Kwilis in the Database Technology Industry

Kwil

kwil

Special thanks to Vishwanath Raman from Oasis Labs and David Whittington from AR.IO for feedback and insights. Can distributed ledger technology , helping companies save over per year? Or can blockchains help us build a , helping reduce the nearly ? Could we even use the same technology that powers Bitcoin and Ethereum to enable humans to ? decrease cloud outages $26.5 billion fully connected and autonomous driving network 1.9 million annual traffic deaths worldwide become an interplanetary species The unifying element in these use cases–cryptocurrency, cloud outages, autonomous vehicle networks, and even space travel–is overcoming byzantine failures. While blockchains can’t yet support the throughput or performance required for many of these applications, overcoming byzantine failures and allowing businesses and institutions to share open data in trustless environments has the potential to realize over . $3 trillion per year in economic value At Kwil, we often describe our database software as a “Byzantine Fault Tolerant Postgres.” This article will explain precisely and practically what “Byzantine Fault Tolerant Postgres” means and how it enables real-world, . trustless digital infrastructure “Byzantine Fault Tolerant Postgres” is the combination of two distinct technologies: byzantine fault tolerant (BFT) consensus algorithms and the PostgreSQL database engine. What is Byzantine Fault Tolerance? Byzantine fault tolerance arises from the “Byzantine Generals Problem,” a distributed systems problem by researchers in 1982. The Byzantine Generals Problem imagines a hypothetical scenario of Byzantine armies, each with its own general, camped outside an enemy city. Without a central authority to command and coordinate attacks, the generals must collectively vote on whether to attack or retreat via messengers. conceived The key challenge in the Byzantine Generals Problem is the generals have no way of knowing whether the other generals are loyal or traitors. For example, General 1 could tell Generals 2 and 3 to attack, and General 2 could tell General 3 that General 1 said to retreat (as shown below). General 3 cannot know whether General 1 or General 2 is the traitor, and thus, the three generals cannot progress safely. For a system of number of traitors to proceed safely, there must be 3 + 1 generals; therefore, >⅔ of the nodes must agree and act honestly. If ≥ ⅓ of the nodes in the system act deceptively, then the nodes will not be able to discern the safe path forward (i.e. the system loses liveness). I won’t go into the mathematical proof for the ⅔ threshold, but you can read a succinct and accessible explanation . n n here The key breakthrough with byzantine fault tolerance is that networks can be run and maintained by parties that do not trust each other. The most popular application of byzantine fault tolerance today is cryptocurrency networks; however, other real-world examples include enabling fault-tolerant safety systems on and protecting against nodes corrupted by radiation in SpaceX’s International Space Station . Boeing airplanes landing procedures In a Postgres database network, byzantine fault tolerance means that more than ⅔ of the nodes must agree on the safe path forward. Assuming a blockchain-based approach, this means that greater than ⅔ of the nodes must agree on each block, including all the transactions within the block, before progressing to the next block. Therefore, BFT Postgres networks can be run without trusting the other parties running nodes. What is Postgres? PostgreSQL, also known as Postgres, is an advanced, open-source, object-relational database engine. It is among the pieces of software worldwide, used in production at organizations ranging from the International Space Station to Reddit and Instagram. Relational databases of the total database market, with Postgres commanding 17.4% alone. In the last week, you have probably used or interacted with an application that relies on a Postgres database. most popular comprise 60% The key to Postgres (and other SQL databases) is that it uses the , storing data in structured tables. Tables are comprised of columns and rows, where each row represents an individual piece of data (also called a record), and each column represents an attribute. By storing data in a relational structure, data can be , enabling better maintainability as a dataset grows and the ability to combine data in ways that yield unique insights. For example, a university that needs to manage information about its academic staff and their professional activities could maintain a relational database with the following normalized structure: relational model normalized In the example above, storing data in a normalized structure offers several advantages. First, the normalized structure minimizes data redundancy: each piece of information is stored only once, reducing the likelihood of inconsistencies and errors. Second, it simplifies updates in the database: changes to a professor’s details, like a department change, only need to be made in one table, making the update process more efficient and less error-prone. Third, the relational structure makes it easier to combine relationships and queries across tables, such as the relationship between courses and publications that are relevant to the subject. Lastly, normalization makes database maintenance easier, especially as the volume of data grows over time. A further benefit of the relational model, especially in contrast to , is transactional integrity, guaranteeing that all or none of a database operation is executed and that concurrent transactions do not interfere (also known as ). This is critical in fields where precision, consistency, and reliability are key, such as banking and healthcare. NoSQL databases ACID transactions What is Byzantine Fault Tolerant Postgres? As inferred from the name, “Byzantine Fault Tolerant Postgres” combines the two technologies mentioned above: a byzantine fault tolerant consensus algorithm running over multiple Postgres databases. In a Kwil network, each node maintains a BFT blockchain and a Postgres database. When a state-changing transaction enters the system (e.g. database creation, database drop, insert/update/delete statement, or a funds transfer), the node first broadcasts the transaction to the other validator nodes. Each validator node then uses the transaction's digital signature to determine whether the transaction’s origin is legitimate. To guarantee and data storage and deletion, nodes must first achieve consensus on the valid transactions in a block. Once a block is confirmed, the nodes execute the transactions on their local Postgres database. To propose or validate the next block, a validator must prove that it correctly applied the changes from the previous block. This is proven by generating a proof containing the result of all the proposed changes from the previous block, applying a schema, and generating a resulting hash. The resulting hash is then included in the next block, and each node must have the correct hash to continue in the block mining process. This process ensures that each node stores the data on its Postgres database, guaranteeing that nodes properly insert, update, and delete data. strong consistency deterministic ordering How is BFT Postgres Useful? The idea of a byzantine fault tolerant relational database is not new; multiple have been published about how a generalizable BFT Database could help improve reliability and uptime while protecting against malicious actors in a distributed system. However, until recently, consensus algorithms were neither sophisticated enough nor could handle the data throughput required to be truly byzantine fault tolerant. Recent interest and funding for blockchain technology, largely due to public interest in cryptocurrencies, has spurred significant advances in BFT research and development. academic articles The main advantage of a byzantine fault tolerant database is that it allows for databases to be operated and shared among parties that may not “trust” each other. In a cryptocurrency network, parties may not “trust” each other because malicious actors seek to subvert the system for financial gain. In space landing systems, parties may not “trust” each other because of that corrupt a computer’s memory. Byzantine fault-tolerant databases enable trustless digital infrastructure. radiation-induced “bit flips” A key advantage of trustless digital infrastructure is that, for the first time, it establishes inviolable rules for how an application will behave. Because no centralized actor can subvert, change, or otherwise disrupt the infrastructure, users can confidently rely on that infrastructure without the de-platforming and high rent-seeking risks in centralized systems. For example, the uses Kwil to store user data across a multi-node, multi-party, BFT database network. The idOS’s top consumers—regulated DeFi protocols such as —require a stable and reliable identity , not an identity , to identify and KYC its users. Using a centralized identity would mean that Gnosis Pay could not make cryptographic guarantees about how its entire protocol would function; the identity company could suddenly disrupt the protocol. Using an identity means that Gnosis Pay has an inviolable guarantee for how its user onboarding and KYC will behave, and it can build its platform on the premise that those guarantees will not change. For the idOS, using a byzantine fault tolerant database allows it to make these inviolable guarantees needed to power regulated DeFi protocols. idOS Network Gnosis Pay protocol company company protocol Conclusion A “Byzantine Fault Tolerant Postgres” database is the unlock for trustless digital infrastructure, enabling a freer, fairer, and more resilient internet. As throughput, latency, and generalized computing capabilities continue to accelerate, trustless technologies like a BFT database will be able to reshape the ways we think about and require trust in technology. Does building with a BFT database sound interesting to you? Kwil’s binaries are available and free to use, and we are excited to go open source with our v0.7 release next month. Anyone can start a multi-node, BFT database network with . just four terminal commands Although the (v0.6) is based on the simpler, less feature-rich , the upcoming release (v0.7) uses the PostgreSQL engine. current Kwil release SQLite database engine Questions or comments? Disagree with this article? Join our , or reach out to luke@kwil.com. As always, we welcome community feedback and the opportunity to push decentralized technologies collaboratively into the future. Discord Footnotes [1] Image Source: https://www.cl.cam.ac.uk/teaching/2122/ConcDisSys/dist-sys-notes.pdf [2] Image Source: https://www.cl.cam.ac.uk/teaching/2122/ConcDisSys/dist-sys-notes.pdf

Fortifying Postgres With Byzantine Fault Tolerance

The world of web applications is rapidly undergoing a fundamental shift from web2 to web3 technologies, with decentralization and composability at the heart of this transformation. For dApp developers, this shift is not just a trend but a necessity. Decentralized databases, exemplified by permanent storage solutions like are becoming the backbone of secure, scalable, and user-centric applications. Arweave What Actually Is a Database? Technically, just an “organized set of data held in a computer.” In theory, this means that anything more structured than a pile of files on a disk somewhere can be called a database. In practice, it’s more specific than that. a database is Databases of course provide a place to put files, but most importantly, they provide . A schema plus a query layer. In this sense, Arweave itself is a database -- one big pool of data, searchable with GraphQL. tools to find the exact data an application needs The catch is that in permissionless systems, it’s a matter of bottom-up standards, not top-down schemas. The Atomic Asset (SWS-1) and Asset Discoverability (ANS-110) standards both specify ways to make Arweave data easy to find and use in any application, promoting interoperability. The Rise of Decentralized Databases Censorship, rent-seeking, data availability problems. Decentralized databases have emerged as the antidote to these pitfalls of centralization. They offer unparalleled security, data immutability, and user sovereignty. , for instance, epitomizes this with its "pay once, store forever" model, making it a cost-effective and pro-user solution for permanent dApp state storage. Arweave In the Arweave ecosystem alone, teams at , , , , and more are working to provide developers with a way to easily use and query Arweave with familiar syntax. Kwil WeaveDB HollowDB Glacier Empowering dApp Developers Security and Immutability The decentralized nature of these databases ensures that once data is stored, it becomes immutable. This is ideal for applications requiring secure, tamper-proof systems. Scalability and Flexibility Decentralized databases scale effortlessly with your application. The architecture of decentralized databases ensures data availability even if some nodes go offline, offering a reliable and scalable solution. User-Centric Design Decentralized databases give users unprecedented control over their data. It’s being stored with whatever database layer you choose, but it’s also there forever on Arweave, and no one can take that away. A compelling feature for dApps aiming to be more user-centric via data portability. Cost Efficiency The absence of central servers means no regular maintenance or need to provision infrastructure, reducing operational costs. The Evolution From Centralization In the Web2 era, centralized databases were the go-to solution for data storage and management. While they offered a controlled environment, their limitations were glaring: susceptibility to hacks, high maintenance costs, and being a single point of failure. However, certain centralized elements, such as APIs and caching layers, still hold value and have been integrated into this new generation of databases. The Hybrid Future: Best of Both Worlds While decentralization offers numerous advantages, a balanced approach that incorporates elements of centralization can be incredibly powerful. After all, the blockchain trilemma states that a system weighted towards decentralization and security will sacrifice scalability. Base layer Arweave is both secure and decentralized, but without centralized helpers (caches, gateway magic, APIs, etc.) databases that leverage it can’t provide the experience users or developers have grown to demand -- we’re accustomed to web2. What was once considered blazingly fast now ™, and anything less capable isn’t an acceptable choice for most developers. just works This hybrid model -- a decentralized base with centralized, hot-swappable helper layers -- acts as a bridge between what web2 expects and what web3 enables. Real 🌏 Impact Decentralized databases are making significant inroads in various sectors, from supply chain management and healthcare to financial services. Arweave's permanence is particularly useful in applications requiring long-term data availability. “WeaveDB can replace centralized databases in all cases,” says founder LensPlay Sahil Kakwani. “It's a standout choice for applications requiring decentralization, resilience, and high performance.” In another WeaveDB user interview, founder Harang Ju noted that decentralized solutions “will easily replace traditional centralized databases in places where transparency and openness are key.” Tepe While building the MEM explorer, the Decent Land Labs team implemented Kwil as an alternative to directly querying MEM interactions from the Arweave L1 chain. Compared to the GraphQL option, which takes several seconds, Kwil is able to return data almost instantly. While mainstream adoption may be a little while down the road, essential components are being built and battle-tested in preparation. Where We Stand… At Decent Land Labs, we believe in user sovereignty, data composability, and the . The future is not just decentralized, it’s also hybrid, leveraging the strengths of both centralization to create highly capable, scalable, and interoperable systems with a strong foundation. storage-based computing paradigm By embracing this shift and leveraging hybrid solutions along with the data permanence decentralization offers, developers are not just following a trend; they are preparing themselves for the future. Check out for more insight on how to integrate your dApps today! docs.mem.tech If you enjoyed our content, please and feel free to comment below! SUBSCRIBE Our new protocol release Molecular Execution Machine is now live! - Beta Testing Sign Up mem.tech

