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The question of from-scratch implementation vs. Python library comes up once in a while, no matter the goal of your project. When it comes up again, I've got you covered. I'm working on a data science project to develop a forecasting model in a public health context. It's a proof of concept, so it's not clear whether my prototype will be implemented in practice, but it is part of my PhD thesis. This brought me to ask the from-scratch vs. Python library question quite a few times. So I searched the 'depths' of the internet to combine the collective knowledge of the community. The Approach I found one discussion on and one on , but both provide a developer perspective. Based on from 2020, Python is increasingly being used for web development, but the proportion of users using it for data analysis is still considerable. Hacker News StackOverflow Python Developer Survey Additionally, project management offers some valuable tools for decision-making and frameworks, so I will first go through what a project management triangle is. If you already know about it, feel free to skip the next paragraph. The project management triangle is a conceptual model of 3 project management constraints - scope, cost, and time. Together, they lead to a certain level of quality. For example, if a project manager suddenly finds out they need to deliver the project earlier, they need to adjust the scope or the cost. Otherwise, this deadline change will negatively impact the quality. You can read more on . Wikipedia How is this relevant to deciding on whether to implement something from scratch? Well, if you have a specific goal and you have a deadline, then what you have is a project, and so you can map the concept of the project triangle to your decision-making. The time simply represents the time you or someone else allocated to solving the problem or a challenge. The scope describes what you are trying to do, e.g., find out whether a password was leaked or apply a machine-learning algorithm to classify cats and dogs. The cost or a resource is you and your capacity and experience. This is a nice start, but it doesn't fit our purpose of deciding whether to use a Python library or implement a solution from scratch completely. So, I tried to combine the developer perspective with the data science point of view and project management principles. The assumption is that you found a Python library with the potential to solve your problem, and the library is active (i.e. the library is regularly updated). If you can't find a good library, you don't have the option of using it. Disclaimer from Ms. Obvious: Implementation vs. Python Library: 7 Things to Consider - do you have a deadline? Is it soon? Time - is your goal to learn or to understand a concept? Learning - how difficult is it to do? Complexity - will you potentially deploy to production? Is it just for you? Project type - how confident are you in your skills? Can somebody help? Python experience - how extensive is the library? Library size - can you use multiple functions from this library? Functionality utilization In terms of the project triangle, the time corresponds to Then, the following three aspects would fall under and the last three under costs or . time. the scope resources In my example at the beginning, I mentioned I was working on a proof of concept data science project. This is part of my PhD, so I'm supposed to learn and push myself outside my comfort zone. My time is limited but still somewhat flexible. I found a few libraries, but they were large, and I only needed one function. Also, I had no experience with these libraries, so I wasn't sure how long it would take to get it all working with my analytic pipeline. Additionally, I didn't want to introduce more libraries. I don't know whether my code will eventually end up in production. Still, I tried to limit dependencies just in case. The task had medium complexity, and I knew I had the required level of experience. I decided to attempt the implementation from scratch. As you can see from this example, the 7 aspects are not equally important. In my case, the learning objective combined with my level of experience and not having a hard deadline were crucial in my decision-making. The rest was secondary. Different aspects can be important in other contexts, but you can always go back and map everything to the 7 factors and the project triangle. When to Use a Python Library Consider using a library when you have little time or are not focused on learning the solution is too complex considering your Python experience your project is not aimed for production, or you don't mind dependencies the size of the library corresponds to the complexity of the problem If you have enough time and want to learn about a topic, you should lean towards implementing your solution from scratch. Python offers thousands of libraries, and they are generally easy to use, so it can be tempting to just go for the library without considering the other option. Rember, when there is no deadline, it's not a project, so you don't have to optimize for time, instead focus on achieving the goal of understanding. On the other hand, if you need to use a complex neural network, it's probably better to use an existing Python library. When to Implement from Scratch Consider implementing from scratch when you have plenty of time or are focused on learning attempting the implementation won't scare you off, considering your Python experience your project is aimed for production, or you want to avoid dependencies the library contains many functionalities that you don't need If you've only started learning Python and you've never coded or programmed before, then attempting to implement an algorithm can be too challenging. So much so that it can kill your enthusiasm. However, if you are a seasoned developer and write code for production, you probably want to avoid being dependent on 3rd party implementations. You also know all this already. Additionally, if the library contains many functionalities that you don't need, consider taking out only the bits you need from the library source code. You might be interested in reading these too: Implementation of Gaussian Naive Bayes in Python from Scratch Why I decided to Build Bitcoin from Scratch

