Author: Maverisk

A major, huge, missing thing in ‘attack trees’ [aren’t they related to access path analysis?] is that they only depict the ‘opportunity’ part of perpetration, and have nothing on the Motivation of Rationalisation parts (as in this easy explanation). And hey, the latter points at insiders, too, that are so often not to be found in attack trees. Why?
That’s two things that broaden the context to anything realistic. So that, e.g., the following can be applied better:

Which goes way back to the physical realm. Allowing for controls to be seen not only as lines of defence [indeed, not the outright stupid kind], but also being of various categories, for differing purposes. To enrich your protection beyond mere data-oriented classical (info)sec which is but an operational subset of what one want, qua information security in its broader scope for the enterprise; figuratively and literally, when combined with this masterpiece method, as rightfully and correctly promoted by this peer.

So, attack trees yes, but why only now, and weren’t you using them already for a long time, implicitly? When not if, not, how can you ever have given any serious opinion about the Design of the control system (being the opinion of its potential Operating Effectiveness!), let alone its Actual Operating Effectiveness which is a mere afterthought when the Design and Implementation are A-OK. If either of the latter isn’t tip-top, actual operating effectiveness is theoretically impossible.
Also, include the various costs of control figures [introducing reasons you can’t achieve perfection by this reason of needing infinite budgets for achieving that, throwing out the baby with the bandwidth bathwater], and Time, as in trend analysis and second-order errors in that.
The more detailed your model, the more rigid it will be. The more comprehensive, the more … it may be inexact but that’s the price of ‘de-modelling’ i.e. making something applicable in reality. Either your model is perfect [into analysis paralysis] OR it makes sense [better be roughly right than a rabbit in the headlights].

Well, leaving you with:

[Awww, isn’t it beautiful, even from a late-80s analog pic? Pierre Blanche near Courchevel 1850 (most recommended)]

As was in some recent waste of power/time by marketing apparently-dunces, “… like Neural Networks, Polymorhic [sic] Sensors, Machine Awareness and Automated Data Monitoring. These techniques all use AI.”
OK. If you believe that, you’ll believe anything. Like, elected presidents are better than monarchies – have a look around the world and weep.

The point being,
a. ‘AI’ is what is still outside any machine’s abilities however complex, by definition. All that machines do, is ML. Yes, even ‘Watson’ (which is …!?) beating humans at Jeopardy is but a flimsy, pathetically failed attempt at a Turing test. [I don’t mean this one.] The missing part is not even that training is on past data (sic) and the future is by sheer logic different from that, but the also missing part, huge, is Random Context. ‘AI’ is still trained in closed environments, including supervision over Right and Wrong outcomes even if through automated learning from feedback loops. Indeed, not Good and Evil even ..! But then, you haven’t understood Nietzsche did you? And remember these quotes, relevant when you see it. Returning to the subject, context is still King [heh], and differentiates the Artificial, the Machine Learning, from the I of Intelligence. The latter ai’s have it. All, and yes I mean ALL, current-day software is insufficiently context-aware or, if approaching context awareness [much like we on Earth approach Proxima Centauri – yeah, not much effect, eh?] like in ‘autos‘ still much too little so (follow the link and weep).
b. … the latter also points to the second part of the point that is being: Intelligence seems to need morals and ethics, that we humans (and your political opponents that I shall not consider under this header) seem to have naturally. Right? Not right as a system choice?
c. Don’t know / not applicable / no opinion.

Hence, do you really want AI, or will you be satisfied with ML that takes over all the mundane tasks that bore humans to death? Not like this Boring Company but like accountancy where Intelligence may be reserved for the human overlords after all. Yes, you may snicker. But the truth is: Your job will be disrupted once it’s rid of the mundane stuff and hopefully you have developed some superiority over the remains. Which is inherently uncertain.
Hence, you may not want this. QED i.e. I rest my case as in:

[Better align with what goes on here; Startup Village Amsterdam]

Well, that escalated quickly.
Only recently, I posted this here thingy that had been lingering for a long time in the back of my mind. And in repeated discussions with various peers. About how things, as in state of the art AI things, converge to bring smart systems to the vineyard.

The post had an open end, how all things put together woud not exist yet in full.
Negative time. As per this: the solution, deployment-ready. Sans the microlocal antidote delivery, that is. But we can consider the viability a closed issue.

Yeaj! There goes my idea of being in the vanguard. But happy that a. I wasn’t a fool, if anyone had noticed .. my posts, b. this may finally get off, and be true innovation helping eco-friendly(er) viticulture.

Hoorde een vraag over de positionering van de Functionaris voor de Gegevensbescherming. Het antwoord (uit een zaal) was ongeveer: In de 2e lijn – u weet wel, als in de 3LoD-Flut (1e alinea van dit, en dit). Omdat, in termen van ‘de’ zaal, de FG een tool is van management om de klantvragen te beantwoorden en zo wat, en niet meer dan dat.

