Artificial Intelligence Vision Research Articles

Advancements and challenges in AI-driven creative translation: A comprehensive analysis

Abstract. This article provides an in-depth exploration of the use of artificial intelligence (AI) in creative translation, highlighting the advancements in machine learning algorithms and the challenges of cultural and emotional congruence. It examines several key aspects: the efficacy of sequence-to-sequence and transformer models in enhancing translation accuracy, the role of AI in cultural contextualization, and the integration of emotional intelligence to handle subtleties in translations. Comparative case studies illustrate AI's performance against human translators in handling culturally dense texts, while adaptive algorithms demonstrate the customization of AI systems for specific cultural contexts. Furthermore, the article delves into the quantitative methods used to evaluate AI translation accuracy, such as BLEU and METEOR scores, and discusses innovative techniques for improving AI translation quality through reinforced learning and context-aware translations. The potential future directions of AI in creative translation, including the use of augmented reality and large-scale language models, are also discussed, presenting a vision of AI's evolving role in breaking linguistic and cultural barriers.

The Effectiveness of using Artificial Intelligence for Building Professional Competencies of Future Specialists in Art Majors in Higher Education Institutions

The research aims to study the impact of the use of artificial intelligence (AI) tools in higher education institutions (HEIs) on building professional competencies of future art specialists. The research employed quantitative and qualitative methods (in particular, modeling methods, pedagogical experiments, and survey of respondents to assess the impact of AI tools on building professional competencies). The author’s definition of the concept of “professional competencies of art specialists” is proposed. Targeted tools were selected and used for building components of professional competencies. For example, VocalAnalysis AI tools were used to form the perceptual component — for students majoring in Musical Art; Art Vision AI — for students majoring in Fine Arts; ChoreoVision AI — for students majoring in Choreography. The results of the study show that students rated their level of ability to use AI as higher than medium. The questionnaire designed to study the impact of the use of AI on building professional competencies of future specialists in art majors, demonstrated a high level of agreement between the assessment of the impact of the use of AI tools on the formation of various components of professional competencies. Further research can be aimed at the development and testing of an algorithm for objective expert evaluation of specific AI tools for the implementation of art projects by students of the specified art majors.

Multifunctional human visual pathway-replicated hardware based on 2D materials

Artificial visual system empowered by 2D materials-based hardware simulates the functionalities of the human visual system, leading the forefront of artificial intelligence vision. However, retina-mimicked hardware that has not yet fully emulated the neural circuits of visual pathways is restricted from realizing more complex and special functions. In this work, we proposed a human visual pathway-replicated hardware that consists of crossbar arrays with split floating gate 2D tungsten diselenide (WSe2) unit devices that simulate the retina and visual cortex, and related connective peripheral circuits that replicate connectomics between the retina and visual cortex. This hardware experimentally displays advanced multi-functions of red–green color-blindness processing, low-power shape recognition, and self-driven motion tracking, promoting the development of machine vision, driverless technology, brain–computer interfaces, and intelligent robotics.

Evaluation of range of motion using vision artificial intelligence (AI) in musculoskeletal medicine

Evaluation of range of motion using vision artificial intelligence (AI) in musculoskeletal medicine

Artificial intelligence vision technology application in sustainability evaluation of solar-driven distillation device

The process of transforming brackish water into freshwater utilizing solar thermal energy is referred to as solar-driven distillation device, or solar still. Such device without fossil fuel provides significant environmental and health benefits by reducing air pollutants and remedying brackish water. The yield of purified water from conventional solar stills remains insufficient, prompting the necessity for research to enhance it by understanding the coupling effect between steam flow, heat transfer, and mass transfer. As such, computer-aided brackish water treatment comparison of the hemispherical solar still and other multi-slope solar stills is conducted in this paper, and an automatic steam&heat flow detection algorithm is developed to solve the problem of difficult data acquisition for gas-liquid transport processes. Firstly, double slope, four slope, and hemispherical solar stills are exposed to the sun in outdoor experiments, therefore the solar thermal performance of each still is analyzed through pairwise comparisons. Secondly, a large amount of experimental image data is input into neural network for training, and by continuously adjusting network parameters, the network can accurately recognize different types of images. Finally, the image to be recognized is input into the trained neural network, which outputs the category labels of the image to achieve automatic image recognition. Based on the data above, the best structure of solar-driven device is hemispherical structure, due to that the hemispherical solar still possesses the best performance in terms of distilled water yield, energy efficiency, exergy efficiency and energy payback.

