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Nextdoor Holdings Forecasts Growth Amidst CEO Change - TipRanks.com

What Happened: Shares of neighborhood social network Nextdoor (NYSE:KIND) jumped 24.8% in the pre-market session after the company announced that Co-founder Nirav Tolia will would return as Chief Executive Officer, President, and Chairperson of the Board, replacing Sarah Friar following a ...

Why Nextdoor (KIND) Stock Is Up Today By Stock Story

That content doesn&#x27;t seem to exist... have you tried going to the home page, or exploring an interest · Meanwhile, here are some important items you&#x27;ll want to know:

Nextdoor Jumps as CEO Exits and Founder Takes Over. It’s Buying Back More 
Stock.

Nextdoor co-founder Nirav Tolia will return as chief executive officer, replacing Sarah Friar, the company announced Friday. The change came after The Information reported Thursday that the local social networing site was considering selling itself or replacing Friar with Tolia.

Briefing: Nextdoor Appoints Co-Founder Tolia as CEO, Replacing Friar — The Information

SAN FRANCISCO, February 23, 2024--Nextdoor Announces Leadership Transition and Reports Preliminary Q4 2023 Financial Results

Nextdoor Announces Leadership Transition and Reports Preliminary Q4 2023 Financial Results

By Will Feuer Nextdoor Holdings said its co-founder, Nirav Tolia, will return as chief executive officer after a steep stock decline over the past two years....

Nextdoor Co-Founder Nirav Tolia to Return as CEO - MarketWatch

SAN FRANCISCO - Nextdoor Holdings, Inc. (NYSE: KIND), known for its neighborhood networking services, announced today that co-founder Nirav Tolia will be rejoining the company as Chief Executive Officer, President, and Chairperson of the Board. Sarah Friar, who has served in these roles since ...

Nextdoor names co-founder Nirav Tolia as new CEO By Investing.com

Board members at the struggling local networking site Nextdoor have been debating the company’s future, including whether to bring in a new CEO or to find a buyer, a person familiar with the matter said. Investment firm Qatalyst has approached companies about their interest, said two people ...

Nextdoor Board Discusses Replacing CEO Friar or Selling Company — The Information

Nextdoor Holdings, Inc. is an American company that operates a hyperlocal social networking service for neighborhoods. The company was founded in 2008 and is based in San Francisco, California. Nextdoor launched in the United States in October 2011, and is available in 11 countries as of May 2023.

Nextdoor - Wikipedia

SAN FRANCISCO (KRON) — San Francisco-based Nextdoor will lay off 25% of its staff, the company announced in a financial report posted Tuesday. The job cuts are part of a broader cost reduction plan that targets reducing the neighborhood network social media company’s personnel expenses ...

Nextdoor layoffs: Company to reduce staff by 25%

Nextdoor Holdings

nextdoor

Privacy Focused Social Network Analysis: Facebook vs. Nextdoor

In one town, police say products like Nextdoor and Ring are helping fight crime. But racism and vigilantism are pervasive on safety platforms. In late August, a resident of Mobile, Ala., posted a video on Nextdoor, a social media app that advertises itself as a place for neighbors to connect. The from a home security camera, showed a man—”Race- black Age- teenager or early 20s”—taking a leaf blower from the poster’s property. clip “Let’s set up a sting and I will have my AK-47 ready for the next one,” a neighbor . responded In the days before and after that post, Mobile residents discussed other suspicious activity on Nextdoor and similar apps. Some posters used barely coded racist language, others mentioned obtaining pistol permits in response to petty thefts or suggested firing warning shots at potential troublemakers. In one post to Nextdoor, a Mobile resident said there were “dogs” going around ripping wiring out of trucks. “You mean like Snoop Dog?” a neighbor responded. “I think it was supposed to say dawgs,” a second said. “They’ve never owned anything, never worked for their own money, have no home training and are just lowdown.… [S]care them by firing outside when they’re seen,” a third wrote. Neighbors have always peered out their windows at each other and discussed passersby. But new surveillance technologies like Nextdoor, Ring cameras, civilian license plate readers, and crime reporting apps—which have quickly attracted police admiration—are fundamentally changing the dynamics of snooping on neighbors. And not necessarily for the best. In Mobile, for instance, the police department has a full-time employee who spends a significant amount of time monitoring Nextdoor posts, Commander Kevin Levy told The Markup. MPD has also entered a special law enforcement partnership with Amazon’s Ring and uses the associated Neighbors app to request footage. “There’s so much more evidence that’s out there, digital evidence, that detectives have at their fingers,” Levy said. “As an agency, we encourage apps like that even though we know we’re going to get a bunch of false positives. That’s the risk you take, but I think otherwise you’re ignoring a really good resource.” Mobile residents can get a discount on Ring surveillance cameras in exchange for registering their cameras with police. The MPD’s list of registered cameras—which includes many Ring devices but is not exclusive to them—has grown from just several hundred cameras when the program started in 2015 to more than 12,000 today, Levy said. But the crowd-sourced safety platforms can also be rife with racism and threats of vigilantism. Recently on Neighbors—an Amazon Ring–owned app similar to Nextdoor that can be directly connected to Ring security cameras—a Mobile resident posted a video of a Black man, whose face is barely visible, picking up a cardboard box from a front porch. The post, titled “Theft,” includes a case ID and phone number for the Mobile Police Department. In the comments, Neighbors app users speculated about who the man was and where he lived. “I think he lives in the section of apartments in the rear on the right,” one user wrote. Another said, “I will be looking for him.” Another Neighbors post from Mobile includes a video from a Ring camera showing a Black man walking across a piece of the poster’s front lawn. “Trespassing,” is all the poster wrote. Ring declined to answer questions on the record. Nextdoor removed the posts mentioned in this story after The Markup inquired about them. “Vigilantism and racism are not allowed on Nextdoor, and we rely on a combination of human and technological moderation capabilities to search for, identify and remove content and neighbors in violation of our community guidelines,” Jenny Mayfield, a spokesperson for the company, wrote in an email. Jelani Drew-Davi, a campaign director at Kairos Fellowship, which is leading the Tech is not Neutral campaign against neighborhood surveillance tools, said the danger of community racism is compounded by the fact that Nextdoor, Ring, and similar services partner with police departments. Speculation about the identity of a person in a video or an accusation of trespassing might previously have been contained to a conversation among neighbors. Now, such comments are monitored by police. In June 2020, Nextdoor removed a feature that allowed Nextdoor users to flag posts and send them directly to police. Government agencies to scroll community news feeds from their official accounts, but police using private accounts can still monitor posts on the platform. are also now not allowed Amazon has with the help of law enforcement agencies and, as of January 2021, had partnerships with . But the company recently so that police must now request footage in public posts. aggressively marketed Ring more than 1,700 departments changed its policies “These companies are actively going after police partnerships,” Drew-Davi said. “In the world that we live in, police are not good to marginalized communities.… Surveillance is not safety. Reports around suspicious neighbors or crime is oftentimes just a proxy for a Black or brown citizen.” But even as concerns grow, so does the availability of new public safety technologies. Recently, neighborhood-owned automatic license plate readers (ALPRs) have become more common. Flock Safety, one of the leaders in this area, claims that t of homeowners associations have purchased its systems to “automate their neighborhood watch.” Such scanners read license plates and check them against police and private databases, sending automatic alerts to police if a wanted car passes by. They can also upload license plates to police databases, creating a powerful tool for tracking drivers’ movements. housands Flock markets its products organizations that are still . specifically to homeowners associations rooted in segregation disproportionately White “It will make it a lot easier for homeowners and [homeowners associations] to track and harass BIPOC visitors and constantly treat anyone who looks different as a threat and to really ramp up the technology of exclusion,” said Albert Fox Cahn, director of the Surveillance Technology Oversight Project. “These tools that are sold as supposedly creating safety: For millions of Americans, they’re viewed as creating a threat.” Flock told The Markup that its system “is not intended to block anyone from entering a neighborhood.” “Flock reduces the need for random traffic stops, by ensuring that officers will be notified if a vehicle is associated with a known wanted suspect or that vehicle is stolen,” Josh Thomas, Flock’s vice president of marketing, wrote in an emailed statement. “We send a near real-time alert to the nearest officer, a picture verifying the image of the vehicle, and the officer is expected to double-verify the data before making a stop.” And then there’s the Citizen app, present in about 60 U.S. cities. Citizen originally launched under the name Vigilante but rebranded after being e for seemingly encouraging citizens to intervene in crimes. Its actions since then have not entirely assuaged fears that it is designed to encourage crowd-served justice. banned from the Apple App Stor Earlier this year, Citizen launched an internet manhunt by offering a $30,000 bounty for information leading to the arrest of a man suspected of starting a brush fire in California, but Citizen employees and users , prompting the company to issue an y. accused and publicly identified the wrong man apolog “Citizen has strict validation protocols to limit the spread of misinformation and ensure safety. This was the first and only instance where these protocols were not followed and, unfortunately, an on-the-ground tip from an LAPD Sergeant was used in place of official confirmation from public safety agencies,” Citizen spokesperson Ebony Bowden wrote in an email to The Markup. She added that the company has “no plans to offer additional rewards to share at this time.” In May, Citizen proposed starting a . (It has since abandoned the plan.) And on Aug. 3 it launched Citizen Protect, a $19.99-per-month service that offers 24/7 </a> to “highly trained Protect Agents” who, among other functions, can send alerts about an incident involving a Protect customer to nearby Citizen users. private police force access Bowden did not directly respond to a question asking whether Citizen encourages its users to respond to alerts generated by “Protect Agents.” “The wisdom of the crowd becomes the tyranny of the crowd, and it’s enabled by technology,” Wayne Logan, a Florida State University law professor who has studied the effects of public safety technology, said. “One hundred years ago, if I put a finger on somebody and the pitchforks came out, it was limited. But now you’ve got the whole internet.” This article was and was republished under the -NonCommercial-NoDerivatives license. The original title was: “ The Downside of Surveilling Your Neighbours” and was written by Todd Feathers. originally published on The Markup Creative Commons Attribution

