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, una frase popular de la que se habla, sigue siendo un misterio para muchos profesionales. Como proveedor de servicios de web scraping, decidimos reunir algunas de las preguntas y respuestas comunes de web scraping para ayudar a desentrañar el misterio. Web scraping 1. ¿Qué es el web scraping? El web scraping, también conocido como web harvesting y extracción de datos, se refiere básicamente a la obtención de datos disponibles en la World Wide Web a través del Protocolo de transferencia de hipertexto (HTTP) o mediante navegadores web. Leer más: Web Scraping: Cómo Comenzó y Qué Sucederá en El Futuro 2. ¿El Web Scraping Es Legal en Algunos Países? El web scraping en sí no es ilegal, ya que es solo una herramienta para recopilar datos más fácilmente. Sin embargo, hacerlo puede violar la ley cuando robas información no pública, o el sitio de destino prohíbe estrictamente el scraping sin permiso previo o sin mencionar ciertos derechos de autor legales relacionados con el uso de sus datos. Se recomienda encarecidamente que lea detenidamente los Términos y condiciones (ToS) del sitio web antes de scraping. Leer más: ¿El Web Scraping Es Legal en Algunos Países? 3. ¿Cuál es la mejor herramienta de web scraping? La elección de una herramienta de scraping depende de la naturaleza del sitio web y su complejidad. Siempre que la herramienta pueda ayudarlo a obtener los datos de manera rápida y sin problemas con un costo aceptable o cero, puede elegir la herramienta que desee. Leer más: Las mejores Herramientas de Data Scraping para 2020 4. ¿Puedo scrape LinkedIn o Facebook? Desafortunadamente, ambos sitios web a través de robots.txt bloquean el web crawling automatizado.Las disputas legales entre LinkedIn y las empresas que han robado datos han sido un tema candente. Sin embargo, si solo obtiene datos públicos y listas de dos sitios web, es legal extraer datos de ambos sitios web. ; Leer más: Scrape la publicación de LinkedIn 5 Cosas que Debes Saber Antes de Scraping de Facebook 5. What is web scraping used for? Web scraping is aimed at collecting data so it can be applied in any industry that needs the data. It is used largely in market research, , human capital optimization, , and many other fields. price monitoring lead generation Read more: Data Insight: 54 Industries Using Web Scraping 6. ¿Puedo extraer datos de toda la web? Muchas personas creen que el web scraping se puede utilizar para scrape datos de toda la World Wide Web o al menos cientos de miles de sitios web. Esto no es factible en la práctica. Dado que los sitios web no siguen una estructura de página universal, sería difícil para un web scraper interactuar con todas las páginas. 7. ¿Web scraping es data mining ? El web scraping y data mining son dos conceptos diferentes. El web scraping consiste en recopilar datos sin procesar, pero data mining es el proceso de descubrir patrones en grandes conjuntos de datos. Leer más: Data Mining (Wiki) Data Mining Explicada con 10 Historias Interesantes 8. ¿Cómo evitar ser bloqueado al scraping un sitio web? Muchos sitios web lo bloquearían si los scrape demasiado. Para evitar ser denegado, debe hacer que el proceso de scraping se parezca más a un humano que navega por un sitio web. Por ejemplo, agregar un retraso entre dos solicitudes, usar proxies o aplicar diferentes patrones de scraping puede ayudarlo a no ser bloqueado. Leer más: ¿Cómo Scrape Websites sin ser bloqueado? 9. ¿Se puede resolver CAPTCHA durante el web scraping? CAPTCHA solía ser una pesadilla para el web scraping, pero ahora se puede resolver fácilmente. Muchas herramientas de web scraping tienen la característica de resolver CAPTCHA automáticamente durante el proceso de extracción. Y hay muchos solucionadores CAPTCHA que pueden integrarse con sistemas de scraping. : Leer más 5 Cosas que Debes Saber al Evitar CAPTCHA para El Web Scraping 10. ¿Puedo volver a publicar el contenido extraído a través del web crawling? La republicación de contenido debe contar con el consentimiento del propietario. Aunque puede scrape el contenido de texto de sitios web que permiten bots, aún necesita usar estos datos de una manera que no infrinja los derechos de autor del editor. 11. ¿Cuál es la diferencia entre el web scraping y el web crawling ? El Web scraping y el web crawling son dos conceptos relacionados. El web scraping como mencionamos anteriormente es un proceso de obtención de datos de sitios web; el web crawling es navegar sistemáticamente por la World Wide Web, generalmente con el fin de indexar la web. Leer Más: Data crawler 12. ¿Qué es un robots.txt file? Robots.txt es un archivo de texto que le dice a los crawlers, bots o spiders si un website podría scrape. Es fundamental comprender el archivo robots.txt para evitar que se bloquee durante el web scraping. 13. ¿Puedo scrape datos detrás de una página de inicio de sesión? Sí, puede scrape los datos detrás de una página de inicio de sesión fácilmente si tiene una cuenta funcional en el sitio web. El proceso de scraping después del inicio de sesión sería similar al de un scraping normal. Leer más: Extraer datos detrás de un inicio de sesión 14. ¿Cómo extraigo el contenido de las páginas web dinámicas? Un sitio web dinámico actualizaría los datos con frecuencia. Por ejemplo, siempre hay nuevas publicaciones en Twitter. Para scrape desde dicho sitio web, es el mismo proceso que scrape otros sitios web, pero permitiría que el scraper acceda al sitio web con cierta frecuencia para obtener los datos actualizados continuamente. Leer más: Ejecutar/Programar tareas en Cloud 15. ¿Puede una herramienta de web scraping descargar archivos de un sitio web directamente? Sí, hay muchas herramientas de scraping que pueden descargar archivos directamente del sitio web y guardarlos en Dropbox u otros servidores al scraping información de texto.