Python Library vs. Implementation From Scratch: 7 Things to Consider

I wrote this article for Stable Report back in September 2019. I am reposting now since it is a good primer for where we are, and I will be posting a update that will reference this article soon. Please note, that the Digital Euro was not live when I first write this article, but China's CBDC is already functional, and the Digital Euro is expected to go live in the coming few years. In a quest to decentralize the globe, entrepreneurs have strived to create a new status quo for the world. Money, and the functions money serve, have been the main target of innovation. In the half decade past, the industry has blossomed tremendously, with projects like Ethereum, R3’s Corda, Hyperledger and more, becoming platforms for a new generation of high tech solutions facilitated by Blockchain and cryptography. These innovations, although initially supported by small teams working on relatively fringe projects, has now reached highs that seems impossible a few years back. Market behemoths, like JP Morgan Chase, Facebook, Walmart, and more, have all become involved in the space with their own blockchain projects. Although Stable Report focusing on stablecoins, and we see the introduction of large corporates as validation of the potential of this market, we recognize that private corporation are not the only players entering the space. Central Banks and their Digital Currency projects are very real, and will enter the market sooner rather than later. The introduction on Facebook’s Libra (read Stable Report’s article on ) has been a wakeup call for Central Banks around the world. The People’s Bank of China has been leading the way, and the industry media are buzzing with updates on their upcoming plans. But what are Central Bank Digital Currencies (CBDCs), and how do they function? Who are the key players in the market, which Central Banks will act first, and what is the expected timeline of CBDCs? Is Bitcoin dead again? Libra here Types and Functions of CBDCs: Through a Bank of International Settlements’(BIS) paper on , a breakdown of money types was presented. The “Money Tree”, a Venn-like diagram the divided money characteristics into four separate categories, is presented below. These characteristics are a) digital, b) central bank issued, c) token/value-based, and d) widely accessible. Central Bank Digital Currency The Money Tree, which should be familiar to researchers of CBDCs, is a good tool for differentiating cryptocurrencies, traditional bank deposits and CBDCs from each other. In this diagram, three separate CBDC categories emerge: a) Central bank, account-based CBDCs, b) Token-based CBDCs for commercial banks, and c) Token-based CBDCs for general use. The differences between the two token-based models is slight but important, since the proposed use of the two models focuses on different users. The commercial bank token model is focused on maintaining the Bank’s reserve targets and alleviating the settlement delays between the central bank and commercial banks. This solves pain points like overnight loans, cross-back payments, and more. The general purpose token-based CBDC model, however, would expand these uses to everyday transactions within the market – something like a central bank-sponsored Venmo, PayPal or CashApp using CBDC tokens. The benefit of this model would be the continuation of existing banking practices with little deviation from today’s banking state of affairs. The third option, Account-based CBDCs, however, would expand the functions of central banks in way not seen in the past. This CBDC model would allow citizens and CBDC users to hold their funds in central bank-based accounts, potentially cutting into the market share of commercial banks. As presented in the BIS survey on CBDCs, the potential for users to shift funds from commercial bank accounts and into CBDC accounts can be quite high in certain occasions. There are a lot of other factors a Central Bank needs to be mindful of before issuing a CBDC, but in many ways, selecting which of the above models a central bank will develop is the most important determinant to how significant a CBDC will be to the banking sector and the future of monetary policy. Key Projects and Players in CBDCs: So which Central Banks are exploring CBDCs, and how close are they to being officially issued? As mentioned above, the interest towards CBDCs skyrocketed when the People’s Bank of China announced that it is expediting the launch of their own project; a decision which in turn was fueled by Facebook’s Libra stablecoin. Media coverage seems to suggest that the CBDC will be launching in late 2019, namely around November, and specifically before the launch of Libra in 2020. Official designs for The People’s Bank of China’s CBDC are still not public, but reports dictate that it will be a two-tier system, with tokens being issued to banks and large telecommunication companies directly from the central bank, and through a private blockchain. The second tier system, from banks and telecom business to users, is still no known, although the PBoC aims for a transaction rate of around 300.000 per second. A more in depth analysis can be found in . Binance Research’s dedicated segment on the topic Although PBoC’s CBDC is the world’s leading CBDC project, with research starting all the way back in 2014, you will find, unsurprisingly, multiple other banks extensively researching CBDCs. In the aforementioned BIS survey indicates that out of the 63 central banks which partook into their research, a whopping 70% (or 44 central banks) are currently, or soon to be, engaged in serious CBDC research. In fact, the first consumer-based pilot CBDC program was launched by the Bank of Uruguay in late 2017. The program, otherwise known as the e-Peso, was an effort to increase financial inclusion in the country, and 20m e-Pesos were minted and distributed to businesses and individuals. The fund transfers were instant and peer-to-peer, either through text messages or through a dedicated e-Peso mobile app. The pilot was a success and was concluded in April of 2018. No further trials or plans are public as of yet. An additional CBDC pilot project is underway in Sweden, however no official date is set as of yet. With the ever falling use of cash throughout Sweden, Sveriges Riksbank is exploring a e-Krona CBDC as a digital cash alternative. For Sveriges Riksbank, the rise of private payment providers, namely Swish - and app that has achieved near 60% market penetration by 2018 – brings with it unwanted third party risk, and the e-Krona could be a more secure solution as money enters the digital age. However, the number of projects being researched and tested are plentiful and quite diverse. Accenture’s Global Blockchain Technology division has been particularly invested in CBDC research and implementation. Their reports and do a great job exploring the technical setup and social need for smart CBDC development by central banks. (R)evolution of Money (R)evolution of Money II (R)evolution of Money II lists a myriad of projects Accenture has been a part of. Often times in partnership with R3, Accenture has been instrumental in leading Project Jasper, Project Jasper-Ubin, Project Stella and more, paving the way for CBDC experimentation and proving that the technology is mature enough to