Artificial Intelligence Search Research Articles

E-learning and AI search decision based on deep learning algorithms application in economic and legal optimization

Economic law is closely related to people's daily social life, which can protect and supervise people's daily and economic behavior, and has fairness and justice. In recent years, deep learning algorithms have significantly developed in the field of economic law. By utilizing this algorithm, not only can we effectively solve relevant problems in economic law, but we can also further optimize economic law efficiently. Based on this, we introduced the algorithm into search decision-making in artificial intelligence and successfully constructed an optimization model for economic law. Firstly, this article analyzes the semi supervised clustering algorithms in deep learning and proposes a learning model for comparing the algorithms. After optimization, a heuristic search algorithm with good performance was obtained. We will design the artificial intelligence search decision technology based on deep learning algorithms into the economic and legal model to obtain the final optimization model. A comparative experiment was conducted on the dataset, and the results showed that the optimization model constructed in this article provides certain assistance for the improvement of China's economic and legal system.

Adoption and Adaptation of Generative Artificial Intelligence in Organizations: Actions for Efficient and Responsible Use in Interaction with Collaborators

The aim of this work was to determine the actions needed to adopt and adapt Generative Artificial Intelligence (GenAI) for efficient and responsible use in the organization, without affecting the contribution of the collaborator to his/her work activities. A bibliographic review of the scientific literature and information published by consulting companies on the current development of research related to GenAI and its impact on organizations was carried out. Artificial intelligence search tools, academic search engines were used, and criteria for the inclusion and exclusion of publications were established. As a result, necessary actions were identified for the adoption, adaptation, efficient and responsible use, and interaction of GenAI with employees in their work environment. These actions include the adjustment of processes, infrastructure, and resources; capacity-building for integration and a culture of innovation, protocol development, staff training, creation of flexible and supportive working environments, collaboration with regulators, transformation of work; and alignment of staff management practices. It was concluded that GenAI is having a major impact on organizations by automating processes and increasing productivity and efficiency. It is essential to address actions in three categories: staff training, fostering a culture of innovation, ethics, and accountability in the use of this technology, and its efficient adoption and adaptation without affecting the contribution of employees. This research helps to identify the elements needed to deepen research development and define, in real contexts, the effectiveness of the adoption and efficient adaptation of GenAI in internal processes and interaction with collaborators, with the aim of promoting best practices that generate value through its use.

Mirage: cyber deception against autonomous cyber attacks in emulation and simulation

AbstractAs the capabilities of cyber adversaries continue to evolve, now in parallel to the explosion of maturing and publicly-available artificial intelligence (AI) technologies, cyber defenders may reasonably wonder when cyber adversaries will begin to also field these AI technologies. In this regard, some promising (read: scary) areas of AI for cyber attack capabilities are search, automated planning, and reinforcement learning. As such, one possible defensive mechanism against future AI-enabled adversaries is that of cyber deception. To that end, in this work, we present and evaluate Mirage, an experimentation system demonstrated in both emulation and simulation forms that allows for the implementation and testing of novel cyber deceptions designed to counter cyber adversaries that use AI search and planning capabilities.

Utility of Artificial Intelligence in Orthopedic Surgery Literature Review: A Comparative Pilot Study.

Literature reviews are essential to the scientific process and allow clinician researchers to advance general knowledge. The purpose of this study was to evaluate if the artificial intelligence (AI) programs ChatGPT and Perplexity.AI can perform an orthopedic surgery literature review. Five different search topics of varying specificity within orthopedic surgery were chosen for each search arm to investigate. A consolidated list of unique articles for each search topic was recorded for the experimental AI search arms and compared with the results of the control arm of two independent reviewers. Articles in the experimental arms were examined by the two independent reviewers for relevancy and validity. ChatGPT was able to identify a total of 61 unique articles. Four articles were not relevant to the search topic and 51 articles were deemed to be fraudulent, resulting in 6 valid articles. Perplexity.AI was able to identify a total of 43 unique articles. Nineteen were not relevant to the search topic but all articles were able to be verified, resulting in 24 valid articles. The control arm was able to identify 132 articles. Success rates for ChatGPT and Perplexity. AI were 4.6% (6 of 132) and 18.2% (24 of 132), respectively. The current iteration of ChatGPT cannot perform a reliable literature review, and Perplexity.AI is only able to perform a limited review of the medical literature. Any utilization of these open AI programs should be done with caution and human quality assurance to promote responsible use and avoid the risk of using fabricated search results. [Orthopedics. 2024;47(3):e125-e130.].

