Oru Kayak has long offered some of the market&#x27;s most compact, conveniently portable watercraft. Now the company is expanding the appeal of its lineup by pairing its vessels with an equally compact, portable electric drive system. Teaming up with the folks at Bixpy, it&#x27;s developed an e-drive ...

Oru motorizes origami kayaks with portable electric outboard

Unlike last year, park visitors can essentially drive right up to the time-limited attraction. Most of the paved roads in the park are open, including Badwater Road, which can be taken to the lake area. (There’s no place to rent a kayak in Death Valley, so those interested in going out on ...

After heavy storms, Death Valley is now open to kayakers: The return of ghostly Lake Manly

Kayaking at Badwater Basin earlier in February. Photograph: Michael Kohler/NPSView image in fullscreen

Kayakers flock to Death Valley after severe rain creates ‘extremely rare’ lake | California | The Guardian

So much rain has fallen in California’s normally bone-dry Death Valley National Park, a temporary lake formed — and it’s deep enough to kayak on, photos show.

Death Valley’s temporary lake is so deep, you can kayak on it — and some have. See it

While Wines is encouraging visitors, it&#x27;s advised that you keep an eye on updates on the Death Valley National Park website, as the temporary lake may be deep enough to kayak for just a couple of weeks but could still be around through April.

Heavy rains form temporary lake in Death Valley; kayaking encouraged

And Kayak, which just turned 20 and sold to Booking Holdings a decade ago for $2.1 billion, is also getting in on the GPT game with a virtual travel assistant that helps users book flights and car rentals. The application is currently ranked first in the GPT Store&#x27;s lifestyle section.

Here's the playbook tech companies like AllTrails, Canva, and Kayak are using to dominate the charts in OpenAI's new GPT Store.

A grainy image captures a woman, dragging a yellow kayak and with a dog at her side, headed into the water at a Wellington beach. For reasons that remain unknown, Michaela Leger allegedly stole the single-seater kayak from a property near Raumati Beach on ...

The woman, the dog and the kayak: Where is Michaela Leger?

A TERRIFIC year of learning new skills, building friendships and taking full advantage of the wonderful amenity of the River Barrow was celebrated by Muine Bheag Kayak and Stand Up Paddle Boarding (SUP) Club recently. The club held its inaugural awards ...

Go-ahead club will teach you how to paddle your own canoe

Matthew Houston did four years in Marist’s fishing program and is now involved with the fishing club at Grand Valley State University, meanwhile, along the way he became involved in kayak fishing and expects to enjoy fishing for his whole life.

One young man’s path in scholastic, collegiate and kayak fishing toward a lifetime love

In 2020, boating fatality numbers soared to 31, tying a 40-year high set in 1996, then rebounded in 2022 back to a near-average 21 deaths, records show. Then in 2022, the number of fatalities swelled to 29. The most fatalities ever happened in 1973, when 47 people died on Tennessee waterways, records show.

Tennessee’s boating-related fatalities in 2023 closing in on total for 2022

KAYAC

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Authors: (1) Savvas Petridis, Google Research, New York, New York, USA; (2) Ben Wedin, Google Research, Cambridge, Massachusetts, USA; (3) James Wexler, Google Research, Cambridge, Massachusetts, USA; (4) Aaron Donsbach, Google Research, Seattle, Washington, USA; (5) Mahima Pushkarna, Google Research, Cambridge, Massachusetts, USA; (6) Nitesh Goyal, Google Research, New York, New York, USA; (7) Carrie J. Cai, Google Research, Mountain View, California, USA; (8) Michael Terry, Google Research, Cambridge, Massachusetts, USA. Table Of Links Abstract & Introduction Related Work Formative Study Constitution Maker Implementation User Study Findings Discussion Conclusion and References 9 CONCLUSION This paper presents ConstitutionMaker, a tool for interactively refining LLM outputs by converting users’ intuitive feedback into principles. ConstitutionMaker’s design is informed by a formative study, where we also collected and classified the types of principles users wanted to write. ConstitutionMaker incorporates three principle-elicitation features: kudos, critique, and rewrite. In a user study with 14 industry professionals, participants felt that ConstitutionMaker helped them (1) write principles that effectively guided the chatbot, (2) convert their feedback into principles more easily, and (3) write principles more efficiently, with (4) less mental demand than the baseline. This was due to ConstitutionMaker supporting their thought processes, including helping them to: identify ways to improve the bot’s responses, convert their intuition into verbal feedback, and phrase their feedback as specific principles. There are many avenues of future work, including supporting users in iterating on and clarifying their principles, organizing larger sets of principles and supporting multiple writers, and helping users test chatbots across multiple user journeys. Together, these findings inform future tools that support interactively customizing LLM outputs. REFERENCES [1] Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, Carol Chen, Catherine Olsson, Christopher Olah, Danny Hernandez, Dawn Drain, Deep Ganguli, Dustin Li, Eli Tran-Johnson, Ethan Perez, Jamie Kerr, Jared Mueller, Jeffrey Ladish, Joshua Landau, Kamal Ndousse, Kamile Lukosuite, Liane Lovitt, Michael Sellitto, Nelson Elhage, Nicholas Schiefer, Noemi Mercado, Nova DasSarma, Robert Lasenby, Robin Larson, Sam Ringer, Scott Johnston, Shauna Kravec, Sheer El Showk, Stanislav Fort, Tamera Lanham, Timothy TelleenLawton, Tom Conerly, Tom Henighan, Tristan Hume, Samuel R. Bowman, Zac Hatfield-Dodds, Ben Mann, Dario Amodei, Nicholas Joseph, Sam McCandlish, Tom Brown, and Jared Kaplan. 2022. Constitutional AI: Harmlessness from AI Feedback. arXiv:2212.08073 [cs.CL] [2] Svetlin Bostandjiev, John O’Donovan, and Tobias Höllerer. 2012. 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Interactively Critiquing LLMs by Converting Feedback into Principle— Conclusion and References

In 2021, hosted the first-ever awards!! HackerNoon Startup of the Year The goal of this campaign was to give recognition to the hardworking startups who pulled through the pandemic -- growing and thriving (despite all of the challenges). More than 44,000 startups were nominated all across the globe! They were selected from over 4,553 cities, with a total number of * cough * “4 billion” votes. Insane, right? To see who is a winner in each respective city, visit and pick a city. The name at the top of the list is the winner. (Compromised awards are under review. Cities with tie votes will be updated to have two winners!) startups.hackernoon.com The will announce the winners per region in their respective cities. Startups Winners Announcement series Here are the winners in the : Asia region Top Startup Cities in East Asia Seoul The Startup of the Year in is with 377 votes! Seoul dable Central is the Startup of the Year! It came out ahead with 121 votes. 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Bet Shemesh Degania Medical Binyamina When it comes to , is the Startup of the Year! Binyamina OncoHost Makkah In , the Startup of the Year is Makkah Scimagine Or Akiva Last but not least, couldn’t be beaten for Startup in the Year in the city of ! WMT Or Akiva More Information: If you’re a winner, please answer ! If you’re a reader, participant, just a curious individual, consider answering one of our interviews . this interview here Read the FAQs about or . Startups here here To see all of the winners you may visit , pick any city and view the name that appears at the top. this site If you’re interested in partnering with us, we invite you to look through some of these or reach out to our business department. unique sponsorship options Perhaps creating your very own tickles your fancy or maybe you have a whole new idea for how we can partner! writing contest Thank you to all of our sponsors! We truly appreciate your contribution to the tech world and thus the future! The Startups Awards are sponsored by: , , , , , , , , , , Blockster Brex FreeCodeCamp LaunchDarkly Coil Udacity Udemy Vercel Asia Pacific Transgender Network Bybit Everscale Network The post was written by Kushal Tamrakar and Valerie Ejegi, edited by Hang Ngo. If you have any questions or concerns or would like to report a discrepancy please email ellen@hackernoon.com.

