Incorrect Output Research Articles

AI Based Hallucination Detector

The main goal of this project is to create an advanced system that can accept questions submitted by users, produce AI-generated answers, and guarantee their accuracy by using an integrated validation method. This involves connecting to external web APIs that have access to trustworthy and authoritative sources, allowing the system to compare AI-generated responses with verified factual data in real time. If the AI-generated answer is accurate, the system will show a confirmation, providing users with assurance of its reliability. However, if the answer is incorrect, the system will flag the error and present the accurate response, addressing the issue of AI generating believable but factually inaccurate answers. In addition, the system records inaccurate responses to detect recurring error trends, which helps to enhance and refine the AI model over time. It also includes an interactive explanation tool that allows users to comprehend the validation process, promoting transparency in decision- making. To increase user involvement, the system can provide information about the origin of the correct answer and offer insights into differences between the AI-generated answers and the correct ones. Additionally, the system will have real-time alerts for critical errors, promptly notifying users when high-risk or sensitive topics are involved. The system's overall accuracy will be evaluated through a periodic review mechanism, which will offer feedback on performance enhancements. Additionally, user feedback will be incorporated into the system to continuously improve it and adapt to changing information sources. Moreover, the system will utilize AI-based learning algorithms to anticipate and prevent potential errors, thus enhancing response quality over time. Ultimately, the project's goal is to establish a reliable and user-friendly AI environment that fosters trust through real-time verification, transparency, continuous enhancement, and minimized risks of incorrect AI outputs.

Commentary on “AI is Changing the World: For Better or for Worse?”

This commentary explores three fundamental premises surrounding the human-AI partnership. First, a human-AI collaboration is perhaps superior to either working independently, as AI enhances human capabilities but requires oversight to ensure ethical and accurate outcomes. Second, AI's effectiveness is limited by the quality and biases of its training data, which underscores the need for diverse, unbiased datasets. Without proper data, AI could perpetuate flawed or biased decisions, impacting areas such as hiring, healthcare, and empathy-driven interactions. Finally, generative AI is prone to “hallucinations,” where it produces plausible yet incorrect outputs. These errors pose significant risks in high-stakes sectors like healthcare and security. As AI becomes more ingrained in society, these challenges raise ethical concerns around job displacement, loss of human autonomy, and biased decision-making. Here, we also examine the implications of AI hallucinations and model collapse, stressing the importance of continuous human intervention to mitigate AI-driven inaccuracies. Ultimately, a balanced partnership between human judgment and AI's scalability, along with rigorous oversight, is necessary to unlock AI's potential while safeguarding societal values.

Comparative Study of Adversarial Defenses: Adversarial Training and Regularization in Vision Transformers and CNNs

Transformer-based models are driving a significant revolution in the field of machine learning at the moment. Among these innovations, vision transformers (ViTs) stand out for their application of transformer architectures to vision-related tasks. By demonstrating performance as good, if not better, than traditional convolutional neural networks (CNNs), ViTs have managed to capture considerable interest in the field. This study focuses on the resilience of ViTs and CNNs in the face of adversarial attacks. Such attacks, which introduce noise into the input of machine learning models to produce incorrect outputs, pose significant challenges to the reliability of machine learning models. Our analysis evaluated the adversarial robustness of CNNs and ViTs by using regularization techniques and adversarial training methods. Adversarial training, in particular, represents a traditional approach to boosting defenses against these attacks. Despite its prominent use, our findings reveal that regularization techniques enable vision transformers and, in most cases, CNNs to enhance adversarial defenses more effectively. Through testing datasets like CIFAR-10 and CIFAR-100, we demonstrate that vision transformers, especially when combined with effective regularization strategies, demonstrate adversarial robustness, even without adversarial training. Two main inferences can be drawn from our findings. Firstly, it emphasizes how effectively vision transformers could strengthen artificial intelligence defenses against adversarial attacks. Secondly, it shows how regularization, which requires much fewer computational resources and covers a wide range of adversarial attacks, can be effective for adversarial defenses. Understanding and improving a model’s resilience to adversarial attacks is crucial for developing secure, dependable systems that can handle the complexity of real-world applications as artificial intelligence and machine learning technologies advance.

Fine-tuning Large Language Models for Rare Disease Concept Normalization.

