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I’m currently cryptocurrency CTO, we’re in the process of launching our cryptocurrency which will change the way we all make payments. But I wanted to share an earlier experience of working with the Ecuadorian government to ensure the cyber and data security of my home nation. It was one of my proudest moments, a unique and rewarding challenge, one that I will never forget. temtum Cyber Security Ecuador I started working with the Ecuadorian government in February 2012. There were many challenges that the government were facing. Firstly in the security of the nation’s systems and networks, and secondly in its data management. There were a number of threats that were the focus at the time: Denial of Service, Malware, Identity theft, Misuse of data and Phishing. I worked along with Christian Chicaiza Security IT Director, Juan Carlos Castillo Infrastructure Director, Gabriel Llumiquinga IT Security Specialist and two IT Security analysts David Arce and Karina Zhamungui. We also interacted with people from the developing team lead by Wilson Flores, and our boss Medardo Mesias, Infrastructure and Security Coordinator. Changing Protocols As part of a team we effectively researched and developed new technology based security projects for the National System of Public Data (SINARDAP, Spanish acronym). Proactively managed security tools, such as: firewalls, IPS, IDS, Antivirus, Load Balancer. Monitored and secured cloud infrastructure, including creating security cloud infrastructure weekly reports. We also designed, developed and managed security procedures for the SINARDAP. Updated, implemented, and monitored standards, policies and security procedures. Proposed strategic maps to improve the security on the cloud infrastructure. We established the requirements to secure the following systems; National Public Data Registration System (SINARDAP, Spanish acronym), National Property Registration System (SNRP, Spanish acronym), National Mercantile Registry System (SNRM, Spanish acronym). We restructured the security backbone of all Registration Institutions and National Public Data Recording Address (DINARDAP Spanish acronym), by implementing endpoint, perimetral, database security amongst others. This meant next generation firewall connected and controlled from the main city and backed up in other cities. IDS, load balancers, access control to web servers. Endpoint security on every computer and server of the institution, which includes antivirus, antispam, port access control and dlp. Access security and database control. Configuration and control changes in servers. CCTV on every Registration Institution to ensure physical security, including biometric systems on specific doors, fire and humidity detection systems, because cybersecurity must be complemented by physical security. As well as dramatically improving cyber security, through these protocols we made it possible for people to get access to their own public data by accessing one website. We have also created the Infodigital System, which is a website that only public institutions can access. They are able to search for citizen public data when they need it, eliminating the use of ID copies and other procedures between institutions. It also reduced the waiting time of the citizen on each procedure. Securing Systems Government systems are a fundamental part of a nation's organisation and political decisions can jeopardise its stability. Some cyber attacks are derived from political decisions that have caused inconvenience to citizens. Therefore, it is advisable to create a business continuity plan, and increase the monitoring of network architecture and systems, especially the most sensitive ones such as health, traffic, national security, among others. Our work reduced the waiting time to complete procedures in commercial registries, property registries and other institutions. On the other hand, the government institutions use the infodigital system internally, so citizens perceive this improvement through the reduction of paperwork. At the beginning, the system generated controversy among citizens since their public data can be consulted from a single access point. However, the standards used, the policies and procedures implemented and the equipment and configurations made generated confidence in the internal and external users of the system. Confidentiality, Integrity and Availability of the Data Sometimes when adding protection to the information, we forget about the users work. When adding layer after layer of protection, we also add obstacles to users making their work complex. This is why we must keep in mind the CIA triad; confidentiality, integrity and availability of the data, to ensure that data must be available to authorized users, so it can be modified only by them. Confidentiality: Data must be accessed only by the authorised users. For example; In a sales company, the sales staff should have access to the customer database, while human resources staff does not need to. Integrity: Data must be correct and accurate, that’s why it should only be accessed  by authorised users. For example; Dr. Alice has access to her patients' medical records and can generate prescriptions. Changing prescription information could result in a patient's death. Availability: Data must be available when need it. For example; The university's academic year begins and students want to register but the system does not show the available courses. Or even worse, the system is unavailable. Securing a Nation Key vulnerabilities are specific to all nations or organisations, it depends on what you are trying to protect, the company, society and even that particular moment. There are some vulnerabilities that exist in the UK, but there are not exploited in South America because we use different devices or systems. For example, more than 50% of people in the US use iOS while in Ecuador is less than 20%. Therefore, the attacks to iOS vulnerabilities is less in Ecuador than in the US. This does not mean we don’t have to protect ourselves, it means it is not a priority. Every system, company and society need to be analysed before listing the vulnerabilities that has to be addressed in order to prioritise them. At that particular time, we were focused on types of vulnerabilities. Network: during data transmission. Internal users: For authorised access to data and prevent misuse of them. Unsolicited system changes. Denial of Service: monitoring of requests to the system. System Registration: Create procedures so that the citizen owner of his/her data is the owner of the account, avoiding identity theft. Personal and Professional Pride Ever since I started working at DINARDAP I felt proud of myself. Being part of an exceptional group to implement large projects such as; SINARDAP, SNRM and SNRP, that reduce the waiting time for citizens, has been a big part of my life. Knowing that I worked for the citizens and not for a company filled me with pride every day. In August 2013, I began my masters in Forensics IT, I achieved a scholarship to continue my studies but this unfortunately bought my role with the government to an end for the time being. As a female leader technology,  the support my government has given me in my professional and academic career has been incredible. It’s allowed my career to flourish and my confidence to continue to grow.  I am now the CTO of t , a groundbreaking new cryptocurrency that I expect will change the landscape of financial payments and centralised payments networks forever. emtum

