90% of shrimp farms in South East Asia already have at least 1 dormant disease living in the water, waiting for a shrimp’s immune system to be compromised

Jala

jala

This paper is available on arxiv under CC 4.0 license. Authors: (1) Ajay Krishnan T. K., School of Digital Sciences; (2) V. S. Anoop, School of Digital Sciences. Table of Links Abstract & Introduction Related Studies Materials and Methods Proposed Approach Results and Discussions Conclusions and References 6 Conclusions Climate change, a pressing global concern, necessitates thorough analysis and understanding across diverse domains to mitigate its impacts effectively. In recent years, the fusion of NLP techniques and machine learning algorithms has emerged as a promising approach for comprehending the complexities and nuances of climate change through the lens of textual data. This paper utilized the advancements in domain-specific large language models to harness the potential of NLP in addressing the challenges posed by climate change through sentiment analysis. By leveraging advanced NLP methodologies, we could identify climate change discourse that may enable uncovering valuable insights and facilitate informed decision-making. References VS Anoop. Sentiment classification of diabetes-related tweets using transformer-based deep learning approach. In International Conference on Advances in Computing and Data Sciences, pages 203–214. Springer, 2023. VS Anoop and S Sreelakshmi. Public discourse and sentiment during mpox outbreak: an analysis using natural language processing. Public Health, 218:114–120, 2023. VS Anoop, Jose Thekkiniath, and Usharani Hareesh Govindarajan. We chased covid-19; did we forget measles?-public discourse and sentiment analysis on spiking measles cases using natural language processing. In International Conference on Multi-disciplinary Trends in Artificial Intelligence, pages 147–158. Springer, 2023. Sina Ardabili, Amir Mosavi, Majid Dehghani, and Annamária R Várkonyi-Kóczy. Deep learning and machine learning in hydrological processes climate change and earth systems a systematic review. In Engineering for Sustainable Future: Selected papers of the 18th International Conference on Global Research and Education Inter-Academia– 2019 18, pages 52–62. Springer, 2020. Ceylan Ceylan. Application of Natural Language Processing to Unstructured Data: A Case Study of Climate Change. PhD thesis, Massachusetts Institute of Technology, 2022. B Jalalzadeh Fard, SA Hasan, and JE Bell. Climedbert: A pre-trained language model for climate and health-related text. arXiv preprint arXiv:2212.00689, 2022. Muhammad Khurram Iqbal, Kamran Abid, Salah u din Ayubi, Naeem Aslam, et al. Omicron tweet sentiment analysis using ensemble learning. Journal of Computing & Biomedical Informatics, 4(02):160–171, 2023. Sarin Jickson, VS Anoop, and S Asharaf. Machine learning approaches for detecting signs of depression from social media. In Proceedings of International Conference on Information Technology and Applications: ICITA 2022, pages 201–214. Springer, 2023. Romieo John, VS Anoop, and S Asharaf. Health mention classification from user-generated reviews using machine learning techniques. In Proceedings of International Conference on Information Technology and Applications: ICITA 2022, pages 175–188. Springer, 2023. S Lekshmi and VS Anoop. Sentiment analysis on covid-19 news videos using machine learning techniques. In Proceedings of International Conference on Frontiers in Computing and Systems: COMSYS 2021, pages 551–560. Springer, 2022. Mustapha Lydiri, Yousef El Mourabit, Youssef El Habouz, and Mohamed Fakir. A performant deep learning model for sentiment analysis of climate change. Social Network Analysis and Mining, 13(1):8, 2022. Nabila Mohamad Sham and Azlinah Mohamed. Climate change sentiment analysis using lexicon, machine learning and hybrid approaches. Sustainability, 14(8):4723, 2022. Uddagiri Sirisha and Sai Chandana Bolem. Aspect based sentiment & emotion analysis with roberta, lstm. International Journal of Advanced Computer Science and Applications, 13(11), 2022. Apoorva Upadhyaya, Marco Fisichella, and Wolfgang Nejdl. A multi-task model for sentiment aided stance detection of climate change tweets, 2022. Milan Varghese and VS Anoop. Deep learning-based sentiment analysis on covid-19 news videos. In Proceedings of International Conference on Information Technology and Applications: ICITA 2021, pages 229–238. Springer, 2022. Francesco S Varini, Jordan Boyd-Graber, Massimiliano Ciaramita, and Markus Leippold. Climatext: A dataset for climate change topic detection. arXiv preprint arXiv:2012.00483, 2020. Nicolas Webersinke, Mathias Kraus, Julia Anna Bingler, and Markus Leippold. Climatebert: A pretrained language model for climate-related text. arXiv preprint arXiv:2110.12010, 2021.

ClimateNLP: Analyzing Public Sentiment Towards Climate Change: Conclusions and References

This paper is under CC 4.0 license. available on arxiv Authors: (1) Marcin W ˛atorek, Faculty of Computer Science and Telecommunications, Cracow University of Technology, ul. Warszawska 24, 31-155 Kraków, Poland and marcin.watorek@pk.edu.pl (M.W.); (2) Jarosław Kwapie ´n, Complex Systems Theory Department, Institute of Nuclear Physics, Polish Academy of Sciences, Radzikowskiego 152, 31-342 Kraków, Poland and jaroslaw.kwapien@ifj.edu.pl; (3)  Stanisław Drozd˙ z, Faculty of Computer Science and Telecommunications, Cracow University of Technology, ul. Warszawska 24, 31-155 Kraków, Poland, Complex Systems Theory Department, Institute of Nuclear Physics, Polish Academy of Sciences, Radzikowskiego 152, 31-342 Kraków, Poland and stanislaw.drozdz@ifj.edu.pl (S.D.). Table of Links Abstract and Introduction Data and Methodology Results and Discussion Conclusion and References 4. Conclusions Based on the multiscale cross-correlation analysis performed for the data covering almost the last three years, it can be concluded that the cryptocurrency market dynamics is substantially tied to the traditional financial markets. Consistently, the most liquid cryptocurrencies, BTC and ETH, cannot serve as a hedge or safe haven for the stock market investments, not only during the turbulent periods like the Covid-19 panic, where this effect is particularly strong, but also during the recent bear market period on tech stocks, which has been accompanied by the parallel bear market on cryptocurrencies. Many observations show that the Covid-19 pandemic may have changed the paradigm that the cryptocurrency market is a largely autonomous market. The recent market developments and the strong US dollar have additionally increased the strength of the cross-correlations between BTC and ETH on the one side and the US tech stocks on the other side. These observations support some earlier findings on the same subject (e.g., [22,70]). In contrast, as the cryptocurrency market was weakly correlated with other markets during 2021, our results cannot support directly a recent hypothesis that the quantitative easing could actually be responsible for these correlations [22]. The existence of links between the global economy and the cryptocurrency market are further strengthened by the reaction of the price changes of BTC and ETH to economic data, such as CPI inflation, in a similar way to traditional financial instruments. These results are able to remove or, at least, to suppress the uncertainty that recent literature on this topic has brought to the cryptocurrency investors. Now it is more clear that the cryptocurrencies can no longer serve as a convenient hedging target for the investors whose purpose is to diversify the risk. Our study brings a strong indication that the cryptocurrency market has finally become a connected part of the global financial markets after 12 years of the maturation process. Whether such a direction of this market evolution remains in agreement with the early vision of the cryptocurrency creators can be disputed, however. We also face a related question: does the fact that we have got “just another part of the global financial market” deserve devoting so huge amounts of energy to it? Sooner or later this question must be addressed by the policy makers. Nevertheless, what becomes evident now is that it allows the market participants to broaden the spectrum of their investment possibilities. Among the limitations that might have influenced our study and, subsequently, our conclusions, we have to mention that only two principal cryptocurrencies were studied. Although they are the most influential, the most frequently traded, and widely discussed cryptocurrencies, they by no means define the entire market. It is possible that an analysis that included some less important cryptocurrencies would bring different outcomes. This is especially likely for the marginal cryptocurrencies without any thinkable “fundamental” value, whose dynamics is driven predominantly by extreme speculation. However, as the cryptocurrency market is looked at by the most investors through the lens of BTC and ETH (as their capitalization indicates), this particular limitation does not seem discouraging to us. Currently, these two assets shape the whole cryptocurrency market and we expect them to continue doing it in the nearest future. Another limitation of our analysis is the particular selection of the traditional financial instruments. Indeed, they constitute only a small fraction of the available ones. We are convinced, though, that they are among the most observed and the most influential ones in the context of the global economy, which fully justifies our choice. A more general observation that the cryptocurrency market has spontaneously coupled to the technological sector of the stock markets by reacting to some trigger provided by the external data inflow resembles analogous effects of the spontaneous emergence of order among the so-far independent degrees of freedom in the various complex systems. However, as complexity allows for flexible reacting of a system to both the external perturbations and internal processes, such effects of ordering in the financial markets have to be eventually counterbalanced by the opposite processes of disordering. Therefore, the market participants must be aware that the inter-market couplings may not last forever and they can significantly be weakened or even removed completely at some point in future. This is why the in-depth studies of the cross-market dependencies have to remain among the principal directions of the cryptocurrency research. Our future work will also deal with energy consumption of the cryptocurrency market. Conceptualisation, S.D., J.K. and M.W.; methodology, S.D., J.K. and M.W.; software, M.W.; validation, S.D., J.K. and M.W.; formal analysis, M.W.; investigation, S.D., J.K. and M.W.; resources, M.W.; data curation, M.W.; writing—original draft preparation, M.W.; writing review and editing, S.D., J.K. and M.W.; visualisation, M.W.; supervision, S.D. All authors have read and agreed to the published version of the manuscript. Author Contributions: This research received no external funding. Funding: Data available freely from Dukascopy [58]. Data Availability Statement: The authors declare no conflict of interest. Conflicts of Interest: References 1. Kwapie ´n, J.; Drozd˙ z, S. Physical approach to complex systems. ˙ Phys. Rep. 2012, 515, 115–226. 2. Bitcoin Pizzaday. Available online: https://www.investopedia.com/news/bitcoin-pizza-day-celebrating-20-million-pizza-order/ (accessed on 1 November 2022). 3. CoinMarketCap. Available online: https://coinmarketcap.com (accessed on 1 November 2022). 4. Gerlach, J.C.; Demos, G.; Sornette, D. Dissection of Bitcoin’s multiscale bubble history from January 2012 to February 2018. R. Soc. Open Sci. 2019, 6, 180643. 5. Bellon, C.; Figuerola-Ferretti, I. Bubbles in Ethereum. Financ. Res. Lett. 2022, 46, 102387. 6. Zitis, P.I.; Contoyiannis, Y.; Potirakis, S.M. Critical dynamics related to a recent Bitcoin crash. Int. Rev. Financ. Anal. 2022, 84, 102368. 7. Aste, T. Cryptocurrency market structure: Connecting emotions and economics. Digit. Financ. 2019, 1, 5–21. 8. Maouchi, Y.; Charfeddine, L.; Montasser, G.E. Understanding digital bubbles amidst the COVID 19 pandemic: Evidence from DeFi and NFTs. Financ. Res. Lett. 2022, 47, 102584. 9. W ˛atorek, M.; Drozd˙ z, S.; Kwapie ´n, J.J.; Minati, L.; O´swi ˛ecimka, P.; Stanuszek, M. Multiscale characteristics of the emerging global ˙ cryptocurrency market. Phys. Rep. 2021, 901, 1–82. 10. Lahmiri, S.; Bekiros, S.; Bezzina, F. Complexity analysis and forecasting of variations in cryptocurrency trading volume with support vector regression tuned by Bayesian optimization under different kernels: An empirical comparison from a large dataset. Expert Syst. Appl. 2022, 209, 118349. 11. Drozd˙ z, S.; G ˛ebarowski, R.; Minati, L.; O´swi ˛ecimka, P.; W ˛atorek, M. Bitcoin market route to maturity? Evidence from return ˙ fluctuations, temporal correlations and multiscaling effects. Chaos 2018, 28, 071101. 12. Drozd˙ z, S.; Minati, L.; O´swi ˛ecimka, P.; Stanuszek, M.; W ˛atorek, M. Competition of noise and collectivity in global cryptocurrency ˙ trading: Route to a self-contained market. Chaos 2020, 30, 023122. 13. Kaiser, L.; Stöckl, S. Cryptocurrencies: Herding and the transfer currency. Financ. Res. Lett. 2020, 33, 101214. 14. Aslanidis, N.; Bariviera, A.F.; Perez-Laborda, A. Are cryptocurrencies becoming more interconnected? Econ. Lett. 2021, 199, 109725. 15. Kwapie ´n, J.; W ˛atorek, M.; Drozd˙ z, S. Cryptocurrency Market Consolidation in 2020-2021. Entropy 2021, 23, 1674. 16. Bae, G.; Kim, J.H. Observing Cryptocurrencies through Robust Anomaly Scores. Entropy 2022, 24, 1643. 17. James, N.; Menzies, M. Collective correlations, dynamics, and behavioural inconsistencies of the cryptocurrency market over time. Nonlinear Dyn. 2022, 107, 4001–4017. 18. Kakinaka, S.; Umeno, K. Asymmetric volatility dynamics in cryptocurrency markets on multi time scales. Res. Int. Bus. Financ. 2022, 62, 101754. 19. Arouxet, M.B.; Bariviera, A.F.; Pastor, V.E.; Vampa, V. Covid-19 impact on cryptocurrencies: Evidence from a wavelet-based Hurst exponent. Phys. A 2022, 596, 127170. 20. Corbet, S.; Hou, Y.G.; Hu, Y.; Larkin, C.; Lucey, B.; Oxley, L. Cryptocurrency liquidity and volatility interrelationships during the COVID-19 pandemic. Financ. Res. Lett. 2022, 45, 102137. 21. Nguyen, A.P.N.; Mai, T.T.; Bezbradica, M.; Crane, M. The Cryptocurrency Market in Transition before and after COVID-19: An Opportunity for Investors? Entropy 2022, 24, 1317. 22. Kumar, A.; Iqbal, N.; Mitra, S.K.; Kristoufek, L.; Bouri, E. Connectedness among major cryptocurrencies in standard times and during the COVID-19 outbreak. J. Int. Financ. Mark. Institutions Money 2022, 77, 101523. 23. W ˛atorek, M.; Kwapie ´n, J.; Drozd˙ z, S. Multifractal cross-Correlations of bitcoin and ether trading Characteristics in the Post-COVID-19 Time. Future Internet 2022, 14, 215. 24. Corbet, S.; Meegan, A.; Larkin, C.; Lucey, B.; Yarovaya, L. Exploring the dynamic relationships between cryptocurrencies and other financial assets. Econ. Lett. 2018, 165, 28–34. 25. Ji, Q.; Bouri, E.; Gupta, R.; Roubaud, D. Network causality structures among Bitcoin and other financial assets: A directed acyclic graph approach. Q. Rev. Econ. Financ. 2018, 70, 203–213. 26. Drozd˙ z, S.; Minati, L.; O´swi ˛ecimka, P.; Stanuszek, M.; W ˛atorek, M. Signatures of the Crypto Currency Market Decoupling from the Forex. Future Internet 2019, 11, 154. 27. Gil-Alana, L.A.; Abakah, E.J.A.; Rojo, M.F.R. Cryptocurrencies and stock market indices. Are they related? Res. Int. Bus. Financ. 2020, 51, 101063. 28. Manavi, S.A.; Jafari, G.; Rouhani, S.; Ausloos, M. Demythifying the belief in cryptocurrencies decentralized aspects. A study of cryptocurrencies time cross-correlations with common currencies, commodities and financial indices. Phys. A 2020, 556, 124759. 29. Urquhart, A.; Zhang, H. Is Bitcoin a hedge or safe haven for currencies? An intraday analysis. Int. Rev. Financ. Anal. 2019, 63, 49–57. 30. Wang, P.; Zhang, W.; Li, X.; Shen, D. Is cryptocurrency a hedge or a safe haven for international indices? A comprehensive and dynamic perspective. Financ. Res. Lett. 2019, 31, 1–18. 31. Shahzad, S.J.H.; Bouri, E.; Roubaud, D.; Kristoufek, L.; Lucey, B. Is Bitcoin a better safe-haven investment than gold and commodities? Int. Rev. Financ. Anal. 2019, 63, 322–330. 32. Shahzad, S.J.H.; Bouri, E.; Roubaud, D.; Kristoufek, L. Safe haven, hedge and diversification for G7 stock markets: Gold versus bitcoin. Econ. Model. 2020, 87, 212–224. 33. Bouri, E.; Shahzad, S.J.H.; Roubaud, D.; Kristoufek, L.; Lucey, B. Bitcoin, gold, and commodities as safe havens for stocks:New insight through wavelet analysis. Q. Rev. Econ. Financ. 2020, 77, 156–164. 34. Thampanya, N.; Nasir, M.A.; Huynh, T.L.D. Asymmetric correlation and hedging effectiveness of gold & cryptocurrencies: From pre-industrial to the 4th industrial revolution. Technol. Forecast. Soc. Chang. 2020, 159, 120195. 35. Zhu, X.; Niu, Z.; Zhang, H.; Huang, J.; Zuo, X. Can gold and bitcoin hedge against the COVID-19 related news sentiment risk? New evidence from a NARDL approach. Resour. Policy 2022, 79, 103098. 36. Conlon, T.; McGee, R. Safe haven or risky hazard? Bitcoin during the Covid-19 bear market. Financ. Res. Lett. 2020, 35, 101607. 37. Kristoufek, L. Grandpa, Grandpa, Tell Me the One About Bitcoin Being a Safe Haven: New Evidence From the COVID-19 Pandemic. Front. Phys. 2020, 8, 296. 38. Grobys, K. When Bitcoin has the flu: On Bitcoin’s performance to hedge equity risk in the early wake of the COVID-19 outbreak. Appl. Econ. Lett. 2021, 28, 860–865. 39. James, N.; Menzies, M.; Chan, J. Changes to the extreme and erratic behaviour of cryptocurrencies during COVID-19. Phys. A 2021, 565, 125581. 40. Barbu, T.C.; Boitan, I.A.; Cepoi, C.. Are cryptocurrencies safe havens during the COVID-19 pandemic? A threshold regression perspective with pandemic-related benchmarks. Econ. Bus. Rev. 2022, 8, 22–49. 41. Jareno, F.; Gonzàlez, M.D.L.O.; Belmonte, P. Asymmetric interdependencies between cryptocurrency and commodity markets: The COVID-19 pandemic impact. Quant. Financ. Econ. 2022, 6, 83–112. 42. Kakinaka, S.; Umeno, K. Cryptocurrency market efficiency in short- and long-term horizons during COVID-19: An asymmetric multifractal analysis approach. Financ. Res. Lett. 2022, 46, 102319. 43. Evrim Mandaci, P.; Cagli, E.C. Herding intensity and volatility in cryptocurrency markets during the COVID-19. Financ. Res. Lett. 2022, 46, 102382. 44. Baker, S.R.; Bloom, N.; Davis, S.J.; Kost, K.; Sammon, M.; Viratyosin, T. The Unprecedented Stock Market Reaction to COVID-19. Rev. Asset Pricing Stud. 2020, 10, 742–758. 45. Yarovaya, L.; Matkovskyy, R.; Jalan, A. The COVID-19 black swan crisis: Reaction and recovery of various financial markets. Res. Int. Bus. Financ. 2022, 59, 101521. 46. Conlon, T.; Corbet, S.; McGee, R.J. Are cryptocurrencies a safe haven for equity markets? An international perspective from the COVID-19 pandemic. Res. Int. Bus. Financ. 2020, 54, 101248. 47. Drozd˙ z, S.; Kwapie ´n, J.; O´swi ˛ecimka, P.; Stanisz, T.; W ˛atorek, M. Complexity in economic and social systems: Cryptocurrency market at around COVID-19. Entropy 2020, 22, 1043. 48. Caferra, R.; Vidal-Tomas, D. Who raised from the abyss? A comparison between cryptocurrency and stock market dynamics during the COVID-19 pandemic. Financ. Res. Lett. 2021, 43, 101954. 49. Jiang, Y.; Lie, J.; Wang, J.; Mu, J. Revisiting the roles of cryptocurrencies in stock markets: A quantile coherency perspective. Econ. Model. 2021, 95, 21–34. 50. James, N. Dynamics, behaviours, and anomaly persistence in cryptocurrencies and equities surrounding COVID-19. Phys. A 2021, 570, 125831. 51. Elmelki, A.; Chaâbane, N.; Benammar, R. Exploring the relationship between cryptocurrency and S&P500: evidence from wavelet coherence analysis. Int. J. Blockchains Cryptocurrencies 2022, 3, 256–268. 52. Wang, P.; Liu, X.; Wu, S. Dynamic Linkage between Bitcoin and Traditional Financial Assets: A Comparative Analysis of Different Time Frequencies. Entropy 2022, 24, 1565. 53. Balcilar, M.; Ozdemir, H.; Agan, B. Effects of COVID-19 on cryptocurrency and emerging market connectedness: Empirical evidence from quantile, frequency, and lasso networks. Phys. A 2022, 604, 127885. 54. Conlon, T.; Corbet, S.; McGee, R.J. Inflation and cryptocurrencies revisited: A time-scale analysis. Econ. Lett. 2021, 206, 109996. 55. Choi, S.; Shin, J. Bitcoin: An inflation hedge but not a safe haven. Financ. Res. Lett. 2022, 46, 102379. 56. James, N.; Menzies, M.; Chin, K. Economic state classification and portfolio optimisation with application to stagflationary environments. Chaos, Solitons Fractals 2022, 164, 112664. 57. Phochanachan, P.; Pirabun, N.; Leurcharusmee, S.; Yamaka, W. Do Bitcoin and Traditional Financial Assets Act as an Inflation Hedge during Stable and Turbulent Markets? Evidence from High Cryptocurrency Adoption Countries. Axioms 2022, 11, 339. 58. Dukascopy. Available online: https://www.dukascopy.com/swiss/pl/cfd/range-of-markets/ (accessed on 1 November 2022). 59. Pearson, K. Note on regression and inheritance in the case of two parents. Proc. R. Soc. Lond. 1895, 58, 240–242. 60. Gopikrishnan, P.; Plerou, V.; Nunes Amaral, L.A.; Meyer, M.; Stanley, H.E. Scaling of the distribution of fluctuations of financial market indices. Phys. Rev. E 1999, 60, 5305–5316. 61. Cont, R. Empirical properties of asset returns: Stylized facts and statistical issues. Quant. Financ. 2001, 1, 223–236. 62. Gabaix, X.; Gopikrishnan, P.; Plerou, V.; Stanley, H.E. A theory of power-law distributions in financial market fluctuations. Nature 2003, 423, 267–270. 63. Kwapie ´n, J.; O´swi ˛ecimka, P.; Drozd˙ z, S. Detrended fluctuation analysis made flexible to detect range of cross-correlated fluctuations. Phys. Rev. E 2015, 92, 052815. 64. Peng, C.K.; Buldyrev, S.V.; Havlin, S.; Simons, M.; Stanley, H.E.; Goldberger, A.L. Mosaic organization of DNA nucleotides. Phys. Rev. E 1994, 49, 1685–1689. 65. Jiang, Z.Q.; Xie, W.J.; Zhou, W.X.; Sornette, D. Multifractal analysis of financial markets: A review. Rep. Prog. Phys. 2019, 82, 125901. 66. O´swi ˛ecimka, P.; Drozd˙ z, S.; Kwapie ´n, J.; Górski, A. Effect of detrending on multifractal characteristics. ˙ Acta Phys. Pol. A 2013, 123, 597–603. 67. Zebende, G. DCCA cross-correlation coefficient: Quantifying level of cross-correlation. Phys. A 2011, 390, 614–618. 68. G ˛ebarowski, R.; O´swi ˛ecimka, P.; W ˛atorek, M.; Drozd˙ z, S. Detecting correlations and triangular arbitrage opportunities in the Forex by means of multifractal detrended cross-correlations analysis. Nonlinear Dyn. 2019, 98, 2349–2364. 69. W ˛atorek, M.; Drozd˙ z, S.; O´swi ˛ecimka, P.; Stanuszek, M. Multifractal cross-correlations between the world oil and other financial markets in 2012–2017. Energy Econ. 2019, 81, 874–885. 70. Zhang, Y.J.; Bouri, E.; Gupta, R.; Ma, S.J. Risk spillover between Bitcoin and conventional financial markets: An expectile-based approach. North Am. J. Econ. Financ. 2021, 55, 101296.

