Saturday, March 18, 2023
Saturday, March 11, 2023
Weekly Sporto bookmarks (weekly)
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(3) Maximizing Productivity with ChatGPT Macros: Tips and Best Practices | LinkedIn
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EDUCAUSE QuickPoll Results: Did ChatGPT Write This Report? | EDUCAUSE
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https://the-decoder.com/chatgpt-guide-five-basic-prompt-strategies-for-better-results/
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What is Markdown & How It Can Help You Write Faster
"Getting back to its roots, Markdown is essentially used to convert plain text into HTML."
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- Getting back to its roots, Markdown is essentially used to convert plain text into HTML.
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How Costco Convinces Brands to Cannibalize Themselves - Napkin Math - Every
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The Case for Full Spectrum “Inclusive” Credentials | Littoraly Don
Saturday, March 4, 2023
Weekly Sporto bookmarks (weekly)
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Knowledge Base In AI: What is it and why do you need one? | KMS Lighthouse
"Knowledge Base In AI: What is it and why do you need one?"
- A knowledge base in artificial intelligence aims to capture human expert knowledge to support decision-making, problem-solving, and more. Through the years, knowledge base systems have been developed to support many organizational processes.
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- Perception
- Learning
- Knowledge representation and reasoning
AI provides the mechanisms that enable machines to “gain knowledge.” It allows them to acquire, process, and use knowledge to perform tasks that display “intelligent” behavior, such as:
- Knowledge is the collection of skills and information a person’s acquired through experience. Knowing how to apply that knowledge to problem- solving and decision-making is intelligence
- A great illustration of knowledge-based AI is AI-powered customer service. When finding a solution to a customer’s problem, customer support agents often search multiple sources of information and seek advice from one or more experts. AI simplifies the process by using keywords and phrases to quickly scour dozens, if not hundreds, of various types of information to speedily answer an agent’s question.
- The fundamental characteristics of an AI-powered knowledge base include:
- Accurate and relevant content
- A consistent voice.
- Faster service
- Simplification
- Improved collaboration
- 4 Key Benefits of Knowledge Base In Artificial Intelligence
- Simplifying knowledge discovery.
- Connecting data from disparate sources.
- Keeping your knowledge base content up-to-date.
- Providing important knowledge management metrics.
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ChatGPT And Generative AI: What This Technology Means For Today’s CIO
- It starts with an understanding that ChatGPT is different from the AI of the past few years, with more advanced natural language processing abilities and a more robust capacity to learn from prompts and fine-tuning.
- from simple help-desk bots to larger solutions that replace entire outbound sales teams.
- Focus on processes that can be optimized with AI and ML, then estimate what business value these improvements could drive.
- As teams evaluate these vendors and their solutions, it will be imperative to understand which solutions are truly leveraging this new generative AI capability and which are just jumping on the AI bandwagon.
- they’ll have to assess the specific infrastructure needed, navigate commercial licensing and resource the team correctly to train the models
- Not only is AI useless without data, but it’s just as useless with the wrong data. Ensuring that the solution gets the right inputs will depend on each company’s needs, of course, but every business will need the right architecture, data model, resources, prompts and training.
- Generative AI will replace some jobs. So what happens to the people performing those jobs today?
- I would imagine that we’ll see a similar fundamental shift over time in all types of roles and functions, from call center agents to engineers.
- how generative AI will be regulated, as well as its impact on an individual’s data privacy and security
- The fact remains, though, that technology always moves faster than governing bodies, so where we will land remains to be seen.
- drive business value while also maintaining trust
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How artificial intelligence can support knowledge management in organizations – RealKM
- How artificial intelligence can support knowledge management in organizations
- this article was published in Jan 2023, so it does not include specifics about ChatGPT.
- AI identified as one of eight emerging innovative concepts in KM.
- The potential role for AI in supporting fundamental dimensions of KM: creation, storage and retrieval, sharing, and application of knowledge
- Practical ways to build the partnership between humans and AI in supporting organizational KM activities
- Implications for the development and management of AI systems based on the components of people, infrastructures, and processes.
