Cybersecurity expert Dawn Cappelli returns from retirement to lead Dragos’ OT-CERT initiative, poised to empower manufacturers.

Industry leader returns with cybersecurity solutions - Manufacturers' Monthly

Dragos has acquired Network Perception to boost its cybersecurity capabilities, offering enhanced visibility and compliance for operational technology networks.

Dragos Expands Cybersecurity Capabilities with Network Perception Acquisition - SDxCentral

Dragos has acquired Network Perception, integrating the NP-View platform to enhance cybersecurity for critical infrastructure sectors, including utilities and manufacturing.

Dragos acquires Network Perception to boost OT cybersecurity

Dragos

dragos

At the beginning of the year, when I started building , I evaluated some cool tech out there. Here’s the list of tech I think will change the world in ways we can’t even predict. Archbee 1. Swift Objective C was perceived as clunky/verbose by many, and hard to teach to younger developers. So Apple decided to brew their second programming language, Swift. They recruited Chris Lattner, notorious from his work on LLVM and conceived one of the most impressive languages ever. What many don’t know is that Swift is open source, and has a big and growing community behind it. They made Swift work on Linux, created Swift Package Manager and are making Swift a workable server-side language, all happening in a very short amount of time. Take a look at a SwiftUI intro from Twilio: So why should you invest your time in learning a bit of Swift? Borrows heavily from functional languages with features like: non-nullable types, immutable data structures, first-class functions, higher-order functions, sum types with exhaustive switch patterns, partial and pure functions, tail-call optimisations for recursion, extensions and many more; Swift is no Haskell, but goes a long way from classic OOP languages like Java and C#; Swift is compiled to LLVM bytecode, so no CPU cycles will ever be wasted; Has Automatic Reference Counting for memory management. Although it can’t compete with something like compile-time memory management in Rust, in my opinion, ARC is the second-best; SwiftUI is a great UI library by Apple — heavily influenced by React, it proves once again that the ideas behind React are great: top-down data flow, immutability and language level declarative views. SwiftUI is not open-source but it’s so cool that it deserved to be on the list; Growing server-side ecosystem — Vapor and Kitura both great for writing backend systems; a bit immature, but very workable with; XCode is a very mature, fast and editor that is a pleasure to work with.Swift playground makes it easy to learn and experiment because of its immediate feedback, no compile required. Swift’s future is bright because of the active and excited community; One of the highest paying jobs; 2. Kotlin A bit of the same story as Swift, Kotlin came to life as a modern less verbose Java alternative on the JVM. Surprisingly, this great language was created and is being developed by JetBrains, a company that makes code editors. Kotlin became popular through Android, when Google made it an official language for its OS, but is becoming a monster alternative for Java in the server-side world too, mainly because Spring Framework made it a priority to have it as first-class citizen. Quick intro from Kotlin’s project lead: So why should you invest your time in learning a bit of Kotlin? Kotlin is multi-platform, meaning you can compile it to JVM, LLVM (and WASM by association) and JavaScript, covering most use cases; Modern type system with non-nullable types, generics, comprehensions, and great influences by functional programming languages. Arrow is an excellent addition to its standard library, take a look here: ; https://arrow-kt.io/ Server-side Kotlin can benefit from one of the most mature server-side ecosystem built over more than two decades since Java/JVM was introduced; Coroutines make it a great programming language to build scalable asynchronous web APIs, but unfortunately, most libraries will block as they were designed for the old thread-per-request paradigm. One of the best IDEs out there — IntelliJ IDEA; React bindings for when you compile to JavaScript to build web apps. Compile to executable files like Rust or Go. 3. Kubernetes When Docker came around, many developers didn’t see the value. Dependency isolation seemingly was not enough for most to make the switch from bare VMs running in the cloud. After Google to open-sourced their internal project Borg, everybody started to see why containers and container orchestration are the way of the future for running systems in the cloud and on-premise. Quick video explanation of Kubernetes: So why should you invest your time in learning a bit of k8s? Declarative infrastructure means you’ll be done with error-prone manual configuring of your system. Yes, YAML is not the best, but solutions like Pulumi make it a lot more reasonable; Distributed systems are more resilient, and Kubernetes makes it a lot easier to build one; Almost Hands-free scaling by just running configuration changes to add nodes or scale containers; Automatic revival of dead/crashed containers, with zero downtime deployments; Readiness and liveness probes for containers; Small to no attack surface if you create your cluster in private networks, only exposing through Ingresses; Greater security than self-managed servers: most hosted k8s clusters will have great security defaults right out the box; Most cloud providers will provide you with a free k8s master, so no actual resources will be added to your cost; As long as you can package your services as Docker containers, everything will work 100% the same in development and in production, making the development process faster; 4. Elixir What a beauty of a dynamic functional language! Built on top of the rock-solid Erlang VM and benefitting from the ecosystem and stability, Elixir marks the spot as one of the crucial technologies developed in the 2010s. They said it best on their homepage so I’m just going to quote it: “The unavoidable truth about software running in production is that things will go wrong. Even more when we take network, file systems, and other third-party resources into account. To cope with failures, Elixir provides supervisors which describe how to restart parts of your system when things go awry, going back to a known initial state that is guaranteed to work: The combination of scalability, fault-tolerance, and event-driven programming via message passing makes Elixir an excellent choice for Reactive Programming and Architectures.” So why should you invest your time in learning a bit of Elixir? Functional language with immutability by default, first-class and higher-class functions, pattern matching, structs, sigils; Phoenix Framework is really great, taking some hints from Ruby On Rails in productivity but scales a lot better and easier on the computing resources; Already true, but in the 2020s non-realtime applications won’t be acceptable anymore for user experience. This means that any language/platform with a focus on realtime data sync and WebSockets will have the edge, and Erlang VM + Elixir does it best in my opinion; Read up on the story of how WhatsApp team productivity skyrocketed with very low computing resources here: ; https://www.wired.com/2015/09/whatsapp-serves-900-million-users-50-engineers/ 5. Elm Elm is a delight. A very simple programming language with guarantees like no other. Have you heard of the expression “Zero runtime exceptions”?. That comes from Elm. Its Haskell inspired type system allows it to do this, meaning you’ll sleep better at night. Elm is also very specific on what problem it solves — frontend development. In my opinion, this is a great choice by Evan Czaplicki acknowledging that “catch-all use-cases” languages won’t cut it anymore. So why should you invest your time in learning a bit of Elm? Rock-solid type system means you’ll refactor easily because the compiler will guide you; Zero runtime exceptions in practice; Built-in toolkit for building user interfaces called The Elm Architecture (TEA). TEA is so great it inspired lots of clones in many other languages; Compiler is top-notch: set the new standard for error messages in the industry, very fast, type inference works great; Great ecosystem with “Enforced Semantic Versioning” meaning your dependencies will always know if something is broken or not — at build-time; Best asset size in the industry — smaller than React and even Preact. As you can see Elm has constantly set a bunch of new standards in our industry. Def one of the top innovations of the 2010s. 6. Flutter Building native UIs is a very hard task. Add that you need to do it for 4–5 operating systems, and you’re in deep trouble. This is why Google started building Flutter, initially focusing on Android and iOS, and eventually adding the Web Platform and even macOS and Windows apps soon. Take a look at this video: So why should you invest your time in learning a bit of Flutter? Helps you build performant mobile apps; Great backing from Google, which apparently is trying to build another OS called Fuchsia based on Flutter UIs; Dart 1 is kind of boring and old-school, but Dart 2 is more functional programming oriented, and also very specific on solving UI problems best; Flutter’s hot reload helps you quickly and easily experiment, build UIs, add features, and fix bugs faster. Experience sub-second reload times, without losing state, on emulators, simulators, and hardware for iOS and Android. Delight your users with Flutter’s built-in beautiful Material Design and Cupertino (iOS-flavor) widgets, rich motion APIs, smooth natural scrolling, and platform awareness. Dart + Flutter has great IDE support in Intellij IDEA and Visual Studio Code; 7. Redis From their homepage: “Redis is an open-source (BSD licensed), in-memory data structure store, used as a database, cache and message broker. It supports data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes with radius queries and streams. Redis has built-in replication, Lua scripting, LRU eviction, transactions and different levels of on-disk persistence, and provides high availability via Redis Sentinel and automatic partitioning with Redis Cluster.” So why should you invest your time in learning a bit of Redis? Redis is insanely fast; Redis is a very versatile toolkit; You can use it as a cache store, database and message broker; You can use it as an in-memory data structure store, which is the most important aspect of Redis in my opinion. In the era of microservices, if you need some shared state that you need to modify concurrently, you basically have no choice but to use Redis; Most cloud providers will provide a managed Redis service for you; Bindings to most programming languages; Very small API surface, and very intuitive; Very scalable through high availability and automatic partitioning. 8. TensorFlow TensorFlow is Google’s machine learning toolkit. From their homepage: “TensorFlow is an end-to-end open-source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.” TensorFlow’s core strength is performance. It was built for taking models from research to production at massive scale and it delivers. Persevere and you’d be able to join the ranks of ML practitioners who use it for incredible things, like finding new planets and pioneering medicine. The TensorFlow community put in a lot of elbow grease to make the initial magic happen, and then more effort again to polish the best gems while scraping out less fortunate designs. The plan was never to force you to use a rough draft forever, but perhaps you habituated so well to the discomfort that you didn’t realize it was temporary. So why should you invest your time in learning a bit of TensorFlow? High-performance machine learning library Bindings to high-level languages like Python, JavaScript and Swift; Can run on both CPU and GPU; Scalable horizontally; Many available trained models; Huge community, and backed by Google and a lot of big companies; Keras — the higher-level abstraction is now built into Tensorflow; Tensorflow 2.0 coming soon — “TensorFlow 2.0 focuses on simplicity and ease of use, with updates like eager execution, intuitive higher-level APIs, and flexible model building on any platform”. 9. Istio Many organizations migrated their existing monolithic workloads/systems to Kubernetes. But that is not where Kubernetes provides the most value, it’s in microservices architectures. But this new way of writing systems brings a new set of problems with it, like: observability, connecting, controlling and securing the microservices. This is where Google saw the opportunity to build Istio, a service mesh. If you are more on the DevOps side, you def need to take a look at Kubernetes and Istio. So why should you invest your time in learning a bit of Istio? Istio is very high performance, written on top of Envoy (C++); Helps you solve most of the problems that arise with microservice-based architectures; Side-car design pattern: most of the stuff every microservice needs to perform (logging, security, figuring out DNS, rate-limiting) will be moved out of the microservice into a “side-car”. This means less code (and error-prone) in each of your microservices; If you run on Google Cloud, you’ll be able to benefit greatly by their integration and ease of deployment. 10. The ELK “So, what is the ELK Stack? “ELK” is the acronym for three open source projects: Elasticsearch, Logstash, and Kibana. Elasticsearch is a search and analytics engine. Logstash is a server‑side data processing pipeline that ingests data from multiple sources simultaneously, transforms it, and then sends it to a “stash” like Elasticsearch. Kibana lets users visualize data with charts and graphs in Elasticsearch.” So why should you invest your time in learning a bit of ELK? Best in Class User Interface for Data Analysis; Data Visualization for the Amazing price of… Free; Extendable Source and Aggregation; Hosted by many cloud providers; Very versatile — works from ingesting logs, to text search, to filtering through huge datasets. Article originally published at https://archbee.io/blog/10-open-source-tech-you-should-take-a-look-at-before-2020

