Abstract: This study examines the relationship between student perceptions and their intention to use generative AI in higher education. Drawing on Expectancy-Value Theory (EVT), a questionnaire was developed to measure students' knowledge of generative AI, perceived value, and perceived cost. A sample of 405 students participated in the study, and confirmatory factor analysis was used to validate the constructs. The results indicate a strong positive correlation between perceived value and intention to use generative AI, and a weak negative correlation between perceived cost and intention to use. As we continue to explore the implications of generative AI in education and other domains, it is crucial to carefully consider the potential long-term consequences and the ethical dilemmas that may arise from widespread adoption. Keywords: Expectancy-Value Theory (EVT); Validated Instrument; Generative AI; ChatGPT Introduction The recent launch of ChatGPT (Schulman et al., 2022), an advanced language model based on the Generative Pre-trained Transformer (GPT) architecture, has generated significant interest and excitement in both academic and industry circles (Agrawal et al., 2022; Chui et al., 2022; Cotton et al., 2023; Mucharraz y Cano et al., 2023). With its impressive capabilities to generate coherent and contextually appropriate responses that closely mimic human-like communication, ChatGPT has the potential to become a game changer in students' lives, influencing various aspects of their personal, social and professional experiences. The increasing prevalence of artificial intelligence (AI) in various industries has led to an unprecedented surge in the demand for AI-related skills and knowledge. Generative AI(GenAI), a subset of AI that focuses on generating new content, has shown tremendous potential in applications across numerous domains, revolutionizing the way humans interact with technology and solve complex problems (Russell & Norvig, 2016). In the field of healthcare, AI has been employed in the development of predictive models, diagnosis, and treatment planning, leading to improved patient outcomes (Topol, 2019).

According to Technavio, the global deep learning market size is projected to grow by USD 11113.13 million from 2022 to 2027.

NEW YORK, Dec. 2, 2022 /PRNewswire/ — According to Technavio, the global deep learning market size is projected to grow by USD 11113.13 million …

New Created by Dr. KM Mohsin Let me tell you my story. I graduated with my Ph. D. in computational nano-electronics but I have been working as a data scientist in most of my career. My undergrad and graduate major was in electrical engineering (EE) and minor in Physics. After first year of my job in Intel as a "yield analysis engineer" (now they changed the title to Data Scientist), I literally broke into data science by taking plenty of online classes. I took numerous interviews, completed tons of projects and finally I broke into data science. I consider this as one of very important achievement in my life. Without having a degree in computer science (CS) or a statistics I got my second job as a Data Scientist. Since then I have been working as a Data Scientist.

Nonparametric supervised learning algorithms represent a succinct class of supervised learning algorithms where the learning parameters are highly flexible and whose values are directly dependent on the size of the training data. In this paper, we comparatively study the properties of four nonparametric algorithms, K-Nearest Neighbours (KNNs), Support Vector Machines (SVMs), Decision trees and Random forests. The supervised learning task is a regression estimate of the time lapse in medical insurance reimbursement. Our study is concerned precisely with how well each of the nonparametric regression models fits the training data. We quantify the goodness of fit using the R-squared metric. The results are presented with a focus on the effect of the size of the training data, the feature space dimension and hyperparameter optimization. The findings suggest k-NN's and SVM's algorithms as better models in predicting welldefined output labels (i.e,

There is an ongoing worldwide pop culture phenomenon which has recently engulfed the entire world, and of course you know what I'm talking about: data science! But you probably knew that. While the story in Endgame revolves more or less around the Infinity Stones -- as has the entire MCU for quite a while -- and their role in saving nothing less than the entire universe (or half of it, anyways), the practice of data science actually also has something to learn from their powers. I know you don't believe me, but let's take a look. Don't forget, Thanos was a bit of a data scientist himself.

It seems to me that the hype about AI makes really difficult for experienced investors to understand where the real value and innovation are. I would like then to humbly try to bring some clarity to what is happening on the investment side of the artificial intelligence industry. We have seen as in the past the development of AI has been stopped by the absence of funding, and thus studying the current investment market is crucial to identify where AI is going. First of all, it should be clear that investing in AI is extremely cumbersome: the level of technical complexity goes out of the pure commercial scope, and not all the venture capitalists are able to fully comprehend the functional details of machine learning. This is why the figures of the "Advisors" and "Scientist-in-Residence" are becoming extremely important nowadays.

It seems to me that the hype about AI makes really difficult for experienced investors to understand where the real value and innovation are. I would like then to humbly try to bring some clarity to what is happening on the investment side of the artificial intelligence industry. We have seen as in the past the development of AI has been stopped by the absence of funding, and thus studying the current investment market is crucial to identify where AI is going. First of all, it should be clear that investing in AI is extremely cumbersome: the level of technical complexity goes out of the pure commercial scope, and not all the venture capitalists are able to fully comprehend the functional details of machine learning. This is why the figures of the "Advisors" and "Scientist-in-Residence" are becoming extremely important nowadays.