The Decentralized Database Revolution

La revolución de las bases de datos descentralizadas

La révolution des bases de données décentralisées

विकेंद्रीकृत डेटाबेस क्रांति

分散型データベース革命

A revolução do banco de dados descentralizado

Cuộc cách mạng cơ sở dữ liệu phi tập trung

去中心化数据库革命

While Web3 is screaming decentralization, the likes of Facebook, Amazon, and Google are hoping it's just a false alarm. As the internet moves closer to the era of decentralization with Web3, it's important to understand clearly how data control will be affected and the likely turn data engineering will take. The internet has welcomed many stages of evolution since its inception with the internet moving from being merely a news board to a place for interaction through the medium of social media. As human experiences are being gradually improved upon, there are concerns about how much data one can control in the bid to enjoy personalized services. What will be the fate of the numerous tech giants that depend on vast quantities of user data to offer personalized user experiences, which is undoubtedly a guaranteed strategy for success? This is the best time to quote Aaron Swartz briskly: Information is power, but like power, there are those who want to keep it to themselves. Web 3 appears to offer a safer online environment with improved data privacy and security. Although adoption is still in its early stages, it is crucial to prepare for what to anticipate in the wake of the next wave of the 21st-century internet. This is a tale of the data control dilemma with the encroachment of decentralization and loudly preached transparency. Let the tales begin! We will start with the basics. ? What Is Web3 Web3 is the next stage of the internet evolution. Powered by technologies like the blockchain, decentralized protocols, and cryptographic systems which store data using to ensure the data is secure, Web3 moves from the era of a centralized web as seen in Web2 to that of a decentralized web. cryptographic algorithms Still in doubt? Listen to Aya Miyaguchi, the Executive Director of Ethereum. Web 3 is about creating a more decentralized and open internet where and interactions, without relying on centralized intermediaries for access and validation. users have control over their data ? What Are Cryptographic Algorithms Imagine you have a secret message to send to your friend. You want to send it through the internet, but you're not sure of the safety of that message. You don't want anyone else to know what it entails, so you decide to use a special code that just you and your friend can comprehend to write the message. Based on the level of your friendship, you may decide to twist some words to mean something else that can only be decoded between you and your friend. That's where cryptography comes in. Adding an algorithm would be including a special set of rules to your codified message to enable your friend to understand it. Now, let's get a definition. Cryptographic algorithms are rules that turn secret messages into secret codes that only the parties involved in the interaction can understand. and cryptographic algorithms used to store data on the blockchain ensure the security and confidentiality of personal information. Privacy is a fundamental pillar of Web 3, By prioritizing privacy and security, Web 3 empowers individuals to take control of their data and protects them from the invasive practices of centralized entities. What Is a Decentralized Web? This term describes an internet environment that doesn't rely on a single entity like Google, Facebook, or Yahoo for control. Instead, it uses blockchain technology to enable direct interaction between users- all the computers and devices are equal and can communicate seamlessly. peer-to-peer In the Web 3 world, we don't need middlemen to trust each other. We can trust the system itself. - Joseph Lubin, Co-founder of Ethereum & CEO of ConsenSys Imagine walking into a room and interacting with everyone without hassle, that's what - feels like! peer to-peer Web3 Is Coming… Thanks to the early adopters of Web3, there are gradually more promising Web3 alternatives for day-to-day tasks that pledge to provide better experiences in terms of data security and speed such as: Brave for browsing Metamask to send money Opensea for buying and selling Mastodon to have more control over your data while still enjoying the benefits of social media. Filecoin to store and manage data rather than uploading to your Google Drive Data storage already has Web3 alternatives. Yeah! An anonymous person source says, " " Rightly so! Whoever controls the data, controls the truth. Web3 is giving more control to individuals so can have a stake in decision-making, and that's how life should be! Data is a valuable asset at times termed as especially in this present era where data is used to improve user experience and help businesses make decisions that lead to growth. you gold, The principles of centralized data management nullify the need for democracy and transparency when dealing with data in the Web2 space. Centralization is not bad We have all benefited from personalized services like having Amazon suggest what you might like based on the last thing you searched for or even bought. Web3 is a new layer of the internet that largely leans on the efforts of centralization to make data more accessible and secure. Decentralization doesn't always mean private Like many newbies in this ecosystem, it's easy to take on the thought that decentralization guarantees privacy. No, it doesn't. " ." Many decentralized applications (dApps) and smart contracts built on decentralized networks like Ethereum collect and store data in transparent, publicly accessible databases Too Many Broken Trusts Many times we have been forced to trust like Facebook with our data, and oftentimes they keep breaking it. tech lords Take the in March 2018 where millions of Facebook users had their data collected without their consent for political advertising reasons. Facebook Cambridge Analytica scandal It was quite easy for Cambridge Analytica, a British political consulting firm, to obtain tons of people’s data through a third-party app - . Your Digital Life The app helped the firm obtain personal details of individuals who signed up as well as details of their friends on Facebook all without their consent. After that incident, which had over 87 million users' data , it became more glaring to the public that Facebook owns and controls the data of everyone on its platform. Till now, users have little visibility on how accessible their personal data is. hijacked Web3 exists to prevent such scandals by giving you the power to control your data. It also promises you that your data is safe by eliminating third-party centralized networks for data storage which are vulnerable to attacks. Talking about centralized networks/servers, I think this is the best time to bring in the discussion on the synergy between big data, data engineering, and centralized servers. Data Engineering & How It Plays Out in Web2 Data engineering refers to the process involved in building an infrastructure with tools such as , , and to collect and transform big data for the benefit of business use which will result in growth. Apache Spark Hadoop Cassandra Big data, on the other hand, refers to large amounts of data- structured, unstructured, and in some cases, semi-structured, generated daily through the internet by individuals and businesses. The importance of data engineering cannot be overlooked- tons of data (big data) are constantly collected, harnessed, and processed through pipelines to provide valuable business insights and decisions. Now, a major limitation of this is that data engineering relies on centralized systems for data storage hence, is prone to a single point of failure - centralized networks have data processed and stored by a single node; in a case where the primary server crashes, there would be a general downtime which could lead to data loss. Also, individuals/businesses have little or no control over their data, and virtually every business now employs centralized servers. What do they say about times and seasons? Well… Here are some limitations of centralized cloud computing: Users' data can be stored and used without their consent. In the place of Google, personal data such as search history, personal preferences, and also location can be arbitrarily used. Data is prone to hacking by third parties just like the Facebook scandal. The operations will be slower leading to stifled scalability- following the dependency on primary servers(less division of labor). Servers are vulnerable to a single point of failure as explained above. Is that the path data management should continue in? I don't think so. There seems to be a way out -thankfully, with the "decentralized mandate" on Web3, multiple nodes can process and store large amounts of data. Now, let me help you understand this. Decentralized storage protocols can be likened to keeping your eggs safe in different baskets- all in the same room each stored in a well secure basket that can only be accessed with permission (on-chain). Centralized storage servers can be likened to keeping all your eggs in one basket relatively safe. The safety of the eggs can be easily tampered with when it's in one basket. That's how vulnerable centralized servers are. Notice that I highlighted that, in the first case scenario, these eggs are in the same room, so there's still a connection between them - that explains the interconnection between decentralized nodes. Just like the eggs in scenario 1, all placed in different baskets interconnected nodes or blocks are more secure and easy to detect when something goes wrong. But then, there's still a problem. Navigating the Early Stage of Innovation Illuminates a Myriad of Challenges The emergence of true data sovereignty in a decentralized environment has uncovered many holes that should be filled. At this current stage where Web2 is still fully in existence, many applications utilize centralized servers including Web3 dApps because there aren't yet fully equipped decentralized storage protocols to handle massive data . yet Web3 developers crave a more sophisticated decentralized database environment following the massive and complex nature of data generated in the ecosystem. Let's face it; with millions of transactions and blocks added daily, developers need to access and query data in real-time. Speed is of necessity for dApps to scale efficiently. Here are some of the problems faced by developers: Some existing decentralized storage protocols like and are limited to the storage of only static files. Arweave IPFS The abundance of Web3 applications means more complex data to deal with like NFT data, tokens, wallets, etc. Following the dynamic nature of Web3 data, these distributed protocols make it difficult for developers to easily query data, leaving most of the data stored in silos unused. Also, such a rigid storage environment slows down deployment. These vast amounts of data lying fallow in silos, which are not maximized are kept to waste because they can't be easily accessed when needed - in real-time. This affects the application lifecycle. : The time and expense required to build a robust database infrastructure to tally with the demands of Web3 complexities will give Web3 developers a second thought to continue with centralized databases which they are even more familiar with. It is a complex process to build from scratch, and there is scarce time to do that while trying to scale. Cost implications One of the pillars of a Web3 environment is . Developers need to trust that they can get reliable data from the emerging decentralized storage infrastructures available. trust Using blockchain specifically to store data takes a lot of time and isn't any different from using centralized servers; transactions are slower and take longer time to validate. In a bid to solve this, developers might resort to building a decentralized on-chain database. While this would be more effective, it is an extremely complex task to get started with. Who Owns the Data in Web3? Various Web3 projects may have distinct approaches to data ownership. However, ownership of data is distributed across nodes or computers to enable transparency, censorship resistance, and immutability. This distribution is carried out by decentralized systems including blockchain technology. Individuals have full control of their data and can decide which data they want to share. How Is Data Stored in Web3? Data storage in Web3 is usually carried out with decentralized technologies like the blockchain, distributed storage networks, and decentralized file systems like (InterPlanetary File System) Now we will take them in bits so you can understand: : A strong force to be reckoned with in the Web3 ecosystem that allows you to store data in blocks and link them securely through cryptography. (Remember the definition above) Blockchain It is usually used to store transactional data through an immutable chain, hence its name- blockchain. :  This decentralized storage type leverages the collective storage ownership of participants in the network and incentivizes them to share more of their unused storage space. The storage space is then broken into bits and distributed across multiple nodes. Distributed storage networks : A peer-to-peer distributed storage environment that leverages on content addressable model: a way to recognize files by their cryptographic hash. It helps in the storage and retrieval of files. Decentralized File Systems (IPFS) Which Web3 databases Are Available? As Web3 is buzzing, many developers have discovered a missing tech stack in the ecosystem, a database for Web3; below are a few emerging databases you can use: Polybase Glacier Ceramic WeaveDB IceFireDB Kwil Space and Time (Web3 data warehouse approach) Thus Far… Some Web3 databases have emerged to solve major pain points that Web3 developers face to scale their dApps efficiently. From the likes of a handful of examples listed above amongst others, their approach in ways is similar by providing: : A better interconnected organized space for Web3 data that allows for easy access and allows developers to build on the infrastructure seamlessly. Programmable composable data network :  With traditional databases rigid on structured data i.e., having rows, tables, and columns, it eliminates the chances to maximize Web3 data which comes in more complex forms. NoSQL databases will help handle Web3 data in any form with more flexibility and efficiency. Using the NoSQL database structure, different types of data can be stored without necessarily fitting into a predefined schema. NoSQL database : Just like building blocks, different parts of the environment can be easily put together or taken out to meet needs per time- it's customizable and dynamic in nature. Modularity : A robust tool that meets the heavy data demands of massive Web3 data without getting overwhelmed or reducing efficiency. Scalable infrastructure : This is one word you may struggle to pronounce but will regularly come across when talking about Web3 applications. I'll try to help you understand it in the best way possible 🙏 Interoperability Imagine that you have a flash drive that can easily connect to 3 computers. That drive is able to share information easily with each of the computers. That's how it works too in Web3- various dApps can share information with each other and work together even if the applications were built by different people. It's more like a meeting point of agreement despite the uniqueness of the applications. Emerging Web3 databases are receptive to allowing various dApps to interact and collaborate seamlessly on their platform. Business applications can interact without intermediaries. Glacier particularly, intends to with the ownership of Web3 data to bring to reality the monetization of Web3 data subsequently. Data monetization: combine NFT technology Exciting Times Ahead for Data Engineers As Web3 gradually encroaches into the way everything on the internet is done, data engineering will also experience interesting steps towards decentralization. Data engineers, the core architects of data pipelines, will have to learn new ways to design and maintain decentralized environments that meet the demands of data sovereignty which covers security, integrity, and privacy through immutability. Also, businesses will garner more trust seeing that their data is protected from unauthorized access, deletion, or manipulation. The smooth transition from Web2 to Web3 is also not leaving out Web2 applications, some of the databases listed above like IceFireDB, is providing a solution for Web2 and Web3 applications to access decentralized storage. Automation is a one-time button for repetitive tasks. Data engineers can employ smart contracts - programmable self-executing contracts to automate some data management practices. How Will Tech Giants React to the Full Unfolding of Web3? Like many things that trended within a particular time frame in the past, there came a time they also had to fade because they weren't able to successfully withstand change. That's the same way this will play out. Facebook, Amazon, and Google may not disappear entirely if they can adapt and implement the necessary changes to transition towards a more open and transparent internet. Facebook has gradually shown support for certain aspects of the Web3 sphere, such as the metaverse and NFTs, despite its to shut down its NFT platform for Facebook and its sister platform, Instagram. recent decision Everyone needs to learn constantly about the evolving direction of the internet because this next phase is really about and you do have a stake. you, Additionally, there would be a more transparent approach to handling consumer data as a business, ensuring direct consent through a decentralized web. It's truly an incredible journey to anticipate, albeit at a leisurely pace. Takeaway Just as you wouldn't be needing another person's phone to make calls, Web3 developers crave an environment for decentralized storage that fully exceeds expectations rather than blindfolding themselves to the many limitations of centralized facilities. Get excited about the new wave of the internet -Web3, where you are more involved and protected.

Who Will Eventually Control Big Data in Web3?

This all started with an email from a publicist offering to provide me with a sample of the in exchange for a review. The Kwilt Shoebox is a device designed to allow you to build a “personal cloud” — it connects to your smartphone and other devices via WiFi and allows you to offload files/photos to it in order to free up storage. Kwilt Shoebox Eventually I received the device. Upon opening the package, I was presented with some tasteful packaging and a series of adapters for the device. The Shoebox is designed to connect to USB storage mediums such as a flash drive or external hard drive. It only features a Micro-USB port for interfacing with peripherals, so a Micro-USB to USB-A adapter is included in the box. The Shoebox can also connect to a TV to display your photos; it comes equipped with a Mini-HDMI connector, so another adapter is included in the box. At this point something started to feel strange. For a device designed to connect directly to storage mediums and TVs, this choice of using adapters instead of just the correct connector was odd. The placement of the ports also felt a little weird — the power and USB port utilize the same connector, and are placed directly adjacent which could cause some confusion. The next thing that caught my eye was a large silver sticker not featured in promotional shots of the device. The sticker is plastered across the side of the Shoebox, not on the bottom like one might expect. Listed in very small print on the bottom of this sticker are the words “removing this label will void your warranty,” which is tech-speak for “something interesting is underneath here.” Naturally, I peeled it off. Seeing what was underneath the sticker made everything click into place. The green PCB, familiar layout of pins, and the word “Raspberry” peeking out from under the red case all immediately reveal that this device is in fact a Raspberry Pi Zero W. This is the first time I’ve ever seen a Raspberry Pi device make it into a consumer product without immediately revealing itself. I have definitely seen other RPi products before; for example, the 3D printer interface is based around the Zero’s bigger sibling. The difference here is that AstroPrint makes it very clear that the device is powered by a Raspberry Pi — it’s targeted towards makers and individuals that would likely want to modify the hardware. The Kwilt website, however, makes no mention of the Raspberry Pi anywhere. This makes sense considering the Shoebox’s targeted market of general consumers who wouldn’t have much of a desire to hack the device. AstroBox Using the Raspberry Pi as a platform is a smart decision on the part of Kwilt for numerous reasons. It has all the necessary capabilities to fulfill the goal of the company, and all of the Zero’s ports can be utilized for real purposes meaning there’s few wasted costs. Additionally, it’s likely that FCC and other licensing is much easier given that the Raspberry Pi Zero is already licensed. And, of course, there are no custom hardware fabrication and design costs. This allows Kwilt to focus their efforts on their well-done web app and mobile app that allows customers to interface with the device. Even is created by the Raspberry Pi foundation. I totally understand Kwilt’s approach to this project — they didn’t want to deal with creating a circuit, manufacturing that circuit, packaging the circuit, and more just to realize their initial concept. Kwilt can focus purely on creating a software interface for this project which is really heart of the whole system. No hardware experience necessary. the case that houses the device My next thought concerned the security of such a platform. I located the IP address of the shoebox and ran a scan via nmap to get networking information about the device. Sure enough, the Shoebox reveals to us that it’s running stock Raspian, and that port 22 is open for SSH like on a stock Raspberry Pi. Kwilt developers were smart enough to change the default SSH password away from “raspberry”, which would have granted easy access to the device. Next, I tried interrogating my poor shoebox for a few days using . Hydra is a password cracking tool that essentially tries to brute force its way into a device by trying different passwords over and over again. I made mine a little smarter by downloading a containing the most frequently used passwords. I got all the way from “123456” to “nachito” without finding a valid key, so the password securing SSH is at least relatively good. An analysis of all of the traffic coming into and out of the Shoebox reveals that all essential operations are performed via HTTPS, which is also good to see. Hydra 60Mb file All in all, the Shoebox appears to be a powerful device that well-utilizes the capabilities of the Raspberry Pi Zero W. The $60 price tag on the device is a significant markup over the $10 Zero W, but given that the company includes a bunch of adapters and a 32-Gb USB key and that they need to pay all their software engineers and marketers this makes sense. Obviously the Pi Zero platform is viable for consumer products, so I’m curious to see where other companies take this next.

The Rise of Raspberry Pi in Consumer Hardware

From Chat-Bots to Killer-Bots?