Integration of Cryptocurrency into the Global Financial Market: Conclusion and References

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

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

The United States v Meta Platforms Court Filing October 24, 2023 is part of . You can jump to any part in this filing . This is part 17 of 100. HackerNoon’s Legal PDF Series here 3. Meta’s Recommendation Algorithms encourage compulsive use, which Meta does not disclose. 151. Instagram and Facebook employ Recommendation Algorithms that curate content from the main feeds and other parts of the Platforms. 152. The Recommendation Algorithms use data points, or “signals,” harvested from individual users to choose and/or arrange each new piece of content to display to a user. Such signals include, but are not limited to, overt actions like Liking a post or following a page as well as such unconscious actions such as lingering on—but not otherwise engaging with—certain content or visiting but not following another user’s page. 153. Meta employs Recommendation Algorithms universally across its Social Media Platforms, including the Instagram Platform’s Main Feed (the scrolling presentation of content immediately visible upon opening the app) and Explore Feed (another scrolling presentation of algorithmically curated content that can be guided by a user’s text input in a search field). 154. Meta designed its Recommendation Algorithms to maximize youth engagement in several ways but did not disclose these engagement-maximization features to the public—instead representing that these algorithms were intended to benefit the user. 155. First, Meta designed the Recommendation Algorithms to present material to young users in an unpredictable sequence rather than displaying posts chronologically. 156. Specifically, Meta’s Recommendation Algorithms display content to young users through a sequencing method referred to by psychologists as “variable reinforcement schedules” or “variable reward schedules.” 157. As Dr. Mark D. Griffiths, Distinguished Professor of Behavioral Addiction at Nottingham Trent University, explains: The rewards [experienced on social media platforms]—which may be physiological, psychological and/or social—can be infrequent but even the anticipation of one of these rewards can be psychologically and/or physiologically pleasing. The rewards are what psychologists refer to as variable reinforcement schedules and is one of the main reasons why social media users repeatedly check their screens. Social media sites are ‘chock-ablock’ with unpredictable rewards. Habitual social media users never know if their next message or notification will be the one that makes them feel really good. In short, random rewards keep individuals responding for longer and has been found in other activities such as the playing of slot machines and video games.[9] 158. Because they do not work in a predictable pattern, these “variable reinforcement schedules” trigger a release of dopamine, a neurotransmitter released by the brain in response to certain stimuli. Dopamine, commonly “seen to be the ‘pleasure chemical,’” is released in anticipation of a potential reward. However, dopamine neurons fire for only a relatively short period of time, and after dopamine is released, an “individual can become disheartened and disengaged.”[10 ] 159. As researchers Rasan Burhan and Jalal Moradzadeh explain, the variable reinforcement schedules baked into social media platforms like Instagram can lead to “addiction with dopamine implicated”: [T]he user can be kept in a loop. Essentially, that’s how the social media apps exploit these innate systems. The way this comes about is through a term referred to as Variable Reward Schedules. This works by positive stimuli being provided at random intervals. By users checking their phones for notifications and updates at periodic intervals for something that could be intrinsically rewarding. Most of the time it’s a neutral stimuli, but on occasion there may be a positive stimuli leading to the rewarding dopamine release hence keeping the user in the feedback loop.[11] 160. [Redacted] 161. By algorithmically serving content to young users according to variable reward schedules, Meta manipulates dopamine releases in its young users, inducing them to engage repeatedly with its Platforms—much like a gambler at a slot machine. 161-164. [Redacted] 165. Nonetheless, and as illustrated above, as recently as 2020, Meta continued to intentionally design its Platforms to manipulate dopamine responses in its young users to maximize time spent on its Platforms. Meta did not disclose that its algorithms were designed to capitalize on young users’ dopamine responses and create an addictive cycle of engagement. 166. Second, Meta uses data harvested from its users to target user engagement on an individual level via its Recommendation Algorithms—making continued engagement even more difficult for young users to resist. 167. In a June 8, 2021 public blog post on Instagram’s website, Mosseri stated that Meta collects and supplies its Recommendation Algorithms with thousands of “signals” across Instagram’s Feed and Stories, including “[y]our activity” and “[y]our history of interacting with someone.” Mosseri’s post explained that the collection of “[y]our activity . . . helps us understand what you might be interested in . . .” and the collection of “[y]our history of interacting with someone . . . gives us a sense of how interested you are generally in seeing posts from a particular person.” 168. Similarly, Facebook’s Vice President of Global Affairs wrote in Medium on March 31, 2021, about Facebook’s Recommendation Algorithms: “The goal is to make sure you see what you find most meaningful—not to keep you glued to your smartphone for hours on end. You can think about this sort of like a spam filter in your inbox: it helps filter out content you won’t find meaningful or relevant, and prioritizes content you will.” 169. Likewise, Meta’s terms of service on data collection state that Meta uses user data to “[p]rovide, personalize and improve our Products,” “[p]rovide measurement, analytics, and other business services,” “[p]romote safety, integrity and security,” “[c]ommunicate with you,” and “[r]esearch and innovate for social good.” 170. In reality, though, Meta tracks and logs the behavior of millions of young users and utilizes that data to refine and strengthen the features that induce young users’ compulsive Social Media Platform use. 171. As young users engage with Meta’s Social Media Platforms, they are unwittingly training Meta’s Recommendation Algorithms to provide the particular flow of content, notifications, and features that will most effectively keep them online. 172. Again, Meta does not disclose to consumers that it is weaponizing young users’ data to capture and keep their attention. 173. Meta admits in its Privacy Policy that it uses data provided by its young users for purposes other than facilitating meaningful social experiences, such as “improv[ing] our Products . . . includ[ing] personalizing features, content and recommendations, such as your Facebook Feed, Instagram feed, Stories, and ads.” 174. This includes using young users’ data to “[t]est out new products and features to see if they work” and to “[g]et feedback on our ideas for products or features.” 175. But Meta’s representations about its Recommendation Algorithms do not effectively apprise young users of the reality that Meta is harvesting vast amounts of personal data to train its Recommendation Algorithms to induce them to keep using the Platforms. 176. Third, the Recommendation Algorithms increase young users’ engagement by periodically presenting those users with psychologically and emotionally gripping content, including content related to eating disorders, violent content, content encouraging negative selfperception and body image issues, bullying content, and other categories of content known by Meta to provoke intense reactions. 177. Meta’s Recommendation Algorithms are optimized to promote user engagement. Serving harmful or disturbing content has been shown to keep young users on the Platforms longer. Accordingly, the Recommendation Algorithms predictably and routinely present young users with psychologically and emotionally distressing content that induces them to spend increased time on the Social Media Platforms. And, once a user has interacted with such harmful content, the Recommendation Algorithm feeds that user additional similar content. 178-182. [Redacted] 183. Again, though, Meta’s public statements regarding its algorithms’ amplification of distressing and problematic content did not reflect Meta’s true awareness of these problems. 184. In fact, Meta has strongly denied that its Social Media Platforms amplify extreme, distressing, or problematic content. 185. For example, on September 30, 2021, Davis denied that Meta promotes harmful content, such as content promoting eating disorders to youth, when she testified before Congress, stating, “we do not direct people towards content that promotes eating disorders. That actually violates our policies, and we remove that content when we become aware of it. We actually use AI to find content like that and remove it.” 186. [Redacted] 187. Likewise, in a June 8, 2021 post on the Instagram website, titled “Shedding More Light on How Instagram Works,” Mosseri describes Meta’s Recommendation Algorithms by providing examples of benign content recommendations (e.g., “if you’re interested in dumplings you might see posts about related topics, like gyoza and dim sum . . .”). The post provides no accompanying examples or warnings disclosing that the Recommendation Algorithms also tend to suggest content that is dangerous or harmful for young users. 188. The Instagram website also boasts that “[a]t Instagram, we have guidelines that govern what content we recommend to people” and specifies that Instagram “avoid[s] making recommendations that may be inappropriate for younger viewers . . . . We use technology to detect both content and accounts that don’t meet these Recommendations Guidelines and to help us avoid recommending them. As always, content that goes against our Community Guidelines will be removed from Instagram.” 189. A parent or young user encountering these and similar communications by Meta could reasonably understand Meta to be representing that its Recommendation Algorithms do not promote content to young users that violates Meta’s Recommendation Guidelines or is otherwise dangerous or inappropriate for young users. 190. But as explained above, Meta does increase young users’ engagement with its Platforms by periodically presenting them with psychologically and emotionally gripping content, including content related to eating disorders, violent content, content encouraging negative self perception and body image issues, bullying content, and other categories of content known by Meta to provoke intense reactions from users. [9] Mark D. Griffiths, Adolescent Social Networking: How Do Social Media Operators Facilitate Habitual Use?, 36 Educ. & Health J. 66, 67 (2018), (internal references omitted). http://archive.today/cPgJ1 [10] Rasan Burhan & Jalal Moradzadeh, Neurotransmitter Dopamine (DA) and its Role in the Development of Social Media Addiction, 11 J. Neurology & Neurophysiology 1, 1 (2020), . http://archive.today/kxldL [11] Id. at 1-2. Continue Reading . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case 4:23-cv-05448 retrieved on October 25, 2023, from is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. Washingtonpost.com