About Ten Open Source Technologies That Are Changing the World

2021 has been the year of NFTs. The space has risen from relative obscurity to experiencing rapid growth throughout the world. The booming NFT world has attracted lots of players with all kinds of projects. Unfortunately, some of those players hurriedly copied what the rest were doing for a quick money grab. The good news is that there are still developers willing to do the required work to achieve excellent results, such as Dragos. The project is owned by coldpizza, Earth Top, and Papi. Dragos Dragos is one of the few NFT projects that are fully built on a blockchain and are decentralized. In simple terms, this means that all the media is stored in a smart contract on the blockchain and no centralized servers have access. Even though some developers claim their NFT is decentralized, most of their media are placed on a web server. That means that in the event of a web server outage, that NFT can go offline. This is not the case for Dragos. You get an NFT that is 100% on-chain and that will live as long as the blockchain exists. The project has different factions of Dragons for like-minded users. As a user, you will be able to access faction-specific Discord chats to communicate with your community. This will allow you to bond with your tribe. Two, you can name your dragon instead of having it appear as a number. This builds an even stronger connection with the project. Three, each dragon has a birthday and is given a special birthday gift as a celebration. The development team behind Dragos NFT has worked on other successful projects like Neo Tokyo. It’s also the team behind byt.io, an NFT marketplace, and launchpad. Dragos NFT is one of the first projects on the byt.io launchpad and part of the byt ecosystem. “Our core team has built multiple 8 figure businesses with large teams, so we understand how to run and grow a business along with hitting targets,” the Dragos NFT team says. “An NFT project is a business at the end of the day, and we have that experience to hit targets and keep our customers happy.” The team admits that making the Dragos NFT project was expensive and more time-consuming than copying what others were doing. However, they are committed to doing things properly. To new people that want to get into the NFT space, their advice is simple. “If you’re looking to launch an NFT, stop looking at it as a money play and start looking at it as an actual business. Real businesses require good products, a great team, and clients/a community that is taken care of. You need realistic goals and a proper team to carry them through. Innovation will always win. Copying will always lose.” Dragos NFT is not a hobby to the owners and operators but a project they plan to see into the future. They plan to continue growing the team and instilling proper SOPs and systems to ensure longevity.

Introducing Dragos: What it Means to be a Fully On-Chain NFT

HackerNoon Reporter: Please tell us briefly about your  background. I have 10+ years of experience as a software architect and tech consultant. Until recently, I solo bootstrapped Archbee after an attempt to build a product as an intrapreneur. In 2015, I tried to start an internal business, more precisely an AI sales prediction software for big fast-food chains. I built an impressive MVP, but I didn't have the skills or the knowledge of what it means to sell to large companies. That's when I realized that the best thing is to build something where I understand the end-user: software developers. For the last 5+ years, I've been following what happens in the startup world, and I envisioned building the next unicorn 🦄. What's your startup called? And in a sentence or two, what does it do? Archbee is a docs tool for software and product teams and solves an unsexy problem, but critical: software documentation. Archbee is built out of frustration with available tools, and we focus on engineering people's needs. It started as an internal tool for software documentation. Still, it expanded, and now it's a CMS & hosting platform for a product, developer, and API docs with the power of team async doc collaboration. It's a unified platform where technical people can work together along with design, sales, or marketing teams. What is the origin story? The origin story starts with a problem I also had. While consulting tech companies, I realized that whenever more than ten people implement a project, most teams need to write documentation because the slows down the development process. tribal knowledge Most of the knowledge management solutions out there are generic — a catch-all for all the teams in a company. Software teams have been using Confluence or Slite, but these don't bring much more than Google Docs. What do you love about your team, and why are you the ones to solve this problem? I tried to build a team with people that are complementary to each other. A startup needs people who understand the scrappy mentality and know-how to get from A to B by their own research. For example, each team member must spend at least 10% of their time doing customer service. We started with a backend dev, a front-end dev, and a growth marketer, looking to expand similarly. We all work remotely, and we try to be asynchronous. If you weren’t building your startup, what would you be doing? I’d be working for a startup, or start an eSports team. At the moment, how do you measure success? What are your core metrics? Our customers are the ones that measure our success, and it shows in the metrics. With a 100% self-service model, Archbee has been growing on average by 15% MoM, and when we get a new customer, they stick and expand with an NRR of 100%+. What’s most exciting about your traction to date? We started with a Product-Led-Growth model, and I knew I was on something when I launched on Product Hunt and got #1 Product of the Day with 0 marketing spend. The most exciting part is the qualitative feedback we get from our users. Most of the time, anecdotes are stronger than raw numbers: "Archbee makes building our docs enjoyable, and I'm a developer, so you know that's saying something" - this is the type of feedback we constantly get when people try our product. What technologies are you currently most excited about, and most worried about? And why? Not excited about the technology itself, but the problems it could solve. It’s a means to an end. What drew you to get published on HackerNoon? What do you like most about our platform? Not only myself, but the team reads HackerNoon. It is a no-brainer to share our story where intelligent people share and read stories. What advice would you give to the 21-year-old version of yourself? Stop f***** around and build it. What is something surprising you've learned this year that your contemporaries would benefit from knowing? Good products always win, because the competition is a marathon with separate tracks and different time clocks. Archbee was nominated as in Startups of the Year hosted by HackerNoon. one of the best startups in Bucharest