Yuan Yi Zhu, an Assistant Professor of International Relations and International Law at Leiden University, recently criticized an article published in The Times of London. The article was written by Matthew Parris, a former Member of the British Parliament. In the article, Parris argues in favor of legalizing euthanasia on the grounds that society simply cannot afford “desperate infirmity for as many such individuals as our society is producing.” Parris argues that it would allow the UK economy to better compete with China and people being coerced to being euthanised due to social pressure is “not a bad thing”. Parris says he does not apologise for how his article "treats human beings as units - in deficit or surplus to the collective". In February 2024, an article was published in the prestigious Scientific Reports journal by Nature, arguing that “AI holds the potential to carry out several major activities at much lower emission levels than can humans.” The piece argues that humans produce far greater CO2 emissions when doing writing or illustration work than AI Large Language Models. The article also notes that: “The freed human time may also incur new, unexpected environmental costs.” Advances in AI haven’t just led to humans being compared to machines but also being compared to animals. Late last year, according to The Guardian, BT’s Chief Innovation Officer, Harmeen Mehta ‘suggested workers whose jobs are threatened by AI accept their fate as “evolution,” comparing them to horses replaced by the car.’ Against this backdrop, one does wonder when we will inevitably see the rise of those who will argue for eugenics such that humans replace machines. This is nevertheless a stark reminder of how humans ultimately are behind killer computers. In a new book I authored called ‘ ,’ I have explored many catastrophic computer disasters. These disasters have included an aircraft entering ‘death dives’, fatal car crashes and lethal radiation doses. How to Protect Yourself from Killer Computers Recently, software reliability has been pushed to the forefront of the public conscience. March 2024 saw system failures, causing McDonald’s outlets in the UK, Australia, and Japan to go offline. Panama Bread in the US saw IT outages, as did the UK high-street giants Tesco, Sainsbury's, and Greggs. As several Boeing executives stood down from their jobs following the death of a whistleblower, it’s worth remembering how the first Boeing 737 MAX groundings began with a computer software problem. Previously unreported, it was also the case that March 2024 saw London police officers report chaos in jails and being unable to begin search warrant applications, as there was also an outage with the Metropolitan Police’s IT systems. The public inquiry into the UK Post Office’s Horizon IT Scandal is underway, a scandal where faulty accounting software resulted in miscarriages of justice, claiming around 700 victims and has been linked to four suicides. This included a pregnant mother being sent to jail. Engineers are now facing the consequences for being complicit in disasters; with FTX’s former Director of Engineering facing up to 75 years in prison, a VW engineer who was complicit in the emissions scandal having been sentenced to 40 months imprisonment despite co-operating with investigators and the architect of the Post Office Horizon IT system currently under investigation by UK police for perjury. In common across all these disasters, whilst the technical factors varied, I found a common theme of wrongdoing, retaliation, and cover-up. Issues were brushed under the carpet instead of addressed directly. For example, one engineering manager at Fujitsu, who built the software behind the Post Office scandal, told the public inquiry how he “was threatened with violence by an individual with a reputation for violence” during one of his early attempts to remedy the issues. This is an industry-wide problem. In November 2023, I led research that found that 53% of software engineers suspected wrongdoing at work, with 75% of software engineers in the UK having experienced retaliation the last time they reported it to their employers. This is a concern as computers do inevitably make mistakes as no software is perfect. Small things like a binary number being flipped (e.g., a “1” becoming a “0”) due to cosmic rays are enough to cause havoc in computer systems, particularly those that haven’t been designed with this risk in mind. There is, however, hope here. The research conducted in the new book finds that resilient systems can be built when humans are part of the system. In these , humans play a role in preventing disaster by raising the alarm and mitigating the issues. socio-technical systems For this to happen, humans must be free to raise the alarm to issues when they come across them. If the on-call engineers who keep our online services and critical national infrastructure running are not able to speak up and act to prevent disaster, these problems emerge. However, this applies to all software engineers and, indeed, everyone who is part of the software development process, including the customers. Killer computers are born in toxic cultures where engineers aren’t able to raise the alarm, and issues are brushed under the carpet. By making sure humans play a key role in the reliability of computer systems, we can help ensure technology serves society.

The Killer Humans Behind Killer Computers

Will a LinkedList be faster? Should I swap the `for each` with an `iterator`? Should this `ArrayList` be an `Array`? This article came to be in response to an optimization so malevolent it has permanently etched itself into my memory. Before going head-on into Java and the ways to tackle interference, either from the garbage collector or from context switching, let's first glance over the fundamentals of writing code for your future self. Premature optimization is the root of all evil. You've heard it before; premature optimization is the root of all evil. Well, sometimes. When writing software, I'm a firm believer in being: as ; you should try to narrate intentions as if you were writing a story. descriptive as possible as ; which means that you should know the fundamentals of the language and apply them accordingly. optimal as possible As Descriptive as Possible Your code should speak intention, and a lot of it pertains to the way you name methods and variables. int[10] array1;        // bad
int[10] numItems;      // better
int[10] backPackItems; // great Just by the variable name, you can already infer functionality. While is abstract, tells you a lot about expected behavior. numItems backPackItems Or say you have this method: List<Countries> visitedCountries() {
    if(noCountryVisitedYet)
        return new ArrayList<>(0);
    }
    // (...)
    return listOfVisitedCountries;
} As far as code goes, this looks more or less ok. Can we do better? We definitely can! List<Countries> visitedCountries() {
    if(noCountryVisitedYet)
        return Collections.emptyList();
    }
    // (...)
    return listOfVisitedCountries;
} Reading is much more descriptive than Collections.emptyList() new ArrayList<>(0); Imagine you're reading the above code for the first time and stumble on the that checks if the user has actually visited countries. Also, imagine this is buried in a lengthy class, reading is definitely more descriptive than , you're also making sure it's immutable making sure client code can't modify it. guard clause Collections.emptyList() new ArrayList<>(0) As Optimal as Possible Know your language, and use it accordingly. If you need a , there's no need to wrap it in a object. The same goes for using a if all you actually need is an . double Double List Array Know that you should concatenate Strings using or if you're sharing state between threads: StringBuilder StringBuffer // don't do this
String votesByCounty = "";
for (County county : counties) {
    votesByCounty += county.toString();
}

// do this instead
StringBuilder votesByCounty = new StringBuilder();
for (County county : counties) {
    votesByCounty.append(county.toString());
} Know how to index your database. Anticipate bottlenecks and cache accordingly. All the above are optimizations. They are the kind of optimizations that you should be aware of and implement as first citizens. How Do You Kill It First? I'll never forget about a hack I read a couple of years ago. Truth be said, the author backtracked quickly, but it goes to show how a lot of evil can spur from good intentions. // do not do this, ever!
int i = 0;
while (i<10000000) {
    // business logic
    
    if (i % 3000 == 0) { //prevent long gc
        try {
            Thread.sleep(0);
        } catch (Ignored e) { }
    }
} A garbage collector hack from hell! You can read more on why and how the above code works and, while the exploit is definitely interesting, this is one of those things you should ever do. in the original article never Works by side effects, has no purpose in this block Thread.sleep(0) Works by exploiting a deficiency of code downstream For anyone inheriting this code, it's obscure and magical Only start forging something a bit more involved if, after writing with all the , you've hit a bottleneck. But steer away from concoctions as the above. default optimizations the language provides How to Tackle Garbage Collector? That If after all's done, the Garbage Collector is still the piece that's offering resistance, these are some of the things you may try: If your service is so latency-sensitive that you can't allow for GC, run with . "Epsilon GC" and avoid GC altogether -XX:+UnlockExperimentalVMOptions -XX:+UseEpsilonGC This will obviously grow your memory until you get an OOM exception, so either it's a short-lived scenario or your program is optimized not to create objects If your service is somewhat latency sensitive, but , run GC1 and feed it something like (default is 250ms) the allowed tolerance permits some leeway -XX:MaxGCPauseTimeMillis=100 , say one of them calls or which are stop-the-world garbage collectors, you can override offending behavior by running with If the issue spurs from external libraries System.gc() Runtime.getRuntime().gc() -XX:+DisableExplicitGC If you're running on a JVM above 11, do try the , performance improvements are monumental! . You may also want to check this . Z Garbage Collector (ZGC) -XX:+UnlockExperimentalVMOptions -XX:+UseZGC JDK 21 GC benchmark VERSION START VERSION END DEFAULT GC Java 1 Java 4 Serial Garbage Collector Java 5 Java 8 Parallel Garbage Collector Java 9 ongoing G1 Garbage Collector Note 1: since Java 15, is , but you still have to explicitly activate it with . ZGC production-ready -XX:+UseZGC Note 2: The VM considers machines as server-class if the VM detects more than two processors and a heap size larger or equal to 1792 MB. If not server-class, it . will default to the Serial GC In essence, opt for GC tuning when it's clear that the application's performance constraints are directly tied to garbage collection behavior and you have the necessary expertise to make informed adjustments. Otherwise, trust the JVM's default settings and focus on optimizing application-level code. - you'll want to read the u/shiphe full comment Other Relevant Libraries You May Want to Explore: Java Microbenchmark Harness (JMH) If you're out of feeling without any real benchmarking, you're doing yourself a disservice. JMH is the Java library to test your algorithms' performance. Use it. optimizing de facto Java-Thread-Affinity Pinning a process to a specific core may improve cache hits. It will depend on the underlying hardware and how your routine is dealing with data. Nonetheless, this library makes it so easy to implement that, if a CPU-intensive method is dragging you, you'll want to test it. LMAX Disruptor This is one of those libraries that, even if you don't need it, you'll want to study. The idea is to allow for ultra-low latency concurrency. But the way it's implemented, from to the brings a lot of new concepts. I still remember when I first discovered it, seven years ago, pulling an all-nighter to digest it. mechanical sympathy ring buffer, Netflix jvmquake The premise of is that when things go sideways with the JVM, you want it to die and not hang. A couple of years ago, I was running simulations on an HTCondor cluster that was on tight memory constraints, and sometimes, jobs would get stuck due to "out of memory" errors. jvmquake This library forces the JVM to die, allowing you to deal with the actual error. In this specific case, HTCondor would auto-re-schedule the job. Final Thoughts The code that made me write this post? I've written way worse. I still do. The best we can hope for is to continuously mess up less. I'm expecting to be disgruntled looking at my own code a few years down the road. And that's a good sign. You can find this and other articles at wasteofserver.com Edits & Thank You: to for catching the mutability error in and the detailed explanation. FlorianSchaetz visitedCountries() to and for explaining that , you get Serial GC u/brunocborges u/BikingSquirrel on lower-end machines to for taking the time to better explain when you should mess with the GC and u/shiphe when you shouldn't to for putting me on the right track regarding what practice u/tomwhoiscontrary should be considered standard to (again) for providing the link to the JDK 21 garbage collector benchmark u/BikingSquirrel Also published on wasteofserver.com

Software Thrives Unless You Kill it First: Premature Optimization and a Tale of Java GC

সফ্টওয়্যার উন্নতি লাভ করে যদি না আপনি এটিকে প্রথমে হত্যা করেন: অকাল অপ্টিমাইজেশান এবং জাভা জিসির একটি গল্প

Software gedeiht, wenn man sie nicht vorher tötet: Vorzeitige Optimierung und eine Geschichte von Java GC

El software prospera a menos que lo elimines primero: optimización prematura y una historia de Java GC

Le logiciel prospère à moins que vous ne le tuiez d’abord : optimisation prématurée et histoire de Java GC

सॉफ्टवेयर तब तक फलता-फूलता है जब तक आप उसे पहले खत्म नहीं कर देते: समयपूर्व अनुकूलन और जावा जीसी की कहानी

ソフトウェアは、最初に殺さない限り繁栄する: 時期尚早な最適化と Java GC の物語

소프트웨어는 먼저 죽이지 않으면 성공한다: 성급한 최적화와 Java GC 이야기

O software prospera a menos que você o elimine primeiro: otimização prematura e uma história de Java GC

Программное обеспечение процветает, если вы не убьете его первым: преждевременная оптимизация и рассказ о Java GC

Yazılım, Siz Önce Onu Öldürmediğiniz Sürece Büyür: Erken Optimizasyon ve Bir Java GC Hikayesi

Phần mềm phát triển mạnh trừ khi bạn tiêu diệt nó trước: Tối ưu hóa sớm và câu chuyện về Java GC