Facebook and Instagram Use Algorithms That Promote Compulsive Use

AI has been slaying it in almost every sector. Healthcare is benefitting a lot from voice based . But this doesn’t mean we can trust them blindly. LLMs and ML Google DeepMind an AI program named AlphaMissense, which predicts the harm a missense mutation can do, when a single DNA letter is altered. The tool, which leverages data from 71 million single-letter mutations affecting human proteins, uses machine learning to “understand” the language of proteins, helping it score the risk level of genetic changes. created What AI is doing in synthetic biology is nothing short of revolutionary. Using AI, scientists are to create new unseen proteins like coding software. We can expect groundbreaking changes in not only medicine, but also in sectors like agriculture, energy, and climate. now programming cells Also, recently, researchers from Edith Cowan University software that quickly scans bone density scans to detect abdominal aortic calcification (AAC), which is a predictor of cardiovascular events and other health risks. The software processed images with 80% accuracy and could revolutionize early disease detection during routine clinical practice. developed In August, a ML model to identify autism spectrum disorder with 95% accuracy. While Stanford Medicine developed an algorithm that reviews images to determine a brain tumour’s aggressiveness, its genetic makeup, and effectiveness of surgery. “It’s sort of a decision support system for the physicians,” one researcher says. international researchers trained Also, Scientists at the Osaka Metropolitan University an AI model that accurately identifies cardiac functions and valvular heart diseases using chest radiographs. The research could supplement traditional echocardiography, improve diagnostic efficiency, and be especially useful in settings lacking specialized technicians. developed Startups in are also upping their game with AI. London-based Ultromics its AI to detect a heart condition that human eyes can often not detect. In July, physician-entrepreneurs at Israel’s started training AI to diagnose cardiac disease on a simple tablet, where the doctors don’t have to be heart experts. With a handheld scanner and tablet, non-cardiologists get a full diagnostic on 8 key heart readings. With its training data, the AI platform already has more experience than any living cardiologist, the physicians say. HealthTech trained Sheba Medical Center In September, , an Indian AI-powered grocery scoring and recommendation platform, tied up with, (Microbiome Research Pvt Ltd), a research platform in the field of gut microbiome and, , a diagnostics startup. With a shared commitment to preventive healthcare, the trio seeks to empower individuals with individualised in-depth diagnostics and convert that information into actionable real-time intelligence. Xume Microbiome.in TruDiagno This can help individuals to make informed choices based on the state of one’s gut microbiome and key diagnostic markers. Microbiome.in is the only private lab with an in-house Illumina and Nanopore (portable) Sequencer enabling unparalleled research on the role of gut microbiome, while TruDiagno adds advanced molecular diagnostic capabilities to the mix. Imagine the power of understanding our microbiome and diagnostic markers to drive actionable intelligence; where the answer is not giving up our favourite foods but simply making better choices based on what our gut and body are telling us Akshaye Jalan, Founder & CEO, Xume “We’re all unique, so how can a generic solution mired in restriction be the answer to good health? Imagine the power of understanding our microbiome and diagnostic markers to drive actionable intelligence; where the answer is not giving up our favourite foods but simply making better choices based on what our gut and body are telling us. This is the future of health and wellness and what our partnership with Microbiome.in and TruDiagno endeavours to do.” said Akshaye Jalan, Founder & CEO, Xume. In September, Oracle introduced voice-activated AI to hospitals, which will help doctors and clinicians to automate medical note-taking, order medications and view lab reviews while also more easily reviewing a patient’s medical record. This is not about going from 10 clicks to seven clicks or six clicks to five clicks. This is about completely eliminating clicks, where we can and where it makes sense. That is not a tomorrow or that’s years away. That is a tomorrow that’s here Mike Sicilia, executive vice president, industries at Oracle “We’ve completely rethought how long it takes somebody to get their job done using the system. I think that we are on the verge of completely eliminating ‘pajama time’ as a result. This is not about going from 10 clicks to seven clicks or six clicks to five clicks. This is about completely eliminating clicks, where we can and where it makes sense. That is not a tomorrow or that’s years away. That is a tomorrow that’s here,” Mike Sicilia, executive vice president, industries at Oracle told the audience at the . Oracle Health Conference But does this mean AI is your next doctor instead of a human? All the above instances are brilliant examples of AI helping humankind become better at healthcare, but they’re also being done under supervision of experts, who are human. The general online population has been turning to Dr. Google even before the Red Hot Chilli Peppers wrote . But there’s a new doctor in town, Dr. ChatGPT. , “the most recent version of ChatGPT made zero “grossly inaccurate” statements when presented with a standard set of eye complaints.” Californication According to NPR However, questions still remain about how to fit in LLMs into healthcare with similar safeguards that are applied when introducing new drugs or medical devices. This article was originally published by in Navanwita Bora Sachdev The Tech Panda.

Dr. ChatGPT vs. Dr. Google: An Assessment of AI Medical Advice

At length Louis XIV, bowed beneath the weight of a reign of sixty years

The field of natural language processing (NLP) has seen a significant shift since the introduction of transformer architectures. First proposed in the "Attention is All You Need" paper [5], these architectures have formed the basis for a variety of powerful models, including and BERT GPT. In this article, we will overview transformer architectures. We will dissect the encoder-only architecture of , exploring its masked language modeling and next-sentence prediction tasks. Next, we cover the decoder-only architecture of GPT models, highlighting its use of causal self-attention and scalability, we will also discuss why this architecture became dominant for general-purpose language models. Additionally, we will revisit the original Transformer architecture, which consists of both an encoder and a decoder. BERT BERT - Bidirectional Encoder Representations from Transformers [1] Overview BERT (Bidirectional Encoder Representations from Transformers) is a pre-trained transformer-based model developed by researchers at AI Language. BERT revolutionized the NLP landscape by introducing a powerful encoder-only architecture that leverages bidirectional context for a wide range of tasks. The primary innovation of BERT lies in its ability to generate context-specific word embeddings, allowing it to capture complex language patterns and dependencies. This understanding of context has several important implications, which we will explore in more detail below. Google Training details The BERT model is trained on two tasks: Masked Language Modeling, where the objective is to predict the masked word based on the context (Fig. 1), and Next Sentence Prediction, where the objective is to classify whether sentence B follows Sentence A in the text. Context-Dependent Word Embeddings Traditional word embedding techniques, such as Word2Vec and GloVe, represent words as fixed-dimensional vectors that capture their general meaning. However, these embeddings do not account for the context in which words appear, which can limit their effectiveness in the modeling of language. BERT addresses this limitation by generating word embeddings that depend on the context in which the words are used. This enables BERT to capture the nuances of language and understand words with multiple meanings based on their surrounding tokens. Understanding word in the context -> Understanding the context One key insight of BERT is that the ability to generate context-dependent word embedding implies that the model understands the context itself. By learning to predict masked tokens based on their surrounding words, BERT demonstrates its ability to infer word meanings from context. This suggests that the model has developed a strong grasp of the language structure and relationships between words, which is essential for effective transfer learning. Transfer Learning and Task Adaptation BERT's understanding of context makes it particularly powerful for transfer learning, as its pre-trained representations can be easily fine-tuned for various downstream tasks. This adaptability is a key strength of BERT, as it allows the model to achieve state-of-the-art performance on a wide range of NLP tasks with minimal architectural changes and relatively small amounts of task-specific data. Examples of tasks for which BERT has been successfully fine-tuned include: Sentiment analysis: BERT can be used to classify text based on sentiment, such as determining whether a movie review is positive or negative. Named entity recognition: Fine-tuning BERT can enable it to identify and classify entities, such as people, organizations, and locations, in a given text. Summary In summary, BERT's encoder-only architecture, context-dependent word embeddings, and deep understanding of context have made it a powerful and versatile model for transfer learning. Its success in a wide variety of NLP tasks has demonstrated the potential of transformer-based models and paved the way for subsequent developments in the field. GPT - Generative Pre-trained Transformer Overview GPT (Generative Pre-trained Transformer) is a state-of-the-art transformer-based model developed by OpenAI. The third iteration in the GPT series, GPT-3 builds upon the success of its predecessors by scaling up the architecture and leveraging a massive amount of pre-training data. In contrast to BERT, GPT is based on the decoder-only Transformer architecture (Fig. 2). This section will explore the core insights of GPT, and highlight its important features that contribute to its success while drawing parallels with BERT. Language Understanding through Next-Word Prediction While BERT learns context-specific word embeddings through its masked language modeling task, GPT-3 focuses on predicting the next word in a given sequence, given the preceding words. Similar to BERT, this word prediction task allows GPT to develop a deep understanding of the language, as it must learn syntactic, semantic, and pragmatic aspects of the text in order to accurately predict what comes next. Efficient Training with Causal Self-Attention The first thing that contributes to the success of GPT models is the efficiency of the training process. GPT employs causal self-attention, a mechanism that allows tokens to only attend to preceding tokens during training. This design choice ensures that the model does not have access to future tokens when predicting the next word, making the training process more efficient. By processing a single text sample of length n, GPT effectively generates n training samples, as it predicts each token based on its preceding context. This increased efficiency in training enables GPT to scale up to larger architectures and datasets, resulting in improved performance and language understanding. In contrast, BERT uses masked language modeling, which involves masking a fixed subset of words and predicting them based on bidirectional context, requiring more computational resources during training. Zero-Shot and Few-Shot Learning The second thing that makes GPT models so interesting is the emergent ability to perform Zero-/ few-shot learning, i.e., by simply providing the model with a task description and zero or few examples of the desired output, GPT-3 can adapt to a wide range of NLP tasks without any task-specific fine-tuning (left image on Fig. 3). GPT-3 develops this remarkable capability only when it gets large enough, i.e., 100Bn+ parameters (right image on Fig.3). It’s possible to train GPT models of this size only due to the training efficiency described in Sec. 3.3, thus, it’s unlikely that BERT-like models will ever demonstrate few-shot learning capabilities and they would always require fine-tuning in practice. The transfer learning capability of GPT-3 is a testament to the model's robust language understanding and its ability to generalize from the vast amount of pre-training data. Conclusion In conclusion, 's success in the field of natural language processing can be attributed to its efficient training based on the next word prediction, and zero-shot/few-shot learning capabilities. Its scalability allows it to handle larger architectures and datasets, enhancing its performance and language understanding. GPT-3 Unlike BERT which requires task-specific fine-tuning and more computational resources during training, GPT-3 can adapt to various NLP tasks with minimal task-specific adjustments, highlighting its capacity for generalization from pre-training data. Encoder-Decoder Architecture in Transformers For completeness here we want to overview the original encoder-decoder architecture (Fig. 4) proposed in the seminal "Attention is All You Need" paper [5]. This architecture is used in practice for a wide range of NLP tasks. We already covered the encoder and decoder architectures, the encoder-decoder structure blends those two parts: the encoder processes the input sequence, and the decoder generates the output sequence, leveraging the representations from the encoder. Here we want to highlight a significant difference between encoder-decoder Transformer models with BERT and GPT. Encoder-decoder models are typically specialized for specific tasks, such as translation or text summarization. This means a single model is typically trained to perform one specific function. On the other hand, BERT and GPT are designed to perform a wide array of tasks, and they excel at transfer learning. This difference underscores the versatility of BERT and GPT, while also highlighting the focused strength of task-specific encoder-decoder models. References: [1] 1810.04805.pdf (arxiv.org) [2] The Illustrated BERT, ELMo, and co. (How NLP Cracked Transfer Learning) – Jay Alammar – Visualizing machine learning one concept at a time. (jalammar.github.io) [3] Transformer Decoder-Only 模型批量生成 Trick - 知乎 (zhihu.com) [4] 2005.14165.pdf (arxiv.org) [5] 1706.03762.pdf (arxiv.org) The lead image for this article was generated by HackerNoon's AI Image Generator via the prompt "Illustrate two humanoid robots squaring off"