"Racism and vigilantism are pervasive on safety platforms"

Some people say that as you grow older, it becomes difficult to make new friends. We are often busy dealing with equally important things such as finding a stable job, paying bills, and saving for the future. Luckily, we are now living in the digital age, and there is much more that can be done with just the click of a button. One of them is getting new people with whom you want to be friends. In this article, we share with you a list of the best apps to make new friends. We have studied them carefully to ensure that they are safe to download and use. If you feel that your social life needs a rejuvenation, then you want to check them out. 1. Meetup Meetup is the perfect place to meet new friends, especially if you are looking for people who have similar interests as you. For example, if you like knitting, cooking, or a particular sport, this app can connect you with a group of friends you can join to enjoy your leisure time. Creating a Meetup account is free, simple, and easy. You have four options: through your FB account, your Google account, your Apple account, or your email account. Once logged in, you can immediately select your city and the things that interest you. From there, you can find many Meetups that may be in your area. If you have a little more energy, you can also start your own group and organize events. You can plan and personalize these events, share photos and chat, and message them to connect all the people in your group with other people. Meetup is a location-based app. So it uses your device's position via GPS and network. We recommend this app to look for clubs or groups where they can become members. If you are interested in a running club, writers club, or book club, you will definitely be sure to sign up with one. 2. Bumblebee BFF Created and launched by the team behind one of the most popular dating apps, Bumble BFF is for those seeking platonic yet meaningful relationships with others. According to the app's creators, Bumble BFF aims to "build a supportive community around them wherever they are." If you have ever tried to use the Bumble dating app, it will be easy for you to use Bumble BFF. Your matches with potential friends are based on your activities and interests. Once you are uploading your photos and filling in your bio, your profile is verified, and you are logged into your other social media account, you are ready to make some new friends. Huh. To switch to sign in or right-left start browsing other people's profiles. Unlike the dating app, where the woman has to take the first step, in the BFF version one can take the first step. 3. Nextdoor Nextdoor is specifically designed for those who have moved into their neighborhoods and are looking to make new friends. It is a private social network specifically for you and your neighbors, which aims to strengthen the relationship between the residents of the local areas. There are many things in this app that you can do besides making new friends. You can do a virtual garage sale and sell goods or household items to your neighbors, and even find part-time jobs such as child care or animal care. Founder Jenny Mayfield assured users of the app that it was safe and secure. Strict security measures are in place to ensure that the neighborhood remains a private environment. You do not have to worry, because only real neighbors are allowed and everyone will have to undergo verification before officially joining. 4. Peanuts Peanut is highly recommended for mothers who feel isolated and want to connect with other mothers who understand what they are doing. The application connects women "through reproduction and motherhood". Thanks to this, you can meet or interact with like-minded women. Michelle Kennedy of Miss Dating App is the founder of this app and launched it in 2013 after giving birth. She described herself as a "new single mom" who wanted to find people who knew and understood what she was doing. She was suffering from postpartum depression and she did not want anyone to feel like it. To help other mothers, they made peanuts whenever they felt isolated or exhausted. On this app, mothers can ask questions and share their knowledge with others. They can also create group conversations and join communities to make connections. If you're a mom and currently feeling the same as Michelle when she had her first childbirth, or simply looking for a new circle of friends who can better understand your mom's issues, Peanut " New Friends "is a great app to download.

