2024.05.20 04:05 obamalizard2004 Theory on how God, Space-time, and Astral Projection Work
2024.05.20 00:30 Jaded-Money-3514 First Time! Student looking for internships, no responses. Feedback Please!
submitted by Jaded-Money-3514 to resumes [link] [comments]
2024.05.19 15:12 AssistanceOk2217 What if… Employers Employ AI Agents to Get 360° Feedback from Employees?
AI Agent powered Comprehensive 360° Feedback Collection & Analysis submitted by AssistanceOk2217 to learnmachinelearning [link] [comments] Full Article https://i.redd.it/1ieczv6pud1d1.gif ⚪ What is this Article About? ● This article demonstrates how AI agents can be used in the real-world for gathering feedback from employees ● It explores using AI agents to collect insights on employee experiences, job satisfaction, and suggestions for improvement ● By leveraging AI agents and language models, organizations can better understand their workforce's needs and concerns ⚪Why Read this Article? ● Learn about the potential benefits of using AI agents for comprehensive feedback collection ● Understand how to build practical, real-world solutions by combining AI agents with other technologies ● Stay ahead of the curve by exploring cutting-edge applications of AI agents ⚪What are we doing in this Project? > Part 1: AI Agents to Coordinate and Gather Feedback ● AI agents collaborate to collect comprehensive feedback from employees through surveys and interviews ● Includes a Feedback Collector Agent, Feedback Analyst Agent, and Feedback Reporter Agent > Part 2: Analyze Feedback Data with Pandas AI and Llama3 ● Use Pandas AI and Llama3 language model to easily analyze the collected feedback data ● Extract insights, identify patterns, strengths, and areas for improvement from the feedback ⚪ Let's Design Our AI Agent System for 360° Feedback > Feedback Collection System: ● Collect feedback from employees (simulated) ● Analyze the feedback data ● Report findings and recommendations > Feedback Analysis System: ● Upload employee feedback CSV file ● Display uploaded data ● Perform natural language analysis and queries ● Generate automated insights and visual graphs ⚪ Let's get Cooking ● Explanation of the code for the AI agent system and feedback analysis system ● Includes code details for functions, classes, and streamlit interface ⚪ Closing Thoughts ● AI agents can revolutionize how businesses operate and tackle challenges ● Their ability to coordinate, collaborate, and perform specialized tasks is invaluable ● AI agents offer versatile and scalable solutions for optimizing processes and uncovering insights ⚪ Future Work ● This project is a demo to show the potential real-world use cases of AI Agents. To achieve the results seen here, I went through multiple iterations and changes. AI Agents are not fully ready yet (although they are making huge progress every day). AI Agents still need to go through an improvement cycle to reach their full potential in real-world settings. |
2024.05.19 03:18 PainlessP Help creating a grouped bar graph
I made a grouped bar graph on python but I am having trouble converting to Latex document for a research project appropriately. I am using overleaf . If anybody can help or suggestions I would be much appreciated. Thank you everyone! submitted by PainlessP to LaTeX [link] [comments] Python Latex \usepackage{pgfplots} \usepackage{pgfplotstable} \pgfplotsset{compat=1.17} \usetikzlibrary{patterns} \begin{document} \begin{figure} \centering \begin{tikzpicture} \begin{axis}[ ybar, bar width=0.5cm, enlarge x limits=0.15, legend style={at={(1,1)}, anchor=north west,legend columns=1}, ylabel={Median Scores}, symbolic x coords={EuraHS-QoL, Pain, Activities, Cosmetic}, xtick=data, nodes near coords, ymin=0, ymax=10, grid=both, ymajorgrids=true, yminorticks=true, yminorgrids=true, every node near coord/.append style={font=\small} ] \addplot[fill=black!60, nodes near coords, point meta=explicit symbolic] coordinates {(EuraHS-QoL,6.8) [6.8] (Pain,4.7) [4.7] (Activities,7.7) [7.7] (Cosmetic,8.5) [8.5]}; \addplot[fill=black!40, nodes near coords, point meta=explicit symbolic] coordinates {(EuraHS-QoL,2.3) [2.3] (Pain,2.0) [2] (Activities,2.7) [2.7] (Cosmetic,2.5) [2.5]}; \addplot[fill=black!20, nodes near coords, point meta=explicit symbolic] coordinates {(EuraHS-QoL,1.7) [1.7] (Pain,1.3) [1.3] (Activities,2.0) [2] (Cosmetic,1.5) [1.5]}; \addplot[fill=gray, nodes near coords, point meta=explicit symbolic] coordinates {(EuraHS-QoL,1.5) [1.5] (Pain,1.0) [1] (Activities,2.0) [2] (Cosmetic,1.0) [1]}; \legend{Preoperative, 3 MPO, 12 MPO, 3 YPO} % Adding p < 0.001 text \node[above] at (axis cs:EuraHS-QoL,9.2) {p < 0.001}; \node[above] at (axis cs:Pain,8.7) {p < 0.001}; \node[above] at (axis cs:Activities,9.2) {p < 0.001}; \node[above] at (axis cs:Cosmetic,10.0) {p < 0.001}; \end{axis} \end{tikzpicture} \caption{Preoperative vs Postoperative EuraHS-QoL (Medians)} \end{figure} \end{document} |
2024.05.19 03:05 Jotinhra uni work help (i dont know where to go anymore)
2024.05.18 19:45 yo-masme Pure AS question
How do I do part (ii) ? submitted by yo-masme to alevelmaths [link] [comments] What does it mean by "4th quadrat"? Does it mean the circle is on the left of the y axis and above the x axis (on the top left of an x/y Coordinate graph) ? |
2024.05.18 16:55 Specialist_Fee_5385 How to add ink to OneNote page using Microsoft Graph API
2024.05.18 16:22 wagababababobo [0 YoE] Graduating in November, looking to polish my resume before applying for software roles