Multi‐Wavelength Optoelectronic Synaptic Transistors Based on Transition Metal Telluride‐Sulfide Heterostructures

AbstractNeuromorphic visual systems based on optogenetic techniques have colossal potential for in‐memory computing with prospects of developing artificial intelligence vision systems. However, conventional transistor architectures face formidable challenges in efficient signal processing owing to limitations in the intrinsic properties of active channel materials. In this work, a novel transition metal telluride‐sulfide hybrid heterojunction‐based optoelectronic synaptic phototransistor is proposed, in which UV–vis responsive zinc oxide encapsulated few‐layer tungsten disulfide channel is decorated with near‐infrared sensitive 0D cobalt ditelluride (CoTe2) nanocrystals (NCs), eliciting the ability to sense, store, and process optical signals across a broad range of the electromagnetic spectrum. This meticulously designed three‐layered heterostructure, based on their interfacial band alignments, enables high photoresponsivity up to ≈2.6 × 103 A W−1 at a back‐gate bias of 20 V, leading to the brain‐inspired synaptic applications with an average power consumption as low as 75 pJ for each training process. The device exhibits excitatory postsynaptic current, paired‐pulse facilitation with an index above 150%, as well as light‐modulated synaptic plasticity by mimicking biological synapses, which mainly originate from trapped holes in Co‐vacancy mediated surface defect states of CoTe2 NCs. Hence, this 2D material‐based hybrid phototransistor appears to be a promising candidate for energy‐efficient next‐generation brain‐inspired neuromorphic vision systems.

Study on Class Management Change and Teacher Orientation under the Vision of Artificial Intelligence

With the rise of artificial intelligence (AI), class management is experiencing unprecedented changes, but it is also facing certain risks and tests. It is of great practical significance to clarify the class management changes and teacher orientation under the vision of AI. Since the application of AI in the field of education, class management has realized the burden reduction and efficiency increase under the ability of technology, and has changed from the traditional mode of standardization, one-sided and unitization to a personalized, systematic and diversified intelligent mode, which provides great convenience for the management of class teachers. However, at the same time, the rise of AI has also caused a series of dilemmas such as the disclosure of students’ privacy and the challenge of the main value, which has put forward higher requirements for teachers’ literacy, positioning and management. Based on this, this paper puts forward the orientation of teachers in class management under the vision of AI. Teachers should improve their intelligent application literacy and boost the level of class management. Teachers should reasonably reflect on intelligent technology and grasp the applicable management situation. Teachers should fill the gaps in intelligent management and pay attention to humanistic emotional assistance to promote the positive integration of AI and class management.

Exploring the Potentials of Large Language Models in Vascular and Interventional Radiology: Opportunities and Challenges

AbstractThe increasing integration of artificial intelligence (AI) in healthcare, particularly in vascular and interventional radiology (VIR), has opened avenues for enhanced efficiency and precision. This narrative review delves into the potential applications of large language models (LLMs) in VIR, with a focus on Chat Generative Pre-Trained Transformer (ChatGPT) and similar models. LLMs, designed for natural language processing, exhibit promising capabilities in clinical decision-making, workflow optimization, education, and patient-centered care. The discussion highlights LLMs' ability to analyze extensive medical literature, aiding radiologists in making informed decisions. Moreover, their role in improving clinical workflow, automating report generation, and intelligent patient scheduling is explored. This article also examines LLMs' impact on VIR education, presenting them as valuable tools for trainees. Additionally, the integration of LLMs into patient education processes is examined, highlighting their potential to enhance patient-centered care through simplified and accurate medical information dissemination. Despite these potentials, this paper discusses challenges and ethical considerations, including AI over-reliance, potential misinformation, and biases. The scarcity of comprehensive VIR datasets and the need for ongoing monitoring and interdisciplinary collaboration are also emphasized. Advocating for a balanced approach, the combination of LLMs with computer vision AI models addresses the inherently visual nature of VIR. Overall, while the widespread implementation of LLMs in VIR may be premature, their potential to improve various aspects of the discipline is undeniable. Recognizing challenges and ethical considerations, fostering collaboration, and adhering to ethical standards are essential for unlocking the full potential of LLMs in VIR, ushering in a new era of healthcare delivery and innovation.

