Unacademy achieved the cult status in 2020, while upGrad, Vedantu and Eruditus reached the $1 bn valuation mark in 2021. LEAD became India’s sixth edtech unicorn in 2022. Clearly, ever since the ...

Why A Career In India's Edtech Industry Is Fraught With Uncertainties

iStock Vedantu said it is adequately funded for now, and does not anticipate fresh funding requirement for the next two years. New Delhi: Edtech startup Vedantu on Thursday said it is on track to be "IPO-ready" in 18-24 months, and is upbeat about the ...

Vedantu on track to be 'IPO-ready' in 18-24 months

What new freedom had he, or all, got? Online live tutoring company Vedantu on Monday said it has onboarded Bollywood actor Aamir Khan as its brand ambassador. Vedantu with its new ad campaign aims ...

Edtech Giant Vedantu Ropes In Bollywood Star Aamir Khan As Brand Ambassador

Get the most recent info and news about Vedantu on HackerNoon, where 10k+ technologists publish stories for 4M+ monthly readers.

Vedantu is a LIVE online learning platform that brings together students and teachers to achieve best results. On it, students can pick from a pool of India’s best teachers and learn from them via LIVE interactive classes. With its superior learning platform, Vedantu ensures that students can get their doubts solved instantly, take part in quizzes & activities and have a personalised learning experience from India’s best teachers. By making LIVE learning engaging and fun, Vedantu students consistently perform well and produce great results! To learn more and try out a free class, visit bit.ly/Vedantu

