Alon Yamin, co-founder and CEO of the AI-based text analysis platform Copyleaks, is helping to combat plagiarism in education, especially in light of the recent Harvard scandal. Following the controversial accusations against the school's former president Claudine Gay, Yamin emphasized that tackling the issue of plagiarism is more important now than ever, especially with the rise in AI. "A year ago, many people considered plagiarism a moot point following the expansion of AI. What was there to worry about if AI was writing everything? But as we've seen in the news over the last few months, plagiarism hasn't gone anywhere. It seems to be as prevalent as ever," Yamin said to Fox News Digital.

In response to growing recognition of the social impact of new AI-based technologies, major AI and ML conferences and journals now encourage or require papers to include ethics impact statements and undergo ethics reviews. This move has sparked heated debate concerning the role of ethics in AI research, at times devolving into name-calling and threats of "cancellation." We diagnose this conflict as one between atomist and holist ideologies. Among other things, atomists believe facts are and should be kept separate from values, while holists believe facts and values are and should be inextricable from one another. With the goal of reducing disciplinary polarization, we draw on numerous philosophical and historical sources to describe each ideology's core beliefs and assumptions. Finally, we call on atomists and holists within the ever-expanding data science community to exhibit greater empathy during ethical disagreements and propose four targeted strategies to ensure AI research benefits society.

The global artificial intelligence in healthcare market size was valued at USD 10.4 billion in 2021 is expected to expand at a compound annual growth rate (CAGR) of 38.4% from 2022 to 2030. The growing datasets of patient health-related digital information, increasing demand for personalized medicine, and the rising demand for reducing care expenses are some of the major driving forces of the market growth. The growing global geriatric population, changing lifestyles, rising prevalence of chronic diseases has contributed to the surge in demand for diagnosing and improved understanding of diseases in their initial stages. Artificial Intelligence (AI) and machine learning (ML) algorithms are being widely adopted and integrated into healthcare systems to accurately predict diseases in their early stage based on historical health datasets. Furthermore, deep learning technologies, predictive analytics, content analytics, and Natural Language Processing (NLP) tools are enabling care professionals to diagnose patients' underlying health conditions at an earlier stage. The Covid-19 pandemic positively influenced the demand for AI technologies and unearthed the potential held by these advanced technologies.

Artificial intelligence (AI) and machine learning (ML) are poised to transform the way health care is delivered. AI is the use of computers to simulate intelligent tasks typically performed by humans. ML is a domain of AI that involves computers automatically learning from data without a priori programming. While AI has been critiqued as being in its "hype cycle" (throughout this article, AI will be used as shorthand for AI and ML), over time, it is likely that every medical specialty will be influenced by AI, and some will be transformed.1 As AI takes on a larger role in clinical practice, it is clear that multiple levels of oversight are needed. However, even with appropriate outside oversight, the importance of clinician review and trust of these technologies cannot be overstated.

Artificial intelligence has permeated nearly every civilian business imaginable. It has altered the way individuals and businesses operate, and it is quickly becoming a necessary component of modern combat. Training and simulation are diverse fields that use system and software engineering principles to build models that can assist soldiers in training on various combat systems used in actual military forces. Many sensor simulation programs have already been launched by the US Navy and Army. Furthermore, augmented and VR technology techniques can be employed to construct effective, accurate, and dynamic training simulations.

Artificial intelligence has infiltrated practically every civilian industry imaginable. It has changed the way people and businesses work, and it is now swiftly becoming a necessary component of modern combat. One of the criteria that determines how powerful a country is the strength of its army. When compared to other parts, investment in this industry is the largest in some of the most developed countries. A significant portion of this investment is dedicated to rigorous research and development in current technologies, such as artificial intelligence (AI) in military applications.

A common concern surrounding automation in recent years is that it will result in widescale job losses as the work previously done by people is taken over by technology. Of course, the reality doesn't really support this narrative, and indeed, companies that invest in technology often end up employing more people as a result of the improvement in their fortunes heralded by the investment. The leadership team of the fintech company Kashat highlight the reality of investing in technology. They reveal that microfinance has traditionally been highly labor intensive, with many of the skills the same as those used in the sector for years. With the introduction of AI, new skills have been introduced into the underwriting process in order to serve at scale, while enabling employees to further expand their skillset and become even more valuable in the future.

As per some research, AI acts better than humans when it is about diagnosing diseases. You can notice that AI-based technologies are outperforming radiologists at identifying malignant tumors during clinical trials. After observing the excellent outcome, the clinicians believe that AI will replace human efforts in the medical field, but not immediately. In this article, you will get to know the potential of Artificial Intelligence and the significant obstacles to the swift execution of AI in healthcare. Machine Learning is perhaps the most common AI application that enables the system to learn the data and improve the real-time experience without any programming.

One of the more permanent changes of the past year relates to technology in the workplace. The pandemic-induced lockdowns accelerated the digital transformation of business that was already underway, and real estate is no exception, especially when it comes to the incorporation of artificial intelligence. To gain a better idea of the perception of AI in property management, AppFolio conducted a study on the crossover of these two disciplines. When asked "I believe I have a basic understanding of artificial intelligence," 85% of the property management executives, decision makers, and generalist property managers surveyed answered in the affirmative. But when asked "Have you ever used or interacted with AI-based technology," only 32% said yes. 49% said no and 19% were unsure.

The purpose of artificial intelligence (AI) is to create intelligent machines. It is used in multiple domains, including finance, manufacturing, logistics, retail, social media, healthcare, and increasingly, cybersecurity. The current discourse about AI and cybersecurity often confuses the different perspectives, as if the intersection of disciplines is monolithic and one-dimensional. Therefore, we need a common language for discussing the various and disparate intersections of AI and cybersecurity that clarifies the differences. I see three parts to the discussion: AI in the hands of defenders, AI in the hands of attackers, and adversarial AI.