Rrrright. Voor wie te bescheten was om de Avg zelf gewoon eens te lezen. En eventuele aanvullende jurisprudentie. Enig idee wat het begrip Onafhankelijkheid inhoudt, wat Toezicht inhoudt ..? Wat het inhoudt dat (niet of) bestuurderen op het matje te roepen zijn als ze zich, vanuit hun onmiddellijke of middellijke (!) taken, hoofdelijk aansprakelijk maken voor privacy-misstappen in de organisatie. Ja, ook vanwege de middellijke besluiten liggen zij wat erg dicht bij het hakblok. Met de rewards, komen de risks.

Waarmee het aloude perspectief van een land iets naar het Oosten interessant wordt, als spiegel. Denk eens terug hoe het ook alweer was; de commissaris van de (dus: communistische) partij die als toezichthouder op het recht in de privacyutopische leer blijven van alle medewerkers in traktorfabriek nummer 43 werd geplaatst. Met sanctiemogelijkheid van buitenaf geëffectueerd.
Het enige verschil met utopischeprivacy is dat de sanctiemogelijkheden nu weleens zwakjes zouden kunnen zijn, cost of doing business. En het enige verschil is dat de traktorfabrieken [ja natuurlijk met een k en niet met een c! we houden het wel histories korrekt ja!] zelf hun partijcommissaris mogen kiezen, en budgetteren en belonen, en mogen inruilen. Maar da’s marginaal.
Niet marginaal is dat het Recht op zo een pietluttig puntje zo diepe greep heeft. Alles en iedereen is schuldig totdat het tegendeel met meters privacydossier is aangetoond nee bewezen.

Wie er meer gebelanceerd mee omgaat, heeft het wat beter begrepen. Maar wie de uitgangspunten niet kent en/of terzijde schuift, zal met de meest hilarische karikatuur van de Partij te maken krijgen. Hoe is het eigenlijk met de directie van de AP; zijn daar al kundige opvolgers aangesteld van de vertrokken kundige bestuurderen …?

Nou ja. En:

[Proper protection is the point; Segovia]

Not drowning – drink less, taste more.
Sometimes, ideas have to ripen to get the full palette of primary to tertiary flavours. Unlike wine, however, the process for ideas is more interpuncted qua quality. But certainly not the smooth ride any G hype cycle curve might suggest – though the suggestion that all subjects mentioned on any, would move at the same speed, is incorrect as the time to maturity is in fact indicated – though so seriously obscured that intent may be presumed as not to be held accountable afterwards for the imminent misses. Nor is the ride along the curve smooth in time for any subject; this suggested by the uniformity of the curve that all subjects are pressed onto. As if all reach the same height of hype.

But that’s beside the point now. What I wanted to mention, is when things get unstuck. Like, I had this idea about
[ drone (swarm or flight pattern) + cameras + visual pattern recognition also outside humans’ vision + some quite trivial machine learning on known patterns ]
equals
[ automated disease detection in their earliest developments, hyperlocally ]
equals
[ hyperlocal antidote delivery by … drones again ].
In itself, OK. When all built robust enough (weather, handling).

But I got stuck in not knowing whether or how this could work.
Along came these great ones.
And these.
And now, all sorts of things come together.
Only need to get these people on board, or their constituents. And work out the details of the potential impact – environmental, but also investment/cost/benefit-wise, and qua impact on labour markets. Dedicated (year-round ..?) and experienced labour may be (very) expensive or not, or available in abundance or the contrary. No business without balance, right?

Oh well. Leaving you with:

[Not your average flat terrain, too; Quinta do whatsitsnameagain oh yeah Vallado, Douro – most heartily recommended!]

Guess we all noticed the somewhat-groundbreaking results of the MIT Moral Machine survey around the world, on the trolley problem and how would you have reacted.
But, apart from the also noted possibly severe bias (towards male, highly educated, affluent hence probably more cosmopolitan, and responding in the first place), there is another aspect:

The survey allowed, maybe even asked, for the brain’s System II (re: Kahneman) to kick in. You know, the cognitive, thinking part that includes ethical and moral reasoning. This, demonstrated by the linkage to language, both on the input side and on the output side. Especially the latter would invalidate the results vis-à-vis practice.

Because in practice, like, human practice, and auto-cars (auto-mobile, remember? “autos”!?), only System I is / will be fast enough to respond in time. Hence, don’t ask but test human responses! And even when in some fancy-dancy VR environment, one cannot preclude a hint of reality-disbelief in the back of ones’ minds (where the appropriate processing takes place!), uncanny valley skews, etc.
And then, “I panicked” is a perfect excuse. Will it be for autos? Legally, too ..? Who will have panicked? etc.