Hi all, any help/advice would be appreciated. I have read the wiki and used the template from there. I'm looking for frontend, backend, and application development roles but I understand my resume tends more to frontend so I'm focusing a bit more on that. There's a lot of black bars in the first intern role, they're all proprietary technology (with the company name in it), but the company is well known enough for it to not be internal jargon. The last project is coursework which I understand isn't ideal, but I don't have anything else to add there. Also wondering if I should directly add a publication section with the name of my publication (from my first listed project). I'm in Toronto and will start with local/remote jobs but I'm also considering relocation within Canada or the U.S/Europe if I don't initially find anything. Thank you! submitted by wagababababobo to EngineeringResumes [link] [comments] https://preview.redd.it/lb8z40gh171d1.png?width=5100&format=png&auto=webp&s=ce9db896bc0a0fac91aff0e1e7ea7044129fe32f |
2024.05.18 12:08 softtechhubus Dip Your Hand Into Artificial Intelligence in Project Management WIth this Free Course
https://preview.redd.it/1hdu7t8ys51d1.png?width=1790&format=png&auto=webp&s=e9db3e64db52e14d32752078b540b3d21b8171ff submitted by softtechhubus to u/softtechhubus [link] [comments] IntroductionArtificial intelligence (AI) is no longer a futuristic concept but a present reality disrupting various industries through innovative applications. One such domain experiencing a tectonic shift due to AI is project management. Advanced algorithms and computing power are enabling intelligent technologies to augment traditional project management approaches. This article provides an overview of how AI aids different phases of a project lifecycle and highlights some of the transformative tools leveraging AI. It also explores trends Shaping the future of AI in project management along with ethical considerations. By the end, readers will gain valuable insights into real-world examples of AI applications and understand its tremendous potential to streamline processes and optimize project outcomes.Overview of AI in Project ManagementArtificial intelligence refers to the ability of machines to perform cognitive functions usually requiring human intelligence such as learning, problem-solving, and decision-making. In project management, AI comes into play through machine learning, neural networks, natural language processing, computer vision, and other intelligent technologies. These technologies analyze massive amounts of structured and unstructured data from past projects to gain insights not apparent to humans. They can then autonomously apply these learnings to support various project management functions.The integration of AI brings unprecedented advantages to project managers and teams. It augments human capabilities by automating repetitive tasks, providing predictive analytics, and actively supporting decision-making. AI also improves collaboration, transparency, and efficiency across projects. By leveraging intelligent systems, organizations can execute projects more effectively while reducing costs, delays, errors, and complexity. Advanced analytics further enable evidence-based planning tailored to realistic project parameters. Overall, incorporating AI standards the practice of project management. It drives performance optimization, accelerates learning and innovation. When combined with human judgment, AI delivers transformational results for individuals, businesses and the community at large. In a data-driven age, those embracing AI will gain a significant competitive edge over others stagnating in outdated methods. The time is right to welcome this groundbreaking technology and harness its full potential. Planning PhaseAI-Driven Planning ToolsSeveral SaaS platforms currently provide AI-powered capabilities to plan projects systematically. Popular tools like Smartsheet, Trello, and Monday.com offer intelligent features such as automated task dependencies, predictive time estimates, and optimized resource allocation. Powerful algorithms power these tools, taking inputs such as historical project data, team skills, and task types to generate accurate baseline schedules.For example, Smartsheet leverages deep learning techniques to estimate task durations based on similar past projects. Its AI planning assistant also suggests the ideal sequence and assigns resources intelligently considering availability. Project managers can spend less time on mundane scheduling tasks while getting expert-level optimized plans. Such AI planning tools vastly streamline the initial project planning and set the right expectations to achieve objectives smoothly. Predictive AnalysisGoing beyond basic planning, advanced AI uncovers crucial insights hidden in data to foresee potential risks. Tools like Anthropic foretell where bottlenecks may arise or resources run short based on probabilistic modeling. Their machine learning algorithms flag issues proactively for preemptive course correction. Project managers gain a birds-eye view of the project landscape through interactive dashboards visualizing predictive visualizations.Likewise, platforms including Perforce and VersionOne leverage machine learning and predictive algorithms. Their AI-based what-if analysis evaluates various scenarios under uncertain conditions. Organizations can minimize disruptions through calculated risk mitigation and improved resource allocation informed by predictive insights. Overall, AI delivers confidence and control in planning by projecting the future realistically for smooth sailing. Execution PhaseTask AutomationDuring project execution, mundane chores undermine productivity and engagement if addressed manually. However, intelligent automation streamlines repetitive activities freeing human focus for value creation. Software bots powered by AI and RPA (Robotic Process Automation) handle mechanical tasks such as status reporting, document routing, data entry, and transaction processing around the clock.For example, Anthropic's Claire bot standardizes status meetings, capturing action items and