Schneier on Security

Clive Robinson • April 28, 2020 11:08 AM

@ ALL,

I guess I should also mention that,

swapping words with synonyms

Has been used since the 1980’s at least to hide a “serial number” or “watermark” in secret and above documents so that if they are leaked they can be traced back to a source…

Clive Robinson • April 29, 2020 2:33 AM

@ Phaete,

Really french words?

What’s wrong with French words, English is full of them, though they might not mean what people think they do (gauche and adroit being but two of many)

But sometimes words just appear with different meanings but without a logical path “sale” being one (comes into English from Old Norse, meaning to “grasp”). Other words with different meanings might have a nebulous connection “on” being one.

However I have often wondered what a French person walking down say London’s Oxford Street would make of the large banners and similar that say “ON SALE”…

Clive Robinson • April 29, 2020 2:49 PM

@ Phaet,

with a 6 kilo cat on their shoulders

That is a big moggie (~13lb in old measurments).

Years ago I mentioned that I once had a baby ‘big cat’ do that to me[1], and you can still just see the claw mark scars where it misjudged how tall I was and rather than drop down it just clawed it’s way up through my cloths… Some of which were “best casual”…

[1] It happened at the home of an “exotic dealer” who re-homed excess animals from captivity (some are more than happy to breed in captivity which gives the problem of what to do with excess stock as they can not realy go back into the wild because of their formed relationship / dependancy with humans).

Clive Robinson • April 29, 2020 5:31 PM

@ MarkH,

Is it possible in principle, that computers could be programmed to do this? I suppose so, but I suggest that noone on Earth knows a strategy by which this might be accomplished.

The two answers are “Yes” and “That does not matter”.

As I’ve mentioned this can be seen as the analogue of the “Halting Problem” that underlied the Alan Turing paper of what is now known as the Church-Turing thesis.

It mattered not a jot to either Alan Turing or Alonzo Church that nobody new “a strategy by which this might be accomplished”. It was sufficient to use it as a “tool of reasoning”.

Have a lay back with your eyes closed and see if you can build something in your mind by which you can investigate the idea. Start of with “broad brush stroke” “black box” functionality. As needed break these large blocks up into smaller more functional focused blocks, keeping things as simple and generic as possible.

For instance one black box would do “functional decomposition of a sentence” this would logically have one input and an unknown number of outputs. These outputs could then be run through some kind of likelyhood estimator to be given not just a ranking but a context indicator. Which immediately tells you, you would need another black box that takes in the context indicators of all the sentences in a paragraph or section to deduce what the subject actually is then feed this into a selection proces to find the most likely meaning.

However how would you deal with an infinite sentance of a single word, such as “buffalo buffalo” or “buffalo buffalo buffalo” which are both valid statments and continue to be valid as you add more buffalo.

Clive Robinson • April 29, 2020 5:56 PM

@ MarkH,

With regards the negating joke, this is more than somewhat related,

https://en.m.wikipedia.org/wiki/That_that_is_is_that_that_is_not_is_not_is_that_it_it_is

@ Wael,

You’d be lucky if he works for you!

Which brings you back to my comment above about,

They’re a real winner

What we are looking for is a sentence that no matter how you “dice it or slice it” you can prove an AI device can not interpret correctly if it does not have a “hard forcing rule” whilst a human can interpret it correctly.

If you find that, then it’s actually game over for “Hard AI” and likewise for “Soft AI” as that is “rule based” any way and “rules are not understanding. See “Chinese Room” argument,

https://plato.stanford.edu/entries/chinese-room/

Clive Robinson • April 30, 2020 4:02 AM

@ MarkH,

Taken from a book titled L.I.A.R.

Two of those sentances “picture, staggering” are based on a different interpretation of the pivot word.

In the case of “staggering” it’s also the difference between who the pivot word applies.

That is in the case of the observed (drunk) it’s his physical actions as seen by the observer.

In the case of the observer it’s being impressed/astounded of the (skillful) behaviour of the observed.

The meaning of the second part of the sentence is effectively irrelevant to the first part before the pivot word and can be freely exchanged with others without effecting the action of the pivot word.

The reason the pivot word works is because there is no frame of refrence or context given in the first half of the sentance.

And whilst I would not claim “context is all” it is important when you consider Searl’s “Chinese Room Argument”.

In the case of both the “picture” and “staggering” pivot words either interpretation is equally valid untill “context” is established.

Thus the observation can not be resolved “without context”. A clear indicator that a “sentence” is “insufficient” as an input block size. Thus the “halting problem” analogy. That is what size of input is sufficient to remove ambiguity? Which is why I possed the “buffalo buffalo” sentance where the meaning keeps changing as more of the input block size is revealed. Further proof of the input block size abiguity can be infinite.