We aim to develop a novel method for rare disease concept normalization by fine-tuning Llama 2, an open-source large language model (LLM), using a domain-specific corpus sourced from the Human Phenotype Ontology (HPO). We developed an in-house template-based script to generate two corpora for fine-tuning. The first (NAME) contains standardized HPO names, sourced from the HPO vocabularies, along with their corresponding identifiers. The second (NAME+SYN) includes HPO names and half of the concept's synonyms as well as identifiers. Subsequently, we fine-tuned Llama2 (Llama2-7B) for each sentence set and conducted an evaluation using a range of sentence prompts and various phenotype terms. When the phenotype terms for normalization were included in the fine-tuning corpora, both models demonstrated nearly perfect performance, averaging over 99% accuracy. In comparison, ChatGPT-3.5 has only ~20% accuracy in identifying HPO IDs for phenotype terms. When single-character typos were introduced in the phenotype terms, the accuracy of NAME and NAME+SYN is 10.2% and 36.1%, respectively, but increases to 61.8% (NAME+SYN) with additional typo-specific fine-tuning. For terms sourced from HPO vocabularies as unseen synonyms, the NAME model achieved 11.2% accuracy, while the NAME+SYN model achieved 92.7% accuracy. Our fine-tuned models demonstrate ability to normalize phenotype terms unseen in the fine-tuning corpus, including misspellings, synonyms, terms from other ontologies, and laymen's terms. Our approach provides a solution for the use of LLM to identify named medical entities from the clinical narratives, while successfully normalizing them to standard concepts in a controlled vocabulary.

Diagnostic decisions of specialist optometrists exposed to ambiguous deep-learning outputs

Artificial intelligence (AI) has great potential in ophthalmology. We investigated how ambiguous outputs from an AI diagnostic support system (AI-DSS) affected diagnostic responses from optometrists when assessing cases of suspected retinal disease. Thirty optometrists (15 more experienced, 15 less) assessed 30 clinical cases. For ten, participants saw an optical coherence tomography (OCT) scan, basic clinical information and retinal photography (‘no AI’). For another ten, they were also given AI-generated OCT-based probabilistic diagnoses (‘AI diagnosis’); and for ten, both AI-diagnosis and AI-generated OCT segmentations (‘AI diagnosis + segmentation’) were provided. Cases were matched across the three types of presentation and were selected to include 40% ambiguous and 20% incorrect AI outputs. Optometrist diagnostic agreement with the predefined reference standard was lowest for ‘AI diagnosis + segmentation’ (204/300, 68%) compared to ‘AI diagnosis’ (224/300, 75% p = 0.010), and ‘no Al’ (242/300, 81%, p = < 0.001). Agreement with AI diagnosis consistent with the reference standard decreased (174/210 vs 199/210, p = 0.003), but participants trusted the AI more (p = 0.029) with segmentations. Practitioner experience did not affect diagnostic responses (p = 0.24). More experienced participants were more confident (p = 0.012) and trusted the AI less (p = 0.038). Our findings also highlight issues around reference standard definition.

Early Perceptions of Teaching and Learning Using Generative AI in Higher Education

This project explored perceptions of ChatGPT in higher education among students and faculty to assess teaching and learning implications of this Generative Artificial Intelligence (Generative AI)–based novel tool. Two theoretical frameworks inspired the project, including Diffusion of Innovation theory (Rogers, 1962) and Technology Acceptance Model (Davis, 1989). An online survey was completed by 380 participants ( N = 380). Participants indicated that they would not use ChatGPT to plagiarize but believed others would. When asked to rate the accuracy of ChatGPT's output, more than half took the incorrect output as correct/somewhat correct or could not tell whether it was correct or incorrect. Results varied based on participant demographics, including age, gender, and occupation. These findings support the need for data literacy. If Generative AI is to be used in higher education to aid in the learning process, it is imperative to continue teaching critical thinking.

Penerapan Metode Interpolasi Linear dan Geometric Mean Filter Pada Citra Resolusi Rendah Hasil Resampling

