Computational Thinking (CT) is a key skill set for students in higher education to thrive and adapt to an increasingly technology-driven future and workplace. While research on CT education has gained remarkable momentum in K12 over the past decade, it has remained under-explored in higher education, leaving higher education teachers with an insufficient overview, knowledge, and support regarding CT education. The proliferation and adoption of artificial intelligence (AI) by educational institutions have demonstrated promising potential to support instructional activities across many disciplines, including CT education. However, a comprehensive overview outlining the various aspects of integrating AI in CT education in higher education is lacking. To mitigate this gap, we conducted this systematic literature review study. The focus of our study is to identify initiatives applying AI in CT education within higher education and to explore various educational aspects of these initiatives, including the benefits and challenges of AI in CT education, instructional strategies employed, CT components covered, and AI techniques and models utilized. This study provides practical and scientific contributions to the CT education community, including an inventory of AI-based initiatives for CT education useful to educators, an overview of various aspects of integrating AI into CT education such as its benefits and challenges (e.g., AI potential to reshape CT education versus its potential to diminish students creativity) and insights into new and expanded perspectives on CT in light of AI (e.g., the decoding approach alongside the coding approach to CT).

They may have a general idea of what AI can do but are unsure of how to implement it effectively. This lack of understanding can lead to misguided investments in AI technologies that do not align with the organization's goals. Another challenge organizations face is the impact of AI on the workforce. As AI becomes more prevalent in the workplace, it may replace certain tasks previously performed by humans, potentially leading to job displacement. The Washington Post reports that the implementation of AI could lead to a significant shift in the labor market, with some jobs becoming obsolete and others emerging to support AI-related technologies.

In conclusion, human leadership remains essential in an AI future. AI can be a powerful tool for improving efficiency, but it is not a replacement for human leadership. Human leadership can bring creativity, empathy, and problem-solving skills to the table that AI simply cannot. Human leaders are also responsible for setting ethical standards for the use of AI and making sure that AI is used in ways that prioritize the safety and well-being of individuals and society. Ultimately, a successful AI future depends on strong human leadership.

The first paper, "Predicting Diabetes Risk from Electronic Health Records: A Machine Learning Approach," uses machine learning to improve diabetes risk prediction accuracy. Diabetes is a chronic disease that affects millions of people worldwide, and accurate risk prediction is important for identifying individuals who are at risk of developing the disease and for targeting preventive interventions. The authors of this paper propose a machine learning approach that uses data from electronic health records (EHRs) to predict diabetes risk, and demonstrate that their approach outperforms other state-of-the-art methods. In the second paper, "Deep Learning for Medical Image Analysis: A Review," deep learning is discussed for the analysis of medical images. For diagnosis and treatment planning, medical images such as X-rays, CT scans, and MRIs are crucial.

Artificial intelligence as part of network infrastructure monitoring has … the marriage of AI and IT operations, in which machine learning is …

Artificial intelligence as part of network infrastructure monitoring has been a popular topic for several years. But only recently has the development of AI network monitoring made it practical to deploy in production networks on a broader scale. With AI network monitoring, the main objectives are to sustain optimal service levels, gain accurate insight into potential infrastructure issues and get that data before business and network operations are affected. To help with this process, machine learning -- a type of AI -- applies algorithms to telemetry and other data streams to gauge a baseline for normal operations. Once the AI network monitoring service establishes that baseline, it can then look for deviations that might indicate a potential infrastructure problem.

Artificial Intelligence was first popularized by a small group of scientist gathered at the Dartmouth College in the United States in 1956. Since then, AI has advanced considerably and is powering many real-world applications ranging from facial recognition to language translators and virtual assistants such as Siri and Alexa. Still, we are far from witnessing AI-powered robots emulating humans. So far that is confined to the Sci-Fi movies. However, AI has created quite a stir in the business world with its many benefits and challenges.

AI or Artificial Intelligence is a technology gaining more attention every year. Many self-learning and intelligent programs are used in the field of software development as well as distinct divisions of IT. Though AI cannot take decisions based on human cognitive abilities, still, innumerable modern machines can efficiently learn, think, and make complex decisions. It offers more opportunities for the process of automation that don't need high creativity, and hence, can be performed by a machine. With AI's efficient to analyze a large volume of data in just a fraction of minutes and swiftly carry available options, it is extensively used in various sectors including IT, marketing, development, recruitment, and more. AI in recruitment is concerned with automating the recruiting process and to identify novel ways of hiring talent. In fact, AI in recruitment can kick off many solutions and uses, which are being revealed in this post. AI or Artificial Intelligence is the development and theory of computer systems able to conduct tasks normally utilizing human intelligence, like speech recognition, visual perception, language translation, decision making, and more.

Tesla, a company by Elon Musk, has changed the automobile market. Everyone should learn how to use technology correctly from Tesla. Today, we will discuss how Tesla uses Artificial Intelligence and will understand its benefits and challenges. Tesla Motors, established in 2003, owns a market value of more than $700 billion. Tesla USP is their electric vehicles, sustainable energy generation products, solar panels, and much more.

In the latest, five US federal agencies are seeking input on how financial institutions are using AI tools. These financial agencies recognize and acknowledge the benefits of AI, noting that AI tools have the potential to augment business decision-making and enhance services available to consumers and businesses. While there are certainly benefits, the agencies recognize that use of AI tools are not without risks, including operational vulnerabilities, cyber threats, heightened consumer protection issues, and privacy concerns. While signaling that they may create more guidance, the agencies point out that there are currently many regulations that govern use of AI tools. This request for comment gives financial institutions an opportunity to bring the benefits and challenges from the use of AI to regulators' attention.