Input Devices Quotes

Just aiming a speely input device, or a Farspark chambre, or whatever you call it... a speelycaptor... at something doesn't collect what is meaningful to me. I need someone to gather it in with all their senses, mix it round in their head, and make it over into words.

For the frequent user, the impact of a cooler, better, easier-to-use input device is profound – so profound that many users are happy to proselytize to their peers. More sneezing of a Purple Cow.

Reality exists in the mind of each. The senses are input devices for incoming data.

Active methods (subscription and search) are better for larger screen devices often used for serious work or study, where session times tend to be longer, and keyboards allow for accurate and fast input. Passive methods of content distribution are, in general, more suitable for the fragmented time and small screens of smartphones.

Three researchers at Stanford University noticed the same thing about the undergraduates they were teaching, and they decided to study it. First, they noticed that while all the students seemed to use digital devices incessantly, not all students did. True to stereotype, some kids were zombified, hyperdigital users. But some kids used their devices in a low-key fashion: not all the time, and not with two dozen windows open simultaneously. The researchers called the first category of students Heavy Media Multitaskers. Their less frantic colleagues were called Light Media Multitaskers. If you asked heavy users to concentrate on a problem while simultaneously giving them lots of distractions, the researchers wondered, how good was their ability to maintain focus? The hypothesis: Compared to light users, the heavy users would be faster and more accurate at switching from one task to another, because they were already so used to switching between browser windows and projects and media inputs. The hypothesis was wrong. In every attentional test the researchers threw at these students, the heavy users did consistently worse than the light users. Sometimes dramatically worse. They weren’t as good at filtering out irrelevant information. They couldn’t organize their memories as well. And they did worse on every task-switching experiment. Psychologist Eyal Ophir, an author of the study, said of the heavy users: “They couldn’t help thinking about the task they weren’t doing. The high multitaskers are always drawing from all the information in front of them. They can’t keep things separate in their minds.” This is just the latest illustration of the fact that the brain cannot multitask. Even if you are a Stanford student in the heart of Silicon Valley.

Thus three conclusions emerge from the eye story: (1) it is easier to inherit a ‘vision acquisition device’ than a full-blown hard-wired visual analyser; (2) the visual analyser, once ‘set up’, is refractory to radical restructuring—hence the existence of a critical period in its development in cats; (3) the eye seems to have evolved in steps from a light-sensitive, innervated cell to our complex organ by common evolutionary mechanisms. Something similar may have been taking place in evolution of the language organ, and may be occurring during individual development. An argument, put forward forcefully by Noam Chomsky and his followers, refers to the ‘poverty of stimulus’. Most permutations of word order and grammatical items in a sentence leads to incomprehensible gibberish. There is no way that children could learn without some internal ‘guide’ which sentence is grammatical and which is not, only on the basis of heard examples. To make matters worse, many parents do not correct their children’s grammatical mistakes (they seem to be much more worried about the utterance of four-letter words). Recent investigations clearly confirm that children’s ‘instinctive’ understanding of grammatical intricacies, between the ages 2 and 4, is far better than one would expect from a conventional learning mechanism. Thus there seems to be a ‘language acquisition device’ (LAD) in the brain, which must be triggered by linguistic input so that its working ultimately leads to proper language. It is the LAD, and not a fully developed linguistic processor, which seems to be innate.

Adding to our understanding of why the brain seems undisturbed by disconnections was not only the notion that it was, in a sense, sending half its decisions into the realm of the unconscious; it was also the discovery of the “interpreter.” This special left brain system kept note of all the behaviors that resulted from the many mental systems. It appeared to be the surveillance camera on our behavior, which, of course, was the evidence that a mental or cognitive act had occurred. The interpreter not only took note; it tried to make “sense” out of the behavior by keeping a running narrative going on about why a string of behaviors was occurring. It is a precious device and most likely uniquely human. It is working in us all the time as we try to explain why we like something or have a particular opinion, or rationalize something we have done. It is the interpreter device that takes the inputs from the massively modularized and automatic brain of ours and creates order from chaos. It comes up with the “makes sense” explanation that leads us to believe in a certain form of essentialism, that is, that we are a unified conscious agent. Nice try, interpreter!

