The poetry of presence c/o Kenner's 'Mechanic Muse'

'... Eliot's poetry responds to yet another of the new century's pervasive experiences, that of being talked to by people we cannot see. This happens whenever we pick up a telephone, a thing we do so many times a day we quite overlook the strangeness of what lappens next. A character in The Cocktail Party remarks that you can't tell the truth on the telephone, meaning probably that you lose three quarters of your communicative power when you cannot be seen and your breathing body is absent, and you must fabricate mere semantic sequences. Your words are only a fraction of what you say, and the telephone throws you back on nothing but words. It is in Sweeney Agonistes, the play that gives the telephone bell a wholly metrical speaking part, that we hear the famous phrase, "I gotta use words vhen I talk to you."
    When the phone rings in that play - 

    Ting a ling ling 

    Ting a ling ling

Dusty snatches it up and says,

Hello Hello are you there?' 

Kenner, H. (1987) The Mechanic Muse, Oxford: OUP. pp.34-35.

Occupational therapist Joël inspires elderly to get in motion ...

Or, "Excuse me. How many movements are there in that watch?"*

"Joël Kruisselbrink’s creativity in designing exercises and games inspires elderly people to keep moving and live life to the fullest.

Nursing home residents are often understimulated and have little motivation to exercise. As an exercise instructor, Joël Kruisselbrink knows better than anyone how important exercise is for the elderly. His passion for creating exercises and games enables older people to continue enjoying life while keeping them moving."

Brightvibes: Exercise is life: Joel’s exercises and games inspire older people to keep moving

Individual

|

      INTERPERSONAL    :     SCIENCES                   

HUMANISTIC  --------------------------------------  MECHANISTIC      

 SOCIOLOGY  :    POLITICAL 

|

Group

What do you think the impact is upon the resident's mental health and emotional well-being? Psycho-
-Social?	Standards & Quality of Care Policy

*Hodges' model: A tool to watch the complications of Care

My source: https://x.com/Rainmaker1973/status/1802268229361967404

Call - Special issue: Learning from Multiple Data Sources for Decision Making in Health Care

The increasing availability of digital data, along with recent developments in Artificial Intelligence, especially in the Machine Learning and Deep Learning fields, led the scientific community to debate whether data alone is sufficient for decision making and scientific exploration. We focus the attention on the healthcare domain, where peculiar issues affect data: indeed, data are usually collected under heterogeneous conditions (i.e., different populations, regimes, and sampling methods), suffer missingness – very often not at random – and their use is strongly constrained by privacy issues. In such a complex setting, this special issue challenges computer scientists to contribute to the above debate by designing and developing innovative methodological approaches, for solving complex decision-making problems in health care, leveraging on observational data.

Topics of interest include, but are not limited to, the following with an emphasis on novel generalizable methods applied to the healthcare domain:

• Causal discovery from multiple data sets.
• Federated causal discovery.
• Causal discovery from heterogeneous data sets.
• Transportability of causal models and inference.
• Neuro-symbolic approaches to learn from heterogeneous data sources.
• Continual learning on streams from multiple data sources.
• Computational intelligent strategies to support causal inference.
• Edge computing for decision making in healthcare.
• Integrative AI methodologies.
• Distributed inference methods.
• Continual Learning.
• Knowledge Discovery and Integration.
• Combination of deductive approaches with ML models.
• Combination of ontologies and/or knowledge-bases with ML to support decision making.

Peer Review Process:

All submitted papers will undergo a rigorous peer-review process featuring at least two reviewers. All submissions should follow the guidelines for authors available at the Journal of Biomedical Informatics website (http://www.elsevier.com/locate/yjbin). JBI’s editorial policy outlined on that page will be strictly enforced by special issue reviewers.

Note that JBI emphasizes the publication of papers that introduce innovative and generalizable methods of interest to the informatics community. Specific applications can be described to motivate the methodology being introduced, but papers that focus solely on a specific application are not suitable. A few examples of papers focused on methods previously published in JBI include: Kyrimi, et al. [1], Huang, et al. [2], Kocbek et al. [3], Houston et al. [4], García Del Valle et al. [5], Graudenzi et al. [6] and Sims et al. [7].

In particular, the authors of [1] showed the relevance of causal models and expert knowledge to develop credible models, i.e., capable of achieving good predictive performances when transported from the study cohort to the target population. Furthermore, [2] tackles the relevant issue of partially overlapping variables when data are collected from multiple data sources. This problem is extremely relevant both in theoretical and practical terms for decision making in the healthcare sector.

