Augmented Intelligence: what is collaboration?

I sat-in on a CSE seminar recently, where a big crowd is exploring the state of the art in human-and-computer-together intelligence.  It was really fun.  The topic was a discussion of a paper on human/computer collaboration from the 1990s:

Grosz, B. J. (1996). Collaborative systems (AAAI-94 presidential address). AI magazine, 17(2), 67.

But just as fun as the classic article and discussion it inspired, was an even older vision of what digital assistants might be, from Apple in the 1980s:

I left thinking that a knowledge navigator like the one Apple envisioned is not really collaboration, but when it makes the Brazil and Sahara simulations work together, that might be collaboration.  But to be a true collaborator, both agents need to want something (or “desire” something?) for themselves.

I hope I have time to attend again soon.

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Child Malnutrition Reading Resources

I’m not sure this list is useful, but at least I’ll find it when I next search:

https://en.wikipedia.org/wiki/Therapeutic_food#cite_ref-manary_1-0
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3144630/

Click to access a57361.pdf

https://academic.oup.com/ajcn/article/81/4/864/4649071
http://onlinelibrary.wiley.com/doi/10.1002/14651858.CD009000.pub2/abstract
https://en.wikipedia.org/wiki/Plumpy%27nut
https://www.huffingtonpost.com/jeffrey-sachs/saying-nuts-to-hunger_b_706798.html

Click to access unicef-plumpy-nut-supply-chain.pdf


http://en.hesperian.org/hhg/New_Where_There_Is_No_Doctor:Malnutrition
http://en.hesperian.org/hhg/New_Where_There_Is_No_Doctor:Malnutrition#Severe.2C_acute_malnutrition
http://hesperian.org/books-and-resources/
http://en.hesperian.org/hhg/New_Where_There_Is_No_Doctor:Malnutrition
http://en.hesperian.org/hhg/New_Where_There_Is_No_Doctor:For_Health_Workers#A_growing_child_is_a_healthy_child
http://en.hesperian.org/hhg/New_Where_There_Is_No_Doctor:Malnutrition#Ready-to-Use_Therapeutic_Foods
http://en.hesperian.org/hhg/A_Community_Guide_to_Environmental_Health:Social_and_Political_Causes_of_Hunger
http://en.hesperian.org/hhg/New_Where_There_Is_No_Doctor:Health_Problems_of_Children

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A paper I just read

I read random papers once in a while from the AMS Math Reviews program, and I read one recently about an MCMC approach to X-ray imaging. It was a fun, detailed look at a few different ways to do sampling, and use effective sample size to figure out which worked better when.

It did also leave me wondering what the giant X-ray machines buried 1,000 feet underground are for, though.

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NLP in Python: n-gram language model for Verbal Autopsy responses

This turned out to be a bit of a downer, but it was a good learning exercise, and the general approach will be useful for generating test data on a different project.  See notebook here.

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Jupyter notebooks for conda environments for python

This was about 2 hours of fuss that I wish I had avoided.  With my updated Jupyter Notebooks, I need to be explicit about what conda environment for python I am using.

It is all laid out clearly, if only I had been looking in this bit of the IPython docs:

For example, using conda environments, install a Python (myenv) Kernel in a first environment:

source activate myenv
python -m ipykernel install --user --name myenv --display-name "Python (myenv)"

And in a second environment, after making sure ipykernel is installed in it:

source activate other-env
python -m ipykernel install --user --name other-env --display-name "Python (other-env)"

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What have I been writing?

Just because I missed posting for the last year, doesn’t mean I have not been writing. Perhaps I have been writing more. Here is something that I just wrote for a perspective on opportunities for machine learning in population health.

Machine learning (ML) is emerging as a technology, climbing the “peak of inflated expectations” or perhaps even starting to slip into the “trough of disillusionment”, in the terms of the technology hype cycle,[ref] and offers both opportunities and threats to population health. ML is a technique for constructing computer algorithms, and what distinguishes ML methods from other computer solutions is that, while the structure of the computer program may be fixed, the details are learned from data. This data-driven approach is now dominant in Artificial Intelligence (AI), especially through deep neural networks, and stands in contrast to the old way, an expert-algorithms approach in which rules summarizing expert knowledge were painstakingly constructed by engineers and domain specialists. ML has succeeded by trading experts and programmers for data and nonparametric statistical models. However, the applications where ML has been successfully deployed remain limited. AI luminary Andrew Ng provides this concise heuristic: “[i]f a typical person can do a mental task with less than one second of thought, we can probably automate it using AI either now or in the near future.”[ref]

The editor only wants 1,000 words, so this is getting cut.

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New Data: A de-identified database of 11,979 verbal autopsy open-ended responses

There is a new sort of publication model in Global Health, and I just gave it a try (editor’s note: just = in the last year). Gates Open Research offers “author-led publication and open peer review”, and is available now to Gates Foundation-funded researchers.

I used it to publish a Data Note, which is a dataset together with a short description of what is in the dataset.

Have a look here: https://gatesopenresearch.org/articles/2-18/v1

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Introducing Vivarium (again)

Just before that year of not writing anything here, I mentioned that I have a new microsimulation platform, and it is called Vivarium.  That is still true, and now it even has some documentation: https://vivarium.readthedocs.io/en/latest/ 

It has been the thing that kept me too busy to blog for the last year.  But it did generate some aesthetically pleasing figures for test purposes, as well as some population health results of interest.  More details to come.

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Woah, one year

I got busy a year ago, and didn’t write anything until now. Well, I wrote some notes, but I never polished anything up enough to hit publish. What was I up to a year ago?

Lots of the same things I am up to now, but I’m going to start writing again, anyway.

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Python throw-back: making mazes again

I had an escapist hobby that is captured in the history of this blog, making maze [link], and I need escape again. And now I have a 5-year-old User for the output! Cool things: the maze-making code from 7 years ago was pretty easy to get working again [links, links]; the scientific python ecosystem now has notebooks! [link]

[Photo of done mazes on paper.]

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