4 Easy-To-Use Apps for Making New Friends in 2021

This Week on Planet Internet: The Investor Next Door

Effortlessly Deploy Django Apps to the Cloud with GitHub Actions and Heroku

In the of the analysis, we will bring back the that play on the ZKP stage. The part is dedicated to showing the “ ” of the ZPK scene. And , we will appeal of “possible” luck from the intruder. Now, let’s present the nature of the abstract and let’s unfold its mysteries. first part actors second magic thirdly rare probabilities Abstract We’ll be using an as the on how to open a mystical door. The nature of the abstract is to turn (such as left/right) into . The mechanism movers are unique to each instance of opening. The the of the structure to the shared . The abstract works when tangible and intangible . The abstract is an which ties them both together to form the . If you play with and have of the ones, you will have chances of the key. Imagine guessing the position of an respective to an . abstract shared knowledge decision gates mechanism movers abstract connects tangible rules intangible knowledge only both exist upper system tangible rules no knowledge intangible close to 0 guessing unknown number x unknown group of y numbers Part 1: The actors As the curtain rises, it reveals to us 3 actors. The is the and , is the and and is . When describing them, the aim is to provide a clear and structural analysis of them, any information regarding the . I will only refer to it but not provide any information until the second part. door giver of access holder of the lock Lisa opener bearer of the key, Bob the verifier outside structure of the lock The door The door is the of the abstract . In its internal knowledge, we find the logic and of the lock. The refers to the required to open each to the by the . Those locks after each opening, ensuring that any given by Lisa can be used for the (which will be passed after a failed/successful attempt). bearer lock hard-coded structure abstract system comparing internal steps individual lock information presented opener iterate successful information strictly current lock If a is reached, because Lisa made a and the , will begin and Lisa will have to provide a based on her knowledge of the . Based on its , the door will depict whether Lisa holds or not the key to enter. critical point mistake passed opening point another lock iteration new key overall (abstract) system internal gears Lisa Lisa, as the bearer of the key, holds knowledge of the and the structure. She knows the of the lock and will her knowledge of that in the form of to the door. She will the lock as if it in front of her. Stating movements such as l which will be the the door will receive and the of the lock accordingly. Once Lisa is sure the lock is in the position, she will state the and . internal system current lock’s limits present logical reasoning imagine opening sits eft/right input adjust internal gears open current key end communication value Bob Bob, as the verifier, can form the of a . Let’s say that Lisa comes and a conversation with the door. She will further state: Left, Right, Left, Left, Right, Right, Right, . After which, she will . Bob now knows how to open of the door. However, has a distinct route to be taken. image potential lock initiates Key end communication iteration 1 iteration 2 As the day passes Bob gathers the information about how to open all . Now, the comes into play and all the 500 iterations. The next day, becomes of the . Now, the will play a crucial role in of guessing. 500 iterations system shuffles Iteration 1 any other 499 logic of shuffling reducing the probability Remember when I said “One could always check the security by checking the memory. Then, questions about gibberish memory come into play to scare off the intruders who may want to bypass. Alluding later to the sense of courage and self-esteem as you broke down 1TB of key complexity.”? For this system to work, it requires that and the have a kind of inside them. of the Door allows for each lock to be and later . Now, the shuffling can come each day or based on . So the door also has to have some kind of (which I will further call the reason of the system) of . both Lisa Door computer Memory uniquely stored shuffled manually algorithmic logic logic shuffling Lisa and all other authorized personnel could very well gather their knowledge of from a “Headquarters”. today’s lock system Or they could have as the Door inside their heads in the case we to create a headquarters. similar shuffling reasoning do not want This allows for both and storage and interactions. A headquarters will allow to gain access. No headquarters would mean that only would be able to open the door. centralized decentralized new personnel limited predefined personnel Both avenues open deeper implementation as a as it could allow to be granted access by providing her with a . decentralized system, newcomers by Lisa unique encrypted key But now I may get too far ahead of myself. Let’s see how the is made. abstract lock Part 2: The lock Maybe is the simplest part to understand. proving it is, however, out of my reach and expertise, but I will try to provide a clear and structural analysis that could allow for mathematical understanding and implementation. the lock Mathematically 1. The prime-nonprime lock system. 1.1 Prime-nonprime lock cipher The cipher at play is made out of . two sets A set of only numbers and a set of only . prime non-primes Both sets have an . equal number of elements Each set can start at an number as long as the . arbitrary previous rule holds true , the set will obtain ‘ ’ as its , whereas the set will obtain ‘ ’ as its . After they are formed prime 3 last element non-prime 2 first element So we have: Prime=[prime, next prime, next next prime…(30 times),3] Non Prime= [2, NP, next NP, next next NP… (30 times)] And pretty much this is the lock. 1.2 Cipher interaction Let’s suppose Lisa comes and tries to unlock the door. We assume that she knows the and (except 3) are in the set. As well as non primes. She knows (except 2) and we find out she how many non-primes there are (we do not want to waste information on what is locally known from the primes set). first prime how many primes the first non prime already knows So the of each or will require knowledge of the from and the from (All the primes and non-primes that exist in math). To later it to the required which to the set of the (which only the authorized personnel will know of). internal knowledge opener intruder set of primes x to y set of non-primes x to y adjust parts correspond actual cipher So Lisa knows supposedly all primes and non-primes. Both up to infinity and divided in the required by the . respective set current lock She, as the authorized personnel, will know that of the starts with the and has (+3 at the end) and the starts with the (+2 at the beginning) and holds 100 (+1) non-primes in total. iteration 1 prime set 30th prime 100 primes non prime set 100th non-prime For a very basic interaction, I will present to you and their interactions with . The text is what know of. The is what is , what Bob can hear. both sets Lisa bolded only the door and the authorized personnel italicized text given outside Iteration 1: Prime set = [5,7,11,13,17,19,(3)] Non Prime set = [(2),4,6,8,9,10,12] Lisa comes to the door and states: ‘ ’. This is the . Now, the door knows that Lisa is aware of . 2,3 initiation both locks inside The process of opening starts. The ‘selected’ prime is 3. The ‘selected’ non-prime is 2. (This is a rule of the internal logic) Lisa says: ‘3, left’. Now, the gears of the prime selection turn to 19. ‘2, right’ The gears of the non prime point now 4. ‘3, left’ (17) ‘2, right’ (6) ‘3, left’ (13) ‘2, right’ (8) Lisa says: Now, the lock has reached a middle place, 13 is selected as the prime and 8 as the non-prime. Lisa has to state their difference (5) and end communication with the door for the door to run its own rules and check. ‘5; 3,2’. Now the door will run internally and check the difference between the middle numbers of each set, if it corresponds to the number specified by Lisa, access is granted. If Lisa however fails, another iteration begins and Lisa has to take a different route. This new deeper layer of failure requires both centralized and decentralized systems. If Lisa was sent from the headquarters and only knew how to open iteration 1, she would have to return to regain information about the second. If the iteration is based on a rule of background iterations (in the case of repeated failed opening), then the iteration will turn instead of 2 to 1.1 iteration. Assuming that Lisa also gained knowledge from the headquarters on how the lock will change in case she fails to open it. (Assuming the cycle doesn’t repeat) At iteration 1.3 (after 3 failed