Cybersecurity Lessons from Working with the Ecuadorian Government

For the Story Teller: Story Telling and Stories to Tell, by Carolyn Sherwin Bailey is part of the HackerNoon Books series. You can jump to any chapter in this book here . The Gingerbread Boy The Gingerbread Boy Act I Place: A Kitchen. Time: Saturday Morning. The Little Old Man Sits in a corner. The Little Old Woman is seen, too, stirring cake dough and singing as she stirs: “Sugar, and spice, and everything nice— That’s what a little girl’s made of; Snaps, and snails, and puppy dogs’ tails; That’s what a little boy’s made of! “Ah, well-a-day, but I wish I had a little boy for all that! Some one to run to the store, and bring in the kindlings, and drive the cows to pasture, and feed the pig, and get into mischief, and be rocked to sleep in the evening.” She calls to the Little Old Man: “Father! Oh, I say, Father! Fetch me the jug of molasses from the pantry. I am making a gingerbread cake for your supper!” The Little Old Man does not move, or stir. The Little Old Woman calls louder: “Fetch me the molasses jug, Father!” The Little Old Woman crosses to the chimney corner, and shakes the Little Old Man, but he is asleep and does not wake. The Little Old Woman holds up her hands in despair. “Dearie me! I might as well have a broom for a Goodman as he. There is nothing done in the house unless I attend to it myself,” she says. She leaves the kitchen for a moment, returning with the jug of molasses. She pours some molasses into the bowl, stirs again, and finally empties the dough out upon the board, rolling it flat with her rolling pin. Suddenly she stops, rolling pin in air. The Little Old Woman: “I have it! I will make me a Gingerbread Boy!” She works very fast, talking as she shapes the Gingerbread Boy with her fingers. The Little Old Woman: “Here is his dear little head, with currants for eyes, and one raisin for his nose, and three raisins for his mouth. Here is his fine little jacket with a row of currants for buttons; and here are his two fine, fat little legs. Here are his arms, and here are his shoes!” She lays the completed Gingerbread Boy in the baking pan and dances about the kitchen with it in her hands, singing as she dances, the Song of the Gingerbread Man: “Hickory, dickory, dickory, dan; Heigho, I sing for a Gingerbread Man! Currants for eyes, and a round raisin nose, Gingerbread shoes on his gingerbread toes, Gingerbread jacket, so tight and neat, Gingerbread smiles on his face so sweet, Hickory, dickory, dickory, dan; Heigho, I sing for a Gingerbread Man!” As she finishes her song, she opens the imaginary oven door, and, kneeling down, puts in the tin which holds the Gingerbread Boy. Then she shakes the Little Old Man again. The Little Old Woman: “Wake up, I say, Father! Wake up! Wake up! The garden’s to be weeded, and the butter’s to be churned! Wake up, I say, and mind the oven. There’s a fine little Gingerbread Boy baking inside!” The Little Old Man wakes very slowly, and looking all about the kitchen says in a dazed sort of way: “What’s that you say, Mother? I don’t see any little Gingerbread Boy.” The Little Old Woman goes to the stove and points to the oven. “He’s in here baking. Do you mind him while I’m away. In twenty minutes by the clock, do you open the oven door, and the Gingerbread Boy will be baked.” The Little Old Man: “Yes, yes, Mother. Do you go and weed the garden and churn. I’ll sit here, and mind the oven.” The Little Old Woman leaves the kitchen. After she has gone, the Little Old Man re-lights his pipe. Then he gets up from his chair and peeps in the oven door. The Little Old Man: “A fine, fat Boy! A very fine, fat Gingerbread Boy! How his buttons shine, and he is swelling so much that his jacket is splitting. I shall eat him for my supper!” He goes back to his chair, and begins smoking, but soon his head nods. He looks up at the clock. The Little Old Man: “In twenty minutes I will take him out. I think I shall have a short nap in the meantime.” The Little Old Man falls fast asleep again, his pipe falling to the floor. As he sleeps, the oven door opens a little as if some one had pushed it from the inside. The real Gingerbread Boy peeps out through the crack. When he sees that the Little Old Man is asleep, he steps out. He begins blowing on his fingers and he puts them in his mouth as if they were burned. He fans himself with the baking tin which he brings with him out of the oven, and he hops about the kitchen on the tips of his toes. The Gingerbread Boy: “My, but that oven was warm! I might have been burned to a crisp before any one remembered to take me out. So this is my new home!” He looks about in all the corners of the kitchen. “And this is my new father!” He goes over to the Little Old Man, and pulls his wig. Then he sits down, cross-legged on the hearth, and goes on talking to himself. The Gingerbread Boy: “I don’t know whether I want to live in this house or not. I know what little boys have to do.” He counts on his fingers: “They have to run to the store, and bring in kindlings, and drive the cows and feed the pigs. I’d rather have a good time. I think I will run away.” He jumps up, and looks around the room, cautiously. “There’s nobody here to see me go. Hurrah! Hurrah! Here I go, off by myself to see the world!” He runs lightly out of the kitchen. Act II Place: A country road. The Gingerbread Boy is discovered, sitting on top of the wall, talking to himself. The Gingerbread Boy: “Here I am, out by myself, seeing the world. The world’s a very pleasant place, only I do wish I were not made of gingerbread, and I do wish that everybody wasn’t so hungry. Wherever I travel some one wants to eat me. Bless my buttons, there comes some one now!” The Mowers come slowly along with their scythes over their shoulders. They sing as they walk: “On Chopnose Day the Mowers rise, As every one supposes, And march upon the grass and flowers, And cut off all their noses.” Suddenly the Mowers discover the Gingerbread Boy. First Mower: “Who sits there on top of the wall?” Second Mower: “It is a little boy made of gingerbread.” First Mower: “Let us eat him!” Second Mower, going up to the Gingerbread Boy: “Good morning, my lad, where do you come from, and where are you going this fine morning?” The Gingerbread Boy hops down from the wall, and dances away on the tips of his toes: “I’ve run away from a Little Old Woman, And a Little Old Man. I can run away from you, I can!Run, run, as fast as you can,You can’t catch me, I’m the Gingerbread Man!” He disappears, followed by the Mowers, but reappears at the other end of the road, looking frightened and out of breath. The Gingerbread Boy: “They didn’t catch me that time, but you never can tell what’s going to happen next. There comes somebody else.” The Threshers are seen passing with their flails over their backs. One of the Threshers: “Who is that by the side of the road?” A Second Thresher: “That is a Gingerbread Boy!” Both of the Threshers, going up to the Gingerbread Boy very fiercely: “Come with us and be eaten, my lad!” The Gingerbread Boy dances a little way ahead of the Threshers as he calls back to them: “I’ve run away from a Little Old Woman, And a Little Old Man, Some Mowers—and—I can run away from you, I can.Run, run, as fast as you can, You can’t catch me, I’m the Gingerbread Man.” He runs away a second time, followed by the Threshers, but he is seen in a moment at the end of the road. He climbs up on the wall again. The Gingerbread Boy: “I wonder who will try and eat me next!” He puts his hand up to his eyes. “There comes some one now!” The Pig enters, grunting. The Pig: “One of us went to market; and one of us stayed at home. One of us had roast beef, but I’m the Pig who had none!” “I’m hungry enough to eat green apples. Ahe! What do I see? A Gingerbread Boy!” He walks up to the wall, and stands on his back feet, but he cannot reach to the top. The Gingerbread Boy dances on top of the wall. “I’ve run away from a Little Old Woman, And a Little Old Man, Some Mowers, some Threshers—and— I can get away from you, I can. Jump, jump, as high as you can, You can’t catch me, I’m the Gingerbread Man!” The Pig tries to get the Gingerbread Boy, but he is not able to, and he walks away, still grunting. The Gingerbread Boy: “Well, he didn’t get me. I believe I am able to take care of myself after all. Why, who is that great creature, coming down the road?” The Fox enters. He sees the Gingerbread Boy, but he pretends that he does not. He sits down and waits. The Gingerbread Boy watches the Fox. Then he speaks to him. “I’ve run away from a Little Old Woman, A Little Old Man, Some Mowers, some Threshers, a Pig—and— I can run away from you, I can!” the Gingerbread Boy says. The Fox speaks in a deep, gruff voice, without moving. The Fox: “Step a little closer, Sonny. I’m very hard of hearing.” The Gingerbread Boy jumps down from the wall, and goes quite close to the Fox, speaking very loudly: “I’ve run away from a Little Old Woman, A Little Old Man. Some Mowers, some Threshers, a Pig—and— I can run away from you, I can!” The Fox speaks again, without moving. The Fox: “You will have to step closer yet, Sonny, I’m very, very hard of hearing.” The Gingerbread Boy goes up to the Fox, shouting in his ear. As he does so, the Fox eats him up. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Bailey, Carolyn Sherwin. 2018. For the Story Teller: Story Telling and Stories to Tell. Urbana, Illinois: Project Gutenberg. Retrieved April 2022 from https://www.gutenberg.org/files/58107/58107-h/58107-h.htm#Page_153 This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org , located at https://www.gutenberg.org/policy/license.html .

