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In such a rapidly evolving industry as tech, where disruptions belong to everyday life, the ability to manage these occurrences is central to maintaining the continuity of operations, and the practice of doing so is widely known as crisis management. In the context of the digital domain, the thing that comes to the fore when an accident arrives is how to save sensitive information from hackers or raise servers from the dead, e.g., technical aspects of the matter. What eludes one’s attention in this rush for a quick patch is how these occurrences affect the people responsible for cleaning this mess, while the consequences run from mere distress caused by the emergency of immediate action to a nervous breakdown. Unless a CEO means to blow the entire company budget on replacing burnt-out staffers every now and then, it is paramount to take precautionary care not only of the software but also of the people who stand at the front of its protection from accidents. This type of crisis management that gives precedence to the staff is defined as human-centered, and those who develop this approach know perfectly well that better preparation of your people for troubles (both in mental and practical terms) increases the probability that their actions will actually help to override the issue, not exacerbate the situation. Leaning back on the principles of this methodology, I suggest the following steps for taking precautionary care of your subordinates to foster their robustness throughout all three stages of a crisis. Stage 1. Preparing for the Worst Though any business can find itself affected by situations that may lead to a crisis, the types of incidents vary greatly from one company to another, even within the digital realm. Consequently, the first step toward a resilient team is to identify what problems the technology you rely upon may open the door to. Spare no effort on blindspot analysis; once done, it will save you money and nerve cells. Next, when potential breaches are identified, it is time to prepare a crisis management plan—a plan of actions your team is expected to take when the disaster steps over your threshold. The more detailed your catastrophe road map is charted, the greater the chances that the issue being tackled will go smoothly and will take place in a timely manner because your company population will know exactly what to do. The major danger that awaits you at this point is to put this plan on the most remote shelf and forget about it until the need arises. Make sure that everyone on your team is acquainted with its contents and can enact them which necessitates fire drills. Simulations of the emergency will highlight weaknesses in the plan and the system in general and showcase team performance which will facilitate your understanding of the business’ preparedness for such occasions. The gaps the drill exposed should be documented and then discussed with the team to show its members how to deal with them. While malfunction simulations will help you spot weaknesses in the system, you are still running the risk of countermeasures going pear-shaped if you are unaware of breaches in the psychological immunity of those who are supposed to face them first. Thus, the next step to take if you seek to improve the robustness of your team is to understand what is capable of breaking them down. Have a talk with each of the team members and identify what stresses them out. Some people easily endure the pressure at the moment but melt down under the continuous torrent of small bugs, or they just won’t be able to deal with the storm of scolding critique from infuriated clients. I bet that one of the major stressors to name will be a fear of making a mistake. Under mentally taxing circumstances, this fear may paralyze the individual’s will to take an action that is dictated by the situation but not outlined in the plan, which may ruin the entire operation. This psychological safety is the bedrock of resilience. That is why it is critically important to show empathy to your subordinates and foster a working environment where an error is a source of knowledge, not a reason for reprimand. These talks will help you get an idea of whom you can place on the disaster recovery team—a dedicated group of people who will shoulder addressing the matter in progress—and whom you can put at the helm as a disaster recovery (DR) leader. Stage 2. In the Midst of a Disaster. When chaos unfolds, it is important to identify whether it is fully digital in character or whether it may do physical damage to your staff. The recent pandemic and a string of armed conflicts erupting across the world reminded us that death danger can transcend the boundaries of imagined probability, so the protection of your employees against such emergencies is an item to include in the plan. Naturally, physical safety is one of our basic necessities, and ensuring it at the workplace is the employer’s duty, so double-check that your people are protected from injury. Furthermore, there is nothing more destabilizing in the moment of danger than unclarity and a sense of lostness caused by a lack of communication and information in general. Thus, keeping the involved up-to-date and providing overall transparency provides them with the level of certainty that forms the solid foundation for a reassured action. What goes hand in hand with informational support is the demonstration that everything goes according to plan: stay calm and collected, and set the example of sangfroid. Panic is contagious, so nipping it in the bud will reduce the stressfulness of the situation. As a continuation of your effort to bolster the mental robustness of your team at the moment, demonstrate to your employees that they are not abandoned to their own devices. There should be a leader who coordinates their actions and points them in the right direction, unfolding a safety net of prompts for occasions when a gap in the protocol calls for a senior manager’s instructions. The right step taken at the right moment will solidify the employee’s confidence in their own abilities to handle a challenge and, as an entailment, boost their mental resistance to tech mishaps. Stage 3. Crisis Aftermath When a crisis is over, the serenity of its aftermath tempts the possibility of consigning it to oblivion. Yet, even though the storm subsided, it is not the time to sit back and relax. Just like a fire drill demands documentation of its results and progress, the real disaster requires a detailed breakdown of the actions and behaviors of the entire team. Moreover, while a simulation may prompt you on the blindspots in the crisis management plan, your employees still know that it is a simulation, so their behavior under real circumstances may digress drastically from what is expected because of the higher level of stress. Consequently, documenting your team’s moves throughout the accident will highlight a) who proved to be more resilient to the disaster and why; and b) which mitigation tactics proved to be effective when enacted by real people on the ground. Keeping track of mistakes helps to adjust the CMP and choose the people who are more fit for a place on the crisis team. A good idea is to arrange a meeting where employees could share their experiences of being in the eye of chaos, what helped them to override it, and what, all the way around, impeded faster resolution. After hitting the hot iron of recording actions during the crisis, fostering resilience in your team demands tackling the imprint the accident might have left on the minds of those who bore the brunt. A corporate psychologist will perfectly do this job for you, or you can at least subsidize a visit or two to a shrink on the side. An honest, one-on-one talk with each participant where you will praise them for apt steps and subtly mention the points for improvement also has the power of alleviating the pressure, helping them slough off the remorse for missteps, learn their lessons, and move on. Wrapping Up However, challenging the laser focus on people at a tech-driven company may be, if you seek to steadily come on top of any hurdle impeding your way, paying attention to the well-being of your employees is imperative. Nurturing resilient teams in hi-tech environments requires mitigation of issues’ effects on people's physical and mental health and calls for creating a space where they feel supported and comfortable sharing their experiences, equipping themselves with hands-on knowledge on handling crises, and adapting to changing situations. In such a rapidly evolving industry as tech, where disruptions belong to everyday life, the ability to manage these occurrences is central to maintaining the continuity of operations, and the practice of doing so is widely known as crisis management. In the context of the digital domain, the thing that comes to the fore when an accident arrives is how to save sensitive information from hackers or raise servers from the dead, e.g., technical aspects of the matter. What eludes one’s attention in this rush for a quick patch is how these occurrences affect the people responsible for cleaning this mess, while the consequences run from mere distress caused by the emergency of immediate action to a nervous breakdown. Unless a CEO means to blow the entire company budget on replacing burnt-out staffers every now and then, it is paramount to take precautionary care not only of the software but also of the people who stand at the front of its protection from accidents. This type of crisis management that gives precedence to the staff is defined as human-centered, and those who develop this approach know perfectly well that better preparation of your people for troubles (both in mental and practical terms) increases the probability that their actions will actually help to override the issue, not exacerbate the situation. Leaning back on the principles of this methodology, I suggest the following steps for taking precautionary care of your subordinates to foster their robustness throughout all three stages of a crisis. Stage 1. Preparing for the Worst Stage 1. Preparing for the Worst Though any business can find itself affected by situations that may lead to a crisis, the types of incidents vary greatly from one company to another, even within the digital realm. Consequently, the first step toward a resilient team is to identify what problems the technology you rely upon may open the door to. Spare no effort on blindspot analysis; once done, it will save you money and nerve cells. Next, when potential breaches are identified, it is time to prepare a crisis management plan—a plan of actions your team is expected to take when the disaster steps over your threshold. The more detailed your catastrophe road map is charted, the greater the chances that the issue being tackled will go smoothly and will take place in a timely manner because your company population will know exactly what to do. The major danger that awaits you at this point is to put this plan on the most remote shelf and forget about it until the need arises. Make sure that everyone on your team is acquainted with its contents and can enact them which necessitates fire drills. Simulations of the emergency will highlight weaknesses in the plan and the system in general and showcase team performance which will facilitate your understanding of the business’ preparedness for such occasions. The gaps the drill exposed should be documented and then discussed with the team to show its members how to deal with them. While malfunction simulations will help you spot weaknesses in the system, you are still running the risk of countermeasures going pear-shaped if you are unaware of breaches in the psychological immunity of those who are supposed to face them first. Thus, the next step to take if you seek to improve the robustness of your team is to understand what is capable of breaking them down. Have a talk with each of the team members and identify what stresses them out. Some people easily endure the pressure at the moment but melt down under the continuous torrent of small bugs, or they just won’t be able to deal with the storm of scolding critique from infuriated clients. I bet that one of the major stressors to name will be a fear of making a mistake. Under mentally taxing circumstances, this fear may paralyze the individual’s will to take an action that is dictated by the situation but not outlined in the plan, which may ruin the entire operation. This psychological safety is the bedrock of resilience. That is why it is critically important to show empathy to your subordinates and foster a working environment where an error is a source of knowledge, not a reason for reprimand. These talks will help you get an idea of whom you can place on the disaster recovery team—a dedicated group of people who will shoulder addressing the matter in progress—and whom you can put at the helm as a disaster recovery (DR) leader. Stage 2. In the Midst of a Disaster. Stage 2. In the Midst of a Disaster. When chaos unfolds, it is important to identify whether it is fully digital in character or whether it may do physical damage to your staff. The recent pandemic and a string of armed conflicts erupting across the world reminded us that death danger can transcend the boundaries of imagined probability, so the protection of your employees against such emergencies is an item to include in the plan. Naturally, physical safety is one of our basic necessities, and ensuring it at the workplace is the employer’s duty, so double-check that your people are protected from injury. Furthermore, there is nothing more destabilizing in the moment of danger than unclarity and a sense of lostness caused by a lack of communication and information in general. Thus, keeping the involved up-to-date and providing overall transparency provides them with the level of certainty that forms the solid foundation for a reassured action. What goes hand in hand with informational support is the demonstration that everything goes according to plan: stay calm and collected, and set the example of sangfroid. Panic is contagious, so nipping it in the bud will reduce the stressfulness of the situation. As a continuation of your effort to bolster the mental robustness of your team at the moment, demonstrate to your employees that they are not abandoned to their own devices. There should be a leader who coordinates their actions and points them in the right direction, unfolding a safety net of prompts for occasions when a gap in the protocol calls for a senior manager’s instructions. The right step taken at the right moment will solidify the employee’s confidence in their own abilities to handle a challenge and, as an entailment, boost their mental resistance to tech mishaps. Stage 3. Crisis Aftermath Stage 3. Crisis Aftermath When a crisis is over, the serenity of its aftermath tempts the possibility of consigning it to oblivion. Yet, even though the storm subsided, it is not the time to sit back and relax. Just like a fire drill demands documentation of its results and progress, the real disaster requires a detailed breakdown of the actions and behaviors of the entire team. Moreover, while a simulation may prompt you on the blindspots in the crisis management plan, your employees still know that it is a simulation, so their behavior under real circumstances may digress drastically from what is expected because of the higher level of stress. Consequently, documenting your team’s moves throughout the accident will highlight a) who proved to be more resilient to the disaster and why; and b) which mitigation tactics proved to be effective when enacted by real people on the ground. Keeping track of mistakes helps to adjust the CMP and choose the people who are more fit for a place on the crisis team. A good idea is to arrange a meeting where employees could share their experiences of being in the eye of chaos, what helped them to override it, and what, all the way around, impeded faster resolution. After hitting the hot iron of recording actions during the crisis, fostering resilience in your team demands tackling the imprint the accident might have left on the minds of those who bore the brunt. A corporate psychologist will perfectly do this job for you, or you can at least subsidize a visit or two to a shrink on the side. An honest, one-on-one talk with each participant where you will praise them for apt steps and subtly mention the points for improvement also has the power of alleviating the pressure, helping them slough off the remorse for missteps, learn their lessons, and move on. Wrapping Up Wrapping Up However, challenging the laser focus on people at a tech-driven company may be, if you seek to steadily come on top of any hurdle impeding your way, paying attention to the well-being of your employees is imperative. Nurturing resilient teams in hi-tech environments requires mitigation of issues’ effects on people's physical and mental health and calls for creating a space where they feel supported and comfortable sharing their experiences, equipping themselves with hands-on knowledge on handling crises, and adapting to changing situations.