attempts) the pace of the “left/right” decision could turn from 1 step to 3 steps and the newly introduced sets will have a total number of 3n+1. Ensuring that no matter how hard you try, you can never select the middle point of the sets. Who knows however if this is necessary. If we allow the locks to repeat themselves we could envision such a protection: After you state “left” when you are positioning the prime selection and you are located at the first prime, you will be sent back at 3. This would assume you are also positioned at the last position of non-primes and thus, stating “right” would turn you to 2. (Or we could never tell the opener that they failed so they will infinitely try to guess, but that of course, would give of no knowledge of whether someone tried to enter and failed or not.) The ‘3,2’ state was achieved and no key was mentioned so the input was wrong, access was denied and the iteration moved to the next lock. 2. Metaphysics (Creativity/Imagination/ ) vs Mathematics (Present state) Possibility The and seem to . What does that mean? It means that we could use and to stop any “ ” or avenues that aim to break the system. It’s not a problem of , but one of of the set limits. Even if you know you can do, you can never know if that 1TB holds 1000 sets of 20 to 1000 numbers of 100 sets of 10.000 numbers. And thus, Especially since those 10.000 numbers could very well be sets that follow one another. possibilities of interaction security measures stop at the power of imagination (endless routes could be taken) reason hidden information creative newly taken computational problem prior knowledge all the actions you will never know your starting point. I will present a view on “cipher interaction” depicted with the help of ChatGPT. (I will lend my personal view in bold and italicized) “The mention of "the power of imagination" in the context of interaction and security measures implies that the system's robustness relies on the ability to anticipate and counteract potential creative or innovative attempts to breach its security . (Any route taken, no matter of creative or smart it is, is not tied to the “ground-truth” knowledge of the system) It suggests that the system designers have incorporated elements that make it resistant to unconventional or unexpected approaches by leveraging reasoning and hidden information. In practical terms, this means that the system is designed in such a way that even if an attacker tries to think outside the box or employ novel strategies to break the system, there are safeguards in place to mitigate such attempts. These safeguards may include: : The system's structure and rules are sufficiently complex and dynamic, making it difficult for an attacker to predict or exploit weaknesses effectively. Complexity : Certain critical information about the system's configuration or operation is kept hidden or encrypted, preventing attackers from gaining a comprehensive understanding of the system's vulnerabilities. Hidden Information : The system can adapt or evolve in response to emerging threats or attack strategies, ensuring that it remains resilient over time. Adaptive Security Measures : Attackers are constrained by their prior knowledge of the system's set limits, preventing them from exploiting vulnerabilities that they are not aware of or cannot anticipate.”- ChatGPT Limits of Prior Knowledge Imagine that you are playing chess. However, you can’t see your opponent’s pieces in exchange for him not seeing yours. This play state could be akin to You do not intend to be offensive at all, in the exchange of being private and aware of your flaws. everybody minding their own business. If during the state of the game, any player sees only once the position of the pieces, then that player will gain relevant irreversible information of position. Without it… who knows how many chess boards are between his pieces and your king. Part 3. The Luck of opening such a system I want to first mention that everything I wrote is simply an idea. Until I see it in action, I will disbelieve it as true. And even if I see it in action, who knows if I will believe it to be the true case. I will still require knowledge of the background sets to know what the opener has done. Now, what do I mean by “Luck”? Let’s turn back to another story. “What can we say about “efficiency” when it comes to our safety? Our long-term health and well-being? Why does “efficient” sound a little too rushy here? Because here, we need proof. We have to be sure as our health is at play.” It indeed seems like has a aspect to it. How could we tie efficiency to our locks? efficiency rushy Well… let’s say that the door only requires the key ‘ ’ and the ‘ ’ ending to open. All the internal structure will itself to the middle of the sets and check. This way, we would the number in novel ways. Lisa could simply state “ ” which for the door would mean “ ” and if the difference between the middle points is indeed 51, access is granted. X 3, 2 run encrypt abc 51 I assume that this approach could offer the same kind of information as before. But then, wouldn’t there be the possibility for someone to get that information out of Lisa? I mean, if they know towards which door she is going they could simply go there, and… maybe we could even implement more doors in this case. What if the quantum world is guided in the end by the classics? I don’t know if it’s just me, but I believe that there exists a . A specific Mathematical that, as long as it is , could drive us into of paving a path for . Like the distribution of primes. Each time you get to a , the pattern , but, it . specific system process kept hidden endless ways true privacy new prime overall changes the slightest changes Based on what we see, 2,3,5,7, and so on, those , of numbers out of the . first prime numbers cut a lot natural number line The the primes we find, the the numbers they . Yet, when talking about , isn’t the same as the part? bigger less influence infinity half 1000th Personal Infinity Could we form a “ ” that works as our ? personal bounded infinity workspace The is and to that . If know those , then only we can depict that to at the to it. overall change static tied infinity only we boundaries overall change stop last prime required exhibit The of someone the is very low since they have to take into account the (Which are at and (but serve as guiding rules) in the process of ). The simplest form of drawing a line is placing 2 points and connecting them. chance exhibiting exact median change starting and end points given among authorized parts creation hidden interaction Now the question comes: Could this be some kind of axiom in math? Or a rule that quantum mathematics could follow? When I say quantum mathematics I mean the interaction between two distinct points of view. Those we have to find the between them, knowing about the , or of the in which they sit. (Those points however cannot move as it would require a meeting and re-organizing among the authorized personnel) two points measured distance nothing shape size, color space Part 4: The synergy Now, extended to people, this space could be comprehended and understood. When it comes to AI or any other computational rule, it can only be analyzed. But what if there is nothing else to analyze except the existence of those two points? Even people peaking into the play cannot see the system. This could be a point where we could ensure protection through a form of “limited” consciousness. Understanding of the system itself (which is different from self-understanding). Mathematical rules of a circle, reflection, inner, out, and so on, all enveloped to form a limited self-awareness. With the lack of human power (acknowledging it can be more) the system will remain forever overall static. Even if the rules that guide it are moving and shaping interactions and outcomes. If those two points were to be humans, their unique perspectives would allow them to potentially observe one another and find smart ways to guess the difference between them. Maybe, they could never guess the exact classical value. But that act of guessing could be akin to seeing the rules. Bots would remain stuck there. But humans could draw. Even if we use sophisticated AI for Lisa, the Door, and Bob, they will still not be able to arrive at a conclusive judgment as there will always be insufficient information. If I am to sum up the whole article in a single phrase, I will say: Bots in the end could create the fastest algorithms we’ve had, but they could never surpass our creativity. I mean… it’s important to keep dreaming. The future looks amazing. But there’s still a lot of work to do x.x (This article is dedicated to Dan. Without your help, my schedule (and also my mood) wouldn’t have allowed to me focus on all those points. Thank you!)

