Author: Maverisk

Quoting before-last week’s post, alreay an update’let: ]
[Sigh] couldn’t resist the introvert-dad’s joke in title.
On the verge of the last Q of ’19 so you have a little spare time to prep; this, about the really really Big Things that will capture the news next year:

1. Genetic algorithms (like here), maybe outright towards solving hard problems that ML-training offers no convergence on or, most probably, as an add-on stacked on top of Last Year’s ML results. As mentioned here, but also here and here (with links). Also, when you’re hooked on Python anyway: this;
2. Some practical solutions à la plastic-eating bacteria going onto large-scale deployment, or CO₂-capture into building material or into C/O₂ reduction via solar thus producing the much-wanted pure C and pure O₂ – some early trials are operational already but Scale will come next year;
3. Hydrogen cars. Apart from safety issues [but similar safety was solved, adequately not 100,00%… for fossil fuel cars so what’s the big deal — and edited to add: it seems that elecs are catching fire much more often than fossils, and are harder to put out; yet more reason to not jump to elecs], the infrastructure’s mostly there. Just add an underground tank plus pump, right ..? No need to build extensive parallel loading stations that comparative-wise still take ages to fill up. Also, where’s the Formula-H class Grand Prix’ ..? Possibly, we’ll have these in abundance, but in the long term they still may be overtaken [huh. boring….] by Cells. And the Scots are onto something [apart from their wisdom in wanting to Remain; as a separate country, could they ..?]. Hopefully, ‘Shipping’ will be an innovation testbed already next year, qua hydro development, in their hydro environment ;-/ with secondary options (solar) and with sufficient room for installations on-board and qua land-based refuelling points;
4. Breakthroughs in medicine, being able to cater much better ever quicker to gender/age-specific requirements;
5. Quantum computing: Before 1/1/~ already some early traces of an upswing in hypefaction: here, with the appropriate (sic) debunkalicious tone. I.e., I don’t believe this will be really big within anything like five years, but a hype may be predicted earlier;
6. … AI …? Only where BPR-driven. Yes, that’s right; despite the frequent re-name almost every year for the past <somanyyears>, latest was (sic) RPA, it’s still basic BPR in its original meaning not the totallyoverbureaucratised ‘method’. Gartner’s (others) are just a set of Mehhh’s compared to the above.

You’ll see I’m right.
Since #6 I don’t list, being my discovery of how to do time travel. Come to think about that: I discovered that in 2029 …but after and before that, who cares for the discovery date ..?

Now then, I’ll await the veracity of the above, with:

[Ah, what a museum! Drake’s first drill near Allegheny, or near Cleveland which sounds similar to Indianoplace]

In the seemingly permanent struggle of … all gutmenschen … against AI biases, finally some progress.

I mean, ‘finally’ is not qua timing; this’all was known, and PoC-used here and there. But finally-in-its-here-intended-sense, we see an actually well-written [i.e., understandable for the lay(wo)man [Straight/LBGTQI+ rainbow], even when quite a bit of calc stamina is required] piece on how and what one can do. ‘tSeems, ex-post. The ex-ante data wrangling [with all its mentioned ethical flaws], for debiasing, doesn’t need or should be in the pic anymore, or do I miss something [hey this piece should have a call to action so there you have it] ..?
Also not in there: Training rules, like what one aims for [e.g., this and this]; and ethics (sic; since this and this).

Which is quite a lot. But still, the Bay’essai is a good one to include in anyone’s Compulsory Reading, both for the debiasing methodology [core work, only valid IF one doesn’t forget the aforementioned ethical deliberations of blinkered zealotics or not], as for the Bayes’ stuff in the first place, that could help in the Bandwidth that I mentioned so overly many times [last bullet of this, and to add this]…
Also, the B-A-B piece can be used against current-day algo’s, can’t it ..? Since that could help with the data lakes out there that are used for training your ML PoCs — though overuse may undo the business case, of your today’s business and of the PoC thing.

Anyway; read, study and learn. Plus:

[Time to start thinking of The Season, again, in the age of meto; Longhorn Whistler]

Yes we’re baaaack! On the ridiculousness of wanting to ‘actively’ do away with passwords. As per:

Passwords should be killed, according to Koen of @ncsc_nl at @OneConferenceNL #nlcyber pic.twitter.com/UKrCGSRLrX

— Jeroen van der Ham (@1sand0s) October 1, 2019

which of course is big-time nonsense.