@ Lurker,

Is the system input written (mechanical typed to satisfy the lawyers), or spoken – for simplicity ignoring regional accents? Chinese might be described as a natural language.

This is another asspect of the failing of Searl’s “Chinese Room Argument” to do with context ambiguity.

If you like it’s the “transducer” problem. Normally we consider a transducer a black box device to convert one input type to another.

Thus a moving coil microphone takes sound and converts it to electricity, and a moving coil speaker does the opposit. Thus you would expect what goes in in terms of inteligence to come out again. The same with other transducers, like a transformer that takes input current converts it to a magnetic field in the first coils and back to current in the second coil. Likewise generators and motors.

However there is something that most people miss that is implicit that most fail to realise and that is that with back to back transducers the intermediate “type” is actually “storage”.

You can see this with the old “mercury delay line storage” from the early days of computing. It works by taking electrical energy converting it to mechanical waves that propagate down a column of mercury to be converted back to electrical energy. You can store quite a number of “bits of information” in series depending on the length of the tube of mercury.

You can take this further, with a magnetic tape recording, where you can store the bits of information for what is effectively an indefinate time.

Both the mercury delay line and magnetic tape are in effect “analogue storage” importantly from our perspective capable of storing a “continuous signal” so capable “in theory” of storing an infinite amount of information resolution.

When Alan Turing designed his thought experiment we now call a “Turing Engine” he had a problem he wanted to deal with discreate “bits” of information represented by “symbols” whilst being able to ignore the issues this incurs. One of which is the problem with all “symbol sets” is that of the number of members in the set or “alphabet size”. This obviously limits the resolution of information to that of the number of members in the alphabet set. As we know with binary the alphabet has two members {0,1}. However there is a subtle flaw which is the empty set {ø} which has real world issues via the likes of “the unasigned value” or the “null value” or “has no meaning value” amongst others. Which means that you have to use “out of band” or “in band” signaling, which in the case of storage as opposed to signalling always becomes “in-band” signalling, which means the alphabet always has to have more members than required for information, which we can see with “C Strings” and “the null terminator” “\0” which is known to cause “ambiguity issues”.

Alan Turing had the same signalling issues, but he needed them to solve the “infinite resolution” issue and data boundries.

Thus rather than “analogue storage” Turing used “Paper Tape” storage which was actually a known technology in the mid nineteenth century (~1850). Put simply the “telegraph codes of which Morse Code was a later variant could be stored on a moving paper tape by using a transducer that lifted and dropped a pen on the moving tape in response to “line current”. In the early twentieth century with the likes of more complex codes such as Baudot and ITU-2 the idea of printing the English Alphabet on tape was how “telexes” and “ticker tape” were delivered in human readable form.

However ticker tape was not suitable for temporary storage for “resend” so the idea of “punched paper tape” was thought up and quickly became well established.

Thus with a “tape punch” and “tape reader” you could store “Plain English Text” on tape in theory not just indefinately but of infinite amount and with a little thought “searchable” by “message number” and “character offset” thus Random Read Access (but Random Write access at only the message level not character level).

The problem that came up with the “32 Character telex codes” such as Baudot and ITU-2 was that whilst fine for a subset of Latin Character based alphabets used in European Languages they were not good for all European languages nor for languages that did not use Latin style Alphabets but Pictograms as many Asian languages do.

In the case of Latin Alphabets the solution was a bad one which was have “different print heads” that is what got printed for a given 5bit code was “print head defined”. Thus if you sent a message using an English print head it would be correct on another English print head Telex machine it would be wrong for a French, Spanish etc print head machine. Thus either the message would have to be prefaced by instructions to change the print head or the operator would have to “pattern recognize” what was happening and retype the message correctly.

The case with Pictograms was not realy any better either. Put simply a subset of “common pictograms” was selected and these were asigned “serial numbers” which became two or more Latin Alphabet charecters forming multi dimensional mappings. So two letters gives 26×26 or 676 pictograms, which is human memorable with practice. But with four letters giving 26x26x26x26 or 456976 or just under a half million pictograms is realistically beyond most humans without some kind of “short hand” rules (which had already been worked out with “oriental dictionaries”). Thus just before WWI the first Oriental Typewriters were invented,

https://en.m.wikipedia.org/wiki/Chinese_typewriter

But neither the oriental codes or typewriters could cover the full number of pictogrames in either Japanese or Chinese logographic alphabets.

So both the multiple ussage of a pictograme –or glyph– and a limited subset issues arise and thus “information is lost” when the use of transducers to storage is used. Not that is usually a problem because “context” usually but not always as a result of not individual sentences/phrases but larger blocks of text make up for.