Unlocking the Magic of Zero-Knowledge-Proofs—Part 2: Artificial Reasoning and Limits of Information

জিরো-নলেজ প্রুফের ম্যাজিক আনলক করা—পার্ট 2: কৃত্রিম যুক্তি এবং তথ্যের সীমা

Die Magie von Zero-Knowledge-Beweisen erschließen – Teil 2: Künstliches Denken und Informationsgrenzen

Descubriendo la magia de las pruebas de conocimiento cero: Parte 2: Razonamiento artificial y límites de la información

Libérer la magie des preuves sans connaissance – Partie 2 : Raisonnement artificiel et limites de l’information

शून्य-ज्ञान प्रमाणों के जादू को खोलना-भाग 2: कृत्रिम तर्क और सूचना की सीमाएँ

ゼロ知識証明の魔法を解く—パート 2: 人工推論と情報の限界

영지식 증명의 마법 풀기 - 2부: 인공 추론과 정보의 한계

Desbloqueando a magia das provas de conhecimento zero – Parte 2: Raciocínio artificial e limites de informação

Раскрытие магии доказательств с нулевым разглашением — Часть 2: Искусственные рассуждения и ограничения информации

Sıfır Bilgi Kanıtlarının Büyüsünün Kilidini Açmak - Bölüm 2: Yapay Muhakeme ve Bilginin Sınırları

Giải phóng sự kỳ diệu của những bằng chứng không có kiến thức—Phần 2: Lý luận nhân tạo và giới hạn thông tin

解锁零知识证明的魔力——第 2 部分：人工推理和信息限制

In an unprecedented move by VERSES AI, today’s announcement of a breakthrough revealing a new path to AGI based on ‘natural’ rather than ‘artificial’ intelligence, appealing to their stated mission VERSES took out a full page ad in the NY Times with an open letter to the Board of Open AI “to build artificial general intelligence (AGI) that is safe and benefits all of humanity.” Specifically, the appeal addresses a clause in the that states in pursuit of their mission “to build artificial general intelligence (AGI) that is safe and benefits all of humanity,” and the concerns about late-stage AGI becoming a “competitive race without time for adequate safety precautions. Therefore, if a value-aligned, safety-conscious project comes close to building AGI before we do, we commit to stop competing with and start assisting this project.” Open AI Board’s charter What Happened? VERSES has achieved an AGI breakthrough within their alternative path to AGI, which is Active Inference. And they are appealing to Open AI “in the spirit of cooperation and in accordance with [their} charter.” According to their today, “VERSES recently achieved a significant internal breakthrough in Active Inference that we believe addresses the tractability problem of probabilistic AI. This advancement enables the design and deployment of adaptive, real-time Active Inference agents at scale, matching and often surpassing the performance of state-of-the-art deep learning. These agents achieve superior performance using orders of magnitude less input data and are optimized for energy efficiency, specifically designed for intelligent computing on the edge, not just in the cloud.” press release Why Is This Significant? In a video published as part of the announcement today titled, “ ” VERSES takes a look at the incredible journey of AI acceleration over this past year and what it suggests about the current path from Artificial Narrow Intelligence (where we are now) to Artificial General Intelligence — AGI (the holy grail of AI automation)… Noting that all of the major players of Deep Learning technology have publicly acknowledged throughout the course of 2023 that “another breakthrough” is needed to get to AGI. For many months now, there has been an overwhelming consensus that machine learning/deep learning cannot achieve AGI. Sam Altman, Bill Gates, Yann LeCunn, Gary Marcus, and many others have publicly stated so. The Year in AI 2023, Just last month, Sam Altman declared at the Hawking Fellowship Award event at Cambridge University that “another breakthrough is needed” in response to a question asking if LLMs are capable of achieving AGI. Even more concerning are the potential dangers of proceeding in the direction of machine intelligence, as evidenced by the “Godfather of AI,” Geoffrey Hinton, creator of backpropagation and the deep learning method, withdrawing from Google early this year over his own concerns of the potential harm to humanity by continuing down the path he had dedicated half a century of his life to. So, What Are The Potential Dangers of Deep Learning Neural Nets? The many problems that pose these potential dangers of continuing down the current path of , are compelling and quite serious. generative AI · Black box problem · Alignment problem · Generalizability problem · Halucination problem · Centralization problem — one corporation owning the AI · Clean data problem · Energy consumption problem · Data update problem · Financial viability problem · Guardrail problem · Copyright problem All Current AI Stems from This 'Artificial' DeepMind Path At University College London, two professors worked right next door to each other. One, Geoffrey Hinton, and the other, Karl Friston. Professor Hinton developed DeepMind, taking an engineering approach to artificial intelligence, resulting in the handful of monolithic foundation models that exist today from which all generative AI is built. Dr. Friston, Chief Science Officer at VERSES and one of the most renowned neuroscientists in history, had other ideas. He pioneered a method called Active Inference, based on a principle he discovered called The Free Energy Principle, opening up an entirely new path to AGI that is grounded in physics and based on the mathematics of natural, biological intelligence rather than mere machines and algorithms. In November 2018, WIRED published an article titled “The Genius Neuroscientist Who Might Hold the Key to True AI,” stating, “Karl Friston’s Free Energy Principle might be the most all-encompassing idea since the theory of natural selection.” In the video released today, Professor Friston says, “You look at machine learning, and you just look at the trajectory. It’s all a trajectory measured in terms of big data, or how many billions of parameters can your large language model handle? That’s exactly the wrong direction.” The AGI Breakthrough In today’s announcement, VERSES explains, “Building on this breakthrough, we developed a novel framework to facilitate the scalable generation of agents with radically improved generalization, adaptability, and computational efficiency. This framework also features superior alignability, interoperability, and governability in accordance with and complemented by the P2874 Spatial Web standards being developed by the Institute of Electrical and Electronics Engineers (IEEE).” VERSES AI is taking a First Principles approach to AI based on the understanding that the path to AGI must be based on natural intelligence. This direction follows the premise that intelligence is multidimensional, adaptive, efficient, and cooperative. The technology they have built demonstrates a systematic blueprint for achieving AGI and even super intelligence resulting from this path. This is the Breakthrough everyone is waiting for. The Research Roadmap Revealed Additionally, outlining the stages of development for Active Inference AI. VERSES unveiled its research roadmap and timeline publicly today, — This is contemporary state-of-the-art Al; namely, universal function approximation-mapping from input or sensory states to outputs or action states — that optimizes some well-defined value function or cost of (systemic) states. Examples include deep learning, Bayesian reinforcement learning, etc. S0: Systemic Intelligence Sentient behavior or active inference based on belief updating and propagation (i.e., optimizing beliefs about states as opposed to states ); where “sentient” means behavior that looks as if it is driven by beliefs about the sensory consequences of action. This entails planning as inference; namely, inferring courses of action that maximize expected information gain and expected value, where value is part of a generative (i.e., world) model; namely, prior preferences. This kind of intelligence is both information-seeking and preference-seeking. It is quintessentially curious, in virtue of being driven by uncertainty minimization, as opposed to reward maximization. S1: Sentient Intelligence — per se Sentient behavior — as defined under S1 — in which plans are predicated on the consequences of action for beliefs about states of the world, as opposed to states . i.e., a move from “what will happen if I do this?” to “what will I believe or know if I do this?”. This kind of inference generally uses generative models with discrete states that “carve nature at its joints”; namely, inference over coarse-grained representations and ensuing world models. This kind of intelligence is amenable to formulation in terms of modal logic, quantum computation, and category theory. This stage corresponds to “artificial general intelligence” in the popular narrative about the progress of AI. S2: Sophisticated Intelligence — per se The deployment of sophisticated AI to recognize the nature and dispositions of users and other AI and — in consequence — recognize (and instantiate) attentional and dispositional states of self; namely, a kind of minimal selfhood (which entails generative models equipped with the capacity for Theory of Mind). This kind of intelligence is able to take the perspective of its users and interaction partners — it is perspectival, in the robust sense of being able to engage in dyadic and shared perspective taking. S3: Sympathetic Intelligence — The kind of collective that emerges from the coordination of Sympathetic Intelligences (as defined in S3) and their interaction partners or users — which may include naturally occurring intelligences such as ourselves, but also other sapient artifacts. This stage corresponds, roughly speaking, to “artificial super-intelligence” in the popular narrative about the progress of AI — with the important distinction that we believe that such intelligence will emerge from dense interactions between agents networked into a hyper-spatial web. We believe that the approach that we have outlined here is the most likely route toward this kind of hypothetical, planetary-scale, distributed super-intelligence.” S4: Shared Intelligence — GENIUS™ Platform Timeline - What to Expect Today, for the first time, . The GENIUS™ Private Beta Program officially launches in January 2024, with previously announced Beta partners like NASA, SimWell, Cortical Labs, and NALANTIS, with more announcements soon. The GENIUS™ Public Beta is set for Summer 2024, with enhanced agent capabilities. Included in the timeline is a projection for Sentient Agents as “Personal Agents” planned for winter 2024 in a Private Beta. VERSES made public their ambitious timeline for the 2024 rollout of their platform Back to Today's Monumental Call for Cooperation and Collaboration Among the AI Community Not only are LLMs unlikely to achieve AGI, they are highly likely to bring about the worst potential for humanity. At the very least, they bring about confusion, misinformation and compound many of the things we already struggle with as a human race. The good news is Active Inference AI can benefit these machine models. They can inherit qualities from Active Inference that solve many of their issues. When faced with a new discovery for a potential path to AGI that is programmable, explainable, self-evolving, governable, and works to scale while preserving individual cultural differences and preferences, aligning with human values.; When it operates in a secure manner — overcoming the security issues and obstacles of using LLMs in an unsecured environment like the centralized World Wide Web; When it has the ability to specify and adhere to rules and regulations for various types, stages of intelligence, and governing needs of a wide gamut of Autonomous Intelligent Systems; We have the potential to do things right from the beginning. We have the potential to embark on this crucial next evolution of technology in cooperation between humans and machines for the ultimate good of the future of human civilization. OpenAI, from its inception, has ensured the public that its mission to develop “AGI that benefits all of humanity” would trump all else, including its own for-profit aspirations. Their charter states very clearly that they would set aside competition to join in support of any leading technology that adheres to that mission. VERSES AI is presenting technology and a timeline that fits the criteria they set in their charter. VERSES AI is demonstrating its own intention and integrity to the public welfare by reaching out to the Open AI board today to appeal to this spirit of cooperation within the AI community. In a space of technology that is rife with fear of a corporate race for domination, it’s hard to imagine a more meaningful gesture than that. At the end of the video, “The Year in AI 2023,” released today, narrated by VERSES CEO Gabriel Rene, states, “ ” We have achieved the breakthrough we believe leads to smarter, safer, and more sustainable general intelligence systems. This isn’t just about achieving AGI. It’s about what we can achieve with it. The natural path invites us to realign our relationship with technology, nature, and each other. And it calls on us to come together to imagine a smarter world and then build it. https://www.verses.ai/year-in-ai-2023-video “The Year in AI 2023,” Full video released today — by permission from VERSES AI , and Explore the future of AI with Active Inference and gain insights into this revolutionary approach! Visit my blog at https://deniseholt.us/ Subscribe to my channels on , YouTube , Spotify , Apple , and more. Subscribe to my newsletter on Amazon for more groundbreaking content. LinkedIn All content for Spatial Web AI is independently created by Denise Holt. Also published . here