软件蓬勃发展，除非你先杀死它：过早优化和 Java GC 的故事

This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license.
Authors:
(1) Yejin Bang, Centre for Artificial Intelligence Research (CAiRE), The Hong Kong University of Science and Technology;
(2) Nayeon Lee, Centre for Artificial Intelligence Research (CAiRE), The Hong Kong University of Science and Technology;
(3) Pascale Fung, Centre for Artificial Intelligence Research (CAiRE), The Hong Kong University of Science and Technology. Table of Links Abstract and Intro
Related Work
Approach
Experiments
Conclusion
Limitations, Ethics Statement and References
A. Experimental Details
B. Generation Results 6.1. Limitations The study is limited by its adherence to the benchmark’s English-based task setup. The analysis is constrained to political ideologies in the United States and the English language. Additionally, the BART model’s 1024 sub-token input limit restricts the number of biased source articles that can be included as an input. It is important to note that these limitations, while potentially impacting the scope of the study’s findings, are not uncommon in natural language processing research. Nonetheless, future research may benefit from addressing these limitations by exploring alternative methods for a broader range of political ideologies (NonU.S. political ideologies) and languages, as well as incorporating longer input texts to capture a more comprehensive range of source articles. 6.2. Ethics Statement The issue of biased articles with framing has been extensively studied, as it can lead to polarization by influencing readers’ opinions toward a certain person, group, or topic. To address this problem, our research focuses on introducing a loss function that can be incorporated to enable the model to reduce framing bias in the generated summary. However, it is important to recognize that automatic technologies can also have unintended negative consequences if not developed with careful consideration of their broader impacts. For example, machine learning models can introduce bias in their output, replacing known source bias with another form of bias (Lee et al., 2022). To mitigate this risk, Lee et al. (2022) have suggested including explicit mention of the source articles alongside automatically generated neutral summaries. Furthermore, while our work aims to remove framing bias in human-generated articles, there is the potential for hallucination in the generation, which is a well-known problem of generative models (Ji et al., 2023). Thus, it is important to equip a guardrail (e.g., a provision of source reference) if such automatic technology is implemented for actual use cases. Despite these challenges, our research can contribute to the effort of mitigating human-generated framing bias in order to reduce polarization in society. One of the use cases can be to aid human experts in the process of providing multi-view synthesized articles without framing bias. In terms of broader societal impact, we hope our work can help online users access more depolarized information online. 6.3. References 2021. Center – what does a "center" media bias rating mean? Ramy Baly, Giovanni Da San Martino, James Glass, and Preslav Nakov. 2020. We can detect your bias: Predicting the political ideology of news articles. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 4982–4991, Online. Association for Computational Linguistics. Adriana Beratšová, Kristína Krchová, Nikola Gažová, and Michal Jirásek. 2016. Framing and bias: a literature review of recent findings. Central European journal of management, 3(2). Dennis Chong and James N Druckman. 2007. Framing theory. Annu. Rev. Polit. Sci., 10:103–126. Robert M Entman. 2002. Framing: Towards clarification of a fractured paradigm. McQuail’s Reader in Mass Communication Theory. London, California and New Delhi: Sage. Robert M Entman. 2007. Framing bias: Media in the distribution of power. Journal of communication, 57(1):163–173. Robert M Entman. 2010. Media framing biases and political power: Explaining slant in news of campaign 2008. Journalism, 11(4):389–408. Alexander R Fabbri, Irene Li, Tianwei She, Suyi Li, and Dragomir R Radev. 2019. Multi-news: A large-scale multi-document summarization dataset and abstractive hierarchical model. arXiv preprint arXiv:1906.01749 Lisa Fan, Marshall White, Eva Sharma, Ruisi Su, Prafulla Kumar Choubey, Ruihong Huang, and Lu Wang. 2019. In plain sight: Media bias through the lens of factual reporting. arXiv preprint arXiv:1909.02670. Matthew Gentzkow and Jesse M Shapiro. 2006. Media bias and reputation. Journal of political Economy, 114(2):280–316. Matthew Gentzkow, Jesse M Shapiro, and Daniel F Stone. 2015. Media bias in the marketplace: Theory. In Handbook of media economics, volume 1, pages 623–645. Elsevier. Erving Goffman. 1974. Frame analysis: An essay on the organization of experience. Harvard University Press Felix Hamborg, Karsten Donnay, and Bela Gipp. 2019. Automated identification of media bias in news articles: an interdisciplinary literature review. International Journal on Digital Libraries, 20(4):391–415. Felix Hamborg, Norman Meuschke, and Bela Gipp. 2017. Matrix-based news aggregation: exploring different news perspectives. In 2017 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pages 1–10. IEEE. Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung. 2023. Survey of hallucination in natural language generation. ACM Comput. Surv., 55(12). Daniel Kahneman and Amos Tversky. 2013. Prospect theory: An analysis of decision under risk. In Handbook of the fundamentals of financial decision making: Part I, pages 99–127. World Scientific. Philippe Laban and Marti A Hearst. 2017. newslens: building and visualizing long-ranging news stories. In Proceedings of the Events and Stories in the News Workshop, pages 1–9. Nayeon Lee, Yejin Bang, Tiezheng Yu, Andrea Madotto, and Pascale Fung. 2022. NeuS: Neutral multi-news summarization for mitigating framing bias. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 3131–3148, Seattle, United States. Association for Computational Linguistics. Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer. 2019. Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv preprint arXiv:1910.13461. Yujian Liu, Xinliang Frederick Zhang, David Wegsman, Nicholas Beauchamp, and Lu Wang. 2022. POLITICS: Pretraining with same-story article comparison for ideology prediction and stance detection. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 1354–1374, Seattle, United States. Association for Computational Linguistics. Saif Mohammad. 2018. Obtaining reliable human ratings of valence, arousal, and dominance for 20,000 english words. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 174–184. Fred Morstatter, Liang Wu, Uraz Yavanoglu, Stephen R Corman, and Huan Liu. 2018. Identifying framing bias in online news. ACM Transactions on Social Computing, 1(2):1–18. Kishore Papineni, Salim Roukos, Todd Ward, and WeiJing Zhu. 2002. Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pages 311–318. Souneil Park, Seungwoo Kang, Sangyoung Chung, and Junehwa Song. 2009. Newscube: delivering multiple aspects of news to mitigate media bias. In Proceedings of the SIGCHI conference on human factors in computing systems, pages 443–452. Dietram A Scheufele. 2000. Agenda-setting, priming, and framing revisited: Another look at cognitive effects of political communication. Mass communication & society, 3(2-3):297–316. All Sides. 2018. Media bias ratings. Allsides.com. Timo Spinde, Christina Kreuter, Wolfgang Gaissmaier, Felix Hamborg, Bela Gipp, and Helge Giese. 2021. Do you think it’s biased? how to ask for the perception of media bias. In 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pages 61–69. IEEE. Esther van den Berg and Katja Markert. 2020. Context in informational bias detection. In Proceedings of the 28th International Conference on Computational Linguistics, pages 6315–6326, Barcelona, Spain (Online). International Committee on Computational Linguistics. George Wright and Paul Goodwin. 2002. Eliminating a framing bias by using simple instructions to ‘think harder’and respondents with managerial experience: Comment on ‘breaking the frame’. Strategic management journal, 23(11):1059–1067. Jingqing Zhang, Yao Zhao, Mohammad Saleh, and Peter J. Liu. 2019a. Pegasus: Pre-training with extracted gap-sentences for abstractive summarization. Tianyi Zhang*, Varsha Kishore*, Felix Wu*, Kilian Q. Weinberger, and Yoav Artzi. 2020. Bertscore: Evaluating text generation with bert. In International Conference on Learning Representations. Yifan Zhang, Giovanni Da San Martino, Alberto BarrónCedeno, Salvatore Romeo, Jisun An, Haewoon Kwak, Todor Staykovski, Israa Jaradat, Georgi Karadzhov, Ramy Baly, et al. 2019b. Tanbih: Get to know what you are reading. EMNLP-IJCNLP 2019, page 223. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Yejin Bang, Centre for Artificial Intelligence Research (CAiRE), The Hong Kong University of Science and Technology; (2) Nayeon Lee, Centre for Artificial Intelligence Research (CAiRE), The Hong Kong University of Science and Technology; (3) Pascale Fung, Centre for Artificial Intelligence Research (CAiRE), The Hong Kong University of Science and Technology. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: Authors: (1) Yejin Bang, Centre for Artificial Intelligence Research (CAiRE), The Hong Kong University of Science and Technology; (2) Nayeon Lee, Centre for Artificial Intelligence Research (CAiRE), The Hong Kong University of Science and Technology; (3) Pascale Fung, Centre for Artificial Intelligence Research (CAiRE), The Hong Kong University of Science and Technology. Table of Links Abstract and Intro Related Work Approach Experiments Conclusion Limitations, Ethics Statement and References A. Experimental Details B. Generation Results Abstract and Intro Abstract and Intro Related Work Related Work Approach Approach Experiments Experiments Conclusion Conclusion Limitations, Ethics Statement and References Limitations, Ethics Statement and References A. Experimental Details A. Experimental Details B. Generation Results B. Generation Results 6.1. Limitations The study is limited by its adherence to the benchmark’s English-based task setup. The analysis is constrained to political ideologies in the United States and the English language. Additionally, the BART model’s 1024 sub-token input limit restricts the number of biased source articles that can be included as an input. It is important to note that these limitations, while potentially impacting the scope of the study’s findings, are not uncommon in natural language processing research. Nonetheless, future research may benefit from addressing these limitations by exploring alternative methods for a broader range of political ideologies (NonU.S. political ideologies) and languages, as well as incorporating longer input texts to capture a more comprehensive range of source articles. 6.2. Ethics Statement The issue of biased articles with framing has been extensively studied, as it can lead to polarization by influencing readers’ opinions toward a certain person, group, or topic. To address this problem, our research focuses on introducing a loss function that can be incorporated to enable the model to reduce framing bias in the generated summary. However, it is important to recognize that automatic technologies can also have unintended negative consequences if not developed with careful consideration of their broader impacts. For example, machine learning models can introduce bias in their output, replacing known source bias with another form of bias (Lee et al., 2022). To mitigate this risk, Lee et al. (2022) have suggested including explicit mention of the source articles alongside automatically generated neutral summaries. Furthermore, while our work aims to remove framing bias in human-generated articles, there is the potential for hallucination in the generation, which is a well-known problem of generative models (Ji et al., 2023). Thus, it is important to equip a guardrail (e.g., a provision of source reference) if such automatic technology is implemented for actual use cases. Despite these challenges, our research can contribute to the effort of mitigating human-generated framing bias in order to reduce polarization in society. One of the use cases can be to aid human experts in the process of providing multi-view synthesized articles without framing bias. In terms of broader societal impact, we hope our work can help online users access more depolarized information online. 6.3. References 2021. Center – what does a "center" media bias rating mean? Ramy Baly, Giovanni Da San Martino, James Glass, and Preslav Nakov. 2020. We can detect your bias: Predicting the political ideology of news articles. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 4982–4991, Online. Association for Computational Linguistics. Adriana Beratšová, Kristína Krchová, Nikola Gažová, and Michal Jirásek. 2016. Framing and bias: a literature review of recent findings. Central European journal of management, 3(2). Dennis Chong and James N Druckman. 2007. Framing theory. Annu. Rev. Polit. Sci., 10:103–126. Robert M Entman. 2002. Framing: Towards clarification of a fractured paradigm. McQuail’s Reader in Mass Communication Theory. London, California and New Delhi: Sage. Robert M Entman. 2007. Framing bias: Media in the distribution of power. Journal of communication, 57(1):163–173. Robert M Entman. 2010. Media framing biases and political power: Explaining slant in news of campaign 2008. Journalism, 11(4):389–408. Alexander R Fabbri, Irene Li, Tianwei She, Suyi Li, and Dragomir R Radev. 2019. Multi-news: A large-scale multi-document summarization dataset and abstractive hierarchical model. arXiv preprint arXiv:1906.01749 Lisa Fan, Marshall White, Eva Sharma, Ruisi Su, Prafulla Kumar Choubey, Ruihong Huang, and Lu Wang. 2019. In plain sight: Media bias through the lens of factual reporting. arXiv preprint arXiv:1909.02670. Matthew Gentzkow and Jesse M Shapiro. 2006. Media bias and reputation. Journal of political Economy, 114(2):280–316. Matthew Gentzkow, Jesse M Shapiro, and Daniel F Stone. 2015. Media bias in the marketplace: Theory. In Handbook of media economics, volume 1, pages 623–645. Elsevier. Erving Goffman. 1974. Frame analysis: An essay on the organization of experience. Harvard University Press Felix Hamborg, Karsten Donnay, and Bela Gipp. 2019. Automated identification of media bias in news articles: an interdisciplinary literature review. International Journal on Digital Libraries, 20(4):391–415. Felix Hamborg, Norman Meuschke, and Bela Gipp. 2017. Matrix-based news aggregation: exploring different news perspectives. In 2017 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pages 1–10. IEEE. Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung. 2023. Survey of hallucination in natural language generation. ACM Comput. Surv., 55(12). Daniel Kahneman and Amos Tversky. 2013. Prospect theory: An analysis of decision under risk. In Handbook of the fundamentals of financial decision making: Part I, pages 99–127. World Scientific. Philippe Laban and Marti A Hearst. 2017. newslens: building and visualizing long-ranging news stories. In Proceedings of the Events and Stories in the News Workshop, pages 1–9. Nayeon Lee, Yejin Bang, Tiezheng Yu, Andrea Madotto, and Pascale Fung. 2022. NeuS: Neutral multi-news summarization for mitigating framing bias. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 3131–3148, Seattle, United States. Association for Computational Linguistics. Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer. 2019. Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv preprint arXiv:1910.13461. Yujian Liu, Xinliang Frederick Zhang, David Wegsman, Nicholas Beauchamp, and Lu Wang. 2022. POLITICS: Pretraining with same-story article comparison for ideology prediction and stance detection. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 1354–1374, Seattle, United States. Association for Computational Linguistics. Saif Mohammad. 2018. Obtaining reliable human ratings of valence, arousal, and dominance for 20,000 english words. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 174–184. Fred Morstatter, Liang Wu, Uraz Yavanoglu, Stephen R Corman, and Huan Liu. 2018. Identifying framing bias in online news. ACM Transactions on Social Computing, 1(2):1–18. Kishore Papineni, Salim Roukos, Todd Ward, and WeiJing Zhu. 2002. Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pages 311–318. Souneil Park, Seungwoo Kang, Sangyoung Chung, and Junehwa Song. 2009. Newscube: delivering multiple aspects of news to mitigate media bias. In Proceedings of the SIGCHI conference on human factors in computing systems, pages 443–452. Dietram A Scheufele. 2000. Agenda-setting, priming, and framing revisited: Another look at cognitive effects of political communication. Mass communication & society, 3(2-3):297–316. All Sides. 2018. Media bias ratings. Allsides.com. Timo Spinde, Christina Kreuter, Wolfgang Gaissmaier, Felix Hamborg, Bela Gipp, and Helge Giese. 2021. Do you think it’s biased? how to ask for the perception of media bias. In 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pages 61–69. IEEE. Esther van den Berg and Katja Markert. 2020. Context in informational bias detection. In Proceedings of the 28th International Conference on Computational Linguistics, pages 6315–6326, Barcelona, Spain (Online). International Committee on Computational Linguistics. George Wright and Paul Goodwin. 2002. Eliminating a framing bias by using simple instructions to ‘think harder’and respondents with managerial experience: Comment on ‘breaking the frame’. Strategic management journal, 23(11):1059–1067. Jingqing Zhang, Yao Zhao, Mohammad Saleh, and Peter J. Liu. 2019a. Pegasus: Pre-training with extracted gap-sentences for abstractive summarization. Tianyi Zhang*, Varsha Kishore*, Felix Wu*, Kilian Q. Weinberger, and Yoav Artzi. 2020. Bertscore: Evaluating text generation with bert. In International Conference on Learning Representations. Yifan Zhang, Giovanni Da San Martino, Alberto BarrónCedeno, Salvatore Romeo, Jisun An, Haewoon Kwak, Todor Staykovski, Israa Jaradat, Georgi Karadzhov, Ramy Baly, et al. 2019b. Tanbih: Get to know what you are reading. EMNLP-IJCNLP 2019, page 223.

Mitigating Framing Bias with Polarity Minimization Loss: Limitations, Ethics Statement & References

পোলারিটি মিনিমাইজেশন লস সহ ফ্রেমিং বায়াস প্রশমিত করা: সীমাবদ্ধতা, নৈতিকতা বিবৃতি এবং উল্লেখ

Abmilderung des Framing Bias durch Polarity Minimization Loss: Einschränkungen, Ethikerklärung und Referenzen

Mitigar el sesgo de encuadre con pérdida de minimización de polaridad: limitaciones, declaración de ética y referencias

Atténuer les biais de cadrage grâce à la perte de minimisation de la polarité : limites, déclaration éthique et références

ध्रुवीयता न्यूनीकरण हानि के साथ फ़्रेमिंग पूर्वाग्रह को कम करना: सीमाएँ, नैतिकता कथन और संदर्भ

極性最小化損失によるフレーミングバイアスの緩和: 制限、倫理声明、参考文献

극성 최소화 손실로 프레이밍 편향 완화: 한계, 윤리 선언문 및 참고 자료

Mitigando o preconceito de enquadramento com perda de minimização de polaridade: limitações, declaração de ética e referências

Уменьшение смещения кадра с помощью минимизации потерь полярности: ограничения, заявление об этике и ссылки

Polarite Minimizasyon Kaybı ile Çerçeveleme Önyargısını Azaltma: Sınırlamalar, Etik Beyanı ve Referanslar

Giảm thiểu thành kiến trong khung với sự mất mát tối thiểu hóa phân cực: Hạn chế, Tuyên bố về đạo đức và tài liệu tham khảo