Strength and manufacturability enhancement of a composite automotive component via an integrated finite element/artificial neural network multi-objective optimization approach

This study addresses the enhancement of an injection-molded fiber-reinforced plastic / metal hybrid automotive structure and its plastic injection molding process through the integration of the finite element method, artificial intelligence and evolutionary search methods. Experiments are conducted to validate the finite element models. The orthogonal array and Latin hypercube methods are employed to generate a database via finite element analysis. The database is then used to train artificial neural networks that accurately evaluate component distortion, manufacturing time, and structural strength. A genetic optimization algorithm is applied to identify optimal process parameters. The procedure was demonstrated to simultaneously reduce product warpage and manufacturing time by 10 and 62 %, respectively, when compared with the reference manufacturing process while strength is kept above the required levels with a reduced number of required data points. A more in-depth investigation into the causes of strength variation and deformation is also provided. The results contribute to the advance of robust composite automotive structures with superior quality, manufactured through efficient processes.

Reflections on tests of AI-search tools in the academic search process

Academic search literacy and artificial intelligence (AI) search are a strategic focus of future researcher support strategies at the Royal Library and affiliated university libraries in Denmark. The ambition is to integrate AI search tools in teaching and services that support literature seeking and hence improve the efficiency of the academic search process.
 This paper is a reflection on the results of tests of AI powered search tools conducted at the Royal Library, Denmark. Information specialists and researchers took part in think-aloud tests, Hackathon and expert quality assessment in the period of April 2021 – February 2022.
 Our observations point to a) opportunities in AI search to let go of traditional approaches to search and broaden our perception of how an academic search can be conducted, b) potential of AI search interfaces to challenge ones preconception and reduce cognitive bias in the search process, c) concerns about the quality of scientific literature AI search identifies, and d) barriers that need to be overcome before practical implementation of Ai search tools become viable in academic searches.
 More research is needed to show the usefulness of AI search tools in systematic search and guidance on how to report the use of them

Retracted: Application of Artificial Intelligence Computer Intelligent Heuristic Search Algorithm

Retracted: Application of Artificial Intelligence Computer Intelligent Heuristic Search Algorithm

Exploring the Intersection of Artificial Intelligence and Neurosurgery: Let us be Cautious With ChatGPT.

ChatGPT is a novel natural language processing artificial intelligence (AI) module where users enter any question or command and receive a single text response within seconds. As AI becomes more accessible, patients may begin to use it as a resource for medical information and advice. This is the first study to assess the neurosurgical information that is provided by ChatGPT. ChatGPT was accessed in January 2023, and prompts were created requesting treatment information for 40 common neurosurgical conditions. Quantitative characteristics were collected, and four independent reviewers evaluated the responses using the DISCERN tool. Prompts were compared against the American Association of Neurological Surgeons (AANS) "For Patients" webpages. ChatGPT returned text organized in paragraph and bullet-point lists. ChatGPT responses were shorter (mean 270.1 ± 41.9 words; AANS webpage 1634.5 ± 891.3 words) but more difficult to read (mean Flesch-Kincaid score 32.4 ± 6.7; AANS webpage 37.1 ± 7.0). ChatGPT output was found to be of "fair" quality (mean DISCERN score 44.2 ± 4.1) and significantly inferior to the "good" overall quality of the AANS patient website (57.7 ± 4.4). ChatGPT was poor in providing references/resources and describing treatment risks. ChatGPT provided 177 references, of which 68.9% were inaccurate and 33.9% were completely falsified. ChatGPT is an adaptive resource for neurosurgical information but has shortcomings that limit the quality of its responses, including poor readability, lack of references, and failure to fully describe treatment options. Hence, patients and providers should remain wary of the provided content. As ChatGPT or other AI search algorithms continue to improve, they may become a reliable alternative for medical information.

Comments: AI Language Tools Hit the Books . . . and Technical Content?

