A grounded theory of abstraction in artificial intelligence

In artificial intelligence, abstraction is commonly used to account for the use of various levels of details in a given representation language or the ability to change from one level to another while preserving useful properties. Abstraction has been mainly studied in problem solving, theorem proving, knowledge representation (in particular for spatial and temporal reasoning) and machine learning. In such contexts, abstraction is defined as a mapping between formalisms that reduces the computational complexity of the task at stake. By analysing the notion of abstraction from an information quantity point of view, we pinpoint the differences and the complementary role of reformulation and abstraction in any representation change. We contribute to extending the existing semantic theories of abstraction to be grounded on perception, where the notion of information quantity is easier to characterize formally. In the author's view, abstraction is best represented using abstraction operators, as they provide semantics for classifying different abstractions and support the automation of representation changes. The usefulness of a grounded theory of abstraction in the cartography domain is illustrated. Finally, the importance of explicitly representing abstraction for designing more autonomous and adaptive systems is discussed.     

人工智能应用于青光眼临床筛查及卫生效益分析

目的:探讨应用人工智能系统对青光眼患者的检出率、筛查的时间成本和经济成本,总结分析其应用于青光眼筛查的优越性与可行性。方法:回顾性研究。收集2019-2020年使用腾讯觅影眼底照片辅助诊断系统进行青光眼筛查的53700例受检者,2012-2018年使用北京同仁医院眼科研究所远程眼科会诊中心的493221例患者,邯郸眼病研究中接受基础眼病筛查的6716名受检者资料。评价人工智能筛查系统对青光眼的检出率,并采用Pearson x~2检验,比较人工智能筛查系统与远程阅片平台、基础眼病筛查对于青光眼检出率的差异,并对三种筛查方式的时间成本和经济成本进行比较分析。结果:临床应用表明,其成本效益的可靠数据及主要指标:检出率、x~2值、时间成本、经济成本等效益显著。人工智能筛查系统对青光眼的检出率为6.56%,远程眼科会诊平台的检出率为2.78%,邯郸眼病研究的检出率为1.70%,人工智能筛查系统与另外两种模式对青光眼诊断检出率的差异有统计学意义(P0.05)。而且使用人工智能筛查系统的患者其医疗时间成本和经济成本花费较低。结论:可行性分析表明,相较于远程眼科会诊平台和基础眼病筛查,人工智能筛查系统对青光眼的检出率较高,同时患者的时间成本和经济成本都得到了大幅度降低,这为青光眼筛查提供了一种有效而快捷的方式,值得临床推广。     

基于深度学习的作物基因组学和遗传改良

随着世界人口的不断增长、食物需求量的不断增加,以及气候的不断变化,如何提高农作物产量已成为人类面临的一个巨大挑战。传统设计育种耗时长、效率低,已经不能满足新时代的育种需求。随着基因型和表型数据成本的不断降低,以及各种组学数据的爆炸式增长,人工智能技术作为能够在大数据中高效率挖掘信息的工具,在生物学领域受到了广泛关注。人工智能指导的设计育种将大大加快育种的效率,给育种带来革命性的变化。介绍了人工智能特别是深度学习在作物基因组学和遗传改良中的应用,并进行了总结与展望,以期为智能设计育种提供新的思路。     

Enzymes and theoretical biology: sketch of an informational perspective of the cell.

In the theoretical scenarios of biology, new insights can be gained by the introduction of information-processing and artificial intelligence concepts, helping to organize the explanation of the many intra- and inter-cellular phenomena that molecular biology is accumulating. Enzymes contain some of the immediate clues; the whole informational processing of prokaryotic cells is another central subject of search. Additionally, prolonging the informational perspective of the cell, a significant parallel can be drawn between informational processes in biological, social and artificial intelligence systems. A more tangible definition of biological complexity and biological intelligence emerges.     

Imaging and artificial intelligence for progression of age-related macular degeneration

Age-related macular degeneration (AMD) is a leading cause of severe vision loss. With our aging population, it may affect 288 million people globally by the year 2040. AMD progresses from an early and intermediate dry form to an advanced one, which manifests as choroidal neovascularization and geographic atrophy. Conversion to AMD-related exudation is known as progression to neovascular AMD, and presence of geographic atrophy is known as progression to advanced dry AMD. AMD progression predictions could enable timely monitoring, earlier detection and treatment, improving vision outcomes. Machine learning approaches, a subset of artificial intelligence applications, applied on imaging data are showing promising results in predicting progression. Extracted biomarkers, specifically from optical coherence tomography scans, are informative in predicting progression events. The purpose of this mini review is to provide an overview about current machine learning applications in artificial intelligence for predicting AMD progression, and describe the various methods, data-input types, and imaging modalities used to identify high-risk patients. With advances in computational capabilities, artificial intelligence applications are likely to transform patient care and management in AMD. External validation studies that improve generalizability to populations and devices, as well as evaluating systems in real-world clinical settings are needed to improve the clinical translations of artificial intelligence AMD applications.     