Las 15 preguntas más frecuentes sobre Web Scraping

Content Overview A space odyssey Demographic targeting isn’t new Targeting the old way The future of AI in political campaigns and its potential impact How is my local politician using AI to target me? AI’s Theory of Mind HAL’s big brother (Artificial General Intelligence) Should I be scared? A space odyssey The sentient Artificial Intelligence (AI) known as HAL-9000 (Heuristically programmed ALgorithmic computer) in the popular 1969 science fiction book is a chilling story of how a computer, programmed with a simple directive, can provide companionship, guidance, and assistance, not unlike your present-day ‘Hey Google’. 2001: A Space Odyssey In the book, the groundbreaking technology denoted as HAL is capable of natural language processing, facial recognition, and speech synthesis. These are purposeful reasons why AI currently exists, be it for improving healthcare through personalized treatments, supporting natural disaster responses using satellite imagery, or simply as a natural language chatbot to answer candidate questions for a political campaign. All good things. Fans of the book will remember that the AI takes a turn for the worse, challenging the human’s motives to protect its existence. The story continues to show how the relationship between its creators and AI can quickly spiral into despair and desperation after a conflict occurs between humans and technology. The book exposes the ethical implications of creating AI systems that appear self-aware and have autonomy, ultimately leading to humanity’s undoing, a popular narrative amongst many science fiction . AI writers Fast forward 50 years to the present day, and HAL makes Siri look like a chimpanzee and Google Assistant like a glorified ) so for now, we’re seemingly safe, you have nothing to worry about, and the world continues to spin without an ominous red light flashing back at us. Dr. Sbastio, (throwback to anyone from the late ’80s Demographic targeting isn’t new This isn’t a book review on Arthur C. Clarke’s remarkably accurate prediction on the , but a post about how AI may be targeting you and your family right now, influencing your decisions based on your political stance, age, ethnicity, and sexual orientation. Demographic targeting isn’t new; before the internet, exit polls, cold calling & paper-based surveys were used to profile communities. They would subsequently be sent direct mailings and cold-called, persuaded to vote for their candidate. Some of these techniques are still used today, although sparingly, given their high degree of variance and inaccuracy. future of AI This is where AI demographic targeting can significantly enhance accuracy. Using machine learning algorithms to analyze large volumes of user data, AI can find patterns where humans have failed. Generally speaking, the more data the model is trained on, the more accurate the predictions may be. What can we do now, that we couldn’t do before All your posts, LinkedIn articles, AMAs, and even that rant you sent last week can be harvested by these AI models. While this may seem terrifying, it depends on how the data is regulated. On the one hand, AI can do things that were not possible before, that is, enhance voter engagement by providing personalized information, as well as help in resource allocation by providing campaigners optimization techniques on which voter groups are more likely to be swayed, thus allowing them to prioritize groups differently. On the other, AI can also be used nefariously to indoctrinate young minds, take advantage of short attention spans, and . How all these ethical and social issues are handled is still undecided, as most countries do not have regulations in place. Facebook Reddit Twitter deep fake opponents’ images to sway decisions Technological revolution Artificial intelligence is transformative; it will undoubtedly change how we work and interact with technology on a monumental scale. This is where we are so far: Perhaps the iPhone launch wasn’t as revolutionary as the discovery of fire, but you get the picture. Targeting the old way Most political campaigns today take advantage of retargeting using cookies or what is known as a pixel on your machine. They follow you around the web like a leach targeting you with ads for products you already own. This is not Artificial Intelligence; this is just a dumb cookie. Ultimately this cookie tracks and retargets advertisements based on your browsing history. Typically this was done in the 2016 & as ad spending shifted from traditional forms such as TV and radio to more cost-effective digital media. Outside the US, Hong Kong also increased online ad spending to f s just to promote one policy speech. 