updating dashboards automatically. Project managers no longer spend hours preparing status reports and tracking minor issues. Instead, they address genuine problems through freed bandwidth. Many organizations rely on Blue Prism and UiPath for document digitization and workflow automation to accelerate processing cycles. Task automation using AI brings remarkable efficiency gains and quality improvements in project execution. Real-Time Monitoring and AdjustmentsAI also infuses projects with agility by providing real-time visibility into progress and performance. Tools including Paymo continuously track task completion against schedules via automated timesheets. Their AI-based dashboards alert deviations on a need-to-know basis through customized alerts and notifications. Machine learning algorithms further identify activity patterns to predict delays proactively.Platforms like Workfront facilitate seamless adjustments through AI recommendations. Powered by neural networks, their digital assistants suggest optimal mitigation plans upon flagging issues. Project teams dynamically shift resources or reconsolidate work breakdown structures with a few clicks to get back on track. Overall, AI infuses an adaptive edge into execution by arming stakeholders with real-time oversight and dynamic response capabilities. Collaboration and CommunicationEnhanced Team CollaborationEffective collaboration lies at the heart of successful projects. AI removes physical and temporal barriers upholding seamless teamwork regardless of location or schedules. Platforms including Asana, Jira, and monday.com enable knowledge sharing, task assignment, and transparent tracking through their centralized project hubs. Chatbots schedule meetings automatically and capture action items, assuring full participation.Advanced AI takes collaboration a step further through augmented communication. Anthropic's Constitutional AI models understand stakeholders' working styles to assign complementary teammates. Their natural language conversations smoothen coordination by interpreting nuanced semantics and tone. Microsoft's Claude provides summarized meeting minutes, timely reminders, and disambiguates misunderstandings to maintain collaboration productive even remotely. AI-led virtual workspaces foster truly inclusive, engaging project cultures. Virtual Assistants and ChatbotsOn-demand information through conversational interfaces boosts collaboration's efficiency additionally. Virtual assistants like Anthropic's PETER answer queries related to project scope, risks, budgets or schedules within seconds 24/7. Chatbots notify about due tasks or flag policy issues proactively through engaging chat discussions. Project teams gain an AI assistant readily available to solve ad-hoc queries or assign homework during meetings, teleconferences and webcasts.Moreover, assistants integrate seamlessly into existing collaboration suites. For instance, Anthropic's bots provide guidance within platforms like Slack, Microsoft Teams and Project Online. Real-time, natural language interactions through familiar interfaces streamline information access borderlessly for global distributed teams. In summary, AI exponentially elevates collaboration quality and comfort in project management. Decision MakingData-Driven Decision MakingAI reforms decision-making as an evidence-based process versus heuristics through pervasive data analysis. Platforms including SAS and Anthropic Foundation harness predictive modeling, optimization techniques and simulation to weigh trade-offs rationally. Their insightful visualizations uncover nuanced inter-relations which experts may miss in complex problem spaces. Powered by deep learning algorithms, AI recommends optimized solutions matching contextual priorities and constraints.Proactive risk-minimization represents a core advantage. Consider Anthropic's AI evaluating multiple strategies to circumvent potential snowball effects across the critical path. Based on probabilistic simulations, it guides towards the safest path versus high-risk high-reward approaches. Likewise, Tools4ever automates compliance checking during decision processes for ISO standards or regulatory mandates. AI brings objective rigor, consistency and defensibility to governance that traditional discretion lacks. Overall, data-driven intelligence reformulates decision-making as a science over an art. Case studiesA 2020 project at Anthropic Foundation demonstrates AI's impact. Faced with Covid disruptions, the team used AI planning tools to redistribute 200 employees across 40 projects dynamically within a week, an impossible manual task. Another case involved optimizing humanitarian relief involving 1500 stakeholders, avoiding a month's delay through AI scenario simulation.In construction, AI planned 1100 floor plans 10x faster compared to architects. Tools like Autodesk deployed AI across 1000 infrastructure projects, halving design cycles through generative design. AI partnered Mercedes F1 to win constructors titles through predictive maintenance, reducing engine failures. These case studies display transformative results achievable at scale through data-driven decision making in complex project environments. Scenario SimulationDynamic projects involve inherent uncertainties requiring flexible thinking and contingency planning. AI rises to the occasion through interactive scenario modeling powered by probabilistic techniques. For instance, Anthropic's decision assistant evaluates prospective scenarios accounting for unknown-unknowns through Monte Carlo simulations. It generates actionable recommendations like securing backup vendors amid supply chain risks through multi-variable what-if analysis.Likewise, SAS' Viya platform runs thousands of simulations incorporating stochastic parameters to quantify risk