In addition to the color representation contained in a digital image, the size of the image will also greatly affect the image processing process. In some cases of image acquisition, sometimes the size of the image obtained does not correspond to the desired one. Pattern recognition processes, such as vehicle license plate recognition, may result in incorrect output if the size of the processed input image is not appropriate. Therefore, determining the size of a digital image is very important in further image processing. To solve such problems can be done by enlarging the size of the image through the process of interpolation or resampling. Image magnification is the process of enlarging the size of a digital image from its original size to a new and different size based on the desired magnification scale. However, as a result of enlarging the image obtained there is always a blur, so the image will look like a checkerboard (split). This situation occurs because during the magnification process the resolution of the new image becomes lower. Thus, additional methods are needed to improve the image quality of the resampling results. This study aims to improve the low-resolution image quality of resampling results by applying Linear Interpolation and Geometric Mean Filter methods. The test results showed that the Linear Interpolation method was able to produce a new image that was larger than the original image and the Geometric Mean Filter method could improve the image quality of the enlarged image so that the details of the information in the image are better and clearly seen visually. And to find out if the image enlarged from the Linear Interpolation and Geometric Mean Filter is good or not, we will use the Mean Squared Error (MSE) and Peak Signal to Noise Ratio (PSNR) methods. Keywords : Image, Interpolasi Linear,Geometric Mean Filter, PSNR, MSE.

Assessing ChatGPT's orthopedic in-service training exam performance and applicability in the field.

ChatGPT has gained widespread attention for its ability to understand and provide human-like responses to inputs. However, few works have focused on its use in Orthopedics. This study assessed ChatGPT's performance on the Orthopedic In-Service Training Exam (OITE) and evaluated its decision-making process to determine whether adoption as a resource in the field is practical. ChatGPT's performance on three OITE exams was evaluated through inputting multiple choice questions. Questions were classified by their orthopedic subject area. Yearly, OITE technical reports were used to gauge scores against resident physicians. ChatGPT's rationales were compared with testmaker explanations using six different groups denoting answer accuracy and logic consistency. Variables were analyzed using contingency table construction and Chi-squared analyses. Of 635 questions, 360 were useable as inputs (56.7%). ChatGPT-3.5 scored 55.8%, 47.7%, and 54% for the years 2020, 2021, and 2022, respectively. Of 190 correct outputs, 179 provided a consistent logic (94.2%). Of 170 incorrect outputs, 133 provided an inconsistent logic (78.2%). Significant associations were found between test topic and correct answer (p = 0.011), and type of logic used and tested topic (p = < 0.001). Basic Science and Sports had adjusted residuals greater than 1.96. Basic Science and correct, no logic; Basic Science and incorrect, inconsistent logic; Sports and correct, no logic; and Sports and incorrect, inconsistent logic; had adjusted residuals greater than 1.96. Based on annual OITE technical reports for resident physicians, ChatGPT-3.5 performed around the PGY-1 level. When answering correctly, it displayed congruent reasoning with testmakers. When answering incorrectly, it exhibited some understanding of the correct answer. It outperformed in Basic Science and Sports, likely due to its ability to output rote facts. These findings suggest that it lacks the fundamental capabilities to be a comprehensive tool in Orthopedic Surgery in its current form. II.

LAFIT: Efficient and Reliable Evaluation of Adversarial Defenses With Latent Features.

Deep convolutional neural networks (CNNs) can be easily tricked to give incorrect outputs by adding tiny perturbations to the input that are imperceptible to humans. This makes them susceptible to adversarial attacks, and poses significant security risks to deep learning systems, and presents a great challenge in making CNNs robust against such attacks. An influx of defense strategies have thus been proposed to improve the robustness of CNNs. Current attack methods, however, may fail to accurately or efficiently evaluate the robustness of defending models. In this paper, we thus propose a unified lp white-box attack strategy, LAFIT, to harness the defender's latent features in its gradient descent steps, and further employ a new loss function to normalize logits to overcome floating-point-based gradient masking. We show that not only is it more efficient, but it is also a stronger adversary than the current state-of-the-art when examined across a wide range of defense mechanisms. This suggests that adversarial attacks/defenses could be contingent on the effective use of the defender's hidden components, and robustness evaluation should no longer view models holistically.

ChatGPT and the Pedagogical Challenge: Unveiling the Impact on Early-Career Academics in Higher Education

Emerging Artificial Intelligence (AI) tools like ChatGPT offer considerable promise for enhancing professional development in higher education. This study focuses on the experiences of early-career lecturers who utilise ChatGPT for professional development. Central questions explore ChatGPT's influence on their overall professional experience and the challenges and benefits of its use. While research on AI in education has been growing, few studies have delved into the specific experiences of young academics using ChatGPT. This study employed qualitative methods to address this gap, pre-cisely 45- to 60-minute in-depth interviews with two purposively selected lecturers, followed by a thematic analysis using a grounded theory approach. Our findings reveal a multifaceted landscape: on the one hand, ChatGPT enhances the ability to construct intricate academic arguments, increases effi-ciency in research and teaching tasks, and heightens critical thinking capabilities. Conversely, chal-lenges such as initial technical hurdles, occasional incorrect outputs, and concerns about over-dependency were also highlighted. Through this investigation, the study contributes to the broader discourse on AI in education by illustrating both the promising opportunities and potential pitfalls of using ChatGPT in academia. The study underscores the need for a balanced approach to AI adoption. It offers insights into the role of AI tools like ChatGPT in shaping the future of professional development in higher education.