Stories for Telling: The Gingerbread Boy

Authors:
(1) Nir Chemaya, University of California, Santa Barbara and (e-mail: nir@ucsb.edu);
(2) Daniel Martin, University of California, Santa Barbara and Kellogg School of Management, Northwestern University and (e-mail: danielmartin@ucsb.edu). Table of Links Abstract and Introduction
Methods
Results
Discussion
References
Appendix for Perceptions and Detection of AI Use in Manuscript Preparation for Academic Journals References Agrawal, Ajay, Joshua Gans, and Avi Goldfarb (2019). The economics of artificial intelligence: an agenda. University of Chicago Press. Akram, Arslan (2023). “An Empirical Study of AI Generated Text Detection Tools”. arXiv preprint arXiv:2310.01423. Altmäe, Signe, Alberto Sola-Leyva, and Andres Salumets (2023). “Artificial intelligence in scientific writing: a friend or a foe?” Reproductive BioMedicine Online Athey, Susan and Guido W Imbens (2019). “Machine learning methods that economists should know about”. Annual Review of Economics 11, pp. 685–725. Beck, J Thaddeus et al. (2020). “Artificial intelligence tool for optimizing eligibility screening for clinical trials in a large community cancer center”. JCO clinical cancer informatics 4, pp. 50–59. Björkegren, Daniel, Joshua E. Blumenstock, and Samsun Knight (2022). (Machine) Learning What Policies Value. arXiv: 2206.00727 [econ.GN]. Bom, Hee-Seung Henry (2023). “Exploring the Opportunities and Challenges of ChatGPT in Academic Writing: a Roundtable Discussion”. Nuclear Medicine and Molecular Imaging, pp. 1– 3. Bommasani, Rishi et al. (2021). “On the opportunities and risks of foundation models”. arXiv preprint arXiv:2108.07258. Bringula, Rex (2023). “What do academics have to say about ChatGPT? A text mining analytics on the discussions regarding ChatGPT on research writing”. AI and Ethics, pp. 1–13. Capra, C. Monica, Matthew Gomies, and Shanshan Zhang (2023). “The Sound of Cooperation and Deception in High Stakes Interactions”. Charness, Gary, Brian Jabarian, and John A List (2023). Generation next: Experimentation with ai. Tech. rep. National Bureau of Economic Research. Chien, Chen-Fu et al. (2020). Artificial intelligence in manufacturing and logistics systems: algorithms, applications, and case studies. Cowen, Tyler and Alexander T Tabarrok (2023). “How to learn and teach economics with large language models, including GPT”. Including GPT (March 17, 2023). Daun, Marian and Jennifer Brings (2023). “How ChatGPT will change software engineering education”. In: Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 1, pp. 110–116. Deza, Arturo, Amit Surana, and Miguel P Eckstein (2019). “Assessment of faster r-cnn in manmachine collaborative search”. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3185–3194. Dietvorst, Berkeley J, Joseph P Simmons, and Cade Massey (2015). “Algorithm aversion: people erroneously avoid algorithms after seeing them err.” Journal of Experimental Psychology: General 144.1, p. 114. Dranove, David and Ginger Zhe Jin (2010). “Quality disclosure and certification: Theory and practice”. Journal of Economic Literature 48.4, pp. 935–63. Farrell, Max H, Tengyuan Liang, and Sanjog Misra (2020). “Deep learning for individual heterogeneity: An automatic inference framework”. arXiv preprint arXiv:2010.14694. Fitria, Tira Nur (2021). “Grammarly as AI-powered English writing assistant: Students’ alternative for writing English”. Metathesis: Journal of English Language, Literature, and Teaching 5.1, pp. 65–78. Franchi, Matt et al. (2023). “Detecting disparities in police deployments using dashcam data”. In: Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, pp. 534–544. Fudenberg, Drew and Annie Liang (2019). “Predicting and understanding initial play”. American Economic Review 109.12, pp. 4112–4141 Fyfe, Paul (2023). “How to cheat on your final paper: Assigning AI for student writing”. AI & SOCIETY 38.4, pp. 1395–1405. Gajos, Krzysztof Z and Lena Mamykina (2022). “Do people engage cognitively with AI? Impact of AI assistance on incidental learning”. In: 27th international conference on intelligent user interfaces, pp. 794–806. Hill-Yardin, Elisa L et al. (2023). “A Chat (GPT) about the future of scientific publishing”. Brain Behav Immun 110, pp. 152–154. Horton, John J (2023). Large language models as simulated economic agents: What can we learn from homo silicus? Tech. rep. National Bureau of Economic Research. Ibrahim, Hazem et al. (2023). “Rethinking Homework in the Age of Artificial Intelligence”. IEEE Intelligent Systems 38.2, pp. 24–27. Jin, Ginger Zhe, Michael Luca, and Daniel Martin (2015). Is no news (perceived as) bad news? An experimental investigation of information disclosure. Tech. rep. National Bureau of Economic Research. Jungherr, Andreas (2023). “Using ChatGPT and Other Large Language Model (LLM) Applications for Academic Paper Assignments”. Korinek, Anton (2023). “Language models and cognitive automation for economic research” Lambrecht, Anja and Catherine Tucker (2019). “Algorithmic bias? An empirical study of apparent gender-based discrimination in the display of STEM career ads”. Management science 65.7, pp. 2966–2981. Malik, Agung Rinaldy et al. (2023). “Exploring Artificial Intelligence in Academic Essay: Higher Education Student’s Perspective”. International Journal of Educational Research Open 5, p. 100296. Mullainathan, Sendhil and Jann Spiess (2017). “Machine learning: an applied econometric approach”. Journal of Economic Perspectives 31.2, pp. 87–106. Obermeyer, Ziad et al. (2019). “Dissecting racial bias in an algorithm used to manage the health of populations”. Science 366.6464, pp. 447–453. Pallathadka, Harikumar et al. (2023). “Applications of artificial intelligence in business management, e-commerce and finance”. Materials Today: Proceedings 80, pp. 2610–2613. Rambachan, Ashesh et al. (2021). “Identifying prediction mistakes in observational data”. Harvard University. Ray, Partha Pratim (2023). “ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope”. Internet of Things and Cyber-Physical Systems. Rolf, Esther et al. (2020). Balancing Competing Objectives with Noisy Data: Score-Based Classifiers for Welfare-Aware Machine Learning. arXiv: 2003.06740 [cs.LG]. Salah, Mohammed, Hussam Al Halbusi, and Fadi Abdelfattah (2023). “May the force of text data analysis be with you: Unleashing the power of generative AI for social psychology research”. Computers in Human Behavior: Artificial Humans, p. 100006 Schmohl, Tobias et al. (2020). “How Artificial Intelligence can improve the Academic Writing of Students”. In: Conference Proceedings. The Future of Education 2020. Shahriar, Sakib and Kadhim Hayawi (2023). “Let’s have a chat! A Conversation with ChatGPT: Technology, Applications, and Limitations”. arXiv preprint arXiv:2302.13817. Singh, Harjit and Avneet Singh (2023). “ChatGPT: Systematic Review, Applications, and Agenda for Multidisciplinary Research”. Journal of Chinese Economic and Business Studies 21.2, pp. 193– 212. Steyvers, Mark et al. (2022). “Bayesian modeling of human–AI complementarity”. Proceedings of the National Academy of Sciences 119.11, e2111547119. Sundar, S Shyam and Eun-Ju Lee (2022). “Rethinking communication in the era of artificial intelligence”. Human Communication Research 48.3, pp. 379–385. Tejeda, Heliodoro et al. (2022). “AI-assisted decision-making: A cognitive modeling approach to infer latent reliance strategies”. Computational Brain & Behavior 5.4, pp. 491–508. Thorp, H Holden (2023). ChatGPT is fun, but not an author. Wang, Xinru, Chen Liang, and Ming Yin (2023). “The Effects of AI Biases and Explanations on Human Decision Fairness: A Case Study of Bidding in Rental Housing Markets”. In: Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, IJCAI-23, Edith Elkind (Ed.). International Joint Conferences on Artificial Intelligence Organization, pp. 3076– 3084 Yang, Nanyin, Marco Palma, and Andreas Drichoutis (2023). “How Does Humanizing Virtual Assistants Affect the Propensity to Follow Their Advice?” Yang, Nanyin, Marco A Palma, and Andreas C Drichoutis (2023). “How Does Humanizing Virtual Assistants Affect the Propensity to Follow Their Advice?” Zuiderwijk, Anneke, Yu-Che Chen, and Fadi Salem (2021). “Implications of the use of artificial intelligence in public governance: A systematic