Learning in the Wild with Incremental Skeptical Gaussian Processes

The ability to learn from human supervision is fundamental for personal assistants and other interactive applications of AI. Two central challenges for deploying interactive learners in the wild are the unreliable nature of the supervision and the varying complexity of the prediction task. We address a simple but representative setting, incremental classification in the wild, where the supervision is noisy and the number of classes grows over time. In order to tackle this task, we propose a redesign of skeptical learning centered around Gaussian Processes (GPs). Skeptical learning is a recent interactive strategy in which, if the machine is sufficiently confident that an example is mislabeled, it asks the annotator to reconsider her feedback. In many cases, this is often enough to obtain clean supervision. Our redesign, dubbed ISGP, leverages the uncertainty estimates supplied by GPs to better allocate labeling and contradiction queries, especially in the presence of noise. Our experiments on synthetic and real-world data show that, as a result, while the original formulation of skeptical learning produces over-confident models that can fail completely in the wild, ISGP works well at varying levels of noise and as new classes are observed.Comment: 7 pages, 3 figures, code: https://gitlab.com/abonte/incremental-skeptical-g

A micro-meso-macro perspective on the methodology of evolutionary economics: integrating history, simulation and econometrics

Applied economics has long been dominated by multiple regression techniques. In this regard, econometrics has tended to have a narrower focus than, for example, psychometrics in psychology. Over the last two decades, the simulation and calibration approach to modeling has become more popular as an alternative to traditional econometric strategies. However, in contrast to the well-developed methodologies that now exist in econometrics, simulation/calibration remains exploratory and provisional, both as an explanatory and as a predictive modelling technique although clear progress has recently been made in this regard (see Brenner and Werker (2006)). In this paper, we suggest an approach that can usefully integrate both of these modelling strategies into a coherent evolutionary economic methodology.

The Precautionary Principle (with Application to the Genetic Modification of Organisms)

We present a non-naive version of the Precautionary (PP) that allows us to avoid paranoia and paralysis by confining precaution to specific domains and problems. PP is intended to deal with uncertainty and risk in cases where the absence of evidence and the incompleteness of scientific knowledge carries profound implications and in the presence of risks of "black swans", unforeseen and unforeseable events of extreme consequence. We formalize PP, placing it within the statistical and probabilistic structure of ruin problems, in which a system is at risk of total failure, and in place of risk we use a formal fragility based approach. We make a central distinction between 1) thin and fat tails, 2) Local and systemic risks and place PP in the joint Fat Tails and systemic cases. We discuss the implications for GMOs (compared to Nuclear energy) and show that GMOs represent a public risk of global harm (while harm from nuclear energy is comparatively limited and better characterized). PP should be used to prescribe severe limits on GMOs

Developmental Bootstrapping of AIs

Although some current AIs surpass human abilities in closed artificial worlds such as board games, their abilities in the real world are limited. They make strange mistakes and do not notice them. They cannot be instructed easily, fail to use common sense, and lack curiosity. They do not make good collaborators. Mainstream approaches for creating AIs are the traditional manually-constructed symbolic AI approach and generative and deep learning AI approaches including large language models (LLMs). These systems are not well suited for creating robust and trustworthy AIs. Although it is outside of the mainstream, the developmental bootstrapping approach has more potential. In developmental bootstrapping, AIs develop competences like human children do. They start with innate competences. They interact with the environment and learn from their interactions. They incrementally extend their innate competences with self-developed competences. They interact and learn from people and establish perceptual, cognitive, and common grounding. They acquire the competences they need through bootstrapping. However, developmental robotics has not yet produced AIs with robust adult-level competences. Projects have typically stopped at the Toddler Barrier corresponding to human infant development at about two years of age, before their speech is fluent. They also do not bridge the Reading Barrier, to skillfully and skeptically draw on the socially developed information resources that power current LLMs. The next competences in human cognitive development involve intrinsic motivation, imitation learning, imagination, coordination, and communication. This position paper lays out the logic, prospects, gaps, and challenges for extending the practice of developmental bootstrapping to acquire further competences and create robust, resilient, and human-compatible AIs.Comment: 102 pages, 29 figure

