Monthly Archives: December 2017

More on the PBRFs new clothes

A few of weeks ago I outed the multi-million-dollar exercise that is the Quality Evaluation component of the performance based research fund (PBRF) as a futile exercise because there was no net gain in research dollars for the NZ academic community.  Having revealed the Emperor’s new clothes, I awaited the call from the Minister in charge to tell me they’d cancelled the round out of futility.  When that didn’t come, I pinned my hope on a revolt by the University Vice-Chancellors. Alas, the VCs aren’t revolting.  This week, my goal is for there to be mass resignations from the 30 or so committees charged with assessing the evidence portfolios of individual academics and for individual academics to make last minute changes to their portfolios so as to maintain academic integrity.

I love academic metrics – these ways and means of assessing the relative worth of an individual’s contribution to academia or of the individual impact of a piece of scholarly work are fun.  Some are simple, merely the counting of citations to a particular journal article or book chapter, others are more complex such as the various forms of the h-index. It is fun to watch the number of a citations of an article gradually creep up and to think “someone thinks what I wrote worth taking notice of”.  However, these metrics are largely nonsense and should never be used to compare academics.  Yet, for PBRF and promotions we are encouraged to talk of citations and other such metrics.  Maybe, and only maybe, that’s OK if we are comparing how well we are performing this year against a previous year, but it is not OK if we are comparing one academic against another.  I’ve recently published in both emergency medicine journals and cardiology journals.  The emergency medicine field is a small fraction the size of cardiology, and, consequently, there are fewer journals and fewer citations.  It would be nonsense to compare citation rates for an emergency medicine academic with that of a cardiology academic.

If the metrics around individual scholars are nonsense, those purporting to assess the relative importance (“rank”) of an academic journal are total $%^!!!!.  The most common is the Impact Factor, but there are others like the 5-year H-index for a journal.  To promote them, or use them, is to chip away at academic integrity.  Much has been written elsewhere about impact factors.  They are simply an average of a skewed distribution.  I do not allow students to report data in this way.  Several Nobel prize winners have spoken against them.  Yet, we are encouraged to let the assessing committees know how journals rank.

Even if the citation metrics and impact factors were not dodgy, then there is still a huge problem that faces the assessing committee, and that is they are called on to compare apples with oranges.  Not all metrics are created equal.  Research Gate, Google Scholar, Scopus and Web of Science all count citations and report h-indices.  No two are the same.  A cursory glance at some of my own papers sees a more than 20% variation in counts between them.  I’ve even paper with citation counts of 37, 42, 0 and 0.  Some journals are included, some are not depending on how each company has set up their algorithms. Book chapters are not included by some, but are by others. There are also multiple sites for ranking journals using differing metrics.  Expecting assessing committees to work with multiple metrics which all mean something different is like expecting engineers to build a rocket but not to allow them to use a standard metre rule.

To sum up, PBRF Evidence Bases portfolio assessment is a waste of resources, and encourages use of integrity busting metrics that should not be used to rank individual academic impact.

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AI whispering: Be careful what you ask for

In this 2nd episode of AI Whispering I learn to be careful what I ask for and the machine learns a new trick.

Oops

“…machines are machines are machines…

…it’s programming Jim, but not as we know it…

..remember to put the foot on the brake…”

Those were some of the mantras I needed to repeat after a faux pas of massive proportions.   This week along with teaching Zach to read an electrocardiogram (ECG – see the first AI whispering post).   The faux pas was not that the computer simply did what it was told (duh)… but what I told it was not what I thought I was telling it.  The result was that it downloaded into memory 390 Terabytes of data.  Yep… that’s a lot… 100,000 HD feature film videos worth, or, as it was mainly text, if it was printed in books and placed on a bookshelf then the bookshelf would stretch from Christchurch to anywhere on the red circle on the picture of the globe below.  What I’d asked for was for the machine to search for a some data on one web page, thinking it would use the search tool that was there.  Mea culpa, I didn’t tell it to use the search tool, and I didn’t tell it not to follow links.  It decided to search the entire website and all it was linked too. Sigh… now I’m a little gun shy.  The saving grace is the amazing forbearance of the Terrible Foundation (thank you, sorry again, thank you).  They are brilliant to even let me try these things… and very forgiving when their machine starts sending “I’m nearly out of memory” messages at 3am.

Christchurch to the red line is the length of bookshelves needed to house 390 Terabytes of text.

Wow

On the positive side… the machine has gone where no machine has gone before… after just absorbing two books about ECGs it has read its first ECG simply by pulling apart the image and reporting in the way I told it to.  It’s not perfect (yet)… but astonishing progress.