'Archbee is Built Out of Frustration with Available Tools': Startup Interview with Dragos Bulugean

This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Dihia LANASRI, OMDENA, New York, USA; (2) Juan OLANO, OMDENA, New York, USA; (3) Sifal KLIOUI, OMDENA, New York, USA; (4) Sin Liang Lee, OMDENA, New York, USA; (5) Lamia SEKKAI, OMDENA, New York, USA. Table of Links Abstract & Introduction Background Related Work Our Methodology Experiments and Results Conclusion, Acknowledgments & References 6 Conclusion The importance of hate speech detection on social networks has encouraged many researchers to build solutions (corpora and classifiers) to detect suspect messages. The literature review shows that most works are interested in text in structured languages like English, French, Arabic, etc. However, few works deal with dialects, mainly the Algerian one, which is known for its complexity and variety. To fill in the gap, we propose in this paper a complete NLP approach to detect hate speech in the Algerian dialect. We built an annotated corpus of more than 13,5K documents, which is used to evaluate various deep learning architectures. The obtained results are very promising, where the most accurate was the DzaraShield . Looking ahead, there is significant potential to enhance inference speed, particularly for the Dziribert-based and multilingual models. While this project primarily focused on Arabic characters, our next step will be to address the dialect when written in Latin characters. Embracing both Arabic and Latin characters will more accurately capture the nuances of the written Algerian dialect. Finally, we plan to expand our corpus size and explore alternative deep-learning architectures. 7 Acknowledgments We would like to thank every person who has contributed to this project: Micha Freidin, Viktor Ivanenko, Piyush Aaryan, Yassine Elboustani, Tasneem Elyamany, Cephars Bonacci, Nolan Wang and Lydia Khelifa Chibout. We would also like to thank Omdena organization for giving us this valuable opportunity References Sean MacAvaney, Hao-Ren Yao, Eugene Yang, Katina Russell, Nazli Goharian, and Ophir Frieder. Hate speech detection: Challenges and solutions. PloS one, 14(8):e0221152, 2019. Mirela P Bogdani, Federico Faloppa, and Xheni Karaj. Beyond definitions. a call for action against hate speech in albania. a comprehensive study november 2021. 2021. JT Nockleby. hate speech in encyclopedia of the american constitution. electronic journal of academic and special librarianship. 2000. Imane Guellil, Ahsan Adeel, Faical Azouaou, Mohamed Boubred, Yousra Houichi, and Akram Abdelhaq Moumna. Ara-women-hate: An annotated corpus dedicated to hate speech detection against women in the arabic community. In Proceedings of the Workshop on Dataset Creation for Lower-Resourced Languages within the 13th Language Resources and Evaluation Conference, pages 68–75, 2022. Thomas Davidson, Dana Warmsley, Michael Macy, and Ingmar Weber. Automated hate speech detection and the problem of offensive language. In Proceedings of the international AAAI conference on web and social media, volume 11, pages 512–515, 2017. Paula Fortuna and Sérgio Nunes. A survey on automatic detection of hate speech in text. ACM Computing Surveys (CSUR), 51(4):1–30, 2018. Anna Schmidt and Michael Wiegand. A survey on hate speech detection using natural language processing. In Proceedings of the fifth international workshop on natural language processing for social media, pages 1–10, 2017. Delphine Battistelli, Cyril Bruneau, and Valentina Dragos. Building a formal model for hate detection in french corpora. Procedia Computer Science, 176:2358–2365, 2020. Fatimah Alkomah and Xiaogang Ma. A literature review of textual hate speech detection methods and datasets. Information, 13(6):273, 2022. Flor Miriam Plaza-del Arco, M Dolores Molina-González, L Alfonso Urena-López, and M Teresa Martín-Valdivia. Comparing pre-trained language models for spanish hate speech detection. Expert Systems with Applications, 166: 114120, 2021. Nuha Albadi, Maram Kurdi, and Shivakant Mishra. Are they our brothers? analysis and detection of religious hate speech in the arabic twittersphere. In 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pages 69–76. IEEE, 2018. Hala Mulki, Hatem Haddad, Chedi Bechikh Ali, and Halima Alshabani. L-hsab: A levantine twitter dataset for hate speech and abusive language. In Proceedings of the third workshop on abusive language online, pages 111–118, 2019. Fréha Mezzoudj, Mourad Loukam, and Fatma Zohra Belkredim. Arabic algerian oranee dialectal language modelling oriented topic. International Journal of Informatics and Applied Mathematics, 2(2):1–14, 2019. Oussama Boucherit and Kheireddine Abainia. Offensive language detection in under-resourced algerian dialectal arabic language. arXiv preprint arXiv:2203.10024, 2022. Djamila Menifi, Wiam Moussa, and Ahmed Cherif Mazari. Transfer Learning and Deep Learning for Multilingual Algerian Dialect Hate Speech Detection. PhD thesis, 2022. Adel Abdelli, Fayçal Guerrouf, Okba Tibermacine, and Belkacem Abdelli. Sentiment analysis of arabic algerian dialect using a supervised method. In 2019 International Conference on Intelligent Systems and Advanced Computing Sciences (ISACS), pages 1–6. IEEE, 2019. Imane Guellil, Ahsan Adeel, Faical Azouaou, Mohamed Boubred, Yousra Houichi, and Akram Abdelhaq Moumna. Sexism detection: The first corpus in algerian dialect with a code-switching in arabic/french and english. arXiv preprint arXiv:2104.01443, 2021. Ona De Gibert, Naiara Perez, Aitor García-Pablos, and Montse Cuadros. Hate speech dataset from a white supremacy forum. arXiv preprint arXiv:1809.04444, 2018. Areej Al-Hassan and Hmood Al-Dossari. Detection of hate speech in social networks: a survey on multilingual corpus. In 6th international conference on computer science and information technology, volume 10, pages 10–5121, 2019. Naaima Boudad, Rdouan Faizi, Rachid Oulad Haj Thami, and Raddouane Chiheb. Sentiment analysis in arabic: A review of the literature. Ain Shams Engineering Journal, 9(4):2479–2490, 2018. Nizar Y Habash. Introduction to Arabic natural language processing. Springer Nature, 2022. Abdul-Baquee M Sharaf and Eric Atwell. Qurana: Corpus of the quran annotated with pronominal anaphora. In Lrec, pages 130–137, 2012. Kheireddine Abainia, Kenza Kara, and Tassadit Hamouni. A new corpus and lexicon for offensive tamazight language detection. In Proceedings of the 7th International Workshop on Social Media World Sensors, pages 1–6, 2022. Ahmed Cherif Mazari and Hamza Kheddar. Deep learning-based analysis of algerian dialect dataset targeted hate speech, offensive language and cyberbullying. International Journal of Computing and Digital Systems, 2023. Imane Guellil, Ahsan Adeel, Faical Azouaou, Sara Chennoufi, Hanene Maafi, and Thinhinane Hamitouche. Detecting hate speech against politicians in arabic community on social media. International Journal of Web Information Systems, 16(3):295–313, 2020. Djamila Mohdeb, Meriem Laifa, Fayssal Zerargui, and Omar Benzaoui. Evaluating transfer learning approach for detecting arabic anti-refugee/migrant speech on social media. Aslib Journal of Information Management, 74(6): 1070–1088, 2022. Reem ALBayari and Sherief Abdallah. Instagram-based benchmark dataset for cyberbullying detection in arabic text. Data, 7(7):83, 2022. Monirah A Al-Ajlan and Mourad Ykhlef. Optimized twitter cyberbullying detection based on deep learning. In 2018 21st Saudi Computer Society National Computer Conference (NCC), pages 1–5. IEEE, 2018. Batoul Haidar, Maroun Chamoun, and Ahmed Serhrouchni. Arabic cyberbullying detection: Enhancing performance by using ensemble machine learning. In 2019 international conference on internet of things (ithings) and ieee green computing and communications (greencom) and ieee cyber, physical and social computing (cpscom) and ieee smart data (smartdata), pages 323–327. IEEE, 2019. Hamdy Mubarak, Kareem Darwish, and Walid Magdy. Abusive language detection on arabic social media. In Proceedings of the first workshop on abusive language online, pages 52–56, 2017. Azalden Alakrot, Liam Murray, and Nikola S Nikolov. Dataset construction for the detection of anti-social behaviour in online communication in arabic. Procedia Computer Science, 142:174–181, 2018. Hatem Haddad, Hala Mulki, and Asma Oueslati. T-hsab: A tunisian hate speech and abusive dataset. In International conference on Arabic language processing, pages 251–263. Springer, 2019. Hamdy Mubarak, Ammar Rashed, Kareem Darwish, Younes Samih, and Ahmed Abdelali. Arabic offensive language on twitter: Analysis and experiments. arXiv preprint arXiv:2004.02192, 2020. Hala Mulki and Bilal Ghanem. Let-mi: an arabic levantine twitter dataset for misogynistic language. arXiv preprint arXiv:2103.10195, 2021. Rehab Duwairi, Amena Hayajneh, and Muhannad Quwaider. A deep learning framework for automatic detection of hate speech embedded in arabic tweets. Arabian Journal for Science and Engineering, 46:4001–4014, 2021. Wassen Aldjanabi, Abdelghani Dahou, Mohammed AA Al-qaness, Mohamed Abd Elaziz, Ahmed Mohamed Helmi, and Robertas Damaševicius. Arabic offensive and hate speech detection using a cross-corpora multi-task learning ˇ model. In Informatics, volume 8, page 69. MDPI, 2021. Batoul Haidar, Maroun Chamoun, and Ahmed Serhrouchni. A multilingual system for cyberbullying detection: Arabic content detection using machine learning. Advances in Science, Technology and Engineering Systems Journal, 2(6): 275–284, 2017. Ahmed Abdelali, Kareem Darwish, Nadir Durrani, and Hamdy Mubarak. Farasa: A fast and furious segmenter for arabic. In Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: Demonstrations, pages 11–16, 2016. Amine Abdaoui, Mohamed Berrimi, Mourad Oussalah, and Abdelouahab Moussaoui. Dziribert: a pre-trained language model for the algerian dialect. arXiv preprint arXiv:2109.12346, 2021. Sourab Mangrulkar, Sylvain Gugger, Lysandre Debut, Younes Belkada, and Sayak Paul. Peft: State-of-the-art parameterefficient fine-tuning methods. https://github.com/huggingface/peft, 2022. Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685, 2021. Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, and Furu Wei. Text embeddings by weakly-supervised contrastive pre-training. arXiv preprint arXiv:2212.03533, 2022. E Moatez Billah Nagoudi, A Elmadany, and M Abdul-Mageed. Arat5: Text-to-text transformers for arabic language understanding and generation. arXiv preprint arXiv:2109.12068, 2021.