VERSES AI Announces AGI Breakthrough: Invokes Open AI's 'Assist' Clause

North and South by Elizabeth Cleghorn Gaskell, is part of the HackerNoon Books Series. You can jump to any chapter in this book . CHAPTER XXI. here CHAPTER XXI. THE DARK NIGHT. “On earth is known to none The smile that is not sister to a tear.” Elliott. Margaret and her father walked home. The night was fine, the streets clean, and with her pretty white silk, like Leezie Lindsay’s gown o’ green satin, in the ballad, “kilted up to her knee,” she was off with her father—ready to dance along with the excitement of the cool, fresh night air. “I rather think Thornton is not quite easy in his mind about this strike. He seemed very anxious to-night.” “I should wonder if he were not. But he spoke with his usual coolness to the others, when they suggested different things, just before we came away.” “So he did after dinner as well. It would take a good deal to stir him from his cool manner of speaking; but his face strikes me as anxious.” “I should be, if I were he. He must know of the growing anger and hardly smothered hatred of his workpeople, who all looked upon him as what the Bible calls a ‘hard man,’—not so much unjust as unfeeling; clear in judgment, standing upon his ‘rights’ as no human being ought to stand, considering what we and all our petty rights are in the sight of the Almighty. I am glad you think he looks anxious. When I remember Boucher’s half mad words and ways, I cannot bear to think how coolly Mr. Thornton spoke.” “In the first place, I am not so convinced as you are about that man Boucher’s utter distress; for the moment, he was badly off, I don’t doubt. But there is always a mysterious supply of money from these Unions; and, from what you said, it was evident the man was of a passionate, demonstrative nature, and gave strong expression to all he felt.” “Oh, papa!” “Well! I only want you to do justice to Mr. Thornton, who is, I suspect, of an exactly opposite nature—a man who is far too proud to show his feelings. Just the character I should have thought beforehand, you would have admired, Margaret.” “So I do—so I should; but I don’t feel quite so sure as you do of the existence of those feelings. He is a man of great strength of character—of unusual intellect, considering the few advantages he has had.” “Not so few. He has led a practical life from a very early age, has been called upon to exercise judgment and self-control. All that developes one part of the intellect. To be sure, he needs some of the knowledge of the past, which gives the truest basis for conjecture as to the future; but he knows this need—he perceives it, and that is something. You are quite prejudiced against Mr. Thornton, Margaret.” “He is the first specimen of a manufacturer—of a person engaged in trade—that I had ever the opportunity of studying, papa. He is my first olive: let me make a face while I swallow it. I know he is good of his kind, and by and by I shall like the kind. I rather think I am already beginning to do so. I was very much interested by what the gentlemen were talking about, although I did not understand half of it. I was quite sorry when Miss Thornton came to take me to the other end of the room, saying she was sure I should be uncomfortable at being the only lady among so many gentlemen. I had never thought about it, I was so busy listening; and the ladies were so dull, papa—oh, so dull! Yet I think it was clever too. It reminded me of our old game of having each so many nouns to introduce into a sentence.” “What do you mean, child?” asked Mr. Hale. “Why, they took nouns that were signs of things which gave evidence of wealth,—housekeepers, under-gardeners, extent of glass, valuable lace, diamonds, and all such things; and each one formed her speech so as to bring them all in, in the prettiest accidental manner possible.” “You will be as proud of your one servant when you get her, if all is true about her that Mrs. Thornton says.” “To be sure, I shall. I felt like a great hypocrite to-night, sitting there in my white silk gown, with my idle hands before me, when I remembered all the good, thorough, house-work they had done to-day. They took me for a fine lady, I’m sure.” “Even I was mistaken enough to think you looked like a lady, my dear,” said Mr. Hale, quietly smiling. But smiles were changed to white and trembling looks when they saw Dixon’s face, as she opened the door. “Oh, master!—Oh, Miss Margaret! Thank God you are come! Dr. Donaldson is here. The servant next door went for him, for the charwoman is gone home. She’s better now; but, oh sir! I thought she’d have died an hour ago.” Mr. Hale caught Margaret’s arm to steady himself from falling. He looked at her face, and saw an expression upon it of surprise and extremest sorrow, but not the agony of terror that contracted his own unprepared heart. She knew more than he did, and yet she listened with that hopeless expression of awed apprehension. “Oh! I should not have left her—wicked daughter that I am!” moaned forth Margaret, as she supported her trembling father’s hasty steps upstairs. Dr. Donaldson met them on the landing. “She is better now,” he whispered. “The opiate has taken effect. The spasms were very bad: no wonder they frightened your maid; but she’ll rally this time.” “This time! Let me go to her!” Half an hour ago Mr. Hale was a middle-aged man; now his sight was dim, his senses wavering, his walk tottering, as if he were seventy years of age. Dr. Donaldson took his arm, and led him into the bedroom. Margaret followed close. There lay her mother, with an unmistakeable look on her face. She might be better now; she was sleeping, but Death had signed her for his own, and it was clear that ere long he would return to take possession. Mr. Hale looked at her for some time without a word. Then he began to shake all over, and, turning away from Dr. Donaldson’s anxious care, he groped to find the door; he could not see it, although several candles, brought in the sudden affright, were burning and flaring there. He staggered into the drawing-room, and felt about for a chair. Dr. Donaldson wheeled one to him, and placed him in it. He felt his pulse. “Speak to him, Miss Hale. We must rouse him.” “Papa!” said Margaret, with a crying voice that was wild with pain. “Papa! Speak to me!” The speculation came again into his eyes, and he made a great effort. “Margaret, did you know of this? Oh, it was cruel of you!” “No, sir, it was not cruel!” replied Dr. Donaldson, with quick decision. “Miss Hale acted under my directions. There may have been a mistake, but it was not cruel. Your wife will be a different creature to-morrow, I trust. She has had spasms, as I anticipated, though I did not tell Miss Hale of my apprehensions. She has taken the opiate I brought with me; she will have a good long sleep; and to-morrow that look which has alarmed you so much will have passed away.” “But not the disease?” Dr. Donaldson glanced at Margaret. Her bent head, her face raised with no appeal for a temporary reprieve, showed that quick observer of human nature that she thought it better that the whole truth should be told. “Not the disease. We cannot touch the disease, with all our poor vaunted skill. We can only delay its progress—alleviate the pain it causes. Be a man, sir—a Christian. Have faith in the immortality of the soul, which no pain, no mortal disease, can assail or touch!” But all the reply he got was in the choked words, “You have never been married, Dr. Donaldson; you don’t know what it is,” and in the deep, manly sobs, which went through the stillness of the night like heavy pulses of agony. Margaret knelt by him, caressing him with tearful caresses. No one, not even Dr. Donaldson, knew how the time went by. Mr. Hale was the first to dare to speak of the necessities of the present moment. “What must we do?” asked he. “Tell us both. Margaret is my staff—my right hand.” Dr. Donaldson gave his clear, sensible directions. No fear for to-night—nay, even peace for to-morrow, and for many days yet. But no enduring hope of recovery. He advised Mr. Hale to go to bed, and leave only one to watch the slumber, which he hoped would be undisturbed. He promised to come again early in the morning. And, with a warm and kindly shake of the hand, he left them. They spoke but few words; they were too much