handle the volumes of transactions expected by CBDCs. This report mentions the exploratory work on CBDCs by the Bank of England, the Bank of Canada, the European Central Bank, the Riksbank, the Bank of Japan, the Monetary Authority of Singapore, and the Eastern Caribbean Central Bank; and is highly recommended reading for anyone that wants to learn more about CBDCs. This piece would be amiss if it didn’t mention the work being conducted by the Bank of Russia, the Bank of Iran, and Venezuela’s Petro. Although the aforementioned projects are under development, little is known about the structure of Iran’s and Russia’s CBDCs, except that both have been touted as responses to US-imposed financial sanctions. Venezuela’s Petro, although state-sponsored, has more characteristics similar to a traditional asset-backed stable cryptocurrency than a CBDC, and has been highly controversial and mistrusted. On a final note, CBDC exploration does not end there, with multiple other Central Banks reviewing the technology and gradually moving forward with it. The Hackernoon article “ ” is a good resource for anyone seeking to explore country-specific projects. CBCD: 19 Countries Creating or Researching the Issuance of a Digital Decentralized Currency The expected timeline for globe-wide CBDC development is yet unknown, but the space in heating up. China, which is leading the way, is expected to launch before 2020, but the timeline for CBDC issuance by western central banks still seems far away. Expected Timeline of CBDC Launch: In conference, the topic of CBDCs was discussed. Michael Warner, strategist for the San Francisco Fed, argued that a FedCoin is still at least a decade way, and that the Fed will be the last institution to explore CBDCs since the US Dollar is already leading the traditional global financial market. IBM’s Think 2019 In the Europe front, the ECB has mentioned that Wholesale CBDCs are a viable option for the future. In most cases, wholesale CBDCs between commercial banks and central banks seem to be the technology that central banks are most excited to explore. All signs show that these will lead the way. Therefore, it seems safe to say that the 2020s might be the decade of financial innovation on money, cryptocurrencies, stablecoins and CBDCs. Everything seems to align with this trajectory, and money is due to change forever. Get ready. Conclusions and Other Thoughts So, where do we stand? CBDCs seem to be becoming a reality sooner rather than later; at least in the East and in emerging markets. What is holding their development back, and are CBDCs a threat to cryptocurrencies? What about stablecoins? The answers are not yet known, but with the information available, let’s try to speculate. For most cases, CBDCs will start through wholesale, with Central Banks issuing token to commercial banks long before you, the end user, gets a hold of them. Why is that? Well, mostly because the technology can help elevate pain points in the settlement process between banks and central banks. Will general purpose, token-based CBDCs enter the retail market? Yes, but probably after wholesale CBDCs become the norm in the back-end of intra-bank and cross-bank transaction. Commercial bank account (or an alternative) will be needed to access these CBDCs when they come. What about the third option? CBDC accounts in the Central Banks for general purposes? The future for such project is unknown, but it seems to bring an unnecessary risk with their implementation. It will be a financial innovation that commercial banks won’t be ready for, since Central Bank accounts will inherently be less risky; and of course less profitable, with no interest returns offered to account holders. But will a meager interest rate counter the uncertainty of keeping your money in commercial bank accounts? Not in times of economic downturn, nor when interest rates are in near historic lows, like today. In most cases, account-based CBDCs will interfere with fractional reserve banking, and I expect them to be the last CBDC version to be explored, and only in countries where financial uncertainty is at its peak. What about Stablecoin competition? CBDCs might just be the main competitor to fiat-backed stablecoins as long as high trust toward the Central Banks exist. However, the cryptocurrency industry was built as a hedge to the whole industry, and decentralized stablecoins will provide exposure to an asset that is theoretically uncorrelated to CBDCs. Would you trust your money with the Central Bank of Greece during the early 2010s financial crisis if they had a CBDC, for example? What about Bitcoin and other pure cryptocurrencies? CBDCs are simply a different beast altogether. No one can offer the privacy protection of Monero, Z-Cash and Dash, and Bitcoin’s value is proven to be uncorrelated to traditional markets and will lead the price of the rest of the blockchain industry. Virtual Machine platforms, like Ethereum, EOS and Cardano, are also significantly different to non-smart contract capable CBDCs, and it looks unlikely that any of the CBDCs researched as the moment will aim to create the smart contract functionality Ethereum or stablecoins like Libra and Terra are focusing on. Lastly, non-backed, algorithmic stablecoins are multi-coin systems that aim to stabilize their price by expanding and contracting the supply of their token. Isn’t that similar to what central banks do since the end of Bretton Woods? It seems that highly innovative projects, like said algorithmic stablecoin, will only flourish when trust and ever expanding usage of their system is established. For that to be the case, it is very likely that a central bank will have to support it – which does beat the purpose initially, but these systems can then be tested accurately, iterated upon securely, and decentralized later on. Does this seem like an impossibility? Well, surprisingly no! Outgoing head of the IMF, Christine Lagarde, has brought up the possibility of as they enter the mainstream financial markets. Of course, existing regulations will need to apply to these companies before any such formal action takes place. But as Ms. Lagarde takes the helm of the European Central Bank, the world will follow closely and monitor the way she handles an industry created to leave her out of a job. Until November 1st, when she takes over the ECB, the IMF paper on is a must-read for anyone interested in the topic. Central Banks acting as a lender of last resort to blockchain businesses the Rise of Digital Money As a final word, the world of money is changing, and the efforts of cryptocurrency entrepreneurs seems to have been successful in pushing the status quo in ways that seemed impossible a few years back. CBDCs, in retrospect, seem like an inevitable response to Bitcoin and Stablecoins. Now is the time. Let’s see what these entrepreneurs can accomplish, and how CBDCs will affect their projects’ future. Editorial note: Since the creation of this article, a number of key steps have been taken. First of all, the Digital Euro seems well on its way in Europe. Libra has intentionally reissued it's whitepaper and rebranded into Diem, and Algorithmic Stablecoins has started to remerge as potentially viable money alternatives. This was Xenofon Kontouris. To follow up on my work on Stabelcoins, CBDCs and Crypto, please follow me on Twitter at @XenofonOfAthens