Synthesizing cellular intelligence and artificial intelligence for bioprocesses

Microbial processes operated under realistic conditions are difficult to describe by mechanistic models, thereby limiting their optimization and control. Responses of living cells to their environment suggest that they possess some "innate intelligence". Such responses have been modeled by a cybernetic approach. Furthermore, the overall behavior of a bioreactor containing a population of cells may be described and controlled through artificial intelligence methods. Therefore, it seems logical to combine cybernetic models with artificial intelligence to evolve an integrated intelligence-based strategy that is physiologically more faithful than the current approaches. This possibility is discussed, together with practical considerations favoring a hybrid approach that includes some mathematical modeling.     

组学时代下机器学习方法在临床决策支持中的应用

随着组学技术的不断发展,对于不同层次和类型的生物数据的获取方法日益成熟。在疾病诊治过程中会产生大量数据,通过机器学习等人工智能方法解析复杂、多维、多尺度的疾病大数据,构建临床决策支持工具,辅助医生寻找快速且有效的疾病诊疗方案是非常必要的。在此过程中,机器学习等人工智能方法的选择显得尤为重要。基于此,本文首先从类型和算法角度对临床决策支持领域中常用的机器学习等方法进行简要综述,分别介绍了支持向量机、逻辑回归、聚类算法、Bagging、随机森林和深度学习,对机器学习等方法在临床决策支持中的应用做了相应总结和分类,并对它们的优势和不足分别进行讨论和阐述,为临床决策支持中机器学习等人工智能方法的选择提供有效参考。     

Distinct neurocognitive strategies for comprehensions of human and artificial intelligence

Although humans have inevitably interacted with both human and artificial intelligence in real life situations, it is unknown whether the human brain engages homologous neurocognitive strategies to cope with both forms of intelligence. To investigate this, we scanned subjects, using functional MRI, while they inferred the reasoning processes conducted by human agents or by computers. We found that the inference of reasoning processes conducted by human agents but not by computers induced increased activity in the precuneus but decreased activity in the ventral medial prefrontal cortex and enhanced functional connectivity between the two brain areas. The findings provide evidence for distinct neurocognitive strategies of taking others' perspective and inhibiting the process referenced to the self that are specific to the comprehension of human intelligence.     

Integrated Artificial Intelligence Approaches for Disease Diagnostics

Mechanocomputational techniques in conjunction with artificial intelligence (AI) are revolutionizing the interpretations of the crucial information from the medical data and converting it into optimized and organized information for diagnostics. It is possible due to valuable perfection in artificial intelligence, computer aided diagnostics, virtual assistant, robotic surgery, augmented reality and genome editing (based on AI) technologies. Such techniques are serving as the products for diagnosing emerging microbial or non microbial diseases. This article represents a combinatory approach of using such approaches and providing therapeutic solutions towards utilizing these techniques in disease diagnostics.     

Current Problems In The Psychology Of Understanding And The Creation Of "Understanding Systems"

In the Resolution of the CPSU Central Committee entitled "On the journal Kommunist," the creation of "artificial intelligence" is pinpointed as one of the most important sociophilosophical and methodological problems of scientific and technical progress ([Resolution…]. P. 5). The psychological aspects of "artificial intelligence" have been studied fruitfully for a long time in our domestic psychology (see, for example, [Artificial intelligence…]), but the steady refinement of computer technology imposes upon scientists the task of deeper study of the interaction between man and computers.     

Views on artificial intelligence (AI) assisted clinical trials

It is of interest to document the views of medical professionals on the application of artificial intelligence (using known data for the prediction of unknown events) in clinical trials using a web survery with a structured questionnaire from 377 subjects. The questionnaire contained 17 statements which were categorised into awareness (1,2 statements), perception (3-10 statements) and opinion (11-17 statements). The data obtained was compared between the subjects using two tailed Fisher''s exact test with p-value <0.05 for data significance analysis. Data shows that majority of professionals have possitive views on the application of artificial intelligence in clinical trials. This will accelarrate the drug evaluation process. However, the use of emerging tools such as AI will not replace human subjects in this context.     

