As Artificial Intelligence (AI) changes how businesses work, there is a growing need for people who can work in this sector. This paper investigates how well universities in United Kingdom offering courses in AI, prepare students for jobs in the real world. To gain insight into the differences between university curricula and industry demands we review the contents of taught courses and job advertisement portals. By using custom data scraping tools to gather information from job advertisements and university curricula, and frequency and Naive Bayes classifier analysis, this study will show exactly what skills industry is looking for. In this study we identified 12 skill categories that were used for mapping. The study showed that the university curriculum in the AI domain is well balanced in most technical skills, including Programming and Machine learning subjects, but have a gap in Data Science and Maths and Statistics skill categories.

Artificial Intelligence (AI) is increasingly acknowledged as a vital component for the advancement and competitiveness of modern organizations, including small and medium enterprises (SMEs). However, the adoption of AI technologies in SMEs faces significant barriers, primarily related to cost, lack of technical skills, and employee acceptance. This study proposes a comprehensive, phased framework designed to facilitate the effective adoption of AI in SMEs by systematically addressing these barriers. The framework begins with raising awareness and securing commitment from leadership, followed by the adoption of low-cost, general-purpose AI tools to build technical competence and foster a positive attitude towards AI. As familiarity with AI technologies increases, the framework advocates for the integration of task-specific AI tools to enhance efficiency and productivity. Subsequently, it guides organizations towards the in-house development of generative AI tools, providing greater customization and control. Finally, the framework addresses the development of discriminative AI models to meet highly specific and precision-oriented tasks. By providing a structured and incremental approach, this framework ensures that SMEs can navigate the complexities of AI integration effectively, driving innovation, efficiency, and competitive advantage. This study contributes to the field by offering a practical, prescriptive framework tailored to the unique needs of SMEs, facilitating the successful adoption of AI technologies and positioning these organizations for sustained growth in a competitive landscape.

AI algorithms used in the public sector, e.g., for allocating social benefits or predicting fraud, often involve multiple public and private stakeholders at various phases of the algorithm's life-cycle. Communication issues between these diverse stakeholders can lead to misinterpretation and misuse of algorithms. We investigate the communication processes for AI fairness-related decisions by conducting interviews with practitioners working on algorithmic systems in the public sector. By applying qualitative coding analysis, we identify key elements of communication processes that underlie fairness-related human decisions. We analyze the division of roles, tasks, skills, and challenges perceived by stakeholders. We formalize the underlying communication issues within a conceptual framework that i. represents the communication patterns ii. outlines missing elements, such as actors who miss skills for their tasks. The framework is used for describing and analyzing key organizational issues for fairness-related decisions. Three general patterns emerge from the analysis: 1. Policy-makers, civil servants, and domain experts are less involved compared to developers throughout a system's life-cycle. This leads to developers taking on extra roles such as advisor, while they potentially miss the required skills and guidance from domain experts. 2. End-users and policy-makers often lack the technical skills to interpret a system's limitations, and rely on developer roles for making decisions concerning fairness issues. 3. Citizens are structurally absent throughout a system's life-cycle, which may lead to decisions that do not include relevant considerations from impacted stakeholders.

Fox News Flash top sports headlines are here. Check out what's clicking on Foxnews.com. Major League Soccer is taking an innovative approach to scouting. The league has partnered with ai.io, the makers of mobile phone scouting app aiScout, in an effort to discover new soccer talent across the North American continent. Every MLS first team clubs, as well as MLS Next Pro and MLS Next teams, are expected to have access to the AI-powered platform.