exhausted by their terror to do more than decide upon the immediate course of action. Mr. Hale was resolved to sit up through the night, and all that Margaret could do was to prevail upon him to rest on the drawing-room sofa. Dixon stoutly and bluntly refused to go to bed; and, as for Margaret, it was simply impossible that she should leave her mother, let all the doctors in the world speak of “husbanding resources,” and “one watcher only being required.” So, Dixon sat, and stared, and winked, and drooped, and picked herself up again with a jerk, and finally gave up the battle, and fairly snored. Margaret had taken off her gown and tossed it aside with a sort of impatient disgust, and put on her dressing-gown. She felt as if she never could sleep again; as if her whole senses were acutely vital, and all endued with double keenness, for the purpose of watching. Every sight and sound—nay, even every thought, touched some nerve to the very quick. For more than two hours, she heard her father’s restless movements in the next room. He came perpetually to the door of her mother’s chamber, pausing there to listen, till she, not hearing his close unseen presence, went and opened it to tell him how all went on, in reply to the questions his baked lips could hardly form. At last he, too, fell asleep, and all the house was still. Margaret sat behind the curtain thinking. Far away in time, far away in space, seemed all the interests of past days. Not more than thirty-six hours ago, she cared for Bessy Higgins and her father, and her heart was wrung for Boucher; now, that was all like a dreaming memory of some former life;—everything that had passed out of doors seemed dissevered from her mother, and therefore unreal. Even Harley Street appeared more distinct; there she remembered, as if it were yesterday, how she had pleased herself with tracing out her mother’s features in her Aunt Shaw’s face,—and how letters had come, making her dwell on the thoughs of home with all the longing of love. Helstone, itself, was in the dim past. The dull gray days of the preceding winter and spring, so uneventless and monotonous, seemed more associated with what she cared for now above all price. She would fain have caught at the skirts of that departing time, and prayed it to return, and give her back what she had too little valued while it was yet in her possession. What a vain show Life seemed! How unsubstantial, and flickering, and flitting! It was as if from some aërial belfry, high up above the stir and jar of the earth, there was a bell continually tolling, “All are shadows!—all are passing!—all is past!” And when the morning dawned, cool and gray, like many a happier morning before—when Margaret looked one by one at the sleepers, it seemed as if the terrible night were unreal as a dream; it, too, was a shadow. It, too, was past. Mrs. Hale herself was not aware when she awoke, how ill she had been the night before. She was rather surprised at Dr. Donaldson’s early visit, and perplexed by the anxious faces of husband and child. She consented to remain in bed that day, saying she certainly was tired; but, the next, she insisted on getting up; and Dr. Donaldson gave his consent to her returning into the drawing-room. She was restless and uncomfortable in every position, and before night she became very feverish. Mr. Hale was utterly listless, and incapable of deciding on anything. “What can we do to spare mamma such another night?” asked Margaret on the third day. “It is to a certain degree, the reaction after the powerful opiates I have been obliged to use. It is more painful for you to see than for her to bear, I believe. But, I think, if we could get a water-bed it might be a good thing. Not but what she will be better to-morrow; pretty much like herself as she was before this attack. Still, I should like her to have a water-bed. Mrs. Thornton has one, I know. I’ll try and call there this afternoon. Stay,” said he, his eye catching on Margaret’s face, blanched with watching in a sick room, “I’m not sure whether I can go; I’ve a long round to take. It would do you no harm to have a brisk walk to Marlborough Street, and ask Mrs. Thornton if she can spare it.” “Certainly,” said Margaret. “I could go while mamma is asleep this afternoon. I’m sure Mrs. Thornton would lend it to us.” Dr. Donaldson’s experience told him rightly. Mrs. Hale seemed to shake off the consequences of her attack, and looked brighter and better this afternoon than Margaret had ever hoped to see her again. Her daughter left her after dinner, sitting in her easy chair, with her hand lying in her husband’s, who looked more worn and suffering than she by far. Still, he could smile now—rather slowly, rather faintly, it is true; but a day or two before, Margaret never thought to see him smile again. It was about two miles from their house in Crampton Crescent to Marlborough Street. It was too hot to walk very quickly. An August sun beat straight down into the street at three o’clock in the afternoon. Margaret went along, without noticing anything very different from usual in the first mile and a half of her journey; she was absorbed in her own thoughts, and had learnt by this time to thread her way through the irregular stream of human beings that flowed through Milton streets. But, by and by, she was struck with an unusual heaving among the mass of people in the crowded road on which she was entering. They did not appear to be moving on, so much as talking, and listening, and buzzing with excitement without much stirring from the spot where they might happen to be. Still, as they made way for her, and, wrapt up in the purpose of her errand, and the necessities that suggested it, she was less quick of observation than she might have been, if her mind had been at ease. She had got into Marlborough Street before the full conviction forced itself upon her, that there was a restless, oppressive sense of irritation abroad among the people; a thunderous atmosphere, morally as well as physically, around her. From every narrow lane opening out on Marlborough Street came up a low distant roar, as of myriads of fierce indignant voices. The inhabitants of each poor squalid dwelling were gathered round the doors and windows, if indeed they were not actually standing in the middle of the narrow ways—all with looks intent towards one point. Marlborough Street itself was the focus of all those human eyes, that betrayed intensest interest of various kinds; some fierce with anger, some lowering with relentless threats! some dilated with fear, or imploring entreaty; and as Margaret reached the small side-entrance by the folding doors, in the great dead wall of Marlborough mill-yard, and waited the porter’s answer to the bell, she looked round and heard the first long far-off roll of the tempest;—saw the first slow-surging wave of the dark crowd come, with its threatening crest, tumble over, and retreat, at the far end of the street, which a moment ago seemed so full of repressed noise, but which now was ominously still; all these circumstances forced themselves on Margaret’s notice, but did not sink down into her pre-occupied heart. She did not know what they meant—what was their deep significance; while she did know, did feel the keen sharp pressure of the knife that was soon to stab her through and through by leaving her motherless. She was trying to realise that, in order that, when it came, she might be ready to comfort her father. The porter opened the door cautiously, not nearly wide enough to admit her. “It’s you, is it, ma’am?” said he, drawing a long breath, and widening the entrance, but still not opening it fully. Margaret went in. He hastily bolted it behind her. “Th’ folk are all coming up here, I reckon?” asked he. “I don’t know. Something unusual seemed going on; but this street is quite empty, I think.” She went across the yard and put up the steps to the house door. There was no near sound,—no steam engine at work with beat and pant,—no click of machinery, no mingling and clashing of many sharp voices; but far away, the ominous gathering roar, deep-clamouring. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Elizabeth Cleghorn Gaskell (2003). North and South. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/4276/pg4276-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