Meet the Winners of Startups in Asia

Smarter Systems, Less Hassle: Inside DBMS Auto-Tuning

Apparate: Early-Exit Models for ML Latency and Throughput Optimization - Conclusion, References

Authors:
(1) Seokil Ham, KAIST;
(2) Jungwuk Park, KAIST;
(3) Dong-Jun Han, Purdue University;
(4) Jaekyun Moon, KAIST. Table of Links Abstract and 1. Introduction 2. Related Works 3. Proposed NEO-KD Algorithm and 3.1 Problem Setup: Adversarial Training in Multi-Exit Networks 3.2 Algorithm Description 4. Experiments and 4.1 Experimental Setup 4.2. Main Experimental Results 4.3. Ablation Studies and Discussions 5. Conclusion, Acknowledgement and References A. Experiment Details B. Clean Test Accuracy and C. Adversarial Training via Average Attack D. Hyperparameter Tuning E. Discussions on Performance Degradation at Later Exits F. Comparison with Recent Defense Methods for Single-Exit Networks G. Comparison with SKD and ARD and H. Implementations of Stronger Attacker Algorithms 5 Conclusion In this paper, we proposed a new knowledge distillation based adversarial training strategy for robust multi-exit networks. Our solution, NEO-KD, reduces adversarial transferability in the network while guiding the output of the adversarial examples to closely follow the ensemble outputs of the neighbor exits of the clean data, significantly improving the overall adversarial test accuracy. Extensive experimental results on both anytime and budgeted prediction setups using various datasets confirmed the effectiveness of our method, compared to baselines relying on existing adversarial training or knowledge distillation techniques for multi-exit networks. Acknowledgement This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2019R1I1A2A02061135), by the Center for Applied Research in Artificial Intelligence (CARAI) grant funded by DAPA and ADD (UD230017TD), and by IITP funds from MSIT of Korea (No. 2020-0-00626). References [1] Maksym Andriushchenko, Francesco Croce, Nicolas Flammarion, and Matthias Hein. Square attack: a query-efficient black-box adversarial attack via random search. In Computer Vision– ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIII, pages 484–501. Springer, 2020. [2] Nicholas Carlini and David Wagner. Towards evaluating the robustness of neural networks. In 2017 ieee symposium on security and privacy (sp), pages 39–57. Ieee, 2017. [3] Sihong Chen, Haojing Shen, Ran Wang, and Xizhao Wang. Towards improving fast adversarial training in multi-exit network. Neural Networks, 150:1–11, 2022. [4] Francesco Croce and Matthias Hein. Minimally distorted adversarial examples with a fast adaptive boundary attack. In International Conference on Machine Learning, pages 2196–2205. PMLR, 2020. [5] Francesco Croce and Matthias Hein. Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks. In International conference on machine learning, pages 2206–2216. PMLR, 2020. [6] Junhao Dong, Seyed-Mohsen Moosavi-Dezfooli, Jianhuang Lai, and Xiaohua Xie. The enemy of my enemy is my friend: Exploring inverse adversaries for improving adversarial training. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 24678–24687, 2023. [7] Yinpeng Dong, Ke Xu, Xiao Yang, Tianyu Pang, Zhijie Deng, Hang Su, and Jun Zhu. Exploring memorization in adversarial training. arXiv preprint arXiv:2106.01606, 2021. [8] Micah Goldblum, Liam Fowl, Soheil Feizi, and Tom Goldstein. Adversarially robust distillation. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 3996–4003, 2020. [9] Dong-Jun Han, Jungwuk Park, Seokil Ham, Namjin Lee, and Jaekyun Moon. Improving lowlatency predictions in multi-exit neural networks via block-dependent losses. IEEE Transactions on Neural Networks and Learning Systems, 2023. [10] Mirazul Haque, Anki Chauhan, Cong Liu, and Wei Yang. Ilfo: Adversarial attack on adaptive neural networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14264–14273, 2020. [11] Sanghyun Hong, Yigitcan Kaya, Ionut,-Vlad Modoranu, and Tudor Dumitras. A panda? no, it’s a sloth: Slowdown attacks on adaptive multi-exit neural network inference. In International Conference on Learning Representations, 2020. [12] Ting-Kuei Hu, Tianlong Chen, Haotao Wang, and Zhangyang Wang. Triple wins: Boosting accuracy, robustness and efficiency together by enabling input-adaptive inference. In International Conference on Learning Representations, 2020. [13] Gao Huang, Danlu Chen, Tianhong Li, Felix Wu, Laurens Van Der Maaten, and Kilian Q Weinberger. Multi-scale dense networks for resource efficient image classification. arXiv preprint arXiv:1703.09844, 2017. [14] Hoki Kim. Torchattacks: A pytorch repository for adversarial attacks. arXiv preprint arXiv:2010.01950, 2020. [15] Alexandros Kouris, Stylianos I Venieris, Stefanos Laskaridis, and Nicholas Lane. Multi-exit semantic segmentation networks. In European Conference on Computer Vision, pages 330–349. Springer, 2022. [16] Alex Krizhevsky, Geoffrey Hinton, et al. Learning multiple layers of features from tiny images. 2009. [17] Ya Le and Xuan Yang. Tiny imagenet visual recognition challenge. CS 231N, 7(7):3, 2015. [18] Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278–2324, 1998. [19] Sungyoon Lee, Hoki Kim, and Jaewook Lee. Graddiv: Adversarial robustness of randomized neural networks via gradient diversity regularization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022. [20] Hao Li, Hong Zhang, Xiaojuan Qi, Ruigang Yang, and Gao Huang. Improved techniques for training adaptive deep networks. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1891–1900, 2019. [21] Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. Towards deep learning models resistant to adversarial attacks. In International Conference on Learning Representations, 2018. [22] Tianyu Pang, Kun Xu, Chao Du, Ning Chen, and Jun Zhu. Improving adversarial robustness via promoting ensemble diversity. In International Conference on Machine Learning, pages 4970–4979. PMLR, 2019. [23] Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, and Ananthram Swami. Distillation as a defense to adversarial perturbations against deep neural networks. In 2016 IEEE symposium on security and privacy (SP), pages 582–597. IEEE, 2016. [24] Mary Phuong and Christoph H Lampert. Distillation-based training for multi-exit architectures. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1355– 1364, 2019. [25] Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al. Imagenet large scale visual recognition challenge. International journal of computer vision, 115:211–252, 2015. [26] Surat Teerapittayanon, Bradley McDanel, and Hsiang-Tsung Kung. Branchynet: Fast inference via early exiting from deep neural networks. In 2016 23rd International Conference on Pattern Recognition (ICPR), pages 2464–2469. IEEE, 2016. [27] Xinglu Wang and Yingming Li. Harmonized dense knowledge distillation training for multi-exit architectures. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 10218–10226, 2021. [28] Qunliang Xing, Mai Xu, Tianyi Li, and Zhenyu Guan. Early exit or not: Resource-efficient blind quality enhancement for compressed images. In European Conference on Computer Vision, pages 275–292. Springer, 2020. [29] Huanrui Yang, Jingyang Zhang, Hongliang Dong, Nathan Inkawhich, Andrew Gardner, Andrew Touchet, Wesley Wilkes, Heath Berry, and Hai Li. Dverge: diversifying vulnerabilities for enhanced robust generation of ensembles. Advances in Neural Information Processing Systems, 33:5505–5515, 2020. [30] Zhuolin Yang, Linyi Li, Xiaojun Xu, Shiliang Zuo, Qian Chen, Pan Zhou, Benjamin Rubinstein, Ce Zhang, and Bo Li. Trs: Transferability reduced ensemble via promoting gradient diversity and model smoothness. Advances in Neural Information Processing Systems, 34, 2021. [31] Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric Xing, Laurent El Ghaoui, and Michael Jordan. Theoretically principled trade-off between robustness and accuracy. In International conference on machine learning, pages 7472–7482. PMLR, 2019. [32] Wangchunshu Zhou, Canwen Xu, Tao Ge, Julian McAuley, Ke Xu, and Furu Wei. Bert loses patience: Fast and robust inference with early exit. Advances in Neural Information Processing Systems, 33:18330–18341, 2020. [33] Jianing Zhu, Jiangchao Yao, Bo Han, Jingfeng Zhang, Tongliang Liu, Gang Niu, Jingren Zhou, Jianliang Xu, and Hongxia Yang. Reliable adversarial distillation with unreliable teachers. arXiv preprint arXiv:2106.04928, 2021. [34] Bojia Zi, Shihao Zhao, Xingjun Ma, and Yu-Gang Jiang. Revisiting adversarial robustness distillation: Robust soft labels make student better. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 16443–16452, 2021. This paper is available on arxiv under CC 4.0 license. Authors: (1) Seokil Ham, KAIST; (2) Jungwuk Park, KAIST; (3) Dong-Jun Han, Purdue University; (4) Jaekyun Moon, KAIST. Authors: Authors: (1) Seokil Ham, KAIST; (2) Jungwuk Park, KAIST; (3) Dong-Jun Han, Purdue University; (4) Jaekyun Moon, KAIST. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Related Works 2. Related Works 3. Proposed NEO-KD Algorithm and 3.1 Problem Setup: Adversarial Training in Multi-Exit Networks 3. Proposed NEO-KD Algorithm and 3.1 Problem Setup: Adversarial Training in Multi-Exit Networks 3.2 Algorithm Description 3.2 Algorithm Description 4. Experiments and 4.1 Experimental Setup 4. Experiments and 4.1 Experimental Setup 4.2. Main Experimental Results 4.2. Main Experimental Results 4.3. Ablation Studies and Discussions 4.3. Ablation Studies and Discussions 5. Conclusion, Acknowledgement and References 5. Conclusion, Acknowledgement and References A. Experiment Details A. Experiment Details B. Clean Test Accuracy and C. Adversarial Training via Average Attack B. Clean Test Accuracy and C. Adversarial Training via Average Attack D. Hyperparameter Tuning D. Hyperparameter Tuning E. Discussions on Performance Degradation at Later Exits E. Discussions on Performance Degradation at Later Exits F. Comparison with Recent Defense Methods for Single-Exit Networks F. Comparison with Recent Defense Methods for Single-Exit Networks G. Comparison with SKD and ARD and H. Implementations of Stronger Attacker Algorithms G. Comparison with SKD and ARD and H. Implementations of Stronger Attacker Algorithms 5 Conclusion In this paper, we proposed a new knowledge distillation based adversarial training strategy for robust multi-exit networks. Our solution, NEO-KD, reduces adversarial transferability in the network while guiding the output of the adversarial examples to closely follow the ensemble outputs of the neighbor exits of the clean data, significantly improving the overall adversarial test accuracy. Extensive experimental results on both anytime and budgeted prediction setups using various datasets confirmed the effectiveness of our method, compared to baselines relying on existing adversarial training or knowledge distillation techniques for multi-exit networks. Acknowledgement This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2019R1I1A2A02061135), by the Center for Applied Research in Artificial Intelligence (CARAI) grant funded by DAPA and ADD (UD230017TD), and by IITP funds from MSIT of Korea (No. 2020-0-00626). References [1] Maksym Andriushchenko, Francesco Croce, Nicolas Flammarion, and Matthias Hein. Square attack: a query-efficient black-box adversarial attack via random search. In Computer Vision– ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIII, pages 484–501. Springer, 2020. [2] Nicholas Carlini and David Wagner. Towards evaluating the robustness of neural networks. In 2017 ieee symposium on security and privacy (sp), pages 39–57. Ieee, 2017. [3] Sihong Chen, Haojing Shen, Ran Wang, and Xizhao Wang. Towards improving fast adversarial training in multi-exit network. Neural Networks, 150:1–11, 2022. [4] Francesco Croce and Matthias Hein. Minimally distorted adversarial examples with a fast adaptive boundary attack. In International Conference on Machine Learning, pages 2196–2205. PMLR, 2020. [5] Francesco Croce and Matthias Hein. Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks. In International conference on machine learning, pages 2206–2216. PMLR, 2020. [6] Junhao Dong, Seyed-Mohsen Moosavi-Dezfooli, Jianhuang Lai, and Xiaohua Xie. The enemy of my enemy is my friend: Exploring inverse adversaries for improving adversarial training. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 24678–24687, 2023. [7] Yinpeng Dong, Ke Xu, Xiao Yang, Tianyu Pang, Zhijie Deng, Hang Su, and Jun Zhu. Exploring memorization in adversarial training. arXiv preprint arXiv:2106.01606, 2021. [8] Micah Goldblum, Liam Fowl, Soheil Feizi, and Tom Goldstein. Adversarially robust distillation. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 3996–4003, 2020. [9] Dong-Jun Han, Jungwuk Park, Seokil Ham, Namjin Lee, and Jaekyun Moon. Improving lowlatency predictions in multi-exit neural networks via block-dependent losses. IEEE Transactions on Neural Networks and Learning Systems, 2023. [10] Mirazul Haque, Anki Chauhan, Cong Liu, and Wei Yang. Ilfo: Adversarial attack on adaptive neural networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14264–14273, 2020. [11] Sanghyun Hong, Yigitcan Kaya, Ionut,-Vlad Modoranu, and Tudor Dumitras. A panda? no, it’s a sloth: Slowdown attacks on adaptive multi-exit neural network inference. In International Conference on Learning Representations, 2020. [12] Ting-Kuei Hu, Tianlong Chen, Haotao Wang, and Zhangyang Wang. Triple wins: Boosting accuracy, robustness and efficiency together by enabling input-adaptive inference. In International Conference on Learning Representations, 2020. [13] Gao Huang, Danlu Chen, Tianhong Li, Felix Wu, Laurens Van Der Maaten, and Kilian Q Weinberger. Multi-scale dense networks for resource efficient image classification. arXiv preprint arXiv:1703.09844, 2017. [14] Hoki Kim. Torchattacks: A pytorch repository for adversarial attacks. arXiv preprint arXiv:2010.01950, 2020. [15] Alexandros Kouris, Stylianos I Venieris, Stefanos Laskaridis, and Nicholas Lane. Multi-exit semantic segmentation networks. In European Conference on Computer Vision, pages 330–349. Springer, 2022. [16] Alex Krizhevsky, Geoffrey Hinton, et al. Learning multiple layers of features from tiny images. 2009. [17] Ya Le and Xuan Yang. Tiny imagenet visual recognition challenge. CS 231N, 7(7):3, 2015. [18] Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278–2324, 1998. [19] Sungyoon Lee, Hoki Kim, and Jaewook Lee. Graddiv: Adversarial robustness of randomized neural networks via gradient diversity regularization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022. [20] Hao Li, Hong Zhang, Xiaojuan Qi, Ruigang Yang, and Gao Huang. Improved techniques for training adaptive deep networks. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1891–1900, 2019. [21] Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. Towards deep learning models resistant to adversarial attacks. In International Conference on Learning Representations, 2018. [22] Tianyu Pang, Kun Xu, Chao Du, Ning Chen, and Jun Zhu. Improving adversarial robustness via promoting ensemble diversity. In International Conference on Machine Learning, pages 4970–4979. PMLR, 2019. [23] Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, and Ananthram Swami. Distillation as a defense to adversarial perturbations against deep neural networks. In 2016 IEEE symposium on security and privacy (SP), pages 582–597. IEEE, 2016. [24] Mary Phuong and Christoph H Lampert. Distillation-based training for multi-exit architectures. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1355– 1364, 2019. [25] Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al. Imagenet large scale visual recognition challenge. International journal of computer vision, 115:211–252, 2015. [26] Surat Teerapittayanon, Bradley McDanel, and Hsiang-Tsung Kung. Branchynet: Fast inference via early exiting from deep neural networks. In 2016 23rd International Conference on Pattern Recognition (ICPR), pages 2464–2469. IEEE, 2016. [27] Xinglu Wang and Yingming Li. Harmonized dense knowledge distillation training for multi-exit architectures. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 10218–10226, 2021. [28] Qunliang Xing, Mai Xu, Tianyi Li, and Zhenyu Guan. Early exit or not: Resource-efficient blind quality enhancement for compressed images. In European Conference on Computer Vision, pages 275–292. Springer, 2020. [29] Huanrui Yang, Jingyang Zhang, Hongliang Dong, Nathan Inkawhich, Andrew Gardner, Andrew Touchet, Wesley Wilkes, Heath Berry, and Hai Li. Dverge: diversifying vulnerabilities for enhanced robust generation of ensembles. Advances in Neural Information Processing Systems, 33:5505–5515, 2020. [30] Zhuolin Yang, Linyi Li, Xiaojun Xu, Shiliang Zuo, Qian Chen, Pan Zhou, Benjamin Rubinstein, Ce Zhang, and Bo Li. Trs: Transferability reduced ensemble via promoting gradient diversity and model smoothness. Advances in Neural Information Processing Systems, 34, 2021. [31] Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric Xing, Laurent El Ghaoui, and Michael Jordan. Theoretically principled trade-off between robustness and accuracy. In International conference on machine learning, pages 7472–7482. PMLR, 2019. [32] Wangchunshu Zhou, Canwen Xu, Tao Ge, Julian McAuley, Ke Xu, and Furu Wei. Bert loses patience: Fast and robust inference with early exit. Advances in Neural Information Processing Systems, 33:18330–18341, 2020. [33] Jianing Zhu, Jiangchao Yao, Bo Han, Jingfeng Zhang, Tongliang Liu, Gang Niu, Jingren Zhou, Jianliang Xu, and Hongxia Yang. Reliable adversarial distillation with unreliable teachers. arXiv preprint arXiv:2106.04928, 2021. [34] Bojia Zi, Shihao Zhao, Xingjun Ma, and Yu-Gang Jiang. Revisiting adversarial robustness distillation: Robust soft labels make student better. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 16443–16452, 2021. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

