityksen. Kolmas komponentti luo tämän esityksen pohjalta täysimittaisen liikeradan teollisuusrobotille. Järjestelmän suorituskykyä arvioidaan fyysisessä ympäristössä ilman lisäkoulutusta Franka Panda -teollisuusrobotilla. Tulokset osoittavat, että kuvatieto voidaan esittää matalaulotteisena affordanssiesityksenä ja tätä esitystä voidaan käyttää säätötehtävän oppimiseen

Matalaulotteisen affordanssiesityksen oppiminen ja tämän hyödyntäminen robottijärjestelmän koulutuksessa

The development of data-driven approaches, such as deep learning, has led to the emergence of systems that have achieved human-like performance in wide variety of tasks. For robotic tasks, deep data-driven models are introduced to create adaptive systems without the need of explicitly programming them. These adaptive systems are needed in situations, where task and environment changes remain unforeseen. Convolutional neural networks (CNNs) have become the standard way to process visual data in robotics. End-to-end neural network models that operate the entire control task can perform various complex tasks with little feature engineering. However, the adaptivity of these systems goes hand in hand with the level of variation in the training data. Training end-to-end deep robotic systems requires a lot of domain-, task-, and hardware-specific data, which is often costly to provide. In this work, we propose to tackle this issue by employing a deep neural network with a modular architecture, consisting of separate perception, policy, and trajectory parts. Each part of the system is trained fully on synthetic data or in simulation. The data is exchanged between parts of the system as low-dimensional representations of affordances and trajectories. The performance is then evaluated in a zero-shot transfer scenario using the Franka Panda robotic arm. Results demonstrate that a low-dimensional representation of scene affordances extracted from an RGB image is sufficient to successfully train manipulator policies.Tietopohjaisten oppimismenetelmien etenkin syväoppimisen viimeaikainen kehitys on synnyttänyt järjestelmiä, jotka ovat saavuttaneet ihmistasoisen suorituskyvyn ihmisälyä vaativissa tehtävissä. Syväoppimiseen pohjautuvia robottijärjestelmiä ollaan kehitetty, jotta ympäristön ja tehtävän muutoksiin mukautuvaisempia robotteja voitaisiin ottaa käyttöön. Konvoluutioneuroverkkojen käyttö kuvatiedon käsittelyssä robotiikassa on yleistä. Neuroverkkomallit, jotka käsittelevät anturitietoa ja suorittavat päätöksenteon ja säädön, voivat oppia monimutkaisia tehtäviä ilman käsin tehtyä kehitystyötä. Näiden järjestelmien kyky mukautua ympäristön muutoksiin on kuitenkin suoraan verrannollinen koulutustiedon monimuotoisuuteen. Syväoppimiseen pohjautuva robottijärjestelmä vaatii oppiakseen suuren määrän ympäristö-, tehtävä-, ja laitteisto-ominaista koulutustietoa, mikä joudutaan yleensä kerätä tehottomasti käsin. Tämän työn tarkoitus on esittää ratkaisu yllämainittuun tehottomuuteen. Esittelemme neuroverkkoarkkitehtuurin, joka koostuu kolmesta erillisestä komponentista. Nämä komponentit koulutetaan erikseen ja koulutus ollaan ainoastaan toteutettu simulaatiossa tai synteettisellä tiedolla ilman fyysisen maailman lisäkouluttautumista Ensimmäinen komponentti tuottaa RGB-kuvasta matalaulotteisen affordanssiesityksen. Tämän esityksen pohjalta toinen komponentti tuottaa matalaulotteisten liikerataesityksen. Kolmas komponentti luo tämän esityksen pohjalta täysimittaisen liikeradan teollisuusrobotille. Järjestelmän suorituskykyä arvioidaan fyysisessä ympäristössä ilman lisäkoulutusta Franka Panda -teollisuusrobotilla. Tulokset osoittavat, että kuvatieto voidaan esittää matalaulotteisena affordanssiesityksenä ja tätä esitystä voidaan käyttää säätötehtävän oppimiseen