Hu: Programming languages are made by people closer to Earth, and the rules of prog-lang are to prevent you from providing instructions, giving way to conditions, in which the chip does not fire, a q.tum-Event. // MIC-ref Post: Therefore, understanding q.tum-mech will always provide one with an elevated programming output, as one’s brain will be tuned to be more aligned with the laws of programming, and thereby, the laws of q.tum-mech.
H3S1: How materials compute:
Hu: A binary is the arrival or non-arrival of a single electron; if an ASCII character can be encoded in max 8-bits, then the order of a few hundred electrons can encode a single sentence. Audio is, as a rule, 10x the bit-density or bit-rate of text, and videos 100x the electron-rate of text, so it should only take a few ten thousand electrons to contain 0.5-s worth of video, assuming 120-char magnitude typed per second. Indeed, 200 kbps is a reasonable min-threshold, for video.
H3S2: Estimating the computational capacity of the universe:
H3S3: History of material science x computing:
H4S1: Telephony latency was already as good as 80.100-ms in 1940<AT&T, a-r>
14:00: This level of latency is correlated with a materials precision to the thousands of an inch. This is also the threshold of microscopic #p H5S1: When engineering descends to the level of materials | science, encoding into binary is assumed, since vacuum tubes operate in a binary | firing | basis. This means that the original telephone, and its modern implementation, is also and has been<fbno>a binary | encoding, a direct-to-binary encoding, and this was enough for ultra.low-latency, despite the lack of modern.electronics|equipment<fbno>Encoding direct to binary, rather than by video frame, eliminates 33-ms latency<WP.MIC-H2S123><WP.MIC-H2S124>
H4S2: Long.distance-telephone,transmission was originally viewed as sound amplification, db-accurate<Turing><#n-p>:
H4S3: The original telephone switch board was a TURN server<Turing>:
H4S4: Edison’s light bulb->Vacuum tube->Transistor->Semi-conductor:
AT&T<1953>: The transistor – a new name, 4:34a new device that can do many of the jobs done by the vacuum tube, 4:39and many the tube can’t do. 4:42Let’s see how the transistor and tube measure up. 4:45First off, the vacuum tube is power hungry. 4:49While a tube like this generally demands a watt or more 4:52of electricity a millionth of a watt is enough for the transistor. 4:56Even a makeshift battery of moist blotting paper wrapped around a coin 5:01can power transistor. 5:13[Electronic signal tone] 5:20The vacuum tube gets pretty hot. 5:22Sometimes a little too hot. That’s why in complex devices 5:26the tubes must be spaced far enough apart for proper ventilation. 5:30Since transistors remain cool 5:34they can be crowded together in a small space. 5:38In size, reliability and ruggedness too, the tiny transistor has many advantages. <WP.MIC-H2S120>
H4S5: The telephone line combined with comp-sci<Shannon><Turing>genesied the idea that machines can talk to machines; those machines would be telephones:
Hu: Therefore, the idea that a communications.relay-server would be the basis point of inter.entity-computing<Turing>is quite righteous in the eyes of history, and this is the idea in which the Flare Messenger design is born:
Hu: The Flare Messenger interface is a natural continuation of the original telephoning interface, and telephones, originally<1961 AT&T, a-r>were conceived of as the machines that would “talk to other machines”, the earliest conception, of a server; note that machines talk to machines, and then to humans, otherwise, it’s a glorified TURN server, which means that p-web is the second, ever, machine to machine computing interface<WP.MIC-H2S70>
H5S1: A telephone machine to telephone machine conversation is a fax machine:
A fax machine, as a communications portal, enables the transmission of rich media, like images. Therefore, the transmission of images, required a ser-ser connection, whereas live audio, was possible with only a cli-ser<TURN>-cli. Text, incidentally, also was not | possible, with TURN-only. An interesting inter-threading, among complexity, of mediums, of differing information densities. [D: 1961, AT&T 8:22] Hu: This fax | printout is analogous to a MySQL-UPDATE statement, but it’s triggered by the opponent’s action, rather than one’s own; this is the exact design, of real-time messaging, on modern mobile devices.