Vedantu

Despite the downturn, EdTech continues to be an attractive investment opportunity for venture capitalists. The sector’s growth prospects remain strong, driven by the increasing adoption of technology and online learning, rising demand for upskilling and reskilling, and the growing number of K-12 and higher education students in the country. According to a report by Blume Ventures, the EdTech market in India is expected to grow at a CAGR of 39% to reach $10.4 billion by 2025. While I doubt they will concur with their report now and are likely to focus on alt sectors, the macro indicators underlying the EdTech investment outbreak in 2021 are still positive. Dealroom reported compared to the previous year due to several factors, including the pandemic-induced economic slowdown, rising inflation, and tighter regulatory scrutiny. The sector’s growth was partly fueled by the surge in online learning during the pandemic, and as things have started to return to normal, the growth has slowed down. This especially followed suit in India, where most EdTech companies saw themselves either running out of runway or raising down-rounds. The real status, of course, becomes clearer in the L2 divisions of the sector. a decline in EdTech activity in private and public markets in 2022 The EdTech sector in India is divided into three major sub-sectors, namely, K-12, higher education, upskilling and reskilling, and each sub-sector presents unique opportunities for investment. The three sub-sectors are solving drastically different problems of different segments. Only one, in my opinion, is slated to create true value. The Kids are Addicted & the Government is Worried K-12: The K-12 EdTech sector focuses on providing digital learning solutions for school students. The sector has witnessed significant growth in recent years, with various companies providing digital content, adaptive learning platforms, and personalised learning solutions. However, investing in this sector can be tricky, as it is highly dependent on government policies and the quality of the education system in the country. BYJU’S: , BYJU’S remains one of the leading EdTech companies in India, providing digital learning solutions for K-12 students. The company has raised over $2 billion in funding and has a valuation of $18 billion. Despite the media frenzy Vedantu: Vedantu is an online tutoring platform that provides live classes for K-12 students. The company has raised over $200 million in funding and has a valuation of $1.1 billion. Toppr: Toppr is an online learning platform that provides personalized learning solutions for K-12 students. The company has raised over $100 million in funding and has a valuation of $800 million. The government of India recently announced , which require EdTech companies to comply with a set of rules and regulations, including minimum qualifications for teachers and accreditation requirements. new guidelines for online education While companies like BYJU’S, Vedantu, and Toppr are leaders in this space, they have faced regulatory challenges. The government of India recently announced , which require EdTech companies to comply with a set of rules and regulations, including minimum qualifications for teachers and accreditation requirements. Therefore, investors looking to invest in K-12 EdTech companies need to keep a close eye on regulatory developments. new guidelines for online education Higher Education: A quality problem borrowed from their offline counterparts The Higher Education EdTech sector focuses on providing digital learning solutions for college and university students. The sector has witnessed significant growth in recent years, with various companies providing online degree programs, online certification courses, and digital content. Some of the top investment opportunities in the Higher Education EdTech sector include: upGrad: upGrad is an online learning platform that provides degree programs, certification courses, and executive education programs. The company has raised over $290 million in funding and has a valuation of $2 billion. Simplilearn: Simplilearn is an online learning platform that provides certification courses in various domains such as data science, cloud computing, and digital marketing. The company has raised over $50 million in funding and has a valuation of $300 million. Emeritus: Emeritus is an online learning platform that provides executive education programs in collaboration with top universities such as MIT, Columbia, and Wharton. The company has raised over $185 million in funding and has a valuation of $2 billion. Companies like upGrad, Simplilearn, and Emeritus are leading players in this space and have raised significant funding in recent years. However, investing in this sector requires carefully analysing the market, competition, and quality of online education offerings. The success of these companies is highly dependent on their ability to offer high-quality education that is comparable to traditional classroom-based education. Upskilling and Reskilling: The Only Way Out The Upskilling and Reskilling EdTech sector focuses on providing digital learning solutions for working professionals. The sector has witnessed significant growth in recent years, with various companies providing online courses and certifications to upskill and reskill