literature review and a research agenda”. Government Information Quarterly 38.3, p. 101577. This paper is available on arxiv under CC 4.0 license. Authors: (1) Nir Chemaya, University of California, Santa Barbara and (e-mail: nir@ucsb.edu); (2) Daniel Martin, University of California, Santa Barbara and Kellogg School of Management, Northwestern University and (e-mail: danielmartin@ucsb.edu). Authors: Authors: (1) Nir Chemaya, University of California, Santa Barbara and (e-mail: nir@ucsb.edu); (2) Daniel Martin, University of California, Santa Barbara and Kellogg School of Management, Northwestern University and (e-mail: danielmartin@ucsb.edu). Table of Links Abstract and Introduction Methods Results Discussion References Appendix for Perceptions and Detection of AI Use in Manuscript Preparation for Academic Journals Abstract and Introduction Abstract and Introduction Methods Methods Results Results Discussion Discussion References References Appendix for Perceptions and Detection of AI Use in Manuscript Preparation for Academic Journals Appendix for Perceptions and Detection of AI Use in Manuscript Preparation for Academic Journals References Agrawal, Ajay, Joshua Gans, and Avi Goldfarb (2019). The economics of artificial intelligence: an agenda. University of Chicago Press. Akram, Arslan (2023). “An Empirical Study of AI Generated Text Detection Tools”. arXiv preprint arXiv:2310.01423. Altmäe, Signe, Alberto Sola-Leyva, and Andres Salumets (2023). “Artificial intelligence in scientific writing: a friend or a foe?” Reproductive BioMedicine Online Athey, Susan and Guido W Imbens (2019). “Machine learning methods that economists should know about”. Annual Review of Economics 11, pp. 685–725. Beck, J Thaddeus et al. (2020). “Artificial intelligence tool for optimizing eligibility screening for clinical trials in a large community cancer center”. JCO clinical cancer informatics 4, pp. 50–59. Björkegren, Daniel, Joshua E. Blumenstock, and Samsun Knight (2022). (Machine) Learning What Policies Value. arXiv: 2206.00727 [econ.GN]. Bom, Hee-Seung Henry (2023). “Exploring the Opportunities and Challenges of ChatGPT in Academic Writing: a Roundtable Discussion”. Nuclear Medicine and Molecular Imaging, pp. 1– 3. Bommasani, Rishi et al. (2021). “On the opportunities and risks of foundation models”. arXiv preprint arXiv:2108.07258. Bringula, Rex (2023). “What do academics have to say about ChatGPT? A text mining analytics on the discussions regarding ChatGPT on research writing”. AI and Ethics, pp. 1–13. Capra, C. Monica, Matthew Gomies, and Shanshan Zhang (2023). “The Sound of Cooperation and Deception in High Stakes Interactions”. Charness, Gary, Brian Jabarian, and John A List (2023). Generation next: Experimentation with ai. Tech. rep. National Bureau of Economic Research. Chien, Chen-Fu et al. (2020). Artificial intelligence in manufacturing and logistics systems: algorithms, applications, and case studies. Cowen, Tyler and Alexander T Tabarrok (2023). “How to learn and teach economics with large language models, including GPT”. Including GPT (March 17, 2023). Daun, Marian and Jennifer Brings (2023). “How ChatGPT will change software engineering education”. In: Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 1, pp. 110–116. Deza, Arturo, Amit Surana, and Miguel P Eckstein (2019). “Assessment of faster r-cnn in manmachine collaborative search”. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3185–3194. Dietvorst, Berkeley J, Joseph P Simmons, and Cade Massey (2015). “Algorithm aversion: people erroneously avoid algorithms after seeing them err.” Journal of Experimental Psychology: General 144.1, p. 114. Dranove, David and Ginger Zhe Jin (2010). “Quality disclosure and certification: Theory and practice”. Journal of Economic Literature 48.4, pp. 935–63. Farrell, Max H, Tengyuan Liang, and Sanjog Misra (2020). “Deep learning for individual heterogeneity: An automatic inference framework”. arXiv preprint arXiv:2010.14694. Fitria, Tira Nur (2021). “Grammarly as AI-powered English writing assistant: Students’ alternative for writing English”. Metathesis: Journal of English Language, Literature, and Teaching 5.1, pp. 65–78. Franchi, Matt et al. (2023). “Detecting disparities in police deployments using dashcam data”. In: Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, pp. 534–544. Fudenberg, Drew and Annie Liang (2019). “Predicting and understanding initial play”. American Economic Review 109.12, pp. 4112–4141 Fyfe, Paul (2023). “How to cheat on your final paper: Assigning AI for student writing”. AI & SOCIETY 38.4, pp. 1395–1405. Gajos, Krzysztof Z and Lena Mamykina (2022). “Do people engage cognitively with AI? Impact of AI assistance on incidental learning”. In: 27th international conference on intelligent user interfaces, pp. 794–806. Hill-Yardin, Elisa L et al. (2023). “A Chat (GPT) about the future of scientific publishing”. Brain Behav Immun 110, pp. 152–154. Horton, John J (2023). Large language models as simulated economic agents: What can we learn from homo silicus? Tech. rep. National Bureau of Economic Research. Ibrahim, Hazem et al. (2023). “Rethinking Homework in the Age of Artificial Intelligence”. IEEE Intelligent Systems 38.2, pp. 24–27. Jin, Ginger Zhe, Michael Luca, and Daniel Martin (2015). Is no news (perceived as) bad news? An experimental investigation of information disclosure. Tech. rep. National Bureau of Economic Research. Jungherr, Andreas (2023). “Using ChatGPT and Other Large Language Model (LLM) Applications for Academic Paper Assignments”. Korinek, Anton (2023). “Language models and cognitive automation for economic research” Lambrecht, Anja and Catherine Tucker (2019). “Algorithmic bias? An empirical study of apparent gender-based discrimination in the display of STEM career ads”. Management science 65.7, pp. 2966–2981. Malik, Agung Rinaldy et al. (2023). “Exploring Artificial Intelligence in Academic Essay: Higher Education Student’s Perspective”. International Journal of Educational Research Open 5, p. 100296. 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Salah, Mohammed, Hussam Al Halbusi, and Fadi Abdelfattah (2023). “May the force of text data analysis be with you: Unleashing the power of generative AI for social psychology research”. Computers in Human Behavior: Artificial Humans, p. 100006 Schmohl, Tobias et al. (2020). “How Artificial Intelligence can improve the Academic Writing of Students”. In: Conference Proceedings. The Future of Education 2020. Shahriar, Sakib and Kadhim Hayawi (2023). “Let’s have a chat! A Conversation with ChatGPT: Technology, Applications, and Limitations”. arXiv preprint arXiv:2302.13817. Singh, Harjit and Avneet Singh (2023). “ChatGPT: Systematic Review, Applications, and Agenda for Multidisciplinary Research”. Journal of Chinese Economic and Business Studies 21.2, pp. 193– 212. Steyvers, Mark et al. (2022). “Bayesian modeling of human–AI complementarity”. Proceedings of the National Academy of Sciences 119.11, e2111547119. Sundar, S Shyam and Eun-Ju Lee (2022). “Rethinking communication in the era of artificial intelligence”. Human Communication Research 48.3, pp. 379–385. Tejeda, Heliodoro et al. (2022). “AI-assisted decision-making: A cognitive modeling approach to infer latent reliance strategies”. Computational Brain & Behavior 5.4, pp. 491–508. Thorp, H Holden (2023). ChatGPT is fun, but not an author. Wang, Xinru, Chen Liang, and Ming Yin (2023). “The Effects of AI Biases and Explanations on Human Decision Fairness: A Case Study of Bidding in Rental Housing Markets”. In: Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, IJCAI-23, Edith Elkind (Ed.). International Joint Conferences on Artificial Intelligence Organization, pp. 3076– 3084 Yang, Nanyin, Marco Palma, and Andreas Drichoutis (2023). “How Does Humanizing Virtual Assistants Affect the Propensity to Follow Their Advice?” Yang, Nanyin, Marco A Palma, and Andreas C Drichoutis (2023). “How Does Humanizing Virtual Assistants Affect the Propensity to Follow Their Advice?” Zuiderwijk, Anneke, Yu-Che Chen, and Fadi Salem (2021). “Implications of the use of artificial intelligence in public governance: A systematic literature review and a research agenda”. Government Information Quarterly 38.3, p. 101577. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