Human-centric Crisis Management: Nurturing Resilient Teams in Hi-tech Environments

USA v. Samuel Bankman-Fried Court Filing, retrieved on March 15, 2024 is part of . You can jump to any part in this filing . This part is 9 of 33. HackerNoon’s Legal PDF Series here V. The Defendant Tried to Deflect Blame and Evade Responsibility After FTX Declared Bankruptcy The defendant’s fraud did not end with FTX’s declaration of bankruptcy. Rather, over the next month, and even after he had been arrested, the defendant disclaimed responsibility, blamed others, and tried to perpetuate his fraud further. For instance, Bankman-Fried told The Wall Street Journal on December 6, 2022, that FTX’s collapse was the result of a large “margin position” on FTX that “got hit a few times, and then got hit massively.” He gave a similar explanation to Forbes on December 13, 2022, and at the New York Times Dealbook event, which is cited in the defendant’s submission. (The evidence at trial proved, however, that the defendant’s fraud was unrelated to Alameda’s margin trading.) In an interview on Good Morning America, BankmanFried blamed the deficit in customer funds on FTX’s borrow/lending facility (GX-923B), despite an earlier conversation with FTX’s General Counsel, Can Sun, after FTX’s collapse that this was not an adequate or genuine explanation for the shortfall in FTX customer funds. (Tr. 1960-63, 1966 (“Q: And was the borrow-lend facility a potential justification that you had discussed with the defendant on November 7, 2022? A: Yes. Q: And what had you said to the defendant about that? A: It was not supported by the facts. Q: And what was his response? A: He acknowledged it.”)). In interviews, Bankman-Fried also tried to distance himself from the misconduct by falsely claiming that he was not running Alameda, that he had not been involved at all in Alameda trading for years, and that he was walled off from Alameda trading decisions in 2022. (See, e.g., GX-2503, 2508, 2508-T). (He, of course, ultimately admitted otherwise on the stand in the face of irrefutable trial evidence.) (Tr. 2570-82). He also claimed that he did not “knowingly commingle funds” and that he was unaware of Alameda’s debts to FTX due to an “accounting mistake”—more lies in the aftermath of FTX’s collapse that were disproved at trial. On January 12, 2023, after he was indicted, the defendant posted a blog post on Substack called “FTX Pre-Mortem Overview” in which he claimed that “Alameda failed to sufficiently hedge,” there were losses in November 2022 when “an extreme, quick, targeted crash precipitated by the CEO of Binance made Alameda insolvent,” and “then Alameda’s contagion spread to FTX and other places.” (The evidence at trial proved this was not true: there was already a $14 billion deficit in customer funds before the “crash” in November 2022.) Bankman-Fried also lied about his intent, publicly claiming, “I did not ever try to commit fraud,” but the evidence overwhelmingly showed that he did intentionally take money that belonged to others and that he lied about it. Continue Reading . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on March 15, 2024, from is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. storage.courtlistener

Bankman-Fried's Attempts to Evade Responsibility After FTX Bankruptcy

Over the last couple of days, I have seen many of my marketer colleagues and AI researchers debating over the potential ban and defending the platform blindly. TikTok As someone who not only runs ad campaigns on social media and has attended human-centered computer design courses for years but also spent some time in libertarian organizations defending the free market and individualism over state control, I have a few insights. Let me be unequivocally clear: my advocacy for AI, free from government control, is mirrored in my stance on social media platforms. However, the crux of the current debate focuses on China's proven exploitation of TikTok to surveil Western people, leaders as well as journalists and shape the ideological trajectory of our youth. open-source Without delving too deeply into politics, let me outline with data and research papers why I strongly support the bipartisan bill to ban TikTok, and why you should delete the app now! Before TikTok, social media was social Instagram, Twitter, and Snapchat; these apps focused on interactions between friends, groups, or communities until TikTok brought the dopamine hit and sucked users' attention spans. The "Likes and Fragments: Examining Perceptions of Time Spent on TikTok" research is a proven example of how TikTok users . often misjudge their usage time Findings highlight that users who engaged more intensively with the platform by liking more videos and opening the app more frequently had a higher discrepancy between their perceived and actual usage times. This suggests that the engaging and immersive nature of TikTok, combined with its algorithmically curated "For You" feed, creates an environment where users are likely to misjudge the amount of time they spend on the app. This is important because it leads us to TikTok's algorithm, which heavily relies on casino-like tactics. You have to be over 18 or 21 to gamble, but this app gets the next generations addicted from a young age. So, what exactly are these tactics? The Devious Tactics of TikTok's Algorithm Significant research from the Brown University School of Public Health highlights the addictive nature of TikTok. The design of TikTok uses classical conditioning and reward-based learning processes to encourage continuous use. Features such as the "For You" stream customize content feeds based on user interactions. Moreover, TikTok employs a variable , with the "swipe down" feature to refresh the feed mirroring the action of pulling a slot machine lever. This intermittent reinforcement keeps users engaged, always anticipating the next "rewarding" video. reward pattern similar to slot machines I have explored the algorithm below more. This strategic manipulation exploits classical conditioning and reward-based learning processes, making the platform highly addictive and challenging to resist. Last year, Utah Attorney General Sean Reyes filed a lawsuit against TikTok, showing how the "For You" recommendation algorithm hooks young users. You can read the here for further exploration. lawsuit showing TikTok's casino tactics “TikTok induces that effect by relying on the same strategy casinos use to hook gamblers on slot machines – keeping them at the machine, spending a quarter at a time, hoping that the next pull will be ‘the one,’” These random dopamine releases and other manipulative tactics hook the not-yet-fully-developed brains of our youth to this state-owned media channel. Now, let's discuss why we should not leave the control of our next generation's brain development to communist China. The Cultural Dichotomy A comparison between Douyin (Chinese version of TikTok) and ours after China's National Intelligence took control of ByteDance shows a massive difference. In 2021, China's National Intelligence . They appointed a government official with a golden vote (this person has the final say in the company), and after this change, Douyin's algorithms entirely changed. took over the control of ByteDance While your children are bombarded with 'trash content', Chinese generations are exposed to . While your one and only child spends hours doom scrolling, their children have mandatory time limits. educational content Marketing luminary Scott Galloway gives a : strong reason to avoid TikTok "If I were part of the CCP and recognized a vested interest in undermining America's strategic position globally, I would subtly and cunningly tip the balance of content to cast America in an unfavorable light. I believe this is exactly what's happening at this moment—it would be foolish for them not to engage in such tactics." The Addiction and Its Impacts An average TikTok user spends about on the app and it is projected to exceed 100 minutes this year. The hyper-personalization algorithm leads to addiction-like behaviors. 60 minutes a day It has been proven repeatedly that TikTok's hyper-personalization algorithm leads to problematic use patterns similar to . addiction-like behaviors TikTok encourages isolation and loneliness among young people, and year-over-year depression statistics show that following the widespread adoption of this soft power weapon. depression among youth has significantly increased "Individuals, particularly young people with social anxiety or depression, might be inclined to spend excessive time online, diminishing real-life interactions," explains Anne Marie Albano, PhD, director of the Columbia University Clinic for Anxiety and Related Disorders. "Avoiding active engagement with the world — through social exchanges, confronting challenges, participating in discussions, or navigating peer conflicts — can intensify feelings of isolation, despair, and emotional distress." The findings reveal that using TikTok elicits negative emotions in users, affecting their intention to continue using these platforms. It leads to feelings of wasted time, addiction, and even impacts mental health when usage becomes excessive or disrupts daily life. research proves that Tiktok's leads to consuming more and ultimately, addiction. "Understanding users’ negative emotions and continuous usage intention in short video platforms" negative emotion flow algorithm Weaponizing TikTok TikTok serves as a weapon for the state-sponsored agenda. The research conducted by the Network Contagion Research Institute in collaboration with Rutgers University Miller Center investigates to the Chinese Government. discrepancies in hashtag ratios on sensitive topics For non-sensitive topics, such as general politics and pop culture, the hashtag ratios between Instagram and TikTok approximately followed the user ratios (~2:1). This pattern of anomalies was consistent across national/regional and international issues sensitive to China, suggesting a systematic approach by TikTok to influence content visibility based on its alignment with Chinese Government interests. NCRI's research extends to geopolitically sensitive topics, demonstrating underrepresentation of hashtags , and overrepresentation of those in alignment, particularly regarding international conflicts where China has strategic interests. favoring narratives opposed to Chinese Government interests The findings raise concerns about TikTok's impartiality and the platform's potential use in furthering the national/regional or international objectives of the Chinese Government. The discrepancies in hashtag representation underscore the need for a more comprehensive analysis to understand TikTok's influence on public narratives and its alignment with the Chinese Government's geostrategic objectives. The NCRI's work highlights the importance of transparency and scrutiny in digital platforms' operations, especially when they have a significant impact on public discourse and possess the potential for governmental influence. Indeed, it is a tool utilized for advancing state-sponsored agendas, with the recent attributed to bin Laden being just one of the more notorious examples. "Letter to America" trend There are two primary uses of this instrument: and . extensive political manipulation concerns regarding data security It has been established that to its younger audience. A Wall Street Journal investigation reveals that TikTok's algorithm has displayed footage of explosions, rockets, and distressed families from war zones to a 13-year-old user who has just signed up, highlighting the platform's exposure of young users to such graphic content. TikTok amplifies content related to self-harm, mental health issues, and extremism Furthermore, research by Newsguard indicates that spending as little as 40 minutes on TikTok leads to exposure to , such as Russia's invasion of Ukraine. false content regarding political events The study " examines how user behaviors and settings affect the recommendations made by TikTok's algorithm. An Empirical Investigation of Personalization Factors on TikTok" The researchers used controlled "sock-puppet" accounts to focus on the impact of personalization factors, including language preference, geographical location, user interactions such as likes and follows, and video viewing durations, on the content that TikTok suggests to its users. The with limited control over the content they are shown leads to echo chambers and filter bubbles, where users are increasingly exposed to content that reinforces their existing beliefs and interests, limiting exposure to diverse viewpoints. "black box" nature of TikTok's recommendation algorithm These researches are showing that this app doesn't only make you addicted while diminishing your ability to pay attention, but it also deliberately changes the future of our very own civilization. The other concern pertains to data security. With over on iPhones and Android devices in the USA, there are nearly 112 million distinct data sources, encompassing not only content but also real-time voice streams. 112 million TikTok installations Data Security The by Zhuoran Liu et al. at ByteDance Inc. showcases ByteDance's position in . "Monolith: Real-Time Recommendation System With Collisionless Embedding Table" leveraging complex algorithms This is a system engineered for real-time recommendation. Monolith addresses critical challenges inherent in recommendation systems, such as handling sparse, categorical data and adapting to non-stationary user behavior, through solutions like a collisionless embedding table and dynamic feature eviction mechanisms. Let's clarify this; Traditional recommendation systems often rely on batch training, where data is collected over a period, processed, and then used to update the model's recommendations. This can lead to delays in the model reflecting the latest user interactions. Monolith's system, by contrast, uses online training. This means it learns from user interactions in real-time, allowing for a much more responsive and personalized user experience. The user's interaction with content is immediately fed back into the system, enabling it to adjust its recommendations on the fly. In the context of machine learning, an embedding table transforms sparse, categorical data into dense vectors of fixed size, which the model can more easily work with. A "collision" in this context would mean different input data points being mapped to the same vector representation, which could lead to inaccurate model predictions. A collisionless embedding table suggests a method where these collisions are minimized or eliminated, improving the model's accuracy and efficiency in handling vast amounts of categorical data. The system's architecture utilizes online training, enabling immediate adaptation based on user interactions, thus significantly refining content personalization. However, this state-of-the-art AI personalization raises important discussions about data privacy, as the continuous, involves , often without or clear data usage policies. real-time processing of user data for personalized content delivery real-time extensive data collection and analysis transparent consent mechanisms . TikTok is under scrutiny for the extent of data it gathers to customize content and refine its algorithm. There is concern that this data could potentially be accessed by the authoritarian government and used against us. One might counter-argue that ByteDance recently proposed transferring their . However, accepting this at face value is not only naive but also highlights that ensuring data security is an almost impossible task. data centers to the US by using Oracle servers The Urgent Need for Action China has been exposing its plans to since 2017. rule the world with artificial general intelligence Deleting TikTok from our phones, creating a public narrative against its use, and sharing these findings and research to enlighten our friends are crucial steps. I believe I am doing my part by writing this article to showcase why we need immediate action against this state-controlled app. "Control over the use of one's mind is integral to human autonomy, and denial of such control undermines freedom of thought and freedom of will." To gain deeper insights into the digital competition and its critical implications for the future, I highly recommend tuning in to the session, if you haven't watched it yet. All-In Summit: Bill Gurley 2,851 Miles Here, Gurley delves into discussions about regulations, the future of AI, and the upcoming intense historical crash between China and the USA. I urge you to share this content with your friends who use TikTok and encourage them to take immediate action by deleting this spyware app. Your participation in this conversation is crucial. Feel free to share your thoughts and comments below, as we continue to explore and address this important topic together. Also published here.