Are you among the millions of ChatGPT users/experimenters around the world? Or impatiently parked on the waitlist? OpenAI launched ChatGPT in late November 2022 with little fanfare. Within a week, the company reported 1 million users. Within the first month, it exceeded 57 million users. Since then, it has been updated several times and a UBS analyst estimated in February that the artificial intelligence (AI) language tool reached 100 million users. The website exceeded 13 million daily visitors as of January. On 1 February, OpenAI announced a ChatGPT Plus monthly subscription plan to give priority access to the chatbot. Clearly, AI language tools are fulfilling the needs and/or satisfying the curiosity of novices and more experienced AI users. Microsoft took notice and in January confirmed the extension of its partnership with OpenAI, an investment rumored to be $10 billion (not confirmed by Microsoft). Microsoft Azure will also continue as the exclusive cloud provider for the tool since OpenAI uses Azure to train its models. ChatGPT (generative pretrained transformer) is not the only AI tool available. Microsoft upgraded its Bing AI search engine, which is powered by an upgraded model of ChatGPT. ChatSonic, also built on top of ChatGPT, can access the internet. Jasper Chat, based on GPT 3.5 with OpenAI as its partner, was built for advertising and marketing businesses. Google Bard is an experimental conversational AI service powered by Google’s own next-generation language model. Character AI revolves around the concept of personas, trained with conversations in mind. Users choose from various personalities (e.g., Elon Musk, Tony Stark, Socrates, US President Biden, Kayne West, etc.) vs. interacting with a single AI chatbot. YouChat is built into a search engine and trained on a ChatGPT model. It holds conversations with full access to the internet. Caktus AI, not available as a free service, is aimed toward students and touted by the company as “the first-ever educational AI tool.” It helps with student content ranging from essays to writing paragraphs and extends to discussions, questions, and coding. There’s also a bot for programmers, GitHub Copilot X, which suggests and completes code and functions in real time. There are many more options and sure to be plenty more developed with specific user types in mind (e.g., technical specialties, business communications, conversational, special interests). All will have their own pros and cons. A retired systems engineer, formerly with General Dynamics Canada, Rob Miller, wrote a column in Canada’s National Observer about his experience using ChatGPT as a research tool for a story about carbon capture and storage—he gave a mixed review. As AI tools gain attention and provide assistance to a wide range of authors and researchers, SPE recently announced its policy for authors who use the tools to generate content for their papers. AI-generated content may be used within SPE publications, but under specific conditions. Open-source libraries, such as those used in ChatGPT and others, are not fail-safe solutions, as evidenced on 20 March when OpenAI took the chatbot offline due to a bug “which allowed some users to see titles from another active user’s chat history,” according to the company. “It’s also possible that the first message of a newly created conversation was visible in someone else’s chat history if both users were active around the same time.” OpenAI also discovered that “the same bug may have caused the unintentional visibility of payment-related information of 1.2% of the ChatGPT Plus subscribers who were active during a specific 9-hour window. … it was possible for some users to see another active user’s first and last name, email address, payment address, the last four digits (only) of a credit card number, and credit card expiration date.” I’d like to hear about your experience with ChatGPT or any of the other options. How did you use it? What type of content were you generating? If you were aiming for assistance with technical content related to oil/gas/energy, how useful was the chatbot? Share your comments and pros and cons at JPT Comments.

Exploring parameter spaces with artificial intelligence and machine learning black-box optimization algorithms

Constraining beyond the Standard Model theories usually involves scanning highly multidimensional parameter spaces and checking observable predictions against experimental bounds and theoretical constraints. Such a task is often timely and computationally expensive, especially when the model is severely constrained and thus leading to very low random sampling efficiency. In this work we tackled this challenge using artificial intelligence and machine learning search algorithms used for black-box optimization problems. Using the constrained minimal supersymmetric standard model and the phenomenological minimal supersymmetric standard model parameter spaces, we consider both the Higgs mass and the dark matter relic density constraints to study their sampling efficiency and parameter space coverage. We find our methodology to produce orders of magnitude improvement of sampling efficiency while reasonably covering the parameter space.

State space search revisited from perspective of deep learning

Recent developments in deep learning have reshaped the landscape of artificial intelligence (AI). In this short research review note, we revisit the popular concept of state in state space search (as well as some related concepts) in AI from the perspective of deep learning. The major takeaway of this examination is that deep learning makes the philosophical concept of emergence operationalizable. In addition, along with other recent developments such as quantum computing, deep learning will have even more profound impact on the development of AI in near future.