THE DARK NIGHT.

Little Brother by Cory Doctorow, is part of the HackerNoon Books Series. You can jump to any chapter in this book . Afterword by Bruce Schneier here Afterword by Bruce Schneier I'm a security technologist. My job is making people secure. I think about security systems and how to break them. Then, how to make them more secure. Computer security systems. Surveillance systems. Airplane security systems and voting machines and RFID chips and everything else. Cory invited me into the last few pages of his book because he wanted me to tell you that security is fun. It's incredibly fun. It's cat and mouse, who can outsmart whom, hunter versus hunted fun. I think it's the most fun job you can possibly have. If you thought it was fun to read about Marcus outsmarting the gait-recognition cameras with rocks in his shoes, think of how much more fun it would be if you were the first person in the world to think of that. Working in security means knowing a lot about technology. It might mean knowing about computers and networks, or cameras and how they work, or the chemistry of bomb detection. But really, security is a mindset. It's a way of thinking. Marcus is a great example of that way of thinking. He's always looking for ways a security system fails. I'll bet he couldn't walk into a store without figuring out a way to shoplift. Not that he'd do it -- there's a difference between knowing how to defeat a security system and actually defeating it -- but he'd know he could. It's how security people think. We're constantly looking at security systems and how to get around them; we can't help it. This kind of thinking is important no matter what side of security you're on. If you've been hired to build a shoplift-proof store, you'd better know how to shoplift. If you're designing a camera system that detects individual gaits, you'd better plan for people putting rocks in their shoes. Because if you don't, you're not going to design anything good. So when you're wandering through your day, take a moment to look at the security systems around you. Look at the cameras in the stores you shop at. (Do they prevent crime, or just move it next door?) See how a restaurant operates. (If you pay after you eat, why don't more people just leave without paying?) Pay attention at airport security. (How could you get a weapon onto an airplane?) Watch what the teller does at a bank. (Bank security is designed to prevent tellers from stealing just as much as it is to prevent you from stealing.) Stare at an anthill. (Insects are all about security.) Read the Constitution, and notice all the ways it provides people with security against government. Look at traffic lights and door locks and all the security systems on television and in the movies. Figure out how they work, what threats they protect against and what threats they don't, how they fail, and how they can be exploited. Spend enough time doing this, and you'll find yourself thinking differently about the world. You'll start noticing that many of the security systems out there don't actually do what they claim to, and that much of our national security is a waste of money. You'll understand privacy as essential to security, not in opposition. You'll stop worrying about things other people worry about, and start worrying about things other people don't even think about. Sometimes you'll notice something about security that no one has ever thought about before. And maybe you'll figure out a new way to break a security system. It was only a few years ago that someone invented phishing. I'm frequently amazed how easy it is to break some pretty big-name security systems. There are a lot of reasons for this, but the big one is that it's impossible to prove that something is secure. All you can do is try to break it -- if you fail, you know that it's secure enough to keep out, but what about someone who's smarter than you? Anyone can design a security system so strong he himself can't break it. you Think about that for a second, because it's not obvious. No one is qualified to analyze their own security designs, because the designer and the analyzer will be the same person, with the same limits. Someone else has to analyze the security, because it has to be secure against things the designers didn't think of. This means that all of us have to analyze the security that other people design. And surprisingly often, one of us breaks it. Marcus's exploits aren't far-fetched; that kind of thing happens all the time. Go onto the net and look up "bump key" or "Bic pen Kryptonite lock"; you'll find a couple of really interesting stories about seemingly strong security defeated by pretty basic technology. And when that happens, be sure to publish it on the Internet somewhere. Secrecy and security aren't the same, even though it may seem that way. Only bad security relies on secrecy; good security works even if all the details of it are public. And publishing vulnerabilities forces security designers to design better security, and makes us all better consumers of security. If you buy a Kryptonite bike lock and it can be defeated with a Bic pen, you're not getting very good security for your money. And, likewise, if a bunch of smart kids can defeat the DHS's antiterrorist technologies, then it's not going to do a very good job against real terrorists. Trading privacy for security is stupid enough; not getting any actual security in the bargain is even stupider. So close the book and go. The world is full of security systems. Hack one of them. Bruce Schneier About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Cory Doctorow (2009). Little Brother. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/30142/pg30142-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

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