ACM International Conference on Information and Knowledge Management October 21--25, 2024 at Boise, Idaho (USA)

The ACM International Conference on Information and Knowledge Management (CIKM) provides a unique venue where both industry and academia can share and discuss cutting-edge research in various fields such as artificial intelligence, search and discovery, data mining, and database systems, all within a single conference. CIKM stands out as an ideal event to showcase technologies and insights that contribute to the realization of the big data and artificial intelligence vision for the future. CIKM 2024 will be held in Boise, Idaho (USA) and will bring together the major international research groups working on information and knowledge management, along with many of the world's leading companies active in e-commerce and other adjacent domains. Proceedings will be published by ACM as part of the ACM Digital Library.

Application of AI intelligent vision detection technology using deep learning algorithm

This study aims to design efficient and reliable artificial intelligence vision detection models to improve detection efficiency and accuracy. The study filters defect-free images by image preprocessing and region of interest detection techniques. AlexNet network is enhanced by introducing attention mechanism modules, deep separable convolutions, and more to effectively boost the network's feature extraction capacity. An area convolutional neural network is developed to rapidly identify and locate defects on steel plate surfaces, utilizing an enhanced AlexNet network for feature extraction. Results demonstrated that the algorithm attained an average detection rate of 98 % and can identify defects in a minimal time of only 0.0011 seconds. For the detection of six types of steel plate defects, the average accuracy of the optimized fast regional convolutional neural network reached more than 0.9, especially for the detection of small-size defects with excellent performance. This improved AlexNet network has a great advantage in F1 value. The conclusion of the study shows that the designed artificial intelligence vision detection model has high detection accuracy, speed, and performance stability in steel plate surface defect detection and has a wide range of application prospects.

A Review of AI-Based Cyber-Attack Detection and Mitigation in Microgrids

In this paper, the application and future vision of Artificial Intelligence (AI)-based techniques in microgrids are presented from a cyber-security perspective of physical devices and communication networks. The vulnerabilities of microgrids are investigated under a variety of cyber-attacks targeting sensor measurements, control signals, and information sharing. With the inclusion of communication networks and smart metering devices, the attack surface has increased in microgrids, making them vulnerable to various cyber-attacks. The negative impact of such attacks may render the microgrids out-of-service, and the attacks may propagate throughout the network due to the absence of efficient mitigation approaches. AI-based techniques are being employed to tackle such data-driven cyber-attacks due to their exceptional pattern recognition and learning capabilities. AI-based methods for cyber-attack detection and mitigation that address the cyber-attacks in microgrids are summarized. A case study is presented showing the performance of AI-based cyber-attack mitigation in a distributed cooperative control-based AC microgrid. Finally, future potential research directions are provided that include the application of transfer learning and explainable AI techniques to increase the trust of AI-based models in the microgrid domain.

Charge Couple Device (CCD) Photoelectric Signal Data Acquisition and Its Application in the Machine Vision of Artificial Intelligence

The development of Charge Couple Device (CCD) technology is particularly rapid in the fields of image sensors and non-contact measurement. In this study, a data acquisition device applied to CCD photoelectric detection system is designed. Among them, the design of the Differential Amplification (DA) module, Analog-to-Digital Converter (ADC) module, First In First Out (FIFO) cache module, and Complex Programmable Logic Device (CPLD) module in this device are emphasized. The ADC circuit in the ADC module converts two 4 MHz analog photoelectric signals generated by the CCD sensor at a frequency of 8 MHz, and then outputs 12-bit digital signals. The collected photoelectric signal is used to detect the damage to the surface of ancient buildings with the machine vision technology of artificial intelligence (AI). In the test, the DA circuit can adjust the voltage range of two photoelectric analog signals output by CCD to a predetermined range (1.5 V∼2.0 V). In the ADC circuit test, there is no data in the FIFO when there is no input conversion, and the converted data will be stored in the internal FIFO during the conversion clock period. Based on machine vision technology, surface damage types of ancient buildings are defined, namely spalling, cracks, and disruption, and surface image samples are generated from collected signals. The samples are trained using the convolutional neural network, and the classifier is generated. The test reveals that the designed photoelectric signal acquisition device and AI machine vision technology can accurately classify the surface damage of ancient buildings.