Hate Speech Detection in Algerian Dialect Using Deep Learning: Conclusion,  Acknowledgments

This paper is available on arxiv under CC 4.0 license. . Authors: Gemini Team, Google Table of Links Abstract and Introduction Model Architecture Training Infrastructure Training Dataset Evaluation Responsible Deployment Discussion and Conclusion, References Contributions and Acknowledgments Appendix 8. Contributions and Acknowledgments Leads Rohan Anil, Co-Lead, Text Sebastian Borgeaud, Co-Lead, Text Yonghui Wu, Co-Lead, Text Jean-Baptiste Alayrac, Co-Lead, MM Vision Jiahui Yu, Co-Lead, MM Vision Radu Soricut*, Co-Lead, MM Vision* Johan Schalkwyk, Lead, MM Audio Andrew M. Dai, Co-Lead, Data Anja Hauth*, Co-Lead, Data* Katie Millican, Co-Lead, Data David Silver, Co-Lead, Fine-Tuning Slav Petrov, Co-Lead, Fine-Tuning Melvin Johnson, Lead, Instruction Tuning Ioannis Antonoglou*, Co-Lead, RL Techniques* Julian Schrittwieser, Co-Lead, RL Techniques Amelia Glaese, Lead, Human Data Jilin Chen, Lead, Safety Emily Pitler, Co-Lead, Tool Use Timothy Lillicrap, Co-Lead, Tool Use Angeliki Lazaridou, Co-Lead, Eval Orhan Firat, Co-Lead, Eval James Molloy, Co-Lead, Infra Michael Isard, Co-Lead, Infra Paul R. Barham, Co-Lead, Infra Tom Hennigan, Co-Lead, Infra Benjamin Lee, Co-Lead, Codebase & Parallelism Fabio Viola, Co-Lead, Codebase & Parallelism Malcolm Reynolds, Co-Lead, Codebase & Parallelism Yuanzhong Xu, Co-Lead, Codebase & Parallelism Ryan Doherty, Lead, Ecosystem Eli Collins, Lead, Product Clemens Meyer, Co-Lead, Operations Eliza Rutherford, Co-Lead, Operations Erica Moreira, Co-Lead, Operations Kareem Ayoub, Co-Lead, Operations Megha Goel, Co-Lead, Operations Core Contributors George Tucker Enrique Piqueras Maxim Krikun Iain Barr Nikolay Savinov Ivo Danihelka Becca Roelofs Core Contributors Anaïs White Anders Andreassen Tamara von Glehn Lakshman Yagati Mehran Kazemi Lucas Gonzalez Misha Khalman Jakub Sygnowski Alexandre Frechette Charlotte Smith Laura Culp Lev Proleev Yi Luan Xi Chen James Lottes Nathan Schucher Federico Lebron Alban Rrustemi Natalie Clay Phil Crone Tomas Kocisky Jeffrey Zhao Bartek Perz Dian Yu Heidi Howard Adam Bloniarz Jack W. Rae Han Lu Laurent Sifre Marcello Maggioni Fred Alcober Dan Garrette Megan Barnes Shantanu Thakoor Jacob Austin Gabriel Barth-Maron William Wong Rishabh Joshi Rahma Chaabouni Deeni Fatiha Arun Ahuja Ruibo Liu Yunxuan Li Sarah Cogan Jeremy Chen Core Contributors Chao Jia Chenjie Gu Qiao Zhang Jordan Grimstad Ale Jakse Hartman Martin Chadwick Gaurav Singh Tomar Xavier Garcia Evan Senter Emanuel Taropa Thanumalayan Sankaranarayana Pillai Jacob Devlin Michael Laskin Diego de Las Casas Dasha Valter Connie Tao Lorenzo Blanco Adrià Puigdomènech Badia David Reitter Mianna Chen Jenny Brennan Clara Rivera Sergey Brin Shariq Iqbal Gabriela Surita Jane Labanowski Abhi Rao Stephanie Winkler Emilio Parisotto Yiming Gu Kate Olszewska Yujing Zhang Ravi Addanki Antoine Miech Annie Louis Laurent El Shafey Denis Teplyashin Geoff Brown Elliot Catt Nithya Attaluri Jan Balaguer Jackie Xiang Pidong Wang Zoe Ashwood Anton Briukhov Albert Webson Sanjay Ganapathy Smit Sanghavi Core Contributors Ajay Kannan Ming-Wei Chang Axel Stjerngren Josip Djolonga Yuting Sun Ankur Bapna Matthew Aitchison Pedram Pejman Henryk Michalewski Tianhe Yu Cindy Wang Juliette Love Junwhan Ahn Dawn Bloxwich Kehang Han Peter Humphreys Thibault Sellam James Bradbury Varun Godbole Sina Samangooei Bogdan Damoc Alex Kaskasoli Sébastien M. R. Arnold Vijay Vasudevan Shubham Agrawal Jason Riesa Dmitry Lepikhin Richard Tanburn Srivatsan Srinivasan Hyeontaek Lim Sarah Hodkinson Pranav Shyam Johan Ferret Steven Hand Ankush Garg Tom Le Paine Jian Li Yujia Li Minh Giang Alexander Neitz Zaheer Abbas Sarah York Machel Reid Elizabeth Cole Aakanksha Chowdhery Dipanjan Das Dominika Rogozińska Vitaly Nikolaev Core Contributors Pablo Sprechmann Zachary Nado Lukas Zilka Flavien Prost Luheng He Marianne Monteiro Gaurav Mishra Chris Welty Josh Newlan Dawei Jia Miltiadis Allamanis Clara Huiyi Hu Raoul de Liedekerke Justin Gilmer Carl Saroufim Shruti Rijhwani Shaobo Hou Disha Shrivastava Anirudh Baddepudi Alex Goldin Adnan Ozturel Albin Cassirer Yunhan Xu Daniel Sohn Devendra Sachan Reinald Kim Amplayo Craig Swanson Dessie Petrova Shashi Narayan Arthur Guez Siddhartha Brahma Jessica Landon Miteyan Patel Ruizhe Zhao Kevin Villela Luyu Wang Wenhao Jia Matthew Rahtz Mai Giménez Legg Yeung Hanzhao Lin James Keeling Petko Georgiev Diana Mincu Boxi Wu Salem Haykal Rachel Saputro Kiran Vodrahalli Core Contributors James Qin Zeynep Cankara Abhanshu Sharma Nick Fernando Will Hawkins Behnam Neyshabur Solomon Kim Adrian Hutter Priyanka Agrawal Alex Castro-Ros George van den Driessche Tao Wang Fan Yang Shuo-yiin Chang Paul Komarek Ross McIlroy Mario Lučić Guodong Zhang Wael Farhan Michael Sharman Paul Natsev Paul Michel Yong Cheng Yamini Bansal Siyuan Qiao Kris Cao Siamak Shakeri Christina Butterfield Justin Chung Paul Kishan Rubenstein Shivani Agrawal Arthur Mensch Kedar Soparkar Karel Lenc Timothy Chung Aedan Pope Loren Maggiore Jackie Kay Priya Jhakra Shibo Wang Joshua Maynez Mary Phuong Taylor Tobin Andrea Tacchetti Maja Trebacz Kevin Robinson Yash Katariya Sebastian Riedel Core Contributors Paige Bailey Kefan Xiao Nimesh Ghelani Lora Aroyo Ambrose Slone Neil Houlsby Xuehan Xiong Zhen Yang Elena Gribovskaya Jonas Adler Mateo Wirth Lisa Lee Music Li Thais Kagohara Jay Pavagadhi Sophie Bridgers Anna Bortsova Sanjay Ghemawat Zafarali Ahmed Tianqi Liu Richard Powell Vijay Bolina Mariko Iinuma Polina Zablotskaia James Besley Da-Woon Chung Timothy Dozat Ramona Comanescu Xiance Si Jeremy Greer Guolong Su Martin Polacek Raphaël Lopez Kaufman Simon Tokumine Hexiang Hu Elena Buchatskaya Yingjie Miao Mohamed Elhawaty Aditya Siddhant Nenad Tomasev Jinwei Xing Christina Greer Helen Miller Shereen Ashraf Aurko Roy Zizhao Zhang Ada Ma Angelos Filos Core Contributors Milos Besta Rory Blevins Ted Klimenko Chih-Kuan Yeh Soravit Changpinyo Jiaqi Mu Oscar Chang Mantas Pajarskas Carrie Muir Vered Cohen Charline Le Lan Krishna Haridasan Amit Marathe Steven Hansen Sholto Douglas Rajkumar Samuel Mingqiu Wang Sophia Austin Chang Lan Jiepu Jiang Justin Chiu Jaime Alonso Lorenzo Lars Lowe Sjösund Sébastien Cevey Zach Gleicher Thi Avrahami Anudhyan Boral Hansa Srinivasan Vittorio Selo Rhys May Konstantinos Aisopos Léonard Hussenot Livio