The contribution provided in [3] stressed the importance of working in a multi-source context by demonstrating how the linking of different repositories can improve the overall understanding of patients' conditions. Similarly, in [4] the authors extended this concept by introducing a methodology to evaluate to audit the data quality of the sources exploited by healthcare information systems. Then, in [5] the multi-source concept is transferred within the multi-modal environment and the authors surveyed the importance of considering different modalities to obtain a better disease understanding.

The works in [6] and [7] focuses on the importance of data. In [6] a data integration framework is defined for characterizing the metabolic deregulations that distinguish cancer phenotypes, by projecting RNA-seq data onto metabolic networks without the need for metabolic measurements; in [7] a biomedical informatics method is introduced that uses multiple public health data sources to perform surveillance of methadone-related adverse drug events. Interestingly, even if patient data are not linked between different data sources, results show that the integration of multiple public data sources can capture more cases and provide more clinical details than individual data sources alone.

Key requirements for JBI ML papers in addition to presenting novel methods (not simply application of existing methods to a new healthcare domain) are as follows: 1) projects must have clinicians involved in research question/problem formulation, defining input data, and assessing the results. 2) An explanation (with clinicians) of how the proposed method would fit into the clinical workflow is expected. It must be translational to practice. 3) Data sets should preferably be collected from hospitals after the research question was formulated, thus avoiding the use of available data (MIMIC) to define a very wide research problem that could potentially be answered with available open datasets (as an example: detecting if someone has COVID from Chest X-Rays would not be acceptable, as the gold standard test is the laboratory test). 4) As for explainability, SHAP values and related diagrams would not be enough: the paper should clearly describe and explain how clinicians use the visualization to make decisions. For further details please refer to https://www.sciencedirect.com/journal/journal-of-biomedical-informatics/publish/guide-for-authors.

Further details available -

Submission process, Questions, and References 

My source: 

https://aixia.it/en/gruppi/hc/

Essay Competition: On the Principles of Intelligence c/o AI-SGES list

On December 17, 1903, two brothers from Dayton,
Ohio, named Wilbur and Orville Wright,
were successful in flying an airplane they built.

Dear all,

Around 1500, Leonardo da Vinci published his sketches of an aircraft with rounded wings, and in 1783 the first manned hot-air balloon took off. It wasn't until 1905 – over 4'000 years after the oldest known depiction of human flight was carved in clay – that the Wright Brothers took off for the first sustained, controlled, and heavier-than-air crewed flight. From today's point of view, the principles of aerodynamics necessary to build operational aircraft seem relatively trivial.

But only in retrospect are the results of such breakthroughs considered part of the 'obvious'. In 1957, psychologist Frank Rosenblatt published the first paper on trainable neural networks. Since then, humans have walked on the moon, the internet has been invented, and smart devices have been built to fit in our pockets – but neural networks have still not led to human-level artificial intelligence. Neural networks appear like wings that fly for a short time but then drop like a stone when confronted with unknown tasks. Therefore, we'd like to propose moving away from the current hype around neural networks and ask you more fundamentally:

Which fundamental principles of intelligence must be considered in the successful design of artificial intelligence?

The appropriate example from aviation would be: "What are the core aspects of flight that must be considered in the successful design of aircraft?" (One possible answer: As a curved airfoil wing flies through the sky, it deflects air and alters the air pressure above and below it. When moving forward, the curved upper part of the wing lowers the air pressure directly above it, so it moves upward.)

With that in mind, we challenge you to write an essay of 2'000 to 3'000 words and become the Wright Brothers in the field of AI! Whether you answer the question from a philosophical, biological, computational, mathematical, neuroscientific, psychological, social, or any other perspective is up to you. We value novel and insightful arguments that are useful in the field of AI.

Go to https://lab42.global/essay/ and hand in your essay by 23:59 GMT on December 31, 2022. The best essays are rewarded with 3'500 USD in total.

Best regards

Oliver Schmid
oliver.schmid AT lab42.global

https://lab42.global/essay

[Do read the guidance on the website. PJ]

Image use here: https://www.nasa.gov/multimedia/imagegallery/image_feature_976.html
original credit - inc. Library of Congress

AI-SGES list

Hodges' model and trial by vampires ...

Hodges' model was created in the mid-1980s.

 

Since then models of nursing, models of care and nursing theory are no longer fashionable.

 

Hodges' model was produced in Manchester, NW England, so as far as the rest of the World is concerned: it was 'not invented here'. 

The eponymous title, means many people may defer: this model is clearly not mine, or ours. Why should we use this one, let alone consider it?

In addition ...

"Academics are novelty vampires. They crave new stuff ..."

Brian Foote