Stanford University’s John Koza, who pioneered genetic programming in 1986, has used genetic algorithms to invent an antenna for NASA, create computer programs for identifying proteins, and invent general purpose electrical controllers. Twenty-three times Koza’s genetic algorithms have independently invented electronic components already patented by humans, simply by targeting the engineering specifications of the finished devices—the “fitness” criteria. For example, Koza’s algorithms invented a voltage-current conversion circuit (a device used for testing electronic equipment) that worked more accurately than the human-invented circuit designed to meet the same specs. Mysteriously, however, no one can describe how it works better—it appears to have redundant and even superfluous parts. But that’s the curious thing about genetic programming (and “evolutionary programming,” the programming family it belongs to). The code is inscrutable. The program “evolves” solutions that computer scientists cannot readily reproduce. What’s more, they can’t understand the process genetic programming followed to achieve a finished solution. A computational tool in which you understand the input and the output but not the underlying procedure is called a “black box” system. And their unknowability is a big downside for any system that uses evolutionary components. Every step toward inscrutability is a step away from accountability, or fond hopes like programming in friendliness toward humans. That doesn’t mean scientists routinely lose control of black box systems. But if cognitive architectures use them in achieving AGI, as they almost certainly will, then layers of unknowability will be at the heart of the system. Unknowability might be an unavoidable consequence of self-aware, self-improving software.

And yet what a potentially dangerous device, what a terrible opportunity to bury valuable, even vital sensory information beneath the fears and prejudices and suppressions of the higher brain! We absolutely must exercise constant discrimination upon the steady barrage of sensations if they are to take on any meaningful form and direct sequential activities; but what bizarre, even ghastly shapes this discrimination is free to invent. Attitudes, moods, neuroses, fixations, and avoidances of all kinds contribute to the sensitivity of the ascending sensory pathways themselves, so that minor irritations can be magnified to overwhelming proportions, pleasures can be erased or actually turned into torments, serious internal difficulties can be blotted completely out of consciousness. The principle of selectivity is crucial to organized behavior, but the possibilities for its abuse are enormous. The mind is capable of distorting incoming information to almost any degree, and it can actually construct a body image that has very little to do with the bulk of sensory data which the body is providing. These two directions of sensory transmission are both occurring all the time, and we cannot say that our idea of reality is more clearly established by one than by the other. Or, if we have to make a choice, we must admit that it is the descending, centrifugal sensory current that is the more important one: We all receive stimulation from the same external world through identical sensory devices, but it is the process of selection and interpretation which makes us respond differently, makes each of us the unique individuals that we are. In this process, discriminating mind descends into and is active in every synapse of the sensory system. The two processes of transmitting data through the nervous system and of interpreting it cannot be separated. Information is processed at each synaptic level of the afferent pathways. There is no one point along the afferent pathways or one particular level beneath the central nervous system below which activity cannot be a conscious sensation and above which it is a recognizable, defineable sensory experience. Perception has many levels, and it seems that the many separate stages are arranged in a hierarchy, with the more complex stages receiving input only after they are processed by the more elementary systems.13 And the more elementary systems are in turn facilitated or inhibited by the higher, more complex ones. The conclusions towards which these observations push us seems unequivocal. The cognitive, associational processes of the higher brain have just as much to do with our construction of physical reality—both within us and outside of us—as do our sensory devices and their specific stimulations. And remember, it is the perception of this sensory reality which initiates and directs our motor responses, our postures, and our behavior.

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The S curve is not just important as a model in its own right; it’s also the jack-of-all-trades of mathematics. If you zoom in on its midsection, it approximates a straight line. Many phenomena we think of as linear are in fact S curves, because nothing can grow without limit. Because of relativity, and contra Newton, acceleration does not increase linearly with force, but follows an S curve centered at zero. So does electric current as a function of voltage in the resistors found in electronic circuits, or in a light bulb (until the filament melts, which is itself another phase transition). If you zoom out from an S curve, it approximates a step function, with the output suddenly changing from zero to one at the threshold. So depending on the input voltages, the same curve represents the workings of a transistor in both digital computers and analog devices like amplifiers and radio tuners. The early part of an S curve is effectively an exponential, and near the saturation point it approximates exponential decay. When someone talks about exponential growth, ask yourself: How soon will it turn into an S curve? When will the population bomb peter out, Moore’s law lose steam, or the singularity fail to happen? Differentiate an S curve and you get a bell curve: slow, fast, slow becomes low, high, low. Add a succession of staggered upward and downward S curves, and you get something close to a sine wave. In fact, every function can be closely approximated by a sum of S curves: when the function goes up, you add an S curve; when it goes down, you subtract one. Children’s learning is not a steady improvement but an accumulation of S curves. So is technological change. Squint at the New York City skyline and you can see a sum of S curves unfolding across the horizon, each as sharp as a skyscraper’s corner. Most importantly for us, S curves lead to a new solution to the credit-assignment problem. If the universe is a symphony of phase transitions, let’s model it with one. That’s what the brain does: it tunes the system of phase transitions inside to the one outside. So let’s replace the perceptron’s step function with an S curve and see what happens.