Baldini Soares Kate Baumli Michael B. Chang Adrià Recasens Ben Caine Alexander Pritzel Filip Pavetic Fabio Pardo Anita Gergely Justin Frye Vinay Ramasesh Dan Horgan Kartikeya Badola Nora Kassner Subhrajit Roy Ethan Dyer Core Contributors Víctor Campos Alex Tomala Yunhao Tang Dalia El Badawy Elspeth White Basil Mustafa Oran Lang Abhishek Jindal Sharad Vikram Zhitao Gong Sergi Caelles Ross Hemsley Gregory Thornton Fangxiaoyu Feng Wojciech Stokowiec Ce Zheng Phoebe Thacker Çağlar Ünlü Zhishuai Zhang Mohammad Saleh James Svensson Max Bileschi Piyush Patil Ankesh Anand Roman Ring Katerina Tsihlas Arpi Vezer Marco Selvi Toby Shevlane Mikel Rodriguez Tom Kwiatkowski Samira Daruki Keran Rong Allan Dafoe Nicholas FitzGerald Keren Gu-Lemberg Mina Khan Lisa Anne Hendricks Marie Pellat Vladimir Feinberg James Cobon-Kerr Tara Sainath Maribeth Rauh Sayed Hadi Hashemi Richard Ives Yana Hasson YaGuang Li Eric Noland Core Contributors Yuan Cao Nathan Byrd Le Hou Qingze Wang Thibault Sottiaux Michela Paganini Jean-Baptiste Lespiau Alexandre Moufarek Samer Hassan Kaushik Shivakumar Joost van Amersfoort Amol Mandhane Pratik Joshi Anirudh Goyal Matthew Tung Andrew Brock Hannah Sheahan Vedant Misra Cheng Li Nemanja Rakićević Mostafa Dehghani Fangyu Liu Sid Mittal Junhyuk Oh Seb Noury Eren Sezener Fantine Huot Matthew Lamm Nicola De Cao Charlie Chen Contributors Gamaleldin Elsayed Ed Chi Mahdis Mahdieh Ian Tenney Nan Hua Ivan Petrychenko Patrick Kane Dylan Scandinaro Rishub Jain Jonathan Uesato Romina Datta Adam Sadovsky Oskar Bunyan Dominik Rabiej Shimu Wu John Zhang Contributors Gautam Vasudevan Edouard Leurent Mahmoud Alnahlawi Ionut Georgescu Nan Wei Ivy Zheng Betty Chan Pam G Rabinovitch Piotr Stanczyk Ye Zhang David Steiner Subhajit Naskar Michael Azzam Matthew Johnson Adam Paszke Chung-Cheng Chiu Jaume Sanchez Elias Afroz Mohiuddin Faizan Muhammad Jin Miao Andrew Lee Nino Vieillard Sahitya Potluri Jane Park Elnaz Davoodi Jiageng Zhang Jeff Stanway Drew Garmon Abhijit Karmarkar Zhe Dong Jong Lee Aviral Kumar Luowei Zhou Jonathan Evens William Isaac Zhe Chen Johnson Jia Anselm Levskaya Zhenkai Zhu Chris Gorgolewski Peter Grabowski Yu Mao Alberto Magni Kaisheng Yao Javier Snaider Norman Casagrande Paul Suganthan Evan Palmer Contributors Geoffrey Irving Edward Loper Manaal Faruqui Isha Arkatkar Nanxin Chen Izhak Shafran Michael Fink Alfonso Castaño Irene Giannoumis Wooyeol Kim Mikołaj Rybiński Ashwin Sreevatsa Jennifer Prendki David Soergel Adrian Goedeckemeyer Willi Gierke Mohsen Jafari Meenu Gaba Jeremy Wiesner Diana Gage Wright Yawen Wei Harsha Vashisht Yana Kulizhskaya Jay Hoover Maigo Le Lu Li Chimezie Iwuanyanwu Lu Liu Kevin Ramirez Andrey Khorlin Albert Cui Tian LIN Marin Georgiev Marcus Wu Ricardo Aguilar Keith Pallo Abhishek Chakladar Alena Repina Xihui Wu Tom van der Weide Priya Ponnapalli Caroline Kaplan Jiri Simsa Shuangfeng Li Olivier Dousse Fan Yang Jeff Piper Nathan Ie Contributors Minnie Lui Rama Pasumarthi Nathan Lintz Anitha Vijayakumar Lam Nguyen Thiet Daniel Andor Pedro Valenzuela Cosmin Paduraru Daiyi Peng Katherine Lee Shuyuan Zhang Somer Greene Duc Dung Nguyen Paula Kurylowicz Sarmishta Velury Sebastian Krause Cassidy Hardin Lucas Dixon Lili Janzer Kiam Choo Ziqiang Feng Biao Zhang Achintya Singhal Tejasi Latkar Mingyang Zhang Quoc Le Elena Allica Abellan Dayou Du Dan McKinnon Natasha Antropova Tolga Bolukbasi Orgad Keller David Reid Daniel Finchelstein Maria Abi Raad Remi Crocker Peter Hawkins Robert Dadashi Colin Gaffney Sid Lall Ken Franko Egor Filonov Anna Bulanova Rémi Leblond Vikas Yadav Shirley Chung Harry Askham Luis C. Cobo Contributors Kelvin Xu Felix Fischer Jun Xu Christina Sorokin Chris Alberti Chu-Cheng Lin Colin Evans Hao Zhou Alek Dimitriev Hannah Forbes Dylan Banarse Zora Tung Jeremiah Liu Mark Omernick Colton Bishop Chintu Kumar Rachel Sterneck Ryan Foley Rohan Jain Swaroop Mishra Jiawei Xia Taylor Bos Geoffrey Cideron Ehsan Amid Francesco Piccinno Xingyu Wang Praseem Banzal Petru Gurita Hila Noga Premal Shah Daniel J. Mankowitz Alex Polozov Nate Kushman Victoria Krakovna Sasha Brown MohammadHossein Bateni Dennis Duan Vlad Firoiu Meghana Thotakuri Tom Natan Anhad Mohananey Matthieu Geist Sidharth Mudgal Sertan Girgin Hui Li Jiayu Ye Ofir Roval Reiko Tojo Contributors Michael Kwong James Lee-Thorp Christopher Yew Quan Yuan Sumit Bagri Danila Sinopalnikov Sabela Ramos John Mellor Abhishek Sharma Aliaksei Severyn Jonathan Lai Kathy Wu Heng-Tze Cheng David Miller Nicolas Sonnerat Denis Vnukov Rory Greig Jennifer Beattie Emily Caveness Libin Bai Julian Eisenschlos Alex Korchemniy Tomy Tsai Mimi Jasarevic Weize Kong Phuong Dao Zeyu Zheng Frederick Liu Fan Yang Rui Zhu Mark Geller Tian Huey Teh Jason Sanmiya Evgeny Gladchenko Nejc Trdin Andrei Sozanschi Daniel Toyama Evan Rosen Sasan Tavakkol Linting Xue Chen Elkind Oliver Woodman John Carpenter George Papamakarios Rupert Kemp Sushant Kafle Tanya Grunina Rishika Sinha Contributors Alice Talbert Abhimanyu Goyal Diane Wu Denese Owusu-Afriyie Cosmo Du Chloe Thornton Jordi Pont-Tuset Pradyumna Narayana Jing Li Saaber Fatehi John Wieting Omar Ajmeri Benigno Uria Tao Zhu Yeongil Ko Laura Knight Amélie Héliou Ning Niu Shane Gu Chenxi Pang Dustin Tran Yeqing Li Nir Levine Ariel Stolovich Norbert Kalb Rebeca Santamaria-Fernandez Sonam Goenka Wenny Yustalim Robin Strudel Ali Elqursh Balaji Lakshminarayanan Charlie Deck Shyam Upadhyay Hyo Lee Mike Dusenberry Zonglin Li Xuezhi Wang Kyle Levin Raphael Hoffmann Dan Holtmann-Rice Olivier Bachem Summer Yue Sho Arora Eric Malmi Daniil Mirylenka Qijun Tan Christy Koh Soheil Hassas Yeganeh Contributors Siim Põder Steven Zheng Francesco Pongetti Mukarram Tariq Yanhua Sun Lucian Ionita Mojtaba Seyedhosseini Pouya Tafti Ragha Kotikalapudi Zhiyu Liu Anmol Gulati Jasmine Liu Xinyu Ye Bart Chrzaszcz Lily Wang Nikhil Sethi Tianrun Li Ben Brown Shreya Singh Wei Fan Aaron Parisi Joe Stanton Chenkai Kuang Vinod Koverkathu Christopher A. Choquette-Choo Yunjie Li TJ Lu Abe Ittycheriah Prakash Shroff Pei Sun Mani Varadarajan Sanaz Bahargam Rob Willoughby David Gaddy Ishita Dasgupta Guillaume Desjardins Marco Cornero Brona Robenek Bhavishya Mittal Ben Albrecht Ashish Shenoy Fedor Moiseev Henrik Jacobsson Alireza Ghaffarkhah Morgane Rivière Alanna Walton Clément Crepy Alicia Parrish Contributors Yuan Liu Zongwei Zhou Clement Farabet Carey Radebaugh Praveen Srinivasan Claudia