2020 United States Primaries our times what it did in the previous year Was all this money well spent? Does targeting individuals this way reach their intended group, or does a healthy majority of the ad spend fall into an abyss of bots, fake accounts, and an unintended audience? Research done by the University of Baltimore suggests that at minimum 20% of ad spending goes to bots, ineligible voters, and other attackers using techniques such as . All of these could potentially be solved with AI trained on the right data, although, as I will discuss further, AI is not a panacea. IP masquerading Dumb cookies & AI’s new consent bar It is interesting to note that there are policies against placing trackers and cookies on users’ devices unless consent is given. You’re certainly aware of that pestering bar that keeps popping up all over the web, the one that you often fail to read before hastily whacking “Accept.” You can thank the Europeans for that. Although perhaps regulation is precisely what AI requires, the European Union may be exactly what we need. Their tight regulatory framework could force the rest of the world to mandate an that controls the ethical use and consent for how AI models ingest our data and disseminate information by way of , thus increasing trust and verifiability, more on this later. AI policy Explainable AI Consider this consent bar: If you hadn’t already noticed, that was an attempt to poke fun at the potential regulations that may be required for AI safety, as almost none currently exists. Expect a facsimile of the above to appear at websites near you; brownie points if you spotted the Borg reference. How did we get here? Political parties used to win races with community outreach, door-to-door canvassing, and engagement with local businesses, it took grit to get your message out, and the people these parties represent saw right through the party leaders’ B.S and, most importantly, made up their own minds with data they collected, the campaign message was human-led, with text generated from the candidate’s mouth. The future of AI in political campaigns and its potential impact The future of AI in political campaigns might be one we should be wary of in its current unregulated state. Instead of winning with good traditional economic policy and honest campaigning, the party with the most computing power combined with the biggest ad budget will win the race. In a very recent case, the Republican National Committee (RNC, a major part of the US republican party) used an criticizing Joe Biden in a doomsday-like dystopia video with fake AI-generated images. It was reported that members of the public believed the . AI-generated video videos were real Three years ago, AI was not really being utilised in election campaigns…You don’t have to be a software designer or a video editing expert to create very realistic-looking videos…We’ve gone beyond Photoshopping small parts of an image to basically generating a completely new image out of thin air. — Darrell West, Center for Technology Innovation at the Brookings Institution Going back to answer the question, was AI successful in reaching its demographics? Well, in the RNC case, yes, a lot of attention was drawn to that doomsday video in which a lot of voters, including democrats, believed the video was real, was it ethical? Probably not, exacerbating existing biases and reinforcing stereotypes has negative consequences for fairness and equality in the political process. Was it legal? Well, that is up to interpretation. According to the FCC, , it would be ‘unconstitutional’ otherwise. you’re pretty much allowed to lie How is my local politician using AI to target me? Politicians have been utilizing technology to market their platforms for years. From Obama’s 2008 ” ” victory to Trump’s hourly tweets to the Tories (UK conservative party) 2015 , there has been a massive move toward online demographic targeting, although misappropriating millions to line Mark Zuckerberg’s pocket may not seem like a scalable plan. email election Facebook campaign The future of AI in politics is predicting which group of people will likely sway to your party and what type of content will coax them. This is already being done and will be a significant timesaver for future Democratic elections. Another way AI will help is the