Yes, people panick and respond intuitively, too. But wasn’t the auto trained to produce System II results for System II and System I occasions? If the latter would be captured under IF SystemIPanic() Then HandBackControlToCluelessHuman(); – emphasis mine, because of this (1st paragraph) – Then translate the pilots’ experience to autos, please:
The more you rely on the auto aspect of autos, and no-one is not aiming at Level 5 independence, the more clearly only the ever more distinctly panicky of System I decision failures will be pushed to the human that still will need to be fully equipped in the cockpit (and think of this, too) by the way contra each and every designer’s wishes, at the ever more latest of latest moments,
the less the human will be able to generate System I responses as these come from experience. So, when not if human override is required, failure will be ever more certain. Can one then still blame the human for a. not having had the ability to get experience properly, b. being called in too late (for almost certain), c. the actual actions taken that transpire, afterwards, to have been an ethical decision? Or will HandBackControlToCluelessHuman(); mean: GiveUpAlreadyAsTheHumanWillAlsoBeClueless()?

OK. The future is bright…? And:

[Just your everyday rush hour in Baltimore]

Wow that seems like two handfuls of something…

This, about a little part of the AI problem complex at the moment: ETL, as Extract, Transform, Load is usually called but few people care to remember, is Data Analytics’ talk for getting the data from some basic simple systems like SAP (E), pruning it and applying all sorts of other manual corrections (yikes! the T), and then slurping it into some Analytics (or visualisation) tool (L).

The L is easiest. Once one would have gone through the E part, plus some T sauce. Not this. The T sauce, this one, however is too hot to handle.

Since it is so error-prone. Error, as in accidental human failing at the medium, data, information and ethical levels. Error, as in failing in bad faith, at the same levels. Bias, anyone?
Problem being, when there would be e.g., bias in the source data, does one toss the respective data points out? Or might they contain valid information [Note: I take it you understand the most kindergarten basic concept of discrimination: distinguishing people on irrelevant criteria – what to do when the criteria are relevant!?] that one misses when dismissing the misfits outright, even before one has a chance to find out whether they had some role in the original data. Ethically-unwantedly biased or not.
And how good would a human be at detecting biases in source data ..? Not very. The very value of many a latter-day ML tool is in finding those hidden patterns that we miss. The experts miss.
Plus, how would you correct? If one were to leave out all cases where it turned out that bias played a role, you’ll end up with ideal cases only. But then your trained ML system will give results that are incomparable with (past) practice and for effective (… → later on) and efficient ML-trained rule-based systems development, one needs to optimise the fit with the past. That’s where your F1 score comes from. Mess with the source data, destroy the learning results.
Above, what to do with later continued learning where self-learing, unsupervised, is all the rage?

In between already, when one prunes to get out the rules one wants and dismisses others, why not turn all found patterns and rules, into a classical expert system, without or preferably with, fuzzy logic? Most explainable, transparent…

But above all, what is ethically unwanted ..? Apparently, the inputs lead to relevant outcomes as they turn out to exist in the source bias. The ML is there to detect such patterns; if there would be no relevance, no pattern would be calculated (sic; and leaving aside small-sample errors that aren’t biases but just errors).
Rather, who determines the vague ideas of what ‘society’ happens to consider just, for some time ..!? E.g., many Western societies have a core of values that are proclaimed to be based on Christianity; either some interpretation of how Jesus Christ’s words would apply in those later centuries, i.e., big fat interpretations on very often shady hidden intents, or hearkening back to the original intent as much as possible – where JS (as a full-on Jew) and those of any intellectual propensity above ignorant peasant level, would have found the idea that salvation or support for one’s neighbours would be available for non-Jews quite despicable, bordering on the unthinkable. The Golden Rule wouldn’t apply to anyone outside the close circle… All ‘ethical’ discussions since are very time- and circumstance-bound even when putting it mildly. As e.g., ‘democracy’ is so much on the decline around the world [fact]. And people don’t care about millions starving but do care about stray dogs in the same countries. Those against discrimination don’t bat an eye over discrimination of e.g., white male elderly (over 30) on the job market. When one wants to ‘correct’ a bias through some measure of equally low or worse moral value, one has no right to enforce [what one loathes oneself; or you’re in breach of the Golden Rule again].

OK, so much for difficulties with manual T. Now, …:

[Non-random colour scheme; Dublin]

[Kinda full book review]
Searching for a sort-of definitive guide on wine-cheese pairing, your intrepid reviewer came across Tasting Wine & Cheese, by Adam Centamore. Browsing through, scepticism started, to be returned to optimism in the introductory chapters. That are quite basic, but with hints of systematic treatment of the subject. And, given the introductory level, miss a few ‘rules’ here and there, in the wine, in the cheese, and in the wine-cheese sections.

