Input Text Quotes

Serious thinking, inspired thinking, can seldom arise from texts sent while eating lunch or driving a car. Responding to these inputs generates as much thought, and as much inspiration, as swatting so many flies. They deaden both the mind and soul.

The Vedic viewpoint presents a type of linguistic realism in which reality is the 'text' which is being processed by the observer. Reality can also be modified by adding text to it similar to how a programmer programs a computer by inputting a computer program.

The genesis block contains a hidden message within it. The coinbase transaction input contains the text “The Times 03/Jan/2009 Chancellor on brink of second bailout for banks.” This

It is important to note that the design of an entire brain region is simpler than the design of a single neuron. As discussed earlier, models often get simpler at a higher level—consider an analogy with a computer. We do need to understand the detailed physics ofsemiconductors to model a transistor, and the equations underlying a single real transistor are complex. A digital circuit that multiples two numbers requires hundreds of them. Yet we can model this multiplication circuit very simply with one or two formulas. An entire computer with billions of transistors can be modeled through its instruction set and register description, which can be described on a handful of written pages of text and formulas. The software programs for an operating system, language compilers, and assemblers are reasonably complex, but modeling a particular program—for example, a speech recognition programbased on hierarchical hidden Markov modeling—may likewise be described in only a few pages of equations. Nowhere in such a description would be found the details ofsemiconductor physics or even of computer architecture. A similar observation holds true for the brain. A particular neocortical pattern recognizer that detects a particular invariant visualfeature (such as a face) or that performs a bandpass filtering (restricting input to a specific frequency range) on sound or that evaluates the temporal proximity of two events can be described with far fewer specific details than the actual physics and chemicalrelations controlling the neurotransmitters, ion channels, and other synaptic and dendritic variables involved in the neural processes. Although all of this complexity needs to be carefully considered before advancing to the next higher conceptual level, much of it can be simplified as the operating principles of the brain are revealed.

From the outset Dickens seemed to take charge even though he was younger than Seymour and less well known. His narrative input seemed to drive the content of the comic plates, which eventually led to the story becoming the main point of interest and with the death of Seymour the plates were reduced to two an instalment whereas the text increased to 16,000 words. Dickens succeeded where his predecessors had failed, making the print more important than the illustration.

How about something like ChatGPT? Well, it has the nice feature that it can do “unsupervised learning”, making it much easier to get it examples to train from. Recall that the basic task for ChatGPT is to figure out how to continue a piece of text that it’s been given. So to get it “training examples” all one has to do is get a piece of text, and mask out the end of it, and then use this as the “input to train from”—with the “output” being the complete, unmasked piece of text.

It is, in fact, natural to think that man may be a finite-state machine, not only in his function as a message source which produces words, but in all his other behavior as well. We can think if we like of all possible conditions and configurations of the cells of the nervous system as constituting states (states of mind, perhaps). We can think of one state passing to another, sometimes with the production of a letter, word, sound, or a part thereof, and sometimes with the production of some other action or of some part of an action. We can think of sight, hearing, touch, and other senses as supplying inputs which determine or influence what state the machine passes into next. If man is a finite-state- machine, the number of states must be fantastic and beyond any detailed mathematical treatment. But, so are the configurations of the molecules in a gas, and yet we can explain much of the significant behavior of a gas in terms of pressure and temperature merely. Can we someday say valid, simple, and important things about the working of the mind in producing written text and other things as well? As we have seen, we can already predict a good deal concerning the statistical nature of what a man will write down on paper, unless he is deliberately trying to behave eccentrically, and, even then, he cannot help conforming to habits of his own.