Digital Regeneration and Database Construction of Hunan Embroidery Needlework under the Perspective of Artificial Intelligence

Abstract In order to explore the development path of Hunan embroidery under the vision of artificial intelligence, promote the digital regeneration and database construction of Hunan embroidery stitches, the communication, and interaction between Hunan embroidery brands and the public, and create more possibilities for revitalizing the culture and industrial development of non-heritage Hunan embroidery. In this paper, a mechanics model of Hunan embroidery stitch is established based on the finite element idea under the view of artificial intelligence. The single yarn in the yarn is regarded as a frictionless articulation of some rows of elastic rods with a circular cross-section. The elastic rod can only be subjected to axial force without a moment, and it is a uniform, continuous, and completely elastic isotropic body. Using the displacement method, the displacement of the unit node is taken as the basic unknown quantity, the displacement in the unit is assumed to be linearly distributed, and the displacement of any node in the unit is obtained by linear interpolation. The strain, stress, and stiffness matrices of the elastic rod unit are derived, the equilibrium equations are given, and a database is established. The results of the study showed that consumers of all age levels thought that the patterns representing Hunan embroidery mainly include Hunan characteristic landscapes, portraits of Hunan great men, traditional flowers, birds and animals, and totems of Chu culture, etc., among which Hunan characteristic landscape accounted for 54% of the largest proportion. It provides a development direction for the inheritance and protection of Hunan embroidery skills.

PD27-07 AUTOMATED OPERATIVE REPORTS FOR ROBOTIC RADICAL PROSTATECTOMY USING AN ARTIFICIAL INTELLIGENCE PLATFORM

You have accessJournal of UrologyCME1 Apr 2023PD27-07 AUTOMATED OPERATIVE REPORTS FOR ROBOTIC RADICAL PROSTATECTOMY USING AN ARTIFICIAL INTELLIGENCE PLATFORM Abhinav Khanna, Alenka Antolin, Maya Zohar, Omri Bar, Danielle Ben-Ayoun, Alexander Krueger, Igor Frank, R. Houston Thompson, Paras Shah, Vidit Sharma, Stephen A. Boorjian, Tamir Wolf, Dotan Asselmann, and Matthew Tollefson Abhinav KhannaAbhinav Khanna More articles by this author , Alenka AntolinAlenka Antolin More articles by this author , Maya ZoharMaya Zohar More articles by this author , Omri BarOmri Bar More articles by this author , Danielle Ben-AyounDanielle Ben-Ayoun More articles by this author , Alexander KruegerAlexander Krueger More articles by this author , Igor FrankIgor Frank More articles by this author , R. Houston ThompsonR. Houston Thompson More articles by this author , Paras ShahParas Shah More articles by this author , Vidit SharmaVidit Sharma More articles by this author , Stephen A. BoorjianStephen A. Boorjian More articles by this author , Tamir WolfTamir Wolf More articles by this author , Dotan AsselmannDotan Asselmann More articles by this author , and Matthew TollefsonMatthew Tollefson More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003305.07AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: The manual creation of operative reports is a tedious documentation task that increases administrative burden among surgeons, which is a primary driver of physician burnout. Using pre-templated notes may reduce this burden but can lead to documentation inaccuracies. Additionally, surgical operative reports are subjective and lack transparency. We aim to develop an artificial intelligence (AI) tool for automatically generating operative reports based on surgical video alone. METHODS: A previously developed computer vision AI algorithm was employed to automatically detect major steps of robotic radical prostatectomy, including pelvic lymph node dissection, Space of Retzius dissection, anterior bladder neck transection, posterior bladder neck transection, seminal vesicle and posterior dissection, lateral/pedicle and apical dissection, urethral transection, vesicourethral anastomosis, and final inspection/extraction. Each step was mapped to pre-specified text, which was then compiled into a narrative operative report based on AI recognition of surgical steps. Accuracy of the AI-generated operative reports was assessed by comparing to operative reports documented in the medical record (human). All discrepancies between AI and human operative reports were adjudicated by independent video review performed by a fellowship-trained urologic oncologist. RESULTS: A total of 117 cases from a single tertiary referral center were included. There was concordance between human and AI operative reports in 107 cases, suggesting that the AI reproduces major components of the human operative report with 91.5% accuracy. Discrepancies between human and AI operative reports were identified in 10 cases, of which 9 were clinically significant. These included 8 discrepancies in lymph node dissection and 1 discrepancy in anterior bladder neck transection. Upon expert video review, the human was inaccurate in 3 discrepancies (2.6%), while the AI was inaccurate in 6 discrepancies (5.1%). CONCLUSIONS: To our knowledge, this is the first report of AI-powered automated creation of operative reports, which achieve high accuracy as compared to human operative reports for major surgical steps. This novel tool has potential to reduce documentation burden, improve operative report accuracy, promote surgical transparency, and decrease subjectivity in surgical documentation. Source of Funding: None © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e744 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Abhinav Khanna More articles by this author Alenka Antolin More articles by this author Maya Zohar More articles by this author Omri Bar More articles by this author Danielle Ben-Ayoun More articles by this author Alexander Krueger More articles by this author Igor Frank More articles by this author R. Houston Thompson More articles by this author Paras Shah More articles by this author Vidit Sharma More articles by this author Stephen A. Boorjian More articles by this author Tamir Wolf More articles by this author Dotan Asselmann More articles by this author Matthew Tollefson More articles by this author Expand All Advertisement PDF downloadLoading ...

