Author: Maverisk

No, this is not about bias in data that you train your “AI”i.e.ML on. I’ve posted (not nearly) enough about that.

It’s more about pointing to an HBR article that should be(come) very influential…
As it was already known that any ‘training’ was about as ineffective as one could get, qua e.g., security awareness transfer [posted about that already, too], this piece elucidates why; among others, because it finds those sent to ‘training’ a priori suspect i.e. guilty and punishes, both creating counter-emotions. But read the whole thing; worth it!

Also, in regard to this earlier post: Thinking in win-lose terms … one is all that you can score [ref. Frankie gtH, Two Tribes; yes you got that or you’re n00bish like here, 10th paragraph].
This may be corrected by the above pointer. But be careful; be very careful. People resent nudging; when they find ou about that, they’ll balk against the brainwashing. ‘tSeems like one has to change little habits, big habits, unconscious ones and blatant ones, of every individual individually, and at societal levels — all and all quite a complex/wicked problem (with this), even leaving aside the thingy about manipulating society towards some better ‘good’ which of course leads to the Utopia dystopia, if it were achievable at all. Like, any strive for an ideal society optimises its positive effects at about the infliction point i.e., halfway through its implementation; after that the negatives begin to outdo the positive effects…

Enough for now; leaving you with:

[Don’t get defensive …! Nancy]

This, about the trend to see play time being over and serious business resuming; with ML.

Where no longer, one just let some business-newbie just out of college (or less) play with a bricks set of tools including a minute subset of all relevant vectors [i.e., a tiniest sliver of context], and then have … Tadaaa! A proof of concept, at best. Now, let’s see how small a part in might take in regular business operations — don’t transform your business too much because that might undo the relevance of rules learned ..! Even when ‘AI translators’ come into fashion, they still take things bottom-up more than they dare to admit, still with an ML focus first, seeking anyplace suitable to deploy it and hence reaping point soultions at best.

Where rather one would want to take things from the other side. Top-down. Maybe not all the way from the top, as insights aboot ML may not live there, and insights about how the business is run, also not [quite completely not, often].
But somewhere-middle-out, where some may understand how the business is run. And where process mining may help to understand what actually goes on, and then plot the following:

to see that a lot of processing in any process step would be repetitive, fetchable-in-an-algorithm-of-some-kind [1] simple data handling, plus a degree of human intervention and deviation based on ad-hoc intelligence applied.

Yes, ‘if any’. But also pointing to this piece on the long-awaited, longed-for demise of ‘six sigma’ and its dehumanising characteristics/effects.

So you can choose which process (step/steps) would lend themselves best for possible (sic) disruption by ML-application of some sort.

The some sort of which you then need to figure out. And also see how the ML (possibly/probably no more than) bits fit into a new, redesigned process. Yes, ML may make sense, to a degree to be ascertained, but humans may need to change – luckily for you, the dot on the horizon, which should be waaayyy before the horizon, is for humans to be rid of the mundane stuuf and to be tasked onto the Intelligent stuff. IF empowered, enriched.

So, in the end, hybrid processes may be a thing to aim for; want.

Plus:

[Artes and Ciencias; Valencia]

[1] The bandwidth of classical algorithms, expert systems, Big Data correlations, classifier ML, more-complex ML, basic neural nets, complex neural nets, evolutionary algorithms [or are they a separate, close but parallel track?] as explained in this.

Omdat er recent alweer hartstikke Foute uitsprakenboel discussies waren over ZP’ers (niet over ZZP’ers), dat die vooral andere belangen zouden hebben – quod non 1 – en dat die zo duur zouden zijn – quod non 2.
QN2, wegens:

Medewerker in vaste dienst verdient € 4000 netto per maand, voor het rekenvoorbeeld. Dat kost de werkgever € 6.487 bruto, volgens alle sites; jaarlijks € 77.844. Plus vakantiegeld € 6.227,52 plus waarschijnlijk een 13e maand € 6.487 plus pensioen € 15.000ballpark plus opleidingsbudget € 1.000 (j..zus wat een kneiterbedragje) en dat alles doorbetaald tijdens vakantie of ziekte… En dan vergeet ik nog een aantal kosten. Oh ja, transitievergoeding; laten we uitgaan van een dienstverband van 10+ jaar dus een halve maand erbij.
Totaal € 109.802,02 voor een 1600 uur aanwezigheid (héél genereus), zijnde [naar is gebleken uit onderzoek, ad 2uur per dag en nog zonder rekening te houden met vakantie-afwezigheid etc.! dus héél genereus] 400 daadwerkelijk productieve uren. Dat is bruto € 194,61 per productief uur.