Technical Change and Industrial Dynamics as Evolutionary Processes

This work prepared for B. Hall and N. Rosenberg (eds.) Handbook of Innovation, Elsevier (2010), lays out the basic premises of this research and review and integrate much of what has been learned on the processes of technological evolution, their main features and their effects on the evolution of industries. First, we map and integrate the various pieces of evidence concerning the nature and structure of technological knowledge the sources of novel opportunities, the dynamics through which they are tapped and the revealed outcomes in terms of advances in production techniques and product characteristics. Explicit recognition of the evolutionary manners through which technological change proceed has also profound implications for the way economists theorize about and analyze a number of topics central to the discipline. One is the theory of the firm in industries where technological and organizational innovation is important. Indeed a large literature has grown up on this topic, addressing the nature of the technological and organizational capabilities which business firms embody and the ways they evolve over time. Another domain concerns the nature of competition in such industries, wherein innovation and diffusion affect growth and survival probabilities of heterogeneous firms, and, relatedly, the determinants of industrial structure. The processes of knowledge accumulation and diffusion involve winners and losers, changing distributions of competitive abilities across different firms, and, with that, changing industrial structures. Both the sector-specific characteristics of technologies and their degrees of maturity over their life cycles influence the patterns of industrial organization ? including of course size distributions, degrees of concentration, relative importance of incumbents and entrants, etc. This is the second set of topics which we address. Finally, in the conclusions, we briefly flag some fundamental aspects of economic growth and development as an innovation driven evolutionary process.Innovation, Technological paradigms, Technological regimes and trajectories, Evolution, Learning, Capability-based theories of the firm, Selection, Industrial dynamics, Emergent properties, Endogenous growth

Bridging spatiotemporal scales in biomechanical models for living tissues : from the contracting Esophagus to cardiac growth

Appropriate functioning of our body is determined by the mechanical behavior of our organs. An improved understanding of the biomechanical functioning of the soft tissues making up these organs is therefore crucial for the choice for, and development of, efficient clinical treatment strategies focused on patient-specific pathophysiology. This doctoral dissertation describes the passive and active biomechanical behavior of gastrointestinal and cardiovascular tissue, both in the short and long term, through computer models that bridge the cell, tissue and organ scale. Using histological characterization, mechanical testing and medical imaging techniques, virtual esophagus and heart models are developed that simulate the patient-specific biomechanical organ behavior as accurately as possible. In addition to the diagnostic value of these models, the developed modeling technology also allows us to predict the acute and chronic effect of various treatment techniques, through e.g. drugs, surgery and/or medical equipment. Consequently, this dissertation offers insights that will have an unmistakable impact on the personalized medicine of the future.Het correct functioneren van ons lichaam wordt bepaald door het mechanisch gedrag van onze organen. Een verbeterd inzicht in het biomechanisch functioneren van deze zachte weefsels is daarom van cruciale waarde voor de keuze voor, en ontwikkeling van, efficiënte klinische behandelingsstrategieën gefocust op de patiënt-specifieke pathofysiologie. Deze doctoraatsthesis brengt het passieve en actieve biomechanisch gedrag van gastro-intestinaal en cardiovasculair weefsel, zowel op korte als lange termijn, in kaart via computermodellen die een brug vormen tussen cel-, weefsel- en orgaanniveau. Aan de hand van histologische karakterisering, mechanische testen en medische beeldvormingstechnieken worden virtuele slokdarm- en hartmodellen ontwikkeld die het patiënt-specifieke orgaangedrag zo accuraat mogelijk simuleren. Naast de diagnostische waarde van deze modellen, laat de ontwikkelde modelleringstechnologie ook toe om het effect van verschillende behandelingstechnieken, via medicatie, chirurgie en/of medische apparatuur bijvoorbeeld, acuut en chronisch te voorspellen. Bijgevolg biedt deze doctoraatsthesis inzichten die een onmiskenbare impact zullen hebben op de gepersonaliseerde geneeskunde van de toekomst

Artificial Intelligence, Mathematical Modeling and Magnetic Resonance Imaging for Precision Medicine in Neurology and Neuroradiology