H5S2: 3 views of the future of communications: H6S1: Snap, a chat.first-computing,paradigm<generous!> H6S2: The iPhone, restoration of the phone, as computing-core H6S3: WeChat, an all.in-one,application; from these, with none having all 3 keys, comes private-web, built on BROWSER, entered by p.dash-flare<WP-MIC><fbno> H6S4: Prophesied by 1961: a telephone, under a video screen, with a hard.coded-keypad, and a payment processor, all in a single interface, and integrated down to the level of hardware<beaut!>
H4S6: 1961 AT&T visualized a scanner and OCR as the next stage of cli-ser-ser-cli<Turing-shared>:
D.R-1: A hand-written note. D.R-@: Insertion into a telephone, for scanning, and OCR. D.R-2: Output to the other side, as a computerized message, that can also be represented in a digital | form, but is printed, at low-cost<tsk.tsk-Lulu!> for convenience. H5S1: Ken Thompson worked, at Bell Labs, where much of this innovation was produced, on improving OCR-reading, to enable this, with Chess Information<r: MIC.ch-16>:
A screen capture from Thompson’s paper, demonstrating a string of chess notation that the computer was instructed to read, including iconography representing zi. == This semantic analysis engine is categorically <r: ch. 8> no different from contextual engines that are used in Google Translate, which also has a feature to scan and read photographs of local street signs and first transcribes the displayed text into a digital version that the computer can process before undergoing the translation and outputting the result for the end user, as well as Apple Siri’s dictation feature, which will retroactively edit prior spoken words based on the context established by next words #, as well as the words that came before the prior words; the sentence structure, grammar, and style based analysis performed by Apple dictation is, likewise to Thompson’s semantic analysis engine, based on the rules of English grammar and speech, while Thompson’s engine was based on the rules of chess. ==
Either way, the semantic analysis | engine was shown to improve accuracy compared to context-less readings of the text, and a high rate of accuracy is important for this application, as the presence of even a single character error can completely change # one’s interpretation of the game or understanding of a position; likewise, Apple’s dictation and the industry of computer-based transcription or translation of human language input in various mediums also has a razor thin margin for error since a single erroneous word can completely reverse the meaning of a sentence, such that users will generally refuse to adopt the product unless a sufficiently high accuracy rate is met <r: ch. 51 H2S1 H3S3>, making this field technically challenging on that vector # Thompson himself recognized the generalizability of his work in chess programming here: In other respects, however, the Informant is representative of a broad class of printed documents. Non-rectilinear layout, tight column- and line-spacing, and broken, touching, or dirty character-images all occur in other documents. For this reason, we believe that several of the methods first applied here will be useful in achieving high performance | in the general case. Baird, Henry S., and Ken Thompson. “Reading Chess.” IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, vol. 12, no. 6, 6 June 1990.
Moreover, Thompson praises the content of Chess Informant, namely, chess games in notation, as being an “effectively and efficiently computable semantics”, making chess writing an ideal model system for the <Ch-16 H2S3 H3S3> work of computer | vision. Thompson’s in depth involvement in chess as well as the sentiments he echoes about the game in Reading Chess suggests that considerations of the game as a model system, for instance in database architecture, via his work in Tablebases, as well as in computer calculation, via his work on Belle, may have influenced considerations of these subjects in his design of the C programming language and the Unix operating system as well, and these potential further connections between chess and computer science are worthy of further investigation. Diagram: Ken Thompson <left> with Dr. Dennis Ritchie.
It is well known in #MIC’s theory of knowledge # that a highly organized mind will seek efficient ways to organize knowledge <r: ch. 44 H2S1 H3S9>, and that when learning is correct, a single concept learned from one field that can be recycled and connected into another will not be represented twice in the mind, and therefore whichever models of logic, conditionals, and reductions that Thompson learned through chess and vice versa will certainly have been funneled into his work in the other field. I went to play against all these <other chess engines> and got my head handed to me, and came to the conclusion that speed was what matters. Yeah no matter if I had an algorithm, and you had an algorithm, and you went twice as fast as me you could emulate what I was doing and set traps. == I would give simultaneous exhibitions <ch. 17 H2S8> and everything, so anyway it <Belle> was a very familiar, very popular member of the local chess club. I take it every week on Friday nights. Ken Thompson at VCF East 2019, referring to his work in chess programming. Ken Thompson, pictured at the Vintage Computer Federation meeting in 2019, sitting, arms folded, beholding, with a single tear, certainly, the audience’s response to the final question: “raise your hands, if your careers depend on Unix and C”. Fin. Post-script: Here is a concluding postulate: if a human woke up tomorrow with a 4,000 elo, which I think is theoretically achievable by studying my textbook at some point, defeated Stockfish 14, and subsequently CS puts their attention back on chess and elevates the state of computing to now be 4,100 elo, and given that the value of all computing worldwide is about $10,000,000,000,000, how much would that act INCREASE the value of the CS industry? Whatever the answer is, that should be the value of the chess industry. A colloquial message written to user Callo on Discord, 4/27/22, to be taken as conjecture.
Baird, Henry S., and Ken Thompson. “Reading Chess.” IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, vol. 12, no. 6, 6 June 1990.