Om de bruto/netto grosso modo € 110.000 bij elkaar te verdienen — want de ZP’er moet er óók nog pensioen uit opbouwen, zélf cursussen betalen, zélf z’n niet-werk vakantiedagen van opslaan, zélf zorgen voor up-to-date hulpmiddelen, etc.etc.etc., en gegeven een ruwe 30%IB over het bruto uurtarief (want de Beldienst geeft nou niet echt korting voor niet-werken wegens vakantie of zo, en bijdragen in ziekteverzekeringen enzo zelf ophoesten ook bij laag inkomen …) na afdracht BTW,
resulteert dat in een uurtarief van € 275,00.

Van een ZP’er mogen we verwachten dat de productieve/totale uren op zo’n 50% staan en dan reken ik nog van me af [feitelijk zelf administratief bijgehouden: ik kom op 90-95%]. Als het zou gaan om productieve bijdrage aan de organisatie, zou het inkomen en uurtarief van de ZP’er dus het dubbele mogen zijn van dat van een medewerker in vaste dienst.

We begrijpen dat de overheid tegen discriminatie op de arbeidsmarkt is, en uitbuiting van ZP’ers wil vermijden.
We begrijpen dat werk-gevers aan ZP’ers niet meer willen betalen dan aan medewerkers in vaste dienst — waarom eigenlijk niet!? de werkgever mag best betalen voor de flexibiliteit niet telkens te hoeven ontslaan en telkens wel verse precies-passende kennis met elders opgedane ervaring (leren van (andermans) fouten) in huis te kunnen halen..!
Wie niet wil zien dat de werkgever betaalt voor de bijdrage aan de organisatie, moet onmiddellijk zelf ontslag nemen wegens verregaande incompetentie.

Dat betekent dat het uurtarief van een ZP’er ongeveer 2,83 keer zo hoog mag zijn als het bruto uurtarief van een medewerker in vaste dienst, voor dezelfde bijdrage.
In de reguliere praktijk is dat, voor de categorieën werk waar normaliter sub-80k per jaar voor staat, niet 2,83 keer zo veel, maar de helft. Dus mag het uurtarief van de ZP’er zo’n 5,65 keer hoger om gelijkuit te komen. Do the math.

Hoe het ook wordt gewend of gekeerd, het idee dat een ZP’er op bruto uurtarief ongeveer even ‘duur’ zou moetenmogen zijn als een medewerker in vaste dienst, is dikke oplichting. Nee, dat is een eufemistische kwalificatie, geen over- maar onderdrijving.

Wachtende op gerechtvaardigde-uurtariefopdrachten…, met:

[Ja maar lekker vast in je hokje zitten is zo fijn! – precies, dus dat voordele in natura mag wel van je inkomen af…? Zuid-As]

On several occasions it struck me that, adviseconsulting on subjects like AI deployment and information risk management [usually not in one go], nowadays the relation between company ‘size’ and headcount seems to have gone less strictly linear. Like, there’s still a lot of big org’s out there that do have large numbers of fte’s

[Skipping for a moment the subject of their productivity towards the bottom line; drilling down one often finds that ‘profit’ or even turnover is more of an emergent property than specifically allocatable to individual KPIs (don’t claim that executives meet their KPIs and are the money makers – that claim is a delirious scam), thus calling into question the idea that there’s tons of dead wood around that could be weeded out. That is against one of my previous hobby horse by the way; thanks for noticing, but I’m not above giving in to nuance, on the contrary huh]

but now, there’s also a fair number of clients with quite limited colleague/’member’ numbers that still have huge turnover — in terms of what counts [ever more]: data processing. Got’ya; I didn’t write ‘information’ for a reason. As in: When impact on clients/customers is the Value rigœur of the latter day [oh not that again], these scale-ups make a splash waaayy beyond their size. Or turnover or profitability even; those become less and less tending to zero relevant anymore. It seems. And it explains valuations better than said three measures of ‘size’ or ‘impact’.

So, shouldn’t we start to compare the soon-to-meet-Schumpeter’ian organisations to similarly-sized-data-processing organisations of any kind, and then conclude which ones are more efficient? Turnover, profits, headcounts don’t count anymore.