Like, what grows together, goes together – yes that is mentioned twice in the book, but elsewhere, piecemeal. Yes, the introduction is about experimenting. Which is what one does when having more experience. If the book is for readers that want to jump into the experimentation, why is the introduction so simple and except a few without systematic rules? If the book is for beginners, why jump to experimentation without first handling the basic thoroughly? And then have near the end of the book with some (…) classic wine-cheese (and –codiment) combinations that all are (some of) the very grow together things that could have been treated upfront. Or in all chapters, where ‘the cheese that loves it’ typically is not from the same area. Often, because some American cheese is mentioned that, like many European brands (yes, often not types only but specific brands that very often can’t be had locally), will be available only here and there. Mostly there.

Except Stilton and port – the Anglophile angle on ‘grow together’, overlooking history where port and Dutch cheeses were styled together when anything out of France was impossible to have at the other side of the Channel. Yes, grows together is a way in which centuries of careful crafting of wines and cheeses to make the perfect fit is captured, so why not have this as the foundation for variation?

Except where Langres is thought ideal for Champagne. Yeah, if you mistake (fact) Langres for being in the Champagne instead of being between the utterly Bourgogne Chablis and the Côte d’Or. Like Durango is in Arizona because it’s close to the Four Corners. One, Langres isn’t in the Champagne of the wines nor of the département; two, Chaource comes from much closer, and is indeed the better pairing. Now, Chaource is mentioned but with Crémants in general where (with the better wines) Langres would be ideal. Why? Noting that with Chaource, having some Champagne in the fontaine makes the pairing outright sublime, yes.

There’s quite a number of outright errors as well. Just browsing around a little, far from complete:
Champagne can only be made of the three grapes ..? Foolish mistake, legally and practically; e.g., the Champagnes with Pinot Blanc that are resurfacing, have funky edges that are perfect for surprises and horizontal tastings. Not to mention Arbane, Pinot Gris and Petit Meslier. Blanc de Noirs being only from Pinot Noir? That’s just stupid. Forgetting to mention the tangerine edge of Noirs, in particular from the Meunier (try a Württemberger Lemberger or a still red from the Champagne and you’ll know), also doesn’t convey much experience with Champagnes outside the annual-million-bottle factory produce of the big companies. Wouldn’t call them ‘houses’, reserve that for the small sometimes artisanal honest producers.
White wine, even when crisp, at 4.4° to 10° …? Wine for which that is ideal, isn’t worth much is it?
Riesling not grown in France ..? Hello, Alsace! Yes tacos will not be served anywhere in Texas because it has in history never belonged to Mexico, right? Alsatian Riesling may have different characteristics but when declared not to exist, how can one tell ..?
Bell pepper aroma (a.k.a paprika everywhere except some local i.e. ‘American’ regions) is mentioned as a characteristic “often found” Cab Sav (p. 147). Right. When your Cab Sav (p.110, can anyone explain the out of order of the Tempranillo and Cab Sav in this section?) has paprika notes, it is not well-crafted, it is very-badly-crafted. Paprika is known to be an indication of serious errors in the making of the wine except in wines with clear Cab Franc influence or dominance and even then. Cab Franc badly made: Green paprika, biting. Cab Franc well-made (e.g., the Canadian ones! Try a Fort Berens or a Burrowing Owl and you’ll know): Mellow yellow paprika, perfect!

[And then one encounters the very rare Frappato brand one has on stock. Other Valle dell’Acate’s are much more interesting. Take a Cerasuolo de Vittoria, excellent on its own but with what cheese ..?]

In general, the wine characteristics are unhelpful as they are either either-or qua style, or incomplete. As mentioned on pp. 88-91 and many places elsewhere, the variety outstretches most of the ‘characteristics’—then why pick a few? And the wines list itself is very incomplete, slightly erratic, as well.
Also I expected to have a good cheese list with wine suggestions as well, not the accidental index lookup. What’s there now, is random examples as if not cheesemakers from some same region make cheeses that are as different from their neighbour’s as what winemakers make.

“In the end, though, pairing cheese and wine is an inexact science, if a science at all.” This, halfway through the book (p.85) so how’s that for in the end, seems to summarise the book quite well. Though one remains unconvinced the author actually intended the self-reference.
Is this book for beginners that need to learn the bars and chords and some simple music pieces, or is it for a seasoned jazz musician? One is lead to believe, both. Starting and ending with general tips & tricks is for the former. But the bewildering details suddenly without too many basics will throw off the beginners, and the lack of systematic treatment (jazz musicians train their bars much more often than beginners..!) will throw off the jazz musician.

Concluding: Not the definitive, systematic wine-cheese and cheese-wine pairing guide I was looking for. Not the guide you should be looking for, either.

We’ll rest with:

[Bayeux is just (?) over the edge of Camembertland, so still perfect with cider – not in the book]