A user-friendly biological workstation

Learning methods developed by artificial intelligence research teams are very efficient for biological sequences analysis but they need running on large computers accessed by terminals. These computers are interfaced with standard displays involving long and unpleasant alphanumerical data handling. The "biological work station" is a personal computer with a color graphic screen providing a user-friendly interface for the artificial intelligence learning programs running on large computers. It provides to biologist a graphical convenient tool for sequence analysis built with efficient man-machine communication methods such as multiwindows, icons and mouse selection. It allows the biologist to edit and display sequences in an efficient and natural way, showing off directly on color pictures the data and the results of learning programs.     

Application of Hierarchical Dissociated Neural Network in Closed-Loop Hybrid System Integrating Biological and Mechanical Intelligence

Neural networks are considered the origin of intelligence in organisms. In this paper, a new design of an intelligent system merging biological intelligence with artificial intelligence was created. It was based on a neural controller bidirectionally connected to an actual mobile robot to implement a novel vehicle. Two types of experimental preparations were utilized as the neural controller including ‘random’ and ‘4Q’ (cultured neurons artificially divided into four interconnected parts) neural network. Compared to the random cultures, the ‘4Q’ cultures presented absolutely different activities, and the robot controlled by the ‘4Q’ network presented better capabilities in search tasks. Our results showed that neural cultures could be successfully employed to control an artificial agent; the robot performed better and better with the stimulus because of the short-term plasticity. A new framework is provided to investigate the bidirectional biological-artificial interface and develop new strategies for a future intelligent system using these simplified model systems.     

A diagnostic expert system

The introduction of new technologies in the field of electronics has influenced the development of technical equipment over the last few years. The progressive miniaturization of integrated circuits makes possible an expansion of the spectrum of functions offered by this equipment. This also applies to medical technology. These more complex units call for new methods of fault detection and diagnosis. In addition to analytical redundancy, tools developed by the artificial intelligence research community, such as expert systems, are becoming more and more important for fault diagnosis. On the basis of a realized diagnosis expert system the possibilities as well as the limits of such system are discussed. Also, possible future developments of artificial intelligence, like machine learning, are considered.     

Artificial intelligence in the development and design of biochemical processes

This article outlines the use of artificial intelligence in three areas of biotechnology: (a) exploration of new production routes for various bioproducts; (b) design of mammalian cell biofermentors; and (c) synthesis of downstream processing schemes for the separation and purification of proteins. Until recently, all of these areas have been ‘knowledge intensive’, driven by the incisive expertise of scientists and engineers, and quite resistant to analytic and rigorous mathematical formulations and solutions. Here we describe the ‘prototype intelligent system’ used in the above three areas, and attempt simple projections on the use of artificial intelligence in biotechnology.     

基于精神影像和人工智能的抑郁症客观生物学标志物研究进展

抑郁症是当今社会上造成首要危害且病因和病理机制最为复杂的精神疾病之一,寻找抑郁症的客观生物学标志物一直是精神医学研究和临床实践的重点和难点,而结合人工智能技术的磁共振影像(magnetic resonance imaging,MRI)技术被认为是目前抑郁症等精神疾病中最有可能率先取得突破进展的客观生物学标志物.然而,当前基于精神影像学的潜在抑郁症客观生物学标志物还未得到一致结论 .本文从精神影像学和以机器学习(machine learning,ML)与深度学习(deep learning, DL)等为代表的人工智能技术相结合的角度,首次从疾病诊断、预防和治疗等三大临床实践环节对抑郁症辅助诊疗的相关研究进行归纳分析,我们发现:a.具有诊断价值的脑区主要集中在楔前叶、扣带回、顶下缘角回、脑岛、丘脑以及海马等;b.具有预防价值的脑区主要集中在楔前叶、中央后回、背外侧前额叶、眶额叶、颞中回等;c.具有预测治疗反应价值的脑区主要集中在楔前叶、扣带回、顶下缘角回、额中回、枕中回、枕下回、舌回等.未来的研究可以通过多中心协作和数据变换提高样本量,同时将多元化的非影像学数据应用于数据挖掘,这将有利于提高人工智能模型的辅助分类能力,为探寻抑郁症的精神影像学客观生物学标志物及其临床应用提供科学证据和参考依据.     

A personal retrospective on the second half of the 20th century

During the Second World War scientists and engineers were involved as never before in all technical phases of the war effort. It included intelligence, logistics and large scale automated computation. Much of this required team work which led to the adoption of interdisciplinary perspectives and found expression after the war in new fields of enquiry such as cybernetics, biophysics and artificial intelligence.