van der Salm Andreas Fidjeland Salvatore Scellato Eri Latorre-Chimoto Hanna Klimczak-Plucińska David Bridson Dario de Cesare Tom Hudson Piermaria Mendolicchio Lexi Walker Alex Morris Ivo Penchev Matthew Mauger Alexey Guseynov Alison Reid Seth Odoom Lucia Loher Victor Cotruta Madhavi Yenugula Dominik Grewe Anastasia Petrushkina Tom Duerig Antonio Sanchez Steve Yadlowsky Amy Shen Amir Globerson Adam Kurzrok Lynette Webb Sahil Dua Dong Li Preethi Lahoti Surya Bhupatiraju Dan Hurt Haroon Qureshi Ananth Agarwal Tomer Shani Matan Eyal Anuj Khare Shreyas Rammohan Belle Lei Wang Chetan Tekur Mihir Sanjay Kale Jinliang Wei Contributors Ruoxin Sang Brennan Saeta Tyler Liechty Yi Sun Yao Zhao Stephan Lee Pandu Nayak Doug Fritz Manish Reddy Vuyyuru John Aslanides Nidhi Vyas Martin Wicke Xiao Ma Taylan Bilal Evgenii Eltyshev Daniel Balle Nina Martin Hardie Cate James Manyika Keyvan Amiri Yelin Kim Xi Xiong Kai Kang Florian Luisier Nilesh Tripuraneni David Madras Mandy Guo Austin Waters Oliver Wang Joshua Ainslie Jason Baldridge Han Zhang Garima Pruthi Jakob Bauer Feng Yang Riham Mansour Jason Gelman Yang Xu George Polovets Ji Liu Honglong Cai Warren Chen XiangHai Sheng Emily Xue Sherjil Ozair Adams Yu Christof Angermueller Contributors Xiaowei Li Weiren Wang Julia Wiesinger Emmanouil Koukoumidis Yuan Tian Anand Iyer Madhu Gurumurthy Mark Goldenson Parashar Shah MK Blake Hongkun Yu Anthony Urbanowicz Jennimaria Palomaki Chrisantha Fernando Kevin Brooks Ken Durden Harsh Mehta Nikola Momchev Elahe Rahimtoroghi Maria Georgaki Amit Raul Sebastian Ruder Morgan Redshaw Jinhyuk Lee Komal Jalan Dinghua Li Ginger Perng Blake Hechtman Parker Schuh Milad Nasr Mia Chen Kieran Milan Vladimir Mikulik Trevor Strohman Juliana Franco Tim Green Program Leads Demis Hassabis Koray Kavukcuoglu Overall Technical Leads (equal contribution) Jeffrey Dean Oriol Vinyals The roles are defined as below: • : Individual(s) responsible for the sub-team throughout the project. Lead Individual that had significant impact throughout the project. • Core Contributor: : Individual that had contributions to the project and was partially involved with the effort. • Contributor Responsible for the organizational aspects of the Gemini effort • Program Lead: Responsible for the technical direction of the overall Gemini effort • Overall Technical Lead: Within each role, contributions are equal, and are listed in a randomized order. Ordering within each role does not indicate ordering of the contributions. Gemini is a cross-Google effort, with members from Google DeepMind (GDM), Google Research (GR), Knowledge and Information (K&I), Core ML, Cloud, Labs, and more. We thank our reviewers and colleagues for their valuable discussions and feedback on the report— Alexandra Belias, Arielle Bier, Eleanor Tomlinson, Emily Hossellman, Gaby Pearl, Helen King, Hollie Dobson, Jaclyn Konzelmann, Jennifer Beroshi, Joel Moss, Jon Small, Jonathan Fildes, Oli Gaymond, Rebecca Bland, Reena Jana, and Tom Lue. Our work is made possible by the dedication and efforts of numerous teams at Google. We would like to acknowledge the support from Abhi Mohan, Adekunle Bello, Aishwarya Nagarajan, Alaa Saade, Alejandro Lince, Alexander Chen, Alexander Kolbasov, Alexander Schiffhauer, Amar Subramanya, Ameya Shringi, Amin Vahdat, Anda Rabatić, Anthonie Gross, Antoine Yang, Anthony Green, Anton Ruddock, Art Khurshudov, Artemis Chen, Arthur Argenson, Avinatan Hassidim, Beiye Liu, Benjamin Schroeder, Bin Ni, Brett Daw, Bryan Chiang, Burak Gokturk, Carl Crous, Carrie Grimes Bostock, Charbel Kaed, Charlotte Banks, Che Diaz, Chris Larkin, Christy Lian, Claire Cui, Clare Bycroft, Corentin Tallec, Daniel Herndon, Dave Burke, David Battle, David Engel, Dipannita Shaw, Donghyun Koo, Doug Ritchie, Dragos Stefanescu, Elissa Wolf, Emre Sargin, Eran Ofek, Eric Herren, Estella King, Fatema Alkhanaizi, Felix Gimeno, Fernando Pereira, Florent Altché, Gabriel Carvajal, Gaurav Gandhi, George Powell, Goran Pavičić, Harry Richardson, Hassan Wassel, Hongji Li, Idan Szpektor, Igor Ivanisevic, Ivan Jambrešić, Ivan Jurin, Jade Fowler, James Assiene, Jay Yagnik, Jeanbastien Grill, Jeff Seibert, Jenna LaPlante, Jessica Austin, Jianxing Lu, Jim O’Keeffe, Jin Huang, Joe Heyward, Johannes Welbl, John Jumper, Jonathan Caton, Josh Woodward, Joshua Foster, Kathryn Tunyasuvunakool, Katrina Wong, Kavya Kopparapu, Kelvin Nguyen, Kira Yin, Konstantin Sharlaimov, Kun Li, Lee Hong, Lilly Taylor, Longfei Shen, Luc Mercier, Maciej Mikuła, Mania Abdi, Manuel Sanchez, Maria Ines Aranguren, Mario Carlos Cortes III, Matthew Tait, Matthias Lochbrunner, Mehdi Ghissassi, Micah Mosley, Michael Bendersky, Michael Figurnov, Michael Harris, Michael Mathieu, Michael O’Neill, Michael Vorburger, Mihir Paradkar, Nandita Dukkipati, Nathan Carter, Nathan Watson, Neil Rabinowitz, Nikhil Dandekar, Nishant Ranka, Obaid Sarvana, Olcan Sercinoglu, Olivier Lacombe, Ottavia Bertolli, Paul Caron, Pranesh Srinivasan, Praveen Kumar, Rahul Sukthankar, Raia Hadsell, Rajagopal Ananthanarayanan, Roberto Lupi, Rosie Zou, Sachin Menezes, Sadegh Jazayeri, Sam Cheung, Sameer Bidichandani, Sania Alex, Sanjiv Kumar, Sara Wiltberger, Sarah Fitzgerald, Saz Basu, Sebastian Nowozin, Shannon Hepburn, Shayne Cardwell, Sissie Hsiao, Srinivasan Venkatachary, Sugato Basu, Sundar Pichai, Sundeep Tirumalareddy, Susannah Young, Swetha Vijayaraghavan, Tania Bedrax-Weiss, Taylor Applebaum, Teiva Harsanyi, Terry Chen, Tim Blyth, Ting Liu, Tom Cobley, Tomas Izo, Trystan Upstill, Varun Singhai, Vedrana Klarić Trupčević, Victor Cai, Vladimir Pudovkin, Vu Dang, Wenbo Zhao, Wesley Crow, Wesley Szeng, Xiaodan Song, Yazhou Zu, Ye Tian, Yicong Wang, Yixing Wang, Yossi Matias, Yunlong Jiao, Zachary Jessup, Zhenchuan Pang, Žiga Avsec, Zimeng Yang, and Zoubin Ghahramani. We’d also like to recognize the AlphaCode team, the Borg Scheduling team, the Facilities team, the Gemini Demo Team, the Global Server Ops (GSO) team, the JAX team, the the Legal team, ML SRE team, the ML Supercomputer (MLSC) team, the PartIR team, the Platforms Infrastructure Engineering (PIE) team, and the XLA Compiler team. We thank everyone at Google not explicitly mentioned above, who have shared excitement, given feedback on early Gemini models or created interesting demo uses of Gemini, and worked with or supported the core Gemini team on many aspects of this project.

