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 Teaching Commons: “an emergent conceptual space for exchange and community among faculty, students, and all others committed to learning as an essential activity of life in contemporary democratic society” (Huber and Hutchings, 2005, p.1) What Is the #iteachmsu Commons?    You teach MSU. We, the Academic Advancement Network, The Graduate School, and The Hub for Innovation in Learning and Technology, believe that a wide educator community (faculty, TAs, ULAs, instructional designers, academic advisors, et al.) makes learning happen across MSU. But, on such a large campus, it can be difficult to fully recognize and leverage this community’s teaching and learning innovations. To address this challenge, the #iteachmsu Commons provides an educator-driven space for sharing teaching resources, connecting across educator networks, and growing teaching practice. #iteachmsu Commons content may be discipline-specific or transdisciplinary, but will always be anchored in teaching competency areas. You will find blog posts, curated playlists, educator learning module pathways, and a campus-wide teaching and learning events calendar. We cultivate this commons across spaces. And through your engagement, we will continue to nurture a culture of teaching and learning across MSU and beyond. How Do I Contribute to the #iteachmsu Commons? Content is organized by posts, playlists and pathways.

Posts: Posts are shorter or longer-form blog postings about teaching practice(s), questions for the educator community, and/or upcoming teaching and learning events. With an MSU email address and free account signup, educators can immediately contribute blog posts and connected media (e.g. handouts, slide decks, class activity prompts, promotional materials). All educators at MSU are welcome to use and contribute to #iteachmsu. And there are no traditional editorial calendars. Suggested models of posts can be found here.
Playlists: Playlists are groupings of posts curated by individual educators and the #iteachmsu community. Playlists allow individual educators to tailor their development and community experiences based on teaching competency area, interest, and/or discipline.
Pathways: Pathways are groupings of educator learning modules curated by academic and support units for badges and other credentialing.

There are two ways to add your contribution to the space:

Contribute existing local resources for posts and pathways: Your unit, college, and/or department might already have educator development resources that could be of use to the wider MSU teaching and learning community. These could be existing blog posts on teaching practice, teaching webinars, and/or open educational resources (e.g classroom assessments, activities). This content will make up part of the posts, playlists, and pathways on this site. Educators can then curate these posts into playlists based on their individual interests. Please make sure to have permission to share this content on a central MSU web space.
Contribute new content for posts: A strength of the #iteachmsu Commons is that it immediately allows educators to share teaching resources, questions and events through posts to the entire community. Posts can take a variety of forms and are organized by teaching competency area categories, content tags, date, and popularity. Posts can be submitted by both individual educators and central units for immediate posting but must adhere to #iteachmsu Commons community guidelines. Posts could be:




About your teaching practice(s): You discuss and/or reflect on the practices you’re using in your teaching. In addition to talking about your ideas, successes, and challenges, we hope you also provide the teaching materials you used (sharing the assignment, slidedeck, rubric, etc.)
Responses to teaching ideas across the web or social media: You share your thoughts about teaching ideas they engage with from other media across the web (e.g. blog posts, social media posts, etc.).
Cross-posts from other teaching-related blogs that might be useful for the #iteachmsu community: You cross-post content from other teaching-related blogs they feel might be useful to the #iteachmsu community.
About teaching-related events: You share upcoming teaching related events as well as their thoughts about ideas they engage with events at MSU and beyond (e.g. workshops, conferences, etc.). If these events help you think in new ways about your practice, share them with the #iteachmsu community.
Questions for our community: You pose questions via posts to the larger community to get ideas for their practice and connect with others considering similar questions.



What Are the #iteachmsu Commons Policies?Part of the mission of the #iteachmsu Commons is to provide space for sharing, reflecting, and learning for all educators on our campus wherever they are in their teaching development. The commons is designed to encourage these types of interactions and reflect policies outlined by the MSU Faculty Senate.  We maintain the right to remove any post that violates guidelines as outlined here and by MSU. To maintain a useful and safer commons, we ask that you:

Follow the MSU Guidelines for Social Media.
Engage across the #iteachmsu commons in a civil and respectful manner. Content may be moderated in accordance with the MSU Guidelines for Social Media.
Do not share private or confidential information via shared content on the #iteachmsu Commons.

Content posted on the #iteachmsu Commons is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International license. Learn more about this licensing here. Posted comments, images, etc. on the #iteachmsu Commons do not necessarily represent the views of Michigan State University or the #iteachmsu Commons Team. Links to external, non-#iteachmsu Commons content do not constitute official endorsement by, or necessarily represent the views of, the #iteachmsu Commons or Michigan State University. What if I Have #iteachmsu Commons Questions and/or Feedback?If you have any concerns about #iteachmsu Commons content, please email us at iteach@msu.edu. We welcome all feedback and thank you for your help in promoting a safer, vibrant and respectful community.  

common myths
for Advanced
AI:A captivating conversation is taking place about the future of artificial intelligence and what it will/should mean for humanity. There are fascinating controversies where the world’s leading experts disagree, such as AI’s future impact on the job market; if/when human-level AI will be developed; whether this will lead to an intelligence explosion; and whether this is something we should welcome or fear. But there are also many examples of boring pseudo-controversies caused by people misunderstanding and talking past each other. 