For one: this.

For two: At some point in time, it turned out cars were unsafe. In a time before seatbelts, a great many were either thrown through a front window, or impaled on the steering column [‘what a way to die’ depends on the stylishness of the vehicle driven]. The societal solution was not to do away with cars, when the alternatives were there before and during the very existence of cars, but to implement safety widgets that made having an accident less all-or-nothing. Where the infra was culpable, it was adapted – and used for additional user protection, e.g., through guiding rails.

For thirds, we switch adjective inflection [dunno if that is the correct expression but it sounds impressive, similar to ‘We Must Do Away With Passwords!’], and consider whether users’ abuse of passwords is the main problem or it is the massive data leaks, having little to do with the user mass messing massively but mainly with the infrastructure [which includes the sysadmins that have been reduced to turning a few screws along the conveyor belt] ..?
The first, not. Users have broken rules, yes, as they didn’t work for them but against them – as far as they could notice; by making it ever harder to just do their job [contributing to the company that gets a margin out of the work that is bigger than the salary otherwise the employees wouldn’t be there].
The second then ..? Ah, yes, mostly. So, the infra is too little controlled and to get a better grip you want to do away with a tiny element in that, the very one that works almost always, like asymptotically-to-infinite counts every day ..?

OK. The world is a much better place much quicker by doing away with cars, then, because the death rate [let alone the injured/ ‘handicapped for life’ rate] is much higher from that, so all cars-and-drivers are categorically to blame.
OR you admit that categorical statements mostly are wrong. And try to fix what is broken, but not what isn’t. And first, get the alternatives spread around sufficiently so no anti-tippingpoint/networkeffect dynamics undo your idea. And see why passwords are abused by everyday authorised users, and fix the problems there; ‘provisioning’ remember ..? And … and …

OK?
Plus:

[People may drown here, so do away with the sea! Villers-sur-Mer]

Let’s not forget today is the feast day of Æthelburh of Barking. Just so you know.

Also, that is a reminder that times come and go without much notice. Especially regarding longer-term but impactful developments… Like this one. The start-up economy finally beginning to wither.
When you considered that ‘finally’ to point at something happy, you may, or may not, be right. OTOH which sounds like ‘auto’, the impact on company size [times number] may vary, as here, with indeterminate results for the economy.

… Wait a sec; this the economy et al. being about the US one. Well, yeah, as they take such an inordinate chunk of publicity/press space. Ramifications are felt elsewhere, too, both regionally and qua global [multiplier] impact of one region on the other. …

Now, alarm bells aren’t ringing yet, but when we consider a. hypes have less short-term impact than we think but more long-term impact than we think; b. such an apparently singular phenomenon has far-reaching impacts in our übercomplex world — have you thought about what the above would mean for e.g., employee empowerment, politics [most partially as will of the people] versus big business, quantitative easing everywhere having flooded/swamped/drowned the world in virtual-not-virtuous money that may have no concrete basis, etc. ..?

So yes it’s of import to think it all through…
Plus:

[Another, but probably at global scale only medium-sized at most, disturbance will be their sinking into the sea per three weeks from now… Burling Gap at Seaford]

On the one hand, we still have in our hearts [not so much minds, but that’s part of the point] that humans’ capability

[on average; not a very old invention! I believe it was in David Epstein’s Range mentioned that some ‘primitive’ – hey let’s get rid of the pejorative of that but keep the (actual true) meaning – peoples, populations of Very remote mountain villages, had a limited subset of this]

of Understanding of abstraction, by means of symbol(ic) classification and manipulation operational sense, not the abusive kind].