TikTok Should Have Been Banned Years Ago, and Here's Why

TikTok বছর আগে নিষিদ্ধ করা উচিত ছিল, এবং এখানে কেন

TikTok hätte schon vor Jahren verboten werden sollen, und hier ist der Grund

TikTok debería haber sido prohibido hace años, y este es el motivo

TikTok aurait dû être interdit il y a des années, et voici pourquoi

टिकटॉक पर वर्षों पहले ही प्रतिबंध लग जाना चाहिए था, और यहां बताया गया है क्यों

TikTokは何年も前に禁止されるべきだった、その理由は次のとおりです

TikTok은 몇 년 전에 금지되었어야 했는데, 그 이유는 다음과 같습니다.

O TikTok deveria ter sido banido há anos e aqui está o porquê

TikTok следовало запретить много лет назад, и вот почему

TikTok Yıllar Önce Yasaklanmalıydı ve İşte Nedeni

Lẽ ra TikTok đã bị cấm từ nhiều năm trước và đây là lý do

TikTok 几年前就应该被禁止，原因如下

This is the 13-year journey that resulted in , a different approach to password management. Here goes articles[0] of articles.length == 2. Neulock Part I It was back in 2011. was still movie material; people communicated using monochromatic doodle faces instead of Pepe the Frog and Shiba Inu doges, and Daenerys Targaryen was the little girl who made the barbaric Khal Drogo find love. Contagion It was then that Google Chrome first made that offer. Do you want Google Chrome to save your password for this site? Save this password? Is that a thing browsers do now? Wow, thanks, Chrome, that’s so convenient! Come to think of it, I type most passwords on a web browser, so this looks like a great place to save them. Besides, what’s the worst that can happen? Chrome is a program installed on my computer. computer! I’m a seasoned Debian Linux power user, and nowhere in the universe is there a safer place than my hard drive! MY Sure, Chrome, go ahead. Save this password, and the next one, and every password I type in your input fields. Deception Until I bought another computer. After ditching the pre-installed Windows for whatever Linux distro I fancied at the time, the next thing to do was install Chrome. Firefox Quantum was still six years away, so there wasn’t much choice. Sign in with your Google account? Sure, why not? Let’s go check Facebook or whatever, and… hey, do you remember my username, Chrome? All right, let’s sign in to Facebook. Wait, why is the password field already filled in with asterisks? No, dear Chrome, this must be a bug, I’ve just bought this computer, your local database is pristine. You’ll realize your mistake once I click the login button. And… I’m in? I went to check Chrome settings immediately. What I saw only increased my horror. All the websites and usernames I had been saving were there, on a list. Plaintext passwords were only a click away. Two expectations had been broken. The first and most important thing is that I never expected Chrome to upload my passwords to Google’s servers without asking for consent. They asked me to my passwords, not to share them! save The second expectation was wishful thinking on my part. Shame on me. Google is one of the coolest tech companies in the world, and its engineers are supposed to be geniuses doing the impossible between ping-pong matches at the Googleplex. I thought Chrome was storing my passwords in some super-secure fashion and only retrieving each password at the right time to inject it like a ninja in the precise input field where it belonged. I didn’t expect to see them all in plaintext. And I wasn’t the only one who was shocked. ”. In a further discussion, Justin Schuh, Chrome’s chief of security, in a rather arrogant manner. Sir Tim Berners-Lee took Kember’s side, calling Chrome’s password manager “ .” Elliott Kember called Chrome’s password security “insane just shrugged it off a way to get your big sister’s passwords After this experience, I never got myself to trust any password manager, even though I knew it was better than confabulating weird passwords in my stupid human head. But, as we say in Brazil, a dog who’s been bitten by a snake fears even sausages. It doesn’t make more sense in Portuguese. A better password manager I spent the next ten years wandering in the desert of password insecurity. I was longing for a better password manager, but all of them seemed to follow the same principle: bundle up all your passwords in a “vault,” encrypt it with your master key and upload this vault to their cloud servers. I could never trust this model. Are they implementing end-to-end encryption, right? Is there a “key recovery” backdoor waiting to be exploited? Or maybe Justin Schuh was right after all: your passwords are “trivially recovered” from any application that stores them, and attempts to make passwords harder to leak are “all just theater.” In this sense, trying to safeguard your passwords any further than trusting a password manager was a novice fool’s errand. In 2019, I landed in the IT Department at the Ministry of Foreign Affairs of Brazil. There, I shadowed the CTO and . I learned a great deal from him and took over after he left. Cybersecurity was always on the agenda since that Ministry produced about 80% of all classified information in the Brazilian Federal government, and its operations were spread over 240 locations in all habitable continents. We were constantly targeted by the world’s scariest acronyms, so we needed to keep our guard up at all times. Over those years, through constant exposure to problems in different domains of cybersecurity, I started to put together principles for a better password manager. tech guru Fabio Cereda Principle #0: all cryptography is broken or eventually will get broken There was a museum of diplomatic communications where I worked. It was full of code books, cast-iron cipher machines, suitcase-portable dish antennas, TELEX adapters, and a few specimens of . It was a showcase of one and a half-century of secret communications between the headquarters and Brazilian embassies and diplomatic missions abroad. All of them relied on some sort of crypto to hold the secrets. All of that crypto is now broken. the infamous Crypto AG C-52 machine If you think about a password manager, the fact that all crypto will eventually get broken isn’t scary. Let’s say it takes, on average, 15 years to break a cryptographic protocol. Just change your passwords every couple of years, and you’ll be fine. A decent password manager should upgrade its crypto every now and then so you’ll always stay ahead of the curve. The scary part is that it’s impossible to know if a protocol has been broken already. Or worse, if it was designed with an intentional, hidden vulnerability, as was the case with the Crypto AG machine. Intelligence agencies and cybercriminal organizations keep their zero-days under the radar for as long as they can. The more I saw last-gen, corporate-grade security systems leaking secret information, sometimes in catastrophically spectacular manners, the less I trusted the vault model employed by all cloud-based password managers. Until very recently, these concerns were merely theoretical. As , fellow hackernooner @hossam26644 wrote There is nothing wrong with using Google’s password manager, Microsoft, Apple, 1password, Bitwarden, or whatever. People have been using them for long, with zero problems till now. He was right on July 25th, 2022. He didn’t trust any of them, though, and he was right again. One year later, there was compelling evidence that and people losing money. Over 150 people, over $35 million in money. the notorious LastPass leaks were resulting in some high-value vaults getting cracked A common misconception is that for an encrypted vault to be cracked, the itself would need to be compromised. LastPass has reminded us how much easier it is to break encryption. AES stands strong, but LastPass made poor implementation decisions: too short master key requirements, too few iterations, and some users were left with vaults that were easily crackable by brute force. AES algorithm Principle #1: logistics are often the weakest security link Government cryptanalysts knew Principle #0 all too well. They are some of the most intelligent people I’ve ever met, and few of them would put money on something protected by cryptography alone. The most wary of them insisted on the use of a for critically secure communications. In fact, they put this requirement in the regulations for the transmission and storage of top-secret documents. One-Time Pad (OTP) In principle, an OTP cypher cannot be broken. It is a simple XOR operation of the plaintext (the data) and a buffer of random values of the same length as the data (the OTP key). By XOR’ing the cipher against the same key again, the plaintext is recovered. Although unbreakable from a cryptanalysis standpoint, the OTP has several inconveniences: Each OTP key can never be reused; An OTP key must be at least as long as the plaintext; The key is symmetrical and must be pre-shared between the sending and the receiving parties. For a worldwide organization, supplying every office with OTP keys is a large ongoing logistical operation. Even worse, it’s the Achilles’ heel of the whole security model! Because, for all of OTP’s crypto-awesomeness to hold true, you need logistics, both in-transit and at-rest. perfect Should Eve intercept your OTP keys while on the road to the destination? All your communications from that point on are compromised. Should Eve physically crack your safe room and steal your OTP key media? All your comms from that point on are compromised, and maybe even your previous ones, if used portions of the key had not been properly erased from the media. If you implement OTP keys as 1 GB chunks of random bytes that get consumed as you send information, it clearly lacks both and backward secrecy. forward In the end, the perfect crypto is a way to shift responsibility from the cybersecurity team to the physical security team. And a similar problem affects offline password managers: they trade-off availability for confidentiality, leaving you with a logistical mess to sort out. As anyone running a local copy of knows, you need to backup your vault. This backup had better be external in case your computer storage dies. You need to keep your backup up-to-date. And make sure nobody has access to it. And don’t keep them all in your house, lest they fall prey to flood and fire. And don’t upload them to Google Drive; otherwise, you’re not using an offline password manager anymore. And… KeePass Principle #2: when a user device is compromised, it’s game over One day, my boss called me. She wanted to share a PDF with ten people from the upper echelon. This information should remain in their eyes only for three days. She knew that they had incentives to share this document with their respective teams to get a head start. This sort of information should be presented in person, but covid. So, she asked me to make this PDF impossible to copy and share. — Can’t be done, I replied. My boss was an Ambassador. This is the diplomatic equivalent of a General. Ambassadors aren’t used to hearing “no” as an answer. I patiently explained that, in computer science, when you give someone read permission to a resource, you implicitly give them permission to copy and distribute. Information access rights don’t obey legal rights. Even if we somehow made it inconvenient to share the file, they could just take pictures of their screens with their phones and share them over WhatsApp. If they can see the document on the screen, so can their cameras. Justin Schuh was right. If someone gains full access to your computer (or phone), they have full access to the information that you, the user, can access through that device. He’s right, but that doesn’t excuse him for implementing such a lousy password manager on Chrome. For a password manager, it means that protecting the user's device is always critical. If the server leaks, the attackers still need to go through the challenge of cracking the vaults. But if a hacker gains complete ownership over the computer running the password manager client, there’s nothing stopping them from stealing all passwords. This happens quite often in the crypto community. User Alice has a few dozen BTC, which makes her a millionaire. She has been doxxed or never hid her identity in the first place. She keeps a copy of her seed phrase in a local password manager on her desktop. “This is not a cloud password manager; I’m safe from leaks,” she thinks. Hackers find a way to make her install a backdoor to her computer. If they’re really good, they can do this without user interaction. The next morning, she wakes up to an empty wallet. For all the doom and gloom about user device compromise, there’s a silver lining: protecting our own devices, as users, is something that’s within our control. A server leak is not. The 150+ people who lost millions to the LastPass server leak bore no responsibility for the leaks or for LastPass implementing inadequate encryption standards. If user device compromise is a catastrophic attack vector no matter the password manager security model, and if we, as users, must protect our devices anyway, wouldn’t it be nice if this were attack vector? If we could build an password manager where no secrets ever leave the user devices so that users don’t need to worry about things out of their control, like server security and AES implementation details? the only online The ingredients are on the table Fellow author @hossam26644 continued, : “maybe I am a security freak, but I don’t want to trust my passwords to an entity, regardless of their promises &/reputation”. He’s right again. Cybersecurity is supposed to be proven and auditable, not based on trust and reputation. on his article The three principles outlined above guided my development of . It took me three years of ideation and iterations to arrive at its current form: an auditable cloud-based password manager that achieves zero knowledge by design and not through encryption. Passwords are synced across user devices using the cloud, but no secrets ever leave these devices. Cloud server compromises cannot leak any secrets because secrets never reach the cloud. Neulock Password Manager The three years of the making of Neulock will be documented in Part II.