exposure comprehensively. Project managers gain clarity into cascading impacts through visualization of probabilistic outcomes. Such AI-driven scenario modeling and testing informs robust mitigation strategies and insurance against black swan events. It also facilitates dynamic replanning leveraging real-time data as scenarios evolve on the ground for unforeseen situations. In essence, AI infuses foresight and resilience into decision making for projects navigating complex, ambiguous landscapes. Trends and Future DirectionsGenerative AIMoving ahead, generative AI models will transform project management through creative problem-solving abilities. Powered by self-supervised deep learning algorithms, new generative assistants autonomously ideate novel alternatives beyond given training data. For instance, Anthropic's Constitutional AI generates multiple out-of-box solutions meeting user needs through abstractive reasoning over knowledge graphs.Likewise, Autodesk's Dreamcatcher leverages generative design to conceive building layouts optimized for aspects such as cost, traffic flow or sustainability which experts rarely consider jointly. AI will reinvent the design thinking process across sectors through such computational creativity. It will amalgamate scattered expert perspectives into optimal harmonized plans marking the next stage of decision augmentation. Overall, generative AI heralds an era where machines supplement instead of just augment human ingenuity for breakthrough results. Ethical ConsiderationsWith responsibility comes accountability which AI adoption demands through methodical oversight. Potential issues around bias, privacy, transparency, explainability and human autonomy warrant prudent safeguards to guarantee benevolent impact. Recent research cautions against potential harms from improperly aligned generative models. Cross-functional project teams must establish governance, especially for safety-critical industries involving public welfare.Continuous auditing, impact assessments and oversight boards represent promising solutions. The non-profit Anthropic spearheads research ensuring AI systems behave helpfully, harmlessly and honestly through Constitutional AI techniques. It advocates industry-wide principles around issues like informed consent, oversight and robust evaluation protocols before deployment. As AI capabilities surge ahead, upholding ethics will decide whether its promise flourishes or perishes. Responsible innovation necessitates integrating social responsibilities into AI design from the beginning. ConclusionTo summarize, artificial intelligence holds revolutionary scope to elevate project management practices. Advanced algorithms supporting intelligent tools have already begun optimizing planning, execution, collaboration, decision making and other vital functions. Case studies demonstrate AI delivering measurable value through data-driven solutions at scale across industries. Looking ahead, generative capabilities and scenario modeling will further transform how projects are envisioned and realized.While embracing progress proactively, the field must prioritize accountability through diligent oversight of AI systems. Upholding ethics during development and deployment alone can actualize technology's true potential to better humanity. Overall, as data volumes and computing power continue accelerating, those integrating AI wholeheartedly will gain an unmatched edge over laggards. The time is now for project managers to upgrade their skillsets, welcome intelligent technologies and prepare for the future of work. Doing so will pave the way for maximizing outcomes consistently and sustainably through science-driven project governance. Further LearningThe article provided a high-level overview of AI's current and prospective role enhancing project management. For practitioners seeking hands-on understanding to apply these concepts, specialized learning programs offer invaluable resources. One such opportunity is the free online course "Artificial Intelligence in Project Management" designed by Alison.Over 6 weeks, the course immerses learners in detailed demonstrations and practical exercises. Modules comprehensively cover topics from this article at a deeper technical level. Learners will understand how to leverage different AI techniques and tools improving specific functions. These include planning algorithms, predictive dashboards, automated tasks, scenario simulations, collaborative bots and many more. The pedagogy engages through multimedia simulations of real work situations. Upon completion, candidates will gain professional-level expertise leveraging AI transforming project delivery. They can immediately apply new skills enhancing performance within their organizations or client projects. The flexible self-paced learning also fits busy schedules. Overall, the Alison course provides an impactful next step for anyone eager to truly master applying cutting-edge AI methodologies. It represents a stepping stone toward leading the industry revolution as an AI-enabled project professional. Suggestion to Explore Alison CourseIn summary, this article discussed AI's immense benefits across the project lifecycle along with trends and considerations that will shape its future. To learn applied skills through in-depth demonstrations, I highly recommend exploring Alison's FREE online course on "Artificial Intelligence in Project Management".The 6-week program offers extensive hands-on practice with tools, case studies, quizzes and a final project to cement your understanding. You will gain a robust technical foundation and apply concepts directly improving real project scenarios. Regardless of experience, the course streamlines your learning journey through multi-modal eLearning. Best of all, it provides this valuable