AI Use in Manuscript Preparation for Academic Journals: References

Authors:
(1) Omri Avrahami, Google Research and The Hebrew University of Jerusalem;
(2) Amir Hertz, Google Research;
(3) Yael Vinker, Google Research and Tel Aviv University;
(4) Moab Arar, Google Research and Tel Aviv University;
(5) Shlomi Fruchter, Google Research;
(6) Ohad Fried, Reichman University;
(7) Daniel Cohen-Or, Google Research and Tel Aviv University;
(8) Dani Lischinski, Google Research and The Hebrew University of Jerusalem. Table of Links Abstract and Introduction
Related Work
Method
Experiments
Limitations and Conclusions
A. Additional Experiments
B. Implementation Details
C. Societal Impact
References 5. Limitations and Conclusions We found our method to suffer from the following limitations: (a) Inconsistent identity — in some cases, our method is not able to converge to a fully consistent identity (without overfitting). As demonstrated in Figure 8(a), when trying to generate a portrait of a robot, our method generated robots with slightly different colors and shapes (e.g., different arms). This may result from a prompt that is too general, for which identity clustering (Section 3.1) is not able to find a sufficiently cohesive set. (b) Inconsistent supporting characters/elements— although our method is able to find a consistent identity for the character described by the input prompt, the identities of other characters, related to the input character (e.g., their pet), might be inconsistent. For example, in Figure 8(b) the input prompt p to our method described only the girl, and when asked to generate the girl with her cat, different cats were generated. In addition, our framework does not support finding multiple concepts concurrently [6]. (c) Spurious attributes — we found that in some cases, our method binds additional attributes, which are not part of the input text prompt, with the final identity of the character. For example, in Figure 8(c), the input text prompt was “a sticker of a ginger cat”, however, our method added green leaves to the generated sticker, even though it was not asked to do so. This stems from the stochastic nature of the text-to-image model — the model added these leaves in some of the stickers generated during the identity clustering stage (Section 3.1), and the stickers containing the leaves happened to form the most cohesive set ccohesive. (d) Significant computational cost — each iteration of our method involves generating a large number of images, and learning the identity of the most cohesive cluster. It takes about 20 minutes to converge into a consistent identity. Reducing the computational costs is an appealing direction for further research. In conclusion, in this paper we offered the first fully-automated solution to the problem of consistent character generation. We hope that our work will pave the way for future advancements, as we believe this technology of consistent character generation may have a disruptive effect on numerous sectors, including education, storytelling, entertainment, fashion, brand design, advertising, and more. Acknowledgments. We thank Yael Pitch, Matan Cohen, Neal Wadhwa and Yaron Brodsky for their valuable help and feedback. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Omri Avrahami, Google Research and The Hebrew University of Jerusalem; (2) Amir Hertz, Google Research; (3) Yael Vinker, Google Research and Tel Aviv University; (4) Moab Arar, Google Research and Tel Aviv University; (5) Shlomi Fruchter, Google Research; (6) Ohad Fried, Reichman University; (7) Daniel Cohen-Or, Google Research and Tel Aviv University; (8) Dani Lischinski, Google Research and The Hebrew University of Jerusalem. Authors: Authors: (1) Omri Avrahami, Google Research and The Hebrew University of Jerusalem; (2) Amir Hertz, Google Research; (3) Yael Vinker, Google Research and Tel Aviv University; (4) Moab Arar, Google Research and Tel Aviv University; (5) Shlomi Fruchter, Google Research; (6) Ohad Fried, Reichman University; (7) Daniel Cohen-Or, Google Research and Tel Aviv University; (8) Dani Lischinski, Google Research and The Hebrew University of Jerusalem. Table of Links Abstract and Introduction Related Work Method Experiments Limitations and Conclusions A. Additional Experiments B. Implementation Details C. Societal Impact References Abstract and Introduction Abstract and Introduction Related Work Related Work Method Method Experiments Experiments Limitations and Conclusions Limitations and Conclusions A. Additional Experiments A. Additional Experiments B. Implementation Details B. Implementation Details C. Societal Impact C. Societal Impact References References 5. Limitations and Conclusions We found our method to suffer from the following limitations: (a) Inconsistent identity — in some cases, our method is not able to converge to a fully consistent identity (without overfitting). As demonstrated in Figure 8(a), when trying to generate a portrait of a robot, our method generated robots with slightly different colors and shapes (e.g., different arms). This may result from a prompt that is too general, for which identity clustering (Section 3.1) is not able to find a sufficiently cohesive set. (b) Inconsistent supporting characters/elements— although our method is able to find a consistent identity for the character described by the input prompt, the identities of other characters, related to the input character (e.g., their pet), might be inconsistent. For example, in Figure 8(b) the input prompt p to our method described only the girl, and when asked to generate the girl with her cat, different cats were generated. In addition, our framework does not support finding multiple concepts concurrently [6]. (c) Spurious attributes — we found that in some cases, our method binds additional attributes, which are not part of the input text prompt, with the final identity of the character. For example, in Figure 8(c), the input text prompt was “a sticker of a ginger cat”, however, our method added green leaves to the generated sticker, even though it was not asked to do so. This stems from the stochastic nature of the text-to-image model — the model added these leaves in some of the stickers generated during the identity clustering stage (Section 3.1), and the stickers containing the leaves happened to form the most cohesive set c cohesive . (d) Significant computational cost — each iteration of our method involves generating a large number of images, and learning the identity of the most cohesive cluster. It takes about 20 minutes to converge into a consistent identity. Reducing the computational costs is an appealing direction for further research. cohesive In conclusion, in this paper we offered the first fully-automated solution to the problem of consistent character generation. We hope that our work will pave the way for future advancements, as we believe this technology of consistent character generation may have a disruptive effect on numerous sectors, including education, storytelling, entertainment, fashion, brand design, advertising, and more. Acknowledgments . We thank Yael Pitch, Matan Cohen, Neal Wadhwa and Yaron Brodsky for their valuable help and feedback. Acknowledgments This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv

The Chosen One: Consistent Characters in Text-to-Image Diffusion Models: Limitations and Conclusions