How NEO-KD Reduces Adversarial Transferability and Improves Accuracy

Step-by-step guide on how to use the .Net Aspire Kafka component in Visual Studio. .Net Aspire framework is used to develop cloud and production-ready distributed applications. It consists of components to handle cloud-native concerns such as Redis, Postgres etc. Prerequisites Install .Net 8
Install Visual Studio 2022 version 17 or higher
.Net Aspire Workload
Container runtime such as Docker Desktop 10 Day .Net Aspire Challenge Objectives Learn how to create a starter project using .Net Aspire with the Apache Kafka component. Github Sample: The solution structure is divided into the following projects: DotnetAspireChallenge.ApiService
DotnetAspireChallenge.AppHost
DotnetAspireChallenge.ServiceDefaults
DotnetAspireChallenge.Web Getting Started Step 1: Install the following NuGet package Install the following Nuget package into the subsequent project “DotnetAspireChallenge.AppHost” dotnet add package Aspire.Hosting.Kafka In the above project, register Kafka UI as shown below var messaging = builder.AddKafka("messaging")
```                           .WithKafkaUI();

Then finally add a reference to both the Producer and Consumer where the producer is “DotnetAspireChallenge.ApiService” and the consumer is “DotnetAspireChallenge.Web” project respectively.
```csharp
    var apiService = builder.AddProject<Projects.DotnetAspireChallenge_ApiService>("apiservice")
        .WithReference(messaging);
    
    
    
    builder.AddProject<Projects.DotnetAspireChallenge_Web>("webfrontend")
        .WithExternalHttpEndpoints()
        .WithReference(cache)
        .WithReference(apiService)
        .WithReference(messaging); Step 2: Add dependency of Kafka Producer Add the dependency in the Program.cs file of the project “**DotnetAspireChallenge.**ApiService” builder.AddKafkaProducer<string, string>("messaging"); and add a relevant minimal API endpoint using the following code. public static class AspireKafkaExtension
    {
        public static void MapAspireKafkaEndpoint(this WebApplication app)
        {
            app.MapGet("/send", async (IProducer<string, string> services, string key, string value) =>
            {
                try
                {
                    var message = new Message<string, string> { Key = key, Value = value };
                    DeliveryResult<string, string>? result = await services.ProduceAsync("messaging", message);
                    return result;
                }
                catch (Exception ex)
                {
    
                    throw;
                }
    
            });
        }
    } The endpoint takes two parameters namely key and value as route values, and produces the message on the docker-hosted Kafka server. https://localhost:7313/send?key=key&value=1 Step 3: Add dependency of Kafka Consumer Now move “DotnetAspireChallenge.Web” project wherein register as a Kafka producer builder.AddKafkaConsumer<string, string>("messaging", options =>
    {
        options.Config.GroupId = "my-consumer-group";
        options.Config.AutoOffsetReset = AutoOffsetReset.Earliest;
        options.Config.EnableAutoCommit = false;
    }); Note: It's mandatory to provide a default group ID. Step 4: Create a Razor Page Create a basic razor page named “KafkaConsumer.razor” to show the consumed message from the Kafka server. @page "/kafka"
    @attribute [StreamRendering(true)]
    @attribute [OutputCache(Duration = 5)]
    @using Confluent.Kafka
    <h3>KafkaConsumer</h3>
    
    @inject KafkaConsumeMessageClient kafaConsumeMessageClient
    <PageTitle>Kafka Consumed Message</PageTitle>
    
    <h1>Kafka</h1>
    
    <p>This component demonstrates showing data loaded from a backend API service.</p>
    
    @if (consumedMessage == null)
    {
        <p><em>Loading...</em></p>
    }
    else
    {
        <table class="table">
            <thead>
                <tr>
                    <th>Topic</th>
                    <th>Value</th>
                </tr>
            </thead>
            <tbody>
                <tr>
                    <td>@consumedMessage.Topic</td>
                    <td>@consumedMessage.Value</td>
                </tr>
    
            </tbody>
        </table>
    }
    @code {
        private ConsumeResult<string, string>? consumedMessage;
    
        protected override async Task OnInitializedAsync() => consumedMessage = kafaConsumeMessageClient.GetKafkaMessage();
    } Step 5: Configure HttpCall to the ApiService public class KafkaConsumeMessageClient(HttpClient httpClient, IConsumer<string, string> _consumer)
    {
    
        public ConsumeResult<string, string>? GetKafkaMessage(CancellationToken cancellationToken = default)
        {
            ConsumeResult<string, string>? deliveryResult = null;
            _consumer.Subscribe("messaging");
            deliveryResult = _consumer.Consume(TimeSpan.FromSeconds(10));
    
            return deliveryResult;
        }
    } Kafka Produce Demo Kafka UI Demo Kafka Consume Demo Github Project: GitHub - ssukhpinder/DotnetAspireChallenge: 10 Day .Net Aspire Challenge
Cheatsheet: Cheat Sheets — .Net Step-by-step guide on how to use the .Net Aspire Kafka component in Visual Studio. .Net Aspire framework is used to develop cloud and production-ready distributed applications. It consists of components to handle cloud-native concerns such as Redis, Postgres etc. Prerequisites Install .Net 8 Install Visual Studio 2022 version 17 or higher .Net Aspire Workload Container runtime such as Docker Desktop Install .Net 8 .Net 8 Install Visual Studio 2022 version 17 or higher Visual Studio 2022 .Net Aspire Workload Container runtime such as Docker Desktop Docker Desktop 10 Day .Net Aspire Challenge 10 Day .Net Aspire Challenge 10 Day .Net Aspire Challenge Objectives Learn how to create a starter project using .Net Aspire with the Apache Kafka component. Github Sample : The solution structure is divided into the following projects: Github Sample DotnetAspireChallenge.ApiService DotnetAspireChallenge.AppHost DotnetAspireChallenge.ServiceDefaults DotnetAspireChallenge.Web DotnetAspireChallenge.ApiService DotnetAspireChallenge.AppHost DotnetAspireChallenge.ServiceDefaults DotnetAspireChallenge.Web Getting Started Step 1: Install the following NuGet package Install the following Nuget package into the subsequent project “ DotnetAspireChallenge.AppHost ” DotnetAspireChallenge.AppHost dotnet add package Aspire.Hosting.Kafka dotnet add package Aspire.Hosting.Kafka In the above project, register Kafka UI as shown below var messaging = builder.AddKafka("messaging")
```                           .WithKafkaUI();

Then finally add a reference to both the Producer and Consumer where the producer is “DotnetAspireChallenge.ApiService” and the consumer is “DotnetAspireChallenge.Web” project respectively.
```csharp
    var apiService = builder.AddProject<Projects.DotnetAspireChallenge_ApiService>("apiservice")
        .WithReference(messaging);
    