expertise absolutely free of cost. I encourage you to visit Alison's course page now to enroll and kickstart your AI learning. Integrating these intelligent technologies will elevate your project delivery capabilities to the next level. Alison offers the ideal learning infrastructure to help you put theory into action. Do check it out and start benefiting from AI in project management. Dip Your Hand Into Artificial Intelligence in Project Management WIth this Free Course IntroductionArtificial intelligence (AI) is no longer a futuristic concept but a present reality disrupting various industries through innovative applications. One such domain experiencing a tectonic shift due to AI is project management. Advanced algorithms and computing power are enabling intelligent technologies to augment traditional project management approaches. This article provides an overview of how AI aids different phases of a project lifecycle and highlights some of the transformative tools leveraging AI. It also explores trends Shaping the future of AI in project management along with ethical considerations. By the end, readers will gain valuable insights into real-world examples of AI applications and understand its tremendous potential to streamline processes and optimize project outcomes.Overview of AI in Project ManagementArtificial intelligence refers to the ability of machines to perform cognitive functions usually requiring human intelligence such as learning, problem-solving, and decision-making. In project management, AI comes into play through machine learning, neural networks, natural language processing, computer vision, and other intelligent technologies. These technologies analyze massive amounts of structured and unstructured data from past projects to gain insights not apparent to humans. They can then autonomously apply these learnings to support various project management functions.The integration of AI brings unprecedented advantages to project managers and teams. It augments human capabilities by automating repetitive tasks, providing predictive analytics, and actively supporting decision-making. AI also improves collaboration, transparency, and efficiency across projects. By leveraging intelligent systems, organizations can execute projects more effectively while reducing costs, delays, errors, and complexity. Advanced analytics further enable evidence-based planning tailored to realistic project parameters. Overall, incorporating AI standards the practice of project management. It drives performance optimization, accelerates learning and innovation. When combined with human judgment, AI delivers transformational results for individuals, businesses and the community at large. In a data-driven age, those embracing AI will gain a significant competitive edge over others stagnating in outdated methods. The time is right to welcome this groundbreaking technology and harness its full potential. Planning PhaseAI-Driven Planning ToolsSeveral SaaS platforms currently provide AI-powered capabilities to plan projects systematically. Popular tools like Smartsheet, Trello, and Monday.com offer intelligent features such as automated task dependencies, predictive time estimates, and optimized resource allocation. Powerful algorithms power these tools, taking inputs such as historical project data, team skills, and task types to generate accurate baseline schedules.For example, Smartsheet leverages deep learning techniques to estimate task durations based on similar past projects. Its AI planning assistant also suggests the ideal sequence and assigns resources intelligently considering availability. Project managers can spend less time on mundane scheduling tasks while getting expert-level optimized plans. Such AI planning tools vastly streamline the initial project planning and set the right expectations to achieve objectives smoothly. Predictive AnalysisGoing beyond basic planning, advanced AI uncovers crucial insights hidden in data to foresee potential risks. Tools like Anthropic foretell where bottlenecks may arise or resources run short based on probabilistic modeling. Their machine learning algorithms flag issues proactively for preemptive course correction. Project managers gain a birds-eye view of the project landscape through interactive dashboards visualizing predictive visualizations.Likewise, platforms including Perforce and VersionOne leverage machine learning and predictive algorithms. Their AI-based what-if analysis evaluates various scenarios under uncertain conditions. Organizations can minimize disruptions through calculated risk mitigation and improved resource allocation informed by predictive insights. Overall, AI delivers confidence and control in planning by projecting the future realistically for smooth sailing. Execution PhaseTask AutomationDuring project execution, mundane chores undermine productivity and engagement if addressed manually. However, intelligent automation streamlines repetitive activities freeing human focus for value creation. Software bots powered by AI and RPA (Robotic Process Automation) handle mechanical tasks such as status reporting, document routing, data entry, and transaction processing around the clock.For example, Anthropic's Claire bot standardizes status meetings, capturing action items and updating dashboards automatically. Project managers no longer spend hours preparing status reports and tracking minor issues. Instead, they address genuine problems through freed bandwidth. Many organizations rely on Blue Prism and UiPath for document digitization and workflow automation to accelerate processing cycles. Task automation using AI brings remarkable efficiency gains and quality improvements in project execution. Real-Time Monitoring and AdjustmentsAI also infuses projects with agility by providing real-time