Chrome Password Manager Betrayed My Trust 13 Years Ago. I Never Forgot

13 বছর আগে Chrome পাসওয়ার্ড ম্যানেজার আমার বিশ্বাসের সাথে বিশ্বাসঘাতকতা করেছে। আমি কখনই ভুলিনি।

Der Chrome Password Manager hat mein Vertrauen vor 13 Jahren missbraucht. Ich habe es nie vergessen.

Chrome Password Manager traicionó mi confianza hace 13 años. Nunca olvidé.

Le gestionnaire de mots de passe Chrome a trahi ma confiance il y a 13 ans. Je n'ai jamais oublié.

क्रोम पासवर्ड मैनेजर ने 13 साल पहले मेरे भरोसे को धोखा दिया। मैं कभी नहीं भूला.

Chrome パスワード マネージャーは 13 年前に私の信頼を裏切りました。私は決して忘れませんでした。

Chrome 비밀번호 관리자가 13년 전에 내 신뢰를 배신했습니다. 나는 결코 잊지 않았다.

O gerenciador de senhas do Chrome traiu minha confiança há 13 anos. Eu nunca esqueci.

Менеджер паролей Chrome предал мое доверие 13 лет назад. Я никогда не забывал.

Chrome Şifre Yöneticisi 13 Yıl Önce Güvenime İhanet Etti. Hiç unutmadım.

Trình quản lý mật khẩu Chrome đã phản bội lòng tin của tôi 13 năm trước Tôi không bao giờ quên.

Chrome 密码管理器 13 年前背叛了我的信任。我从未忘记。

102 Stories To Learn About Scrum

268 Stories To Learn About Project Management

Non-fungible tokens (NFTs) have taken the world by storm. Within 2024, the revenues from the NFT market are expected to reach $2,378.00m, and by 2028, the projected revenue amount is $3,369.00m, with an annual growth rate of 9.10%. It means that NFTs’ impact on the global economy is expected to be growing exponentially. What are the reasons for such a growth rate? While the majority of users associate NFTs with art and content creation, this understanding is very limited. So, what about checking the perspectives of NFTs in art, business, and economy, and whether non-fungible tokens are going to have any relevant impact on the world in the years to come? Are non-fungible tokens revolutionizing the art and content creation industries? Blockchain is widely praised for being secure and immutable. Once an NFT is minted, it cannot be counterfeited, eliminated, or replaced. This is why NFTs are expected to play a crucial role in combatting piracy and counterfeiting. One of the main proofs that NFTs may work out as expected is the fact that even the European Union Intellectual Property Office has issued an about NFTs and virtual goods. Brands willing to move their intellectual assets in the Web3 world, have to ensure that there is a physical asset backing the virtual one, while the term NFT on its own is “ ”. So, even from this practice, we can see that there are still many limitations on the way for NFTs to represent copyright. NFTs are certificates recorded on a blockchain, and these certificates shall represent physical assets. official practice not acceptable Tokenization of real-world assets: real estate, valuable items, etc. There are many platforms that benefit from this technology by allowing to tokenize real-world assets such as homes, airplanes, yachts, valuable art pieces, and other worthy items. Further, fractions of these tokens are sold to investors. This practice is highly beneficial to everybody. People can buy a property that was earlier not accessible due to its high prices. Property owners can get money from sales or rent faster than if they were relying on high-net-worth individuals only. Geographical limitations are not as restrictive as they were before the tokenization introduction because blockchain is accessible from any point in the world. But how NFTs are related to tokenization? Tokenization relies on NFTs to happen. Real-world assets are turned into NFTs which are recorded on a blockchain. Each NFT has its identification code and metadata, these things make the NFT unique. This uniqueness is what gives NFTs value in the world of business. NFTs for solving ethical issues One more type of NFT is expected to rise - those that will be taking care of the ethical part of the game. Here, we are talking about tokenization again but from a different perspective. Take a newly developed medication for a super contagious disease that is spreading in Africa - counterfeits are the major problem in such a case. NFTs would allow to ensure that the medication comes from a reputable pharmaceutic company and can be trusted. Another example would be gems, for instance, diamonds. It is not a secret that many gems are mined in Africa, and plenty of mines rely on child labor to benefit more. NFTs would help to track the gem's origin to ensure it doesn’t come from a mine where child labor is used. While it is still long for this use case to be applied, it for sure may add value to the NFT industry and boost its potential. NFTs for fighting scam Have you ever bought fake tickets to a concert or another important event? If yes, you know this pain. NFTs will eliminate the danger of buying fake tickets by forging the title of ownership on a blockchain. Well, tickets are just an example. I expect that in the future, NFTs can be widely used to prove the authenticity of items. Bottom line NFTs are still evolving, and like any new technology, they will be susceptible to ups and downs. However, one thing is clear: in the future, everything will be about utility. NFTs as images will sustain but this use case won’t be the main one anymore. What are the future use cases of NFTs? Some of them we can already determine, however, I am sure that many more things are to come.