La tesi affronta la possibilità di utilizzare metodi matematici, tecniche di simulazione, teorie fisiche riadattate e algoritmi di intelligenza artificiale per soddisfare le esigenze cliniche in neuroradiologia e neurologia al fine di descrivere e prevedere i patterns e l’evoluzione temporale di una malattia, nonché di supportare il processo decisionale clinico. La tesi è suddivisa in tre parti. La prima parte riguarda lo sviluppo di un workflow radiomico combinato con algoritmi di Machine Learning al fine di prevedere parametri che favoriscono la descrizione quantitativa dei cambiamenti anatomici e del coinvolgimento muscolare nei disordini neuromuscolari, con particolare attenzione alla distrofia facioscapolo-omerale. Il workflow proposto si basa su sequenze di risonanza magnetica convenzionali disponibili nella maggior parte dei centri neuromuscolari e, dunque, può essere utilizzato come strumento non invasivo per monitorare anche i più piccoli cambiamenti nei disturbi neuromuscolari oltre che per la valutazione della progressione della malattia nel tempo. La seconda parte riguarda l’utilizzo di un modello cinetico per descrivere la crescita tumorale basato sugli strumenti della meccanica statistica per sistemi multi-agente e che tiene in considerazione gli effetti delle incertezze cliniche legate alla variabilità della progressione tumorale nei diversi pazienti. L'azione dei protocolli terapeutici è modellata come controllo che agisce a livello microscopico modificando la natura della distribuzione risultante. Viene mostrato come lo scenario controllato permetta di smorzare le incertezze associate alla variabilità della dinamica tumorale. Inoltre, sono stati introdotti metodi di simulazione numerica basati sulla formulazione stochastic Galerkin del modello cinetico sviluppato. La terza parte si riferisce ad un progetto ancora in corso che tenta di descrivere una porzione di cervello attraverso la teoria quantistica dei campi e di simularne il comportamento attraverso l'implementazione di una rete neurale con una funzione di attivazione costruita ad hoc e che simula la funzione di risposta del modello biologico neuronale. E’ stato ottenuto che, nelle condizioni studiate, l'attività della porzione di cervello può essere descritta fino a O(6), i.e, considerando l’interazione fino a sei campi, come un processo gaussiano. Il framework quantistico definito può essere esteso anche al caso di un processo non gaussiano, ovvero al caso di una teoria di campo quantistico interagente utilizzando l’approccio della teoria wilsoniana di campo efficace.The thesis addresses the possibility of using mathematical methods, simulation techniques, repurposed physical theories and artificial intelligence algorithms to fulfill clinical needs in neuroradiology and neurology. The aim is to describe and to predict disease patterns and its evolution over time as well as to support clinical decision-making processes. The thesis is divided into three parts. Part 1 is related to the development of a Radiomic workflow combined with Machine Learning algorithms in order to predict parameters that quantify muscular anatomical involvement in neuromuscular diseases, with special focus on Facioscapulohumeral dystrophy. The proposed workflow relies on conventional Magnetic Resonance Imaging sequences available in most neuromuscular centers and it can be used as a non-invasive tool to monitor even fine change in neuromuscular disorders and to evaluate longitudinal diseases’ progression over time. Part 2 is about the description of a kinetic model for tumor growth by means of classical tools of statistical mechanics for many-agent systems also taking into account the effects of clinical uncertainties related to patients’ variability in tumor progression. The action of therapeutic protocols is modeled as feedback control at the microscopic level. The controlled scenario allows the dumping of uncertainties associated with the variability in tumors’ dynamics. Suitable numerical methods, based on Stochastic Galerkin formulation of the derived kinetic model, are introduced. Part 3 refers to a still-on going project that attempts to describe a brain portion through a quantum field theory and to simulate its behavior through the implementation of a neural network with an ad-hoc activation function mimicking the biological neuron model response function. Under considered conditions, the brain portion activity can be expressed up to O(6), i.e., up to six fields interaction, as a Gaussian Process. The defined quantum field framework may also be extended to the case of a Non-Gaussian Process behavior, or rather to an interacting quantum field theory in a Wilsonian Effective Field theory approach