Objective technical skill assessment is crucial for effective training of new surgeons in robot-assisted surgery. With advancements in surgical training programs in both physical and virtual environments, it is imperative to develop generalizable methods for automatically assessing skills. In this paper, we propose a novel approach for skill assessment by transferring domain knowledge from labeled kinematic data to unlabeled data. Our approach leverages labeled data from common surgical training tasks such as Suturing, Needle Passing, and Knot Tying to jointly train a model with both labeled and unlabeled data. Pseudo labels are generated for the unlabeled data through an iterative manner that incorporates uncertainty estimation to ensure accurate labeling. We evaluate our method on a virtual reality simulated training task (Ring Transfer) using data from the da Vinci Research Kit (dVRK). The results show that trainees with robotic assistance have significantly higher expert probability compared to these without any assistance, p < 0.05, which aligns with previous studies showing the benefits of robotic assistance in improving training proficiency. Our method offers a significant advantage over other existing works as it does not require manual labeling or prior knowledge of the surgical training task for robot-assisted surgery.

As the world becomes increasingly more digital and connected, artificial intelligence (AI) is transforming how we work and live. From chatbots, such as OpenAI's ChatGPT, and virtual assistants to predictive analytics and machine learning, AI is revolutionizing industries and creating new opportunities for innovation and growth. However, with these opportunities come challenges, particularly in the workforce. As jobs become more automated and AI systems become more sophisticated, it's becoming increasingly important for workers to have the skills and knowledge necessary to thrive in an AI-enabled workplace. In this article, we'll explore some of the essential AI skills that workers will need in the future, as well as strategies for upskilling and reskilling the workforce to prepare them for the challenges and opportunities presented by AI.

Josh Tobin, the co-founder and CEO of machine learning tool provider Gantry, didn't want to believe it at first. But Tobin, who previously worked as a research scientist at OpenAI, eventually came to the conclusion that it was true: The end of traditional ML modeling is upon us. The idea that you didn't need to train a machine learning model anymore and can get better results by just using off-the-shelf models without any tuning on your own custom data seemed wrong to Tobin, who spent years learning how to build these systems. When he first heard of the idea after starting his ML tool business Gantry, which he co-founded in 2021 with fellow OpenAI alum Vicky Cheung, he didn't want to believe it. "The first four or five times I heard that, my thinking was like, okay, these companies just don't know what they're doing," Tobin said.

Many organizations are determining how to strengthen their teams amid economic uncertainty and skills shortages. Building technical talent is key to helping teams withstand the challenges of undergoing digital transformation. Your approach can differentiate your organization and set it up for success. Whether you're focusing inward or hiring, what matters is a well-defined strategy to help make informed decisions with a positive long-term impact. Melanie Kalmar, CIO at Dow, recently wrote about why you should be focused on building digital acumen; "Building digital acumen is essential across our organization if we're going to realize the true potential of what we're trying to do with digitalization – from the CIO and information systems teams to sales, supply chain, communications, manufacturing, R&D, and more."

A recent report by the Institute for the Future, in partnership with Dell, predicts that 85% of jobs that will be available in 2030 haven't been invented yet. I don't think it's as crazy as it seems, especially when we think of everything that has changed in the last ten years, like social media, artificial intelligence, and automation. The work human beings do will continue to shift as some jobs become obsolete and new jobs emerge – and the experience and skill set we'll need in the future look very different from the ones we need today. Soft skills will grow in importance as the demand for the things machines can't do continues to increase. However, the ability to understand and work confidently with technology will still be critical.

Training and education are imperative in many facets of healthcare -- from understanding clinical systems, to improving technical skills, to understanding regulations and professional standards. Technology often presents unique training challenges because of the ways in which it disrupts existing workflow patterns, alters clinical practice, and creates both predictable and unforeseen challenges. The emergence of artificial intelligence (AI), its anticipated expansion in healthcare, and its sheer scope point to significant training and educational needs for medical students and practicing healthcare providers. These needs go far beyond developing technical skills with AI programs and systems; rather, they call for a shift in the paradigm of medical learning. An AMA Journal of Ethics article titled "Reimagining Medical Education in the Age of AI" discusses how traditional medical education -- which focuses on information acquisition, retention, and application -- is insufficient, counterproductive, and potentially harmful in the era of digital medicine.