Systematic AI Approach for AGI: Addressing Alignment, Energy, and AGI Grand Challenges

AI faces a trifecta of grand challenges the Energy Wall, the Alignment Problem and the Leap from Narrow AI to AGI. Contemporary AI solutions consume unsustainable amounts of energy during model training and daily operations.Making things worse, the amount of computation required to train each new AI model has been doubling every 2 months since 2020, directly translating to increases in energy consumption.The leap from AI to AGI requires multiple functional subsystems operating in a balanced manner, which requires a system architecture. However, the current approach to artificial intelligence lacks system design; even though system characteristics play a key role in the human brain from the way it processes information to how it makes decisions. Similarly, current alignment and AI ethics approaches largely ignore system design, yet studies show that the brains system architecture plays a critical role in healthy moral decisions.In this paper, we argue that system design is critically important in overcoming all three grand challenges. We posit that system design is the missing piece in overcoming the grand challenges.We present a Systematic AI Approach for AGI that utilizes system design principles for AGI, while providing ways to overcome the energy wall and the alignment challenges.Comment: International Journal on Semantic Computing (2024) Categories: Artificial Intelligence; AI; Artificial General Intelligence; AGI; System Design; System Architectur

A visual approach for probing learned models

Deep learning models are complex neural networks that are able to accomplish a large range of tasks effectively, including machine translation, speech recognition, and image classification. However, recent research has shown that transformations of input data can deteriorate the performance of these models dramatically. This effect is especially startling with adversarial perturbations that aim to fool a deep neural network while being barely perceptible. The complexity of these networks makes it hard to understand where and why they fail. Previous work has attempted to provide insights into the inner workings of these models in various different ways. A survey of these existing systems is conducted and concludes that they have failed to provide an integrated approach for probing how specific changes to the input data are represented within a trained model. This thesis introduces Advis, a visualization system for analyzing the impact of input data transformations on a model's performance and on its internal representations. For performance analysis, it displays various metrics of prediction quality and robustness using lists and a radar chart. An interactive confusion matrix supports pattern detection and input image selection. Insights into the impact of data distortions on internal representations can be gained by the combination of a color-coded computation graph and detailed activation visualizations. The system is based on a highly flexible architecture that enables users to adapt it to the specific requirements of their task. Three use cases demonstrate the usefulness of the system for probing and comparing the impact of input transformations on performance metrics and internal representations of various networks. The insights gained through this system show that interactive visual approaches for understanding the effect of input perturbations on deep learning models are an area worth further investigation

Hardware and Software Optimizations for Accelerating Deep Neural Networks: Survey of Current Trends, Challenges, and the Road Ahead

Currently, Machine Learning (ML) is becoming ubiquitous in everyday life. Deep Learning (DL) is already present in many applications ranging from computer vision for medicine to autonomous driving of modern cars as well as other sectors in security, healthcare, and finance. However, to achieve impressive performance, these algorithms employ very deep networks, requiring a significant computational power, both during the training and inference time. A single inference of a DL model may require billions of multiply-and-accumulated operations, making the DL extremely compute-and energy-hungry. In a scenario where several sophisticated algorithms need to be executed with limited energy and low latency, the need for cost-effective hardware platforms capable of implementing energy-efficient DL execution arises. This paper first introduces the key properties of two brain-inspired models like Deep Neural Network (DNN), and Spiking Neural Network (SNN), and then analyzes techniques to produce efficient and high-performance designs. This work summarizes and compares the works for four leading platforms for the execution of algorithms such as CPU, GPU, FPGA and ASIC describing the main solutions of the state-of-the-art, giving much prominence to the last two solutions since they offer greater design flexibility and bear the potential of high energy-efficiency, especially for the inference process. In addition to hardware solutions, this paper discusses some of the important security issues that these DNN and SNN models may have during their execution, and offers a comprehensive section on benchmarking, explaining how to assess the quality of different networks and hardware systems designed for them

Mobile Wound Assessment and 3D Modeling from a Single Image

The prevalence of camera-enabled mobile phones have made mobile wound assessment a viable treatment option for millions of previously difficult to reach patients. We have designed a complete mobile wound assessment platform to ameliorate the many challenges related to chronic wound care. Chronic wounds and infections are the most severe, costly and fatal types of wounds, placing them at the center of mobile wound assessment. Wound physicians assess thousands of single-view wound images from all over the world, and it may be difficult to determine the location of the wound on the body, for example, if the wound is taken at close range. In our solution, end-users capture an image of the wound by taking a picture with their mobile camera. The wound image is segmented and classified using modern convolution neural networks, and is stored securely in the cloud for remote tracking. We use an interactive semi-automated approach to allow users to specify the location of the wound on the body. To accomplish this we have created, to the best our knowledge, the first 3D human surface anatomy labeling system, based off the current NYU and Anatomy Mapper labeling systems. To interactively view wounds in 3D, we have presented an efficient projective texture mapping algorithm for texturing wounds onto a 3D human anatomy model. In so doing, we have demonstrated an approach to 3D wound reconstruction that works even for a single wound image