Thacker wondered. Given the way prices were falling and processor speeds were rising, why not just build a bunch of very simple controllers and then let the computer’s central processor use software to do all the really hard work of input and output? The result would be a kind of internal time-sharing, Thacker realized. The processor would still cycle very, very quickly among all its users, but now only one of those users would be human; the rest would be input/output devices. “The payoff,” he explains, “would be an enormous simplification of the machine.

A NAND gate is an AND gate with inverted output; that is, it is a device whose output is "0" when all inputs are “1”.  See the truth table and symbols below.

The MIT Media Lab lists our device as the first of what would later be called wearable computers, namely, computers that are worn on the body as part of their function. In late 1961 I built the second wearable computer, a knockoff to predict the wheel of fortune or money wheel. As in the roulette computer, my device used the toe-operated switch for input, the speaker for output, and just a single unijunction transistor; it required only one person. Matchbox-sized, it worked well in the casinos, but the game had too little action to conceal the spectacular consequences of my late bets. Several times when I placed bets on 40:1 as the wheel was spinning, the croupier would give the wheel an extra push.

In June 2008, Amazon.com filed for a new patent with a Microsoft Kinect–like feature for making purchases with body movements. Anticipating computers and other devices that can track a user’s movements, the new Amazon patent is titled “Movement recognition as input mechanism.” Forget keypads and mice, you may soon be able to make a purchase simply by nodding your head at your computer, Kindle, or cell phone. Industry wags have dubbed it the “1-Nod patent.

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,

Paradoxically, the tendency to accumulate a huge backlog of random inputs to deal with, and the number of people troubled with that, have increased dramatically, as the digital revolution has “streamlined” our lives. Implementing standard tools and procedures for capturing ideas and input will become more and more critical as your life and work become more sophisticated. As you proceed in your career, for instance, you’ll probably notice that your best ideas about work will not come to you at work. The ability to leverage that thinking with good collection devices that are always at hand is key to staying on top of your world.

VR will be the ultimate input-output device. Some people call VR “the last medium” because any subsequent medium can be invented inside of VR, using software alone.