ability to quickly find donors at a fraction of what it would typically cost. This can be achieved by using regression techniques, a type of supervised learning that trains on existing donor data. Your local politician will know now if you’re worth their time inviting you to that fancy fundraiser, the likelihood of your donation, and the approximate amount. This doesn’t sound too good, what is the future of AI? AI progress is inevitable and will continue to be used as a force for good as long as research emerges from the field of . Limiting the amount of identifiable data amassed from users’ machines is one regulatory hurdle. As you read this, there is a high chance of telemetry data ( ) being sent from your machine to the cloud. Your data and search queries are crowdsourced, contributing to advancements in natural language processing, deep learning, and large-scale training. Pat yourself on your back, you’re helping humanity, and your data is now part of the collective! AI safety cite Knowledge is power As the saying goes, “Knowledge is power,” thanks to your data, political campaigns are harnessing that power to gain insights into your behavior and preferences. As we have seen, campaigners can now target potential voters with surgical precision, personalize messages with increasing complexity, and have the ability to pinpoint where campaign benefactors live; this is just the tip of the AI iceberg. AI’s Theory of Mind The future of AI is evolving rapidly. In the last 3 years, AI has developed the Theory of Mind, a measurement of cognitive ability and strategic thinking. Scientists use this test to measure if humans or animals have self-consciousness and morality in social interactions. This same test was applied to GPT, and the results were astounding. The last test conducted in Nov 2022 concluded that GPT 3.5 had reached the strategic level of a 9-year-old child. What does this mean for demographic targeting? Well, If the president calls you to say , perhaps it could be AI-Joe Biden. hello HAL’s big brother (Artificial General Intelligence) As AI improves through (RLHF), which is a fancy way of assigning a thumbs up or down to a prompt response, the next iteration of AI could be (AGI). A hypothetical HAL-like agent that theoretically can accomplish any task autonomously without intervention. How this progress to AGI will affect the world is currently unknown, although many, including OpenAI, the company behind the infamous GPT, . Reinforcement Learning with Human Feedback Artificial General Intelligence are urging clear regulation How can this technology be improved? Whether AI is used for demographic targeting, natural language processing for virtual assistants, weather forecasting, or healthcare, there is much improvement to be made. AI is still embryonic, and regulation will need to be in lockstep with the fast pace of iteration. As Satya Nadela puts it, we’re still in the early stages of AI development: We’ve gone from the bicycle to the steam engine with the launch of ChatGPT3 — Satya Nadela, CEO, Microsoft — May 2023 Build Conference Improvements are needed in Long-term safety, a key concern of in its recent build conference. “ ” includes safe testing of user data and monitoring of inconsistencies in NPL to detect violence, self-harm, and hate speech. Microsoft Azure AI Safety Explainable AI Furthermore, trust needs to be reinforced by way of (XAI), which is the process that allows users to trust the output of machine learning algorithms. What ChatGPT believes to be true may not actually be true, and you, as a consumer, should be able to verify how the result of the algorithm came to be. Deep fake political videos could include fine print as to how the video was generated and what data is fact or fiction. A lot needs to be done to create a more transparent and reliable AI system that is safe for the general public to use. Explainable Artificial Intelligence Should I be scared? You need not be scared but educated and vigilant, look out for red flags, read the fine print, and know how the AI results are generated. Protect minors and teach them about the pitfalls and inaccuracies of such technologies. Know that propaganda can come in many forms, including something that may look real, feel real but entirely AI-generated. The last of human-led elections has begun, and you will be targeted. First, they ignore you, then they laugh at you, then they fight you, then you win. – Anonymous, but it’s highly possible it was written by HAL-9000. Originally posted . here