Lip syncing method for realistic expressive three-dimensional face model

Lip synchronization of 3D face model is now being used in a multitude of important fields. It brings a more human and dramatic reality to computer games, films and interactive multimedia, and is growing in use and importance. High level realism can be used in demanding applications such as computer games and cinema. Authoring lip syncing with complex and subtle expressions is still difficult and fraught with problems in terms of realism. Thus, this study proposes a lip syncing method of realistic expressive 3D face model. Animated lips require a 3D face model capable of representing the movement of face muscles during speech and a method to produce the correct lip shape at the correct time. The 3D face model is designed based on MPEG-4 facial animation standard to support lip syncing that is aligned with input audio file. It deforms using Raised Cosine Deformation function that is grafted onto the input facial geometry. This study also proposes a method to animate the 3D face model over time to create animated lip syncing using a canonical set of visemes for all pairwise combinations of a reduced phoneme set called ProPhone. Finally, this study integrates emotions by considering both Ekman model and Plutchik’s wheel with emotive eye movements by implementing Emotional Eye Movements Markup Language to produce realistic 3D face model. The experimental results show that the proposed model can generate visually satisfactory animations with Mean Square Error of 0.0020 for neutral, 0.0024 for happy expression, 0.0020 for angry expression, 0.0030 for fear expression, 0.0026 for surprise expression, 0.0010 for disgust expression, and 0.0030 for sad expression

Smart Technologies for Precision Assembly

This open access book constitutes the refereed post-conference proceedings of the 9th IFIP WG 5.5 International Precision Assembly Seminar, IPAS 2020, held virtually in December 2020. The 16 revised full papers and 10 revised short papers presented together with 1 keynote paper were carefully reviewed and selected from numerous submissions. The papers address topics such as assembly design and planning; assembly operations; assembly cells and systems; human centred assembly; and assistance methods in assembly

Designing concept mapping models with neural architecture search

Artificial neural networks are widely used in all sorts of applications, many of which directly impact the public’s lives. For all of their qualities, these systems have a major flaw: their black-box nature impedes us from interpreting their behavior, which harms public trust and their overall applicability. Explainable AI is a field that focuses on developing interpretable AI systems. However, the current solutions for black-box models do not provide fully accurate or easy-to-understand explanations. Concept mapping, proposed by Sousa Ribeiro and Leite [60], promises to do both. In this method, classifiers - dubbed mapping networks - are used to map a black-box model’s sub-symbolic internal representations into symbolic, human-understandable ontology concepts, opening the way to explainability. However, little investigation was done in the original work on consistently designing quality architectures for concept mapping. In this dissertation, we fill the existing knowledge gaps by conducting extensive empirical evaluation of architectures for concept mapping. We create a custom-made image classification dataset designed to facilitate observing how the black-box model’s task affects concept mapping. Further, we employ a custom adaption of differentiable architecture search (DARTS [33]) to automatically find good architectures. Our adaption of DARTS for concept mapping proves capable of consistently learning exemplary architectures and shows more resilience to context changes than manual trial-and-error.A rede neuronal artificial tem tido vasto uso em todo o tipo de aplicações, muitas das quais têm um impacto direto na vida pública. Apesar de todas as suas qualidades, estes sistemas têm uma fraqueza crucial: a sua natureza opaca impede-nos de interpretar o seu comportamento, algo que tem um impacto negativo na sua aceitação publica e aplicabilidade. Explainable AI é uma área que se foca em desenvolver sistemas de inteligência artificial interpretáveis, mas muitas das soluções atuais para modelos opacos não providenciam justificações acertadas ou fáceis de entender. Mapeamento de conceitos, proposto por Sousa Ribeiro e Leite, promete ambos. Neste método, classificadores adicionais - chamados de redes mapeadoras - são criados para mapear as representações internas subsimbólicas de um modelo em conceitos pertencentes a uma ontologia: simbólicos e passíveis de compreensão humana. Todavia, pouca investigação foi feita no trabalho original sobre as arquitecturas destas peças instrumentais, as redes mapeadoras. Nesta dissertação, preenchemos as atuais brechas de conhecimento realizando extensos testes empíricos sobre arquitecturas para mapeamento de conceitos. Usamos um dataset de classificação de imagens, gerado por nós especificamente para facilitar a observação de como o mapeamento de conceitos é afetado pela tarefa do modelo original. Para além disso, usamos uma versão de procura de arquiteturas diferencial (DARTS [33]), adaptada para aprender automaticamente boas arquitecturas mapeadoras. Essa nossa adaptação prova ser capaz de consistentemente encontrar arquitecturas competentes, e demonstra uma maior resiliência a mudanças de contexto do que o método original de tentativa e erro