Adversarial Neural Collaborative Filtering with Embedding Dimension Correlations

ABSTRACT Recently, convolutional neural networks (CNNs) have achieved excellent performance for the recommendation system by extracting deep features and building collaborative filtering models. However, CNNs have been verified susceptible to adversarial examples. This is because adversarial samples are subtle non-random disturbances, which indicates that machine learning models produce incorrect outputs. Therefore, we propose a novel model of Adversarial Neural Collaborative Filtering with Embedding Dimension Correlations, named ANCF in short, to address the adversarial problem of CNN-based recommendation system. In particular, the proposed ANCF model adopts the matrix factorization to train the adversarial personalized ranking in the prediction layer. This is because matrix factorization supposes that the linear interaction of the latent factors, which are captured between the user and the item, can describe the observable feedback, thus the proposed ANCF model can learn more complicated representation of their latent factors to improve the performance of recommendation. In addition, the ANCF model utilizes the outer product instead of the inner product or concatenation to learn explicitly pairwise embedding dimensional correlations and obtain the interaction map from which CNNs can utilize its strengths to learn high-order correlations. As a result, the proposed ANCF model can improve the robustness performance by the adversarial personalized ranking, and obtain more information by encoding correlations between different embedding layers. Experimental results carried out on three public datasets demonstrate that the ANCF model outperforms other existing recommendation models.

H2C-TM: A Hybrid High Coverage Test Method for Improving the Detection of HtD Faults in STT-MRAMs

This paper proposes a design-for-testability (DFT) scheme and a test method to improve the detection of hard-to-detect (HtD) faults of STT-MRAMs. The proposed DFT scheme considers HtD faults which are undefined state faults (USFs). These states do not always lead to incorrect functionality during the test process resulting in test escapes. The proposed DFT scheme, which can be deployed as a standalone technique, is called USFA-DFT (an undefined state fault-aware design-for-testability technique). In this scheme, the USF detection is achieved through using a weak write before reading the cell. The weak write overwrites defective cells, causing an incorrect read output, without flipping the content of defect-free cells. To avoid test escapes or yield loss (over-testing) caused by process variation (PV) effects, we make use of a post-silicon calibration scheme. Next, to improve the detectability of HtD faults in STT-MRAMs, we suggest a hybrid high coverage test method (called H2C-TM) which enhances the previous method called LCHC-DFT (a low-cost high-coverage design-for-testability technique) by combining it with USFA-DFT. This test method improves the HtD fault detection, by applying the weak write (controlled by USFA-DFT) before the stressed read operation (controlled by LCHC-DFT). This hybrid approach has a negligible area overhead (3.18% for a 1 kbit array). The results for the performance evaluation of the H2C-TM scheme show that for intra-cell and inter-cell defects, on average, the improvements of 14.4% and 11.3% of HtD fault coverage, compared to those of LCHC-DFT, are achieved. Also, for intra-cell and inter-cell defects, the proposed hybrid approach outperforms USFA-DFT by 49.8% and 49.3% respectively. Finally, in the presence of the process variation, the proposed test method guarantees a robust DFT with only a maximum deviation of 10% from the nominal value.

Beyond Trade-Off: An Optimized Binocular Stereo Vision Based Depth Estimation Algorithm for Designing Harvesting Robot in Orchards