Gemini -  A Family of Highly Capable Multimodal Models: Contributions and Acknowledgments

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Yesterday, the non-profit organization Center for AI Safety about the existential risks of AI: released a one-sentence statement “ ” Mitigating the risk of extinction from A.I. should be a global priority alongside other societal-scale risks, such as pandemics and nuclear war The statement was signed by 350 prominent AI researchers and industry leaders such as OpenAI CEO, , Google CEO, Demis Hassabis, and Anthropic CEO, Dario Amodei. Earlier this month, the same three tech leaders to discuss future AI regulations.  AI is one of the few industries where major companies ask the regulators to impose boundaries on what they can and cannot do, usually, it works the other way around. Sam Altman DeepMind met with the Biden administration The mid-to-long-term AI risks are serious, although difficult to understand and predict. In this light, new copyright infringement issues posed by gigantic generative AI models, may not seem like a big deal. There is certainly not any life-or-death urgency involved in clarifying the legal landscape. On the other hand, the tension between generative AI models and copyright law is imminent and the dangers are easy to understand and explain. The Copyright Issues Before Open AI’s commercial breakthrough with , and before the competitive race among BigTech companies to develop larger and more impressive AI models, legal scholars were discussing who would own the right to a piece of original work created by an AI. As it turns out, the question of authorship has had very little practical relevance.  Neither providers nor users of generative AI models have shown much interest in claiming rights over content generated by AI. ChatGPT More relevant and pressing issues are: If using large volumes of copyright-protected material in the training process of large AI models is infringing the rights of copyright holders, and 2) What if outputs generated by AI models look suspiciously similar to copyrighted works used in the training process? In my next post, I will attempt to answer both questions by looking at the AI image model which was recently targeted with lawsuits from two different fronts. In this post, I will provide some relevant background information on the EU’s upcoming AI Act and the debate about transparency requirements. Stable Diffusion The EU AI Act and Foundation Models The European Union agreed on a on the 11th of May. Among the changes from previous drafts comes additional regulation of so-called “foundation models”. Compromise Text for its EU AI Act A  foundation model is defined as . Examples of AI model that falls into this category is ChatGPT and Stable Diffusion. "an AI system model that is trained on broad data at scale, is designed for the generality of output, and can be adapted to a wide range of distinctive tasks" The Compromise Text sets out requirements for developers of foundational models to register the model in an EU database. In this context, they have to disclose certain information and technical documentation of a range of factors related to the development of the model. According to Section C in ANNEX VIII, foundation model providers such as OpenAI and would be required to disclose data sources and which training resources are used in the development of their models. Google In addition to the requirements in ANNEX VIII, providers of foundational models are among other things required to “ (Article 28b (4) (c)). document and make publicly available a sufficiently detailed summary of the use of training data protected under copyright law” This provision is interesting and I am curious to learn how it will be interpreted in practice. OpenAI has famously not revealed anything about how or with what data GPT-4 was trained. Once the EU's AI Act enters into force, this has to change if they wish to continue operating in the EU. OpenAI, Upcoming Regulations & Copyright Issues It seems like OpenAI is speaking in two tongues. On one hand, Sam Altman and his team support AI regulation and worry about the future implications of AI and its dangers to humanity. On the other hand, they have so far refused to open up GPT-4 for public scrutiny due to competitive concerns. OpenAI’s split-personality is foreseeable since the company has a stated mission , while they are also riding on a ten-billion-dollar investment from Microsoft. The strange mixture of altruistic concerns for humanity and colossal funding from BigTech makes me wonder if OpenAI is willing to play ball with regulators, or if it will be more profitable for them to not do so and remain closed. “to ensure that artificial general intelligence benefits all of humanity” While the training process behind GPT-4 is closed for public scrutiny, we can be sure of one thing: it includes the use of copyright-protected material on an unfathomably large scale. which consists of more than 15 million domains and it still only accounts for a relatively small part of the size of GPT-3’s complete training data set. Presumably, the large majority of text GPT-3 used to analyze and learn from was protected by copyright. Washington Post analyzed the Google C4 dataset I wonder if and how OpenAI and other providers of foundation models can prepare a “sufficiently detailed summary" of all this data in accordance with EU's AI Act. The interpretation is important. Last Wednesday, if OpenAI could not comply with the upcoming EU regulations. He has since reversed the statement in a Tweet from last Friday, where he clarified: " . " Sam Altman floated the idea of leaving EU We are excited to continue to operate here and of course have no plans to leave At a recent event in London during , the : Sam Altman’s “Euro tour” OpenAI CEO said " " The current draft of the EU AI Act would be over-regulating, but we have heard it's going to get pulled back Dragos Tudorache, an EU parliament member who is leading the drafting of EU proposals disagrees: "I don't see any dilution happening anytime soon (..) These provisions relate mainly to transparency, which ensures the AI and the company building it are trustworthy. I don't see a reason why any company would shy away from transparency."