employees. Some of the top investment opportunities in the Upskilling and Reskilling EdTech sector include: Simplilearn: Simplilearn is an online learning platform that provides certification courses in various domains such as data science, cloud computing, and digital marketing. The company has raised over $50 million in funding and has a valuation of $300 million, with its latest round led by global venture capital firm, Sixth Sense Ventures. Springboard: Springboard is an online learning platform that provides certification courses in emerging technologies such as AI, data science, and machine learning. The company has raised over $30 million in funding and has a valuation of $350 million, with its latest round led by Reach Capital. Scaler: Scaler is an online learning platform that provides coding and programming courses to upskill and reskill. It raised USD 20 million in funding led by Strive. The upskilling and reskilling sub-sector of the EdTech industry presents a unique opportunity. With the world becoming more technologically advanced, companies are constantly seeking employees with new skill sets. According to by 2025, over half of all employees will require significant reskilling and upskilling. This presents a massive opportunity for EdTech companies to fill this skills gap, making the upskilling and reskilling sub-sector a lucrative investment option. a report by the World Economic Forum, But why this over that? A report by HolonIQ states that the upskilling and reskilling market has witnessed a surge in investment, with total funding reaching USD $87bn+ of Global EdTech funding predicted through 2030. $32bn last decade. The report also highlights that upskilling and reskilling programs are the most sought-after offerings by corporates, with 64% of them citing this as a priority. Furthermore, suggests that the upskilling and reskilling of employees can significantly impact a company’s bottom line. The report highlights that companies that invest in upskilling and reskilling can see a 6-9% increase in profit margins. a report by McKinsey Global Institute Looking closely, there are only two facts to marry: There are over 100 million individuals set to enter the workforce in India over the next 5 years. The wider higher-ed environment is not equipped to churn out quality candidates That is the gap. HolonIQ identified a similar gap in 2019 on a global level. The upskilling and reskilling business model is attractive due to its (1) high-profit margins, (2) shorter course durations, and (3) less vulnerability to external factors. Upskilling and reskilling are able to extract a higher revenue per user (RPU) than K-12 & Higher-Ed players as they charge corporate clients, and the courses they offer are typically shorter in duration. The upskilling and reskilling business model typically operates in the B2B space, catering to the needs of corporate clients. EdTech companies provide short-term, high-quality courses designed to upskill and reskill employees in various domains such as data science, cloud computing, and digital marketing. These courses provide specific and valuable skill sets that the job market demands, and consequently tend to be priced far higher than K-12 or higher education course. This leads to a higher revenue per user (RPU) and a shorter customer acquisition cost (CAC), resulting in a higher lifetime value (LTV) per customer. These companies typically have a diversified client base across various industries, which reduces the risk of revenue loss in case of any economic downturn or regulatory scrutiny. Overall, the upskilling and reskilling business model is attractive due to its (1) high-profit margins, (2) shorter course durations, and (3) less vulnerability to external factors. In Conclusion According to the , the decline in EdTech activity is part of a broader trend of a slowdown in the Indian startup ecosystem and not an isolated event due to changes in the EdTech market. The report highlights that the total value of investments in Indian startups declined by 26% in 2022 compared to the previous year. EY PE/VC Trend Book The report also states that investors are becoming more cautious and are focusing on companies with strong fundamentals, which could be a reason for the decline in EdTech activity. According to the , the decline in EdTech activity is part of a broader trend of a slowdown in the Indian startup ecosystem, and not an isolated event due to changes in the EdTech market. EY PE/VC Trend Book The EdTech industry in India continues to present a multitude of investment opportunities that were originally recognised at the start of 2020, with each sub-sector presenting unique prospects. While the K-12 and higher education segments have witnessed significant growth in recent years, investing in the upskilling and reskilling segment can be a better option due to the untapped market, diversification of client base, and higher profitability. As the demand for upskilling and reskilling programs continues to rise, investing in EdTech companies that provide these services can yield significant returns. However, as with any investment, it is essential to conduct thorough due diligence and invest in companies with strong fundamentals and growth prospects. (Originally published on - Siddhant’s thoughts on data, defi, design, and digital) Draftt

