Problems in probability theory prove to be one of the most challenging for students. Here, we formulate and discuss four related problems in probability theory that proved difficult for first to fourth-year undergraduate students whose first language was not English. These examples emphasize how crucial it is to understand the conditions and requirements of the problems precisely before starting to solve them. We discuss the solutions to those problems in detail, complement them with numerical estimations, and link the conditions in the problems to the logical statements in Python programming language. We also tested two widely used chatbots (GPT-4o and Claude 3.5 Sonnet) by checking their responses to these problems.

Personalized adaptive learning (PAL) stands out by closely monitoring individual students' progress and tailoring their learning paths to their unique knowledge and needs. A crucial technique for effective PAL implementation is knowledge tracing, which models students' evolving knowledge to predict their future performance. Recent advancements in deep learning have significantly enhanced knowledge tracing through Deep Knowledge Tracing (DKT). However, there is limited research on DKT for Science, Technology, Engineering, and Math (STEM) education at Historically Black Colleges and Universities (HBCUs). This study builds a comprehensive dataset to investigate DKT for implementing PAL in STEM education at HBCUs, utilizing multiple state-of-the-art (SOTA) DKT models to examine knowledge tracing performance. The dataset includes 352,148 learning records for 17,181 undergraduate students across eight colleges at Prairie View A&M University (PVAMU). The SOTA DKT models employed include DKT, DKT+, DKVMN, SAKT, and KQN. Experimental results demonstrate the effectiveness of DKT models in accurately predicting students' academic outcomes. Specifically, the SAKT and KQN models outperform others in terms of accuracy and AUC. These findings have significant implications for faculty members and academic advisors, providing valuable insights for identifying students at risk of academic underperformance before the end of the semester. Furthermore, this allows for proactive interventions to support students' academic progress, potentially enhancing student retention and graduation rates.

Mariza Ferro is a professor at the Federal Fluminense University and a visiting professor at Bordeaux University. She has been working in the field of AI since 2002. She works on AI for good, including human-centric AI, ethical and trustworthy AI, green and sustainable AI, and AI for sustainable development goals. She guides her research based on the principle that AI must benefit humankind. Furthermore, she is also working with public outreach by making science available for all.

Women seeking a relationship have revealed which messages a prospective partner should definitely not send on dating apps. Researchers led by Purdue University, Indiana, found that among 275 participants -- mostly female -- starting a conversation with a sexually explicit message was the biggest turn-off, especially for people looking for a long-term relationship. Conversely, someone whose initial message included a greeting and a question was more likely to get a positive response. It comes as a separate group of scientists also revealed that tall men would prioritize tall women for relationships, but see short ones as just a fling. Women seeking a long-term relationship on dating apps find a sexually explicit opening line surprising and a violation, according to new research.

The National Science Foundation (NSF) has designated the Center for Research in Computer Vision at the University of Central Florida (UCF) as a site for Research Experiences for Undergraduates (REU) in the area of Computer Vision for 2021-2023. The purpose of the REU is to encourage undergraduate students to pursue graduate school and research careers. UCF has continued to be an REU site in Computer Vision since the inception of REU by NSF in 1987. Through the longest REU program in the country, Dr. Shah and his colleagues have trained more than 300 REU students from more than 75 universities all over the USA, resulting in more than 80 high quality journal and conference publications. All previous REU participants successfully completed their degree in computer science related areas; about half have continued with graduate studies, several participants are now faculty members at different universities, and several participants have started their own companies.

We report on Shallow Blue (SB), an autonomous chess agent constructed by a small group of faculty and undergraduate students at Canisius College. In addition to pushing the limits of consumer grade components at low cost, SB is a focal point for interdisciplinary student projects spanning computer science, engineering, and physics. We demonstrate that undergraduate students can engage in rich, long-term robotic design and applied Artificial Intelligence (AI) from both hardware and software perspectives. Student outcomes of SB include senior theses, conference presentations, peer-reviewed publications, and admission to graduate programs. Students who participated also report substantial development in skills and knowledge applicable to their post-undergraduate education and careers.