The Legal Copyright Battle Against AI: An Introduction to the EU’s Requirements

has tightly entered the life of every crypto enthusiast and it’s no surprise. The very idea of the existence of an independent and transparent financial ecosystem, which is not influenced by regulators and human factors, imperceptibly brings us closer to the new digital era. DeFi’s rise to fame did not fall out of the sky, it was a natural development of the and this is . DeFi new financial system how it happened in 2019 : Vitalik Buterin said “I’m very excited about the potential that DeFi offers in principle. The idea that anyone anywhere in the world can have access and choose their financial exposure is a very powerful thing.” Recently, the sector of decentralized finance (DeFi) has become much more popular. According to , the DeFi market’s TVL is (as of March 29, 2022), and reports the total locked assets being at (excluding staking). This indicates that this sector’s annual . DeFi Pulse $94.41B DeFi Llama $225.78B growth is reported to be approximately 200% was a among DeFi applications that gained wide popularity in late 2017. Since then, the total amount of funds placed in DeFi protocols (TVL, Total Value Locked) has been constantly . The came in the midst of the global coronavirus pandemic. For instance, in  2018, TVL was $928 millions, in May 2019 - $7.568 billion, in May 2020 - $9.276 billion, and in November 2021 the TVL reached . MakerDAO pathbreaker growing DeFi boom a record with $107.506 billion Thanks to the rapid growth of DeFi, its boundaries are constantly expanding, which creates unique products built on this technology. Let's have a look at what we will see this year. DeFi trends Defi trends is a cryptocurrency market data aggregator that provides tools for analyzing and tracking individual cryptocurrencies and the market as a whole, some trends for 2022: Messari predicted or remain at the same level. Ethereum will be the main blockchain for DeFi, and DAI will be the main decentralized stablecoin. The popularity of stablecoins will grow . Their currency rate will not be pegged to the US dollar or other fiat currencies and will not be as volatile as cryptocurrencies. Non Pegged stablecoins will appear – projects created by publicly known development teams. They will meet all regulatory requirements. – projects that are released by anonymous development teams. They will work regardless of regulatory requirements. DeFi will be divided into 2 parts . CeDeFi AnonFi in 2022 as many DeFi protocols are now using DAOs to drive their future. Meanwhile, many new NFT DAOs are emerging to support collective investment in the art of NFTs. DAOs (Decentralized Autonomous Organizations) will soar to new heights One of the most successful crypto stories of the year 2021 was , with daily active users from 38,000 in April to 2.7 million in mid-November. This rise in blockchain-based gaming is likely to spark a parallel , with DeFi obviously playing a big role in this. Axie Infinity rising rise in GameFi , CDO and Co-Founder at . “As we examine gaming’s performance, we see how vast its impact has been on the blockchain industry and just how powerful a propeller the play-to-earn narrative has become. Gaming accounts for half the industry’s usage and a fifth of the NFT trading volume for what’s been a record-breaking quarter. Axie beating blue-chips like CryptoPunks and NBA Top Shots to become the most traded collection ever poignantly illustrates this,” said Dragos Dunica DappRadar predicts that each popular musician will try to issue their own , as major record labels look to break into the NFT space. There were some successful examples at the end of 2021 by , and . Finance Monthly magazine Music NFT 3lau Nas Mike Shinoda on many fronts, sometimes in completely different directions. Trends can always change, depending on financial needs and situations in the world. However, there is a certain clear vector of DeFi’s development, this is . However, we should never forget about one very important detail. In order to take advantage of all the benefits the DeFi world has to offer, you need to procedures. What is KYC? And what is wrong with it? Read on. DeFi is evolving only movement forward go through KYC KYC problem DeFi’s growth – as expected – from governmental agencies and state institutions that drew attention to the fact that DeFi services do not restrict usage of their services by criminals in any way, given that AML & KYC haven’t been readily available in the sector. provoked a reaction (Know Your Customer) is a financial institution’s primary modus operandi, which requires them to the private information of a person before this person can any transactions. This identification serves many purposes: clientele understanding, monitoring operations, reducing liability, combating bribery and corruption. This from dealing with fraudsters and ensures the safety of customer assets. KYC verify conduct protects companies (which are binding across the EU) and (which set goals). For KYC, the two most relevant pieces of European legislation are the (which is a regulation and hence uniform across the EU) and the fifth AML (a directive, along with its four predecessors). By transposing directives into national laws, countries can impose stricter requirements. European law consists of regulations directives GDPR AML KYC verification is a that includes and of customers' personal information. Through this procedure, financial institutions and service providers can determine each client’s risk level. multi-stage procedure collection analysis Unfortunately, some users believe that full verification is a of blockchain technology’s . Users’ personal data is collected by many servers, as each service has its own database. No wonder users are afraid of data breaches and try to protect their personality. However, violation principle of anonymity there is a solution to this problem – Hashbon Pass. Hashbon Pass: born to solve the KYC problem , or by , is an and a decentralized protocol for reusable identity verification on blockchains such as Ethereum, BNB Chain, Polygon and other EVM-compatible networks. Our main is to a privacy-oriented KYC procedure. We're helping , the way it's supposed to be, not completely eradicate it, since users still have to pass KYC after creating their NFT passport. Hashbon Pass NFT Passport Hashbon FiRe NFT token mission facilitate run KYC procedures in a safe manner How it works? It works , which gives you access to vital services like banking, exchanges, crypto products, medicine and 18+ content. After all, there will no longer be the need to go through KYC procedure for different services or sites as Hashbon Pass represents a . like a passport or ID card in real life over and over again unified admission Hashbon Pass protocol is the first ever and the utilization of Tokenized All Web3 Pass ( ). Through which DeFi services, including DEXes, IDOs, & lending platforms, understand that active on their platform, determine their , and also their presence on , as well as calculate the user’s and all without needing access to their personal data. Anonymous KYC TAWP they have a verified user age citizenship blacklists credit score social rating, The an NFT Passport consists of the following steps: process of obtaining The user , fills up their nickname and sends their of their choice. registers biometric data to the verifier The user pays a and gets an NFT Passport through licensed verifiers. one-time verification fee The user’s personal data is who issue NFT passports. Such verifiers have licenses that comply with , which gives them the authority to work with clients’ personal data. stored only by verifiers GDPR The user’s is recorded on the blockchain – nickname, citizenship and date of birth. Scanned documents and personal data are stored by verifiers and are without the user's prior authorization. basic information not accessible by third parties Hashbon Pass protocol involves the participation of three parties: that require user identification. Verifiers, Users, & DeFi protocols We strive to achieve two : the user's interaction with the constantly-evolving DeFi space, and the core values of the industry in the face of looming government interference and regulation. You can read the detailed to learn more about Hashbon Pass. main goals simplify preserve whitepaper Versatile opportunities of Hashbon Pass Hashbon Pass meets the demands of the three different groups of clients, paving the road for . new DeFi and Web3 blockchain standards Users: to different services using an NFT Passport; Limitless access Convenience provided by Hashbon Pass. Users can pass as well as through Hashbon Pass; 18+ verification CAPTCHA Hashbon Pass provides additional options to secure your accounts through ; 2FA Social Rating for user ; interaction on Web 3.0 . Loan security Services: user identification; Secure and seamless , personal data can now be transferred to services, but strictly with the user's permission; SSI system Embeddability of ; marketplaces Hashbon Pass protocol is into all popular DeFi protocols as it is released on popular blockchains such as Ethereum, BNB Chain, Polygon, and other EVM-compatible blockchains. easily integrated Verifiers: Become Hashbon partners and join the initiative to bring a new standard to Web2 & Web3 ID procedures. , we’ll be launching . Services that are ready to enter the new era of the NFT ID industry can and integrate Hashbon Pass before official launch. On June 30 Hashbon Pass book a demo here

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