The Future of EdTech Funding in India

Educational Institutes are easy prey for hackers to compromise and covertly launch Cyber Attacks/Malicious Campaigns under the hood, without divulging their real identity. This is majorly due to the reckless attitude of IT Administrators towards their Network Assets as the software programs (which are being used in the production) need to undergo several patches/updates to mitigate any exposed risk. School of Villains by Naolito | Source: DeviantArt Here, we are going to discuss each Threat Vectors where Educational Institutes are being targeted and being used for possible malicious campaigns or exploitation. DATABASE TRADING It is notable that there is a significant rise of “ ” detected on various underground hacking forums or DarkNet Marketplaces during this pandemic (COVID-19). The and are bagging huge amounts (in the form of Cryptocurrency) into their Hot or Cold wallets. Database Trading compromised databases (University/Schools) are being sold on different underground forums Student Record of Brazil advertised in Raid Forum Compromised FTP Servers of Academic Sites The above-listed screenshots are just an example to back the statement for Database Trading at large ( ) and sometimes, it is distributed for free on such communities. if you note the Timestamps It is also important to note that the Online Educational Platforms are also being targeted on a continual basis. One of the largest breaches was the compromise of an Online learning Platform “ ” that got hacked and put for sale on Dark Web Marketplace in May, 2020. Unacademy for $2,000 consisting of 22M Userbase Advertised in Empire Market Note : The Empire Market (Dark Web Marketplace) had gone exit scammed in August 2020, after a long standing for 3 years. Another Indian Tutor website “ ” observed a data breach in 2019, . Vedantu exposing ~700K Student/Tutor Records EDUCATIONAL INFRASTRUCTURE FOR SALE It is common to find the vulnerable/open systems (Academic Infra) among the listing with other compromised servers in the Cyber Criminal Black Markets, but uncommon for the general public. Adversaries gain access to such systems using various methods like Spear Phishing Emails, Open RDP Access, Unprotected Elastic Servers etc. It is a common practice among criminals to conceal their presence (in the victim environment) after compromising the targeted systems, prevailing a backdoor access for extended network access(commonly called Lateral Movement) in order to sell the same on various Marketplaces. Some of the smooth offerings made in this arena are:- Webmail CPAnel Access Microsoft Webmail O365 Access Cpanel Access of various Educational Institutes across the globe Microsoft O365 Domain Accounts for Sale While offering the offensive services, it is remarkable that the hackers/adversaries are redefining their rules of etiquette by providing the legitimacy of the sources like , hence showcasing a professional line. The proves that the listed hacked accounts are recent, which again amplifies the trust factor for the Exploit Seekers. whether the listed assets are in working condition timestamps : Once the actor gains access to the University Infrastructure (such as Email), the same can be against any entity, as Educational Domains are (generally) not included in any of the Blacklist, hence clearing the initial level of defense (traffic from blacklisted IPs are usually blocked). This can also be used for , hence leveraging access to any restricted system (which is being defined via admin policies). Outcome used to launch a Spear Phishing Email Campaigns Impersonation Attacks DOMAIN LIST OFFERING[.EDU] Another kind of novice threat to the Academic Domain is the selling of a large number of Educational Domain lists, hence narrowing down the effort of hackers to find vulnerable systems. Domain Sale Once identified, the actor can scan for the targeted network look for the unsecured Ports/Services, mirroring the website, Exploiting the Weaknesses (in case of unpatched), Gaining Unauthorized Access, Stealthy Integration to a Bot Army (Botnet), Launch DDoS and any imaginable Cyber Destruction. — Note: This threat is not only pertaining to the Educational domain, but also affects other TLDs and Country-Specific TLDs. CREDENTIAL LEAK FROM EDU-PLATFORMS There are ample Credential leaks for Online Educational Platforms (or MOOCs) surfaced on Deep Web, which are being captured by various methods such as etc. and are shared on various Deep Web channels. Keylogging, Spyware Activity, Running Malicious Stealthy Programs, Below is a list of Email-Password combination of Udemy Accounts which appeared on a Turkish forum:- Udemy Accounts listed for free Here is another listing of found on an individual blog:- Username-Password Combo for Code Academy Accounts CodeAcademy Account Credentials It is notable that the passwords are in plain-text which would facilitate adversaries to launch a on various digital platforms of the targeted individual. Password-Spraying Attack There are various dedicated checker programs available for each Educational Platform such as etc. Udemy, CodeAcademy, Coursera Checker Program found for Code Academy Note : Checker Programs are used by attackers to launch Brute Force logins on multiple platforms to check whether the acquired credentials are in working state or not. Similarly, there are various cracked Academic Programs which are being offered for cheaper prices on the Underground Dark Web Marketplaces. Premium Courses offered at cheaper prices As the education is provided for cheaper or free, it can be considered as a Robinhood Act, but cracking into the personal accounts are not justifiable. ATTACKS— RISE OF RANSOMWARE INFECTION & HACKTIVISM It is an undeniable fact that there are many prestigious Academic Institutes hit by numerous ransomware programs at different timelines. By closely inspecting the same fact, it is evident that Ransomware Attacks seen a sudden uptick since 2016; as the victims became ready for the negotiation offered by the hackers in order to recover the files, by paying the ransom. This ignited the interest of attackers and began to invest more resources for the Ransomware Programs. : Now, you may know several RaaS (Ransomware as a Service) programs like along with major Ransomware Gangs like and their experimental business strategies like etc. Outcome Smaug, GandCrab, Project Root Maze, Clop, Netwalker, Nefilim, REvil, Snake Live Data Auction, Storage as a Service, Affiliate Programs, Feedback Collection Here is a list of few Universities who had paid Ransomware Operators to gain back the compromised data:- UNIVERSITY OF CALGARY — 2016 — Paid $20,000 HORRY COUNTY SCHOOL DIST. — 2017 — Paid $8,500 LOS ANGELES VALLEY COLLEGE — 2017 — Paid $30,000 UNIVERSITY OF MAASTRICHT — 2020 — Paid $220,000 UNIVERSITY OF UTAH — 2020 — Paid $457,059 to Netwalker (Suspected) UNIVERSITY OF CALIFORNIA — 2020 — Paid $1.14M to Netwalker MAYNOOTH UNIVERSITY — 2020 — Paid Undisclosed Amount UNIVERSITY HOSPITAL NEW JERSEY — 2020 — Paid $670,000 to SunCrypt It is also interesting to note that the took place recently (May 2020) that compromised more than 10 Universities from the UK, US and Canada. BlackBaud Hack Note : BlackBaud is a Cloud Hosting Provider majorly used by Educational Institutes and various Non-Profit Organizations. Here is a of Educational Institutes who were hit by various Ransomwares in 2020. list The Hacking Campaigns orchestrated by APT Groups such as (Iran) against Global Universities is the that the attacks against Educational Institutes are on a rise. Silent Librarian newest evidence Nowadays, though many institutes are aided by ( is one such), it would be a healthy practice to patch up the old legacy tools in order to keep the cyber attacks at a bay. Cyber Extortion Insurances Cyber Extortion Coverage from IRMI KEY-TAKEAWAYS > Quick fix to the Open/Unsecured ports, especially RDP and Elastic/Mongo > Never to fall for any phishing email attachments/links > Not to blindly trust request coming from EDU Domains > Disown the used passwords in Educational Moocs Platform > Regular Data Backup > Isolate the Mirror/Backup Archives from Mainframe Systems > Never pay demanded ransom, in case of Ransomware Attack The largest IoT Botnet “ ” brought down various online services such as etc was created in a . Mirai OVH, Netflix, Spotify, PayPal University Dorm Room So, Dear Sys-Admins! Never under-estimate the power of Educational Platforms!!! Follow me on for interesting DarkWeb/InfoSec Short findings! ;-) Twitter Note:- The Article is purely an Individual Research and is not subjected to be used/published anywhere without the Author’s consent.

Why Educational Platforms are a Favorite Target Among Attackers?