Why Is GPT Better Than BERT? A Detailed Review of Transformer Architectures

Hybridism

Moby-Dick; or The Whale, by Herman Melville is part of HackerNoon’s Book Blog Post series. You can jump to any chapter in this book here: [ ]. Chapter 72:The Monkey-Rope LINK TO TABLE OF LINK CHAPTER 72. The Monkey-Rope. In the tumultuous business of cutting-in and attending to a whale, there is much running backwards and forwards among the crew. Now hands are wanted here, and then again hands are wanted there. There is no staying in any one place; for at one and the same time everything has to be done everywhere. It is much the same with him who endeavors the description of the scene. We must now retrace our way a little. It was mentioned that upon first breaking ground in the whale’s back, the blubber-hook was inserted into the original hole there cut by the spades of the mates. But how did so clumsy and weighty a mass as that same hook get fixed in that hole? It was inserted there by my particular friend Queequeg, whose duty it was, as harpooneer, to descend upon the monster’s back for the special purpose referred to. But in very many cases, circumstances require that the harpooneer shall remain on the whale till the whole flensing or stripping operation is concluded. The whale, be it observed, lies almost entirely submerged, excepting the immediate parts operated upon. So down there, some ten feet below the level of the deck, the poor harpooneer flounders about, half on the whale and half in the water, as the vast mass revolves like a tread-mill beneath him. On the occasion in question, Queequeg figured in the Highland costume—a shirt and socks—in which to my eyes, at least, he appeared to uncommon advantage; and no one had a better chance to observe him, as will presently be seen. Being the savage’s bowsman, that is, the person who pulled the bow-oar in his boat (the second one from forward), it was my cheerful duty to attend upon him while taking that hard-scrabble scramble upon the dead whale’s back. You have seen Italian organ-boys holding a dancing-ape by a long cord. Just so, from the ship’s steep side, did I hold Queequeg down there in the sea, by what is technically called in the fishery a monkey-rope, attached to a strong strip of canvas belted round his waist. It was a humorously perilous business for both of us. For, before we proceed further, it must be said that the monkey-rope was fast at both ends; fast to Queequeg’s broad canvas belt, and fast to my narrow leather one. So that for better or for worse, we two, for the time, were wedded; and should poor Queequeg sink to rise no more, then both usage and honor demanded, that instead of cutting the cord, it should drag me down in his wake. So, then, an elongated Siamese ligature united us. Queequeg was my own inseparable twin brother; nor could I any way get rid of the dangerous liabilities which the hempen bond entailed. So strongly and metaphysically did I conceive of my situation then, that while earnestly watching his motions, I seemed distinctly to perceive that my own individuality was now merged in a joint stock company of two; that my free will had received a mortal wound; and that another’s mistake or misfortune might plunge innocent me into unmerited disaster and death. Therefore, I saw that here was a sort of interregnum in Providence; for its even-handed equity never could have so gross an injustice. And yet still further pondering—while I jerked him now and then from between the whale and ship, which would threaten to jam him—still further pondering, I say, I saw that this situation of mine was the precise situation of every mortal that breathes; only, in most cases, he, one way or other, has this Siamese connexion with a plurality of other mortals. If your banker breaks, you snap; if your apothecary by mistake sends you poison in your pills, you die. True, you may say that, by exceeding caution, you may possibly escape these and the multitudinous other evil chances of life. But handle Queequeg’s monkey-rope heedfully as I would, sometimes he jerked it so, that I came very near sliding overboard. Nor could I possibly forget that, do what I would, I only had the management of one end of it.* *The monkey-rope is found in all whalers; but it was only in the Pequod that the monkey and his holder were ever tied together. This improvement upon the original usage was introduced by no less a man than Stubb, in order to afford the imperilled harpooneer the strongest possible guarantee for the faithfulness and vigilance of his monkey-rope holder. I have hinted that I would often jerk poor Queequeg from between the whale and the ship—where he would occasionally fall, from the incessant rolling and swaying of both. But this was not the only jamming jeopardy he was exposed to. Unappalled by the massacre made upon them during the night, the sharks now freshly and more keenly allured by the before pent blood which began to flow from the carcass—the rabid creatures swarmed round it like bees in a beehive. And right in among those sharks was Queequeg; who often pushed them aside with his floundering feet. A thing altogether incredible were it not that attracted by such prey as a dead whale, the otherwise miscellaneously carnivorous shark will seldom touch a man. Nevertheless, it may well be believed that since they have such a ravenous finger in the pie, it is deemed but wise to look sharp to them. Accordingly, besides the monkey-rope, with which I now and then jerked the poor fellow from too close a vicinity to the maw of what seemed a peculiarly ferocious shark—he was provided with still another protection. Suspended over the side in one of the stages, Tashtego and Daggoo continually flourished over his head a couple of keen whale-spades, wherewith they slaughtered as many sharks as they could reach. This procedure of theirs, to be sure, was very disinterested and benevolent of them. They meant Queequeg’s best happiness, I admit; but in their hasty zeal to befriend him, and from the circumstance that both he and the sharks were at times half hidden by the blood-muddled water, those indiscreet spades of theirs would come nearer amputating a leg than a tail. But poor Queequeg, I suppose, straining and gasping there with that great iron hook—poor Queequeg, I suppose, only prayed to his Yojo, and gave up his life into the hands of his gods. Well, well, my dear comrade and twin-brother, thought I, as I drew in and then slacked off the rope to every swell of the sea—what matters it, after all? Are you not the precious image of each and all of us men in this whaling world? That unsounded ocean you gasp in, is Life; those sharks, your foes; those spades, your friends; and what between sharks and spades you are in a sad pickle and peril, poor lad. But courage! there is good cheer in store for you, Queequeg. For now, as with blue lips and blood-shot eyes the exhausted savage at last climbs up the chains and stands all dripping and involuntarily trembling over the side; the steward advances, and with a benevolent, consolatory glance hands him—what? Some hot Cognac? No! hands him, ye gods! hands him a cup of tepid ginger and water! “Ginger? Do I smell ginger?” suspiciously asked Stubb, coming near. “Yes, this must be ginger,” peering into the as yet untasted cup. Then standing as if incredulous for a while, he calmly walked towards the astonished steward slowly saying, “Ginger? ginger? and will you have the goodness to tell me, Mr. Dough-Boy, where lies the virtue of ginger? Ginger! is ginger the sort of fuel you use, Dough-boy, to kindle a fire in this shivering cannibal? Ginger!—what the devil is ginger? Sea-coal? firewood?—lucifer matches?—tinder?—gunpowder?—what the devil is ginger, I say, that you offer this cup to our poor Queequeg here.” “There is some sneaking Temperance Society movement about this business,” he suddenly added, now approaching Starbuck, who had just come from forward. “Will you look at that kannakin, sir: smell of it, if you please.” Then watching the mate’s countenance, he added, “The steward, Mr. Starbuck, had the face to offer that calomel and jalap to Queequeg, there, this instant off the whale. Is the steward an apothecary, sir? and may I ask whether this is the sort of bitters by which he blows back the life into a half-drowned man?” “I trust not,” said Starbuck, “it is poor stuff enough.” “Aye, aye, steward,” cried Stubb, “we’ll teach you to drug a harpooneer; none of your apothecary’s medicine here; you want to poison us, do ye? You have got out insurances on our lives and want to murder us all, and pocket the proceeds, do ye?” “It was not me,” cried Dough-Boy, “it was Aunt Charity that brought the ginger on board; and bade me never give the harpooneers any spirits, but only this ginger-jub—so she called it.” “Ginger-jub! you gingerly rascal! take that! and run along with ye to the lockers, and get something better. I hope I do no wrong, Mr. Starbuck. It is the captain’s orders—grog for the harpooneer on a whale.” “Enough,” replied Starbuck, “only don’t hit him again, but—” “Oh, I never hurt when I hit, except when I hit a whale or something of that sort; and this fellow’s a weazel. What were you about saying, sir?” “Only this: go down with him, and get what thou wantest thyself.” When Stubb reappeared, he came with a dark flask in one hand, and a sort of tea-caddy in the other. The first contained strong spirits, and was handed to Queequeg; the second was Aunt Charity’s gift, and that was freely given to the waves. 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. Urbana, Illinois: Project Gutenberg. Retrieved April 2022 from Melville, Herman, 2001. Moby-Dick: or The Whale. https://www.gutenberg.org/files/2701/2701-h/2701-h.htm#link2HCH0072 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 , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

Moby-Dick; or The Whale: Chapter 72 -The Monkey-Rope

From long-awaited sequels to brand-new fascinating ideas, 2022 is set to be a huge year for indie games. However, before I jump into the excitement surrounding all the upcoming games, there's something that a lot of you might want to know. What are indie games, and why should you play them? To put it plainly, indie (short for "independent") games are made by independent developers or individuals who fund their creations a lot differently than "AAA" studios and publishers - hence, the title "indie." These games are produced in a variety of ways, including crowdfunding, Patreon supporters, or by the developer themselves. For years, indie games have delivered unique and extraordinary gaming experiences, and let's face it, sometimes these experiences are ten times better than what big studio games have to offer. Also, indie games have become plentiful and more accessible over time, with most developers choosing to publish their games across several popular platforms. With the advancement of gaming engines and consoles, creators have more tools at their fingertips to create true masterpieces, with of a variety of genres. each year packed with indie releases So, what can we expect from the best indie games coming this year? Here are the Best Indie Games to be Released in 2022 Some of these titles have already been released this January, but some of them are planned to be released later this year and others don't have a set date yet. These games span across a variety of genres and aim to deliver an impressive range of gaming experiences. Lastly, these titles are presented in no specific order and will be available across different consoles upon release. With all of this in mind, sift through the list and see what is going to be your own best indie game for 2022. 1. Tchia Exploring a wonderfully rich cultural atmosphere is the goal of this sandbox adventure. Inspired by the culture of the New Caledonian people, players will spend much of the game traveling around the archipelago, swimming, climbing, sailing, gliding, and playing the ukulele. Not much is known about the overarching story, except players will take on the role of the protagonist, Tchia, as she goes on a tropical adventure across the archipelago and meets different types of people along the way. The game will also feature cutscenes that will be voiced in the traditional language, which will provide an extra immersive experience. 2. Bear & Breakfast Ever wanted to live the life of a bear in the forest? You can in this game, but there’s a twist. In Bear & Breakfast, players assume the role of a grizzly bear who manages a bed and breakfast in the woods - dangerous woods. Gameplay will consist of managing and setting up each room in the inn with Sims-like controls, as well as meeting other characters while exploring the forest. 3. Nobody Saves the World Shapeshifting into either a ghost, dragon, or slug (and more as you progress) is the core gameplay of this action-RPG. It’s also one of few games on this list that has already been released (and in case you’re wondering, it’s receiving rave reviews as we speak). Players take on the role of Nobody, a shapeshifter, who is tasked with saving the world. By completing quests, players unlock over fifteen forms to swap back and forth between and are given new abilities that open the chance to complete more quests. An additional perk of playing this game is that it supports co-op play. 4. Have a Nice Death This is one game that is sure to provide a unique experience. The plot is simple: Death is overloaded with work and surrounded by employees who offer no help and have thrown off “the balance of souls.” As a result of this, Death embarks on a cleaning crusade within the company by using his scythe to put things back in order. In an interesting mix of office work meets the afterlife, Magic Design Studios will bring forth their cute 2D action rogue-lite in March. 5. Thirsty Suitors Announced during this year’s Game Awards, Thirsty Suitors is expected to be a delightful mix of unique turn-based battles, skating, and cooking competitions. Players follow Jala, a young woman who is on a journey to find herself by confronting her past, dealing with overbearing family members, and more, all in time for her sister’s upcoming wedding. The game’s trailer is sprinkled with humorous moments and over-the-top action. There is no set date for the game’s release, but more information will be revealed soon. 6. Strange Horticulture Here’s another 2022 game that has already been released - yay! Strange Horticulture is a puzzle game where players run a local plant store. The overall goal of the game is to discover and identify new plants, as well as tend to your pet cat. It sounds simple, but the backdrop of the story involves players using their discoveries to dictate the occult-centered story. Other characters are thrown in the mix as players navigate the town of Undermere and solve an occult mystery. 7. Stray Stray is one of those titles you’ll see pop up on Twitter a lot if you browse through indie gamer threads for a while. It has created a strong fan base already even though it hasn’t even been released yet - wow! Players take on the role of a stray cat who is lost in a cybercity that contains its own secrets for our feline protagonist to bring to light. 8. Palworld Palworld has been making waves in the gaming world due to gamers comparing it to Nintendo's popular Pokemon franchise. In this indie game by Pocketpair, players travel around an open world, befriending and collecting "pals," mysterious creatures that can be found throughout the land. Players will use these pals in fighting, building, farming, and even working in factories in order to survive in this very bizarre world. The game is expected to have a multiplayer mode as well. 9. Planet Crafter Players will spend their time terraforming and surviving on a hostile planet in this space adventure title. The goal is to collect resources and shape the environment to make the planet safe and inhabitable for humans. Those who enjoy science fiction survival games will want to look into this one. 10. Cuphead - The Delicious Last Course Cuphead fans have been waiting forever for news of another Cuphead game. Needless to say, the news of a Cuphead DLC was one of the Game Awards’ biggest announcements this year. Continuing in the classic style of the original Cuphead game, Cuphead and Mugman team up with Ms. Chalice on an adventure to D.L.C. Isle. With the help of her abilities, the two brothers use their new weapons and magical charms to take on the new peril and aid Chef Saltbaker in the final quest. The game is expected to be released on the 30th of June. 11. Sea of Stars Sea of Stars has been gaining a lot of attention lately due to its colorful aesthetic and enchanting premise. This turn-based RPG follows the story of Children of the Solstice who must combine their sun and moon powers to use Eclipse Magic, a power that is essential in fighting off monsters created by The Fleshmancer - an enemy alchemist. 12. Mineko's Night Market This game is all about enjoying and appreciating Japanese culture. Players will play as protagonist Mineko, a young girl who moves to a new, yet worn-out, home on a Japanese-inspired island at the base of Mount Fugu. However, the island is in an uproar over reported sightings of Sun Cat, Abe, who was long thought to be a mythical figure. Mineko sets out to unravel the mystery behind the town and help restore it to what it once was - but she also must tend to her daily duties of working jobs, completing quests, and crafting items for the Night Market. 13. Coffee Talk Episode 2: Hibiscus & Butterfly Last but not least, Toge Productions is bringing back their talking simulator with a freshly brewed chapter. Players will be given the chance to create beautiful latte art, immerse themselves in a relaxing atmosphere, and brew a variety of coffee drinks that are sure to help the customers “warm up” to them, as they all share their own unique stories. It’s the continuation of the original Coffee Talk game, where two things in life are mixed together: coffee and heartfelt conversation. Final Thoughts on The Best Indie Games of 2022 As you can tell by the list, 2022 is another big year for the indie game world, and there’s something out there for everyone from the looks of it. Whether you’re a new or old gamer who is interested in trying something different this year, you can’t go wrong with looking into the best indie games of the year.