장소와 그 가치를 저장하는 배측과 중간 해마의 차별적 역할

학위논문(박사) -- 서울대학교대학원 : 자연과학대학 뇌인지과학과, 2021.8. 강민수.오래전부터 해마는 자신의 경험, 즉 일화 사건의 기억에 필수적인 영역으로 알려져왔습니다. 이러한 일화 사건에는 특정 장소에서 겪은 감정적 경험들이 기억으로 저장됩니다. 이러한 일화 기억의 특성을 고려하면, 해마는 감정 정보를 처리할 가능성이 매우 높고, 실제로 중간 해마와 복측 해마는 편도체로부터 해부학적으로 직접 연결되어 있습니다. 또한 중간 해마는 배측 해마로부터 많은 공간 정보를 받아드린다고 알려져있습니다. 따라서 중간 해마는 장소의 위치와 그 장소에서 경험한 감정정보를 연합할 가능성이 높습니다. 하지만, 중간 해마의 이러한 장소-감정 연합 기억의 역할은 거의 알려지지 않았다. 그래서, 저는 중간 해매가 특정 공간에서 발생하는 사건의 가치를 저장하는데 중요하고, 배측 해마는 정확한 위치 정보를 표상하는데 중요하다는 가설을 세웠습니다. 이를 검증하기 위해 쥐의 배측과 중간 해마의 개별 뉴런을 동시에 리코딩하였으며, 선호도가 다른 먹이를 이용해 장소의 가치 정보를 변화시키는 실험을 진행했습니다. 이 학위 논문의 첫 파트에서는 쥐가 2차원 공간에서 자유롭게 돌아다닐 때의 배측부터 복측해마의 장소 세포가 어떻게 달라지는지 살펴보았습니다. 구체적으로, 중간해마보다 배측 해마에서 장소 선택적 활동이 더 강하게 나타났으며, 복측 해마에서는 장소 세포의 활동이 거의 관찰되지 않았습니다. 두번째 파트에서, 해마가 필요없는 간단한 과제에서 먹이의 가치가 바뀐 이후에, 배측과 복측 해마의 장소 세포의 공간 표상 변화를 살펴보았습니다. 주어진 공간에서 제공되던 맛있는 먹이가 맛없는 먹이로 바뀌고 나면, 중간 해마의 장소세포는 재빠르게 공간 표상을 재배열하게 됩니다. 하지만, 동일한 조작에서 배측 해마의 장소세포는 공간 표상을 일정하게 유지하였습니다. 마지막으로, 세번째 파트에서는 해마가 필요한 기억 과제에서 가치-의존적 공간 재배열을 추가적으로 알아보았습니다. T 모양의 미로에서 장소 선호 과제를 진행하는 동안, 중간 해마의 장소 세포는 맛있는 먹이가 나오는 공간을 집중적으로 표상하며, 이러한 집중된 표상은 맛있는 먹이의 위치가 바뀌어도 동일하게 관찰됩니다. 반면, 배측 해마 장소 세포의 공간 표상은 이러한 조작에 거의 영향을 받지 않습니다. 그리고 이 장소 선호 학습을 하는 동안, 배측 해마보다 중간 해마의 신경망 상태가 빠르게 변하는 모습을 보였습니다. 종합하자면, 위 결과들은 배측 해마와 복측 해마는 서로 다른 기능을 맡고 있다는 점을 보여줍니다. 즉, 배측 해마는 동물의 정확한 장소를 표상하는데 특화되어 있으며, 중간 해마는 장소와 그 가치 정보를 연합하는 역할을 맡고 있습니다. 이러한 발견은 중간 해마가 행동 선택과 밀접한 정보를 처리하며, 이러한 정보를 내측 전두엽을 통해 다른 뇌 영역과 소통하는 기능적으로 중요한 영역이라는 것을 시사합니다.It has long been postulated that the hippocampus is vital for memorizing autobiographical episodic events. Because an episodic event often entails memories for certain places associated with their emotional and motivational significance, it is promising that the hippocampus processes spatial information in conjunction with its associated valence. Among the hippocampal subregions (i.e., dorsal, intermediate, and ventral), the amygdala, which plays key roles in processing valence information, sends direct axonal projection to the intermediate and ventral hippocampus. Also, there are extensive recurrent collaterals and associational projections (presumably spatial information) from the dorsal hippocampus to the intermediate hippocampus. Thus, the intermediate hippocampus may integrate emotional/motivational information in association with locational information. However, it is largely unknown that how the intermediate hippocampus process value-associated spatial information processing. Therefore, I hypothesized that encoding the value of an event at a specific location takes priority in the intermediate hippocampus, compared to the dorsal hippocampus, whose priority resides in representing the precise location of an animal, presumably in the cognitive map. To test this hypothesis, I simultaneously recorded single units from the dorsal and intermediate hippocampus while rats performed a battery of tasks in which the level of motivational significance of a place was controlled by foods with different palatability. In this dissertation of Chapter 1, I examined the changes in spatial firing patterns along the dorsoventral axis while rats foraged in an open field maze. Specifically, spatially selective firing was more eminent in the dorsal than in the intermediate hippocampus, and spatial signals were hardly observed in the ventral hippocampus. In Chapter 2, after changes in reward value during non-mnemonic tasks, differential global remappings of place cells were found between the dorsal and intermediate hippocampus. When more-palatable reward (i.e., sunflower seeds) were replaced with less-palatable one (Cheerios) in a given location, place cells in the intermediate hippocampus remapped immediately. In contrast, place fields recorded from the dorsal hippocampus maintained their spatial representations stably in the same manipulation. In Chapter 3, value-dependent remappings were further investigated in hippocampal-dependent tasks. During the place-preference task in the T-maze, place fields obtained from the intermediate