This paper is available on arxiv under CC 4.0 license. Authors: (1) Muzhaffar Hazman, University of Galway, Ireland; (2) Susan McKeever, Technological University Dublin, Ireland; (3) Josephine Griffith, University of Galway, Ireland. Table of Links Abstract and Introduction Related Works Methodology Results Limitations and Future Works Conclusion, Acknowledgments, and References A Hyperparameters and Settings B Metric: Weighted F1-Score C Architectural Details D Performance Benchmarking E Contingency Table: Baseline vs. Text-STILT 6 Conclusion In this work, we addressed the challenge of training multimodal meme sentiment classifiers on a limited number of labelled memes by incorporating unimodal sentiment analysis data. We did so by proposing the first instance of STILT that applies unimodal intermediate tasks to a multimodal target task. Specifically, we tested image-only and text-only sentiment classification as intermediate tasks in training a meme sentiment classifier. We showed that this approach worked – unimodal text improved meme classification performance to a statistically significant degree. This novel approach allowed us to train a meme classifier that outperforms meme-only finetuning with only 60% as many labelled meme samples. As possible explanations for our observations, we discuss apparent similarities and differences in the roles of image and text modalities between unimodal and multimodal sentiment analysis tasks. Acknowledgments This work was conducted with the financial support of the Science Foundation Ireland Centre for Research Training in Digitally-Enhanced Reality (d-real) under Grant No. 18/CRT/6224; and the provision of computational facilities and support from the Irish Centre for High-End Computing (ICHEC). References Christian Bauckhage. 2011. Insights into internet memes. Proceedings of the International AAAI Conference on Web and Social Media, 5(1):42–49. Efrat Blaier, Itzik Malkiel, and Lior Wolf. 2021. 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Image sentiment polarity - dataset. Available at https://data.world/ crowdflower/image-sentiment-polarity. Accessed: 2023-01-15. Baishan Duan and Yuesheng Zhu. 2022. Browallia at memotion 2.0 2022 : Multimodal memotion analysis with modified ogb strategies (short paper). In DeFactify @ AAAI 2022. First Workshop on Multimodal Fact-Checking and Hate Speech Detection, CEUR Workshop Proceedings. AAAI. Noam Gal, Limor Shifman, and Zohar Kampf. 2016. “it gets better”: Internet memes and the construction of collective identity. New Media & Society, 18(8):1698–1714. Yue Gu, Kangning Yang, Shiyu Fu, Shuhong Chen, Xinyu Li, and Ivan Marsic. 2018. Hybrid Attention based Multimodal Network for Spoken Language Classification. In Proceedings of the 27th International Conference on Computational Linguistics, pages 2379–2390, Santa Fe, New Mexico, USA. Association for Computational Linguistics. Muzhaffar Hazman, Susan McKeever, and Josephine Griffith. 2023. 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In De-Factify @ AAAI 2022. First Workshop on Multimodal Fact-Checking and Hate Speech Detection, CEUR Workshop Proceedings. AAAI. Jason Phang, Thibault Fevry, and Samuel R. Bowman. ´ 2019. Sentence encoders on stilts: Supplementary training on intermediate labeled-data tasks. arXiv preprint, arXiv:1811.01088. Mohammad Taher Pilehvar and Jose Camacho-Collados. 2019. WiC: the word-in-context dataset for evaluating context-sensitive meaning representations. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 1267–1273, Minneapolis, Minnesota. Association for Computational Linguistics. Clifton Poth, Jonas Pfeiffer, Andreas Ruckl ¨ e, and Iryna ´ Gurevych. 2021. What to pre-train on? Efficient intermediate task selection. 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MOMENTA: A Multimodal Framework for Detecting Harmful Memes and Their Targets. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 4439–4455, Punta Cana, Dominican Republic. Association for Computational Linguistics. Yada Pruksachatkun, Jason Phang, Haokun Liu, Phu Mon Htut, Xiaoyi Zhang, Richard Yuanzhe Pang, Clara Vania, Katharina Kann, and Samuel R. Bowman. 2020. Intermediate-task transfer learning with pretrained language models: When and why does it work? In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5231–5247, Online. Association for Computational Linguistics. Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, et al. 2021. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, pages 8748–8763. PMLR. Sathyanarayanan Ramamoorthy, Nethra Gunti, Shreyash Mishra1, S Suryavardan, Aishwarya Reganti, Parth Patwa, Amitava Das, Tanmoy Chakraborty, Amit Sheth, Asif Ekbal, and Chaitanya Ahuja. 2022. Memotion 2: Dataset on sentiment and emotion analysis of memes. In De-Factify @ AAAI 2022. First Workshop on Multimodal Fact-Checking and Hate Speech Detection, CEUR Workshop Proceedings. AAAI. Elad Segev, Asaf Nissenbaum, Nathan Stolero, and Limor Shifman. 2015. Families and Networks of Internet Memes: The Relationship Between Cohesiveness, Uniqueness, and Quiddity Concreteness. Journal of Computer-Mediated Communication, 20(4):417–433. Lanyu Shang, Yang Zhang, Yuheng Zha, Yingxi Chen, Christina Youn, and Dong Wang. 2021. AOMD: An Analogy-Aware Approach to Offensive Meme Detection on Social Media. Inf. Process. Manage., 58(5). Chhavi Sharma, Deepesh Bhageria, William Scott, Srinivas PYKL, Amitava Das, Tanmoy Chakraborty, et al. 2020. SemEval-2020 task 8: Memotion analysisthe visuo-lingual metaphor! In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 759–773, Barcelona (online). International Committee for Computational Linguistics. Shivam Sharma, Mohd Khizir Siddiqui, Md. Shad Akhtar, and Tanmoy Chakraborty. 2022. Domainaware self-supervised pre-training for label-efficient meme analysis. In Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 792–805, Online only. Association for Computational Linguistics. Limor Shifman. 2014. The cultural logic of photo-based meme genres. Journal of Visual Culture, 13(3):340–358. Pranaydeep Singh, Aaron Maladry, and Els Lefever. 2022. Combining language models and linguistic information to label entities in memes. 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Thomas McCoy, Roma Patel, Najoung Kim, Ian Tenney, Yinghui Huang, Katherin Yu, Shuning Jin, Berlin Chen, Benjamin Van Durme, Edouard Grave, Ellie Pavlick, and Samuel R. Bowman. 2019. Can you tell me how to get past sesame street? sentence-level pretraining beyond language modeling. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4465–4476, Florence, Italy. Association for Computational Linguistics. Ron Zhu. 2020. Enhance multimodal transformer with external label and in-domain pretrain: Hateful meme challenge winning solution. arXiv preprint, arXiv:2012.08290. Yan Zhuang and Yanru Zhang. 2022. Yet at memotion 2.0 2022 : Hate speech detection combining bilstm and fully connected layers (short paper). In De-Factify @ AAAI 2022. First Workshop on Multimodal Fact-Checking and Hate Speech Detection, CEUR Workshop Proceedings. AAAI.