    
    
    builder.AddProject<Projects.DotnetAspireChallenge_Web>("webfrontend")
        .WithExternalHttpEndpoints()
        .WithReference(cache)
        .WithReference(apiService)
        .WithReference(messaging); var messaging = builder.AddKafka("messaging")
```                           .WithKafkaUI();

Then finally add a reference to both the Producer and Consumer where the producer is “DotnetAspireChallenge.ApiService” and the consumer is “DotnetAspireChallenge.Web” project respectively.
```csharp
    var apiService = builder.AddProject<Projects.DotnetAspireChallenge_ApiService>("apiservice")
        .WithReference(messaging);
    
    
    
    builder.AddProject<Projects.DotnetAspireChallenge_Web>("webfrontend")
        .WithExternalHttpEndpoints()
        .WithReference(cache)
        .WithReference(apiService)
        .WithReference(messaging); Step 2: Add dependency of Kafka Producer Add the dependency in the Program.cs file of the project “**DotnetAspireChallenge.**ApiService” builder.AddKafkaProducer<string, string>("messaging"); builder.AddKafkaProducer<string, string>("messaging"); and add a relevant minimal API endpoint using the following code. public static class AspireKafkaExtension
    {
        public static void MapAspireKafkaEndpoint(this WebApplication app)
        {
            app.MapGet("/send", async (IProducer<string, string> services, string key, string value) =>
            {
                try
                {
                    var message = new Message<string, string> { Key = key, Value = value };
                    DeliveryResult<string, string>? result = await services.ProduceAsync("messaging", message);
                    return result;
                }
                catch (Exception ex)
                {
    
                    throw;
                }
    
            });
        }
    } public static class AspireKafkaExtension
    {
        public static void MapAspireKafkaEndpoint(this WebApplication app)
        {
            app.MapGet("/send", async (IProducer<string, string> services, string key, string value) =>
            {
                try
                {
                    var message = new Message<string, string> { Key = key, Value = value };
                    DeliveryResult<string, string>? result = await services.ProduceAsync("messaging", message);
                    return result;
                }
                catch (Exception ex)
                {
    
                    throw;
                }
    
            });
        }
    } The endpoint takes two parameters namely key and value as route values, and produces the message on the docker-hosted Kafka server. https://localhost:7313/send?key=key&value=1 https://localhost:7313/send?key=key&value=1 Step 3: Add dependency of Kafka Consumer Now move “ DotnetAspireChallenge.Web ” project wherein register as a Kafka producer DotnetAspireChallenge.Web builder.AddKafkaConsumer<string, string>("messaging", options =>
    {
        options.Config.GroupId = "my-consumer-group";
        options.Config.AutoOffsetReset = AutoOffsetReset.Earliest;
        options.Config.EnableAutoCommit = false;
    }); builder.AddKafkaConsumer<string, string>("messaging", options =>
    {
        options.Config.GroupId = "my-consumer-group";
        options.Config.AutoOffsetReset = AutoOffsetReset.Earliest;
        options.Config.EnableAutoCommit = false;
    }); Note: It's mandatory to provide a default group ID. Note: It's mandatory to provide a default group ID. Step 4: Create a Razor Page Create a basic razor page named “KafkaConsumer.razor” to show the consumed message from the Kafka server. @page "/kafka"
    @attribute [StreamRendering(true)]
    @attribute [OutputCache(Duration = 5)]
    @using Confluent.Kafka
    <h3>KafkaConsumer</h3>
    
    @inject KafkaConsumeMessageClient kafaConsumeMessageClient
    <PageTitle>Kafka Consumed Message</PageTitle>
    
    <h1>Kafka</h1>
    
    <p>This component demonstrates showing data loaded from a backend API service.</p>
    
    @if (consumedMessage == null)
    {
        <p><em>Loading...</em></p>
    }
    else
    {
        <table class="table">
            <thead>
                <tr>
                    <th>Topic</th>
                    <th>Value</th>
                </tr>
            </thead>
            <tbody>
                <tr>
                    <td>@consumedMessage.Topic</td>
                    <td>@consumedMessage.Value</td>
                </tr>
    
            </tbody>
        </table>
    }
    @code {
        private ConsumeResult<string, string>? consumedMessage;
    
        protected override async Task OnInitializedAsync() => consumedMessage = kafaConsumeMessageClient.GetKafkaMessage();
    } @page "/kafka"
    @attribute [StreamRendering(true)]
    @attribute [OutputCache(Duration = 5)]
    @using Confluent.Kafka
    <h3>KafkaConsumer</h3>
    
    @inject KafkaConsumeMessageClient kafaConsumeMessageClient
    <PageTitle>Kafka Consumed Message</PageTitle>
    
    <h1>Kafka</h1>
    
    <p>This component demonstrates showing data loaded from a backend API service.</p>
    
    @if (consumedMessage == null)
    {
        <p><em>Loading...</em></p>
    }
    else
    {
        <table class="table">
            <thead>
                <tr>
                    <th>Topic</th>
                    <th>Value</th>
                </tr>
            </thead>
            <tbody>
                <tr>
                    <td>@consumedMessage.Topic</td>
                    <td>@consumedMessage.Value</td>
                </tr>
    
            </tbody>
        </table>
    }
    @code {
        private ConsumeResult<string, string>? consumedMessage;
    
        protected override async Task OnInitializedAsync() => consumedMessage = kafaConsumeMessageClient.GetKafkaMessage();
    } Step 5: Configure HttpCall to the ApiService public class KafkaConsumeMessageClient(HttpClient httpClient, IConsumer<string, string> _consumer)
    {
    
        public ConsumeResult<string, string>? GetKafkaMessage(CancellationToken cancellationToken = default)
        {
            ConsumeResult<string, string>? deliveryResult = null;
            _consumer.Subscribe("messaging");
            deliveryResult = _consumer.Consume(TimeSpan.FromSeconds(10));
    
            return deliveryResult;
        }
    } public class KafkaConsumeMessageClient(HttpClient httpClient, IConsumer<string, string> _consumer)
    {
    
        public ConsumeResult<string, string>? GetKafkaMessage(CancellationToken cancellationToken = default)
        {
            ConsumeResult<string, string>? deliveryResult = null;
            _consumer.Subscribe("messaging");
            deliveryResult = _consumer.Consume(TimeSpan.FromSeconds(10));
    
            return deliveryResult;
        }
    } Kafka Produce Demo Kafka UI Demo Kafka Consume Demo Github Project: GitHub - ssukhpinder/DotnetAspireChallenge: 10 Day .Net Aspire Challenge Cheatsheet: Cheat Sheets — .Net Github Project: GitHub - ssukhpinder/DotnetAspireChallenge: 10 Day .Net Aspire Challenge GitHub - ssukhpinder/DotnetAspireChallenge: 10 Day .Net Aspire Challenge Cheatsheet: Cheat Sheets — .Net Cheat Sheets — .Net

10 Days of .Net Aspire: Day 5 — Integrating Apache Kafka 

What Are General Search Engines?