Depth estimation is one of the bottleneck parts for harvesting robots to determine whether the operation of grasping or picking succeeds or not directly. This paper proposed a novel disparity completion method combined with bilateral filtering and pyramid fusion to improve the issues of incorrect outputs due to the missed or wrong matching when achieving 3D position from 2D images in open-world environments. Briefly, our proposed method has two significant advantages in general. Firstly, occlusion between leaves, branches, and fruits is a universal phenomenon in unstructured orchard environments, which results in the most depth estimation algorithms facing great challenges to obtain accurate outputs in these occluded regions. To alleviate these issues, unlike other research efforts that already exist, we optimized the semi-global matching algorithm to obtain high accuracy sparse values as an initial disparity map; then, an improved bilateral filtering algorithm is proposed to eliminate holes and discontinuous regions caused by occlusion to obtain precise and density disparity outputs. Secondly, due to taking the practical high-efficiency requirements of the automated harvesting robot in its working status into consideration, we attempted to merge multiple low-resolution bilateral filtering results through the pyramid fusion model which goes beyond the trade-off mechanism to improve the performance of both accuracy and time cost. Finally, a prototype harvesting robot was designed to conduct experiments at three kinds of different distances (0.6~0.75 m, 1~1.2 m, and 1.6~1.9 m). Experiment results showed that our proposed method achieved density disparity maps and eliminated holes and discontinuous defects in the disparity map effectively. The average absolute error of our proposed method is 3.2 mm, and the average relative error is 1.79%. In addition, the time cost is greatly reduced more than 90%. Comprehensive experimental results demonstrate that our proposed algorithm provides a potential possibility for designing harvesting.

How Can IJDS Authors, Reviewers, and Editors Use (and Misuse) Generative AI?

How Can <i>IJDS</i> Authors, Reviewers, and Editors Use (and Misuse) Generative AI?

Traditional Chinese Medicine Synonymous Term Conversion

Background: The medical records of traditional Chinese medicine (TCM) contain numerous synonymous terms with different descriptions, which is not conducive to computer-aided data mining of TCM. However, there is a lack of models available to normalize synonymous TCM terms. Therefore, construction of a synonymous term conversion (STC) model for normalizing synonymous TCM terms is necessary. Methods: Based on the neural networks of bidirectional encoder representations from transformers (BERT), four types of TCM STC models were designed: Models based on BERT and text classification, text sequence generation, named entity recognition, and text matching. The superior STC model was selected on the basis of its performance in converting synonymous terms. Moreover, three misjudgment inspection methods for the conversion results of the STC model based on inconsistency were proposed to find incorrect term conversion: Neuron random deactivation, output comparison of multiple isomorphic models, and output comparison of multiple heterogeneous models (OCMH). Results: The classification-based STC model outperformed the other STC task models. It achieved F1 scores of 0.91, 0.91, and 0.83 for performing symptoms, patterns, and treatments STC tasks, respectively. The OCMH method showed the best performance in misjudgment inspection, with wrong detection rates of 0.80, 0.84, and 0.90 in the term conversion results for symptoms, patterns, and treatments, respectively. Conclusion: The TCM STC model based on classification achieved superior performance in converting synonymous terms for symptoms, patterns, and treatments. The misjudgment inspection method based on OCMH showed superior performance in identifying incorrect outputs.

Detecting Adversarial Examples by Input Transformations, Defense Perturbations, and Voting.

Over the past few years, convolutional neural networks (CNNs) have proved to reach superhuman performance in visual recognition tasks. However, CNNs can easily be fooled by adversarial examples (AEs), i.e., maliciously crafted images that force the networks to predict an incorrect output while being extremely similar to those for which a correct output is predicted. Regular AEs are not robust to input image transformations, which can then be used to detect whether an AE is presented to the network. Nevertheless, it is still possible to generate AEs that are robust to such transformations. This article extensively explores the detection of AEs via image transformations and proposes a novel methodology, called defense perturbation, to detect robust AEs with the same input transformations the AEs are robust to. Such a defense perturbation is shown to be an effective counter-measure to robust AEs. Furthermore, multinetwork AEs are introduced. This kind of AEs can be used to simultaneously fool multiple networks, which is critical in systems that use network redundancy, such as those based on architectures with majority voting over multiple CNNs. An extensive set of experiments based on state-of-the-art CNNs trained on the Imagenet dataset is finally reported.

Robust and Secure Quality Monitoring for Welding through Platform-as-a-Service: A Resistance and Submerged Arc Welding Study

For smart manufacturing systems, quality monitoring of welding has already started to shift from empirical modeling to knowledge integration directly from the captured data by utilizing one of the most promising Industry 4.0’s key enabling technologies, artificial intelligence (AI)/machine learning (ML). However, beyond the advantages that they bring, AI/ML introduces new types of security threats, which are related to their very nature and eventually, they will pose real threats to the production cost and quality of products. These types of security threats, such as adversarial attacks, are causing the targeted AI system to produce incorrect or malicious outputs. This may undermine the performance (and the efficiency) of the quality monitoring systems. Herein, a software platform servicing quality monitoring for welding is presented in the context of resistance and submerged arc welding. The hosted ML classification models that are trained to perform quality monitoring are subjected to two different types of untargeted, black-box, adversarial attacks. The first one is based on a purely statistical approach and the second one is based on process knowledge for crafting these adversarial inputs that can compromise the models’ accuracy. Finally, the mechanisms upon which these adversarial attacks are inflicting damage are discussed to identify which process features or defects are replicated. This way, a roadmap is sketched toward testing the vulnerability and robustness of an AI-based quality monitoring system.