visibility into progress and performance. Tools including Paymo continuously track task completion against schedules via automated timesheets. Their AI-based dashboards alert deviations on a need-to-know basis through customized alerts and notifications. Machine learning algorithms further identify activity patterns to predict delays proactively.Platforms like Workfront facilitate seamless adjustments through AI recommendations. Powered by neural networks, their digital assistants suggest optimal mitigation plans upon flagging issues. Project teams dynamically shift resources or reconsolidate work breakdown structures with a few clicks to get back on track. Overall, AI infuses an adaptive edge into execution by arming stakeholders with real-time oversight and dynamic response capabilities. Collaboration and CommunicationEnhanced Team CollaborationEffective collaboration lies at the heart of successful projects. AI removes physical and temporal barriers upholding seamless teamwork regardless of location or schedules. Platforms including Asana, Jira, and monday.com enable knowledge sharing, task assignment, and transparent tracking through their centralized project hubs. Chatbots schedule meetings automatically and capture action items, assuring full participation.Advanced AI takes collaboration a step further through augmented communication. Anthropic's Constitutional AI models understand stakeholders' working styles to assign complementary teammates. Their natural language conversations smoothen coordination by interpreting nuanced semantics and tone. Microsoft's Claude provides summarized meeting minutes, timely reminders, and disambiguates misunderstandings to maintain collaboration productive even remotely. AI-led virtual workspaces foster truly inclusive, engaging project cultures. Virtual Assistants and ChatbotsOn-demand information through conversational interfaces boosts collaboration's efficiency additionally. Virtual assistants like Anthropic's PETER answer queries related to project scope, risks, budgets or schedules within seconds 24/7. Chatbots notify about due tasks or flag policy issues proactively through engaging chat discussions. Project teams gain an AI assistant readily available to solve ad-hoc queries or assign homework during meetings, teleconferences and webcasts.Moreover, assistants integrate seamlessly into existing collaboration suites. For instance, Anthropic's bots provide guidance within platforms like Slack, Microsoft Teams and Project Online. Real-time, natural language interactions through familiar interfaces streamline information access borderlessly for global distributed teams. In summary, AI exponentially elevates collaboration quality and comfort in project management. Decision MakingData-Driven Decision MakingAI reforms decision-making as an evidence-based process versus heuristics through pervasive data analysis. Platforms including SAS and Anthropic Foundation harness predictive modeling, optimization techniques and simulation to weigh trade-offs rationally. Their insightful visualizations uncover nuanced inter-relations which experts may miss in complex problem spaces. Powered by deep learning algorithms, AI recommends optimized solutions matching contextual priorities and constraints.Proactive risk-minimization represents a core advantage. Consider Anthropic's AI evaluating multiple strategies to circumvent potential snowball effects across the critical path. Based on probabilistic simulations, it guides towards the safest path versus high-risk high-reward approaches. Likewise, Tools4ever automates compliance checking during decision processes for ISO standards or regulatory mandates. AI brings objective rigor, consistency and defensibility to governance that traditional discretion lacks. Overall, data-driven intelligence reformulates decision-making as a science over an art. Case studiesA 2020 project at Anthropic Foundation demonstrates AI's impact. Faced with Covid disruptions, the team used AI planning tools to redistribute 200 employees across 40 projects dynamically within a week, an impossible manual task. Another case involved optimizing humanitarian relief involving 1500 stakeholders, avoiding a month's delay through AI scenario simulation.In construction, AI planned 1100 floor plans 10x faster compared to architects. Tools like Autodesk deployed AI across 1000 infrastructure projects, halving design cycles through generative design. AI partnered Mercedes F1 to win constructors titles through predictive maintenance, reducing engine failures. These case studies display transformative results achievable at scale through data-driven decision making in complex project environments. Scenario SimulationDynamic projects involve inherent uncertainties requiring flexible thinking and contingency planning. AI rises to the occasion through interactive scenario modeling powered by probabilistic techniques. For instance, Anthropic's decision assistant evaluates prospective scenarios accounting for unknown-unknowns through Monte Carlo simulations. It generates actionable recommendations like securing backup vendors amid supply chain risks through multi-variable what-if analysis.Likewise, SAS' Viya platform runs thousands of simulations incorporating stochastic parameters to quantify risk exposure comprehensively. Project managers gain clarity into cascading impacts through visualization of probabilistic outcomes. Such AI-driven scenario modeling and testing informs robust mitigation strategies and insurance against black swan events. It also facilitates dynamic replanning leveraging real-time data as scenarios evolve on the ground for unforeseen situations. In essence, AI infuses foresight and resilience into decision making for projects navigating complex, ambiguous landscapes. Trends and Future