ChatGPT in Classrooms: Opportunities, Challenges, and Ethical Considerations

Authors: (1) Renato P. dos Santos, CIAGE – Centre for Generative Artificial Intelligence in Cognition and Education. Table of Links Abstract and Introduction Materials And Methods Results and Analyses Prompts and generated texts Conceptualizing chemical reactions Deepening on understanding of chemical reactions Question about combustion Question about a graph of gases turning into water over time Question about the difference between atoms, molecules, and moles Deepening on the concept of mole Question about changing of state Question about an animated representation of water molecules undergoing phase changes Question about plasma, a state of matter Question about chemical bondings Question about illustration of chemical bonds Question about the essence of the type of chemical bonding Further analysis Conclusions Limitations of the study and possible future studies Author Contributions, Conflicts of interest, Acknowledgements, and References Limitations of the study and possible future studies Despite its inherent limitations, including its single case design and the potential for bias, the exploratory depth of the study uncovered hidden potential within these systems, even amid serious concerns about generalizability. Future research could include: • Refining the crafting of prompts. • Exploring new features of these and other GenAIbots being introduced with increasing frequency. • Establishing standardised benchmarks to evaluate and compare chatbots and AI systems' performance, accuracy, and reliability. • Conducting long-term studies to observe the evolution of chatbots' capabilities and their impact on user interactions over time. • Conducting research with real students in classroom settings and beyond to assess these AI systems' practical educational applications and challenges. • Investigating chatbots' learning and adaptation capabilities to individual user needs and preferences over time. • Researching the integration of multimodal inputs (e.g., text, voice, image) to enhance chatbot capabilities and user interaction experiences. When implementing GenAIbots in Chemistry learning, it is crucial to evaluate benefits and drawbacks judiciously, ensuring accurate information delivery and considering the implications of reduced human interaction. These concerns may be alleviated by integrating GenAIbots with other educational tools or activities promoting collaborative dialogue among learners. Author Contributions The author confirms sole responsibility for the following: study conception and design, data collection, analysis and interpretation of results, and manuscript preparation. Conflicts of interest There are no conflicts to declare. Acknowledgements The authors warmly acknowledge Melanie Swan for her invaluable suggestion, which led to the transition from using the term 'objects-to-think-with' to 'agents-to-thinkwith'. References Adiguzel, T., Kaya, M. H., & Cansu, F. K. (2023). Revolutionising education with AI: Exploring the transformative potential of ChatGPT. Contemporary Educational Technology, 15(3), ep429. https://doi.org/10.30935/cedtech/13152 Baidoo-Anu, D., & Owusu Ansah, L. (2023). Education in the Era of Generative Artificial Intelligence (AI): Understanding the Potential Benefits of ChatGPT in Promoting Teaching and Learning. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4337484 Bardin, L. (1977). L’analyse de contenu. PUF - Presses Universitaires de France. Bitzenbauer, P. (2023). ChatGPT in physics education: A pilot study on easy-to-implement activities. Contemporary Educational Technology, 15(3), ep430. https://doi.org/10.30935/cedtech/13176 Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., … Amodei, D. (2020). Language Models are Few-Shot Learners. ArXiv, 2005.14165. http://arxiv.org/abs/2005.14165 Castro Nascimento, C. M., & Pimentel, A. S. (2023). Do Large Language Models Understand Chemistry? A Conversation with ChatGPT. Journal of Chemical Information and Modeling, 63(6), 1649–1655. https://doi.org/10.1021/acs.jcim.3c00285 David, E. (2023, September 21). Microsoft to add DALL-E 3 to Bing Chat. The Verge; Vox Media. https://www.theverge.com/2023/9/21/23873690/microsoft-new-ai-features-bingsearch-shopping-dall-e-3 Dewi, C. A., Pahriah, P., & Purmadi, A. (2021). The Urgency of Digital Literacy for Generation Z Students in Chemistry Learning. International Journal of Emerging Technologies in Learning, 16(11), 88–103. https://doi.org/10.3991/ijet.v16i11.19871 Dunlop, L., Hodgson, A., & Stubbs, J. E. (2020). Building capabilities in chemistry education: happiness and discomfort through philosophical dialogue in chemistry. Chemistry Education Research and Practice, 21(1), 438–451. https://doi.org/10.1039/C9RP00141G Flavell, J. H. (1976). Metacognitive aspects of problem-solving. In L. B. Resnick (Ed.), The Nature of Intelligence (pp. 231–236). Lawrence Erlbaum. Floridi, L., & Chiriatti, M. (2020). GPT-3: Its Nature, Scope, Limits, and Consequences. Minds and Machines, 30(4), 681–694. https://doi.org/10.1007/s11023-020-09548-1 Franciscu, S. (2023). ChatGPT: A Natural Language Generation Model for Chatbots. https://doi.org/10.13140/RG.2.2.24777.83044 Gregorcic, B., & Pendrill, A.-M. (2023). ChatGPT and the frustrated Socrates. Physics Education, 58(3), 035021. https://doi.org/10.1088/1361-6552/acc299 Latour, B. (1991). Nous n’avons jamais été modernes: Essai d’anthropologie symétrique. La Découverte. Leon, A. J., & Vidhani, D. (2023). ChatGPT Needs a Chemistry Tutor, Too. Journal of Chemical Education. https://doi.org/10.1021/acs.jchemed.3c00288 Liu, F., Budiu, R., Zhang, A., & Cionca, E. (2023, October 1). ChatGPT, Bard, or Bing Chat? Differences Among 3 Generative-AI Bots. https://www.nngroup.com/articles/ai-botcomparison/ Metz, C., & Hsu, T. (2023, September 20). ChatGPT Can Now Generate Images, Too. New York Times - Technology. https://www.nytimes.com/2023/09/20/technology/chatgpt-dalle3-images-openai.html Mishra, A., Soni, U., Arunkumar, A., Huang, J., Kwon, B. C., & Bryan, C. (2023). PromptAid: Prompt Exploration, Perturbation, Testing and Iteration using Visual Analytics for Large Language Models. ArXiv, 2304.01964. https://doi.org/10.48550/arXiv.2304.01964 Mollick, E. R. (2023, April 26). A guide to prompting AI (for what it is worth): A little bit of magic,but mostly just practice. One Useful Thing Blog. https://www.oneusefulthing.org/p/a-guidetoprompting-ai-for-what Okonkwo, C. W., & Ade-Ibijola, A. (2021). Chatbots applications in education: A systematic review. Computers and Education: Artificial Intelligence, 2, 100033. https://doi.org/10.1016/j.caeai.2021.100033 OpenAI. (2023). GPT-4 Technical Report. OpenAI. https://cdn.openai.com/papers/gpt-4.pdf Papert, S. A. (1980). Mindstorms - Children, Computers and Powerful Ideas. Basic Books. http://www.arvindguptatoys.com/arvindgupta/mindstorms.pdf Permatasari, M. B., Rahayu, S., & Dasna, W. (2022). Chemistry Learning Using Multiple Representations: A Systematic Literature Review. J.Sci.Learn.2022, 5(2), 334–341. https://doi.org/10.17509/jsl.v5i2.42656 Pimentel, A., Wagener, A., Silveira, E. F. da, Picciani, P., Salles, B., Follmer, C., & Jr., O. N. O. (2023). Challenging ChatGPT with Chemistry-Related Subjects. ChemRxiv. https://doi.org/10.26434/chemrxiv-2023-xl6w3 Schlosser, M. (2019). Agency. In The Stanford Encyclopedia of Philosophy. https://plato.stanford.edu/archives/win2019/entries/agency/ Swan, M. (2015, January 21). We Should Consider the Future World as one of Multi-Species Intelligence. Response to The Edge Question 2015: What Do You Think About Machines That Think?; Edge.org. http://edge.org/response-detail/26070 Taylor, C. A., Hogarth, H., Hacking, E. B., & Bastos, E. (2022). Posthuman Object Pedagogies: Thinking with Things to Think with Theory for Innovative Educational Research. Cultural and Pedagogical Inquiry, 14(1), 206–221. https://doi.org/10.18733/cpi29662 Timilsena, N. P., Maharjan, K. B., & Devkota, K. M. (2022). Teachers’ And Students’ Experiences In Chemistry Learning Difficulties. Journal of Positive School Psychology, 6(10), 2856–2867. https://www.journalppw.com/index.php/jpsp/article/view/13764 Tümay, H. (2016). Reconsidering learning difficulties and misconceptions in chemistry: emergence in chemistry and its implications for chemical education. Chemistry Education Research and Practice, 17(2), 229–245. https://doi.org/10.1039/C6RP00008H Turkle, S. (1984). The Second Self: Computers and the Human Spirit. Simon and Schuster. Yin, R. K. (2011). Applications of Case Study Research. SAGE Publications, Inc. This paper is under CC BY-SA 4.0 DEED license. available on arxiv