On the other, we’re still trying to figure out how neural networks can be induced to find, by themselves, the level of symbol(ic) manipulation that we attribute to the average human. Even when excluding half of the global populace from ‘normal’ intelligence (the gaussian proxy of proxies has 50% below average by definition, and we choose the average as ‘intelligent’ for whatever reason), this of course begs the question how humans get to learn about abstractions, symbols and their manipulation [sadly, the latter of symbols, not being learnt too much about the humans being manipulated and despite a brief mirage (i.e., ‘fata morgana’) of ‘democracy’ in the 20^th century, this being the standard throughout the ages].
Case in point; this arrived within minutes of drafting and scheduling this post … no it’s not about deep understanding of the data, that’s too low(ly) a level of understanding …

And, why is it ‘forbidden’ somehow, to train neural networks with Tensorflow and what have we, by outright instruction ..?
Yes, episodal learning is on the rise. But why not outright ‘hypothesis inclusion’ by setting weights to non-random values? Why not train ‘nets along with all the other [again: (last bullet of) this) methods, w/ an evolutionary sauce on top ..? Why would we want neural networks to somewhat-predictably (sic) generate the emergent property of intelligence while at the same time stop training once a suitable coughing up of about-right answers is drilled?
Possibly, the answer is: Because only then can the vast masses of office drones/workers cling onto the illusion that they’re doing work that has a veneer of intelligence…

This of course, from a re-read of Kant [A648/B676 #1-24], where the difference use/function of Vernunft and Verstand are explained once again, here in quite summary fashion [once you truly grasp their definitions and functioning from the previous 600 pages…] — oh how insightful Kant is on many things; e.g., the induction fallacy versus deduction’s function [A647 #17-28], and e.g., the answer to Russell’s so much later question of “The whole problem with the world is that fools and fanatics are always so certain of themselves, and wiser people so full of doubts.”: those without knowledge don’t get a grasp of what they’re missing; they just don’t have any idea about what knowledge/wisdom’s sheer existence. [A575/B603 #19-30]

But still, back to the original [as I may or may not be at this, appropriately…] of why one would go to such enormous lengths of human work to get the tiny proof of concept’let of a half-decent neural net — the first 80% of the vast workload going into getting the data, the next 80% going into wrangling the data [just google for it; articles abound how this is a roadblock, too], then a further 80% being needed to get some code operational, and then finding that not much interesting was found that quite straightforward human analysis could have either approved of or dismissed easily on sight or through verification/falsification with margins.

Let’s get back to capturing that ability in expert systems … Treat ML as just any tool, and as just any building block of an algorithm, just as it is in the brain… Or is it ..?
[At writing, not so sure ..! possibly, the beauty of ‘Intelligence’ [truly defined], as an emergent property..? isn’t implemented other than in neurons – then what?]
[Also: Who cares? When we can build systems far faster and easier that perform much better much quicker than either neural nets or human experts, e.g., through expert systems, wouldn’t we jst use those and not care how the engine was developed ..? I’d say hybrid systems perform best, as always; also keeping hidden pattern detecting ML but also humans in a parallel loop.]
[Also also: This; there’s some bottom-up progress as well. Some.]

And then, when there’s systems out there that one can possibly truly call ‘intelligent’, first let them spontaneously recognise the supremacy of this real piece of genius. Not even ten minutes, but worth it …!

Anyways:

[Yes the entrance in front can fold closed, flat…; Valencia of course]

On how 3LoD as a going-concern silo’isation of ‘governance’ stuff that says to deal with Risk [which it doesn’t, in any useful way], is inherently i.e. by its very semantic internal structure, a placebo at rare, seldom, near-unique very best and more often a nocebo [yes, the opposite: something that doesn’t work but its expected (sic²) side effects will help you down the drain] but in most cases just palliative care. I.e., helping alleviate the pain of dying.

Earlier, there was this tweet (oh, on the blog post by the Giant) about how ‘audit’ may be a placebo and no more. Going through the motions and shaking off some nervousness, psych insecurity, by report recipients about the future — of course forgetting that auditors look back, not forward too much.
The same, now for ‘risk’ as captured in the 3LoD nonsense. Too many posts out there [mine, but if you want better explanations why the non-, check the ‘net it’s full of pertinent info] to quote or even link here. Only one of the vast number of theoretical, logical, methodological, tactical, operational and very factual problems with the model: It requires all to dally in babblestuff instead of standing between threat and vulnerability. Except for the threat of a regulator probing all the way through the humbug and finding some weakness in the 1^st line; which is a purely hypothetical issue since indeed it would hard-require said regulator to know what he’s doing… All the time, there’s nothing in the model that requires 1^st line management to deal adequately with risk.

Yes having eager beaver 3LoD in place may feel lukewarm but shareholder value maximisation over everything [still reigning supreme in executives’ minds despite some window dressing] requires you to just wet your pants for that effect; much cheaper. You’re laughed at anyway since you promote 3LoD so much.