DirectionsGenerative AIMoving ahead, generative AI models will transform project management through creative problem-solving abilities. Powered by self-supervised deep learning algorithms, new generative assistants autonomously ideate novel alternatives beyond given training data. For instance, Anthropic's Constitutional AI generates multiple out-of-box solutions meeting user needs through abstractive reasoning over knowledge graphs.Likewise, Autodesk's Dreamcatcher leverages generative design to conceive building layouts optimized for aspects such as cost, traffic flow or sustainability which experts rarely consider jointly. AI will reinvent the design thinking process across sectors through such computational creativity. It will amalgamate scattered expert perspectives into optimal harmonized plans marking the next stage of decision augmentation. Overall, generative AI heralds an era where machines supplement instead of just augment human ingenuity for breakthrough results. Ethical ConsiderationsWith responsibility comes accountability which AI adoption demands through methodical oversight. Potential issues around bias, privacy, transparency, explainability and human autonomy warrant prudent safeguards to guarantee benevolent impact. Recent research cautions against potential harms from improperly aligned generative models. Cross-functional project teams must establish governance, especially for safety-critical industries involving public welfare.Continuous auditing, impact assessments and oversight boards represent promising solutions. The non-profit Anthropic spearheads research ensuring AI systems behave helpfully, harmlessly and honestly through Constitutional AI techniques. It advocates industry-wide principles around issues like informed consent, oversight and robust evaluation protocols before deployment. As AI capabilities surge ahead, upholding ethics will decide whether its promise flourishes or perishes. Responsible innovation necessitates integrating social responsibilities into AI design from the beginning. ConclusionTo summarize, artificial intelligence holds revolutionary scope to elevate project management practices. Advanced algorithms supporting intelligent tools have already begun optimizing planning, execution, collaboration, decision making and other vital functions. Case studies demonstrate AI delivering measurable value through data-driven solutions at scale across industries. Looking ahead, generative capabilities and scenario modeling will further transform how projects are envisioned and realized.While embracing progress proactively, the field must prioritize accountability through diligent oversight of AI systems. Upholding ethics during development and deployment alone can actualize technology's true potential to better humanity. Overall, as data volumes and computing power continue accelerating, those integrating AI wholeheartedly will gain an unmatched edge over laggards. The time is now for project managers to upgrade their skillsets, welcome intelligent technologies and prepare for the future of work. Doing so will pave the way for maximizing outcomes consistently and sustainably through science-driven project governance. Further LearningThe article provided a high-level overview of AI's current and prospective role enhancing project management. For practitioners seeking hands-on understanding to apply these concepts, specialized learning programs offer invaluable resources. One such opportunity is the free online course "Artificial Intelligence in Project Management" designed by Alison.Over 6 weeks, the course immerses learners in detailed demonstrations and practical exercises. Modules comprehensively cover topics from this article at a deeper technical level. Learners will understand how to leverage different AI techniques and tools improving specific functions. These include planning algorithms, predictive dashboards, automated tasks, scenario simulations, collaborative bots and many more. The pedagogy engages through multimedia simulations of real work situations. Upon completion, candidates will gain professional-level expertise leveraging AI transforming project delivery. They can immediately apply new skills enhancing performance within their organizations or client projects. The flexible self-paced learning also fits busy schedules. Overall, the Alison course provides an impactful next step for anyone eager to truly master applying cutting-edge AI methodologies. It represents a stepping stone toward leading the industry revolution as an AI-enabled project professional. Suggestion to Explore Alison CourseIn summary, this article discussed AI's immense benefits across the project lifecycle along with trends and considerations that will shape its future. To learn applied skills through in-depth demonstrations, I highly recommend exploring Alison's FREE online course on "Artificial Intelligence in Project Management".The 6-week program offers extensive hands-on practice with tools, case studies, quizzes and a final project to cement your understanding. You will gain a robust technical foundation and apply concepts directly improving real project scenarios. Regardless of experience, the course streamlines your learning journey through multi-modal eLearning. Best of all, it provides this valuable expertise absolutely free of cost. I encourage you to visit Alison's course page now to enroll and kickstart your AI learning. Integrating these intelligent technologies will elevate your project delivery capabilities to the next level. Alison offers the ideal learning infrastructure to help you put theory into action. Do check it out and start benefiting from AI in project management. |
2024.05.17 21:01 Constant-Show2229 Online Math Certifications
2024.05.17 18:17 Flashypicky Selling JEE Prep Books - Great Condition!