Comparing and integrating artificial intelligence and similarity search detection techniques: application to seismic sequences in Southern Italy

SUMMARYUnderstanding mechanical processes occurring on faults requires detailed information on the microseismicity that can be enhanced today by advanced techniques for earthquake detection. This problem is challenging when the seismicity rate is low and most of the earthquakes occur at depth. In this study, we compare three detection techniques, the autocorrelation FAST, the machine learning EQTransformer, and the template matching EQCorrScan, to assess their ability to improve catalogues associated with seismic sequences in the normal fault system of Southern Apennines (Italy) using data from the Irpinia Near Fault Observatory (INFO). We found that the integration of the machine learning and template matching detectors, the former providing templates for the cross-correlation, largely outperforms techniques based on autocorrelation and machine learning alone, featuring an enrichment of the automatic and manual catalogues of factors 21 and 7, respectively. Since output catalogues can be polluted by many false positives, we applied refined event selection based on the cumulative distribution of their similarity level. We can thus clean up the detection lists and analyse final subsets dominated by real events. The magnitude of completeness decreases by more than one unit compared to the reference value for the network. We report b-values associated with sequences smaller than the average, likely corresponding to larger differential stresses than for the background seismicity of the area. For all the analysed sequences, we found that main events are anticipated by foreshocks, indicating a possible preparation process for main shocks at subkilometric scales.

Towards the Neuroevolution of Low-level artificial general intelligence.

In this work, we argue that the search for Artificial General Intelligence should start from a much lower level than human-level intelligence. The circumstances of intelligent behavior in nature resulted from an organism interacting with its surrounding environment, which could change over time and exert pressure on the organism to allow for learning of new behaviors or environment models. Our hypothesis is that learning occurs through interpreting sensory feedback when an agent acts in an environment. For that to happen, a body and a reactive environment are needed. We evaluate a method to evolve a biologically-inspired artificial neural network that learns from environment reactions named Neuroevolution of Artificial General Intelligence, a framework for low-level artificial general intelligence. This method allows the evolutionary complexification of a randomly-initialized spiking neural network with adaptive synapses, which controls agents instantiated in mutable environments. Such a configuration allows us to benchmark the adaptivity and generality of the controllers. The chosen tasks in the mutable environments are food foraging, emulation of logic gates, and cart-pole balancing. The three tasks are successfully solved with rather small network topologies and therefore it opens up the possibility of experimenting with more complex tasks and scenarios where curriculum learning is beneficial.

The Application of AI-Based Technology in Computer Network Operation and Maintenance

As an emerging IT technology, artificial intelligence has unique advantages in data extraction and data processing. In this paper, an artificial intelligence search method based on the K-means algorithm is proposed. According to the similarity between data samples, the samples are divided into many different categories. The ARIMA algorithm is used to perform regression analysis on the traffic data, and a Boosting model is established to improve the time series prediction accuracy. And, we use the relative distance between elements to represent the degree of dissimilarity of different types of variables. It can process massive data in a short time and obtain user behavior information. A large number of experiments show that the method proposed in this paper can complete information search more effectively and complete information classification quickly.

Application of variable-order fractional calculus in neural networks: where do we stand?

After decades of evolution and advancement, artificial intelligence has profoundly affected all fields of science and engineering, and has started to revolutionize all aspects of today's life. In this regard, neural networks, which are a stepping stone in the search for artificial intelligence, play an important role. Motivated by this, the current special issue aims to explore recent trends and developments in the modeling, analysis, synchronization, and practical application of chaotic variable-order fractional neural networks.

A Linear Time Pattern Based Algorithm for N-Queens Problem

The n-queens problem is the placing of n number of queens on an nxn chessboard so that no two queens attack each other. This problem is important due to various usage fields such as VLSI testing, traffic control job scheduling, data routing, dead-lock or blockage prevention, digital image processing and parallel memory storage schemes mentioned in the literature. Besides, this problem has been used as a benchmark for developing new Artificial Intelligence search techniques. However, it is known that backtracking algorithms, one of the most frequently used algorithms to solve this problem, cannot produce all solutions in large n values due to the exponentially growing time complexity. Therefore, various methods have been proposed for producing one or more solutions, not all solutions for each n value. In this study, a pattern based approach that produces at least one unique solution for all n values (n>3) was detected. By using this pattern, a new algorithm that produces at least one unique solution for even very large n values in linear time was developed. The developed algorithm with О(n) time complexity produces quite faster solution to n-queens problem and even in some values, this algorithm produces (n-1)/2 unique solutions in linear time.