hippocampus accumulated near the arm associated with more-preferred rewards, and overrepresented patterns shifted toward opposite arm after the locations of more-preferred and less-preferred rewards were reversed. However, spatial representations of place cells in the dorsal hippocampus were rarely affected by such manipulation. And, during the acquisition of the place-preference task, the ensemble network state in the iHP changed faster than that in the dHP. Taken together, our results suggest that there are functional segregations between the dorsal and intermediate subregions of the hippocampus. That is, the dorsal hippocampus is specialized in representing the animal's precise locations in the environment, whereas the intermediate hippocampus takes part in the integration of spatial information and its motivational values. These findings imply that the intermediate hippocampus is a functionally significant hippocampal subregion through which critical action-related information (i.e., spatial information from the dorsal hippocampus and emotional/motivational information from the amygdala) is integrated and communicated to the rest of the brain via the medial prefrontal cortex.BACKGROUND AND HYPOTHESIS. 1 1.1 BACKGROUND 1 1.1.1 Episodic memory and hippocampus. 2 1.1.2 Introduction of the rodent hippocampal researches. 2 1.1.3 Single-cell recording from the rodent hippocampus 4 1.1.3.1 Basic firing properties of place cells 4 1.1.3.2 Spatial representation of place cells. 5 1.1.3.3 Non-spatial representation of place cells 6 1.1.3.4 Value representation in the hippocampus. 6 1.1.4 Difference in anatomical connectivities along the dorsoventral axis. 7 1.1.5 Difference in functions along the dorsoventral axis. 10 1.2 HYPOTHESIS 12 CHAPTER 1. 13 2.1 Introduction. 14 2.2 Methods. 15 2.2.1 Subjects. 15 2.2.2 Maze familiarization and pre-training 15 2.2.3 Surgical implantation of the hyperdrive. 15 2.2.4 Electrophysiological recording procedures 16 2.2.5 Histological verification of tetrode tracks 16 2.2.6 Unit isolation 16 2.2.7 Basic firing properties 17 2.2.8 Definition of place fields 17 2.2.9 Theta-modulation and burst index 18 2.3 Results. 19 2.3.1 Anatomical boundary between dorsal, intermediate and ventral hippocampus. 19 2.3.2 Comparison of basic firing properties between hippocampal subregions 20 2.3.3 Degree of spatially selective firing patterns sharply decreased at the border between dHP and iHP. 23 2.4 Discussion. 28 CHAPTER 2. 30 3.1 Introduction. 31 3.2 Methods. 32 3.2.1 Behavior paradigm. 32 3.2.1.1 Food preference test. 32 3.2.1.2 Spatial alternation task. 33 3.2.2 Post-surgical training and main recording 33 3.2.3 Constructing the population rate map. 34 3.2.4 Categorization of place field responses 34 3.2.5 Reward-type coding analysis. 34 3.2.6 Speed-correlated cells. 35 3.3 Results. 35 3.3.1 Rat's food preference for sunflower seeds and Froot Loops over Cheerios. 35 3.3.2 Place cells in iHP, but not dHP, encode changes in motivational values of place via global remapping. 36 3.3.3 Identity of reward type is coded in the iHP by rate remapping, but not in the dHP. 49 3.3.4 Neural activity of single cells of vHP in response to motivational value changes. 51 3.4.5 Immediate coding of the changes in motivational values in iHP, but not in dHP. 53 3.4 Discussion 60 CHAPTER 3. 64 4.1 Introduction 65 4.2 Methods 65 4.2.1 Behavior paradigm. 65 4.2.2 Principal component analysis for neural ensemble state 66 4.2.3 Synchronization of spiking activity. 67 4.3 Results. 68 4.3.1 Overrepresentation of the motivationally significant place by the place cells in iHP, but not in dHP 68 4.2.2 Rapid changes of the ensemble network changes in iHP, compared to those in dHP. 77 4.2.3 Place cells in the dHP and iHP co-fire more strongly during a mnemonic task than non-mnemonic tasks. 79 4.4 Discussion 82 GENERAL DISCUSSION. 87 5.1 Conclusion 88 5.2 Limitation 88 5.3 Implication and perspective. 89 5.4 Future research direction. 93 BIBLIOGRAPHY 94 ACKNOWLEDGMENT 111 국문초록 112박