The End of Everything 

দ্য এন্ড অফ এভরিথিং

Das Ende von allem

El fin de todo

La fin de tout

हर चीज़ का अंत

すべての終わり

모든 것의 끝

O fim de tudo

Конец всего

Herşeyin Sonu

Sự kết thúc của mọi thứ

一切的终结

Drone deliveries could be the next big thing if logistics companies find a way to overcome the current hurdles. How long will it take them to figure things out? What Drone Delivery Could Look Like So far, the commercial sector has shown interest in various short-range drone applications. Last-mile delivery is one of the most likely uses for drones in logistics since the technology is better suited for brief trips. While conventional vans can travel incredibly long distances, the cost of deploying them in the last leg of shipping is absurdly high. Same-day delivery is another likely application because drones are small, fast, and lightweight. More importantly, they can typically only hold one package at a time, making them perfect for urgent order fulfillment. The food and grocery delivery surge highlights another potential application for cost-effective drones. While app-based services are popular, companies lose money paying their drivers. For example, Uber only — it lost millions every year prior. started making a profit in 2023 Pros of Using Drones for Package Deliveries There are numerous benefits to using drones for package deliveries. Fast Delivery Autonomous drones could cut delivery times in half. They can deliver lightweight packages because they’re agile and small. A large fleet could easily make thousands of deliveries daily. faster than conventional vehicles Inexpensive Operation Semi-trucks and delivery vans use diesel or gas, which is expensive and fluctuates in price often. On the other hand, drones use electricity, which is relatively inexpensive. In other words, their cost per trip is typically much lower. Higher Efficiency Drones are fast and use little fuel because roadways, traffic lights, congestion, or car accidents don’t hinder them. Instead, they can fly in a semi-direct path toward their target address. They're more efficient since they can achieve a higher delivery quota than trucks. Increased Accessibility Addresses in remote or congested areas can be challenging to get to by truck. Drones can travel to these spots with little trouble, increasing delivery accessibility — and potentially increasing order frequency. Automatic Delivery Autonomous drones pose less of a risk to public safety than self-driving vehicles. At the same time, they offer all of the benefits of automation — they can deliver packages around the clock, never have to take breaks, and won’t suffer from human-error-related mistakes. Cons of Using Drones for Package Deliveries As great as the benefits of drone deliveries are, downsides do exist. Unreliable Unfortunately, modern drones aren’t the most robust technology — a strong gust of wind or unpleasant weather can easily knock them off course. If conditions aren’t ideal, they won’t be able to leave the fulfillment center to make deliveries. Complex Currently, there are few out-of-the-box solutions for commercial drone delivery. Autonomous flight , requiring technology like cloud computing, artificial intelligence, computer vision, and GPS-independent navigation to work. Properly developing and integrating everything can be a highly complex undertaking. is very resource-intensive Involved Logistics companies can only deploy a fleet if they have storage space, chargers, navigation software, insurance, and maintenance. Developing the infrastructure to make drone deliveries is time-consuming and expensive. Expensive Commercial drones are expensive — on average, in 2024. Unfortunately, the price will only drop to $500 by 2028. Logistics companies will have to invest a significant portion of their budget into the upfront expenses alone. a single one costs $520 Error-Prone While conventional vehicles can experience a flat tire or a dead battery, the driver can fix it or call for assistance. If a drone suffers from a technical issue mid-air, it could land in a tree, on a roof or in the middle of a road, irreversibly damaging it and its package. Finding and retrieving it might be impossible if its geolocation features malfunction. Restricted Drones can only carry relatively lightweight packages. They’d have to be much larger to lift bulk or heavy objects — and who wants all that weight flying precariously above their head? Preventable Challenges to Widespread Adoption One of the most preventable challenges to the widespread adoption is drop-off uncertainty. Media often depicts drones gracefully swooping down and gingerly placing a package right where it needs to be. In reality, they need a pre-determined location like a parcel locker. Unoperated drones aren’t capable of the precision that placing a package at the right doorstep requires, so pick-up locations are ideal. However, they have to get relatively low to the ground to make these deliveries, meaning there’s a high likelihood of interference. While logistics companies can hope people remain civil, only some are fond of this novel delivery method. Residents of one town after the tech giant rolled them out. Although they’d face repercussions from destroying or damaging anything, the ensuing legal battles and replacement costs could be high. discussed shooting down Amazon’s drones At the very least, general public hesitation is another challenge halting widespread adoption. According to one survey, feel worried about potential drone accidents. Their concerns aren’t unfounded, either — drones are far more likely to crash than aircraft. nearly three in 10 people The issue of red tape is technically preventable but would be challenging in reality. The Federal Aviation Administration (FAA)’s Part 107 rule between the pilot and their drone at all times. Australia and the United Kingdom have similar regulations. This technicality all but prevents autonomous deliveries. requires a line of sight Moreover, operators must get a remote pilot certificate, register, certify, and pass tests before ever being able to fly — or watch over — a drone. Even if they have all these credentials, they can’t fly above 400 feet or over congested areas. The regulations are strict. While these challenges are all preventable with enough effort, money, and time, they still are massive obstacles to standardizing drone delivery. Unless a pioneering logistics company works to change things, they will remain in the way. Unavoidable Challenges to Widespread Adoption Logistics companies can’t remove every hurdle to adoption. Weather is one of the biggest obstacles — drones can’t fly in hail, fog, rain, sleet, or windy conditions. If it’s too hot out, their lifting capacity drops drastically, and their batteries drain much faster in the cold. Essentially, they are relatively useless unless it’s mild out. Their range is another longstanding challenge. Currently, autonomous drones can only get so far — a few miles, to be precise. For logistics companies to make an adequate return on investment, they’d need to build a micro-fulfillment center next to every major city. Obstacles are another frequently overlooked — and unavoidable — challenge. While autonomous delivery drones are often equipped with computer vision, artificial intelligence, and sensors, they won’t be able to recognize objects as well as humans. In other words, they have a much higher chance of running into power lines, low-flying aircraft, trees, birds, and people. What the Future of Drone Delivery Will Look Like While drone technology has potential, it’s facing some significant hurdles. Even if the FAA’s regulations suddenly disappeared, their success isn’t guaranteed. In fact, companies have yet to make much progress even though the FAA has already pulled back. Its 2021 rule change , enabling them to fly at night and over moving vehicles without a waiver. loosened commercial drone restrictions Still, the winds of change are definitely blowing. In 2021, registered their drones. More and more logistics enterprises are recognizing the potential of this technology. roughly 1.3 million commercial pilots Crucially, the general public’s awareness and acceptance of this technology is shifting. According to one study, to use package delivery drones if the option existed. While logistical adoption of the drone market, it likely won’t be an option at checkout for years. seven in 10 people would prefer is the fastest-growing portion Drones Could Be the Future of Logistics While delivery drones might not become a thing for years — possibly even decades — it’s clear that logistics brands are hard at work trying to make this technology happen. It could soon become the standard.

How Close Are We to Widespread Drone Deliveries?

আমরা ড্রোন ডেলিভারি ব্যাপকভাবে কতটা কাছাকাছি?

Wie nah sind wir an einer weit verbreiteten Verbreitung von Drohnenlieferungen?

¿Qué tan cerca estamos de las entregas generalizadas de drones?

À quel point sommes-nous proches des livraisons généralisées par drones ?

हम व्यापक ड्रोन डिलीवरी के कितने करीब हैं?

ドローン配送の普及はどのくらい近づいているのでしょうか?

드론 배송이 널리 확산되는 데 얼마나 가까이 다가왔나요?

Quão perto estamos das entregas generalizadas de drones?

Насколько мы близки к распространению доставки дронами?

Yaygın Drone Teslimatlarına Ne Kadar Yakınız?

Chúng ta đã tiến gần đến việc giao hàng bằng máy bay không người lái rộng rãi đến mức nào?