Stakeholders’ views on drug development: the congenital disorders of glycosylation community perspective

BackgroundCongenital disorders of glycosylation (CDG) are a large family of rare genetic diseases for which therapies are virtually nonexistent. However, CDG therapeutic research has been expanding, thanks to the continuous efforts of the CDG medical/scientific and patient communities. Hence, CDG drug development is a popular research topic. The main aim of this study was to understand current and steer future CDG drug development and approval by collecting and analysing the views and experiences of the CDG community, encompassing professionals and families. An electronic (e-)survey was developed and distributed to achieve this goal.ResultsA total of 128 respondents (46 CDG professionals and 82 family members), mainly from Europe and the USA, participated in this study. Most professionals (95.0%) were relatively familiar with drug development and approval processes, while CDG families revealed low familiarity levels, with 8.5% admitting to never having heard about drug development. However, both stakeholder groups agreed that patients and families make significant contributions to drug development and approval. Regarding their perceptions of and experiences with specific drug development and approval tools, namely biobanks, disease models, patient registries, natural history studies (NHS) and clinical trials (CT), the CDG community stakeholders described low use and participation, as well as variable familiarity. Additionally, CDG professionals and families shared conflicting views about CT patient engagement and related information sharing. Families reported lower levels of involvement in CT design (25.0% declared ever being involved) and information (60.0% stated having been informed) compared to professionals (60.0% and 85.7%, respectively). These contrasting perceptions were further extended to their insights and experiences with patient-centric research. Finally, the CDG community (67.4% of professionals and 54.0% of families) reported a positive vision of artificial intelligence (AI) as a drug development tool. Nevertheless, despite the high AI awareness among CDG families (76.8%), professionals described limited AI use in their research (23.9%).ConclusionsThis community-centric study sheds new light on CDG drug development and approval. It identifies educational, communication and research gaps and opportunities for CDG professionals and families that could improve and accelerate CDG therapy development.

Technological Progress, Artificial Intelligence Development and Ethical Paradigms

In the historical landscape, technological discovery and development have significantly impacted human life, individually and communally. The significant impact of technological development is positive or negative depending on the influence of human vision, plans, strategies, and implementation on the design of these technological developments. Given that human-technology relations are symbiotic in the sense that it is affecting each other, or furthermore along with human-nature relations which make triadic symbiosis, ethics has a crucial role in maintaining humanity level and the stability of life, including the anticipation that technology will not become a disaster in the future. This research seeks to analyze the artificial intelligence (AI) development in Indonesia, one of the developing countries with a high level of attention to technological development, by offering an ethical paradigm as its basis. This paradigm is based on three principles, namely, (1) responsibility, (2) awareness, and (3) sustainability. Against the historical-philosophical backdrop of the technological development in human life and its relationships and possibilities, the offer of the ethical paradigm is intended to be the basis for the design of AI vision, strategy, and implementation as management of all the impacts of AI development, including the positive potential.

Ultrasensitive Flexible Memory Phototransistor with Detectivity of 1.8×1013 Jones for Artificial Visual Nociceptor

Emerging intelligent devices that can simulate an artificial intelligence vision system are of great interest for the development of modern information technology. Nociceptor is a crucial sensory neuron that recognizes harmful inputs and sends pain signals to the central nervous system to avoid injury; however, visual nociceptors, considered to be a key bionic function to protect eyesight based on optoelectronic devices, have yet to be developed. Herein this study, a three‐terminal flexible memory phototransistor (MPT) is first fabricated, which simulates the visual nociceptive behavior by adjusting light stimulation. The CsPbBr3 quantum‐dots (QDs)‐few‐layered black phosphorous nanosheets (FLBP NSs) heterojunction MPT demonstrates high responsivity of 7.2 × 103 AW−1 and high detectivity of 1.8 × 1013 Jones due to the high absorption coefficient of CsPbBr3 QDs materials and a high carrier transport property of FLBP NSs. Moreover, the proposed device can be used to emulate ultraviolet‐stimuli‐induced characteristics of visual nociceptors such as a threshold, no adaption, relaxation, allodynia, and hyperalgesia. It provides a new avenue for the realization of next‐generation neural‐integrated devices via its visual pain sense‐perception abilities.