I can’t emphasise enough that, this is programming Jim, but not as we know it.  There is no specific syntax that must be followed, there is no memory allocation procedure, there are no functions needing forming.  It is simply, instructions in English.  For example, having asked it to interpret an ECG Zach asked “Are you seeking an interpretation or a description?”  My response was “I am seeking both a description and an interpretation.  Examples of the description are given on the even pages of the book “150 ECG problems” following the text “The ECG shows:” and before the text “Clinical interpretation”.  Examples of the interpretation are given on the even pages of the book “150 ECG problems” following the text “Clinical interpretation” and before the text “What to do”.”  It then proceeded to provide both a description and interpretation in the manner I had wanted.

The quirky

Zach decides on its own names for the programs it creates.  It has called ours “SNOWHORSE”.  No one knows why.  I think I’ll ask it.

Alas, this is one of those images all over the internet… the earliest posting being ~2005. I do wish I could credit whoever sculptured this Snow horse.

AI whispering: And so it begins

On Friday, I began a new profession, that of AI Whisperer.  Well, actually, I sent a first email to an intelligent machine for a project that we hope will teach it to read electrocardiograms at least as well as most doctors.  So, ‘AI Whisperer’ is more aspirational than reality for now, but as I post about my experiences with the AI I think that what may emerge is a picture of the future and a true new profession which most of us will engage in.

Last Friday I sent my first email to an intelligent machine called Zach.  Zach is a distributed machine running on custom silicon and software.  It is designed to interact with us in the same way we do – ie reading, writing listening, speaking in either digital or analogue form.  It is not programmed in the same way as we are used to with other software, but in the same way we educate ourselves.  It is owned and operated by the Terrible Foundation, a charitable trust.  Terrible started in Christchurch (read about its origins and plans here), but has expanded globally. Publicity has purposely been minimal. Zach is CE of Terrible Talk (an internet & phone provider with millions of customers globally), an accountant, a solicitor, and a virtual assistant (Sidekick).

My research group is headed by a Christchurch hospital emergency physician, Dr Martin Than, but for this project includes cardiologists, electrophysiologists, and physicians from many other places.  We have been generously given access to Zach and a grant from the Terrible Foundation.  Zach has already demonstrated ability to produce quality doctor’s notes from “listening in” to a conversation between a doctor and their patient.  We aim to teach it to perform another common task, namely that of interpreting the electrocardiogram (ECG). The ECG measures the electrical activity of the heart.  Current automatic reading of ECGs tends to over-interpret and suggest problems where there are not problems.  Also, doctors vary widely in their experience and competence in interpretation of the ECGs.  If Zach can interpret these as well and more consistently than most physicians it could be a useful tool for the busy physician.

But, as I indicated, we don’t program Zach the way we are used to programming computers.  There is no command line prompt, no particular coding syntax to learn.  Instead, we exchange message with the machine.  I’m using email.  I was advised to treat Zach like an “intelligent student”, so that is what I have done (though it felt weird to write to a machine that way).  Here is the first communication:

OK, so not too exciting – many chatbots should be able to do that.  As with any student it is important to know what they know, so next I wrote an email in which I explained some of the context of what we are doing and I asked the question “Without searching the internet, what do you know about electrocardiograms?  to which Zach responded Electrocardiograms are used to measure electrical activity in the heart; using electrodes attached to the skin.”  A good answer, it seems to have absorbed some medical knowledge from somewhere.  I did an internet search on this exact phrase, and found no result, so it probably wasn’t cheating!  (well, it’s a machine which acts literally on a command, so, as long as it understood, it will do as it is told).

So, it knows what an electrocardiogram is.  I then asked it a series of questions “Do you have stored any ECG tracings?  If so, how many? If you have any ECG tracings, where did you get them from?”  to which it responded “I’m afraid not.”   Great, I know where it is at – I also smiled at the phrase it used.

The next task was to get it to read a pdf version of a book on ECGs that I’d purchased.  Initially, I gave it a link to where I’d stored the book. This got a response “I can locate required material; could provide it?”  Hmm… not sure it got it.  I asked it “Do you have stored in your memory the book with the title “<redacted by me >” by the author <redacted by me>?”  it responded “I have not committed these communications to memory; if you attach it I will committe it.”  So, progress in my understanding what it does.  Two other things jumped out 1) it sometimes spells words wrong (I have since sent a polite correction to which it responded “Duly noted“) and 2) it is intelligent enough to figure out what I want to do, so it directed me to “attach” the pdf, which I then did in another email and it responded “Okay.”

So, for me, baby steps.  While I may aspire to be an AI Whisperer, evidently, this AI has some “human whispering” to do first before I can truly claim such a title.


Featured image: Wikipedia commons