13 Best Indie Games to Look forward to in 2022

In this thread, the team at Hackernoon discusses the best pizza topping. Be warned, this slogging thread gets a bit spicy, with our team members heavily disagreeing on what the best topping is. slogging This Slogging thread by Jack Boreham, Mónica Freitas, Sara Pinto, Limarc Ambalina, Adrian Morales and Blake Cram occurred in slogging's official #random channel, and has been edited for readability. Jack Boreham Dec 20, 2021, 12:29 PM INCOMING...heavily debated topic: what is the Best Pizza topping? Mónica Freitas Dec 20, 2021, 5:56 PM Jack Boreham PINEAPPLE! PINEAPPLE! 🍍 AND CHEESE! 🧀 Sara Pinto Dec 21, 2021, 12:42 PM Jack Boreham I'm gonna go ahead and say it: pineapple (please don't shoot) Jack Boreham Dec 21, 2021, 1:34 PM OH GOSHHHHH Mónica Freitas Sara Pinto,  naaa to be honest it's not half bad, but not the best, right Limarc Ambalina? 😁 Mónica Freitas Sara Pinto how can it beat a pepperoni though.... Mónica Freitas Dec 21, 2021, 4:51 PM Jack Boreham I'll fight you on this! I'll change pepperoni for mushrooms or peppers easily. Jack Boreham and NOW I'm hungry Jack Boreham Dec 22, 2021, 12:21 PM Mónica Freitas HOWWW, pepperoni was gifted to us from the gods 😂 are you vegan or veggie? 😂 Mónica Freitas Dec 22, 2021, 3:16 PM Nah, I couldn't give up on meat. I just find it an overpowering flavor; it erases the rest of the pizza. 😂. Speaking of veggie, Sara Pinto come through. What's the perfect pizza like? Sara Pinto Dec 22, 2021, 3:26 PM Jack Boreham I can trade pepperoni for pineapple every day, but I guess I'm biased since I'm a vegetarian 😂. Mónica Freitas Jack Boreham I'll tell you what - olives are a pretty strong runner up! Limarc Ambalina Dec 23, 2021, 1:05 PM I'll be honest Jack Boreham the fact that the first answer to this thread were pineapple from 2 separate people caused me to ignore this thread for 22 hours. I'm almost offended. I'm with jack that you can't beat good ol' pepperoni, and my second would be bacon. I wish I had the strength to go vegan Limarc Ambalina Dec 23, 2021, 1:07 PM I'll give an out of the box one. There's this place in my hometown called A Little Pizza Heaven, and their biggest differentiator is that they top the crust and the pizza off with a smear of special garlic butter......the taste is HEAVENLY Jack Boreham Dec 23, 2021, 1:29 PM Mónica Freitas mushrooms are slimy; I don't want to add any more slime to my greasy pizza😂. Pep is crisp, tasty and compliments the cheese and balances out the texture.  Mushroom belongs in a soup and a fry up (what we British call a proper breakfast) or perhaps a mushroom tartare (I don't know what it is, I heard it on Masterchef). 😂 Jack Boreham Dec 23, 2021, 1:30 PM Sara Pinto, I respect that; I wish to go vegan, but the meats are far too good! Jack Boreham Dec 23, 2021, 1:32 PM Limarc Ambalina, you know what? I completely agree with you! I didn't want to go full ham, as I was outnumbered... but I couldn't agree more. I'm offended as well 😉! That pizza sounds amazing, bro 💪 . Sara Pinto Dec 23, 2021, 1:44 PM Limarc Ambalina Garlic butter on pizza... that's what I'll dream about when I go to sleep today. Jack Boreham Haha, I respect what you're saying as well, but I have to challenge you to do a pizza without the meat (I won't even mention pineapple) 😂 Mónica Freitas Dec 23, 2021, 2:54 PM Jack Boreham soup???? mental! ahahah. Limarc Ambalina Jack Boreham Stop pineapple hate! Are we moving to the cereal first vs. milk first discussing next?! 😂 Jack Boreham Dec 24, 2021, 9:37 AM Sara Pinto you mean veggie pizza. I've had one and I like it 😂 Jack Boreham Dec 24, 2021, 9:38 AM Mónica Freitas never had a good old mushroom soup...it's the bee's knees 😂. Mónica Freitas hahahhhaha #newthreadstarting. I bet you can't list three reasons why pineapple is better 😁 Mónica Freitas Dec 24, 2021, 12:12 PM Jack Boreham It's not something I've ever seen here. Maybe someday I'll try that soup out. If you go ahead with that thread, we best build a wall between this community ahaha. Mónica Freitas Dec 24, 2021, 12:16 PM Jack Boreham challenge accepted: It's sweet Cuts the grease out of the pizza It's fruit, so it adds that sprinkle of healthiness 😂 😂 1 Adrian Morales Dec 24, 2021, 2:29 PM I firmly stand with pineapple belonging on pizza. A pineapple and ham pizza? I'm there, save me a slice. 😂 💚 2 Mónica Freitas Dec 27, 2021, 2:36 PM Yay, team pineapple! Blake Cram Dec 27, 2021, 11:11 PM Holy smokes. Never thought I'd see so many pineapple advocates. I don't think it's the best topping... but I think pineapple can be delicious, but it's easy to f--- up. I think there are a few essential "pineapple on pizza rules"... Pineapple as the only topping is an abomination.                                                             It's basically dessert. Texturally confusing if the pineapple is mushy or mealy. You have to have fresh pineapple with at least a little crunch. Pineapple is an acidic fruit. And it add a nice acidity to the sweet and savory of cheese, sauce and bread. But, you can't let the pineapple be the star of the show or you get too much sweetness. can You have to break up that sweetness with one of 3 things: something cool in the cheese mix like pecorino romano, parm or asiago does a lot to break up Mozz or Provalone's sweetness. you also have to have a spicier, herby sauce. finally have to agree with, gotta pair it with something awesome like Canadian Bacon... although my personal faves are Pep or Jalapeño when I'm feeling crazy. 😂 1 Blake Cram Dec 27, 2021, 11:19 PM As far as toppings, I think I side more with Jack Boreham and Limarc Ambalina that Pepperoni is the classic barometer for quality at a pizza joint. If you're vegetarian then it's the Margherita slice (maybe with light anchovies in NY). To me, either of those slices shows off a pizzerias skill without anything in the way. best Jack Boreham Dec 29, 2021, 11:36 AM Blake Cram, a man that speaks the truth! Couldn't have put it better myself. Mónica Freitas Sara Pinto any counterpoints before we declare VICTORY? Mónica Freitas Dec 31, 2021, 12:31 PM Yes! My preference! Ahahaha

What is the Best Pizza Topping? (Slogging Insights)

To determine the relative smartness level of a city, there are several indicators that have been defined to arrive as such a number. These indicators, among many others, are: , Natural Environment , Water and waste , Transport , Energy , Economy , Education , Health and ICT Governance In the swamp of these unbalanced indicators lies a mathematical law which is common to them all. It is called as Kleiber's law. Background Kleiber's Law was found by scientist Max Kleiber, who was a Swiss mathematician doing his undergraduate studies in Zurich in 1910. He moved to California to study agriculture at the University of California Davis. He started his research on the metabolism rates in animals which was important to determine how much food the animals would require and how much meat they would produce. It was then he stumbled upon a mysterious pattern that would be found in almost all of the animals. It was called as . If mass vs metabolism was plotted on a logarithmic scale, the result was a perfectly straight line that was common to all animals. "negative power quarter scaling" The equation was clear, metabolism scales to mass to the negative quarter power. If you take square root of 1000, it is almost 31, and square root of 31 is almost 5.5 . If a cow that weighs 1000 times as much as a bird, the cow will live 5.5 times as long as a bird, and the heart rate would be 5.5 times slower than that of a bird. Physicist Geoffery West decided to apply Kleiber's law to the largest (mimicking) organism on the planet, cities. Did they obey it? Did they slow down as they expanded in size? His team conducted a research and found that cities did follow this law. Number of street lights, gas stations, length of roads, electricity used by the city, all these indicators followed Kleiber's law, which was found in biological life. They also found that a city that was 10 times as bigger than another was not 10 times more innovative, but 17 times more innovative. A city 50 times bigger was 130 times more innovative. West proved that similar to animals, as cities get bigger, they slow down. But, they generate ideas faster than a smaller city. Now, we are going to see it in action. The code used in this article can be found . This is called as super linear scaling. here Applying Kleiber's Law Now that we are familiar with Kleiber's law, i will now apply it to the cities of today. I have gathered data ( ) for several different cities for 3 indicators, street lights, road length and electricity. The data in it's raw form looks like this. source City Name, Length Roads ( km), Population ( k)
Davanagere, , of in 100 1278.7 4.5 The data for electricity and street lights is in the same format. After some cleaning and aggregating, I then calculate log to the base 10 of length of roads and log to the base 10 of the population. The code is After aggregation the data looks like this: City Name  Length Roads ( km)  Population  RoadLengthPerCapita  RoadLengthPerCapitaLog  PopulationLog  LengthOfRoadsLog Davanagere Jalandhar Belagavi indore Gwalior Jhansi Namchi raipur Muzaffarpur Dahod of in 2 1278.700 4.50 284.155556 8.150537 0.653213 3.106769 4 2360.000 8.62 273.781903 8.096883 0.935507 3.372912 0 979.670 4.88 200.752049 7.649271 0.688420 2.991080 8 3477.577 20.00 173.878850 7.441939 1.301030 3.541277 3 1732.000 10.70 161.869159 7.338684 1.029384 3.238548 5 731.575 5.07 144.294872 7.172876 0.705008 2.864259 7 41.410 0.40 103.525000 6.693835 -0.397940 1.617105 9 1254.920 16.42 76.426309 6.255997 1.215373 3.098616 6 550.360 48.00 11.465833 3.519269 1.681241 2.740647 1 112.145 21.30 5.265023 2.396440 1.328380 2.049780 The data for street lights is almost the same. For electricity, it is a little different. When log of population to the base 10 (x axis) is plotted against log of length of roads to the base 10, using the following code We get the following graph: Let us try to make a comparison. Let's take the cities and . Jhansi Indore Jhansi has a population of 5 * 10^6, and Indore has a population of 20 * 10^6, almost 4 times as big. If we compare log of length of roads, we see that the difference is more than 4 times. Log of length of roads for Indore is 10^3.5, and log of length of roads for Jhansi is 10^2.75. 10^3.5 / 10^2.75 is 5.6 . So, Indore is 5.6 times more creative than Jhansi, even though it is 4 times as bigger. Similarly, we can make a comparison based on street light indicator. The data looks like the following: City Name  Number of Poles  Population  PolesPerCapita  PolesPerCapitaLog  PopulationLog  NumberofPolesLog Dharamshala Kohima Thanjavur Atal Nagar Bareilly KOTA Srinagar Raipur Bhopal 3 3548 0.50 0.070960 -3.816850 -0.301030 3.549984 5 837 1.15 0.007278 -7.102191 0.060698 2.922725 8 11124 2.91 0.038227 -4.709272 0.463893 4.046261 0 7904 5.60 0.014114 -6.146700 0.748188 3.897847 1 31280 9.04 0.034602 -4.853010 0.956168 4.495267 4 12909 10.00 0.012909 -6.275479 1.000000 4.110893 7 63750 11.80 0.054025 -4.210218 1.071882 4.804480 6 54068 16.42 0.032928 -4.924535 1.215373 4.732940 2 20000 18.00 0.011111 -6.491853 1.255273 4.301030 The plot of log of population (x axis) vs log of number of street lights is as follows: If we take and for the purpose of comparison, Kohima has a population of 1.15 * 10⁶, and Raipur has a population of 16.42 * 10⁶, which means Raipur is 14 times as big as Kohima, population wise. Log of number of street lights to the base 10 for Kohima is 10³, and for Raipur is 10^(4.75). That means Raipur is 10^(4.75) / 10³ , or 56 times more creative than Kohima. Kohima Raipur If we continue to Electricity, log of electricity consumption to the base 10 does not yield a straight line that follows Kleiber’s law. Instead, if we plot log of electricity consumption per capita to the base 2 (x axis) vs log of population to the base 2, we get a line that follows kleiber’s law. The code to aggregate data is: The aggregated data looks like: City Name   Electricity  Population  ElectricityPerCapita  ElectricityLog  ElectricityPerCapitaLog  PopulationLog Tiruchirappalli Belagavi Agartala Jaipur Bhopal Thiruvananthapuram Davanagere Amritsar Indore Bengaluru NAGPUR 10 23.37000 9.17 2.548528 1.368659 1.349664 3.196922 2 40.30000 4.88 8.258197 1.605305 3.045827 2.286881 0 319.10000 4.38 72.853881 2.503927 6.186934 2.130931 7 2873.50100 30.70 93.599381 3.458411 6.548427 4.940167 4 7339.37013 18.00 407.742785 3.865659 8.671516 4.169925 9 4040.40000 9.58 421.753653 3.606424 8.720257 3.260026 5 2627.36000 5.21 504.291747 3.419520 8.978115 2.381283 1 8059.09000 11.30 713.193805 3.906286 9.478150 3.498251 6 17576.37000 19.90 883.234673 4.244929 9.786653 4.314697 3 131675.76680 84.30 1561.990116 5.119506 10.609170 6.397461 8 90644.85000 24.10 3761.197095 4.957343 11.876976 4.590961 The code to plot the data is: The plot looks like: Let’s take and for comparison. Population of Agartala is 4.38 * 10⁶, and of Indore is 20 * 10⁶. Which means Indore is almost 5 times as big as Agartala, population wise. Log of electricity per capita for Agartala is 2⁶, and for Indore is 2¹⁰. 2¹⁰ / 2⁶ is 16. Which means Indore is almost 16 times more creative than Agartala, despite being 5 times as big. Agartala Indore Conclusion In this post we saw that if a city is n times as bigger that the other, it is > n times more creative. It means a bigger city churns out more patents than it’s counterpart. The levels of innovation required for reducing crime, installing new electric poles, constructing new roads and bike lanes, waste disposal system, etc. is higher in a bigger city. The law that governs metabolism of energy in bacteria to plants also governs how a city expands. And I am sure it is plausible that if more research is conducted, this law would also be visible in how galaxies and supernovas expand.