A Primer on Central Bank Digital Currency – What’s going on? 

Authors: (1) Savvas Petridis, Google Research, New York, New York, USA; (2) Ben Wedin, Google Research, Cambridge, Massachusetts, USA; (3) James Wexler, Google Research, Cambridge, Massachusetts, USA; (4) Aaron Donsbach, Google Research, Seattle, Washington, USA; (5) Mahima Pushkarna, Google Research, Cambridge, Massachusetts, USA; (6) Nitesh Goyal, Google Research, New York, New York, USA; (7) Carrie J. Cai, Google Research, Mountain View, California, USA; (8) Michael Terry, Google Research, Cambridge, Massachusetts, USA. Table Of Links Abstract & Introduction Related Work Formative Study Constitution Maker Implementation User Study Findings Discussion Conclusion and References 9 CONCLUSION This paper presents ConstitutionMaker, a tool for interactively refining LLM outputs by converting users’ intuitive feedback into principles. ConstitutionMaker’s design is informed by a formative study, where we also collected and classified the types of principles users wanted to write. ConstitutionMaker incorporates three principle-elicitation features: kudos, critique, and rewrite. In a user study with 14 industry professionals, participants felt that ConstitutionMaker helped them (1) write principles that effectively guided the chatbot, (2) convert their feedback into principles more easily, and (3) write principles more efficiently, with (4) less mental demand than the baseline. This was due to ConstitutionMaker supporting their thought processes, including helping them to: identify ways to improve the bot’s responses, convert their intuition into verbal feedback, and phrase their feedback as specific principles. There are many avenues of future work, including supporting users in iterating on and clarifying their principles, organizing larger sets of principles and supporting multiple writers, and helping users test chatbots across multiple user journeys. Together, these findings inform future tools that support interactively customizing LLM outputs. 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Interactively Critiquing LLMs by Converting Feedback into Principle— Conclusion and References

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.

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remberg

remberg.de

51-200 employees

Since 2018

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REMBERG

EVERGREEN INDEX #8791

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