通过极性最小化损失减轻框架偏差：局限性、道德声明和参考文献

If you've been following the VR industry, you know it's been around for decades. Companies have been trying to make virtual reality a mainstream success for ages. While Apple coined the buzz-worthy term “spatial computing” with their Vision Pro headset, they're certainly not the first to try. From the failed Virtual Boy in the 90s to the moderately successful Oculus and HTC Vive VR headsets, tech giants have struggled to find that elusive killer app. But if you just scroll through TikTok or Instagram, you'll see what's really on people's minds these days. And it definitely isn't about bigger screens to watch Netflix or play games. No, what's driving the conversation is beauty, looks, fashion, and how to glow up. So, as transformative as spatial computing could become, the multi-trillion-dollar global beauty and fashion space will lead AR. Our universal desire to look and feel good provides the real-life use case everyone’s looking for. The promises and challenges of AR beauty The core premise of AR beauty is simple but compelling: Using advanced computer vision and rendering to virtually overlay any makeup look, hairstyle, outfit, or accessory onto your face and body with hyper-realistic precision. Consumers can experiment endlessly, sampling different looks and products with no mess, effort, or commitment. If you've ever ordered a piece of clothing online only for it to look completely different (or just not fit right) when it arrives, you know how frustrating that can be—or bought an expensive watch or pair of kicks, only to regret the purchase when it doesn’t look quite how you expected. What if you could virtually try on anything before committing your money? That's the big idea of AR beauty and fashion—getting rid of the guesswork and potential disappointment by allowing you to realistically experiment with any look or product before pulling the trigger. But, some rudimentary AR makeup apps illustrate the significant technological challenges involved. Poor facial tracking, clunky interfaces, and unconvincing visual realism hamper the experience. Faithfully simulating the intricacies of how cosmetics interact with diverse facial geometries, skin tones, and movements at a high fidelity is an enormously complex computational problem. Implementing AI and AR technologies The latest breakthroughs in AI and AR technologies have proven useful in cracking these challenges. For example, Perfect Corp's AI Makeup Virtual Try-On uses machine learning to realistically render different makeup colors and textures like lipstick, eyeshadow, and more, with the simulations customized to each individual's unique facial features and microexpressions for an ultra-personalized virtual experience. If you're not all that into fashion and beauty, this may not seem like a big deal. But for those obsessed with looking their best, the ability to visualize any look or product before buying could be hugely transformative. Their AI Skin Analysis tech goes further, using artificial intelligence to scan faces and detect 14 different skin conditions and concerns, then surfacing customized product and skincare recommendations tailored to the user. And the AgileHand technology allows realistic virtual try-ons to extend beyond just faces by precisely tracking hand motions. This enables photorealistic AR visualizations for intricate products like jewelry, watches, and nail polish. Some other AR beauty solutions include Makeup AR for virtual makeup try-ons, AI Foundation Shade Finder and Matcher for accurate skin tone detection and foundation shade matching, AI Skin Analysis for accurate skin concern detection and personalized product recommendations of products, and Emulation Technology for Skincare Treatment for accurate representations of the expected results of a course of treatment, overlaid directly onto their faces with the latest advancements in AR technology. The AI tech for AR beauty The recent renaissance of AI breakthroughs, especially around areas like generative adversarial networks (GANs), facial semantic segmentation, and neural rendering, is what's enabling the kind of hyper-realistic and personalized AR experiences that were merely SciFi just years ago. Using GANs, AI models can now learn to generate highly realistic representations of human faces, facial attributes, and facial animations from just a handful of source images. This opens up possibilities for instantly generating virtual makeup looks and hairstyles that are custom-tailored to each user's individual facial geometry and characteristics. Facial semantic segmentation allows AI to precisely understand and map out different facial features like skin, facial hair, eyelashes and more - enabling AR effects to seamlessly integrate and move in harmony with the user's face. Neural rendering techniques leveraging AI can also realistically synthesize the complex ways light interacts with different materials and textures in real time, far beyond what's possible with traditional rendering pipelines. Looking ahead, AI is set to transform AR beauty experiences into something much more akin to having a personal beauty guru. Using deep learning, an AI assistant could continuously analyze your unique attributes, habits, and preferences, automatically surfacing ideal products, trendy looks, and personalized recommendations for you to try virtually in AR with just a voice command or subtle gesture. The possibilities extend far beyond just virtual try-ons. AI could help formulate bespoke beauty products optimized for your individual needs and environmental conditions. Or DTC brands could let customers co-create new cosmetic products and shades with the assistance of AI. The takeaway: Beauty and fashion are the AR killer app While the metaverse is still largely vapourware, the humble makeup tutorial may push spatial computing into the mainstream sooner than we think. With an insatiable consumer appetite for looking our best and plenty of money to be made, the global beauty and fashion industry has both the motivation and resources to drive AR innovation and adoption harder than any other sector. If executed well and key technical barriers around rendering, tracking, and hardware are overcome, AR holds the transformative potential to reshape how we discover, purchase, use, and even perceive beauty products. Immersive virtual try-on experiences, AI-driven personalization, and participatory product development could not only reinvent the beauty consumer experience but also make individual self-expression more accessible than ever before. So, as the tech world covers each new OS update and app ecosystem announcement, keep your eye on the influencers and beauty fanatics pioneering the spatial computing frontier with a fresh face of makeup in every video. They may low-key catalyze the next ubiquitous computing paradigm while just trying to look fly. If you've been following the VR industry, you know it's been around for decades. Companies have been trying to make virtual reality a mainstream success for ages. While Apple coined the buzz-worthy term “ spatial computing ” with their Vision Pro headset, they're certainly not the first to try. spatial computing spatial computing From the failed Virtual Boy in the 90s to the moderately successful Oculus and HTC Vive VR headsets, tech giants have struggled to find that elusive killer app. But if you just scroll through TikTok or Instagram, you'll see what's really on people's minds these days. And it definitely isn't about bigger screens to watch Netflix or play games. No, what's driving the conversation is beauty, looks, fashion, and how to glow up. So, as transformative as spatial computing could become, the multi-trillion-dollar global beauty and fashion space will lead AR. Our universal desire to look and feel good provides the real-life use case everyone’s looking for. The promises and challenges of AR beauty The core premise of AR beauty is simple but compelling: Using advanced computer vision and rendering to virtually overlay any makeup look, hairstyle, outfit, or accessory onto your face and body with hyper-realistic precision. Consumers can experiment endlessly, sampling different looks and products with no mess, effort, or commitment. If you've ever ordered a piece of clothing online only for it to look completely different (or just not fit right) when it arrives, you know how frustrating that can be—or bought an expensive watch or pair of kicks, only to regret the purchase when it doesn’t look quite how you expected. frustrating frustrating What if you could virtually try on anything before committing your money? That's the big idea of AR beauty and fashion—getting rid of the guesswork and potential disappointment by allowing you to realistically experiment with any look or product before pulling the trigger. But, some rudimentary AR makeup apps illustrate the significant technological challenges involved. Poor facial tracking, clunky interfaces, and unconvincing visual realism hamper the experience. Faithfully simulating the intricacies of how cosmetics interact with diverse facial geometries, skin tones, and movements at a high fidelity is an enormously complex computational problem. Implementing AI and AR technologies The latest breakthroughs in AI and AR technologies have proven useful in cracking these challenges. For example, Perfect Corp's AI Makeup Virtual Try-On uses machine learning to realistically render different makeup colors and textures like lipstick, eyeshadow, and more, with the simulations customized to each individual's unique facial features and microexpressions for an ultra-personalized virtual experience. AI Makeup Virtual Try-On If you're not all that into fashion and beauty, this may not seem like a big deal. But for those obsessed with looking their best, the ability to visualize any look or product before buying could be hugely transformative. Their AI Skin Analysis tech goes further, using artificial intelligence to scan faces and detect 14 different skin conditions and concerns, then surfacing customized product and skincare recommendations tailored to the user. AI Skin Analysis And the AgileHand technology allows realistic virtual try-ons to extend beyond just faces by precisely tracking hand motions. This enables photorealistic AR visualizations for intricate products like jewelry, watches, and nail polish. Some other AR beauty solutions include Makeup AR for virtual makeup try-ons, AI Foundation Shade Finder and Matcher for accurate skin tone detection and foundation shade matching, AI Skin Analysis for accurate skin concern detection and personalized product recommendations of products, and Emulation Technology for Skincare Treatment for accurate representations of the expected results of a course of treatment, overlaid directly onto their faces with the latest advancements in AR technology. The AI tech for AR beauty The recent renaissance of AI breakthroughs, especially around areas like generative adversarial networks (GANs), facial semantic segmentation, and neural rendering, is what's enabling the kind of hyper-realistic and personalized AR experiences that were merely SciFi just years ago. Using GANs, AI models can now learn to generate highly realistic representations of human faces, facial attributes, and facial animations from just a handful of source images. This opens up possibilities for instantly generating virtual makeup looks and hairstyles that are custom-tailored to each user's individual facial geometry and characteristics. Facial semantic segmentation allows AI to precisely understand and map out different facial features like skin, facial hair, eyelashes and more - enabling AR effects to seamlessly integrate and move in harmony with the user's face. Neural rendering techniques leveraging AI can also realistically synthesize the complex ways light interacts with different materials and textures in real time, far beyond what's possible with traditional rendering pipelines. Looking ahead, AI is set to transform AR beauty experiences into something much more akin to having a personal beauty guru. Using deep learning, an AI assistant could continuously analyze your unique attributes, habits, and preferences, automatically surfacing ideal products, trendy looks, and personalized recommendations for you to try virtually in AR with just a voice command or subtle gesture. The possibilities extend far beyond just virtual try-ons. AI could help formulate bespoke beauty products optimized for your individual needs and environmental conditions. Or DTC brands could let customers co-create new cosmetic products and shades with the assistance of AI. The takeaway: Beauty and fashion are the AR killer app While the metaverse is still largely vapourware, the humble makeup tutorial may push spatial computing into the mainstream sooner than we think. With an insatiable consumer appetite for looking our best and plenty of money to be made, the global beauty and fashion industry has both the motivation and resources to drive AR innovation and adoption harder than any other sector. If executed well and key technical barriers around rendering, tracking, and hardware are overcome, AR holds the transformative potential to reshape how we discover, purchase, use, and even perceive beauty products. Immersive virtual try-on experiences, AI-driven personalization, and participatory product development could not only reinvent the beauty consumer experience but also make individual self-expression more accessible than ever before. So, as the tech world covers each new OS update and app ecosystem announcement, keep your eye on the influencers and beauty fanatics pioneering the spatial computing frontier with a fresh face of makeup in every video. They may low-key catalyze the next ubiquitous computing paradigm while just trying to look fly.

Why the Beauty and Fashion Industry is the Future of AR and Spatial Computing

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Yici Zhong, Department of Physics, Graduate School of Science, University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan;
(2) Kazumi Kashiyama, Research Center for the Early Universe, Graduate School of Science, University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan and Kavli Institute for the Physics and Mathematics of the Universe (Kavli IPMU,WPI), The University of Tokyo, Chiba 277-8582, Japan;
(3) Shinsuke Takasao, Department of Earth and Space Science, Graduate School of Science, Osaka University, Toyonaka, Osaka 560-0043, Japan;
(4) Toshikazu Shigeyama, Research Center for the Early Universe (RESCEU), School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan and Department of Astronomy, School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan;
(5) Kotaro Fujisawa, Research Center for the Early Universe (RESCEU), School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan and Department of Liberal Arts, Tokyo University of Technology, Ota-ku, Tokyo 144-0051, Japan. Table of Links Abstract and Intro Setup Result Summary and Discussion Appendix A. Dual Energy Formalism B. Convergence of Results C. Change of the Mass Loss Rate in MHD Regime References REFERENCES Briggs, G. P., Ferrario, L., Tout, C. A., Wickramasinghe, D. T., & Hurley, J. R. 2015, MNRAS, 447, 1713, doi: 10.1093/mnras/stu2539 Bryan, G. L., Norman, M. L., Stone, J. M., Cen, R., & Ostriker, J. P. 1995, Computer Physics Communications, 89, 149, doi: 10.1016/0010-4655(94)00191-4 Bucciantini, N., Thompson, T. A., Arons, J., Quataert, E., & Del Zanna, L. 2006, MNRAS, 368, 1717, doi: 10.1111/j.1365-2966.2006.10217.x Caiazzo, I., Burdge, K. B., Fuller, J., et al. 2021, Nature, 595, 39, doi: 10.1038/s41586-021-03615-y Cheng, S., Cummings, J. D., M´enard, B., & Toonen, S. 2020, ApJ, 891, 160, doi: 10.3847/1538-4357/ab733c Contopoulos, I., & Spitkovsky, A. 2006, ApJ, 643, 1139, doi: 10.1086/501161 Ferreira, J. M. 2000, MNRAS, 316, 647, doi: 10.1046/j.1365-8711.2000.03540.x Garc´ıa-Berro, E., Lor´en-Aguilar, P., Aznar-Sigu´an, G., et al. 2012, ApJ, 749, 25, doi: 10.1088/0004-637X/749/1/25 Gvaramadze, V. V., Gr¨afener, G., Langer, N., et al. 2019, Nature, 569, 684, doi: 10.1038/s41586-019-1216-1 Iben, I., J., & Tutukov, A. V. 1984, ApJS, 54, 335, doi: 10.1086/190932 Iijima, H. 2016, PhD thesis, University of Tokyo, Department of Earth and Planetary Environmental Science Jardine, M., & Collier Cameron, A. 2019, MNRAS, 482, 2853, doi: 10.1093/mnras/sty2872 Kashiyama, K., Fujisawa, K., & Shigeyama, T. 2019, ApJ, 887, 39, doi: 10.3847/1538-4357/ab4e97 Kashiyama, K., Ioka, K., & Kawanaka, N. 2011, PhRvD, 83, 023002, doi: 10.1103/PhysRevD.83.023002 Kashiyama, K., & Murase, K. 2017, ApJL, 839, L3, doi: 10.3847/2041-8213/aa68e1 Kilic, M., Kosakowski, A., Moss, A. G., Bergeron, P., & Conly, A. A. 2021, ApJL, 923, L6, doi: 10.3847/2041-8213/ac3b60 Kirsten, F., Marcote, B., Nimmo, K., et al. 2022, Nature, 602, 585, doi: 10.1038/s41586-021-04354-w Ko, T., Suzuki, H., Kashiyama, K., et al. 2023, arXiv e-prints, arXiv:2304.14669, doi: 10.48550/arXiv.2304.14669 Kremer, K., Piro, A. L., & Li, D. 2021, ApJL, 917, L11, doi: 10.3847/2041-8213/ac13a0 Lu, W., Beniamini, P., & Kumar, P. 2022, MNRAS, 510, 1867, doi: 10.1093/mnras/stab3500 Lykou, F., Parker, Q. A., Ritter, A., et al. 2022, arXiv e-prints, arXiv:2208.03946, doi: 10.48550/arXiv.2208.03946 Matt, S. P., MacGregor, K. B., Pinsonneault, M. H., & Greene, T. P. 2012, ApJL, 754, L26, doi: 10.1088/2041-8205/754/2/L26 Michel, F. C. 1969, ApJ, 158, 727, doi: 10.1086/150233 Miyoshi, T., & Kusano, K. 2005, Journal of Computational Physics, 208, 315, doi: 10.1016/j.jcp.2005.02.017 Nomoto, K., & Iben, I., J. 1985, ApJ, 297, 531, doi: 10.1086/163547 Oskinova, L. M., Gvaramadze, V. V., Gr¨afener, G., Langer, N., & Todt, H. 2020, A&A, 644, L8, doi: 10.1051/0004-6361/202039232 Parker, E. N. 1965, SSRv, 4, 666, doi: 10.1007/BF00216273 Raives, M. J., Coleman, M. S. B., & Thompson, T. A. 2023, arXiv e-prints, arXiv:2302.05462, doi: 10.48550/arXiv.2302.05462 Ritter, A., Parker, Q. A., Lykou, F., et al. 2021, ApJL, 918, L33, doi: 10.3847/2041-8213/ac2253 Saio, H., & Nomoto, K. 2004, ApJ, 615, 444, doi: 10.1086/423976 Schwab, J. 2021, ApJ, 906, 53, doi: 10.3847/1538-4357/abc87e Schwab, J., Quataert, E., & Kasen, D. 2016, MNRAS, 463, 3461, doi: 10.1093/mnras/stw2249 Stone, J. M., Tomida, K., White, C. J., & Felker, K. G. 2020, The Astrophysical Journal Supplement Series, 249, 4, doi: 10.3847/1538-4365/ab929b Takasao, S., Tomida, K., Iwasaki, K., & Suzuki, T. K. 2019, ApJL, 878, L10, doi: 10.3847/2041-8213/ab22bb Tout, C. A., Wickramasinghe, D. T., Liebert, J., Ferrario, L., & Pringle, J. E. 2008, MNRAS, 387, 897, doi: 10.1111/j.1365-2966.2008.13291.x Townsend, R. H. D., & Owocki, S. P. 2005, MNRAS, 357, 251, doi: 10.1111/j.1365-2966.2005.08642.x Ud-Doula, A., Owocki, S. P., & Townsend, R. H. D. 2009, MNRAS, 392, 1022, doi: 10.1111/j.1365-2966.2008.14134.x Webbink, R. F. 1984, ApJ, 277, 355, doi: 10.1086/161701 Weber, E. J., & Davis, Leverett, J. 1967, ApJ, 148, 217, doi: 10.1086/149138 Wu, C., Xiong, H., Lin, J., et al. 2023, ApJL, 944, L54, doi: 10.3847/2041-8213/acb6f3 Yao, P. Z., Quataert, E., & Goulding, A. 2023, MNRAS, doi: 10.1093/mnras/stad1934 This paper is available on arxiv under CC 4.0 license. Authors: (1) Yici Zhong, Department of Physics, Graduate School of Science, University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan; (2) Kazumi Kashiyama, Research Center for the Early Universe, Graduate School of Science, University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan and Kavli Institute for the Physics and Mathematics of the Universe (Kavli IPMU,WPI), The University of Tokyo, Chiba 277-8582, Japan; (3) Shinsuke Takasao, Department of Earth and Space Science, Graduate School of Science, Osaka University, Toyonaka, Osaka 560-0043, Japan; (4) Toshikazu Shigeyama, Research Center for the Early Universe (RESCEU), School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan and Department of Astronomy, School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan; (5) Kotaro Fujisawa, Research Center for the Early Universe (RESCEU), School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan and Department of Liberal Arts, Tokyo University of Technology, Ota-ku, Tokyo 144-0051, Japan. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Yici Zhong, Department of Physics, Graduate School of Science, University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan; (2) Kazumi Kashiyama, Research Center for the Early Universe, Graduate School of Science, University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan and Kavli Institute for the Physics and Mathematics of the Universe (Kavli IPMU,WPI), The University of Tokyo, Chiba 277-8582, Japan; (3) Shinsuke Takasao, Department of Earth and Space Science, Graduate School of Science, Osaka University, Toyonaka, Osaka 560-0043, Japan; (4) Toshikazu Shigeyama, Research Center for the Early Universe (RESCEU), School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan and Department of Astronomy, School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan; (5) Kotaro Fujisawa, Research Center for the Early Universe (RESCEU), School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan and Department of Liberal Arts, Tokyo University of Technology, Ota-ku, Tokyo 144-0051, Japan. Table of Links Abstract and Intro Abstract and Intro Setup Setup Result Result Summary and Discussion Summary and Discussion Appendix Appendix A. Dual Energy Formalism A. Dual Energy Formalism B. Convergence of Results B. Convergence of Results C. Change of the Mass Loss Rate in MHD Regime C. Change of the Mass Loss Rate in MHD Regime References References REFERENCES Briggs, G. P., Ferrario, L., Tout, C. A., Wickramasinghe, D. T., & Hurley, J. R. 2015, MNRAS, 447, 1713, doi: 10.1093/mnras/stu2539 Bryan, G. L., Norman, M. L., Stone, J. M., Cen, R., & Ostriker, J. P. 1995, Computer Physics Communications, 89, 149, doi: 10.1016/0010-4655(94)00191-4 Bucciantini, N., Thompson, T. A., Arons, J., Quataert, E., & Del Zanna, L. 2006, MNRAS, 368, 1717, doi: 10.1111/j.1365-2966.2006.10217.x Caiazzo, I., Burdge, K. B., Fuller, J., et al. 2021, Nature, 595, 39, doi: 10.1038/s41586-021-03615-y Cheng, S., Cummings, J. D., M´enard, B., & Toonen, S. 2020, ApJ, 891, 160, doi: 10.3847/1538-4357/ab733c Contopoulos, I., & Spitkovsky, A. 2006, ApJ, 643, 1139, doi: 10.1086/501161 Ferreira, J. M. 2000, MNRAS, 316, 647, doi: 10.1046/j.1365-8711.2000.03540.x Garc´ıa-Berro, E., Lor´en-Aguilar, P., Aznar-Sigu´an, G., et al. 2012, ApJ, 749, 25, doi: 10.1088/0004-637X/749/1/25 Gvaramadze, V. V., Gr¨afener, G., Langer, N., et al. 2019, Nature, 569, 684, doi: 10.1038/s41586-019-1216-1 Iben, I., J., & Tutukov, A. V. 1984, ApJS, 54, 335, doi: 10.1086/190932 Iijima, H. 2016, PhD thesis, University of Tokyo, Department of Earth and Planetary Environmental Science Jardine, M., & Collier Cameron, A. 2019, MNRAS, 482, 2853, doi: 10.1093/mnras/sty2872 Kashiyama, K., Fujisawa, K., & Shigeyama, T. 2019, ApJ, 887, 39, doi: 10.3847/1538-4357/ab4e97 Kashiyama, K., Ioka, K., & Kawanaka, N. 2011, PhRvD, 83, 023002, doi: 10.1103/PhysRevD.83.023002 Kashiyama, K., & Murase, K. 2017, ApJL, 839, L3, doi: 10.3847/2041-8213/aa68e1 Kilic, M., Kosakowski, A., Moss, A. G., Bergeron, P., & Conly, A. A. 2021, ApJL, 923, L6, doi: 10.3847/2041-8213/ac3b60 Kirsten, F., Marcote, B., Nimmo, K., et al. 2022, Nature, 602, 585, doi: 10.1038/s41586-021-04354-w Ko, T., Suzuki, H., Kashiyama, K., et al. 2023, arXiv e-prints, arXiv:2304.14669, doi: 10.48550/arXiv.2304.14669 Kremer, K., Piro, A. L., & Li, D. 2021, ApJL, 917, L11, doi: 10.3847/2041-8213/ac13a0 Lu, W., Beniamini, P., & Kumar, P. 2022, MNRAS, 510, 1867, doi: 10.1093/mnras/stab3500 Lykou, F., Parker, Q. A., Ritter, A., et al. 2022, arXiv e-prints, arXiv:2208.03946, doi: 10.48550/arXiv.2208.03946 Matt, S. P., MacGregor, K. B., Pinsonneault, M. H., & Greene, T. P. 2012, ApJL, 754, L26, doi: 10.1088/2041-8205/754/2/L26 Michel, F. C. 1969, ApJ, 158, 727, doi: 10.1086/150233 Miyoshi, T., & Kusano, K. 2005, Journal of Computational Physics, 208, 315, doi: 10.1016/j.jcp.2005.02.017 Nomoto, K., & Iben, I., J. 1985, ApJ, 297, 531, doi: 10.1086/163547 Oskinova, L. M., Gvaramadze, V. V., Gr¨afener, G., Langer, N., & Todt, H. 2020, A&A, 644, L8, doi: 10.1051/0004-6361/202039232 Parker, E. N. 1965, SSRv, 4, 666, doi: 10.1007/BF00216273 Raives, M. J., Coleman, M. S. B., & Thompson, T. A. 2023, arXiv e-prints, arXiv:2302.05462, doi: 10.48550/arXiv.2302.05462 Ritter, A., Parker, Q. A., Lykou, F., et al. 2021, ApJL, 918, L33, doi: 10.3847/2041-8213/ac2253 Saio, H., & Nomoto, K. 2004, ApJ, 615, 444, doi: 10.1086/423976 Schwab, J. 2021, ApJ, 906, 53, doi: 10.3847/1538-4357/abc87e Schwab, J., Quataert, E., & Kasen, D. 2016, MNRAS, 463, 3461, doi: 10.1093/mnras/stw2249 Stone, J. M., Tomida, K., White, C. J., & Felker, K. G. 2020, The Astrophysical Journal Supplement Series, 249, 4, doi: 10.3847/1538-4365/ab929b Takasao, S., Tomida, K., Iwasaki, K., & Suzuki, T. K. 2019, ApJL, 878, L10, doi: 10.3847/2041-8213/ab22bb Tout, C. A., Wickramasinghe, D. T., Liebert, J., Ferrario, L., & Pringle, J. E. 2008, MNRAS, 387, 897, doi: 10.1111/j.1365-2966.2008.13291.x Townsend, R. H. D., & Owocki, S. P. 2005, MNRAS, 357, 251, doi: 10.1111/j.1365-2966.2005.08642.x Ud-Doula, A., Owocki, S. P., & Townsend, R. H. D. 2009, MNRAS, 392, 1022, doi: 10.1111/j.1365-2966.2008.14134.x Webbink, R. F. 1984, ApJ, 277, 355, doi: 10.1086/161701 Weber, E. J., & Davis, Leverett, J. 1967, ApJ, 148, 217, doi: 10.1086/149138 Wu, C., Xiong, H., Lin, J., et al. 2023, ApJL, 944, L54, doi: 10.3847/2041-8213/acb6f3 Yao, P. Z., Quataert, E., & Goulding, A. 2023, MNRAS, doi: 10.1093/mnras/stad1934