- creating, storing and retrieving, sharing, and applying knowledge.
- This table is an excellent reference.
- Harvesting, classifying, organizing, storing, and retrieving explicit knowledge
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- Analyzing and filtering multiple channels of content and communication
- Facilitating knowledge reuse by teams and individuals
- Retrieve dispersed nuggets of information related to a troubleshooting situation
- Connecting people working on the same issues by fostering weak ties and know-who
- Facilitating collaborative intelligence and shared organizational memory
- Creating more coordinated, connected systems across organizational silos
- Enhancing situated knowledge application by searching and preparing knowledge sources
- knowledge production and management are inherently human-centered
- Therefore, they propose that the most effective roles assigned to AI in KM will mostly augment humans rather than replace them, thereby achieving collaborative intelligence in which AI and humans enhance each other’s complementary strengths.
- This also aligns with the findings of a previous paper that explored combining humans and AI for organizational decision-making under uncertainty.
- Figure 1,
- this is an excellent reference table.
- An emerging genre of AI systems called personal intelligent assistants can occupy a unique position in personal KM.
- Information overload is one of the key challenges of the information environment for knowledge workers. Personal intelligent assistants can help broaden the cognitive bandwidth of knowledge workers and change the way they digest relevant knowledge by providing more effective capabilities for processing, filtering, sorting, and navigating information resources.
- ChatGPT definitely fits into the personal assistant conceptual model.
- AI presents specialized intelligence that enables sensing the environment, learning from experience, and creating possibilities for action in relation to specific task contexts. General intelligence, however, remains a human-centered characteristic
- Specifically, the application of knowledge for strategic-level thinking and decision-making requires elements of general intelligence, and builds from the uniquely human prerogatives such as foresight, social and emotional intelligence, self-development, imagination, and curiosity.
- Two dimensions of KM can be supported by IT uses: codification of knowledge and human collaboration.
- streamline the tasks of collecting, classifying, analyzing, and presenting content, and in doing so, free up knowledge workers for higher value-added tasks
- In regard to collaboration, AI technologies provide great capabilities for generating know-who (i.e., sources of expertise) within and across organizational boundaries and for extending and augmenting knowledge networks
- ransferring tacit knowledge remains a highly human-centered practice, and attempts to turn inherently tacit knowledge into explicit knowledge and to facilitate its transfer through technological mechanisms have failed in the past.
- AI systems can now self-learn to develop and improve know-how and know-what, offering more effective outputs as they process new data. However, this self-learning makes it difficult, if not impossible, to explain the inferences generated by the black box of AI. This is a particularly serious problem in evidence-based fields such as medicine and law, where there are clear obligations for explaining how AI systems may weigh the inputs they receive to inform certain recommendations
- the role of humans is indispensable in formulating know-why for AI-based inferences; know-why is essential for alleviating the black box of AI, justifying decisions, training budding human experts, and garnering organizational support.
- But years of research indicates that for an IT deployment to be successful, there need to be accompanying organizational changes.
- the value of AI for KM lies not only in technology, but also in new infrastructures, trained people, and redesigned processes.
- a symbiotic relationship, one that both recognizes the irreplaceable contributions of humans to knowledge work and that seeks ways to reinvent and elevate their role. One way in which AI can elevate knowledge workers is through reskilling and upskilling.
- knowledge scientists and data scientists, who collect and prepare training data sets for machine learning algorithms. Knowledge scientists can contribute to the process of combining the two distinct AI strategies – symbolic AI (using more traditional approaches) and statistical AI (based on neural networks) – by helping to build knowledge graphs that represent background knowledge and that complement training data
- AI champions can be instrumental in presenting an alternative narrative that emphasizes augmenting knowledge workers rather than replacing them – an alternative narrative that describes the expected improvement in the kinds of tasks knowledge workers perform.