How to Determine the Relative Smartness Level of a City

What is BERT? How does one use BERT to solve problems? Google Colab, Tensorflow, Kubernetes on Google Cloud Overview This for people who want to create a REST service using a model built with , the best NLP base model available. I spent a lot of time figuring out how to put a solution together so I figured I would write up how to deploy a solution and share! BERT Why should you read this? Today we have machine learning engineers, software engineers, and data scientists. The trend in deep learning is that models are getting so powerful that there is little need to know about the details of the specific algorithm and can be immediately applied to custom use cases. This trend will turn the job of machine learning engineers into a skill that software engineers have. There will still be data scientists because there will be the need to apply traditional machine learning methods and incorporate domain knowledge on “small” data to solve problems and get the desired performance. What is ? BERT BERT, or Bidirectional Encoder Representations from Transformers, is a new method of pre-training language representations which obtains state-of-the-art results on a wide array of Natural Language Processing (NLP) tasks. — Github Project Page Why is important? BERT #1 When performed state-of-the-art on 11 NLP tasks. released On , BERT achieves 93.2% F1 score (a measure of accuracy), surpassing the previous state-of-the-art score of 91.6% and human-level score of 91.2% SQuAD v1.1 BERT also improves the state-of-the-art by 7.6% absolute on the very challenging , a set of 9 diverse Natural Language Understanding (NLU) tasks. GLUE benchmark #2 The main driver for performance Visual Common Sense Deep text-only models perform much better: most notably, BERT [17] obtains 35.0% accuracy. […] Our model, R2C obtains an additional boost over BERT by 9% accuracy, reaching a final performance of 44%. […] The model suffers most when using GloVe representations instead of BERT: a loss of 24%. The empirical evidence demonstrated by using BERT on different tasks across different domains gives good reason to try BERT. Let’s use BERT to solve a classification problem! Find a problem to solve started . Sebastian Ruder a project to track the progress in Natural Language Processing tasks This walkthrough is going to tackle a text classification problem. For simplicity, we are going to choose the since the dataset is a reasonable size and closely aligns with a traditional classification problem. AG News Dataset Refer to this article to get the AG News Dataset ready for training. Part 1: Fine-tune BERT using AG News Dataset The result of following this entire tutorial is . The Google Colab notebook to process the dataset, train, and export the model can be found . The following will go over recreating needed resources for the REST service at a high level. here here , clone the so that we extend the project to meet our needs. First BERT repo , is the file to extend for sentence classification. To add a new data source add a class that extends DataProcessor. The DataProcessor class specifies the methods to implement and you can refer to the other classes that extend DataProcessor to figure out reasonable implementations. Second run_classifier.py class AgnewsProcessor(DataProcessor):"""Processor for the MultiNLI data set (GLUE version).""" def get_train_examples(self, data_dir):"""See base class."""return self._create_examples(self._read_tsv(os.path.join(data_dir, "train.tsv")), "train") def get_dev_examples(self, data_dir):"""See base class."""return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev.tsv")),"dev_matched") def get_test_examples(self, data_dir):"""See base class."""return self._create_examples(self._read_tsv(os.path.join(data_dir, "test.tsv")), "test") def get_labels(self):"""See base class."""return ["World","Entertainment","Sports","Business",] def _create_examples(self, lines, set_type):"""Creates examples for the training and dev sets."""examples = []for (i, line) in enumerate(lines):if i == 0:  # for headercontinuesingle_example = self._create_example(line, set_type)examples.append(single_example)return examples def _create_example(self, line, set_type):guid = "%s-%s" % (set_type, tokenization.convert_to_unicode(line[0]))text_a = tokenization.convert_to_unicode(line[1])if set_type == "test":label = "World"else:label = tokenization.convert_to_unicode(line[-1])single_example = InputExample(guid=guid, text_a=text_a, label=label)return single_example , copy this to train, evaluate and export the model. For training, this tutorial will use Google Colab notebooks. To use the notebook as designed, create a Google Storage Bucket to store the base model and trained model. Third notebook Why use Google Colab notebooks? They are a free compute resource and easy to share. The initial evaluation results are not very promising. For a deeper analysis look here . ***** Eval results *****eval_accuracy = 0.25eval_loss = 7.6698585global_step = 12000loss = 7.3534603 At the end of Part 1, you should have exported a fine-tuned BERT model trained on data of your choosing. This tutorial saves the model into Google Cloud Storage. Part 2: Write Tenorflow Serving Client Now that we have a saved model. We need to create some code to call the Tensorflow model and get predictions. The code needs to run on a server and the easiest lightweight code to use is . Flask , parse and process the input with FullTokenizer using a call to which outputs an which the BERT model expects. First _create_example InputExample inputExample = processor._create_example([request_id, content['description']], 'test') transform to using Second, InputExample tf.train.Example from_record_to_tf_example tf_example = classifiers.from_record_to_tf_example(3, inputExample, label_list, 64, tokenizer) now send the formatted input to the Tensorflow Serving container. Third, ### create predict request objectmodel_request = predict_pb2.PredictRequest() ### specify name of modelmodel_request.model_spec.name = 'bert' ### specify name of function to callmodel_request.model_spec.signature_name = 'serving_default' ### specify dimetion of requestdims = [tensor_shape_pb2.TensorShapeProto.Dim(size=1)]tensor_shape_proto = tensor_shape_pb2.TensorShapeProto(dim=dims) ### create model input objecttensor_proto = tensor_pb2.TensorProto(dtype=types_pb2.DT_STRING,tensor_shape=tensor_shape_proto,string_val=[model_input]) ### actually send request to model and wait for outputmodel_request.inputs['examples'].CopyFrom(tensor_proto)result = stub.Predict(model_request, 10.0)  # 10 secs timeoutresult = tf.make_ndarray(result.outputs["probabilities"])pretty_result = "Predicted Label: " + label_list[result[0].argmax(axis=0)]app.logger.info("Predicted Label: %s", label_list[result[0].argmax(axis=0)]) Part 3: Build Docker Containers for Tensorflow-Serving and Tenorflow-Serving Client Using the flask app and Tensorflow model, we can create docker containers and deploy them using Kubernetes. (k8s) is an open-source system for automating deployment, scaling, and management of containerized applications. — Project Page Kubernetes If you don’t know anything about Kubernetes I suggest you go over this set of . tutorials One of the real benefits of using Kubernetes is the abstraction of networking. You can have different pieces of an application deployed in different pods on different nodes and talk to each other internally seamlessly while exposing a single point of entry called a ‘service’. Image Credit: https://kubernetes.io/docs/tutorials/kubernetes-basics/expose/expose-intro/ Build Docker Containers and Push Containers to Dockerhub Prerequisite: Create a Dockerhub account # Create Tensorflow Serving Container and host on DockerhubIMAGE_NAME=tf_serving_bert_agnewsVER=1547919083_v2MODEL_NAME=bertDOCKER_USER=lapolonio ~ run -d --name $IMAGE_NAME tensorflow/serving ~/models cp -r  gs://bert-finetuning-ag-news/bert/export/AGNE/1547919083 ~/models cp ~/models/1547919083/ $IMAGE_NAME:/models/$MODEL_NAME cd docker mkdir gsutil docker MODEL_NAME is an internal convention in Tensorflow Serving used to refer to the model and create/name the corresponding model REST endpoint_docker_ commit --change "ENV MODEL_NAME $MODEL_NAME" $IMAGE_NAME $USER/$IMAGE_NAME ### the tag $USER/$IMAGE_NAME $DOCKER_USER/$IMAGE_NAME:$VER push $DOCKER_USER/$IMAGE_NAME:$VER docker docker # Create client to call Bert Model clone https://github.com/lapolonio/bert.git ~/bert git cd CLIENT_IMAGE_NAME=bert_agnews_clientCLIENT_VER=v3DOCKER_USER=lapolonio asset cp gs://cloud-tpu-checkpoints/bert/uncased_L-12_H-768_A-12/vocab.txt asset/ build -t $USER/$CLIENT_IMAGE_NAME . tag $USER/$CLIENT_IMAGE_NAME $DOCKER_USER/$CLIENT_IMAGE_NAME:$CLIENT_VER push $DOCKER_USER/$CLIENT_IMAGE_NAME:$CLIENT_VER mkdir gsutil docker docker docker Create a Kubernetes cluster, deploy containers to Kubernetes in Google Cloud container clusters create bert-cluster ### create k8s clustergcloud config set container/cluster bert-cluster ### set the target cluster to the created clutergcloud container clusters get-credentials bert-cluster --zone us-east1-b --project bert-227121 ### get credentials from google cloud to allow deploymentgcloud convert --stdout | apply -f - ### deploy containerskompose kubectl In Google Cloud Console there is an option to connect to Google Cloud when you select that option the following prompt opens: Image Credit: Google Cloud Console Interface Conclusion: Enterprise-Ready vs Production-Ready This tutorial goes over deploying a Tensorflow model built using BERT in Kubernetes on Google Cloud. We used BERT as the base model to solve an NLP classification task. This solution pattern creates a proof of concept ecosystem to test if Kubernetes is a viable solution to provide Tensorflow models in production in the cloud. There could be barriers to using Kubernetes / Tensorflow Serving such as networking or performance constraints. If Kubernetes is viable there is still a number of steps to get this solution Production-Ready: AutoScaling (to handle different workloads) Logging CNAMES (unless you are ok with referring to possibly changing IPs) Alerts (to notify if service goes down) Automatic builds and deployments (CI/CD) Different Environments for testing Kubernetes is an excellent solution for serving stateless applications at scale. Tensorflow Serving is a robust platform for serving Tensorflow models. Tensorflow Serving provides model versioning functionality and model status endpoints that fit nicely with heartbeats and ready to be used for alerts. __________ Additional Resources: _The year 2018 has been an inflection point for machine learning models handling text (or more accurately, Natural…_jalammar.github.io The Illustrated BERT, ELMo, and co. (How NLP Cracked Transfer Learning) https://github.com/google-research/bert#sentence-and-sentence-pair-classification-task _TensorFlow Variables are the best way to represent shared, persistent state manipulated by your program…_www.tensorflow.org Save and Restore | TensorFlow _The RESTful APIs support a canonical encoding in JSON, making it easier to share data between systems. For supported…_www.tensorflow.org RESTful API | TensorFlow Serving | TensorFlow _This will run the docker container and launch the TensorFlow Serving Model Server, bind the REST API port 8501, and map…_www.tensorflow.org Using TensorFlow Serving with Docker | TensorFlow Serving | TensorFlow _To interact with the Terminal, please use the desktop/tablet version Continue to Module 3›_kubernetes.io Interactive Tutorial — Deploying an App _Google Cloud delivers secure, open, intelligent, and transformative tools to help enterprises modernize for today’s…_cloud.google.com Deploying a containerized web application | Kubernetes Engine Tutorials | Google Cloud _Google Cloud Platform (GCP) is a cloud computing service by Google that offers a set of enterprise cloud services that…_scotch.io Google Cloud Platform I: Deploy a Docker App To Google Container Engine with Kubernetes _Google Cloud delivers secure, open, intelligent, and transformative tools to help enterprises modernize for today’s…_cloud.google.com Exposing applications using services | Kubernetes Engine | Google Cloud

Enterprise™ AF Solution for Text Classification (using BERT)