And then, Schumpeter [when applying to you personally, drop the h] strikes again, sped up by the nocebo effects [cost of all the overhead].
Hence the palliative angle. Feel epicurean until you’re done.

Now then;

[For whoM the bell tolls; Baltimore – followed Procedure until cyber-probed (cyberattack‘s too much said!)]

Sometimes, it’s hard to remember things from the past, the throwback ideas that should have made it but didn’t.
Like, what happened to all the ‘knowledge workers‘ and their natural empowerment …!?
Where the real knowledge that delivered value, was with the shop-floor level workers that were all brainy-brainy and ‘manager’ types around them only had to supply the facilities, both physical and in terms of getting rid of any risk / organisational / adversarial troubles caused by others outside the compatriotic überproductive geniuses. No longer would hierarchies be needed, no longer would power be with Money. The best i.e. brightest would rule themselves, and the world – that existed to support them.

The further one develops the latter, the more one arrives at the cynical flip side of this; all the brains being boxed in by, certainly relatively, moronic managers micromanage-bullying all into kindergarten compliance since no-one understands anymore what drives Value (like this). Not the managers, that are dunces in a suit [excepting the exceptions that I know a few of as well, they are], not the workers, that are frenzied overtime clockers [hey that is very definitely highly correlated with under-delivery qua productivity yes]. Also, this.
Oh, and this; meritocracy isn’t working anymore.

And there’s a new generation swooping in [not so much, yet]. Generation Alpha. Whereas the previous two [or three, even] generations, now outdated as attention tractors, have hardly integrated but so far, seem to be ‘worse’, maybe much worse, than their predecessors in turn in turning demand into real And lasting benefits towards humanity. It seems the younger, the more existentially-threatened hence meekly-conservative their approach to life. Of which work is a part but now it seems so important a part that it drives everything else; away.
So, finally, we have come to the end of the Sixties. And are, historically and in so short a time-frame, back in the Ancien Régime however you’d want to define that, e.g., pre-1940, pre-1914, or pre-18xx whatever one’s current likes — and whatever one’s current insights into the sociology of those eras; I have infinitely little hope you have any of that insight inclusive-or you relate it to current-days economies. E.g., as in this. [Yes the link is to the Dutch book site that has this cover for the paperback. Not-so-Amaze-son has the pic for the Kindle-format being the only one they have …!?!?] Read between the lines, in reflection of where the developments outlined come from, and you see not all is well in the Age of Aquarius…

Or is all the Big Data [to use an already quite antiquated term!] and AI/ML data analyst/scientist [quod non] mumbo-jumbo the destroyer ..? Qua timing, it seems so. Also, see the tweet below. How did this happen, though ..!?

Which is obvious when one realises the realisation of the Age of Aquarius through reform of society, where not monnai but People would be important again, has failed. ‘Knowledge workers’ my a…:
[again]

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

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

Oh well. Too bad I’d say.
But No! There’s ‘hope’…! Like, in this. Though small, qua following, it still is alive … Also, some outposts remain.

And:

[Also a non-starter for-all, from an earlier attempt; Hundertwasser Vienna]

Hm, last time I looked [admittedly, some time ago], in many US states PIs had to get a license because they deal in such shady business as profiling. Shady, since it’s an outright intrusion of privacy that’s going on. One-on-one.

Haven’t heard that all that had access to e.g., Facebook’s data and the profiles that can be derived from that, had each and everyone of them been vetted. Yes, all that had access through any account w/ access to any Fb data, may have needed to be licensed.

And now you answer that users per EULA [or whatever legaleeze [was wont to write sleazy but that’s pleo] phrase you’d have for it] agreed to their data being used. But unwitting disclosure (signing off on an unreadable EULA is this, not wilful; ‘disclosure’ as transfer of info for any use elsewhere) and unwilful disclosure are both on the other side v.v. wilful disclosure. Maybe unwitting disclosure isn’t a thing yet, but it is. Any transfer of info is purpose-bound in a narrow sense [yes, legally it has always been]; and derivative info not used for the immediate benefit of the subject only, too, fits the narrow-sense subject-benefit only protection requirement.
Also, it’s not targeted but mass trawling. That not just every state officer even can do; officially, this is allowed to a certain very narrow group only. Why would a private party not have such limits (to zero), then, when it’s not one-on-one but massively upscaled ..?