- workers will need to learn how to interact with intelligent systems rather than with humans for many of these tasks
- AI literacy is a key component needed for upskilling both managers and workers interacting with AI systems
- This requires knowledge workers to develop a fuller appreciation of their artificial counterparts
- Deep-learning approaches, for example, require rather large sets of training data to produce reliable outcomes.
- data are dynamically being generated in real time, and most such data are unstructured
- Knowledge graphs are an emerging way in which organizations can harness this data
- Designing for mutual learning recognizes the limits of the AI system in managing knowledge and precipitates the need for constant auditing and involvement of human supervisors
- The redesign of workflows and the identification of ways in which algorithms’ recommendations can augment various knowledge activities require a continual conversation and negotiation between technology and domain experts
- Elevating humans necessitates that organizations look for opportunities to free knowledge workers from arduous and monotonous work by automating it.
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The Connection between Artificial Intelligence and Knowledge Management | KMInstitute
- KM and AI at its core is about knowledge. AI provides the mechanisms to enable machines to learn. AI allows machines to acquire, process and use knowledge to perform tasks and to unlock knowledge that can be delivered to humans to improve the decision-making process. I believe that AI and KM are two sides of the same coin. KM allows an understanding of knowledge to occur, while AI provides the capabilities to expand, use, and create knowledge in ways we have not yet imagined.
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How Artificial Intelligence Can Improve Organizational Decision Making
- organizations should be able to achieve organizational agility powered by AI.
- There are multiple examples of implementing AI in the supply chain, transportation, education, operations, marketing, and pretty much every industry that’s moving toward digitalization, and switching from manual activities to technology-assisted ones.
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Knowledge Base In AI: What is it and why do you need one? | KMS Lighthouse
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Leveraging Artificial Intelligence in knowledge management - Document360
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AI Ethics and its Impact on Knowledge Management | For Librarians | Springer Nature
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Are You Ready for the Era of Co-Creation With AI? | Built In
"Are You Ready for the Era of Co-Creation With AI?"
- Artificial intelligence is unlikely to replace humans who work in creative fields anytime soon, but it is rapidly changing the landscape. Designers, developers, and copywriters who embrace AI the use of AI tools in their work will gain a competitive edge in their careers.
- Co-creation with AI refers to the practice of humans and machines working together to create something new or to solve a problem.
- AI can be a great co-creator, improving the efficiency of designers and developers, but it still requires a human moderator to review its output. The moderator should have relevant experience in the field to evaluate and refine the result.
- AI tools can streamline the ideation process
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Saturday, February 25, 2023
Saturday, February 18, 2023
Saturday, February 11, 2023
Saturday, February 4, 2023
Saturday, January 28, 2023
Weekly Sporto bookmarks (weekly)
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"“It sends a signal for those colleges to be even more strategic of designing dual enrollment to lead students into meaningful pathways at the community college.”"
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How Micro-credentials Can Support Social Mobility in Rural Communities - Digital Promise
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The Rapidly Changing Global Landscape of Microcredentials - The EvoLLLution The EvoLLLution
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Latino college enrollment was on an upswing. But Covid has taken a toll.
- Students dropped out for are a variety of reasons: Family members became sick or lost their jobs and the student had to help support the family. Or, they just couldn’t afford it anymore. And, instead of falling further behind, they decided to drop out.
- The pandemic also worsened some of the problems already faced by Latino college students including language barriers, challenges due to immigration status, or lack of knowledge of the application due to them being first-generation students.
- looking at the bigger picture, in one year, we saw five years of growth lost in terms of enrollment and representation and that is big.”
- Institutions across the country had to quickly adapt to the uncertainty of the Covid pandemic requiring them to make changes in order to provide their students with some sense of normalcy.
- The University of Texas Rio Grande Valley, said that their enrollment numbers actually increased.
- the fact that UTRGV saw enrollment growth, while other schools experienced declines, to their financial assistance programs and other initiatives
- provide summer employment to their students and created a summer relief program. The institution continued these relief programs all the way through the fall semester.