Our new approach to making and launching The importance of user feedback and community support before launching a new product is huge. Users are full of surprises, it’s long been established that developers make many assumptions about how a user will approach, use, or interpret their application. Many of which will be wrong! What follows is the story of our first app built completely in the open from start to finish — an attempt on our part to help with this problem. Let us begin by setting the scene… Chapter 1 — Idea ‘Twas the month before Christmas, and all through the house, not a creature was is stirring, not even a… Okay, so not quite, but what was actually happening was Black Friday. Yes, Black Friday, that slightly repulsive holiday focussed on sucking as much money out of consumers as possible by offering countless deals and pretties. One can’t browse the web at all without being inundated with last chances. No I don’t want 40% off everything at Cacti-R-Us, free shipping on a couch, or a half-price jalapeño burger THANK YOU! Actually the last one I could go for. But not because of your dumb deals! maybe But this isn’t just another Black Friday bashing article (even though some places went to “Cyber Tuesday” this year! Come on, that’s pushing it). No, what caught our attention on this most illustrious of days was the emails! So many marketing emails! Most of which I have no memory of signing up for, and certainly no intention of buying from again. I understand that this is a great medium to capture consumers attention, but after 24 hours I felt like my attention had been blasted out of my ears. Even opting out or unsubscribing from these emails is a drain on my energy. It’s annoying to hunt for the opt out link, especially because that usually means I have to read your stupid marketing garbage. And if you were naïve enough to simply move them to the trash, then you’ll quickly realise that this doesn’t solve anything, the following week your inbox is swamped again. As it turns out there is a solution to this problem. However, in return for using services that unsubscribe to these things for you, you have to agree to let them sell all your data for marketing purposes. Call me suspicious, but aren’t I trying to get away from marketing purposes? So what can we do about this? Chapter 2 — Validation Well we’ve discovered a problem, let’s see if it’s something that bothers other people. So we picked a name and threw together a 10 minute landing page and started sharing it around on social media. The first draft of the landing page The response was overwhelmingly positive! Leave Me Alone was to become an Within a few hours we had 50 potential beta users, and a load of ideas and feature requests. All this before we’d written a single line of code. ethical and privacy first email unsubscription service. We decided that going forward we would get community feedback on This included features, design, colours, domain names, and even bits of copy. every decision we made. So I guess the next step was to actually build something? Chapter 3 — Coding itself is the piece of the product puzzle that we are most familiar with, and as we’re beginning to understand, it’s also arguably the least important part. Coding Nonetheless it had to be done. We whipped together our ideas and in a few days had a working prototype. scanned a users inbox for marketing emails, and allowed them to unsubscribe from them with a single click. Leave Me Alone Gmail We’re pleased to say that we managed this without exposing a single piece of email content on our servers. Thus achieving the privacy aspect that was essential to us. We still store a little meta data from any emails that are unsubscribed (so that we know they have already been actioned if we see them again), but this information is encrypted and so we think this is a small concession. (Though if anyone can think of a way for us to have the same functionality without storing even this little bit of data then let us know!) Anyway, with something now working it was time to let our beta testers run free! Chapter 4 — Closed beta Beta testing is a scary prospect, letting users into your app before it’s quite ready? What if things break? Well it turns out that things do break, and sometimes they break hard. But beta users aren’t expecting a finished product, and they are surprisingly forgiving! In our case we swapped free use of our beta product in return to listening intently to absolutely everything they had to say about the app. As a result we found and fixed a LOT of bugs, tweaked the UI, and came up with some new features that we hadn’t thought of that are now . essential to the app We’re of the opinion now that without letting users loose as soon as possible, wouldn’t be half as effective as it is now. Leave Me Alone Chapter 5— Open Beta Since the beta was so successful, we are now opening up to anyone. A soft launch if you like. This will help us to spread the word, as happy users make for the best salespeople! Leave Me Alone Our wall of love ❤️ Epilogue We approached making a lot differently from our previous apps. We wanted to build in the open and share our progress with other makers and potential customers. We shared our progress on , logged our tasks on , and asked for feedback in Slack and Telegram communities that we’re active in. Leave Me Alone Twitter Makerlog This turned out to be an excellent decision for two reasons; We were hostage to accountability. We were openly responsible for building and delivering an idea that we had committed to make. Immediately, enquires started about promised features, progress updates, and the launch date. This helped by validating that the product was needed, and gave us motivation to keep developing. Having a community of users that want to see our product succeed was invaluable. We discovered isolated bugs that would have been insanely difficult to track down without being able to work one-on-one with dedicated users. For instance one user has a single malformed email that caused the entire app to crash if it was seen. One other had the biggest inbox of spam I have ever seen, which quickly smashed our email streamer 😂. Overall, we had a busy 72 hours handling bug reports, pouring through logs, and pushing fixes. Without this process we would never have known about any of these problems and we would have launched a very broken product. Final thoughts We want to say a tremendously huge 🎉 THANK YOU 🎉 to all of our beta testers for their help. is now ready to be released into the wild. We’ll continue to develop and improve the service. You can see our public roadmap — let us know which features you’d like us to work on next. Leave Me Alone here If you’ve made it this far and you’d like to try you can head on over to and use the coupon WE_HATE_SPAM for 10% off any scans! Leave Me Alone https://leavemealone.xyz We hope you find our email service useful and that it can help you. If you have any questions or feedback don’t hesitate to contact us on or via email at . Twitter leavemealone@squarecat.io Enjoy your clean inbox!

Building Leave Me Alone in the Open

One of the key events of the year took place last week in Zurich´s “Crypto Valley”. The was — for me — both engaging and stimulating because the broad focus and main topic of it was the tokenization of securities, which is where — with — I am more involved. Cryptosummit Untitled-INC Among the high level speakers were in video streaming, Charles Hoskinson, , , and many other top businesspeople. ConsenSys Joseph Lubin CoinDesk´s Michael Casey VC investor Jalak Jobanputra Outlier Venture´s Jamie Burke Of course, with such a packed agenda and thought-provoking panels running simultaneously on 3 stages, I had to make a choice and follow only those which were — for me at least — the most compelling. Most certainly I have missed interesting stuff, but the following were for me the key “take-aways” from the event. , formerly Ethereum CEO and currently at , gave me comfort that the global adoption path of crypto is nowadays irreversible. He captivated the audience mentioning his experiences while travelling for crypto projects in lands such as Mongolia where — perhaps not surprisingly — cryptocurrency adoption is progressing at a faster rate than in many western countries. He emphasized that will be the key instrument for driving the growth and capital allocation to developing countries. This is the first time in history that developing countries are not constrained in accessing capital for development. And of course this will be a radical shift for those countries which will have the unique opportunity to free themselves from the chokehold of western powers and financial institutions such as the . For the first time in history those countries can now sell tokenized bonds backed up by their commodities and rare earth resources, without the need to sell them out to multinationals in exchange of “peanuts”. The first time in history that they can access a global, decentralized market of investors to fuel their growth. I find this is pretty exciting. Charles Hoskinson Input Output HK Securities Token Offerings (STOs) IMF Juwan Lee, , presented interesting datasets to show where crypto investments stand today when compared with the hedge funds in the 90s. Crypto investments stand today where the hedge funds sector stood back in 1997, at the very beginning of its growth phase. Since then the hedge fund sector has grown in volume more than 15 times, going from the early phases, through institutionalization, consolidation and finally to the current maturity phase. founder and CEO of Nexchange Courtesy of Juwan Lee´s Presentation — Nexchange Crypto funds are also still quite minuscule in terms of managed funds. The large majority (approx. 208) have less than US$10m under management. Only the largest funds — approx. 28 — manage over US$200m. This is laughable compared to traditional hedge funds. The main point here to be made is that there is still tremendous growth opportunity for crypto assets and for penetration of crypto funds, considering that global capital markets and other assets classes are worth approx US$ 513trillion and the crypto sector only US$ 0,2trillion. Of course some of this data must be taken with caution — specially in such a young and fast evolving sector — but you surely get the point, even when a few billions can be added or deducted to those numbers the prospects are still extremely exciting. Finally, because many funds are deep in the red at this juncture, one can expect that they will be compelled to liquidate their assets by year end. This could possibly cause the crypto market to drop lower before it can start trading higher. Definitely something worth keeping in mind. Throughout the event you could feel a justified excitement for the perspectives of the sector but also some unrealistic expectations and — at times — superficiality and a lack of in-depth analysis of what could be really achieved today with a security token. This was obviously from companies touting their products and services. But the “we tokenize the world” mantra is — for now at least — still unrealistic. , chief scientist and Managing Director at , brilliantly cut through the hype to make clear what is still needed before security tokenization can fulfil the expectations. To summarize his excellent presentation and his many slides in a few words is not easy, but that is in essence what is needed: liquidity and market makers, decentralised exchanges, on-chain programmability, trustless disclosure mechanisms about the underlying and . As you can see we are not quite there yet. Jesus Rodriguez Invector Labs possibly even ad hoc blockchains Courtesy of Jesus Rodriguez presentation — The case for a Security Token Blockchain — Invector Labs Courtesy of Jesus Rodriguez presentation — The case against a Security Token Blockchain — Invector Labs Let´s draw then some conclusions on the complex topic of security tokenization. The going live of the first for trading security tokens is to be expected sometime in the next 6 to 18 months, together with some traditional centralized exchanges — such as the , the , the of the — and some specialized crypto exchanges such as the or , just to name a few, who are also hard at work to go live as soon as possible. The STO-go-live of innovative (decentralized) exchanges together with large traditional exchanges — which can bring vital liquidity pools to the market — will inevitably shake the sector and will likely cause a concentration among the numerous tokenization platforms which sprung up recently and which offer little value proposal for tokenizing securities; the majority are basically no more than a window for token sales, with little or no liquidity and do not differ substantially from a crowd-funding platform. Many of them will likely disappear, the best few maybe will be consolidated. Therefore some level of disruption in the sector is to be expected. decentralized exchanges Swiss SIX LSE Bakkt platform ICE GBX SCX Despite the current hype on tokenized equity issuances, I would rather expect tokenized debt securities or other types of non-equity profit participation to become the broadest use-case for tokenized securities. The reason is — as I — that debt or other forms of non-equity profit participation can be easily tokenized, made freely transferable on the blockchain and efficiently programmed to automatically execute “smart contract” functions such as paying dividend-yields. All this without the need for third party off-chain trusted entities — such as Companies´ Registries, Notaries, Land Registries or complex settlement and custody mechanisms — which instead come into play when transferring company shares or real assets. wrote here Yet, in this fast evolving sector there are few certainties, both on the technical and regulatory side. This is indeed the main concern when advising an STO project. We must always walk a fine line to balance out all the pros and cons of every technical and regulatory choice and try to maintain as much flexibility as possible, just in case we need to rapidly change the project’s course of action. Easier said than done.

The latest on security tokens from Zurich´s Cryptosummit

Quarter four is fast upon us and it’s packed with unmissable blockchain and cryptocurrency events. This time of year, Europe takes the lead in innovation, speaker line-up, dazzling venues, and original tracks. So if you thought you’d seen this year’s best already, think again. Check out the top five crypto conferences in Europe this fall: 1. Blockchain Live | London September 26 Source: Newsbtc If you’re looking for a lineup of the industry’s heaviest hitters, won’t disappoint. With speakers including ConsenSys CEO Joseph Lubin, Digital Asset’s Blythe Masters, and smart contract creator Nick Szabo, the only problem with this one-day event is finding time to fit everything in. Blockchain Live Spread out over nine stages and with over 100 speakers, there’s something for everyone, from the blockchain novice to the earliest adopter. Watch insightful speeches, see practical case studies, and hear from the industry pioneers making blockchain work in their business. Just don’t expect a party afterward. This conference promises to deliver on content, but networking is entirely up to you. 2. Crypto Summit | Zurich October 28–29 Source: Twitter The third edition of takes place in Zurich this October, promising to deliver even more powerful keynotes, panel discussions, roundtables, and practical workshops spread out over nine blocks. Confirmed speakers so far include Pantera Capital’s Joey Krug, Outlier Ventures’ Jamie Burke, and FuturePerfect Ventures’ Jalak Jobanputra. Crypto Summit Past speakers at Crypto Summit have included Ledger’s Eric Larchevêque, and recognized thought-leader, Alex Tapscott. With more than 2,000 delegates and over 120 speakers in this two-day event, you’ll get exclusive access to leading minds in the industry. Some of the discussion topics include the tokenization of assets, bonds on the blockchain, and the emergence of new technologies, including IoT and AI. 3. Malta Blockchain Summit | Malta November 1–2 Source: CoinRevolution Malta made headlines this year becoming the first country to officially pass new legislation regarding cryptocurrencies, ICOs, and DLT. With some of the most forward-thinking minds in regulation and innovation on this tiny island, the looks set to be an insightful event. Malta Blockchain Summit Hear from Maltese Prime Minister Hon. Joseph Muscat and other key members of the Malta Blockchain Taskforce. Watch several ICO pitches and a blockchain hackathon, and find out what the Blockchain Island has in store for the future of regulation and other emerging technologies. 4. Web Summit | Lisbon November 5–8 Source: Mindus If you want glitz and glamour, is the event for you, taking place over four insanely intense days. With more than 70,000 attendees and over 1,200 speakers, Web Summit has rightly earned the title of the biggest and best tech conference on the planet by both Forbes and Inc publications. Web Summit Lisbon There’s a lot more than blockchain and crypto to be found at Web Summit — from the Queen of Jordan to the Founder of Pinterest. Ex-Barcelona player Ronaldinho will even be giving a speech, as will Tim Draper, Imogen Heap, and André Villas-Boas. Think tech, entertainment, sports, networking, and parties — just don’t forget about the conference. 5. European Blockchain Convention | Barcelona November 29 Source: Coinmarketcap If you’re still getting over the crowds of Web Summit, promises to be a much smaller, more intimate event. Confirmed speakers so far include Hyperledger’s Marta Piekarska, Smart Valor’s Olga Feldmeier, and ConsenSys MD Lory Kehoe, as well as prominent members of the EU parliament. European Blockchain Convention This one-day event in the heart of Barcelona provides five full hours of networking opportunities, 50 accomplished speakers, and a convergence of industry leaders, politicians, developers, regulators, entrepreneurs, and investors. Wrapping It Up If you needed an excuse to head to the old continent, now you’ve got five! Informative and action-packed events in some of the world’s most stunning settings. Discuss the future of crypto investing against the backdrop of snow-capped mountains. Mingle with celebrities and tech entrepreneurs in the colorful streets of Lisbon. Or soak up the last of the autumn sunshine in Barcelona while making invaluable contacts to help you drive the industry forward.