In the clamoring industry scene of Myanmar, opening learning experiences and molding achievement depends on grasping the market elements. Statistical surveying assumes a urgent part in directing organizations towards informed choices and key headways. How about we dive into the domain of best market research companies in Myanmar and investigate the top organizations that are preparing for industry greatness! best market research companies in Myanmar -amtmarketresearch We assist our clients with opening learning experiences and shape With regards to exploring the perplexing business sector scene of Myanmar, one organization stands apart for its excellent ability in statistical surveying - amtmarketresearch. With a mission to assist clients with opening learning experiences and shape their prosperity direction, this unique firm blows away in conveying noteworthy bits of knowledge. amtmarketresearch brags a group prepared specialists who are knowledgeable in the subtleties of the Myanmar market. Their fitted way to deal with research guarantees that clients get custom tailored techniques that line up with their interesting objectives and difficulties. By utilizing state of the art procedures and industry mastery, they engage organizations to pursue informed choices that drive unmistakable outcomes. In a serious commercial center like Myanmar, having a believed accomplice like amtmarketresearch can have a significant effect. Their devotion to greatness and obligation to client achievement put them aside as one of the most mind-blowing statistical surveying organizations in the country. Prologue to Statistical surveying in Myanmar Myanmar, a country wealthy in culture and history, is likewise a place that is known for arising potential open doors for organizations. As the market scene advances quickly, understanding shopper conduct and industry patterns becomes urgent for progress. This is where statistical surveying assumes an essential part. best market research companies in Myanmar includes assembling and examining information to reveal experiences that drive informed direction. By digging into socioeconomics, inclinations, and buying designs, organizations can fit their methodologies to actually address the issues of the nearby market. From conventional overviews to creative advanced devices, statistical surveying procedures keep on developing in Myanmar. Organizations are utilizing innovation to contact more extensive crowds and accumulate continuous input on items and administrations. In this powerful climate, remaining in front of the opposition requires a profound comprehension of the nearby subtleties and elements forming shopper conduct. Statistical surveying fills in as a compass directing organizations towards development and maintainability in Myanmar's energetic commercial center. Significance of Statistical surveying for Organizations in Myanmar Statistical surveying holds massive significance for organizations in Myanmar. It goes about as a directing light, enlightening the way towards outcome in the powerful market scene of this Southeast Asian country. By leading careful statistical surveying, organizations can acquire important experiences into buyer inclinations, contest investigation, and arising patterns. Understanding the requirements and needs of the neighborhood populace is critical for fitting items or administrations that reverberate with Myanmar's different segment portions. Statistical surveying assists organizations with pursuing informed choices in view of information driven proof as opposed to suppositions or mystery. In a quickly developing business sector like Myanmar, remaining in front of contenders requires constant checking and examination of industry patterns. Statistical surveying empowers organizations to adjust rapidly to changing economic situations and jump all over chances before their rivals do. In addition, putting resources i

but not in the way it is intended to be.3 For an example of a chain of unintended uses, let us start with Phase One, the computer. The mathematical discipline of combinatorics, here basic science, derived from propositional knowledge, led to the building of computers, or so the story goes. (And, of course, to remind the reader of cherry-picking, we need to take into account the body of theoretical knowledge that went nowhere.) But at first, nobody had an idea what to do with these enormous boxes full of circuits as they were cumbersome, expensive, and their applications were not too widespread, outside of database management, only good to process quantities of data. It is as if one needed to invent an application for the thrill of technology. Baby boomers will remember those mysterious punch cards. Then someone introduced the console to input with the aid of a screen monitor, using a keyboard. This led, of course, to word processing, and the computer took off because of its fitness to word processing, particularly with the microcomputer in the early 1980s. It was convenient, but not much more than that until some other unintended consequence came to be mixed into it. Now Phase Two, the Internet. It had been set up as a resilient military communication network device, developed by a research unit of the Department of Defense called DARPA and got a boost in the days when Ronald Reagan was obsessed with the Soviets. It was meant to allow the United States to survive a generalized military attack. Great idea, but add the personal computer plus Internet and we get social networks, broken marriages, a rise in nerdiness, the ability for a post-Soviet person with social difficulties to find a matching spouse. All that thanks to initial U.S. tax dollars (or rather budget deficit) during Reagan’s anti-Soviet crusade.

The methods I’ve described can be used to implement any function that stays constant in time, but a more interesting class of functions are those that involve sequences in time. To handle such functions, we use a device called a finite-state machine. Finite-state machines can be used to implement time-varying functions—functions that depend not just on the current input but also on the previous history of inputs. Once you learn to recognize a finite-state machine, you’ll notice them everywhere—in combination locks, ballpoint pens, even legal contracts. The basic idea of a finite-state machine is to combine a look-up table, constructed using Boolean logic, with a memory device. The memory is used to store a summary of the past, which is the state of the finite-state machine.

One consequence of this principle of universality is that the only important difference in power between two computers is their speed and the size of their memory. Computers may differ in the kinds of input and output devices connected to them, but these so-called peripherals are not essential characteristics of a computer, any more than its size or its cost or the color of its case. In terms of what they are able to do, all computers (and all other types of universal computing devices) are fundamentally identical.

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.