Poison Neural Network-Based mmWave Beam Selection and Detoxification With Machine Unlearning

Deep neural network-based learning methods have been considered promising techniques used in beam selection problems. However, existing research ignores the peculiar vulnerabilities of neural networks. The adversaries can use data poisoning to embed predefined triggers into a model during training time such that the neural network-based beam model may make an incorrect output decision of a test example when patched with the trigger. Data poisoning offers attackers the possibility to build backdoors. The goal of backdoors is often unethical, such as giving users a poor experience by manipulating infected models to output inappropriate beams. In this paper, first, we introduce a simple backdoor attack method by using data poisoning in a mmWave beam selection system. By numerical simulations, we verify that this poisoning attack is effective for neural networks with different structures. In addition, we explore the effect of poisoned data volume on the effect of backdoor attacks. The results show that the backdoor can be successfully implanted into the beam selection neural network. Besides, we fine-tune the trained model for a new wireless communication environment, and the results show that backdoors still exist even when the model is tuned with data from new scenarios. Then, we propose a machine unlearning solution to mitigate the backdoor of the trained beam selection model. The problem of eliminating backdoors is modeled as a minimax optimization problem. We propose a novel adversarial unlearning method along with label smoothing to solve the backdoor removal problem. We compared the proposed backdoor elimination method with the classical fine-tuning elimination method and the neural network pruning method through numerical simulations. The results show that the fine-tuning and the pruning methods cannot effectively remove the backdoor. The proposed machine unlearning method can make the trained model forget about the backdoor under the condition that the performance of the benign task (beam selection tasks when the trigger does not appear) is guaranteed to be slightly degraded. In summary, our work illustrates that data poisoning-based backdoor attacks may exist in wireless networks, and we propose a scheme to eliminate backdoors.

Development of Employee Attendance Management Information System During the Covid-19 Pandemic Based on Website using QR Code and PHP Native

Hero Web Design is a small-scale company that is growing, the company is engaged in technology. Attendance activities at Hero Web Design still use the conventional method, every day employees carry out attendance activities manually. This results in fraud, loss, and damage to the processed data because they are not integrated. As a result, with the emergence of these problems the company could not develop. With the problems that Hero Web Design has, here an online attendance information system is made with a QR-Code and face detection using the Scrum method. In this study using the Scrum method using 3 Sprints, each Sprint has a process of not more than one month. The data sources used in this study are observational data in the form of attendance management activities and report management. The second data source is interview data in the form of an answer from a question-and-answer discussion with Hero Web Design. The third data source is literature study data in the form of supporting journals that have similar research. A website-based attendance management information system with a program working process using the PHP programming language, javascript, CSS, and bootstrap framework. The results of the information system that has been created will be tested using the black box method. This method aims to check for missing or incorrect functions, interfaces, performance, program initialization and output errors, data structures, or database access errors. Furthermore, from making this system, it is hoped that it can be used as a remote or online attendance without having to come face to face and overcome the problems found in Hero Web Design.

Digital IC Tester For 74XX Series using PIC18F4550

A microcontroller-based circuit known as a "digital IC tester" determines if an integrated circuit is in good operating order or not. Product testing is a costly and time-consuming procedure in industries. Testing is required before the system is fully operational to prevent mistakes and undesirable outcomes. Similar to this, in educational institutions, it is required to verify whether the IC is excellent or bad before doing experiments during practicals. Numerous minor IC flaws cause the system to operate erratically and generate incorrect outputs. Therefore, the purpose of this research is to use a PIC microcontroller to construct an IC tester that can test the majority of integrated circuits in the 74xx series logic gates. The main goal of this project is to simulate the features of a logic gate IC and use the truth table of that specific IC to verify the state of the gates in that IC. Several design philosophies were contrasted before one was ultimately put into practice. Following the successful completion of simulation and testing, they were combined to produce the final version.