But as the “Shi shi shi” poem shows similarly to the “Buffalo” issue there are always exceptions.

Which further reduces the “Chinese Room Argument”.

@ Wael,

Obviously there’s more to the meaning of a sentence than the words that comprise it, right? AI doesn’t take that into account, which leads to the fact that humans also need “hard forcing rules”.

The answers are “Yes”, “Yes currently”, and “No”.

A sentence can be “stand alone” in which case it needs to be entirely unambiguous at many levels. That is it contains all “context” and other higher level meanings. In practice even with questions and statements few sentences are ever entirely free of ambiguity, which is why we have larger blocks of text made up of sentences such as paragraphs, sections, chapters, books, and libraries.

There is a “truism” that a five page scientific paper contains as much actual information as a book, and that the book, in turn contains as much information as a library. That is to understand the paper you must know what is in the book, and to understand what is in the book you must know what is in effect a specialist refrence and teaching library. The problem is you still need more than the library can give you…

This is because importantly unlike a “Database” which the library is you must be able to “understand” the information within and be able to form new patterns and conclusions from it. To do this you have to be able to not only understand concepts that our five senses give us but something more we glibly lable “inteligence” but have no idea how to show it even exists let alone quantify it in a way that alows measurands to be agreed and thus information acurately transfered.

Whilst humans have physical limits, as far as we are aware they do not come with “hard forcing rules”. That is we can not just recognize our limitations we can understand them and thus learn to adapt around them.

@ Lurker, MarkH, Wael,

So we come onto the problem with Searl’s “Chinese Room Argument”.

There are three places where “understanding” or “inteligence” is required and none of them are in the room at any time.

The room is in reality is just like a tape reader, indexable tape store, and tape writer.

The understanding is required to formulate a question and understand it, nothing more.

The first occasion understanding is required is in building the “tape store” database not in using it. This is done before the room exists, thus never goes inside it.

The second occasion understanding is required is in formulating a question to ask/use the room. Thus this understanding is again outside the room in the head of the person writing the question.

The third time again is outside the room in the head of the person reading the answer from the room.

To try to make an argument that the databse is somehow some kind of inteligence is an absurdity. It’s like saying that you imbue a sheat of paper with intelligence when you doodle on it or a toodler makes random circling and lines as they scrible with a pencile, or a snail leaves a trail on it. To make that make sense you would have to argue that “sampled chaos” put on a magnetic tape or platter of a hard drive is actually inteligence, but where is the ibteligence, which oarticle of rust has it? Such an argument falls to the “Reductio ad absurdum” or “turtles all the way down” falacy where you would just go on forever trying to argue for what does not exist by the false premise you are using. The false premise is saying “Knowledge” is “Intelligence” it’s not nore ever was.

Knowledge is stored or communicated information. That is information that has no physical form is impressed or modulated onto physical matter or energy which are at the end of the day the same thing, or if you prefere interchangable forms of the same thing.

Where inteligence exists is in the processing of knowledge to make new knowledge by which we obtain a better understanding of our physical universe around us.

Whilst simple to say, so are “random” and “chaos” but how do you not just define them but differentiate them?

At the moment we have the same trouble with “Intelligence”, any definition we come up with is either nebulous or insufficient, therefore we don’t have a way to quantify it, measure it, communicate about it, or realy understand it.

Which is why both Hard AI and Easy/Soft AI are a “shell game” as beloved by confidence tricksters and those deluding themselves they somehow have the ability to understand under which shell the pea is (those slightly wiser know that the pea is effectively under none of them 😉

The fun realy starts however when you consider Soft/Easy AI, it was once called “expert systems” and they were at their simplest “rule following” systems little more than glorified Ladder Logic or Simple Non-feedback State Machines. When we replaced the hard logic with Bayesian Estimation we got the basis of Fuzzy Logic, it alowed for less hard rules and faster response. That is simple statistics had “softened” the edges of the hard rules thus needing less rules with less hard limits. In effect we have moved on rather than humans doing the statistical analysis of data and comming up with Bayesian rules, we have alowed the software via what are sometimes called “genetic or annealing” processes to find it’s own rules, and so on. At no point does “Intelligence” magically arise there is no “understanding” involved just an analysis of data and desired outcomes making what is yet another statistical series of rules.

Whilst this is a very powerful process no understanding or knowledge generation is required. There is no more Inteligence in there than there is in a Digital Signal Processing system. And like all rule based systems it is by definition either bounded or incompleate, thus “finding edge or corner cases” is realy about not sanitising data into a fully defined context.