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Saturday, January 21, 2023
Weekly Sporto bookmarks (weekly)
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Supporting Two-Year Universities During COVID-19 · The Hunt Institute
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- The data released today shows that of the 2.6 million students who entered college as first-time freshmen in the fall of 2019, 74 percent returned for their second year — an unprecedented two percentage point drop, the lowest level since 2012.
- Not surprisingly, community colleges showed the steepest decline in persistence rates, down 3.5 percentage points to 58.5 percent. Community colleges attract a disproportionate number of low-income and minority students, and they have seen the most dramatic enrollment and persistence drops
- In most cases, the explanations are straightforward: These students needed to get jobs — even low-skill, low-paying jobs — to support their families. In theory, these students could return to college now that the pandemic has eased, but there’s little evidence in enrollment trends to suggest this is happening.
- Instead, they appear to be forming a missing generation of college students, an unprecedented phenomenon likely to affect the nation’s productivity rate for years. Any country’s international competitiveness is forecast by the skills acquired by young people entering the workforce.
- Before today’s data release, there was ample evidence to suggest a missing generation was taking shape. This spring, overall college enrollment fell by 603,000 students, from 17.5 million to 16.9 million — a drop that is seven times worse than the year before when the pandemic first hit and marks the sharpest year-over-year decline since 2011, the first year the Clearinghouse began keeping track.
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Persistence & Retention - National Student Clearinghouse Research Center
- Seventy-five percent of students who started college in the first fall of the COVID-19 pandemic returned for their second year. This persistence rate represents a one-year increase of 1.1 percentage points but has yet to recover to the pre-pandemic level in this important early student success indicator.
- Persistence rate is measured by the percentage of students who return to college at any institution for their second year, while retention rate represents the percentage of students who return to the same institution.
- The overall persistence rate of 75.0 percent represents a one-year gain of 1.1 percentage points (pp), more than the historical average, but the persistence rate is still below the pre-pandemic level (75.9%).
- The latest persistence rate gain is due to first-time students transferring out in their first year more than those remaining at their starting institution (+0.9 pp in the transfer-out rate vs. +0.2 pp in the retention rate). This is a reversal of last year’s trend, where the decline in the transfer-out rate had caused the first-year persistence rate to drop.
- Community colleges and private for-profit four-year institutions have led this year’s improvement. Other four-year institutions experienced small drops in both rates.
- Latinx students had a small rebound in persistence (+0.7 pp) following a major decline the year prior (-2.6 pp). Persistence rates increased in other major racial/ethnic groups except for Native American students, who saw a 2.8 pp decline. There was no notable improvement in retention, regardless of race and ethnicity
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New Federal Survey Data Show the Pandemic Has Hit Would-Be College Students Hard
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Hispanic-Serving Institutions (HSIs): 2020-21 | Excelencia in Education
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For the first time in 20 years, the number of HSIs has decreased from 569 in 2019-20 to 559 in 2020-21. This decrease is due, in part, to enrollment declines, institutional closings, and consolidation during the pandemic.
In 2020-21, HSIs are located in 29 states, the District of Columbia, and Puerto Rico. These institutions represent 18% of all institutions of higher education and enroll 66% of Latino undergraduates.
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Challenges and Strategies for Assisting Latino Students with College Access and Completion –
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Latino/a Student Enrollment Declines Amid COVID-19 | BestColleges
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HSI Facts - Hispanic Association of Colleges and Universities
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Hispanic-serving institutions seeing enrollment return after pandemic decline
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Latino college enrollment was increasing until the pandemic hit - The Washington Post
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Number of Hispanic Serving Institutions falls as pandemic cuts Latino enrollment
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Latino college students dropped out, quit classes during COVID-19
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COVID-19: Stay Informed - National Student Clearinghouse Research Center
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College enrollment is nowhere near pre-pandemic levels, experts worry - The Washington Post