Non-Fungible Tokens, Their Use Cases and Perspectives in 2024

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Social Media. ACM Trans. Soc. Comput. 2018, 1 (1), 1–27. . https://doi.org/10.1145/3140565 (46) Cinelli, M.; De Francisci Morales, G.; Galeazzi, A.; Quattrociocchi, W.; Starnini, M. The Echo
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(58) Khaund, T.; Kirdemir, B.; Agarwal, N.; Liu, H.; Morstatter, F. Social Bots and Their Coordination During Online Campaigns: A Survey. IEEE Trans. Comput. Soc. Syst. 2022, 9 (2), 530–545. https://doi.org/10.1109/TCSS.2021.3103515. (59) Dang, S.; Paul, K.; Chmielewski, D. Do spam bots really comprise under 5% of Twitter users? Elon Musk wants to know | Reuters https://www.reuters.com/technology/do-spam-botsreally-comprise-under-5-twitter-users-elon-musk-wants-know-2022-05-13/ (accessed Sep 1, 2022). (60) Luceri, L.; Deb, A.; Giordano, S.; Ferrara, E. Evolution of Bot and Human Behavior during Elections. First Monday 2019, 24 (9). https://doi.org/10.5210/fm.v24i9.10213. (61) Bugra Torusdag, M.; Kutlu, M.; Selcuk, A. A. Are We Secure from Bots? Investigating Vulnerabilities of Botometer. 5th Int. Conf. Comput. Sci. Eng. UBMK 2020 2020, 343–348. https://doi.org/10.1109/UBMK50275.2020.9219433. (62) Balestrucci, A. How Many Bots Are You Following? 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N.; Laato, S.; Talukder, S.; Sutinen, E. Misinformation Sharing and Social Media Fatigue during COVID-19: An Affordance and Cognitive Load Perspective. Technol. Forecast. Soc. Change 2020, 159, 120201. https://doi.org/10.1016/j.techfore.2020.120201. (68) Törnberg, P. Echo Chambers and Viral Misinformation: Modeling Fake News as Complex Contagion. PLoS One 2018, 13 (9), 1–21. https://doi.org/10.1371/journal.pone.0203958. (69) IFCN. International Fact-Checking Network – Poynter https://www.poynter.org/ifcn/ (accessed Jul 27, 2020). (70) Bruns, A.; Harrington, S.; Hurcombe, E. ‘Corona? 5G? Or Both?’: The Dynamics of COVID-19/5G Conspiracy Theories on Facebook. Media Int. Aust. 2020, 177 (1), 12–29. https://doi.org/10.1177/1329878X20946113. (71) de-Lima-Santos, M.-F.; Kooli, A. Instagrammable Data: Using Visuals to Showcase More Than Numbers on AJ Labs Instagram Page. Int. J. Commun. 2022, 16, 2821–2842. https://doi.org/1932–8036/20220005. (72) Reuters. Facebook removes dozens of vaccine misinformation “superspreaders” https://www.reuters.com/technology/facebook-removes-dozens-vaccine-misinformationsuperspreaders-2021-08-18/ (accessed Oct 11, 2021). (73) Bastian, M.; Heymann, S.; Jacomy, M. Gephi: An Open Source Software for Exploring and Manipulating Networks. Icwsm 2009. (74) Blondel, V. D.; Guillaume, J. L.; Lambiotte, R.; Lefebvre, E. Fast Unfolding of Communities in Large Networks. J. Stat. Mech. Theory Exp. 2008, 2008 (10), 10008. https://doi.org/10.1088/1742- 5468/2008/10/P10008. (75) Fortunato, S. Community Detection in Graphs. Phys. Rep. 2010, 486 (3–5), 75–174. https://doi.org/10.1016/J.PHYSREP.2009.11.002. (76) Howard, P. N.; Woolley, S.; Calo, R. Algorithms, Bots, and Political Communication in the US 2016 Election: The Challenge of Automated Political Communication for Election Law and Administration. J. Inf. Technol. Polit. 2018, 15 (2), 81–93. https://doi.org/10.1080/19331681.2018.1448735. (77) Theocharis, Y.; Jungherr, A. Computational Social Science and the Study of Political Communication. Polit. Commun. 2021, 38 (1–2), 1–22. https://doi.org/10.1080/10584609.2020.1833121. (78) Girvan, M.; Newman, M. E. J. Community Structure in Social and Biological Networks. Proc. Natl. Acad. Sci. U. S. A. 2002, 99 (12), 7821–7826. https://doi.org/10.1073/pnas.122653799. (79) Watts, D. J.; Strogatz, S. H. Collective Dynamics of ’small-World9 Networks. Nature 1998, 393 (6684), 440–442. https://doi.org/10.1038/30918. (80) Walter, N.; Salovich, N. A. Unchecked vs. Uncheckable: How Opinion-Based Claims Can Impede Corrections of Misinformation. Mass Commun. Soc. 2021, 15205436.2020.1864406. https://doi.org/10.1080/15205436.2020.1864406. (81) Mozilla Foundation. INSIDE THE SHADOWY WORLD OF DISINFORMATION FOR HIRE IN KENYA; 2021. (82) Culliford, E. Facebook cracks down on German anti-COVID restrictions group over “social harm” https://www.reuters.com/technology/facebook-shuts-down-network-linked-germananti-covid-group-launches-rules-social-2021-09-16/ (accessed Oct 10, 2021). (83) Angus, D.; Bruns, A.; Hurcombe, E.; Harrington, S.; Glazunova, S.; Montaña-Niño, S. X.; Obeid, A.; Coulibaly, S.; Copland, S.; Graham, T.; Wright, S.; Dehghan, E. ‘Fake News’ and Other Problematic Information: Studying Dissemination and Discourse Patterns. AoIR Selected Papers of Internet Research. 2021. https://doi.org/10.5210/spir.v2021i0.12089 (84) Dehghan, E.; Glazunova, S.. ‘Fake news’ discourses. Journal of Language and Politics. 2021. 20(5), 741–760. https://doi.org/10.1075/jlp.21032.deh (85) de-Lima-Santos, M.-F.; Ceron, W. Coordinated Amplification, Coordinated Inauthentic Behavior, Orchestrated Campaigns? A Systematic Literature Review of Coordinated Inauthentic Content on Online Social Networks. In T. Filibeli & M. Ö. 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Disinformation Echo-Chambers on Facebook: Funding and References

Disinformation Echo-Chambers on Facebook: Abstract & Intro

Generative AI and Contextual Confidence: Abstract & Intro

Let's learn about via these 53 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Machine Learning Uses /Learn Repo 1. Use Cascade Models to Get Better Speed and Accuracy in Computer Vision Tasks Great way to improve your Computer Vision models metrics 2. The Four Types of Machine Learning | Part 2 In the previous post, we saw the first two types of machine learning. In this post, we will discuss the other two types of machine learning. These are — Semi-su 3. The Convergence of AI + IOT is Imminent & Your Competitor is Already Preparing for It With the emergence of ever-cheaper and robust hardware, 5G connectivity around the corner, and most importantly, a growing list of real world use cases, we can all agree that IOT projects are here to stay. But is that where it ends ? 4. Adversarial Examples In Machine Learning Explained There are easy ways to build adversarial examples that can fool any deep learning model and create security issues no matter how complex the model is. 5. Reimagining Customer Behavior Through Machine Learning Here what you need to know about Machine Learning in business market strategy 6. Machine Learning: Santa’s little helper He’s making a list and checking it… well… once, because his model has a 98% accuracy 7. How AI Is Transforming The Field Of Education Artificial Intelligence (AI) is one of the wonders of the modern world, which isn’t going to cease to amaze the most intelligent of human beings. Like other fields, the field of education is also gaining the maximum amount of benefits out of the promises of the AI-powered technology. 8. Artificial Intelligence on Making Court Decisions In Estonia, artificial intelligence will play a crucial role in the future of human beings. It will play a crucial role in the functioning of judicial institutions, which is a great innovation. According to the authorities in this Baltic country, they are empowered to arbitrate cases of minor offenses autonomously. Unbelievable but true! 9. The GPUs for Deep Learning: NVIDIA vs AWS vs Azure and More Take a deeper dive into what a GPU is, when you should use it or shouldn’t for Deep Learning tasks, and what is the best GPU on-premises and in the cloud in 202 10. Debunking 4 Common Myths About Machine Learning Machine learning is a subset of artificial intelligence that involves the use of algorithms and statistical models to enable computers to improve them. 11. How Document Classification Can Improve Business Processes The process of labeling documents into categories based on the type of the content is known as document classification. It can also be defined as the process of assigning one or more classes or categories to a document (depending on the type of content) to make it easy to sort and manage images, texts, and videos. Document classification can be done using artificial intelligence, machine learning, and python. 12. How To Get Started With Machine Learning With The Right Mindset You got intrigued by the machine learning world and wanted to get started as soon as possible, read all the articles, watched all the videos, but still isn’t sure about where to start, welcome to the club. 13. How Companies Are Actually Using AI in Everyday Practices When thinking of AI (artificial intelligence), mixed emotions often come to mind. For movie buffs, we might immediately see images of Will Smith battling it out with humanoid AI creatures in IRobot or the even more realistic looking depiction of artificial intelligence in the movie aptly called AI. In our human minds, AI is something that could potentially lead to a catastrophic apocalypse as machines take over the world. 