Digital boards, also known as interactive or electronic whiteboards, have revolutionized the way information is presented and shared in various settings, ranging from classrooms to corporate boardrooms. These sophisticated devices combine the benefits of traditional whiteboards with cutting-edge technology, providing a dynamic and interactive platform for communication. Unlike static whiteboards, digital boards are equipped with touch-sensitive screens that respond to both stylus and finger input, allowing users to write, draw, and manipulate content with ease. This versatility enhances collaboration and engagement, making learning and business meetings more interactive and productive. The potential applications of digital boards are vast, from facilitating remote collaboration to enhancing creative brainstorming sessions. As technology continues to advance, we can expect further innovations in digital board design, with features such as artificial intelligence integration and enhanced interactivity. In essence, digital boards have become indispensable tools in modern communication, fostering dynamic and collaborative environments across educational, professional, and creative domains.

the software team needs some way to validate their work before the responses from actual hardware are available. Simulators model behaviors from subsystems through the full system in virtual space — including user behavior. An emulator is a physical device that imitates the behavior of one subsystem, providing realistic inputs and outputs so that another subsystem can be developed independently.

Aided language input means using the learner’s AAC system to talk naturally while saying the same words verbally at the same time. Using both spoken language and the device simultaneously is shown to increase the learner’s receptive and expressive language skills.12 This simple strategy improves language outcomes for a couple of big reasons. First, the speaker tends to slow down their rate of speech as they go back and forth between saying a word verbally and with the device. A slower rate of speech gives the learner more time to process the words. Second, aided language input serves as a model for the user. The learner hears a word verbally, hears and sees another way to say that same word, and observes the word being used in appropriate contexts.

A wasteful, even silly, contest for technical prowess ensued. At one point, for example, researchers in Palo Alto heard that their Dallas counterparts had fashioned a hand-held input device like the mouse invented by Douglas Engelbart and improved at PARC. Dallas called its tool “the cat.

Carnot declares, such a machine cannot exist. It is a perpetual motion machine, which scientists had long declared an impossibility. For centuries, people had dreamed of building devices that did something useful, but which needed no input of effort from animal muscle, flowing water, or wind. None had ever worked, and in 1775 the Royal Academy of Sciences in Paris stated that they would no longer consider proposals concerning perpetual motion.

In a stunning 1971 paper, Twenty Things to Do with a Computer, Seymour Papert and Logo co-creator Cynthia Solomon proposed educative computer-based projects for kids. They included composing music, controlling puppets, programming, movie making, mathematical modeling, and a host of other projects that schools should aspire to more than 40 years later. Papert and Solomon also made the case for 1:1 computing and stressed the three game changers discussed later in this book. The school computer should have a large number of output ports to allow the computer to switch lights on and off, start tape recorders, actuate slide projectors and start and stop all manner of little machines. There should also be input ports to allow signals to be sent to the computer. In our image of a school computation laboratory, an important role is played by numerous “controller ports” which allow any student to plug any device into the computer… The laboratory will have a supply of motors, solenoids, relays, sense devices of various kids, etc. Using them, the students will be able to invent and build an endless variety of cybernetic systems.

Why is owning equity in a business important to becoming rich? It’s ownership versus wage work. If you are paid for renting out your time, even lawyers and doctors, you can make some money, but you’re not going to make the money that gives you financial freedom. You’re not going to have passive income where a business is earning for you while you are on vacation. [10] This is probably one of the most important points. People seem to think you can create wealth—make money through work. It’s probably not going to work. There are many reasons for that. Without ownership, your inputs are very closely tied to your outputs. In almost any salaried job, even one paying a lot per hour like a lawyer or a doctor, you’re still putting in the hours, and every hour you get paid. Without ownership, when you’re sleeping, you’re not earning. When you’re retired, you’re not earning. When you’re on vacation, you’re not earning. And you can’t earn nonlinearly. If you look at even doctors who get rich (like really rich), it’s because they open a business. They open a private practice. The private practice builds a brand, and the brand attracts people. Or they build some kind of a medical device, a procedure, or a process with an intellectual property. Essentially, you’re working for somebody else, and that person is taking on the risk and has the accountability, the intellectual property, and the brand. They’re not going to pay you enough. They’re going to pay you the bare minimum they have to, to get you to do their job. That can be a high bare minimum, but it’s still not going to be true wealth where you’re retired but still earning. [78] Owning equity in a company basically means you own the upside. When you own debt, you own guaranteed revenue streams and you own the downside. You want to own equity. If you don’t own equity in a business, your odds of making money are very slim. You have to work up to the point where you can own equity in a business. You could own equity as a small shareholder where you bought stock. You could also own it as an owner where you started the company. Ownership is really important. [10]

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