1.1M    ./scripts 58M     ./cloud9 74M     . You can also use tee to write the output to several files at the same time, as shown in this example: root@beaglebone:/opt# du ‐d1 ‐h | tee /tmp/1.txt /tmp/2.txt /tmp/3.txt Filter Commands (from sort to xargs) There are filtering commands, each of which provides a useful function: sort: This command has several options, including (‐r) sorts in reverse; (‐f) ignores case; (‐d) uses dictionary sorting, ignoring punctuation; (‐n) numeric sort; (‐b) ignores blank space; (‐i) ignores control characters; (‐u) displays duplicate lines only once; and (‐m) merges multiple inputs into a single output. wc (word count): This can be used to calculate the number of words, lines, or characters in a stream. For example: root@beaglebone:/tmp# wc < animals.txt  4  4 18 This has returned that there are 4 lines, 4 words, and 18 characters. You can select the values independently by using (‐l) for line count; (‐w) for word count; (‐m) for character count; and (‐c) for the byte count (which would also be 18 in this case). head: Displays the first lines of the input. This is useful if you have a very long file or stream of information and you want to examine only the first few lines. By default it will display the first 10 lines. You can specify the number of lines using the ‐n option. For example, to get the first five lines of output of the dmesg command (display message or driver message), which displays the message buffer of the kernel, you can use the following: root@beaglebone:/tmp# dmesg | head ‐n5   [    0.000000] Booting Linux on physical CPU 0x0   [    0.000000] Initializing cgroup subsys cpuset   [    0.000000] Initializing cgroup subsys cpu   [    0.000000] Initializing cgroup subsys cpuacct   [    0.000000] Linux version 3.13.4-bone5(root@imx6q-sabrelite-1gb-0) tail: This is just like head except that it displays the last lines of a file or stream. Using it in combination with dmesg provides useful output, as shown here: root@beaglebone:/tmp# dmesg | tail ‐n2   [   36.123251] libphy: 4a101000.mdio:00 - Link is Up - 100/Full   [   36.123421] IPv6:ADDRCONF(NETDEV_CHANGE): eth0:link becomes ready grep: A very powerful filter command that can parse lines using text and regular expressions. You can use this command to filter output with options, including (‐i) ignore case; (‐m 5) stop after five matches; (‐q) silent, will exit with return status 0 if any matches are found; (‐e) specify a pattern; (‐c) print a count of matches; (‐o) print only the matching text; and (‐l) list the filename of the file containing the match. For example, the following examines the dmesg output for the first three occurrences of the string “usb,” using ‐i to ignore case: root@beaglebone:/tmp# dmesg |grep ‐i ‐m3 usb   [    1.948582] usbcore: registered new interface driver usbfs   [    1.948637] usbcore: registered new interface driver hub   [    1.948795] usbcore: registered new device driver usb You can combine pipes together. For example, you get the exact same output by using head and displaying only the first three lines of the grep output: root@beaglebone:/tmp# dmesg |grep ‐i usb |head ‐n3   [    1.948582] usbcore: registered new interface driver usbfs   [    1.948637] usbcore: registered new interface driver hub   [    1.948795] usbcore: registered new device driver usb xargs: This is a very powerful filter command that enables you to construct an argument list that you use to call another command or tool. In the following example, a text file args.txt that contains three strings is used to create three new files. The output of cat is piped to xargs, where it passes the three strings as arguments to the touch command, creating three new files a.txt, b.txt,

Neither inherently good nor evil, electronic communication platforms are 100 percent dependent on user input.

When there is input text, complete_​greet() returns a list of friends that match. Otherwise, the full list of friends is returned.