14. Every Way Natural Language is Better Than SQL Since the dawn of time, humans have communicated through gestures, drawings, smoke, or speech. Along the way, Structured Query Language (SQL) made its way into human life so we could speak to databases. However, it’s time to revert back to our natural language and rethink how we talk to our data. 15. How Machine Learning Can Help With Fraud Prevention Technology, without a doubt, has eased up a lot of issues for us, including the likes of fraud prevention. But before we start talking about the technological inputs pertaining to the same, it is necessary to understand why fraud prevention is actually required by businesses and why this genre of functionality is wide-spread and extremely popular. Firstly, financial firms are probably the most affected ones as fraudsters are actually interested in siphoning off money more than anything else. Secondly, fraudulent activities aren’t restricted to one vertical and it is a challenge for the firms to develop newer strategies for combating evolving threats. 16. Introduction to Different Machine Leaning Tools Machine Learning is one of the emerging technologies of the present IT industry. This technology has now become the talk of the town and has seen  an abnormally high growth over the few years. 17. Deploy Computer Vision Models with Triton Inference Server There are a lot of Machine Learning courses, and we are pretty good at modeling and improving our accuracy or other metrics. 18. Basic Use Cases of AI, ML, Deep Learning and Internet of Things The world’s most influential companies and technologies are influenced by the efficiency of Artificial intelligence and similar technologies. Whether it is Facebook or Amazon, Google or Microsoft, all firms are harnessing AI techniques and algorithms to introduce high-level performance and streamlined operations. 19. How is AI-powered Software Development influencing Pet Tech Technology is revolutionizing all aspects of our lives. Let's take a look at how AI-powered software development influencing pet tech. 20. An Introduction to AI-Based Visual Inspection for Defect Detection “Why should I care about a cool new technology until it’s solving any of my problems?” – this was the exact conversation I had with the executive of a water purification plant over a warm cup of coffee. 21. Announcing ModelDB 2.0 release Since we wrote ModelDB 1.0, a pioneering model versioning system, we have learned a lot and adapting it to the evolving ecosystem became a challenge. Hence we decided to rebuild from the ground up to support a model versioning system tailored to make ML development and deployment reliable, safe, and reproducible. 22. Most Useful ML Ops Applications Many ML Ops tools allow overseeing the entire machine learning model life cycle. Here are some of the most worthwhile ones to consider. 23. Vladimir Vapnik's New Learning Model Vladimir Vapnik recently gave a talk about a new theory of learning he is working on. 24. Evaluating Regression Models in Machine Learning Model evaluation is very important since we need to understand how well our model is performing. 25. Breaking into Deep Learning: Transforming the World Without Expert Input Deep learning is a subdivision of machine learning in which Artificial Neural Networks (ANNs) learn from a huge influx of data to produce high-quality output. 26. Model Tests are Essential for Building Domain Knowledge Testing protects against regressions. But the most important product of testing is domain knowledge and powerful capabilities. 27. Can Graph Neural Networks Solve Real-World Problems? In this article, we will learn about GNNs and its structure as well as its applications 28. The Future of Artificial Intelligence: To Kill or To Heal? The evil cyber-intelligence from the Matrix and a cyborg killing machine from the Terminator movies - that’s what most people used to imagine when talking about the future of artificial intelligence. 29. What’s the Difference Between AI And Machine Learning? It’s nearly impossible to have a conversation about technology without mentioning artificial intelligence (AI) or machine learning (ML). 30. Here’s How Libratus, a Poker-Playing AI, Bluffed Four Professional Texas Holdem Players The world of technology is changing at a faster rate than we can possibly fathom. Long gone are the days when we were the sole trailblazers in a human-tech relationship when the incentive resided in our hands. 31. What's the Difference Between MLOps and AIOps An overview of the MLOps and AIOps worlds to understand what they mean, how they relate to DevOps, and how they compare in terms of benefits. 32. Machine Learning Applications Across Different Industries 1952 witnessed the world’s first computer that could learn while it was running. It was a game of checkers developed by Arthur Samuel. It has barely been half a century since then and we’re already having a conversation about whether we should commercialize self-driving cars or not. Machine Learning gave birth to some of these great advancements in technology and we’re going to dive deep into everything it can do to make our lives much easier than ever before. 33. How AI and Machine Learning Changes Modern-day Industries It’s important to know exactly how AI and Machine Learning are transforming the industries and their role in our technological advancements. 34. Applications of Artificial Intelligence in Business Artificial Intelligence, the concept of ‘machines with brains’ has been in the spotlight around the last seven decades. A theoretical notion that started as simple rule-based automation in the 1950s' has now grown so much that now the scientists are trying to make human-like robots. The question- What AI might do to us? — has created a lot of controversies in and out of the scientific community. 35. 8 Machine Learning Trends that Impact Business in 2021 and Beyond Let’s discover the latest innovations in machine learning in 2021-2022 and go over various examples of how this technology can benefit you and your business. 36. Success With Machine Learning Projects in Python In this article, we will give you a sense of the applications for machine learning and explain why Python is a perfect choice for getting started. 37. Build A Commission-Free Algo Trading Bot By Machine Learning Quarterly Earnings Reports [Full Guide] Introduction 38. What is Machine learning and Why is it Important? Machine learning is quite an exciting field to study and rightly so. It is all around us in this modern world. From Facebook’s feed to Google Maps for navigation, machine learning finds its application in almost every aspect of our lives. 39. Is Automation Jeopardizing Our Future? Is automation jeopardizing our future? What's its impact on our future and the job market? And how can we cope up? 40. Universal Data Tool Introduction: Weekly Update 2 If you haven’t heard of the Universal Data Tool, it’s an open-source web or desktop program to collaborate, build and edit text, image, video, and audio datasets with labels and annotations. 41. An Introduction to Variational Autoencoders Using Keras In this tutorial, we’ll explore how Variational Autoencoders simply but powerfully extend their predecessors, ordinary Autoencoders. 42. How To Improve Data Quality When With Unsupervised Machine Learning There won’t be any business insights if the data quality is poor. 43. How Machine Learning is Used in Astronomy Is Astronomy data science? 44. Will Machine Learning Algorithms Encroach on Content Marketers? Since its inception, the field of online content marketing has been in a constant state of flux. This tendency is built into it — since it operates in an ever changing online world, all it can ever do is change along with the internet. For better or for worse, the rapid advent of machine learning algorithms in online processes will only lead to more change in the field of content marketing. 45. How To Identify Trees with Deep Learning Idea / inspiration 46. Pecan.ai Raises 11 Million to Bring Machine Learning to Business Analysts Pecan.ai has just come out of stealth, raising an $11M Series A, to enable business analysts to build machine learning models automatically. Dell Capital led the round, joined by S capital and bringing the total funding of the company to $15M. 47. Embeddings in Machine Learning: Everything You Need to Know Here's a deep dive into the history of machine learning embeddings, common uses, and current infrastructure solutions, including the vector database. 48. Quantum Machine Learning Using TensorFlow Quantum INTRODUCTION 49. How Data Analysis Helps Unveil the Truth of Coronavirus These days we are all scared of the new airborne contagious coronavirus (2019-nCoV). Even if it is a tiny cough or low fever, it might underlie a lethargic symptom. However, what is the real truth? 50. Big Tech Companies Need Machine Learning Engineers Machine learning is no longer a sci-fi concept, but an actual application of AI technology we use every day. 51. No Such Thing As The Intelligent Edge But it will be much bigger than the Internet. 52. Why It’s Very Difficult to Create AI-Based Slow Motion Over the last few years a number of open source machine learning
projects have emerged that are capable of raising the frame rate of
source video to 60 frames per second and beyond, producing a smoothed,
'hyper-real' look. 53. Optuna Vs. Hyperopt: Which Hyperparameter Optimization Library You Should Choose Thinking which library should you choose for hyperparameter optimization? Thank you for checking out the 53 most read stories about Machine Learning Uses on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