Machine Learning with Explainable Artificial Intelligence Vision for Characterization of Solution Conductivity Using Optical Emission Spectroscopy of Plasma in Aqueous Solution

AbstractThis study presents an explainable artificial intelligence (XAI) vision for optical emission spectroscopy (OES) of plasma in aqueous solution. We aim to characterize the plasma and OES with XAI. Trained with 18000 spectra, a multilayer artificial neural network (ANN) model accurately predicted the solution conductivity. Local interpretable model‐agnostics explanations (LIME), an XAI method, interpreted the model through perturbing spectral features and fitting the feature contribution with a linear model. LIME showed that OH, Hγ, and Hβ emission lines were critical to the model, differing from the lines typically selected by humans. The results demonstrated that machine captured the spectral features neglected by humans. We believe using XAI for plasma OES analysis impacts the fields of plasma and analytical chemistry.

Dual-Mode Learning of Ambipolar Synaptic Phototransistor Based on 2D Perovskite/Organic Heterojunction for Flexible Color Recognizable Visual System.

Artificial intelligence vision systems (AIVSs) with information sensing, processing, and storage functions are increasingly gaining attention in the science and technology community. Although synapse phototransistor (SPT) is one of the essential components in AIVSs, solution-processed large-area photonic synapses that can detect and recognize multi-wavelength light are highly desirable. One of the major challenges in this area is the inability of the available materials to distinguish colors from the visible light to the near-infrared (NIR) light for single carrier (hole-only or electron-only) SPTs owing to lack of cognitive elements. Herein, 2D perovskite/organic heterojunction (PEA2 SnI4 /Y6) ambipolar SPTs (POASPTs) are developed via solution process. The POASPTs can display dual-mode learning process, which can convert light signals into postsynaptic currents with excitement/inhibition modes (hole-transporting region) or inhibition/excitement (electron-transporting region). The POASPTs exhibit high responsivity to visible light (104 A W-1 ) and NIR light (200 A W-1 ), and effectively perform learning and memory simultaneously. The flexible POASPT arrays can successfully recognize the images of different colors of light. This study reveals that the fabricated POASPTs have great potentials in the development of large-area, high-efficiency, and low-cost AIVSs.

Evaluation and Comparison of Artificial Intelligence Vision Aids: Orcam MyEye 1 and Seeing AI

Introduction: We compared the print-to-speech properties and human performance characteristics of two artificial intelligence vision aids, Orcam MyEye 1 (a portable device) and Seeing AI (an iPhone and iPad application). Methods: There were seven participants with visual impairments who had no experience with the two reading aids. Four participants had no light perception. Two individuals with measurable acuity and one with light perception were tested while blindfolded. We also tested performance with text of varying appearance in varying viewing conditions. To evaluate human performance, we asked the participants to use the devices to attempt 12 reading tasks similar to activities of daily living. We assessed the ranges of text attributes for which reading was possible, such as print size, contrast, and light level. We also assessed if individuals could complete tasks with the devices and measured accuracy and completion time. Participants also completed a survey concerning the two aids. Results: Both aids achieved greater than 95% accuracy in text recognition for flat, plain word documents and ranged from 13 to 57% accuracy for formatted text on curved surfaces. Both aids could read print sizes as small as 0.8M (20/40 Snellen equivalent, 40 cm viewing distance). Individuals successfully completed 71% and 55% ( p = .114) of tasks while using Orcam MyEye 1 and Seeing AI, respectively. There was no significant difference in time to completion of tasks ( p = .775). Individuals believed both aids would be helpful for daily activities. Discussion: Orcam MyEye 1 and Seeing AI had similar text-reading capability and usability. Both aids were useful to users with severe visual impairments in performing reading tasks. Implications for Practitioners: Selection of a reading device or aid should be based on individual preferences and prior familiarity with the platform, since we found no clear superiority of one solution over the other.