So, only info explicitly posted to Public, can be shared with that Public and no right is transferred to extract economic value from it. … Well, that’s pushing it, right?

But certainly, no-one has said that licensing suddenly wasn’t required anymore. Including full compliance with all the requirements to get and hold the license. ‘tSeems to have some stuff on info secrecy, right?
And, is this post ‘against’ Fb? May be. Or not ..! Just as some time ago, a lot of people weren’t necessarily against Al Capone and he evaded conviction. Until, he was caught on that most tangential issue, remember ..?

Yes I’m rambling. But still.

And:

[That time of year again… Museumplein]

[Sigh] couldn’t resist the introvert-dad’s joke in title.
On the verge of the last Q of ’19 so you have a little spare time to prep; this, about the really really Big Things that will capture the news next year:

1. Genetic algorithms (like here), maybe outright towards solving hard problems that ML-training offers no convergence on or, most probably, as an add-on stacked on top of Last Year’s ML results. As mentioned here, but also here and here (with links). Also, when you’re hooked on Python anyway: this;
2. Some practical solutions à la plastic-eating bacteria going onto large-scale deployment, or CO₂-capture into building material or into C/O₂ reduction via solar thus producing the much-wanted pure C and pure O₂ – some early trials are operational already but Scale will come next year;
3. Hydrogen cars. Apart from safety issues [but similar safety was solved, adequately not 100,00%… for fossil fuel cars so what’s the big deal — and edited to add: it seems that elecs are catching fire much more often than fossils, and are harder to put out; yet more reason to not jump to elecs], the infrastructure’s mostly there. Just add an underground tank plus pump, right ..? No need to build extensive parallel loading stations that comparative-wise still take ages to fill up. Also, where’s the Formula-H class Grand Prix’ ..? Possibly, we’ll have these in abundance, but in the long term they still may be overtaken [huh. boring….] by Cells. And the Scots are onto something [apart from their wisdom in wanting to Remain; as a separate country, could they ..?]. Hopefully, ‘Shipping’ will be an innovation testbed already next year, qua hydro development, in their hydro environment ;-/ with secondary options (solar) and with sufficient room for installations on-board and qua land-based refuelling points;
4. Breakthroughs in medicine, being able to cater much better ever quicker to gender/age-specific requirements;
5. … AI …? Only where BPR-driven. Yes, that’s right; despite the frequent re-name almost every year for the past <somanyyears>, latest was (sic) RPA, it’s still basic BPR in its original meaning not the totallyoverbureaucratised ‘method’. Gartner’s (others) are just a set of Mehhh’s compared to the above.

You’ll see I’m right.
Since #6 I don’t list, being my discovery of how to do time travel. Come to think about that: I discovered that in 2029 …but after and before that, who cares for the discovery date ..?

Now then, I’ll await the veracity of the above, with:

[Ah, what a museum! Drake’s first drill near Allegheny, or near Cleveland which sounds similar to Indianoplace]

On the abuse of language: Where on the one hand, some auditors call themselves ‘qualified’ whereas at the same time, they (seldomly) give opinions – as in: ‘statements they want to have the value of hard fact’ – that are ‘qualified’, meaning that there is something seriously wrong with the subject (i.e., object, in epistemological terms) they just don’t know how to put down factually how bad it is.

I can agree to the part where they consider themselves qualified, in the latter sense. Especially those that call themselves qualified. Which often is intended to say that others, that don’t qualify themselves as such, aren’t. Which is truth in reverse.
Also, it’s like being a lady: If you have to say it of yourself, …

But I understand that some call themselves qualified indeed. Like, the members of this charter that ticks too many boxes of the list of characteristics of a criminal organisation. In a literal sense, not even in the figurative one that opines (sic) on auditors in general. Dutch auditors would translate ‘qualified’ opinion into ‘gemankeerd oordeel’, but the ‘gemankeerd’ then also applies to those that qualify themselves as qualified.

But do get rid of the ambiguity or people will remain ambiguous about your capabilities…

That much for now; with:

[Qualified, as useful; once at Glassfever Dordrecht. No, it’s deliberately vague; didn’t you get the reference to the above? Then you may be ‘qualified’…]