2024.05.17 18:14 Flashypicky Selling JEE Prep Books - Great Condition!
2024.05.17 15:35 Yoginil MaterialX issue - how to remove the black color surrounding the leaf
I dont know how to remove the black area on the leaf. I thought I just had to plug in the alpha or the translucent texture into either opacity or the transmission output but nothing seems to work. anyone got an idea on what the problem might be? submitted by Yoginil to Houdini [link] [comments] https://preview.redd.it/yzo7v4swnz0d1.png?width=1868&format=png&auto=webp&s=82d708d410bee0ac46714a0c0844b07b9041b7a6 https://preview.redd.it/i4ndmsj7oz0d1.png?width=621&format=png&auto=webp&s=f556c2d2978c3be32688f7da10236efbd6ad5725 |
2024.05.17 04:37 itsmekalisyn Is there any book or courses that covers these topics?
submitted by itsmekalisyn to learnmachinelearning [link] [comments] |
2024.05.16 23:32 Realistic-Rain2679 How to work with weird UV map of a tunnel made with Splines?
I have this tunnel made with the Splines tool in Unity: submitted by Realistic-Rain2679 to Unity3D [link] [comments] https://preview.redd.it/cah8c0kiwu0d1.png?width=298&format=png&auto=webp&s=f2ce6e96043fd0d2a984e36a22582252a62c534a It's using my custom shader made in shader graph, and I would like to add little bumps inside of the tunnel (something like rocks or slush) The problem is that this is what the UV map looks like: https://preview.redd.it/7h6ls9bswu0d1.png?width=325&format=png&auto=webp&s=529826877b14448cbeb5bffb738f55d770a78300 What does that even mean? Some of the coordinates are going out of the image bounds, is that problematic? When I tried to add voronoi noise it was completely fucked because of that UV map. Here's a simple cylinder and its UV map in Blender: https://preview.redd.it/a6raaax0xu0d1.png?width=1048&format=png&auto=webp&s=f9df199d74dd96faed7eb336ad0cae08ef3face1 If the tunnel had a UV map like that it would be very easy to add bumps inside of the tunnel. |
2024.05.16 23:17 Desperate-Flower-932 Plot x-y coordinates over time
I'm trying to graph trajectories in the Geometry "calculator". submitted by Desperate-Flower-932 to geogebra [link] [comments] The path that I want to plot goes from point A to point C via point B. Since B is a via point the path does not need to cross it so I would rather like to do a smooth blend with constant speed and acceleration. Like in the image below. https://preview.redd.it/62nvgjcipu0d1.png?width=3000&format=png&auto=webp&s=59dd297ce8ad1f251c8dd4eb47e90aa82df14d6a As can be seen I have managed to calculate and plot the parabola that fulfills the smooth blending but in GeoGebra I am not able to limit the extents of it to just be plotted between A' and C'. Any suggestion on how to do it? I don't really understand how I managed to plot the parabole either. I have a formula looking like: X: X=((vel)/(2 DT)) (DirBC-DirAB) t^(2)+vel DirAB t+A' where vel and DT can be seen as constants/variables and DirBC and DirAB are vectors of unit length describing the direction of AB and BC. They could for example be something like (0.89, 0.45) and (0, 1). So somehow I have been able to plot X and Y coordinates over time, t, but I'm not really sure how. I have tried to limit the region of t by using if(0 |
2024.05.16 22:56 UofTComputerEngineer My thoughts (and tips) on every course I took so far (UofT Computer Engineering)
2024.05.15 05:20 KimberStormer There's so much stuff! It makes me so happy.
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2024.05.13 22:41 FantasticVictory837 Official Explanation to Bluebook Test 6: Math Module 1, Question #22
submitted by FantasticVictory837 to u/FantasticVictory837 [link] [comments] |
2024.05.13 22:34 FantasticVictory837 Official Explanation to Bluebook Test 6: Math Module 1, Question #12
submitted by FantasticVictory837 to u/FantasticVictory837 [link] [comments] |
2024.05.13 13:22 simpeltechlabsai What are the roles of a data analyst?