Uhm. Now what.

Oh, at least, this:

[Sending data to once a mighty empire …? Coincidence: The Empire Home truck; London]

Training ML systems is bound to use personally identifiable information, PII usually dubbed personal information. This latter thing diminishes the scope, way too much, by leaving out that any bit of information that in conjunction with outside sources of any kind, can be used to identify a person, is PII.[1]
Under GDPR, there’s the right to be forgotten… Now there’s two problems:

• Sometimes, data points can be retrieved literally from the trained system, like here. Clearly, such data points need to not be reproduced anymore, then. But how to un-learn an ML system when the data point involved, needs to be forgotten? [2]
• Similar less literal cases apply. E.g., when it’s not one data point that’s regurgitated but the one does have an off-average value in the weight/trained parameter. Which is probable, since an ML system hardly learns from n times an average value [it may but then, that’s not ML but fixed function learning, fixed ‘algorithm’ wise] but from n different values, the one of concern among them. How to get the contribution out of the weights, and how to prove (which you may have to, under GDPR obligations though only when push comes to shove) that your ML weights no longer include that one data point its impact on the weights ..?

It’ll be fun, they said. For lawyers, they said.

Still, the whole thing may need to be figured out before anyone can deploy any ML system that included European citizens’ data — since the GDPR has global effect.
Now you have fun, I say.

With:

[You probably are on camera here…]

[1] Side note: I was wont to write ‘can and will’ which is true but sounds too much like ‘anything you say can and will be used against you in a court of law’ [disregarding the exact wording], which will of fact alter what I may say as I now include the consideration of what and how I say things subsequently. To which I ask: When not if, not all that I’d say is actually used in a court of law, does this invalidate the statement made to me, rendering the ‘can’ part invalid i.e., the respective speech part(s) that are used, illegal(ly obtained) evidence ..? Since I say things other and/or differently than without the statement at arrest i.e. based on a statement by a sworn officer that is later proven false, perjurious even. Entrapment? That’s illegal in many circumstances…
Would want to know from a legal scholar how this works.

[2] Most probably, you will not be able/allowed to keep that data point for any specific reason. To say that it’s too difficult to get the data point out of the trained system: Does. not. work. The law just require you to do the near-impossible; your mistake. Just train the system all over again why would anyone care for your interest? GDPR requires you to only ask how high you have to jump and then do that, whether you’d have to set a world record or not.

Already, we were aware that

• With ML systems, the lines between software/fixed algorithms, parametrisation and semantic meaning of the outcomes, are blurred. We have no ‘place’ where the ‘logic’ sits or is stored/used; it’s all getting mushy and that’s not a good thing;
• The law wants neat yearsteryears’ algorithms (protocols, parameters, provable actions upon intent, etc.);
• Adversarial AI exists, whether we call it AI or the mere ML that it is.

All these three in concert, don’t give hope. Like explained here and more profoundly here, ‘hacking’ may not be appropriately defined, if it currently is at all, once one uses ad-AI to mess with ML-driven (literally) systems. The latter is more like solicitation or so…?
To expound, I copy a little from the article:

“Unless legal and societal frameworks adjust, the consequences of misalignment between law and practice include (i) inadequate coverage of crime, (ii) missing or skewed security incentives, and the (iii) prospect of chilling critical security research. This last one is particularly dangerous in light of the important role researchers can play in revealing the biases, safety limitations, and opportunities for mischief that the mainstreaming of artificial intelligence appears to present.”

“… why this lack of clarity represents a concern. First, courts and other authorities will be hard-pressed to draw defensible lines between intuitively wrong and intuitively legitimate conduct. How do we reach acts that endanger safety—such as tricking a driverless car into mischaracterizing its environment—while tolerating reasonable anti-surveillance measures—such as makeup that foils facial recognition—which leverage similar technical principles, but dissimilar secondary consequences?
Second, and relatedly, researchers interested in testing whether systems being developed are safe and secure do not always know whether their hacking efforts may implicate federal law … Third, designers and distributors of AI-enabled products will not understand the full scope of their obligations with respect to security.”

Yes there’s a call to action.
Since “We are living in world that is not only mediated and connected, but increasingly intelligent. And that intelligence has limits. Today’s malicious actors penetrate computers to steal, spy, or disrupt. Tomorrow’s malicious actors may also trick computers into making critical mistakes or divulging the private information upon which they were trained.”
Haven’t heard too much reflection on this, yet.
Would definitely want to hear yours. Please.