This guide is designated to anybody with basic programming knowledge or a computer science background interested in becoming a Research Scientist with on Deep Learning and NLP. You can go Bottom-Up or Top-Down both works well and it is actually crucial to know which approach suites you the best. If you are okay with studying lots of mathematical concepts without application then use Bottom-Up. If you want to go hands-on first then use the Top-Down first. The Mathematical Foundation part is for all Artificial Intelligence branches such as Machine Learning, Reinforcement Learning, Computer Vision and so on. AI is heavily math-theory based so a solid foundation is essential.

New Delhi, September 10, 2020: As part of its ongoing efforts to promote skilling as a national priority, NASSCOM FutureSkills and Microsoft have joined hands to launch a nation-wide AI skilling initiative. The initiative aims to skill 1 million students in AI by 2021. The collaboration will provide Microsoft's AI, machine learning and data science expertise to students through easy to consume modules including live demos, hands on workshops and assignments. These introductory sessions on AI will be available for undergraduate students at no cost and will cover the basics of data science, machine learning models on Azure, and understanding of cognitive services to build intelligent solutions. The partnership with NASSCOM FutureSkills is an extension of Microsoft's global skilling initiative to help 25 million people worldwide acquire new digital skills, needed to thrive in a digital economy.

In today's software development industry, jobs have become more cognitively complex and require workers who are more collaborative and creative in their problem-solving techniques.14 Employees also must be able to combine diverse specializations rather than just having routine knowledge in one domain.22 While the "hard" technical skills associated with programming remain a prerequisite for new hires, the industry also wants software developers who can readily demonstrate a range of so-called "soft" skills, including the capacity to communicate clearly, facilitate an open and inclusive workplace environment, and demonstrate the resiliency and flexibility to work on a range of tasks.24 Our own past research4 interviewing software industry hiring managers indicates that discerning such soft skills among new hires is an overwhelming priority across companies. The industry hiring managers and directors we interviewed over the past two years stated that while the capacity to code is a necessity for employment, these managers actually spend the vast majority of their recruitment time assessing a candidate's soft skills, as these suggest the presence of adaptive expertise (AE) and the candidate's potential for persistence and continual learning on the job.4 What was also intriguing to us in discussion with a wide range of hiring managers was their expressed willingness to consider graduates from alternative educational settings--in particular, so-called "coding bootcamps"--alongside more traditional hires from undergraduate computer science (CS) programs.4 While there is no single representative model of a coding bootcamp, these intense training programs extend, on average,14 weeks in duration, cost approximately $12,000, and emphasize teaching the programming skills that employers look for from new software developer hires (particularly front-end programming) while also enabling their graduates to grasp the most essential aspects of coding.6 Much of this expressed willingness to hire codecamp graduates stemmed directly back to hiring managers' perceptions that what boot-camp students may lack in rigorous CS knowledge is counterbalanced with greater work experience and the interpersonal and intrapersonal skills to join a wider team while remaining resilient in the face of unexpected challenges. This, of course, represented only one party's perspective.

Harvey Mudd computer science professor Jim Boerkoel works with a student in his robotics lab, where ... [ ] he focuses on using AI to develop human-robot teamwork. The rapid expansion of artificial intelligence from facial recognition and self-driving cars to understanding human speech is having a major impact on business and society, which is why the lack of diversity among the people developing AI tools is so troubling. A recent study published by the AI Now Institute of New York University concluded that a "diversity disaster" has resulted in flawed AI systems that perpetuate gender and racial biases. The report found that more than 80 percent of AI professors are men and only 15% of AI researchers at Facebook and 10 percent of AI researchers at Google are women. The numbers reflect a larger issue facing the computer sciences where, in 2018, less than 25 percent of PhDs were awarded to females and/or minorities, who are historically underrepresented in computing. Industry and academia are taking steps to increase diversity among AI researchers through steps designed to ensure that future technology benefits all people and not just a homogenous group of white males.