Soon after that, Eno briefly joined a group called the Scratch Orchestra, led by the late British avant-garde composer Cornelius Cardew. There was one Cardew piece that would be a formative experience for Eno—a piece known as “Paragraph 7,” part of a larger Cardew masterwork called The Great Learning. Explaining “Paragraph 7” could easily take up a book of its own. “Paragraph 7”’s score is designed to be performed by a group of singers, and it can be done by anyone, trained or untrained. The words are from a text by Confucius, broken up into 24 short chunks, each of which has a number. There are only a few simple rules. The number tells the singer how many times to repeat that chunk of text; an additional number tells each singer how many times to repeat it loudly or softly. Each singer chooses a note with which to sing each chunk—any note—with the caveats to not hit the same note twice in a row, and to try to match notes with a note sung by someone else in the group. Each note is held “for the length of a breath,” and each singer goes through the text at his own pace. Despite the seeming vagueness of the score’s few instructions, the piece sounds very similar—and very beautiful—each time it is performed. It starts out in discord, but rapidly and predictably resolves into a tranquil pool of sound. “Paragraph 7,” and 1960s tape loop pieces like Steve Reich’s “It’s Gonna Rain,” sparked Eno’s fascination with music that wasn’t obsessively organized from the start, but instead grew and mutated in intriguing ways from a limited set of initial constraints. “Paragraph 7” also reinforced Eno’s interest in music compositions that seemed to have the capacity to regulate themselves; the idea of a self-regulating system was at the very heart of cybernetics. Another appealing facet of “Paragraph 7” for Eno was that it was both process and product—an elegant and endlessly beguiling process that yielded a lush, calming result. Some of Cage’s pieces, and other process-driven pieces by other avant-gardists, embraced process to the point of extreme fetishism, and the resulting product could be jarring or painful to listen to. “Paragraph 7,” meanwhile, was easier on the ears—a shimmering cloud of sonics. In an essay titled “Generating and Organizing Variety in the Arts,” published in Studio International in 1976, a 28-year-old Eno connected his interest in “Paragraph 7” to his interest in cybernetics. He attempted to analyze how the design of the score’s few instructions naturally reduced the “variety” of possible inputs, leading to a remarkably consistent output. In the essay, Eno also wrote about algorithms—a cutting-edge concept for an electronic-music composer to be writing about, in an era when typewriters, not computers, were still en vogue. (In 1976, on the other side of the Atlantic, Steve Jobs and Steve Wozniak were busy building a primitive personal computer in a garage that they called the Apple I.) Eno also talked about the related concept of a “heuristic,” using managerial-cybernetics champion Stafford Beer’s definition. “To use Beer’s example: If you wish to tell someone how to reach the top of a mountain that is shrouded in mist, the heuristic ‘keep going up’ will get him there,” Eno wrote. Eno connected Beer’s concept of a “heuristic” to music. Brecht’s Fluxus scores, for instance, could be described as heuristics.

which reads a little input and returns the token. When it needs another token, it calls yylex() again. The scanner acts as a coroutine; that is, each time it returns, it remembers where it was, and on the next call it picks up where it left off.

Use manual sanity checks in data pipelines. When optimizing data processing systems, it’s easy to stay in the “binary mindset” mode, using tight pipelines, efficient binary data formats, and compressed I/O. As the data passes through the system unseen, unchecked (except for perhaps its type), it remains invisible until something outright blows up. Then debugging commences. I advocate sprinkling a few simple log messages throughout the code, showing what the data looks like at various internal points of processing, as good practice — nothing fancy, just an analogy to the Unix head command, picking and visualizing a few data points. Not only does this help during the aforementioned debugging, but seeing the data in a human-readable format leads to “aha!” moments surprisingly often, even when all seems to be going well. Strange tokenization! They promised input would always be encoded in latin1! How did a document in this language get in there? Image files leaked into a pipeline that expects and parses text files! These are often insights that go way beyond those offered by automatic type checking or a fixed unit test, hinting at issues beyond component boundaries. Real-world data is messy. Catch early even things that wouldn’t necessarily lead to exceptions or glaring errors. Err on the side of too much verbosity.