53 Stories To Learn About Machine Learning Uses

278 Stories To Learn About Machine Learning

103 Stories To Learn About Banking

Let's learn about via these 75 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Datasets /Learn Repo 1. Top 15 Datasets for Autonomous Driving A2D2, ApolloScape, and Berkeley DeepDrive are among the best autonomous driving datasets available today. 2. Web Scraping API para Extracción de Datos: Una Guía para Principiantes ¿Alguna vez te sucede cuando la gente te pide que escribas una API separada para integrar datos de redes sociales y guardar los datos sin procesar en tu base de datos de análisis en el sitio? Definitivamente quieres saber qué es la API, cómo se usa en web scraping y qué puede lograr con ella. Echemos un vistazo. 3. 10 Best Stock Market Datasets for Machine Learning For those looking to build predictive models, this article will introduce 10 stock market and cryptocurrency datasets for machine learning. 4. Evolution of The Data Production Paradigm in AI The long-term success of an AI-based product relies on having the infrastructure for scalable, flexible, and cost-effective data labeling for its learning. 5. Collecting Data from 1.1M Hacker News Curated Comments In this test we use the data collection of 1.1M Hacker News curated comments with numeric fields from https://zenodo.org/record/45901. 6. 10 Biggest Image Datasets for Computer Vision Data is very important in building computer vision models and these are the 10 Biggest Datasets for Computer Vision. 7. Top 20 Image Datasets for Machine Learning and Computer Vision Computer vision enables computers to understand the content of images and videos. The goal in computer vision is to automate tasks that the human visual system can do. 8. The Art of Data Storytelling: How to Make Your Data Impactful Data is everywhere: whether you choose a new location for your business or decide on the color to use in an ad, data is an invisible advisor that helps make impactful decisions. With quite a number of resources to choose from, data is becoming more accessible, day by day. But as soon as it has been collected, one inevitable question arises: how do I turn this data into insights that can be acted upon? 9. 10 Best Hugging Face Datasets for Building NLP Models Hugging Face offers solutions and tools for developers and researchers. This article looks at the Best Hugging Face Datasets for Building NLP Models. 10. How This Open Litter Database Will Save the Planet The Litterati app has been around for a couple years on iOS + Android. Over that time, well over 100k people have downloaded the app and been a part of a global team that is 'crowdsource cleaning the Earth'. Over that time, people only had access to the data that they themselves generated. 11. Sort Through Online Data  via Web Scraping [101] How Can You Sort Through Online Data? 12. 6 Work from Home Positions in AI Data Collection and Data Annotation For digital nomads, college students, stay-at-home parents or anyone looking for remote work positions, this article introduces online/remote work positions that are available today in the fields of AI Data Collection and Data Annotation. 13. Universal Data Tool Introduction: Weekly Update 1 If you haven’t heard of the Universal Data Tool, it’s an open-source web or desktop program to collaborate, build and edit text, image, video and audio datasets with labels and annotations. You can get started with the Universal Data Tool at universaldatatool.com 14. Spending Weekend with GraphQL Have you ever experienced an itch you just can’t scratch? If yes, then you will feel my pain. A few days back, everything was fine, I was happily writing code(!) and doing standup meetings regularly. Just before the weekend, my boss called me and shared this problem with me. 15. How Data Analysis Helps Unveil the Truth of Coronavirus These days we are all scared of the new airborne contagious coronavirus (2019-nCoV). Even if it is a tiny cough or low fever, it might underlie a lethargic symptom. However, what is the real truth? 16. 10 Best Image Classification Datasets for ML Projects To help you build object recognition models, scene recognition models, and more, we’ve compiled a list of the best image classification datasets. These datasets vary in scope and magnitude and can suit a variety of use cases. Furthermore, the datasets have been divided into the following categories: medical imaging, agriculture & scene recognition, and others. 17. What is Web Data Collection? Everything you need to know to automate, optimize and streamline the data collection process in your organization! 18. 14 Best Tableau Datasets for Practicing Data Visualization This article focuses on the 14 Best Tableau Datasets for Practicing Data Visualization, which is essential for business analysts and data scientists. 19. Artificial Intelligence is No Match for Natural Stupidity A Lazy Introduction to AI for Infosec. 20. How Big Data and Artificial Intelligence Will Go Hand in Hand? The emergence of technology is playing an inevitable role in business. It’s drastically transforming the way people work together in an organization. Both these technologies are revolutionizing every aspect of our life.  These technologies are creating a culture where the collaboration of IT leaders and businesses results in realizing values from all generated data. 21. 17 Open Crime Datasets for Data Science and Machine Learning Projects For those looking to analyze crime rates or trends over a specific area or time period, we have compiled a list of the 16 best crime datasets made available for public use. 22. Build Data-Driven Web App Without Backend During the last couple of decades websites' functionally has increased dramatically - from simple landing pages serving simple static ads to complex progressive web apps whose functionality close to native applications including user authorization, location tracking, bluetooth handling, and offline mode. 23. 12 Best Pre-Installed R Datasets Commonly Used for Statistical Analysis R programming is mostly used in statistical analysis and ML.
This article looks at the Best Pre-Installed R Datasets Commonly Used for Statistical Analysis. 24. MODEL-CENTRIC vs DATA-CENTRIC Approaches in Machine Learning Machine learning is an area of artificial intelligence (AI) and computer science that focuses on using data and algorithms to mimic the way humans learn 25. The Hunt for Data: Creating a Computer Vision Dataset for Road Safety In this article, I would like to share my own experience of developing a smart camera for cyclists with an advanced computer vision algorithm 26. 13 Best Datasets for Power BI Practice In 2022, Gartner named Microsoft Power BI the Business Intelligence and Analytics Platforms leader. These are the 13 Best Datasets for Power BI Practice. 27. Encoding Categorical Data for ML Algorithms Encoding is a technique used to convert categorical data to numerical representations to be able to use the data in machine learning algorithms. 28. Things to Consider When Looking For Data Science Roles There is a great demand for data scientists presenting market dynamics that are favourable for the community. More so than your peers in other professions, you will be able to evaluate a company for what it is able to offer you, rather than solely being the one that is being evaluated. So what should you look for when comparing and evaluating data science roles? Here is a list of some commonly known factors plus some less discussed ones that will help you in your evaluation. 29. 10 Best Reddit Datasets for NLP and Other ML Projects In this post, I wanted to share a Reddit dataset list that gained a lot of traction on social media when it was first posted. 30. 10 Best Datasets for Geospatial Analytics (Open and Public Access) Scientists use geospatial analytics to build visualizations such as maps, graphs and cartograms. These are the Best Public Datasets for Geospatial Analytics. 31. How to Aid Disease Research with a Biomedical Knowledge Graph Building a biomedical knowledge graph using publicly available datasets to better aid disease research and biomedical data modelling. 32. What is Data Analytics and How It Can  Be Used WHAT IS DATA ANALYTICS? 33. Alternatives to Web Scraping with Python Is Python really the easiest and most efficient way to scrape a website? There are other options out there. Find out which one is best for you! 34. Six Ways For Effective Data Visualization With Tableau Tableau 35. 10 Best Keras Datasets for Building and Training Deep Learning Models This article looks at the Best Keras Datasets for Building and Training Deep Learning Models, accessible to developers and researchers worldwide. 36. 14 Open Datasets for Text Classification in Machine Learning Text classification datasets are used to categorize natural language texts according to content. For example, think classifying news articles by topic, or classifying book reviews based on a positive or negative response. Text classification is also helpful for language detection, organizing customer feedback, and fraud detection. Though time consuming when done manually, this process can be automated with machine learning models. The result saves companies time while also providing valuable data insights. 37. 11 Torchvision Datasets for Computer Vision You Need to Know With torchvision datasets, developers can train and test their machine learning models on a range of tasks, such as image classification and object detection. 38. Merging Datasets from Different Timescales One of the trickiest situations in machine learning is when you have to deal with datasets coming from different time scales. 39. An Intro to No-Code Web Scraping Web scraping has broken the barriers of programming and can now be done in a much simpler and easier manner without using a single line of code. 40. How To Monitor a Forum for Keywords Using Python and AWS Lambda While building ScrapingBee I'm always checking different forums everyday to help people about web scraping related questions and engage with the community. 41. 8 Best Human Behaviour Datasets for Machine Learning Human behaviour describes how people interact and in this article, we will look at the 8 Best Human Behaviour Datasets for Machine Learning. 42. 20 Best PyTorch Datasets for Building Deep Learning Models PyTorch has gained a reputation as a research-focused framework, and these are the Best PyTorch Datasets for Building Deep Learning Models available today. 43. Data Set and Data Augmentation for Face Detection and Recognition When it comes to building an Artificially Intelligent (AI) application, your approach must be data first, not application first. 44. A Deep Dive Into Facebook’s AI Transcoder Just over a week, most of you would have heard that Facebooks AI research team (FAIR) developed a neural transcompiler, that converts code from high level programming language like C++, Python, Java, Cobol into another language using ‘unsupervised translation’ . The traditional approach had been to tokenize the source language and convert it into an Abstract Syntax Tree (AST) which the transcompiler would use to translate to the target language of choice, based on handwritten rules that define the translations, such that abstract or the context is not lost. 45. Las 15 preguntas más frecuentes sobre Web Scraping Previously published at https://www.octoparse.es/blog/15-preguntas-frecuentes-sobre-web-scraping 46. Is Data Catalog an Indispensable Tool for Corporate? To understand the concept of data catalog, we need an assessment of the fundamentals that constitute the process on an elementary level. At the most rudimentary stage lies the idea of arrangement and the order of things. 47. Introducing theHolopix50k Dataset for Image Super-Resolution Depth estimation and stereo image super-resolution are well-known tasks in the field of computer vision. To help researchers get high-quality training data for these tasks, industry-leading lightfield hardware provider Leia Inc. used their social media app, Holopix™, to create Holopix50k, the world’s largest “in-the-wild” stereo image dataset. 48. 7 Effective Ways to Deal With a Small Dataset In a real-world setting, you often only have a small dataset to work with. Models trained on a small number of observations tend to overfit and produce inaccurate results. Learn how to avoid overfitting and get accurate predictions even if available data is scarce. 49. Top 10 Best Web Scraper And Data Scraping Tools Data extraction has many forms and can be complicated. From Preventing your IP from getting banned to bypassing the captchas, to parsing the source correctly, headerless chrome for javascript rendering, data cleaning, and then generating the data in a usable format, there is a lot of effort that goes in. I have been scraping data from the web for over 8 years. We used web scraping for tracking the prices of other hotel booking vendors. So, when our competitor lowers his prices we get a notification to lower our prices to from our cron web scrapers. 50. Top 3 Face Datasets and How to Work with Them An image dataset contains specially selected digital images intended to help train, test, and evaluate an artificial intelligence (AI) or machine learning (ML) 51. 11 Best Climate Change Datasets for Data Science Projects Data is a central piece of the climate change debate. With the climate change datasets on this list, many data scientists have created visualizations and models to measure and track the change in surface temperatures, sea ice levels, and more. Many of these datasets have been made public to allow people to contribute and add valuable insight into the way the climate is changing and its causes. 52. InfluxDB Continuous Downsampling  - Optimize Your TSDB Today Introduction 53. Top 10 Open Datasets for Linear Regression On Hacker Noon, I will be sharing some of my best-performing machine learning articles. This listicle on datasets built for regression or linear regression tasks has been upvoted many times on Reddit and reshared dozens of times on various social media platforms. I hope Hacker Noon data scientists find it useful as well! 54. Going From Data Lakes to Oceans Aggregating into data lakes is the solution of today — but are Federated Sources the solution of tomorrow? 55. How To Scrap Product Information With Python & BeautifulSoup Module From Amazon Listings [Tutorial] Intro 56. 10 Best Datasets for Time Series Analysis In order to understand how a certain metric varies over time and to predict future values, we will look at the 10 Best Datasets for Time Series Analysis. 57. 10 Data Table Libraries for JavaScript Tables are a useful tool for visualizing, organizing and processing data in JavaScript. To start using them, you need to download a free library or one for a reasonable price. Here is a list of 10 useful, functional, and reliable JS libraries that will help you work with tables. 58. Top 15 Chatbot Datasets for NLP Projects An effective chatbot requires a massive amount of training data in order to quickly solve user inquiries without human intervention. However, the primary bottleneck in chatbot development is obtaining realistic, task-oriented dialog data to train these machine learning-based systems. 59. Machine Learning Food Datasets Collection An essential part of my company's Machine Learning team is working with different food datasets, and we spend a lot of time before for searching, combining or intersecting different datasets to get data that we need and can use in our work. Given that it might help someone else, I decided to list all helpful datasets in one place. 60. The Pain Points of Scaling Data Science While building a machine learning model, data scaling in machine learning is the most significant element through data pre-processing. Scaling may recognize the difference between a model of poor machine learning and a stronger one. 61. 5 Million Face Images for Facial Recognition Model Training This article on face recognition datasets is one of my best-performing articles I wrote originally on Lionbridge AI. I'm happy to share it with the Hacker Noon community! 62. Kannada-MNIST:A new handwritten digits dataset in ML town TLDR: 63. Here's Why We Built An Open-Source Goldmine of Crypto-Markets Datasets How to run a distributed data-mining operation to source and process crypto market data at zero cost. 64. Top 20 Twitter Datasets for Machine Learning Projects It is often very difficult for AI researchers to gather social media data for machine learning. Luckily, one free and accessible source of SNS data is Twitter. 65. Creating A Data Science Pipeline That Works Correctly An easy, automated, repeatable way to check your data science solution is doing exactly what it's designed to do. 66. 15 Excel Datasets for Data Analytics Beginners Excel is an indispensable tool for data manipulation, data visualization and statistical analysis. These are 15 Excel datasets for data analytics beginners. 67. What Is Big Data? Understanding The Business Use of Big Data Analytics Big data analytics can be applied for all and any business to boost their revenue and conversions and identify their common mistakes. 68. Gender Prediction Using Mobile App Data Сreate a model for the gender prediction based on the list of installed applications on a mobile device. 69. Towards an ImageNet Moment for Speech-to-Text: A Deep Dive Speech-to-text (STT), also known as automated-speech-recognition (ASR), has a long history and has made amazing progress over the past decade. Currently, it is often believed that only large corporations like Google,  Facebook, or Baidu (or local state-backed monopolies for the Russian language) can provide deployable “in-the-wild” solutions. 70. Build A Commission-Free Algo Trading Bot By Machine Learning Quarterly Earnings Reports [Full Guide] Introduction 71. Getting Started with Data Visualization: Building a JavaScript Scatter Plot Module Scatter plots are a great way to visualize data. Data is represented as points on a Cartesian plane where the x and y coordinate of each point represents a variable. These charts let you investigate the relationship between two variables, detect outliers in the data set as well as detect trends. They are one of the most commonly used data visualization techniques and are a must have for your data visualization arsenal! 72. 10 Best African Language  Datasets for Data Science Projects A list of African language datasets from across the web that can be used in numerous NLP tasks. 73. Introducing a Simple Module for Parsing CSV Files This Slogging thread by  and Arthur Tkachenko occurred in slogging's official #programming channel, and has been edited for readability. 74. The Rise of Reusable SQL-based Data Modeling Tools and DataOps services The resurgence of SQL-based RDBMS 75. How To Master Elasticsearch Query DSL Photo by Evgeni Tcherkasski on Unsplash Thank you for checking out the 75 most read stories about Datasets on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

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301 Stories To Learn About Cryptocurrency Top Story