Magnetic Wind From a Massive White Dwarf Merger: References

The Data Revolution: AI Takes the Wheel

Happiness Is Based on a Flawed Equation — Here's How to Fix It

সুখ একটি ত্রুটিপূর্ণ সমীকরণের উপর ভিত্তি করে - এটি কীভাবে ঠিক করা যায় তা এখানে

Glück basiert auf einer fehlerhaften Gleichung – so lässt sie sich beheben

La felicidad se basa en una ecuación defectuosa: aquí se explica cómo solucionarla

Le bonheur est basé sur une équation erronée : voici comment y remédier

खुशी एक दोषपूर्ण समीकरण पर आधारित है - इसे ठीक करने का तरीका यहां बताया गया है

幸福は間違った方程式に基づいている — これを修正する方法はこれだ

행복은 잘못된 방정식에 기초합니다. 문제를 해결하는 방법은 다음과 같습니다.

A felicidade é baseada em uma equação falha – veja como consertar

Счастье основано на ошибочном уравнении — вот как это исправить

Mutluluk Kusurlu Bir Denkleme Dayalı - İşte Nasıl Düzeltileceği

Hạnh phúc dựa trên một phương trình sai lầm - Đây là cách khắc phục

幸福建立在一个有缺陷的等式上——下面是如何修复它

Few franchises are as confusing as the X-Men franchise, but that doesn’t take away from how amazing the movies are (most of them anyway). The series has been going on since 2000, and most of their movies aren’t numbered, so it can be hard to know where to start or what entry comes next. So, to help with that, here are the X-men movies in order of release date. P.S. This list will only cover the core X-Men movies to make it less confusing for beginners. This means that only the movies that drive the main plot further will be included. So, the Deadpool movies, the New Mutants, X-Men Origins: Wolverine, and Logan won’t be included. The X-Men Movies in Order X-Men
X2
X-Men: The Last Stand
X-Men: First Class
The Wolverine
X-Men: Days of Future Past
X-Men Apocalypse
Dark Phoenix 1. X-Men (2000) The X-Men had been popular for decades, especially in the 90s, and it culminated with them getting their first Hollywood movie in 2000. It starred a fairly unknown Hugh Jackman as Wolverine and seasoned acting veterans Sir Patrick Stewart and Sir Ian McKellan as Professor Xavier and Magneto. Through fate, the lonely wanderer Wolverine finds himself joining the X-Men and becoming a reluctant hero. There, he’ll team up with other heroes such as Cyclops, Storm, and Jean Grey. 2. X2 (2003) Mutant distrust is at an all-time high after a mutant attacks the White House, and unfortunately, the X-Men are caught in the crossfire. The main person leading the crusade against mutants is Colonel William Stryker, and he won’t stop until they’re all dead and buried. Separated and with their backs against the wall, the X-Men will have to turn to an old foe if they want to take down Stryker. But even though he hates mutants, Stryker seemingly has a past with them, specifically, with Wolverine. As the X-Men and Magneto work together to take down the Colonel, Wolverine has his own side mission: to discover how he and Stryker are connected. 3. X-Men: The Last Stand (2006) News breaks out that there is a serum that can take away one’s mutant powers, which, of course, catches the attention of all mutants, including Professor Xavier and his school of mutants, and Magneto and his Brotherhood of Mutants. Although the serum is optional for any mutant who wants it, Magneto and his Brotherhood are certain that this is only the beginning. That in the near future, mutants will be forced to take it. Lines are drawn within the mutant community, and they go to war with each other. In the middle of all this is a mutant child. He has the power to take away other mutants’ powers, and it was because of him that the serum was able to be made. As long as he’s alive, the serum will always be available, and so the X-Men take a last stand to protect him against Magneto. 4. X-Men: First Class (2011) Although we had seen the X-Men in three different movies before, we still didn’t know a ton about them such as how they were formed, why, and how Charles Xavier and Erik Lehnsherr came to know each other. Well, X-Men: First Class answers all of those questions and some that you didn't even know you had. The first iteration of the X-Men come together to take down former Nazi Sebastian Shaw as he pulls the strings of the U.S. and the Soviet Union. His ultimate goal is for both countries to wage nuclear war against one another. Charles, Erik, and their rag-tag team of mutants do everything they can to stop him. 5. The Wolverine (2013) You might be wondering why the first and third Wolverine movies aren’t included on this list but the second one in the trilogy is. I know; it’s kind of a confusing thing to do. However, the other two movies can be watched in a vacuum; they give more insight into the Wolverine character, but they don’t necessarily have much to do with the X-Men team itself. The Wolverine movie is special in that it directly deals with the aftermath of X-Men: The Last Stand. And on top of that, it leads right into Days of Future Past. 6. X-Men: Days of Future Past (2014) The past and present clash in Days of Future Past. After everything they have fought for, the X-Men have failed, and their worst nightmare has come true. Mutants are persecuted, hunted down, and killed by robots called sentinels. It is a grim, apocalyptic time, but not all is lost. They have one shot to make things right, and the only man right for the job is Wolverine. He gets sent back in time to the 70s to stop the sentinels from ever being created. There, he’ll meet up with the younger versions of Professor Xavier, Magneto, Beast, and more as they fight to prevent that ugly future from happening. 7. X-Men Apocalypse (2016) The X-Men have saved the world from the Sentinels, but a new threat emerges. Maybe new isn’t the right word considering this person is from ancient Egypt. We’re talking about Apocalypse, one of the first mutants ever. He has awakened in the present, but he’s disappointed in the modern world and plans to restructure it in his image. It’s up to the X-Men to stop him, and who’s a part of the line-up? It’s a mix between veterans such as Beast and Raven along with newcomers Jean Grey, Cyclops, and Quicksilver. 8. X-Men: Dark Phoenix (2019) Jean Grey has always been one of the strongest X-Men, so it’s a good thing she’s on our side. However, after a mission goes awry, she awakens the Phoenix Force within her. With Jean becoming even stronger than she already was, it’s up to the X-Men to not only protect her from herself but from others such as Magneto. Technically, this isn’t the final film in the franchise as The New Mutants was released a year later, but that could be watched in a vacuum. Dark Phoenix can be considered the true ending to Fox’s X-Men film series. Read More 1. How to Watch the Harry Potter Movies in Order The MCU Phase 3 Movies in Order
The MCU Phase 4 Movies in Order Feature image source Few franchises are as confusing as the X-Men franchise, but that doesn’t take away from how amazing the movies are (most of them anyway). The series has been going on since 2000, and most of their movies aren’t numbered, so it can be hard to know where to start or what entry comes next. So, to help with that, here are the X-men movies in order of release date. P.S. This list will only cover the core X-Men movies to make it less confusing for beginners. This means that only the movies that drive the main plot further will be included. So, the Deadpool movies, the New Mutants, X-Men Origins: Wolverine, and Logan won’t be included. The X-Men Movies in Order X-Men X2 X-Men: The Last Stand X-Men: First Class The Wolverine X-Men: Days of Future Past X-Men Apocalypse Dark Phoenix X-Men X2 X-Men: The Last Stand X-Men: First Class The Wolverine X-Men: Days of Future Past X-Men Apocalypse Dark Phoenix 1. X-Men (2000) The X-Men had been popular for decades, especially in the 90s, and it culminated with them getting their first Hollywood movie in 2000. It starred a fairly unknown Hugh Jackman as Wolverine and seasoned acting veterans Sir Patrick Stewart and Sir Ian McKellan as Professor Xavier and Magneto. Through fate, the lonely wanderer Wolverine finds himself joining the X-Men and becoming a reluctant hero. There, he’ll team up with other heroes such as Cyclops, Storm, and Jean Grey. 2. X2 (2003) Mutant distrust is at an all-time high after a mutant attacks the White House, and unfortunately, the X-Men are caught in the crossfire. The main person leading the crusade against mutants is Colonel William Stryker, and he won’t stop until they’re all dead and buried. Separated and with their backs against the wall, the X-Men will have to turn to an old foe if they want to take down Stryker. But even though he hates mutants, Stryker seemingly has a past with them, specifically, with Wolverine. As the X-Men and Magneto work together to take down the Colonel, Wolverine has his own side mission: to discover how he and Stryker are connected. 3. X-Men: The Last Stand (2006) News breaks out that there is a serum that can take away one’s mutant powers, which, of course, catches the attention of all mutants, including Professor Xavier and his school of mutants, and Magneto and his Brotherhood of Mutants. Although the serum is optional for any mutant who wants it, Magneto and his Brotherhood are certain that this is only the beginning. That in the near future, mutants will be forced to take it. Lines are drawn within the mutant community, and they go to war with each other. In the middle of all this is a mutant child. He has the power to take away other mutants’ powers, and it was because of him that the serum was able to be made. As long as he’s alive, the serum will always be available, and so the X-Men take a last stand to protect him against Magneto. 4. X-Men: First Class (2011) Although we had seen the X-Men in three different movies before, we still didn’t know a ton about them such as how they were formed, why, and how Charles Xavier and Erik Lehnsherr came to know each other. Well, X-Men: First Class answers all of those questions and some that you didn't even know you had. The first iteration of the X-Men come together to take down former Nazi Sebastian Shaw as he pulls the strings of the U.S. and the Soviet Union. His ultimate goal is for both countries to wage nuclear war against one another. Charles, Erik, and their rag-tag team of mutants do everything they can to stop him. 5. The Wolverine (2013) You might be wondering why the first and third Wolverine movies aren’t included on this list but the second one in the trilogy is. I know; it’s kind of a confusing thing to do. However, the other two movies can be watched in a vacuum; they give more insight into the Wolverine character, but they don’t necessarily have much to do with the X-Men team itself. The Wolverine movie is special in that it directly deals with the aftermath of X-Men: The Last Stand. And on top of that, it leads right into Days of Future Past. 6. X-Men: Days of Future Past (2014) The past and present clash in Days of Future Past. After everything they have fought for, the X-Men have failed, and their worst nightmare has come true. Mutants are persecuted, hunted down, and killed by robots called sentinels. It is a grim, apocalyptic time, but not all is lost. They have one shot to make things right, and the only man right for the job is Wolverine. He gets sent back in time to the 70s to stop the sentinels from ever being created. There, he’ll meet up with the younger versions of Professor Xavier, Magneto, Beast, and more as they fight to prevent that ugly future from happening. 7. X-Men Apocalypse (2016) The X-Men have saved the world from the Sentinels, but a new threat emerges. Maybe new isn’t the right word considering this person is from ancient Egypt. We’re talking about Apocalypse, one of the first mutants ever. He has awakened in the present, but he’s disappointed in the modern world and plans to restructure it in his image. It’s up to the X-Men to stop him, and who’s a part of the line-up? It’s a mix between veterans such as Beast and Raven along with newcomers Jean Grey, Cyclops, and Quicksilver. 8. X-Men: Dark Phoenix (2019) Jean Grey has always been one of the strongest X-Men, so it’s a good thing she’s on our side. However, after a mission goes awry, she awakens the Phoenix Force within her. With Jean becoming even stronger than she already was, it’s up to the X-Men to not only protect her from herself but from others such as Magneto. Technically, this isn’t the final film in the franchise as The New Mutants was released a year later, but that could be watched in a vacuum. Dark Phoenix can be considered the true ending to Fox’s X-Men film series. Read More 1. How to Watch the Harry Potter Movies in Order How to Watch the Harry Potter Movies in Order The MCU Phase 3 Movies in Order The MCU Phase 4 Movies in Order The MCU Phase 3 Movies in Order The MCU Phase 3 Movies in Order The MCU Phase 4 Movies in Order The MCU Phase 4 Movies in Order Feature image source Feature image source source