我们距离广泛的无人机送货还有多远？

Jingle Codes, Jingle Codes, Secure All The Way: Harness the Holiday Cheer to Fortify Your Code with Zero Trust Security Practices Unwrap the gift of impenetrable security this holiday season as we take you on a sleigh ride through the snowy realm of Zero Trust architecture. Get ready to deck your code with the latest in cybersecurity as we illuminate the path to a joyful and breach-free coding wonderland. Building Secure Software with Cheer: A Guide to Code-Mas Eve 🎅🎁 In this festive edition of our developer's guide, we embark on a joyful coding journey filled with the spirit of the holiday season. Imagine every line of code as a gift of security, wrapped in the warmth of Christmas cheer. Let us guide our readers through a winter wonderland of secure coding. Developers ensure the security of their devices, just like Santa checks his sleigh. With digital reindeer reins, devices are verified and trusted, keeping potential threats at bay. 🎄🔒🎅 Device security is crucial. Developers authenticate and safeguard devices to prevent unauthorized access. Only trusted devices can participate in holiday coding festivities. This is just a glimpse into secure coding practices. In a future blog post, we will explore enhancing device security and application integrity. 🔒💻🎄 Let's continue our journey through the coding wonderland, ensuring every step is imbued with the spirit of zero trust. Together, we can create a digital holiday season that is merrier and safer for all. Happy coding! 🎅💻❄️ Jingle Bells, Hackers Smell, Keep Them Far Away 🎶🔒 Imagine every developer wearing a Santa hat, each line of code a gift of security. It's not enough to leave milk and cookies out for Santa; we must also leave a trail of obfuscated cookie crumbs (code) so convoluted that even the smartest cyber-Grinch's GPS fails to map it. 🎅🎄🔒 (Please send me a private message to receive the mermaid diagram code.) 😊 Pillar 1: A Candy Cane Fence Around User Identity 🍬🔒 Every time a bell rings, a developer ensures secure protection of user IDs. Authentication checks are twisted into the code, creating a robust defense against fake Santas trying to sneak into our holiday app party. This peppermint wall of security guarantees only genuine users can access the festivities. Developers diligently build and maintain this protective barrier, ensuring our holiday app remains safe and joyful for all. 🍬🔒🧑‍💻 To delve deeper into the importance of user authentication and effective security measures, developers can These measures create a fortified fortress of security that allows only authorized users into the holiday app wonderland. 🔒🔑🎉 incorporate multi-factor authentication, strong password policies, and secure session management. This is just a glimpse into secure coding practices. In another blog post, we will explore additional ways developers can protect user data and enhance application security. 🔒🔐💻 Pillar 2: Sleigh Integrity - No Reindeer Games Here 🛷🔒 Just as Santa checks the integrity of his sleigh before every flight, developers meticulously scrutinize the security of their devices. Picture wrapping each device in the digital equivalent of reindeer reins, ensuring its identity and trustworthiness. With a resounding "On Dasher, on Dancer..." these reins grant access only to the verified members of the fleet, keeping the wolves in reindeer clothing at bay. 🛷🔒🦌 Device security is a crucial aspect of our coding winter wonderland. Developers take great care in confirming the authenticity and reliability of each device, safeguarding against unauthorized access and potential threats. By implementing stringent security measures and robust device authentication, developers create a fortified environment where only trusted devices can participate in holiday coding festivities. 🔒💻🎄 Pillar 3: Securely Singing Through the Snowy Network ❄️🔒 A sleigh bell's jingle ensures it's heard through the snow, much like how we need our network traffic to sing clearly and securely through the internet's blizzard. This ensures that our messages and information remain protected, like whispered secrets, as they traverse the digital landscape. ❄️🔒🔔 Developers meticulously tune each data packet, transitioning it from the normal tone to a cryptographic melody. Pillar 4: Backend Workshop Security - Elves Only Beyond This Point 🧑‍💻🔒 Keeping Santa's workshop secure is of utmost importance, just like safeguarding the backend of our applications. While cookies are delightful treats, we must ensure they don't become stepping stones for cyber-criminals. Developers take on the role of vigilant elves, Only fellow elves or authorized users possess the knowledge and tools to unlock these defenses. 🎅🧑‍💻🔒 fortifying the workshop doors with intricate puzzles and robust locks in the form of secure code. Pillar 5: Encrypting the Presents (Data) Under the Tree 🎁🔒 In the spirit of the holiday season, developers take great care in ensuring the secure delivery of data, just like wrapping presents. They meticulously wrap data chunks with encryption so complex that it's comparable to ribbons that require an engineering degree and a pair of pliers to untie. Just as kids should never find their gifts before Christmas morning, This dedication to data security reflects the commitment of developers to create a winter wonderland of cybersecurity, where the joy and surprise of secure communication are preserved. 🎁🔒🎄 developers ensure that the wrapped data remains a mystery until it reaches its intended recipient. Pillar 6: Visions of Logs Danced in Their Heads - Analytics & Monitoring 📊🔒 Imagine logs (the data kind) burning bright, with developers cozy around the fire analyzing the flames. We ensure that , keeping the holiday cybersecurity hearth safe and warm. 📊🔥🔒 not a single ember (threat) pops out of the fireplace unexpectedly Developers meticulously analyze logs and monitor application activities to identify and address any potential threats or vulnerabilities. By keeping a watchful eye on the logs, they can ensure a secure and protected environment for the holiday coding festivities. Pillar 7: Automation All the Way - Oh What Fun It Is to Code 🤖💻🔒 There's no time for manual sleigh steering in a Zero Trust wonderland. Developers embrace automation to streamline security measures, These automated security protocols self-correct their courses at each jolly jingle junction, freeing developers to enjoy the holiday code cocoa. 🤖💻🔒 allowing smart algorithms to handle the grunt work. Conclusion: Embracing Zero Trust for Secure Coding in a Winter Wonderland As we wrap up our coding journey through the winter wonderland of secure coding, let's reflect on the importance of our efforts. Just as we string the lights of secure protocols and hang the mistletoe of encrypted data, we ensure that our codebases are fortified against cyber threats. The meticulous work we do as developers ensures that every digital stocking is hung with care and every gingerbread code-man is buttoned up against the cold, making our cyber celebrations merry and bright. 🎅🎄🔒 To recap, we have explored the seven pillars of secure coding in a winter wonderland: 🏰🔒❄️ : We create a robust foundation for safeguarding our applications and data by implementing secure protocols and encryption techniques. 🎄🔒💡 String the lights of secure protocols and hanging the mistletoe of encrypted data Incorporating multi-factor authentication, strong password policies, and secure session management adds an extra layer of protection to ensure that only authorized users can access our systems. 🍬🔒🔑 Build a candy cane fence around user identity: Thoroughly vetting and securing the devices used in our development process helps prevent unauthorized access and potential threats. 🛷🔒🦌 Ensure sleigh integrity and device security: Tuning our network traffic to sing a cryptographic melody ensures that only intended recipients can decipher the information, keeping it safe from prying eyes. ❄️🔒🔔 Sing securely through the snowy network: : Implementing robust security measures for our application's backend, such as secure coding practices and encryption, safeguards against potential breaches and unauthorized access. 🧑‍💻🔒🎁 Protect the backend workshop and encrypt the presents (data) Analyzing logs and monitoring our applications' activities allows us to identify and address any potential threats or vulnerabilities, ensuring a safe and secure environment. 📊🔥🔒 Embrace the power of logs and analytics: Leveraging automation and smart algorithms streamline security processes, allowing developers to focus on creating a delightful user experience while maintaining a strong security posture. 🤖💻🔒 Automate security measures for a joyful coding experience: In the world of software development, our job as developers is never done. It goes beyond creating apps that sing songs—it involves ensuring user security day and night. By incorporating Zero Trust principles into our coding, we're not just writing code; we're crafting a secure winter wonderland for our digital world. ❄️🔒🎅 So, let's imagine ourselves as the conductors of this production, blending technology and trust. Just as a maestro composes each note, we infuse our code with Zero Trust. It becomes an art form, a masterpiece that captures the holiday spirit. Embracing the power of Zero Trust guides our decisions, ensuring that every feature is built with care for security. We think like adversaries, staying ahead of threats and safeguarding our users. 🔒🛡️🤖 May our coding journeys be filled with wonder and excitement. Let's build a winter wonderland of cybersecurity that stands the test of time. As we embrace the challenge, remember that true magic happens in secure coding. Let our creativity shine, and may our digital world be filled with joyful code. ✨💻❄️ Wishing you a Merry Code-Mas and a successful deployment! 🎄⌨️🎁 May Infosec Be with You.

The 7 Pillars of Zero Trust Security: A Developer's Zero Trust Christmas Carol 

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

Gemini - A Family of Highly Capable Multimodal Models: Discussion and Conclusion, References