Bayesian Rationality Revisited: Integrating Order Effects

Bayes’ inference cannot reliably account for uncertainty in mental processes. The reason is that Bayes’ inference is based on the assumption that the order in which the relevant features are evaluated is indifferent, which is not the case in most of mental processes. Instead of Bayes’ rule, a more general, probabilistic rule of inference capable of accounting for these order effects is established. This new rule of inference can be used to improve the current Bayesian models of cognition. Moreover, it should play an essential role in the search for artificial emotional intelligence.

204 Anatomical Modeling of Two-Trajectory Laser Amygdalohippocampotomy

INTRODUCTION: Laser interstitial thermal therapy (LITT) for mesial temporal lobe epilepsy (mTLE) is typically performed with one laser trajectory to target the medial temporal lobe (MTL). MTL structures such as piriform and entorhinal cortex are epileptogenic, but due to their relative geometry, are difficult to target with one trajectory while simultaneously maintaining adequate ablation of the amygdala and hippocampus. METHODS: Magnetic resonance imaging (MRI) from the Human Connectome Project (HCP) was used to create subject-specific target structures consisting of hippocampus, amygdala, and piriform/entorhinal/perirhinal cortex. An algorithm searched for safe potential trajectories along the hippocampal axis (catheter one) and along the amygdala-piriform axis (catheter two) and compared this to a single trajectory optimized over all structures. The proportion of each structure ablated at various burn radii was evaluated. A cohort of 11 consecutive patients with mTLE received two laser LITT. RESULTS: The two-laser trajectory approach was superior at nearly all burn radii for all hippocampal subfields and amygdala nuclei (p < 0.05). Two-laser trajectories achieved full ablation of MTL cortical structures at physiologically realistic burn radii, while one-laser trajectories could not. 5 patients with at least 1 year follow-up (mean = 21.8 months) experienced Engel class I outcomes; 6 patients with less than 1 year follow-up (mean = 6.6 months) are on track for Engel I outcomes. CONCLUSION: Large-scale artificial intelligence search through high-quality HCP imaging data reveals the theoretical advantage of two-laser LITT over one-laser LITT.

Evaluation of Inspection Efficiency of Diatom Artificial Intelligence Search System Based on Scanning Electron Microscope.

To explore the application values of diatom artificial intelligence (AI) search system in the diagnosis of drowning. The liver and kidney tissues of 12 drowned corpses were taken and were performed with the diatom test, the view images were obtained by scanning electron microscopy (SEM). Diatom detection and forensic expert manual identification were carried out under the thresholds of 0.5, 0.7 and 0.9 of the diatom AI search system, respectively. Diatom recall rate, precision rate and image exclusion rate were used to detect and compare the efficiency of diatom AI search system. There was no statistical difference between the number of diatoms detected in the target marked by the diatom AI search system and the number of diatoms identified manually (P>0.05); the recall rates of the diatom AI search system were statistically different under different thresholds (P<0.05); the precision rates of the diatom AI system were statistically different under different thresholds(P<0.05), and the highest precision rate was 53.15%; the image exclusion rates of the diatom AI search system were statistically different under different thresholds (P<0.05), and the highest image exclusion rate was 99.72%. For the same sample, the time taken by the diatom AI search system to identify diatoms was only 1/7 of that of manual identification. Diatom AI search system has a good application prospect in drowning cases. Its automatic diatom search ability is equal to that of experienced forensic experts, and it can greatly reduce the workload of manual observation of images.

SYSTEMATIC BIBLIOMETRIC REVIEW OF ARTIFICIAL INTELLIGENCE TECHNOLOGY IN ORGANIZATIONAL MANAGEMENT, DEVELOPMENT, CHANGE AND CULTURE

Even now, in the times of advanced digitization, the planning and implementation of many organizational measures remain human-driven. Corresponding to a global trend of application of artificial intelligence in all areas of life, it has received more attention in the last few years and garnered emerging clusters of research in usage of this technology for organizational issues. Some companies already offer tools that support different management tasks in the area of organizational development, but they are not holistic. According to the google trend analysis of the search for artificial intelligence, the inquiry to this topic continues increasing. The purpose of the described investigation was to identify the academic trends in research interaction between such scientific fields, as Artificial Intelligence, Organizational Management, Organizational Development, Organizational Change, and Organizational Culture using bibliometric and network publication analysis. In order to achieve this purpose, we systematically analysed 191 publications between 1983 and 2020 as well as cited and citing publications. The findings of this study provide important conclusions of the current research state. The insightful results are presented in the form of critical review and frame the body of knowledge.