The X-Men Movies in Order: How to Watch Them By Release Date

Social Media and the Battlefield: Human Error in Modern Warfare

The Linux command line is a powerful tool with a wide range of functions. Users mostly use it to interact with their computers, and it gives them access to a variety of tools that can expedite and streamline their work. To fully utilize this capacity, you must be familiar with a few fundamental commands and shortcuts. Furthermore, grasping the fundamental commands of Linux is essential to making the most of all of its features. I recently posed the question, “What is your favourite Linux command?” on X (previously Twitter), and I received a lot of responses. https://twitter.com/Dhanush_Nehru/status/1789549671385575858?embedable=true After viewing most of the comments, I’ve selected a couple that might be significant and also important to know. rm Command: rm removes all the files from the current directory with a prompt. rm -RF removes all the files and directories recursively without a prompt. Example: rm test.txt touch Command: An empty file is created in the current directory by the touch command. Example: touch test.txt rmdir Command: The rmdir command deletes an empty directory from the current working directory. Example: rmdir example_directory cd Command: It’s used to navigate between directories or folders within a file system. To go directly to the root directory, we use the cd command alone. Example: cd /home/user ls Command: This command is used to list files and directories in a directory. When used with the -lrh options, it provides a detailed list with specific formatting. -l: Produces a long listing format, showing additional information such as permissions, owner, group, size, and modification time.
-r: Lists files and directories in reverse order.
-h: Prints sizes in a human-readable format (e.g., kilobytes, megabytes, gigabytes). Example: ls -lrh cat Command: The cat command displays the contents of a text file. Example: cat example.txt pwd Command: The pwd command displays the path of the current working directory. Example: pwd man Command: To access the manual pages for a specific command, use the man command. It offers comprehensive details on the syntax, options, and operation of a command. Example: man ls sudo Command: In Unix-like operating systems, the sudo command represents “superuser do.” As determined by the security policy set up on the system, it permits a permitted user to run a command as the superuser (root) or as any other user. Example: sudo nano /etc/hosts Apart from these commands, there are a few more commands that you might not be aware of, so check out this video for further insights. https://youtu.be/sIG3ForhRRI?si=SNhgY8gchpryKatD&embedable=true Thanks for reading, please give a like as a sort of encouragement, and also share this post on socials which might benefit someone. Connect ⬇️ Twitter / Instagram / Github / Youtube / Newsletter / Discord The Linux command line is a powerful tool with a wide range of functions. Users mostly use it to interact with their computers, and it gives them access to a variety of tools that can expedite and streamline their work. To fully utilize this capacity, you must be familiar with a few fundamental commands and shortcuts. Furthermore, grasping the fundamental commands of Linux is essential to making the most of all of its features. I recently posed the question, “What is your favourite Linux command?” on X (previously Twitter), and I received a lot of responses. https://twitter.com/Dhanush_Nehru/status/1789549671385575858?embedable=true https://twitter.com/Dhanush_Nehru/status/1789549671385575858?embedable=true After viewing most of the comments, I’ve selected a couple that might be significant and also important to know. rm Command: rm removes all the files from the current directory with a prompt. rm -RF removes all the files and directories recursively without a prompt. Example: rm test.txt touch Command: An empty file is created in the current directory by the touch command. Example: touch test.txt rmdir Command: The rmdir command deletes an empty directory from the current working directory. Example: rmdir example_directory cd Command: It’s used to navigate between directories or folders within a file system. To go directly to the root directory, we use the cd command alone. Example: cd /home/user ls Command: This command is used to list files and directories in a directory. When used with the -lrh options, it provides a detailed list with specific formatting. -lrh -l: Produces a long listing format, showing additional information such as permissions, owner, group, size, and modification time. -r: Lists files and directories in reverse order. -h: Prints sizes in a human-readable format (e.g., kilobytes, megabytes, gigabytes). -l : Produces a long listing format, showing additional information such as permissions, owner, group, size, and modification time. -l -r : Lists files and directories in reverse order. -r -h : Prints sizes in a human-readable format (e.g., kilobytes, megabytes, gigabytes). -h Example: ls -lrh cat Command: The cat command displays the contents of a text file. Example: cat example.txt pwd Command: The pwd command displays the path of the current working directory. Example: pwd man Command: To access the manual pages for a specific command, use the man command. It offers comprehensive details on the syntax, options, and operation of a command. Example: man ls sudo Command: In Unix-like operating systems, the sudo command represents “superuser do.” As determined by the security policy set up on the system, it permits a permitted user to run a command as the superuser (root) or as any other user. Example: sudo nano /etc/hosts Apart from these commands, there are a few more commands that you might not be aware of, so check out this video for further insights. Apart from these commands, there are a few more commands that you might not be aware of, so check out this video for further insights. https://youtu.be/sIG3ForhRRI?si=SNhgY8gchpryKatD&embedable=true https://youtu.be/sIG3ForhRRI?si=SNhgY8gchpryKatD&embedable=true Thanks for reading, please give a like as a sort of encouragement, and also share this post on socials which might benefit someone. Thanks for reading, please give a like as a sort of encouragement, and also share this post on socials which might benefit someone. Connect ⬇️ Connect Twitter / Instagram / Github / Youtube / Newsletter / Discord Twitter Twitter / Instagram Instagram / Github Github / Youtube Youtube / Newsletter Newsletter / Discord Discord

The Most Important Linux Commands You Need To Know!!!

Satoshi Nakamoto and Bitcoin weren’t exactly popular in the early days, not even among their peers. Privacy-inclined people, who were also experts in programming, and were looking for an alternative to banks and traditional money, were the first ones to read the Bitcoin whitepaper —and most of them didn’t think highly of it. Gregory Maxwell, a self-declared cypherpunk, was one of them. Maxwell is an American programmer well-known for his deep involvement in Bitcoin, to the point of being one of the most important developers of Bitcoin Core (its main implementation) for years. It wasn’t like that since the beginning, though. He first got involved in other open-source and collaborative systems, being an early contributor to Wikipedia and later an employee at the Mozilla Foundation. However, he believed that decentralized consensus wasn’t possible. “When Bitcoin first came out, I was on the cryptography mailing list. When it happened, I sort of laughed. Because I had already proven that decentralized consensus was impossible.” Before really paying enough attention to the Bitcoin code, he was helping to develop the now widely-used Opus Audio Codec. This is an audio format specially designed to compress and transmit audio over the Internet. It offers high-quality sound while using less data, making it ideal for applications like voice calls, video conferencing, and streaming music. Household names like YouTube, Spotify, Netflix, Discord, and Skype use this technology today. Maxwell on Bitcoin Even before Bitcoin, Maxwell was involved in the Reusable Proof-of-Work (RPOW) project created by Hal Finney —who would also turn to Bitcoin later. This system created RPOW tokens that could be transferred between users and exchanged for new units, but it wasn’t exactly like decentralized money. Maxwell was aiming to create conditional transfers (similar to smart contracts) in the platform, but the adoption of it wasn’t high. He came back to Bitcoin in late 2010, as a miner first, just trying to use his GPUs in something useful. Soon after, he paid closer attention to the whitepaper and the source code and changed his mind about the project. He then started to collaborate at a technical level, and he’s been doing it since 2011. In those early days, he focused on protocol design, incentives, and cryptographic review. ** ** In 2014, he also co-founded Blockstream, a company mainly created to boost and fund Bitcoin development. While there, Maxwell helped to create the two-way peg, which makes sidechains possible. These are separate chains connected to a parent distributed ledger, like Bitcoin, enabling experimentation with new features and functionalities without risking the main chain's security. The two-way peg would allow assets to move between chains without losing their value. As a Bitcoin Core developer, Maxwell also helped to build the homomorphic key derivation in Bitcoin Improvement Proposal (BIP) #32, which allows for the generation of multiple public and private key pairs from a single master key. Additionally, he co-authored the BIP-9 to establish a standard for light updates (softforks), and the BIP-173 to define a new type of address for SegWit —another improvement update to increase efficiency. He left Blockstream in 2018 and focused on the creation of smart contract features for Bitcoin. That way, he became the author of a new important improvement for Bitcoin, the Taproot update. This technique combines multiple spending conditions into a single signature, enhancing privacy and reducing transaction costs —which encourages the creation of complex smart contracts. Some Concerns, and Time As much as other fellow cypherpunks, Maxwell has also expressed some concerns about Bitcoin's internal functionality. This is the system that really spread the idea of decentralized money, but it’s not perfect. For starters, Maxwell told the story about his early days as a miner, and how the overheating caused by the machines even drew the police to his home. Mining is, indeed, worrisome, given the great amount of electric energy that it needs. Privacy is another issue in the Bitcoin ledger. People tend to believe that Bitcoin is anonymous, but that’s quite far from the truth. Bitcoin is actually pseudonymous, which means instead of names and ID documents it uses cryptographic addresses. However, that doesn’t mean you can’t be tracked, especially because every transaction is publicly available in a blockchain explorer. And they include data like amount, date, and connected addresses (senders and recipients). Maxwell is also trying to fix this issue in the first cryptocurrency, as he commented in an interview. “Obviously privacy is a big area where Bitcoin needs improvement and I've tried to come up with fancy technology to improve the situation, but it's always a balancing act. Privacy is the one area where if Bitcoin doesn't get better at it... it might leave the world a worse off place than if Bitcoin hadn't existed. The worst case for Bitcoin privacy is pretty bad.  There has been a lot of progress but more is needed.” Despite current flaws, Maxwell also thinks that mass adoption of cryptocurrencies is just a matter of time. Impediments like insecure hardware, buggy software, tax barriers, or self-custody risks will find a solution over the years, and we’re on our way to it. Obyte as a Step into the Future Obyte has emerged as a compelling alternative to Bitcoin, addressing concerns regarding privacy, and decentralization. Unlike Bitcoin, which requires miners as inevitable intermediaries to approve transactions, Obyte employs a Directed Acyclic Graph (DAG) structure where transactions don’t need any approval. This approach eliminates the need for blocks and their creators, making Obyte fully decentralized. Moreover, Obyte offers enhanced privacy features, such as the integration of the privacy coin Blackbytes and in-wallet encrypted chat functionality. These options provide users with greater confidentiality in their transactions and communications, addressing the privacy concerns prevalent in Bitcoin's ledger. By prioritizing decentralization and privacy, Obyte ensures that users can freely conduct transactions on their own and have the option to do so without compromising their sensitive information to public scrutiny, distinguishing it as a more privacy-focused and decentralized option in the cryptocurrency landscape. This ecosystem is really the next step into the future, as many cypherpunks have dreamed of. Read more from Cypherpunks Write Code series: Tim May & Crypto-anarchism Wei Dai & B-money Nick Szabo & Smart Contracts Adam Back & Hashcash Eric Hughes & Remailer St Jude & Community Memory Hal Finney & RPOW John Gilmore & EFF Satoshi Nakamoto & Bitcoin Featured Vector Image by Garry Killian / Freepik Photograph of Gregory Maxwell by Blockstream Satoshi Nakamoto and Bitcoin weren’t exactly popular in the early days, not even among their peers. Privacy-inclined people, who were also experts in programming, and were looking for an alternative to banks and traditional money, were the first ones to read the Bitcoin whitepaper —and most of them didn’t think highly of it. Gregory Maxwell, a self-declared cypherpunk, was one of them. Maxwell is an American programmer well-known for his deep involvement in Bitcoin, to the point of being one of the most important developers of Bitcoin Core (its main implementation) for years. It wasn’t like that since the beginning, though. He first got involved in other open-source and collaborative systems, being an early contributor to Wikipedia and later an employee at the Mozilla Foundation. However, he believed that decentralized consensus wasn’t possible. Maxwell is an American programmer well-known for his deep involvement in Bitcoin, to the point of being one of the most important developers of Bitcoin Core (its main implementation) for years. Maxwell Maxwell he believed he believed “When Bitcoin first came out, I was on the cryptography mailing list. When it happened, I sort of laughed. Because I had already proven that decentralized consensus was impossible.” “When Bitcoin first came out, I was on the cryptography mailing list. When it happened, I sort of laughed. Because I had already proven that decentralized consensus was impossible.” Before really paying enough attention to the Bitcoin code, he was helping to develop the now widely-used Opus Audio Codec. This is an audio format specially designed to compress and transmit audio over the Internet. It offers high-quality sound while using less data, making it ideal for applications like voice calls, video conferencing, and streaming music. Household names like YouTube, Spotify, Netflix, Discord, and Skype use this technology today. Maxwell on Bitcoin Even before Bitcoin, Maxwell was involved in the Reusable Proof-of-Work (RPOW) project created by Hal Finney —who would also turn to Bitcoin later. This system created RPOW tokens that could be transferred between users and exchanged for new units, but it wasn’t exactly like decentralized money. Maxwell was aiming to create conditional transfers (similar to smart contracts) in the platform, but the adoption of it wasn’t high. He came back to Bitcoin in late 2010, as a miner first, just trying to use his GPUs in something useful. Soon after, he paid closer attention to the whitepaper and the source code and changed his mind about the project. He then started to collaborate at a technical level, and he’s been doing it since 2011. In those early days, he focused on protocol design, incentives, and cryptographic review. He then started to collaborate at a technical level, and he’s been doing it since 2011. he focused he focused ** ** In 2014, he also co-founded Blockstream, a company mainly created to boost and fund Bitcoin development. While there, Maxwell helped to create the two-way peg, which makes sidechains possible. These are separate chains connected to a parent distributed ledger, like Bitcoin, enabling experimentation with new features and functionalities without risking the main chain's security. The two-way peg would allow assets to move between chains without losing their value. As a Bitcoin Core developer, Maxwell also helped to build the homomorphic key derivation in Bitcoin Improvement Proposal (BIP) #32 , which allows for the generation of multiple public and private key pairs from a single master key. Additionally, he co-authored the BIP-9 to establish a standard for light updates (softforks), and the BIP-173 to define a new type of address for SegWit —another improvement update to increase efficiency. Maxwell also helped to build the homomorphic key derivation in Bitcoin Improvement Proposal ( BIP ) #32 BIP BIP He left Blockstream in 2018 and focused on the creation of smart contract features for Bitcoin. That way, he became the author of a new important improvement for Bitcoin, the Taproot update . This technique combines multiple spending conditions into a single signature, enhancing privacy and reducing transaction costs —which encourages the creation of complex smart contracts. Taproot update Taproot update Some Concerns, and Time As much as other fellow cypherpunks , Maxwell has also expressed some concerns about Bitcoin's internal functionality. This is the system that really spread the idea of decentralized money, but it’s not perfect. For starters, Maxwell told the story about his early days as a miner, and how the overheating caused by the machines even drew the police to his home. Mining is, indeed, worrisome, given the great amount of electric energy that it needs. cypherpunks cypherpunks Privacy is another issue in the Bitcoin ledger. People tend to believe that Bitcoin is anonymous, but that’s quite far from the truth. Bitcoin is actually pseudonymous, which means instead of names and ID documents it uses cryptographic addresses. However, that doesn’t mean you can’t be tracked, especially because every transaction is publicly available in a blockchain explorer. And they include data like amount, date, and connected addresses (senders and recipients). Privacy is another issue in the Bitcoin ledger. Maxwell is also trying to fix this issue in the first cryptocurrency, as he commented in an interview. an interview. an interview. “Obviously privacy is a big area where Bitcoin needs improvement and I've tried to come up with fancy technology to improve the situation, but it's always a balancing act. Privacy is the one area where if Bitcoin doesn't get better at it... it might leave the world a worse off place than if Bitcoin hadn't existed. The worst case for Bitcoin privacy is pretty bad.  There has been a lot of progress but more is needed.” “Obviously privacy is a big area where Bitcoin needs improvement and I've tried to come up with fancy technology to improve the situation, but it's always a balancing act. Privacy is the one area where if Bitcoin doesn't get better at it... it might leave the world a worse off place than if Bitcoin hadn't existed. The worst case for Bitcoin privacy is pretty bad.  There has been a lot of progress but more is needed.” Despite current flaws, Maxwell also thinks that mass adoption of cryptocurrencies is just a matter of time. Impediments like insecure hardware, buggy software, tax barriers, or self-custody risks will find a solution over the years, and we’re on our way to it. Obyte as a Step into the Future Obyte has emerged as a compelling alternative to Bitcoin, addressing concerns regarding privacy, and decentralization. Unlike Bitcoin, which requires miners as inevitable intermediaries to approve transactions, Obyte employs a Directed Acyclic Graph (DAG) structure where transactions don’t need any approval. This approach eliminates the need for blocks and their creators, making Obyte fully decentralized. Obyte Obyte Obyte employs a Directed Acyclic Graph (DAG) structure where transactions don’t need any approval. Moreover, Obyte offers enhanced privacy features, such as the integration of the privacy coin Blackbytes and in-wallet encrypted chat functionality . These options provide users with greater confidentiality in their transactions and communications, addressing the privacy concerns prevalent in Bitcoin's ledger. Blackbytes Blackbytes chat functionality chat functionality By prioritizing decentralization and privacy, Obyte ensures that users can freely conduct transactions on their own and have the option to do so without compromising their sensitive information to public scrutiny, distinguishing it as a more privacy-focused and decentralized option in the cryptocurrency landscape. This ecosystem is really the next step into the future, as many cypherpunks have dreamed of. Read more from Cypherpunks Write Code series: Read more from Cypherpunks Write Code series: Tim May & Crypto-anarchism Tim May & Crypto-anarchism Tim May & Crypto-anarchism Tim May & Crypto-anarchism Wei Dai & B-money Wei Dai & B-money Wei Dai & B-money Wei Dai & B-money Nick Szabo & Smart Contracts Nick Szabo & Smart Contracts Nick Szabo & Smart Contracts Nick Szabo & Smart Contracts Adam Back & Hashcash Adam Back & Hashcash Adam Back & Hashcash Adam Back & Hashcash Eric Hughes & Remailer Eric Hughes & Remailer Eric Hughes & Remailer Eric Hughes & Remailer St Jude & Community Memory St Jude & Community Memory St Jude & Community Memory St Jude & Community Memory Hal Finney & RPOW Hal Finney & RPOW Hal Finney & RPOW Hal Finney & RPOW John Gilmore & EFF John Gilmore & EFF John Gilmore & EFF John Gilmore & EFF Satoshi Nakamoto & Bitcoin Satoshi Nakamoto & Bitcoin Satoshi Nakamoto & Bitcoin Satoshi Nakamoto & Bitcoin Featured Vector Image by Garry Killian / Freepik Featured Vector Image by Garry Killian / Freepik Freepik Freepik Photograph of Gregory Maxwell by Blockstream Photograph of Gregory Maxwell by Blockstream Blockstream Blockstream