[Edited to add: Do also read between the lines of this, qua probably mostly surreptitios data capture contra the GDPR… And what if I want my data to be removed from the ML-parameters ..?? See upcoming Monday’s post]

Oh, and:

[On Mare Nostrum I mean Mare Liberum, the legal ship may have sailed. On a vast expanse of not much. Outside Porto]

Just when you thought about getting into it, maybe, from somewhere near the bottom… One should be careful to know what the bottom looks like.
Qua diving into ‘Data Science’ quod non, that so many have put their personal hopes in, but … tempted how and why ..?

Earlier, I posted this, on how all the Fourth Estate – as far as independent and also focused on others that might still be independent, now apparently unwanted and to be turned into 4thE sheeple – wrote about how one would have to slave oneself to death for the most minute chance of Making It.

Then, news came around that actually, it seems like the Model doesn’t work anymore… In this more recent piece, and various linked posts (and external articles) therein.

Today, even more support for the above warning. Maybe not for some (e.g., him), that had the appropriate insights long ago already and surfaced to surf, if we may express it that way, and only still need to get a suitable spot – or this, if you know the place or (have) be(en) there.

Also:

"You definitely need a PHD to do data science" he says while manually renaming 75 csv files

— Gordon Shotwell (Hiring) (@gshotwell) September 9, 2019

Now then, I’ll leave you with the Today’s Link to study and weep, and:

[Not quite the above, but close …?? Just North of Siena]

When humans are much, much less far off from [other; ed.] animals than we commonly think, and a look at ‘presidents’ around the world may push that into a negative margin, there was this piece about how “AI” might better have a look at animals’ trained brains for a path forward from ANI to AGI.

Well, wasn’t it that the Jeopardy-winning “AI” system actually was 42 subsystems working each in their own direction ..?
How would ‘rebuilding’ a human brain also not be the same, at a somewhat larger scale ..?

Like, this post about ‘getting’ physics being done by some quite neatly identified parts of the brain — what about building massively (complex but) connected massive numbers of subsystems, all focused at particular areas of human thought (with each possibly developing their own specialty – not much asked when that already happens in larger neural nets by itself, eh?). Building from the ground up, as humans do when they develop their brains (recognising mama first, before papa, then saying ‘papa’ first, before ‘mama’). The map of brain regions is developing at light speed anyway; as here and here.

This may take decades of all-out development, like with humans. [Noting that with the explosion of complexity in society, the age of full development has jumped from adolescence to twenties, even when the latter also includes full development of a conscience and sense of responsibility/ies. Once, centuries ago, there was so little to learn about the world that many were done by adolescence-to-18th. Now, there’s so much already of basic stuff to ‘get’ society and one’s role in it, that the age should be much above 20; also: Life-long learning.
All moves to lower the age of … whatever, qua maturity e.g., in driving, drinking (hopefully separating the ages between the two so responsible behaviour in both and in combi is (not) developed properly), and criminal culpability versus youth crime, all backfire grossly already for this reason: Brains haven’t developed earlier, only opportunity and incompetence. By, among others but prominent, parental protective hugging-into-debilitation of generations of youths that haven’t learned to fence for themselves in hostile environments that still require cooperation to survive. Never having learned give-and-take (Give first !!! Duties before rights), means never having learned to be a responsible human. Which shows, in many societies.
Edited to add: this, about how a hand’s ‘nerves’ may learn about objects; any one that has dealt with baies, knows this drill….

Or, one stops the development after a handful of years, and ends up with ‘presidents’.
Or, one goes on a bit, beyond the proven ‘95% of human behaviour is reflexes on outside impulses the neocortex just puts a semi-civilised sauce on it’ onto e.g., Kritik der reinen Vernuft, Die Welt als Wille und Vorstelling, and Tractatus Logico-Philosophicus (plus the Theologico-Politicus I hope). To have a system with Explainability towards these masterpieces, among others, would be a great benefit to society. But I’m digressing; the Turing Test was about average humans, not us.

