Highlight and annotate at least 2 areas for each question. The annotations should be 1-2 sentences explaining the following: A. New learning B. Familiar with this C. Use this in practice

With respect to the predictive text portion of ChatGPT, a good non-technical (non-mathematical) description of a related mathematical model is described in chapter 3 of:

Pierce, John Robinson. An Introduction to Information Theory: Symbols, Signals and Noise. Second, Revised. Dover Books on Mathematics. 1961. Reprint, Mineola, N.Y: Dover Publications, Inc., 1980. https://www.amazon.com/Introduction-Information-Theory-Symbols-Mathematics/dp/0486240614.

Is LaMDA really able to "learn higher-level concepts" or is it just a large, straight-forward information theoretic-based prediction engine?

Lehnen, N., Glasauer, S., Schröder, L., Regnath, F., Biersack, K., Bergh, O. V. den, & Werder, D. von. (2022). Post-COVID symptoms in the absence of organic deficit—Lessons from diseases we know. PsyArXiv. https://doi.org/10.31234/osf.io/yqar2

Delz, Louise Aurora Katharina, Keith Gaynor, Ellen Somers, Rachel O. Connor, and Luisa Schmieder. ‘A CONFIRMATORY FACTOR ANALYSIS OF A COGNITIVE MODEL OF COVID-19 RELATED DISTRESS’. PsyArXiv, 18 February 2022. https://doi.org/10.31234/osf.io/zmf5d.

American Medical Association (AMA). (2021, December 6). Peter Hotez, MD, PhD, on the omicron variant and Delta winter surge | COVID-19 Update for Dec. 6, 20. https://www.youtube.com/watch?v=WnfpC1_N2Mg

News, B. N. O. (2021, November 26). Tracking COVID-19 variant Omicron. BNO News. https://bnonews.com/index.php/2021/11/omicron-tracker/

Art Poon. (2021, November 28). Our first https://filogeneti.ca/CoVizu update with B.1.1.529. As expected, number of mutations is well over molecular clock prediction (~13 diffs). Relatively low numbers of identical genomes implies large number of unsampled infections. We update every two days from GISAID. https://t.co/m8w2CjL1c0 [Tweet]. @art_poon. https://twitter.com/art_poon/status/1465001066194481162

Pisanu, E., Benedetto, A. D., Infurna, M. R., & Rumiati, R. I. (2021). Psychological impact in Healthcare Professionals during emergencies: The Italian experience with COVID-19. PsyArXiv. https://doi.org/10.31234/osf.io/5rzj9

Possibly useful summaries of a collection of papers and resources, including some by Andy Clark.

A brief overview of predictive processing.

Data Collection and Integration to Enhance Public Health Registration, Thu, Jun 10, 2021 at 1:00 PM | Eventbrite. (n.d.). Retrieved May 28, 2021, from https://www.eventbrite.com/e/data-collection-and-integration-to-enhance-public-health-registration-156146370999

Szabelska, A., Pollet, T. V., Dujols, O., Klein, R. A., & IJzerman, H. (2021). A Tutorial for Exploratory Research: An Eight-Step Approach. PsyArXiv. https://doi.org/10.31234/osf.io/cy9mz

predictive processing is a process model that is suggested (or constrained) by the FEP.

Yang, Scott Cheng-Hsin, Chirag Rank, Jake Alden Whritner, Olfa Nasraoui, and Patrick Shafto. ‘Unifying Recommendation and Active Learning for Information Filtering and Recommender Systems’. Preprint. PsyArXiv, 25 August 2020. https://doi.org/10.31234/osf.io/jqa83.

Does BMI Predict the Early Spatial Variation and Intensity of COVID-19 in Developing Countries? Evidence from India. COVID-19 and the Labor Market. (n.d.). IZA – Institute of Labor Economics. Retrieved July 29, 2020, from https://covid-19.iza.org/publications/dp13444/

Maarten van Smeden on Twitter: “Let’s talk about the ‘risk factors’ for COVID-19 for a moment 1/n” / Twitter. (n.d.). Twitter. Retrieved July 11, 2020, from https://twitter.com/maartenvsmeden/status/1249702560442785794

Shmueli, G. (2010). To Explain or to Predict? Statistical Science, 25(3), 289–310.

It is this contortion that will make it hard to ask SNA driven research questions. You must think about describing patterns rather than making predictions.

Need to create a risk management plan associated with the use of predictive analytics. Talking as an organization about the risks is important - that way we can help keep each other responsible for using analytics in a responsible way.

Useful breakdown. Research, predictive models, and recognition are quite distinct from one another and the approaches to data that they imply are quite different. In a way, the “personalized learning” model at the core of the second topic is close to the Big Data attitude (collect all the things and sense will come through eventually) with corresponding ethical problems. Through projects vary greatly, research has a much more solid base in both ethics and epistemology than the kind of Big Data approach used by technocentric outlets. The part about recognition, though, opens the most interesting door. Microcredentials and badges are a part of a broader picture. The data shared in those cases need not be so comprehensive and learners have a lot of agency in the matter. In fact, when then-Ashoka Charles Tsai interviewed Mozilla executive director Mark Surman about badges, the message was quite clear: badges are a way to rethink education as a learner-driven “create your own path” adventure. The contrast between the three models reveals a lot. From the abstract world of research, to the top-down models of Minority Report-style predictive educating, all the way to a form of heutagogy. Lots to chew on.

Easier to do with a strict model for success.