In the fast-changing world of cryptocurrency, the question remains: Is crypto trading a high-stakes gamble or a golden path to financial triumph? Let's take a thrilling journey into the heart of this ongoing debate. I started my crypto journey while living in Bangkok, Thailand in 2017. This was during the ICO boom and I became a top-rated expert with ICOBench (hey don’t judge me). I read over 300 white papers, roadmaps and field of dreams type websites. I became an advisor to some of these dream chasers. Sadly 90% of those projects failed. The gamble did not pay off for investors, developers and communities. But the hype and sentiment were awesome. By November 2021, the total crypto market cap reached a record high of around $3 trillion. We know crypto changes faster than my two-year-old niece’s mood after ice cream. As of today, December 28, 2023, the total market cap has plummeted to around $800 billion. A staggering 73% decrease from its peak. Trillions of dollars in potential wealth wiped out. The gamble or the golden path? With just months to go until the Bitcoin halving, electric excitement is brewing across the cryptosphere. Emotions are fueled by the long-awaited potential approval of a Bitcoin ETF and the possibility of finally leaving the bear market behind. Sentiment is approaching peak greed. Cryptocurrency trading has captured the imagination of risk-takers and dreamers alike. But is it a risky bet or the key to unlocking unprecedented success? Let's move further into the intriguing world of crypto to find out. Understanding Cryptocurrency Trading From Wall Street wolves to tech-savvy teens, a digital gold rush has gripped the world: cryptocurrency trading. But what exactly is this phenomenon that has everyone buying and selling invisible coins? Imagine a lively marketplace, not of stocks and bonds, but of digital currencies dancing across glowing screens. This is the heart of crypto trading, where you buy and sell digital currencies on various exchanges, free from the shackles of traditional markets. But be warned, the thrill of potential riches comes hand in hand with the dizzying dance of 24/7 price fluctuations. Are you ready to join the digital gold rush? Before diving in it's crucial to understand some basic terms. First off, wallets are used to store your digital assets securely. They come in different forms such as software wallets (which can be downloaded onto your computer or mobile device) and hardware wallets (physical devices that provide an extra layer of security). Secondly, blockchain technology serves as the underlying infrastructure for crypto. It is a decentralized ledger that records all transactions made with cryptocurrencies. Imagine blockchain as a colossal Jenga tower, each block representing a transaction, securely stacked on top of the last. Every new block adds to the stability and transparency of the entire structure. With this foundational knowledge in place, you're now ready to embark on your crypto trading journey. It's important to continue learning about the details of different cryptocurrencies. You should also stay updated on market trends before making any investment decisions. Keep these tips in mind as you begin exploring the thrilling yet unpredictable world of crypto trading. Exchanges Unveiled: Where & How to Trade Safely One of the first steps in getting started with cryptocurrency trading is finding a reputable exchange platform. These platforms act as middlemen. They empower you to buy, sell, and trade various cryptocurrencies. According to Chainalysis, central and southern Asia are leading the way in . These are the countries where average, everyday people are embracing crypto the most. grassroots crypto adoption The buy button is a portal to a digital gold rush, where fortunes can be made (or lost) in a heartbeat. This is the adrenaline-pumping world of crypto trading. However, with , it's crucial to choose one that prioritizes security and user safety. more than 1,500 crypto exchanges available today When selecting an exchange, look for platforms that have a solid reputation in the industry and offer enhanced security features. Exchanges like Coinbase or Binance are popular choices known for their robust security measures and user-friendly interfaces. They employ technologies such as cold storage wallets and encryption protocols to safeguard users' funds. BYDFI is another crypto platform making waves. Forbes, a trusted source in financial insights, tackles the . BYDFi offers margin accounts, derivatives trading and relatively low fees. While trading complex crypto derivative contracts isn’t for beginners, BYDFi aims to make the process as simple as possible. BYDFI (formerly BitYard) phenomenon in a review SafetyDetectives had an informative , to get his perspective on the challenges facing the crypto industry. His insights could reshape your view on crypto trading. He said security risks in cryptocurrency trading include phishing attacks, exchange hacks and wallet vulnerabilities. conversation with Michael Hung, the CEO of BYDFI In addition to choosing a trustworthy exchange, it's important to practice safe trading habits on these platforms. One essential measure is enabling Two-Factor Authentication (2FA) for your exchange account. This adds an extra layer of protection by requiring you to provide two forms of identification when logging in. It significantly reduces the risk of unauthorized access even if your password gets compromised. I always recommend opting for reputable exchanges that prioritize security and implementing strong authentication practices like 2FA. These practices can help you navigate the world of crypto trading more confidently. Equally important they help in minimizing potential risks associated with online transactions. The Psychological Game in Crypto Trading In the world of cryptocurrency trading, emotions often run high. The allure of quick profits can fuel a sense of fear and anxiety, as traders constantly grapple with the fear of missing out (FOMO) on the next big opportunity. This psychological game is not for the faint-hearted. "Mastering your emotions is the ultimate hack in the 2023 crypto game. Knowledge is power, but self-control is the secret sauce." - Michael Lopez, Crypto Educator One moment, you may find yourself caught up in a whirlwind of excitement, witnessing coin prices skyrocket and feeling an overwhelming urge to jump on board before it's too late. That’s FOMO. The fear of missing out leads many traders to make impulsive decisions driven purely by emotion rather than reasoned analysis. On the other hand, there's another emotional driver that often grips crypto traders: fear, uncertainty, and doubt (FUD). We've all seen instances where negative news or market rumors instigate panic within the crypto community. The implosion of FTX serves as a stark example of how negative news and market rumors can trigger panic within the crypto community. Here's why: FTX faced accusations of mismanaging funds and improper accounting practices. These rumors ignited fears about the exchange's solvency. Founder Sam Bankman-Fried attempted to reassure investors on social media. But panic escalated when FTX temporarily halted withdrawals, further fueling suspicion. Within a few days, a massive wave of investors tried to pull their funds. This storming of the gates led to FTX's bankruptcy and subsequent liquidation. The FTX collapse sent shockwaves through the crypto market. Prices of major coins like Bitcoin and Ethereum plummeted as investors feared it could be the tip of the iceberg. Is this situation  indicating broader financial instability within the industry? Social media platforms like Twitter were flooded with panic-stricken posts. Reddit was swamped with users voicing anxieties about the future of crypto and the potential for further exchange failures. News outlets ran headlines highlighting the "FTX contagion," raising concerns about other exchanges and crypto projects potentially being exposed to FTX's troubles. The FTX failure highlighted the importance of transparency and trust in the industry. It increased the demand for stricter regulations, better risk management and harsher penalties for bad actors in the crypto space. FTX is only one example. But it shows how negative news and rumors can rapidly trigger panic within the broader crypto ecosystem. This can lead to significant market downturns where  traders, investors and communities are left holding an empty bag of coins. It is important to educate yourself about various cryptocurrency fundamentals. This will enable you to  invest with confidence instead of blindly taking panic or greed-driven actions. Develop patience combined with disciplined risk management. These skills will help you navigate the crypto rollercoaster ride and eventually yield desirable results. Crystal Ball Gazing: Predictions for the Future of Crypto Trading I’ve shown you that cryptocurrency trading has been a wild and unpredictable ride since it started. But what does the future hold for this volatile market? As adoption and  mainstream recognition increase, many “experts” are making bold predictions about where crypto trading is headed. One major trend that experts believe will shape the future of crypto trading is the increased adoption of blockchain technology by businesses. With its decentralized and secure nature, blockchain has potential applications across various industries. Back in 2019 I wrote about “ .” 21 Inspirational Blockchain Quotes — with Use Cases Today there are more applications in finance, supply chain management, healthcare, and other industries. Widespread adoption could lead to an increase in demand for cryptocurrencies as they become an integral part of these innovative solutions. Another prediction is that regulatory clarity will play a significant role in shaping the future of crypto trading. As governments around the world grapple with how to regulate this new digital asset class, clearer guidelines could provide much-needed stability and confidence to investors. Regulatory frameworks may also help address concerns related to security breaches, fraudulent activities and money laundering risks associated with cryptocurrency trading. In addition, technological advancements are expected to pave the way for improvements in crypto trading infrastructure. Faster transaction speeds through Layer-2 scaling solutions like Lightning Network, or Ethereum's upcoming updates could enhance user experience while reducing fees. Furthermore, developments in decentralized exchanges (DEXs) aim to provide greater security measures without relying on centralized intermediaries. Tectum Softnotes While no one can accurately predict what will happen in the ever-evolving world of crypto trading with certainty, it's clear that exciting changes are on the horizon. As technology continues to advance and regulations adapt, cryptocurrency traders must stay informed about these trends. Only time itself will reveal if these predictions bear fruit or offer unexpected surprises along the way. Is Crypto Trading Risky Business or Your Golden Opportunity? Let’s circle back to my original question and consider what we’ve learned in this post. Crypto trading has emerged as a popular investment avenue. It continues to attract both seasoned traders and beginners looking to dive into the world of digital currencies. But along with the potential for substantial gains, crypto trading also comes with its fair share of risks. One of the first risks that should be addressed is the volatility factor. Cryptocurrencies are known for their wild price swings. We’ve seen dramatic ups and downs within a matter of hours. While this volatility presents opportunities for high returns on investment, it also means there is a higher chance of significant losses. Another risk we identified with crypto trading lies in the unregulated nature of the market. Unlike traditional financial markets where regulations provide some level of protection to investors, cryptocurrencies operate in a decentralized and largely unregulated environment. “Be Your Own Bank” is the war cry. But this lack of regulation opens doors to fraudulent activities including rug pulls, scams and hacks. While this may be true, amidst these risks lie golden opportunities if approached with caution and careful research. Dogecoin, initially created as a joke in 2013, skyrocketed in value during 2021. The pump was heavily fueled by social media hype and celebrity endorsements, particularly from Elon Musk. This "meme coin" frenzy saw Dogecoin's price rise from fractions of a cent to a peak of over $0.70, generating massive profits for early investors. Virtual land in metaverse platforms like The Sandbox and Decentraland saw renewed interest in 2023, with some parcels selling for millions of dollars. Some investors who acquired virtual land early, anticipating its potential value in future metaverse economies, profited from its appreciation. Despite setbacks in 2022, DeFi projects continued to evolve in 2023, with a focus on improving security, scalability and user experience. Those investors who supported promising DeFi 2.0 protocols with strong fundamentals and engaged communities were able to reap rewards as these projects gained traction. As a beginner trader, it is crucial to conduct thorough research before getting involved in any specific cryptocurrency or exchange platform. Stay informed about market trends, and news updates. Follow regulatory developments that may impact prices. Track market sentiment. And always Do Your Own Research. Knowledge is power when it comes to crypto trading. As you grow, explore technical analysis tools like candlestick patterns and charts alongside fundamental analysis techniques. Get an understanding of each project's purpose and utility value before you invest. Conclusion It’s clear that cryptocurrency trading can be an exhilarating rollercoaster ride for beginners. However, with the right knowledge and approach, it can also be a money-making endeavor. We've explored the appeal of cryptocurrencies, their volatile nature and some tips for success in this fast-paced market. Remember to start by choosing a reliable crypto wallet that suits your needs. Understand the risks involved and never invest more than you are willing to lose. Keep up with news and trends in the crypto world to make informed decisions. And lastly, practice safe trading practices such as setting stop-loss orders and diversifying your portfolio. If you're hungry for more information on cryptocurrency trading, there are plenty of resources available online. Here on . Websites like CoinMarketCap provide real-time data on prices and market capitalizations of various cryptocurrencies. Books such as my “ ” explain how to learn more about the world of digital currencies. HackerNoon you can find more than 8,329 stories on cryptocurrency Bitcoin and Crypto for Beginners Whether you're looking for financial gains or simply want to understand this emerging technology better, diving into cryptocurrency trading is an adventure worth embarking on - just remember to buckle up!

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