TIMELINE MYTHS



The first myth regards the timeline: how long will it take until machines greatly supersede human-level intelligence? A common misconception is that we know the answer with great certainty.
One popular myth is that we know we’ll get superhuman AI this century. In fact, history is full of technological over-hyping. Where are those fusion power plants and flying cars we were promised we’d have by now? AI has also been repeatedly over-hyped in the past, even by some of the founders of the field. For example, John McCarthy (who coined the term “artificial intelligence”), Marvin Minsky, Nathaniel Rochester, and Claude Shannon wrote this overly optimistic forecast about what could be accomplished during two months with stone-age computers: “We propose that a 2 month, 10 man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College […] An attempt will be made to find how to make machines use language, form abstractions, and concepts, solve kinds of problems now reserved for humans, and improve themselves. We think that a significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together for a summer.”

CONTROVERSY MYTHS



Another common misconception is that the only people harboring concerns about AI and advocating AI safety research are Luddites who don’t know much about AI. When Stuart Russell, author of the standard AI textbook, mentioned this during his Puerto Rico talk, the audience laughed loudly. A related misconception is that supporting AI safety research is hugely controversial. In fact, to support a modest investment in AI safety research, people don’t need to be convinced that risks are high, merely non-negligible — just as a modest investment in home insurance is justified by a non-negligible probability of the home burning down.

Skills are like apps for Alexa, and provide a new channel for your content and services. Skills let customers use their voices to perform everyday tasks like checking the news, listening to music, playing a game, and more. Organizations and individuals can publish skills in the Alexa Skills Store to reach and delight customers on hundreds of millions of Alexa devices.

If you're trying to learn Greek Articles you will find some useful resources including a course about Definite and Indefinite Articles... to help you with your Greek grammar. Try to concentrate on the lesson and notice the pattern that occurs each time the word changes its place. Also don't forget to check the rest of our other lessons listed on Learn Greek. Enjoy the rest of the lesson!

If you're trying to learn Greek Articles you will find some useful resources including a course about Definite and Indefinite Articles... to help you with your Greek grammar. Try to concentrate on the lesson and notice the pattern that occurs each time the word changes its place. Also don't forget to check the rest of our other lessons listed on Learn Greek. Enjoy the rest of the lesson!

A management system describes the way in which companies organize themselves in their structures and processes in order to act systematically, ensure smooth processes and achieve planned results Modern management systems usually follow the PDCA cycle of planning, implementation, review and improvement (Plan-Do-Check-Act).
An effective management system is based on and controls structured and optimized processes. Thus, it establishes the systematic and continuous improvement of the organization through clear rules, roles and processes.
Management systems can be used in all areas - depending on where your company operates and what goals are to be achieved. This can be in a specific industry, such as transport and logistics, the automotive industry or healthcare, or even across industries.
Modern management systems according to ISO standards follow the same logic, the so-called High Level Structure, but cover different aspects. The most widely used is the internationally known ISO 9001 standard for a quality management system.

A management system describes the way in which companies organize themselves in their structures and processes in order to act systematically, ensure smooth processes and achieve planned results Modern management systems usually follow the PDCA cycle of planning, implementation, review and improvement (Plan-Do-Check-Act).
An effective management system is based on and controls structured and optimized processes. Thus, it establishes the systematic and continuous improvement of the organization through clear rules, roles and processes.
Management systems can be used in all areas - depending on where your company operates and what goals are to be achieved. This can be in a specific industry, such as transport and logistics, the automotive industry or healthcare, or even across industries.
Modern management systems according to ISO standards follow the same logic, the so-called High Level Structure, but cover different aspects. The most widely used is the internationally known ISO 9001 standard for a quality management system.

Big data:Big data refers to the large, diverse sets of information that grow at ever-increasing rates. It encompasses the volume of information, the velocity or speed at which it is created and collected, and the variety or scope of the data points being covered (known as the "three v's" of big data).

Big data is a great quantity of diverse information that arrives in increasing volumes and with ever-higher velocity.
Big data can be structured (often numeric, easily formatted and stored) or unstructured (more free-form, less quantifiable).
Nearly every department in a company can utilize findings from big data analysis, but handling its clutter and noise can pose problems.
Big data can be collected from publicly shared comments on social networks and websites, voluntarily gathered from personal electronics and apps, through questionnaires, product purchases, and electronic check-ins.
Big data is most often stored in computer databases and is analyzed using software specifically designed to handle large, complex data sets.
How Big Data Works
Big data can be categorized as unstructured or structured. Structured data consists of information already managed by the organization in databases and spreadsheets; it is frequently numeric in nature. Unstructured data is information that is unorganized and does not fall into a predetermined model or format. It includes data gathered from social media sources, which help institutions gather information on customer needs.






 






Big data can be collected from publicly shared comments on social networks and websites, voluntarily gathered from personal electronics and apps, through questionnaires, product purchases, and electronic check-ins. The presence of sensors and other inputs in smart devices allows for data to be gathered across a broad spectrum of situations and circumstances.