Pre-Training Phase The first phase, pre-training, is an essential step in training ChatGPT. During pre-training, the model learns from a large amount of text data from the internet. It's like feeding the model with a rich buffet of information, allowing it to absorb and understand the patterns and structures of human language. We have the supervised and non-supervised pre-training approach. The main objective of a supervised training approach is to train the model to understand and accurately map inputs to corresponding outputs. It's important to know the limitations of this approach regarding scalability. The reliance on human trainers

Writing a book is both rewarding and inspiring The preparation, research and introduction of new chapters to an ever increasing text provides enormous excitement as one gets closer and closer to completion The culmination of all the hours of work combined with the emotional input in its creation cannot describe the sense of pride and accomplishment when it is finally published

The first stop in the oft-repeated looking up of a word is the massive Egyptian-German dictionary, Wörterbuch der ägyptischen Sprache, a project begun in 1897, its five volumes of entries eventually published between 1926 and 1931. The archive of note slips that were used to compile this dictionary—still a standard reference for hieroglyphic texts—has now been digitized. By inputting the page number and word entry from the published Wörterbuch, we can scroll through scans of paper slips containing handwritten copies of snippets of ancient Egyptian texts containing a particular word.

Note that this differs from type=text input elements in this way: in type=text input elements, the value attribute sets the default value of the form, but in type=checkbox elements, the value attribute controls what value is sent if the checkbox is turned on when the form is submitted.

input Get data from the "outside world". This might be reading data from a file, or even some kind of sensor like a microphone or GPS. In our initial programs, our input will come from the user typing data on the keyboard. output Display the results of the program on a screen or store them in a file or perhaps write them to a device like a speaker to play music or speak text. sequential execution Perform statements one after another in the order they are encountered in the script. conditional execution Check for certain conditions and then execute or skip a sequence of statements. repeated execution Perform some set of statements repeatedly, usually with some variation. reuse Write a set of instructions once and give them a name and then reuse those instructions as needed throughout your program.

Structured data includes simple data inputs like numerical values, dates, currencies, or addresses. Unstructured data includes data types that are more complicated to analyze, such as text, images, and video.

We’ve seen the emergence of a gift economy in digital products such as software, text, images, and video; the natural course of events would see this pattern extend to APM product-design files, leading (aside from the cost of input materials) to a gift economy in physical objects (but within what mandated constraints?).

Similarly computers can be made entirely from just a single kind of simple component which does a comparison: if both inputs are true then the output is false, otherwise the output is true. The rest is “just organisation” of these elements. A powerful approach is to set up the components so they can manifest a symbolic machine (software), and the software can then be further organised to make ever higher level software “machines”.

The mining process for bitcoin is a continual cycle of hashing a few pieces of data together in pursuit of an output that meets a predetermined difficulty level, mainly the number of 0s that the output starts with. We call this output the golden hash. Recall that a hash function takes data—for example the text in this sentence—and hashes it into a fixed-length string of alphanumeric digits. While the output of a hash function is always of fixed length, the characters within it are unpredictable, and therefore changing one piece of data in the input can drastically change the output. It’s called a golden hash because it bestows the privilege of that miner’s block of transactions being appended to Bitcoin’s blockchain. As a reward, that miner gets paid in a coinbase transaction, which is the first transaction in the block. Currently, that transaction delivers 12.5 bitcoin to the lucky miner.

A lightweight review tool would look much like the 360 tool I described above. It would ask managers to rate their employees for each category, and ask them to input text when the employee is in the bottom or top two ratings. The overall rating would be automatically calculated, and the manager’s rating distribution would be compared to the expected distribution. If the manager’s distribution falls outside of what’s expected, the manager must explain.

I have been told that to look at history, theology, philosophy from the female perspective is myopic – one-eyed. It is commonly assumed that these disciplines have been regarded from the “human” perspective, that the male has incorporated both female and male perspectives, that he has been fair in all these matters, and indeed, capable. I have been expected to disregard it if the female is rarely mentioned as a factor in the first two million years of human existence, or if she is, that it is in a secondary placement or in a slanderous context. A casual perusal of most history, and “pre-history”, from a “fair” perspective would leave one wondering how the human species reproduced itself, let alone that the female had any further creative input to the human enterprise. As an example, one such weighty tome called The Last Two Million Years, has in all its four hundred and eighty-eight pages of text and plates, remarkably little evidence of female presence to the human enterprise. She rates a mention every now and then in relation to “problems of reproduction”, greater sexual receptivity than female apes, and men insisting that “their sisters married outside the family”. The very occasional Goddess or woman of note is most often, a mistress, consort or wife. Queen Elizabeth I stands alone as a woman of power in the last two million years, and even then the caption under her portrait is couched in a negative, reading “Defeat of the Armada