The bottleneck being the hardware, obviously. Plugging in USB/UTP cables between two systems isn’t as much fun as incepting/building human massively-complex systems-to-be-raised.
Also, there may be a build-by-biology versus build-by-design difference; have a look at the numbers here and you get what it would take. On the hardware side, things/boundaries are moving as well.
Edited to add: The flip side is that any above-such trained system, is most quickly and infinitly-copiable. Hence, should one go for ‘average human’ intelligence, or variate on purpose [who calls the shots on this, qua human-like-systems eugenetics ..??], or aim for the highest intelligence [of possibly non-human form] achievable? And what if the latter, and it turns out that decides to do away with stoopid humans quickly to protect the earth, its power supplies and its’self ..? What if too many of such extreme intelligencies prove to be too many / all, as whacko as many human ‘super’intelligents are ..?

Oh and I am aware that one doesn’t ‘need’ to rebuild a human brain, to get to something similar to human intelligence [Big-IF there’s such a thing; have a look around at your colleagues]; my point here is that we may want to strive for something similar and let it veer off as close to the Edge [not the browser, that’s a demo of non-intel] as possible to prevent it from developing ‘intelligence’ of a kind that we have no clue how to deal with — which would potentially make it much, much more dangerous.
What if the System were beyond, on a different path, of the Sensation/Impression–Grasping(? Verstand)–Understanding(? Vernunft) line of Kant (relevantexplained in the Kritik der Reinen Vernunft, I. Transzedentale Elementarlehre II. Teil Der Transzendentalen Logik II. Abteilung Die transzendentale Dialektik, Einleitung II Von der reinen Vernunft als dem Sitze des transzendentalen Scheins, C Von dem reinen Gebrauche der Vernunft B363/10-30, obviously)..? Our brain does work that way, but other substrates not necessarily should, too.

But there is no systemic logical block to this all, is there ..? Your thoughts, please.

[Edited to add: this and this.]
[Edited to add: this, on “AI” passing school tests but how (mostly!) irrelevant hat is.]
Plus, about the word before last:

Plus:

[Ah, music appreciation … there‘s one …; Aan het IJ, Amsterdam]

“If you can’t explain it simply, you don’t understand it well enough” – Albert Einstein. Or was it Feynman? Or what was it, by Einstein, or ..?

“An alleged scientific discovery has no merit unless it can be explained to a barmaid” popularly attributed to Lord Rutherford of Nelson in as stated in Einstein, the Man and His Achievement By G. J. Whitrow, Dover Press 1973. Einstein is unlikely to have said it since his theory of relativity was very abstract and based on sophisticated mathematics.
to which I found
“Unrelated, but reminds me of the joke about the mathematicians who were trying to play a joke on their colleague in a bar and coached the “barmaid” to reply “one third x cubed” when they offhand asked her what the integral of x^2 was. When the colleague comes back and they try to play the prank she responds as they prompted her, and then nonchalantly adds, “plus a constant””.
Did you get the constant, or were merely reminded, or don’t know what it is about or don’t care ..? And:

“If I could explain it to the average person, I wouldn’t have been worth the Nobel Prize.” [Einstein, in various variants]
Hence:
“You should make things as simple as possible” – Albert Einstein — really ..?? From the man who gave this quote:
“It can scarcely be denied that the supreme goal of all theory is to make the irreducible basic elements as simple and as few as possible without having to surrender the adequate representation of a single datum of experience.” From “On the Method of Theoretical Physics,” the Herbert Spencer Lecture, Oxford, June 10, 1933.
Hence: “You should make things as simple as possible, BUT NOT SIMPLER“

[My bold caps]; the purpose of this post. The ones to forget that, will be the onces laughed at for their sheer dunceness.

Also:

[Describe the full detail in ten words or less. At the Fabrique, Utrecht/Maarssen]

You may have been attracted by the title – I won’t give away your name, address, phone and social security number, and credit card and bank accounts to the first bidder since I may not be in the business you assumed from the title.

Rather, I’m referring to the age-old stupidity of trying to capture complex systems in merely complicated models, not even to study and understand but toy around with it and at your pleasure do prognostic and prescriptive things with it. Which was found to be Wrong already long ago, as per e.g., this and this and this and …

Now, there´s the addition of this, listing among others “I think it was the physicist Murray Gell-man that said: “The only valid model of a complex system is the system itself.”” – my bold face [to insert an association with the difference between a font and a typeface …].

When Gell-Man states something, you better study it. This one, too.

I’ll leave you with:

[Look at those babies climbing up ..! Prague. No tricks, they’re there!]