How AI Will Impact Political Campaigns in the Near Future—It All Started With Hal-9000

কীভাবে এআই অদূর ভবিষ্যতে রাজনৈতিক প্রচারাভিযানকে প্রভাবিত করবে—এটি Hal-9000 দিয়ে শুরু হয়েছিল

Wie sich KI in naher Zukunft auf politische Kampagnen auswirken wird – alles begann mit Hal-9000

Cómo afectará la IA a las campañas políticas en un futuro próximo: todo comenzó con Hal-9000

Comment l’IA aura un impact sur les campagnes politiques dans un avenir proche : tout a commencé avec Hal-9000

एआई निकट भविष्य में राजनीतिक अभियानों को कैसे प्रभावित करेगा—यह सब एचएएल-9000 से शुरू हुआ

AI は近い将来の政治キャンペーンにどのような影響を与えるか—すべては Hal-9000 から始まった

AI가 가까운 미래에 정치 캠페인에 어떤 영향을 미칠 것인가? 모든 것은 Hal-9000에서 시작되었습니다

Como a IA impactará as campanhas políticas no futuro próximo – tudo começou com o Hal-9000

Как ИИ повлияет на политические кампании в ближайшем будущем: все началось с Hal-9000

Yapay Zeka Yakın Gelecekte Siyasi Kampanyaları Nasıl Etkileyecek? Her Şey Hal-9000 ile Başladı

AI sẽ tác động đến các chiến dịch chính trị như thế nào trong tương lai gần—Tất cả đều bắt đầu với Hal-9000

人工智能将如何在不久的将来影响政治竞选——一切都从 Hal-9000 开始

He Began to Twist and Turn, as Though Torn by Some Invisible Force

Comenzó a girar y girar, como si lo desgarrara una fuerza invisible

Il a commencé à se tordre et à tourner, comme s'il était déchiré par une force invisible