What do you need to ask yourself (and answer) about each e-mail, text, voice mail, memo, page of meeting notes, or self-generated idea that comes your way? This is the component of input management that forms the basis for your personal organization. Many people try to get organized but make the mistake of doing it with incomplete batches of stuff. You can’t organize what’s incoming—you can only capture it and process it. Instead, you organize the actions you’ll need to take based on the decisions you’ve made about what needs to be done. The whole deal—both the capturing and organizing phases—is represented in the center “trunk” of the decision-tree model shown here.

Meadows also received a text on December 20, 2020, from Mike Lindell, a mustachioed, self-described former crack addict who’d made a fortune as CEO of the bedding company My Pillow. Lindell, who was an infomercial star, major Trump rally fixture, and financial backer of various protests against the former president’s loss, implored Meadows to have federal agents seize voting machines in key states. He was famous for wearing a large cross necklace and his message was an overheated blend of Christian prayer and internet insanity. “Hey Mark, I felt I was suppose to text you this message … You being a man a faith and on the front line of the decisions that are going to be historical! I would ask that you pray for wisdom and discernment from God! You are one of the people the president trusts the most. That being said I want to add my input.… Everything Sidney has said is true!” Lindell wrote. “We have to get the machines and everything we already have proves the President won by millions of votes! I have read and not validated yet that you and others talked him out of seizing them … If true . I pray it is part of a bigger plan … I am grateful that on the night of the election the algorithms of the corrupt machines broke and they realized our president would win in spite of the historical fraud! I look for deviations every day in my business … when I find one I investigate relentlessly until I know why it happened and how it happened … (this is my gift from God that has made my business so successful) From 11:15 pm on the night of the election I have spent all my time running impossible deviations and numbers from this election … I also was blessed to be able to get info and help Sidney Lin General Flynn and everyone else out there gathering all the massive evidence! I have been sickened by politicians (especially republicans) judges, the media not wanting to see truth (no matter what the truth would be!) This is the biggest cover up of one of the worst crimes in history! I have spent over a million$to help uncover this fraud and used my platform so people can get the word not to give up! The people on both sides have to see the truth and when they do.… There will not be no civil war, people (including politicians!) are fearing! The only thing any of us should fear is fear of the Lord! Every person on this planet needs to know the truth and see the evidence!!! Mark . God has his hand in all of this and has put you on the front line … I will continue praying for you to have great wisdom and discernment! Blessings Mike.” Meadows seemed grateful

DeepArt.io: What it’s for: Transforming photos into artwork. How it works: Whether it’s Van Gogh’s starry swirls or Warhol’s pop art you’re after, DeepArt brings a touch of classic artistry to your designs. 2. Lumen5: What it’s for: Video content creation for products. How it works: Convert those text descriptions into vibrant video narratives. Lumen5 crafts visual stories tailored to your product, making your designs not just seen but experienced. 3. Crello: What it’s for: Design and animation. How it works: As your on-call graphic designer, you can feed Crello a theme and get back a flurry of design elements and animations. Perfect for those captivating store banners and promotional materials. 4. Everbee: What it’s for: Automated design creation. How it works: Everbee’s AI processes trends and popular designs to suggest fresh, market-ready creations. It’s like having a personal design assistant who’s always in the know. 5. RelayThat: What it’s for: Brand consistency in designs. How it works: Maintain a consistent look and feel across all your products. Input your brand elements— colors, fonts, logos—and RelayThat ensures every design harmonizes with your brand voice. 6. Printful’s Mockup Generator: What it’s for: Product mockup visualization. How it works: A gem for visualizing your designs in the real world. See how they’d look on T-shirts, mugs, posters, and more—basically, a digital fitting room for your art.