Innovative Strategies in Meme Sentiment Analysis: The Impact of Unimodal Training

Transductive Learning for Textual Few-Shot: Limitations, Acknowledgements, & References

This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Ke Hong, Tsinghua University & Infinigence-AI; (2) Guohao Dai, Shanghai Jiao Tong University & Infinigence-AI; (3) Jiaming Xu, Shanghai Jiao Tong University & Infinigence-AI; (4) Qiuli Mao, Tsinghua University & Infinigence-AI; (5) Xiuhong Li, Peking University; (6) Jun Liu, Shanghai Jiao Tong University & Infinigence-AI; (7) Kangdi Chen, Infinigence-AI; (8) Yuhan Dong, Tsinghua University; (9) Yu Wang, Tsinghua University. Table of Links Abstract & Introduction Backgrounds Asynchronized Softmax with Unified Maximum Value Flat GEMM Optimization with Double Buffering Heuristic Dataflow with Hardware Resource Adaption Evaluation Related Works Conclusion & References 8 Conclusions We propose FlashDecoding++, a fast Large Language Model inference engine in this paper. FlashDecoding++ accelerates mainstream LLMs with multiple hardware backend support. FlashDecoding++ proposes three novel designs: the asynchronized softmax with unified max value, the flat GEMM optimization with double buffering, and the heuristic dataflow with hardware resource adaption, achieving up to × and × speedup on NVIDIA and AMD GPUs compared with Hugging Face implementations. FlashDecoding++ also achieves an average of × speedup compared with state-of-the-art LLM inference engines, FlashDecoding, on various LLMs. 4.86 2.18 1.37 References [1] Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288, 2023. [2] Arun James Thirunavukarasu, Darren Shu Jeng Ting, Kabilan Elangovan, Laura Gutierrez, Ting Fang Tan, and Daniel Shu Wei Ting. Large language models in medicine. Nature medicine, 29(8):1930–1940, 2023. [3] Rohan Anil, Andrew M. 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FlashDecoding++: Faster Large Language Model Inference on GPUs: Conclusion & References

Generate and Pray: Using SALLMS to Evaluate the Security: Conclusion & References

Revolutionizing Creative Text Generation with Quality-Diversity through AI Feedback

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

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