Top 5 Crypto Conferences in Europe This Fall

If you work with technology, then you’re probably asked this question all the time: “How do you stay up-to-date on industry news?” blockchain And to be honest, it’s difficult to give a consistent answer. The blockchain industry is changing constantly. It’s not a space with a lot of definitive long-form print media available. Even one of the first was out of date by the time it made it to print. Blockchain for Dummies books Information is a challenge in this space. Everyone wants to know what’s new and where the breakthroughs are coming from. But getting sucked into the constant churn of news can become time-consuming, draining and counterproductive. That’s why it’s best to get your information from the initial source — a blog, thought leader, a founder, press releases, and other news direct from a company or government agency. Double-checking stories and thinking twice about unfamiliar sources can also help ensure what you read, hear and share is the real deal. Here’s how you can help ensure the news you consume is both timely and accurate: 1. Look to thought leaders in the space. Even people within the blockchain industry can have a tough time telling whether news — or even a company — is legitimate or not. To avoid as much falsity as possible, get your information from people who have been in the industry since the beginning. They should have a track record to back their assertions. An easy way to start cultivating a dependable news ecosystem is to follow the companies and founders that interest you on various channels. Medium and Twitter are useful and productive channels for getting your news and insights right from the source. This is where many of the thought leaders you may want to follow post both news and musings on the blockchain industry. To start, look to Michael Casey and Paul Vigna, the authors of and . Then, consider Laura Shin of Unchained, Kelly Weaver of Melrose PR, Nick Szabo, Caitlin Long — all reliable sources for what’s happening in various areas of the blockchain space. The Age of Cryptocurrency The Truth Machine: The Blockchain and the Future of Everything Remember, you still have to stay alert for fake accounts that offer misleading or incorrect information and news. There have been fake accounts created for thought leaders in the past, so exercise caution when you first begin following someone. 2. Use social media for fast-paced news, but beware of misinformation and hacks. Social media is an excellent way to stay on top of news “as it’s happening.” But the danger inherent in these platforms is evident — not all the news you see on social media is actually true. In some cases, small errors and inconsistencies can add up to a misleading account of what’s happening. But there are also plenty of trolls and fake accounts out there that deliberately attempt to manipulate what people see online. Twitter understands this and is . They’ve suspended 70 million accounts in May and June alone, with more scheduled in the coming months. And Facebook is being pressured to change policies and for sharing misleading content. cracking down pay hefty fines Unfortunately, online communities and social media sites present more risks than just “fake news.” You have to be careful of account hacks, malicious links, and even coordinated market manipulations. It’s not unusual for people online to sensationalize a company, or attempt to for their own personal gain. manipulate the price of a cryptocurrency The best practice is to take breaking news with a grain of salt. And if you’re unsure about any of the information you see on your feed, wait to double-check its veracity. 3. Reference multiple publications to cross-check news and company information. led by MIT researcher Soroush Vosoughi came to a somewhat depressing, if not all that surprising, conclusion: false information travels faster than the truth on social media. A new study While there are plenty of bad actors working to spread misinformation, there are ways to help counteract the problem. Verifying information before sharing has to become a standard, and the best way to do that is by getting a second — or even third and fourth — opinion. Cross-referencing different publications and sources is the easiest and most effective way to find inconsistencies in news reports. You can get some news from Twitter or Reddit, but it’s a good idea to then look elsewhere for confirmation. Try checking Medium for posts related to what’s happening but also look to major publications who build fact-checking into their process. A fact-checked article is much more reliable, and it will absolutely have source links or explanations for where the information was originally posted. Truthfully, it’s not easy to stay up-to-date in an industry that evolves as quickly as blockchain — especially as its rise has coincided with that of fake news and propaganda online. Still, a strong dose of skepticism and a habit of checking multiple sources are the best ways to combat falsehoods while staying on the cutting edge of the space. If you’re looking to begin following thought leaders on Twitter, here are a few handles to get started: @srolondon, @andreatinianow, @mvparadigm, @ariannasimpson, @cryptokelley, @e_rossiello, @jutta_steiner, @daltonan, @josh_blockchain, @ambresoub, @leanne_kemp, @iam_preethi, @satisgroup, @jalak, and @carol_vancleef. (Full disclosure, you can also follow my updates at @iamsamsterdam). This article originally appeared on Forbes.com. Thanks for reading! Follow me on Twitter and Quora for more insights on blockchain technology. Or get in touch with the Chronicled team here .

3 Tips For Staying On Top Of The Rapidly-Changing Blockchain Industry

How technology made me less of a man and more of a machine 9 lbs, 4 oz Firmly into the 95th percentile, I was the heaviest newborn in the hospital by a comfortable margin. In fact, that trend continued until quite recently. With the help of an assortment of new food brands and technologies though, I lost 45 pounds this year, prompting countless questions about about how someone so clearly to food suddenly turned the tables. addicted On the surface, I followed a tried and true method: eat healthy, eat less, and workout. Behind the scenes, I waged an all-out war to lose weight in the digital age. It was expensive, exhausting, and controversial. I can’t say if the approach I took is for everyone, but perhaps it’s helpful to someone. Welcome to the battlefield. Motivation Every meaningful journey – adversity yields heroics. That’s true for dieting, but not in an obvious way. Being overweight in and of itself isn’t an adequate catalyst since it’s difficult to perceive yourself as the enemy to overcome. All the new health and fitness technologies made possible by the digital age are frustratingly inaccessible without an initial spark of motivation. needs a villain Motivation for me unexpectedly struck as I sat through what I thought would be the least interesting of the seminars at an economics forum last year. The speaker, a researcher named Gary Taubes, had just published his latest work, . Instead of lecturing about health or biology, he zeroed in on the industrial complex that perpetuates misinformation about the supposed benefits of carbohydrates and the harms of fats. The Case Against Sugar …if you create a new market with a brand-new manufactured food, give it a brand-new fancy name, put a big advertising budget behind it, you can have a market all to yourself and force your competitors to catch up. You can’t do that with fruits and vegetables. It’s harder to differentiate an apple from an apple. Taubes explored a between private and public sectors that anyone who dares question the food pyramid orthodoxy. Despite repeatedly gaining favorable placement in government-produced nutrition guides, processed carbohydrates are likely the primary cause of obesity. The ramifications have infiltrated every aspect of American culture, inside and outside supermarkets. It turns out that while no one else can lose weight for you, . Knowing that was just the impetus I needed. I had found my villain. decades-old collusion marginalizes almost everyone is trying to make you fat Mindset Zoomed out, my weight loss chart today looks like a steep slope downward, perhaps the result of some sudden shift in lifestyle. But zoom in and you’ll observe that weight loss is a series of ups and downs — the latter consisting of battles that I lost for any number of reasons. The very notion that weight loss can be summarized with a ‘before-and-after’ picture is just one of many misconceptions. In fact, I discovered that much of the ‘expert advice’ and ‘conventional wisdom’ about dieting was unhelpful at best or counterproductive at worst. For example, deliberately planned ‘cheat days’ tend to be unnecessary within an urban lifestyle. Vacations, social events, and an assortment of other activities essentially guarantee that they happen with or without planning. My road to svelte frequently involved losing the same pounds over and over. Each plateau left me legitimately wondering if that was the end of the road, the most I could lose within reason. The ‘body positivity’ movement, frequently present in conversations along the journey, repeatedly proclaimed ‘you did it — great job!’ But I wasn’t happy when I looked in the mirror, so I kept pushing. The key for me at first was to continually refocus on the Goliathan nemesis – that nameless, complex system of harmful incentives designed to make us eat unhealthfully. But as anyone who has failed at dieting before knows, motivation alone only gets you so far. Without permanently changing my underlying lifestyle and perspective, I was betting on no more than fleeting willpower and a string of lucky days to continue. At any given moment, I was a misstep away from going back to my old ways. I needed air support. By ruthlessly experimenting with different technologies and foods, I assembled a personalized counter-system of routines and habits which helped me gain a sustainable advantage. Eating Dieting for me was like building a sand castle, with processed carbohydrates being the nearby rising tide. Before I could achieve any meaningful results, I was forced to internalize how mind-numbingly impossible it was to lose weight whilst mentally and physically . dependent on sugar ‘Everything in moderation’ proved to be yet another dieting fallacy. Any amount of processed carbs threatened to disrupt weeks of progress. Sugar has a special place in dieting Hell: the slightest taste of it of eating, craving, and obesity. It seems obvious to say now but triggers a flywheel if you aren’t capable of feeling full, you aren’t capable of dieting. I remember feeling angry, especially about all the people who seemed to be able to eat processed carbs without negative ramifications. I imagine that every dieter has at some point wished obesity upon people with whatever genetic predisposition lets them blissfully eat pizza while maintaining their waist size. But I was tired of excuses. Eventually I picked a month and removed all human contact outside of work and home from my calendar. I used to do the Whole30 challenge and eliminated processed carbs and gluten from my diet. It was one of the most difficult aspects of my journey. Kettlebell Kitchen I added a recurring event to my calendar at 3pm that said “I am going to have a migraine and I am going to survive.” Anticipating the pain was cathartic — a piercing reminder that my biology had been exploited. One afternoon, the migraine didn’t come. Food began tasting different and keeping me full longer. I had a lot more physical and mental energy. I realized that ‘normalcy’ had in actuality been a state of constant bloat. Bread suddenly looked and smelled like plastic. I may be the only New Yorker who didn’t have a bagel in 2018. By the time I reached my target weight, my diet looked like this: (~200 calories) Breakfast Super Coffee Two hard-boiled eggs (~160 calories) Late-morning 1 cup mixed berries Almond-milk cappuccino (~400 calories) Lunch salad 2 cups spinach 4 oz of chicken breast 1/3 cup red onion 1/3 mango 1 oz goat cheese 3 egg whites Lemon juice, salt, & pepper (~200 calories) Late-afternoon 1 apple Macadamia-milk matcha latte (~350 calories) Dinner salad 6oz turkey meatballs 2 cups spinach 1/3 cup red onion 1 cup butternut squash (~270 calories) Snack 1oz Turkey Perky Jerky 1 cup mixed berries 1 container Good Culture Cottage Cheese Meticulous tracking and optimization enabled me to rotate in and out alternate foods, testing and learning like a mad scientist while maintaining sanity on a daily budget of 1,600 calories. Optimization Even within the constraints of a Paleo diet, there is a lot of optionality. Eliminating the guesswork of what contributed to weight loss was a critical next step. For $39.99, I bought a premium annual membership to for calorie counting. Why the premium version, when the free option was more than adequate? Because, despite how behavioral economists mischaracterize it, is a strong motivator to continue behaviors you’re mentally committed to before being physically committed to. Tricking myself into utilizing past purchases became one of my favorite dieting techniques and was a recurring theme throughout my journey. LoseIt sunk cost fallacy I then purchased and connected an and . I weighed myself at the same time every morning and evening to pinpoint causes of short-term and long-term fluctuations. I didn’t start out as obsessive regarding weigh-ins, but there were few aspects of dieting more discouraging than feeling like you had a good day but still gained weight the next day. It made me want to throw the scale out the window because the whole process felt like an exercise in futility. The sooner I understood my body’s reactions to foods, the sooner I could avoid those experiences. Apple Watch Withings Body Scale By carefully cross-referencing LoseIt with the data from my scale, I categorized the impact that a comprehensive list of foods and brands had on my weight. Aside from those listed in my average daily diet, some of the best contributors to my weight loss included: , , , kelp noodles, seaweed salad, and coconut aminos. PB2 Trader Joe’s cauliflower gnocchi From the Ground Up cauliflower crackers Then there are the traditionally-considered ‘healthy’ foods that I primarily avoided because they either weren’t filling or routinely led to weight gain: nuts, seeds, avocado, açaí bowls, peanut butter, yogurt, and hummus. I’m still on the fence about popcorn. Without working out, here were roughly the total amount of calories I could eat per day to still lose weight: 1,950 calories at 200 pounds 1,900 calories at 190 pounds 1,825 calories at 180 pounds 1,725 calories at 170 pounds 1,600 calories at 160 pounds As you might expect, losing weight got progressively more difficult. It is far more difficult to construct a flywheel to lose weight than it is to fall back on our biological flywheels fine-tuned to help us gain weight. Attaining aggressively lower calorie ceilings increasingly involved appetite suppressants like caffeine along with a variety of ‘hacks.’ Travel hacks Over time, I discovered that travel and vacation were the biggest obstacles to weight loss. I couldn’t depend on my go-to routine outside of my usual environment. In fact, I gave up on trying altogether and instead went into ‘maintenance mode.’ I relied on a few techniques specifically designed for travel: Energy bars instead of hotel breakfast: and Thunderbird GFB Bites to mitigate jet lag and reset my 💩 schedule Senna Tea Booking early morning coffee meetings to prevents myself from partying and drinking too heavily the night before Insatiable hunger hacks Every now and then, endless bouts of hunger struck. As the late Mitch Hedberg explained about the phenomenon: Rice is great if you’re really hungry and want to eat two thousand of something. Unfortunately, given its calorie count, rice wasn’t an option when I wanted to eat two thousand of something. But three foods were, in a way: Cauliflower rice: so low calorie that it was hardly worth logging in LoseIt. A large head of it, which is a preposterous amount to eat, is just 200 calories. In addition to cauliflower rice, I often roasted it, sautéd it, or dehydrated it and turned it into ‘cauliflour’ for pizza (with more cauliflower on top). I sincerely believe cauliflower is the future as it is the most versatile food in the world. Jalapeños: What was better than eating 2,000 of something? Eating a bunch of sliced hot peppers and then wanting to eat 0 of anything else, because my palette was wrecked. And if that didn’t work… Super salty jerky: With my , I perfected a mango habanero jerky that was so salty and spicy that I got full from one piece because it made me drink two cups of water immediately afterward. Excalibur dehydrator Weekends are for working, working out, and jerky. Psychology hacks As I mentioned earlier, utilizing the sunk cost fallacy was a critical and reusable psychological strategy for weight loss. Beyond that, I found the following two approaches to be thoroughly effective: Aspirations: I woke up every single day and told myself that obesity was a mindset. To turn aspirations into reality, I regularly purchased clothes a size too small. They hung in my closet, continually mocking me until I could fit into them. Versioning: On the flip side, I sustained momentum by eradicating the previously heavier version of myself. The moment I shrunk a size, I got rid of everything I owned that was the size above. There’d be no going back after that. Fitness Once I had a reliable system in place for ruthlessly cutting caloric intake, I began working out. I think doing so earlier would have added too many variables simultaneously, thereby complicating optimization. Not only that, but I have always hated working out, and making it a part of the process too quickly may have created an adverse effect. About 15 pounds into my journey though, I yet again tapped the magical powers of sunk costs. At “noon on Monday,” I joined thousands of people who raced to reserve their bike for the week’s quickly-filling available classes. Class at 8am on Saturday? Sure, I’d be up to to spin then. Going on vacation? Spin classes booked for every morning. So long as I paid ahead of time, I showed up. Soul Cycle Spin or get off the bike. Eventually, I graduated to , a sunk-cost-as-a-service with a superbly defensible breakeven analysis (especially when you reach 100 rides in 120 days as I did). Then came , , , and who knows what’s next. Peloton boxing rowing lifting All I know is that something always has to come next. Because something both fantastic and awful happens now if I miss a day of working out, hunger hacking, or counting calories: I go through signs of withdrawal. Addiction That brings us to today, and perhaps the rest of my life. There’s a prevailing assumption that weight loss is purely about motivation, as though I didn’t really want to be thin until now. Over the years, I broke countless promises to myself about eating right and still can’t be trusted anywhere near a plate of cookies. At any moment, a switch could go off and I’ll again be impulsively eating. Motivation was the ignition, but I have only been able to lose weight thanks to an emerging field of health and fitness technologies that enabled me to from ‘food’ to ‘something other than food.’ Becoming neurotic about a literally insane web of techniques is by no means what I expected to happen or something I advocate for, but it’s the reality. redirect my addiction In one sense, technology made me less of a man and more of a machine. In another, it made me realize just how human I am, albeit 45 lbs and thousands of dollars less than the one I was before. If you’ve always been overweight but truly want to diet, I can’t offer a silver bullet. It’s not about throwing money at the problem — many others have spent countless more than myself on surgeries, personal trainers, meal replacements, and weight loss communities, all without sustainable success. You can bookmark every technology and brand I mentioned, but they won’t lose weight for you. Neither can I and neither can anyone but yourself. If there’s one thing I hope you take from my story, it’s this: so long as it works, do . you you I owe so much gratitude to my girlfriend who recognized my pseudo-anarchist skepticism of authority and leveraged Taubes’ talk into full-blown action. She continues to push me every day.

Stories, News & Mentions about JALA

Jala’s Stories on HackerNoon