वह मुड़ना और मुड़ना शुरू कर दिया, जैसे कि किसी अदृश्य बल द्वारा फाड़ा गया हो

目に見えない力に引き裂かれたように、彼はねじれて回転し始めました

Ele começou a torcer e virar, como se fosse rasgado por alguma força invisível

Anh ta bắt đầu vặn và xoay, mặc dù bị xé nát bởi một lực lượng vô hình nào đó

他开始扭曲和转动，好像被某种无形的力量撕裂了

You may have seen some suggestions that cyber security started with the first antivirus, and a date given of 1987 as the first year of commercial antivirus. Sorry to say that it’s wrong (at least I find it wrong). If we try again, then we could say 1971 when the first worm (Creeper) was released, along with arguably the first antivirus in the single-purpose Reaper in 1972. While Reaper was designed to remove copies of Creeper, it did move and replicate through the ARPANET, so arguably was another worm itself. While I can see the argument for Creeper and Reaper as a start date, I can’t bring myself to agree. Either we point at arbitrary events to describe when cyber security started, which could take us back to 1966 (the time-sharing mainframe CTSS suffered a password breach as the welcome message got switched with the master password file), 1971 (the launch of the ARPANET), or even 2003 when the US government established the National Cyber Security Division. The Beginning of Cybersecurity Cyber security began between 1970 and 1972 with the publications of the Ware and Anderson reports. I will explain below how I came to this conclusion and the important historical points you need to be aware of for this answer to make sense. To get to the start, we need to decide what cyber security really means. I go with the definition that it’s the discipline of security applied to the cyber domain. The cyber domain is ‘a global domain within the information environment consisting of the interdependent networks of information, technology infrastructures and resident data, including the internet, telecommunications networks, computer systems, and embedded processors and controllers’. ( ) NIST Security on the other hand is protection of assets from threats. Assets can be anything, including people, information, or physical systems. Threats (when we’re dealing with security) can ultimately always be traced back to a person. If Cyber Security Didn’t Start in 1987, Then When? I tend to stick to a few different dates. 1969 was when the first two nodes of the ARPANET came online, even if it wasn’t declared operational until 1971. Just because something exists, though, doesn’t mean that a formal security discipline exists around it. Really we need to look at when people started formally considering approaches to security in the cyber domain. That leaves us with a couple of reports which were the first to formally consider the aspects of security. The Ware Report The first of these is the creatively named ‘ ’ by Willis H. Ware (and others), published in 1970. Published here is a generous word, as originally the report was classified as Confidential until DARPA declassified it in 1975. The Ware report set out a number of different controls which would be recognised as fundamental by any cyber security professional today. Even where some terminology has changed over time, concepts are familiar to anyone who has studied or practised cyber security. Security Controls for Computer Systems: Report of Defense Science Board Task Force on Computer Security The Ware report set out a number of what it calls vulnerabilities, although nowadays we would refer to them as threats since they are external and uncontrolled rather than weaknesses within a system. - what we now might call a negligence threat, the result of failure of some component or control whether hardware, software, or configuration by an operator Accidental Disclosure - a malicious threat, although the report then makes a distinction between a passive approach (such as wire tapping, covered under Passive Subversion) and an active approach referred to as Active Infiltration which includes compromise or elevation of privilege by a legitimate user Deliberate Penetration - mentioned by the report, but specifically called out as outside the scope Physical Attack What’s fascinating about the Ware report, and the next ones we’ll be talking about, is how modern many of the approaches taken in them are. As an example, (or modelling) is often considered to have started in 2004 when Frank Swiderski and Window Snyder wrote the ** **on it. There are some earlier references, but most consider it to have begun at the earliest in the 90s. Given that (good) threat modelling is now seen as essential to designing secure systems (though far too often overlooked, leaving us in the insecure world we inhabit), we’d expect to see it popping up fairly early on in the timeline. threat modeling first book Image taken from the Ware Report, showing something anyone aware of threat modelling should find very familiar. The diagram above should be recognisable to anyone as at least a basic threat model, showing different attacks and vulnerabilities of a system. Further in the report we have recommendations for controls, what we might now recognise as architectural principles for secure system development, consideration given to risk management and costs, and far more. Some terminology has changed over the years, and the report itself considers multi-use systems (i.e. what we’d now consider mainframes and terminals) rather than the true peer to peer networking that would come along shortly, but even a brief reading shows the foundational thinking that underlies even the most modern cyber security approaches. The Anderson Report So we’ve got an argument for 1970 as the date cyber security started. The only problem is that while there’s mention of networks, in 1970 the ARPANET wasn’t really in an operational state so the definition of network was more about a multi-user mainframe with remote terminals. Lets step a little further into the future-past, with the publication of the equally creatively named ‘Computer Security Technology Planning Study’ and . Volume I Volume II https://www.youtube.com/watch?v=4djue-tOaFY?embedable=true James P. Anderson also worked on the Ware report previously, and his two-volume follow up drew heavily from and expanded upon it. Many of the recommendations he made for designing and implementing secure systems have, frankly, been ignored over the decades to the detriment of the cyber security ecosystem, and some are being rediscovered and renamed. To take an example, if you’ve heard of zero-trust network architecture, you can find the core concepts that lead to the approach in volume 2 of the report. More importantly for those of us trying to define a conception date for cyber security, the Anderson Report talks a lot more about the ‘movement toward the establishment of large dispersed networks of related computer systems’. This gives us a more modern perspective of networks being key to cyber security. So When Did Cyber Security Start? We’ve got our answer. Admittedly, it might cover a range, but there’s a stronger argument for it than there is for saying it began with the first software tool of a particular type. Cyber security is about a methodical, considered approach to security within the cyber domain. Since the cyber domain didn’t exist (in any practical way) before the 1970s, we’re not going to need to look back further, but the pioneers of the field were very much active and thinking about the problems we still struggle with today back in that time. My answer - cyber began in the years between 1970 and 1972 with the publications of the Ware and Anderson reports. And if you read those reports carefully, it’s changed surprisingly little since then.

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