Jingle Bells, Hackers Smell: Riding the CVE Sleigh to Cyber-Safe Holidays (Santa's Alert for Appearances!) While we deck the halls and rock around the Christmas tree, hackers treat CVEs like the ultimate Secret Santa—a gift that keeps on giving (them access to our networks). But don't let your sing the blues this festive season; with some smart moves, we can wrap those CVEs up tighter than the most stubborn of holiday gifts. 🎅🔒🎁 cybersecurity Introduction Imagine you're all snug in your bed while visions of sugarplums (or perhaps encrypted data) dance in your head. In the merry land of cybersecurity, however, there's no rest for the weary, as CVEs become the Grinch plotting to steal the Christmas of our network's tranquillity. But take heart! We can outsmart these humbugs and keep our cyber-chimneys clear of unwanted visitors. 🛡️🔒 The Advent Calendar of CVEs: A Not-So-Jolly Countdown Think of a CVE like the calendar countdown to the big day, but instead of chocolates, there's a different kind of surprise in every window: : This is when the elves spot a potential toy malfunction before it hits Santa's sleigh. 🕵️‍♂️🔍 Identification : Imagine announcing over the North Pole PA system which toys might break. 📢🎁 Disclosure : If the mischievous elves (aka hackers) get there first, it's coal for everyone. ⚠️👿 Exploitation Hackers' Winter Wonderland: Why They're Merrymaking with CVEs 'Tis the season for hackers to spread their malicious cheer, as they find joy in exploiting CVEs during the holiday season. Just like characters from Tim Burton's "Nightmare Before Christmas," these cyber-Scrooges have their own twisted version of festive fun. : Picture bots zipping through the digital snow faster than St. Nick's Midnight Express. These automated agents are quick to identify vulnerabilities, constantly searching for weaknesses in systems and networks. They navigate with precision, leaving no stone unturned in their quest to find a way in. Sleigh Ride Scouts : Unfortunately, patches sometimes arrive fashionably late, just like a Christmas card from your dear aunt in July. While security researchers and software vendors work diligently to develop fixes, there can be delays in their release. This gives hackers an extended window of opportunity to exploit the vulnerabilities, like a delayed gift that arrives just in time for the holidays. Patchwork Presents : Security teams find themselves bombarded with an overwhelming number of CVEs, much like trying to discern your own pitch in a chaotic round of '12 Days of Christmas'. With so many alerts, it becomes challenging for security professionals to prioritize and address each vulnerability effectively. Carol of Alerts But why do hackers find such delight in exploiting these vulnerabilities? The excitement and thrill of successfully exploiting a CVE and gaining unauthorized access to systems fuel their motivation. Just as a laser pointer captures a cat's attention, CVEs give hackers an enticing target to focus their efforts on. It's like watching a mischievous cat chasing a laser pointer. So, as we enjoy the holiday season, let's not forget that hackers are also in a festive mood, seeking opportunities to exploit vulnerabilities. By understanding their tactics and taking proactive measures to protect our systems, we can stay one step ahead and ensure a cyber-safe holiday season for ourselves and our organizations. The Naughty List: How the CVE System Can Coal Things Up Our digital Santa Claus faces challenges in keeping his list up-to-date when it comes to managing CVEs: : The teams responsible for managing CVEs are like Santa's reindeer powering a sleigh built for NASA—full of spirit but in need of an upgrade. They work tirelessly to identify and address vulnerabilities, but with the ever-growing software and hardware complexity, they could benefit from increased resources and support. 🦌🔌🚀 Reindeer Resources : Just like that fruitcake from last year that often goes unnoticed but has the potential to cause a disruption, many vulnerabilities may slip through the cracks. These vulnerabilities, although not always in the spotlight, can pose significant risks if left unaddressed. It's crucial to ensure comprehensive coverage and actively identify and patch these vulnerabilities to maintain strong cybersecurity defenses. 🎂🔍 The Great Gingerbread Gap : With millions of toys (or software components) being developed and deployed, Santa's workshop faces the challenge of checking every single one twice. It's a monumental task to ensure that each component is thoroughly vetted and free of vulnerabilities. This requires efficient coordination, collaboration, and quality assurance practices among developers, testers, and security professionals to minimize the chances of introducing vulnerabilities into the software ecosystem. 🧸✅✅ Toy Factory Tangles Expanding on these challenges and exploring how developers play a crucial role in addressing CVEs and strengthening cybersecurity defenses will be the focus of a future blog post. Stay tuned for more insights on this topic! A Carol of Cyber Tactics: How the Grinchy Hackers Move In the world of cybersecurity, hackers have mastered the art of exploiting vulnerabilities, and their tactics are as cunning as a Grinch stealing Christmas presents. Let's take a closer look at two of their most common tactics: : These villains are like carolers who show up at your door before you even have a chance to sing along. Zero-day exploits refer to vulnerabilities that hackers discover and exploit before developers are aware of them. It's like finding a flaw in Santa's sleigh and taking advantage of it before anyone has a chance to fix it. These exploits can cause significant damage as they target vulnerabilities that are unknown to the vendor and, therefore, unpatched. Zero-Day Carolers : Imagine if someone took apart Santa's toys to see how they were built, but instead of putting them back together, they created traps. That's what hackers do when they reverse-engineer patches. When a patch is released to fix a vulnerability, hackers can analyze it to understand the vulnerability and develop an attack to exploit systems that haven't been updated yet. It's like using Santa's own tools against him. Reverse-Engineering Workshop Understanding these tactics is crucial in the . By staying informed and implementing robust security measures, businesses and individuals can protect themselves from these Grinchy hackers and ensure a safer digital environment. 🎄🔒💻 fight against cybercrime Jingling All the Way: The Real-World Impact of CVE Delays In the fast-paced world of cybersecurity, every moment that vulnerabilities go unpatched is like leaving our holiday lights tangled in a box—frustrating and unproductive. Just as we eagerly anticipate the joy of unwrapping presents, cybercriminals eagerly await the opportunity to exploit vulnerabilities that have not been addressed in a timely manner. 🎄🔌 In this winter cybersecurity wonderland, losing time to a CVE is like dropping your roasted chestnuts all over the floor—annoying, messy, and entirely preventable with a little foresight. Just as Santa is said to be watching, hackers are vigilantly observing too, and instead of presents, they're ready to hand out security incidents. We must be proactive in addressing vulnerabilities to ensure a secure digital environment for ourselves and our organizations. 🌰🚫🔒👿💻 It's crucial to understand the real-world impact of CVE delays. Every minute that vulnerabilities are left unpatched increases the risk of data breaches, system downtime, and other costly incidents. These incidents can lead to reputational damage, financial losses, and compromised security. ⏰💥💻 To prevent such consequences, it is essential to prioritize timely patching and implement robust security measures. By promptly addressing CVEs and applying necessary security updates, organizations can minimize the window of opportunity for cybercriminals to exploit vulnerabilities. Remember, in the cybersecurity race, time is of the essence, and staying ahead requires proactive action. ⏳🔒🏃 Let us take the necessary steps to protect our valuable data and assets, ensuring a cyber-safe environment not just during the holiday season but all year round. 🛡️💻🔒 Wrapping Up Your Network: The Cybersecurity Gift-Giving Guide Fear not, for here are some effective strategies to secure your cyber defenses tightly, leaving Santa's elves unable to sneak a peek: : Just like Santa's fleet on Christmas Eve, ensure that your monitoring is omnipresent. Keep a watchful eye on your systems and networks, ready to detect any potential threats. 🦌👀🛡️ Reindeer Reconnaissance : Employ automated tools that tirelessly patch up vulnerabilities, much like Santa's elves wrapping gifts in his workshop. Let these tools work diligently to apply necessary security updates, minimizing the risk of exploitation. 🤖🔧🎁 Automated Elves : Train your team to be as vigilant and alert as a frosty friend. Equip them with the knowledge and awareness to identify and respond to phishing scams, ensuring that they don't fall for any tricks disguised as Santa's outfit. ⛄🎣🎅 Savvy Snowmen (and Women) With these powerful strategies at your disposal, you are not merely waiting for Santa; instead, you're joining him on the rooftop, standing ready to protect your valuable data and assets. 🎅🔒🍪 By implementing these proactive measures, you can confidently safeguard your digital environment, outpacing hackers in this ever-evolving landscape. Conclusion: Sleighing the CVE Grinches In our cyber fairytale, might seem like they're leading the Grinch-conga line toward our digital goodies. But even Santa needs a little help sometimes, and with our advanced preparation and cyber-smart strategies, we're ready to hang our stockings with care—and confidence. 🎄🎅🔒 CVEs Remember, in the cybersecurity night sky, be the brightest star atop the tree, guiding Santa’s sleigh through the fog—not the blinking warning light on a hacker's dashboard. ✨🌟🎅🔒 Because after all, 'tis the season to be secure! 🎄🔒 FAQ Can you give me a festive refresher on CVEs? 🎅🔒 Q: CVEs, also known as Common Vulnerabilities and Exposures, are like those tricky Christmas lights; if one bulb is faulty, the entire string can go dark. Just as you need to regularly check your Christmas lights to ensure they are all functioning properly, CVEs require constant monitoring to identify and address any vulnerabilities in software or hardware. 🕵️‍♀️💡 A: How do CVEs impact cybersecurity? Q: CVEs pose a significant threat to cybersecurity. When vulnerabilities are left unaddressed, hackers can exploit them to gain unauthorized access to systems, leading to data breaches, system downtime, and other costly incidents. Promptly addressing CVEs and implementing robust security measures are crucial to mitigating their impact and protecting valuable assets. 🛡️💻💥 A: What are some challenges with the CVE system? Q: While the CVE system serves as a valuable resource for cataloging and tracking vulnerabilities, it faces certain limitations and challenges. These include limited resources for assigning and tracking vulnerabilities, incomplete coverage of less popular or niche software and hardware, and the complexity of coordinating vulnerability identification and assigning CVEs across various vendors and organizations. 🚫🔍🌐 A: How can organizations defend against CVEs? Q: Organizations can adopt proactive measures to defend against CVEs. This includes staying vigilant through continuous monitoring of systems and networks, implementing automated patch management systems to ensure timely application of critical security updates, and educating employees about cybersecurity risks and best practices. By combining these strategies, organizations can stay one step ahead of hackers and enhance their cybersecurity defenses. 👀🔒🚀 A: May Infosec Be with You.

'Tis the Season to Secure: How CVEs Are the Grinch for Cybersecurity

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