Cypherpunks Write Code: Gregory Maxwell and Bitcoin Core

সাইফারপাঙ্কস কোড লিখুন: গ্রেগরি ম্যাক্সওয়েল এবং বিটকয়েন কোর

Cypherpunks schreiben Code: Gregory Maxwell und Bitcoin Core

Código de escritura de Cypherpunks: Gregory Maxwell y Bitcoin Core

Cypherpunks écrit du code : Gregory Maxwell et Bitcoin Core

साइफरपंक्स ने कोड लिखा: ग्रेगरी मैक्सवेल और बिटकॉइन कोर

サイファーパンクがコードを書く: グレゴリー・マクスウェルとビットコイン・コア

Cypherpunks의 코드 작성: Gregory Maxwell 및 Bitcoin Core

Código de gravação Cypherpunks: Gregory Maxwell e Bitcoin Core

Шифропанки пишут код: Грегори Максвелл и Bitcoin Core

Şifrepunklar Kod Yazıyor: Gregory Maxwell ve Bitcoin Core

Cypherpunks viết mã: Gregory Maxwell và Bitcoin Core

密码朋克编写代码：Gregory Maxwell 和比特币核心

Understanding and Generating Dialogue between Characters in Stories: Limitations and References

In recent decades, automakers around the world have spent countless billions on autonomous vehicle technology. They're doing so because everyone agrees that self-driving cars will be the next killer app in the industry. Unfortunately, the road to getting there hasn't been as smooth as those automakers have hoped. On the contrary, the short history of autonomous vehicle technology is one filled with countless technological failures and accidents. In San Francisco, CA, a hotbed for autonomous vehicle testing, there have already been dozens of accidents—including at least one that seriously injured a pedestrian. However, fully autonomous vehicles aren't the only type of self-driving technology out there. There are also countless new vehicles on US roads with complex driver-assist systems. These vehicles have the same features as self-driving models, except that they require a driver's hands on the wheel at all times. And those vehicles are causing havoc on the roads. In one 10-month span, in fact, automakers reported 400 accidents involving vehicles with driver-assist systems in use. All of this is to say that the development of self-driving vehicle technology is not without risk. And at this stage, it begs a serious question—are smart autonomous vehicle technologies making us less safe on the road? Here's the answer to that question, based on all available data. The Safety Potential of Autonomous Driving Technology Without a doubt, the various technological elements that make up today's driver-assist systems and tomorrow's fully autonomous vehicles have the potential to save lives. How many lives? To figure that out, consider the following. Each year in the US, there are approximately 1.7 million rear-end vehicle collisions. Those accidents create approximately 1,700 fatalities and 500,000 additional injuries. And yet, a study of vehicles that include forward collision warning (FCW) systems and autonomous emergency braking (AEB) functionality indicates that those two technologies alone could reduce such accidents by a whopping 50%. That means universal adoption of FCW and AEB technology could save at least 850 lives every year. Another study indicated that equipping vehicles with FCW, lane departure warning (LDW), blind spot warning, and curve-adaptive headlights could cut car accident rates by up to one-third. If you consider that there are approximately 43,000 fatal car crashes in the US every year, those technologies alone could prevent 14,190 of those. Assessing the Human Element You'll notice that so far, I've only mentioned driver-assist technologies that don't directly control the operation of a vehicle. And therein lies the problem. It seems that the more self-driving technology you put in a vehicle, the less responsible drivers become. According to a recent survey, drivers are developing some disturbing habits connected to autonomous driving technology. In the survey, a shocking 61% of respondents indicated that advanced driver-assist technologies make them more comfortable looking away from the road while driving. A full 58% of those with vehicles equipped with pedestrian detection admitted that they've stopped looking around for pedestrians. And maybe worst of all, 57% of those with AEB-equipped vehicles report it making them comfortable with looking away from the road while driving. If you examine those results together, a pretty clear picture starts to emerge. It's that any vehicle technology that takes the onus of safety off the driver makes drivers less careful and more distracted. And that tendency is nowhere more pronounced than it is in a specific driver cohort: Tesla vehicle owners. The Electric Elephant in the Room If you go looking for information about self-driving technologies and vehicle safety, it won't take long for you to notice a trend. It's that vehicles from all-electric automaker Tesla seem to get involved in a shocking number of car crashes. And you're not imagining it. According to insurance company data, Tesla owners have the highest accident rate of any type of vehicle. Between 2022 and 2023, Tesla drivers had an accident rate of 23.54 per 1,000 drivers. And if you dig a bit deeper, you'll find that a huge percentage of those crashes happened either completely or in part due to the automaker's Autopilot feature. So far, Tesla's self-driving feature is responsible for 736 crashes and 17 fatalities. And there are some important reasons for that which tie into this discussion. One of them is the fact that Tesla's Autopilot isn't capable of fully autonomous operation. It's just the automaker's name for its particular flavor of driver-assist technologies. Like others in its class, Autopilot requires drivers to keep their hands on the steering wheel at all times. And yet, the company has spent over nine years advertising the Autopilot feature as a Full Self-Driving Capability. And given what we know about driver tendencies surrounding autonomous technologies, likely explains the high accident rate among Tesla drivers. Those drivers, lulled by the promise of illusory self-driving capabilities, seem to take chances they wouldn't take in other vehicles. To wit, most of the accidents involving the Autopilot feature happen as onboard computers record the driver taking their hands off the steering wheel for extended periods. The Takeaway The main takeaway from all of this is simple. It's that self-driving and autonomous vehicle technology can and should make driving much safer. However, until those technologies mature into truly autonomous vehicles, they're having the opposite effect. While features like FCW and AEB do decrease fatality rates in accidents, they may be contributing to driver behavior that causes more accidents. Of course, there's no precise way to tell if that particular tradeoff is worth it, safety-wise. Furthermore, it also seems clear that the more drivers believe that their vehicles can handle the driving for them, the less engaged in the task they get. Therefore, until vehicles can handle all of the driving for their occupants without human intervention, we may see an ever-increasing number of injuries and fatalities connected to driver-assist technologies. That reality puts more pressure than ever before on the automakers working toward full vehicle autonomy to get their current technologies and designs to the finish line. In recent decades, automakers around the world have spent countless billions on autonomous vehicle technology. They're doing so because everyone agrees that self-driving cars will be the next killer app in the industry. Unfortunately, the road to getting there hasn't been as smooth as those automakers have hoped. autonomous vehicle autonomous vehicle On the contrary, the short history of autonomous vehicle technology is one filled with countless technological failures and accidents. In San Francisco, CA, a hotbed for autonomous vehicle testing, there have already been dozens of accidents —including at least one that seriously injured a pedestrian . dozens of accidents dozens of accidents seriously injured a pedestrian seriously injured a pedestrian However, fully autonomous vehicles aren't the only type of self-driving technology out there. There are also countless new vehicles on US roads with complex driver-assist systems. These vehicles have the same features as self-driving models, except that they require a driver's hands on the wheel at all times. And those vehicles are causing havoc on the roads. In one 10-month span, in fact, automakers reported 400 accidents involving vehicles with driver-assist systems in use. reported 400 accidents reported 400 accidents All of this is to say that the development of self-driving vehicle technology is not without risk. And at this stage, it begs a serious question—are smart autonomous vehicle technologies making us less safe on the road? Here's the answer to that question, based on all available data. The Safety Potential of Autonomous Driving Technology Without a doubt, the various technological elements that make up today's driver-assist systems and tomorrow's fully autonomous vehicles have the potential to save lives. How many lives? To figure that out, consider the following. make up today's driver-assist systems make up today's driver-assist systems Each year in the US, there are approximately 1.7 million rear-end vehicle collisions . Those accidents create approximately 1,700 fatalities and 500,000 additional injuries. And yet, a study of vehicles that include forward collision warning (FCW) systems and autonomous emergency braking (AEB) functionality indicates that those two technologies alone could reduce such accidents by a whopping 50% . That means universal adoption of FCW and AEB technology could save at least 850 lives every year. 1.7 million rear-end vehicle collisions 1.7 million rear-end vehicle collisions by a whopping 50% by a whopping 50% Another study indicated that equipping vehicles with FCW, lane departure warning (LDW), blind spot warning, and curve-adaptive headlights could cut car accident rates by up to one-third . If you consider that there are approximately 43,000 fatal car crashes in the US every year, those technologies alone could prevent 14,190 of those. by up to one-third by up to one-third Assessing the Human Element You'll notice that so far, I've only mentioned driver-assist technologies that don't directly control the operation of a vehicle. And therein lies the problem. It seems that the more self-driving technology you put in a vehicle, the less responsible drivers become. According to a recent survey , drivers are developing some disturbing habits connected to autonomous driving technology. a recent survey a recent survey In the survey, a shocking 61% of respondents indicated that advanced driver-assist technologies make them more comfortable looking away from the road while driving. A full 58% of those with vehicles equipped with pedestrian detection admitted that they've stopped looking around for pedestrians. And maybe worst of all, 57% of those with AEB-equipped vehicles report it making them comfortable with looking away from the road while driving. If you examine those results together, a pretty clear picture starts to emerge. It's that any vehicle technology that takes the onus of safety off the driver makes drivers less careful and more distracted. And that tendency is nowhere more pronounced than it is in a specific driver cohort: Tesla vehicle owners. The Electric Elephant in the Room If you go looking for information about self-driving technologies and vehicle safety, it won't take long for you to notice a trend. It's that vehicles from all-electric automaker Tesla seem to get involved in a shocking number of car crashes . And you're not imagining it. According to insurance company data, Tesla owners have the highest accident rate of any type of vehicle. Between 2022 and 2023, Tesla drivers had an accident rate of 23.54 per 1,000 drivers . And if you dig a bit deeper, you'll find that a huge percentage of those crashes happened either completely or in part due to the automaker's Autopilot feature. car crashes car crashes 23.54 per 1,000 drivers 23.54 per 1,000 drivers So far, Tesla's self-driving feature is responsible for 736 crashes and 17 fatalities . And there are some important reasons for that which tie into this discussion. One of them is the fact that Tesla's Autopilot isn't capable of fully autonomous operation. It's just the automaker's name for its particular flavor of driver-assist technologies. Like others in its class, Autopilot requires drivers to keep their hands on the steering wheel at all times. 736 crashes and 17 fatalities 736 crashes and 17 fatalities And yet, the company has spent over nine years advertising the Autopilot feature as a Full Self-Driving Capability . And given what we know about driver tendencies surrounding autonomous technologies, likely explains the high accident rate among Tesla drivers. Those drivers, lulled by the promise of illusory self-driving capabilities, seem to take chances they wouldn't take in other vehicles. To wit, most of the accidents involving the Autopilot feature happen as onboard computers record the driver taking their hands off the steering wheel for extended periods. Full Self-Driving Capability Full Self-Driving Capability The Takeaway The main takeaway from all of this is simple. It's that self-driving and autonomous vehicle technology can and should make driving much safer. However, until those technologies mature into truly autonomous vehicles, they're having the opposite effect. While features like FCW and AEB do decrease fatality rates in accidents, they may be contributing to driver behavior that causes more accidents. Of course, there's no precise way to tell if that particular tradeoff is worth it, safety-wise. Furthermore, it also seems clear that the more drivers believe that their vehicles can handle the driving for them, the less engaged in the task they get. Therefore, until vehicles can handle all of the driving for their occupants without human intervention, we may see an ever-increasing number of injuries and fatalities connected to driver-assist technologies. That reality puts more pressure than ever before on the automakers working toward full vehicle autonomy to get their current technologies and designs to the finish line.

Self-Driving Cars and Personal Injury: Are Smart Features Making Cars Safer or More Dangerous?

স্ব-ড্রাইভিং গাড়ি এবং ব্যক্তিগত আঘাত: স্মার্ট বৈশিষ্ট্যগুলি কি গাড়িগুলিকে নিরাপদ বা আরও বিপজ্জনক করে তোলে?

Selbstfahrende Autos und Personenschäden: Machen intelligente Funktionen Autos sicherer oder gefährlicher?

Los vehículos autónomos y las lesiones personales: ¿las funciones inteligentes hacen que los coches sean más seguros o más peligrosos?

Voitures autonomes et blessures corporelles : les fonctionnalités intelligentes rendent-elles les voitures plus sûres ou plus dangereuses ?

स्वचालित कारें और व्यक्तिगत चोट: क्या स्मार्ट फीचर्स कारों को सुरक्षित बना रहे हैं या अधिक खतरनाक?

自動運転車と人身傷害: スマート機能は車をより安全にするのか、それともより危険にするのか?

자율주행차와 개인 상해: 스마트 기능이 자동차를 더 안전하게 만드는가, 아니면 더 위험한가?

Carros autônomos e ferimentos pessoais: os recursos inteligentes estão tornando os carros mais seguros ou mais perigosos?

Беспилотные автомобили и травматизм: делают ли интеллектуальные функции автомобили безопаснее или опаснее?

Sürücüsüz Arabalar ve Kişisel Yaralanma: Akıllı Özellikler Arabaları Daha Güvenli mi, yoksa Daha Tehlikeli mi Hale Getiriyor?

Ô tô tự lái và thương tích cá nhân: Các tính năng thông minh giúp ô tô an toàn hơn hay nguy hiểm hơn?

自动驾驶汽车与人身伤害：智能功能是否使汽车更安全还是更危险？

Stories, News & Mentions about KWIL

Kwil’s Stories on HackerNoon