Overcoming Limitations in AI Chatbot Research for Future Educational Impact

ভবিষ্যতের শিক্ষাগত প্রভাবের জন্য এআই চ্যাটবট গবেষণায় সীমাবদ্ধতা অতিক্রম করা

Überwindung von Einschränkungen in der KI-Chatbot-Forschung für zukünftige Bildungseffekte

Superar las limitaciones en la investigación de chatbots de IA para lograr un impacto educativo futuro

Surmonter les limites de la recherche sur les chatbots IA pour un impact éducatif futur

भविष्य के शैक्षिक प्रभाव के लिए AI चैटबॉट अनुसंधान में सीमाओं पर काबू पाना

将来の教育への影響のために AI チャットボット研究の限界を克服する

미래 교육적 영향을 위한 AI 챗봇 연구의 한계 극복

Superando as limitações na pesquisa do AI Chatbot para um impacto educacional futuro

Преодоление ограничений в исследованиях чат-ботов с использованием искусственного интеллекта для будущего воздействия на образование

Gelecekteki Eğitim Etkisi için Yapay Zeka Chatbot Araştırmalarında Sınırlamaların Aşılması

Khắc phục những hạn chế trong nghiên cứu Chatbot AI để tạo ra tác động giáo dục trong tương lai

克服人工智能聊天机器人研究的局限性，为未来教育带来影响

Welcome back to our 'Behind the Startup' series where we dive into the journeys of innovative companies reshaping industries. Today, we're revisiting Travala.com through a conversation with Juan Otero, who last joined us in May 2020. At that time, Juan forecasted a recovery in the travel sector which largely rang true. As we catch up with him today, we'll learn how Travala.com has navigated the evolving landscape of the travel industry, especially in light of the ongoing shifts towards digital and decentralized technologies. Join us as Juan shares insights on the company's growth, the integration of web3 technologies, and how these strategies are positioning Travala.com for future challenges and opportunities in the travel sector. Ishan Pandey: Welcome back, Juan; it's a pleasure to have you in our 'Behind the Startup' series. Last time we spoke in May 2020, you correctly predicted about a recovery starting in Q3, particularly for domestic travel post Covid-19, with a full recovery including international travel by Q4. As we continue our 'Behind the Startup' series, could you update us on how Travala.com has evolved since then, especially considering the ongoing shifts in the travel industry? Juan Otero: Thanks Ishan, great to be speaking with you again. So much has happened at Travala.com in the last four years as the travel industry rebounded and the web3 industry continued to swell. In that time, we’ve significantly expanded our offering through partnerships with some of the biggest names in travel, including Expedia and KAYAK. Our accommodation selection multiplied to over 2.2 million properties, and we introduced flights with 600+ airlines as well as 410,000+ activities, with a total of over 3 million travel products now available on Travala.com. Our team has also more than doubled in size to accommodate our growth, with hires across all departments from the management team to security, developers, marketing, and customer support. All of these developments have culminated in tremendous growth in customers, bookings, and revenue, with our gross revenue growing from less than $1 million in Q1 2020 to over $22 million in Q1 2024. Ishan Pandey: During our last discussion, you highlighted some pivotal adjustments made at Travala.com in response to the pandemic. Looking at the current times, how has your business model or strategy changed to align with the new dynamics of the travel industry? Juan Otero: From the rise of domestic travel to the influx of digital nomads across the globe, the pandemic has prompted the emergence of many new trends that look likely to continue well into the future. Where we once might’ve promoted a weekend away, we’re now encouraging longer, more immersive experiences through marketing, exclusive mobile-only deals on stays, and by offering an average of 16% savings on hotels after booking a flight. One of the often overlooked developments in recent years has been the rapid emergence of metasearch engines. With shifting behaviours in how savvy travellers search for, compare, and book travel, we recognised the importance of these metasearch engines—which have quickly gained market share when it comes to the discovery phase of travel—and became the first crypto-friendly online travel agency to be listed on leading metasearch engines KAYAK and Google Hotels, alongside incumbents in the travel industry. Ishan Pandey: Cryptocurrencies have seen substantial growth in value and popularity since we last talked. How is Travala.com planning to capitalise on this trend? Are there new strategies or technologies you're implementing? Juan Otero: We’ve honed our web3 strategy by partnering with projects to promote cross-community growth and leverage the power of highly engaged web3 audiences. As a result, we now have over 100 cryptocurrency payment options and frequently conduct campaigns with partners to promote the ability to travel with crypto on Travala.com. From leading web3 events like TOKEN2049 and Paris Blockchain Week to traditional brands interested in expanding their audience—such as Marriott International and the Catalan Tourist Board—we’re leveraging our position as the bridge between the world of traditional travel and the web3 space to continue underscoring our reputation as the leading crypto-native travel booking service. We’ve also introduced Bitcoin rewards for Platinum and Diamond members of the AVA Smart Program, the travel rewards program available on Travala.com in partnership with the AVA Foundation. These two membership groups can receive up to 10% back in Bitcoin on bookings made via Travala.com, which can be put towards future bookings or transferred out of the platform. Ishan Pandey: With rising competition and shifting consumer behaviors, how does Travala.com intend to differentiate itself in the travel industry? Juan Otero: As consumer behaviours continue evolving and products become increasingly tailored to the millennial and Gen Z audience that now make up the majority of the travel market, the key advantage that we have over more established players in the travel industry is our agility as a team, coupled with our innovation-first approach. We predict that crypto payments will become more commonplace as the web3 industry continues to mature, to the point where it will no longer be a point of differentiation. Other web3 elements (and how successfully they can be incorporated into the travel experience) will therefore be the main area of differentiation for Travala.com. From areas like web3-oriented loyalty programs to digital collectibles, we’re continuously exploring new ways that enhance the experience and make it more rewarding, pushing the boundaries of what travel is in today’s age. Ishan Pandey: Previously, you mentioned the impact of Bitcoin halving on your operations. Can you expand on how recent cryptocurrency events have influenced Travala.com's market dynamics and user behavior? Juan Otero: The positive market action driven by the Bitcoin halving saw the cryptocurrency reach an all-time high in mid-March earlier this year and resulted in our bookings soaring, leading to the strongest quarter in Travala.com’s history. As approximately 75% of our bookings are purchased with crypto, we experience a strong correlation between a crypto bull market and high booking volume on our platform, given the additional value available for travellers to derive from their crypto assets in dollar-value terms.
With the fourth Bitcoin halving now behind us, we expect the crypto market to continue performing well in the next 12–18 months, based on historic trends following previous Bitcoin halving events. Ishan Pandey: Finally, considering all the challenges and opportunities that have emerged, what is your long-term vision for Travala.com in this new era of travel and technology? Juan Otero: We see Travala.com as becoming a hub for crypto travellers to create genuine connections with the world, themselves, and each other. Web3 is a community movement, and our community is constantly brimming with discussions about travel tips, destinations to explore, the broader web3 space, and suggestions for Travala.com. We want Travala.com to be more than just a platform where travellers go to book their travel; in the long term, we envision Travala.com solidifying its position as the world's leading web3 travel ecosystem, where knowledge, creativity, and of course the act of travel itself is rewarded. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Welcome back to our 'Behind the Startup' series where we dive into the journeys of innovative companies reshaping industries. Today, we're revisiting Travala.com through a conversation with Juan Otero, who last joined us in May 2020. At that time, Juan forecasted a recovery in the travel sector which largely rang true. As we catch up with him today, we'll learn how Travala.com has navigated the evolving landscape of the travel industry, especially in light of the ongoing shifts towards digital and decentralized technologies. Join us as Juan shares insights on the company's growth, the integration of web3 technologies, and how these strategies are positioning Travala.com for future challenges and opportunities in the travel sector. Ishan Pandey: Welcome back, Juan; it's a pleasure to have you in our 'Behind the Startup' series. Last time we spoke in May 2020, you correctly predicted about a recovery starting in Q3, particularly for domestic travel post Covid-19, with a full recovery including international travel by Q4. As we continue our 'Behind the Startup' series, could you update us on how Travala.com has evolved since then, especially considering the ongoing shifts in the travel industry? Ishan Pandey: Welcome back, Juan; it's a pleasure to have you in our 'Behind the Startup' series. Last time we spoke in May 2020, you correctly predicted about a recovery starting in Q3, particularly for domestic travel post Covid-19, with a full recovery including international travel by Q4. As we continue our 'Behind the Startup' series, could you update us on how Travala.com has evolved since then, especially considering the ongoing shifts in the travel industry? Ishan Pandey: Welcome back, Juan; it's a pleasure to have you in our 'Behind the Startup' series. Last time we spoke in May 2020, you correctly predicted about a recovery starting in Q3, particularly for domestic travel post Covid-19, with a full recovery including international travel by Q4. As we continue our 'Behind the Startup' series, could you update us on how Travala.com has evolved since then, especially considering the ongoing shifts in the travel industry? Travala.com Juan Otero: Thanks Ishan, great to be speaking with you again. So much has happened at Travala.com in the last four years as the travel industry rebounded and the web3 industry continued to swell. In that time, we’ve significantly expanded our offering through partnerships with some of the biggest names in travel, including Expedia and KAYAK. Juan Otero: Travala.com Our accommodation selection multiplied to over 2.2 million properties, and we introduced flights with 600+ airlines as well as 410,000+ activities, with a total of over 3 million travel products now available on Travala.com . Travala.com Our team has also more than doubled in size to accommodate our growth, with hires across all departments from the management team to security, developers, marketing, and customer support. All of these developments have culminated in tremendous growth in customers, bookings, and revenue, with our gross revenue growing from less than $1 million in Q1 2020 to over $22 million in Q1 2024. Ishan Pandey: During our last discussion, you highlighted some pivotal adjustments made at Travala.com in response to the pandemic. Looking at the current times, how has your business model or strategy changed to align with the new dynamics of the travel industry? Ishan Pandey: During our last discussion, you highlighted some pivotal adjustments made at Travala.com in response to the pandemic. Looking at the current times, how has your business model or strategy changed to align with the new dynamics of the travel industry? Ishan Pandey: During our last discussion, you highlighted some pivotal adjustments made at Travala.com in response to the pandemic. Looking at the current times, how has your business model or strategy changed to align with the new dynamics of the travel industry? Travala.com Juan Otero: From the rise of domestic travel to the influx of digital nomads across the globe, the pandemic has prompted the emergence of many new trends that look likely to continue well into the future. Where we once might’ve promoted a weekend away, we’re now encouraging longer, more immersive experiences through marketing, exclusive mobile-only deals on stays, and by offering an average of 16% savings on hotels after booking a flight. Juan Otero: One of the often overlooked developments in recent years has been the rapid emergence of metasearch engines. With shifting behaviours in how savvy travellers search for, compare, and book travel, we recognised the importance of these metasearch engines—which have quickly gained market share when it comes to the discovery phase of travel—and became the first crypto-friendly online travel agency to be listed on leading metasearch engines KAYAK and Google Hotels, alongside incumbents in the travel industry. Ishan Pandey: Cryptocurrencies have seen substantial growth in value and popularity since we last talked. How is Travala.com planning to capitalise on this trend? Are there new strategies or technologies you're implementing? Ishan Pandey: Cryptocurrencies have seen substantial growth in value and popularity since we last talked. How is Travala.com planning to capitalise on this trend? Are there new strategies or technologies you're implementing? Ishan Pandey: Cryptocurrencies have seen substantial growth in value and popularity since we last talked. How is Travala.com planning to capitalise on this trend? Are there new strategies or technologies you're implementing? Travala.com Juan Otero: We’ve honed our web3 strategy by partnering with projects to promote cross-community growth and leverage the power of highly engaged web3 audiences. Juan Otero: As a result, we now have over 100 cryptocurrency payment options and frequently conduct campaigns with partners to promote the ability to travel with crypto on Travala.com . From leading web3 events like TOKEN2049 and Paris Blockchain Week to traditional brands interested in expanding their audience—such as Marriott International and the Catalan Tourist Board—we’re leveraging our position as the bridge between the world of traditional travel and the web3 space to continue underscoring our reputation as the leading crypto-native travel booking service. Travala.com We’ve also introduced Bitcoin rewards for Platinum and Diamond members of the AVA Smart Program, the travel rewards program available on Travala.com in partnership with the AVA Foundation. These two membership groups can receive up to 10% back in Bitcoin on bookings made via Travala.com , which can be put towards future bookings or transferred out of the platform. Travala.com Travala.com Ishan Pandey: With rising competition and shifting consumer behaviors, how does Travala.com intend to differentiate itself in the travel industry? Ishan Pandey: With rising competition and shifting consumer behaviors, how does Travala.com intend to differentiate itself in the travel industry? Ishan Pandey: With rising competition and shifting consumer behaviors, how does Travala.com intend to differentiate itself in the travel industry? Travala.com Juan Otero: As consumer behaviours continue evolving and products become increasingly tailored to the millennial and Gen Z audience that now make up the majority of the travel market, the key advantage that we have over more established players in the travel industry is our agility as a team, coupled with our innovation-first approach. Juan Otero: We predict that crypto payments will become more commonplace as the web3 industry continues to mature, to the point where it will no longer be a point of differentiation. Other web3 elements (and how successfully they can be incorporated into the travel experience) will therefore be the main area of differentiation for Travala.com . From areas like web3-oriented loyalty programs to digital collectibles, we’re continuously exploring new ways that enhance the experience and make it more rewarding, pushing the boundaries of what travel is in today’s age. Travala.com Ishan Pandey: Previously, you mentioned the impact of Bitcoin halving on your operations. Can you expand on how recent cryptocurrency events have influenced Travala.com's market dynamics and user behavior? Ishan Pandey: Previously, you mentioned the impact of Bitcoin halving on your operations. Can you expand on how recent cryptocurrency events have influenced Travala.com's market dynamics and user behavior? Ishan Pandey: Previously, you mentioned the impact of Bitcoin halving on your operations. Can you expand on how recent cryptocurrency events have influenced Travala.com 's market dynamics and user behavior? Travala.com Juan Otero: The positive market action driven by the Bitcoin halving saw the cryptocurrency reach an all-time high in mid-March earlier this year and resulted in our bookings soaring, leading to the strongest quarter in Travala.com ’s history. Juan Otero: Travala.com As approximately 75% of our bookings are purchased with crypto, we experience a strong correlation between a crypto bull market and high booking volume on our platform, given the additional value available for travellers to derive from their crypto assets in dollar-value terms.
With the fourth Bitcoin halving now behind us, we expect the crypto market to continue performing well in the next 12–18 months, based on historic trends following previous Bitcoin halving events. Ishan Pandey: Finally, considering all the challenges and opportunities that have emerged, what is your long-term vision for Travala.com in this new era of travel and technology? Ishan Pandey: Finally, considering all the challenges and opportunities that have emerged, what is your long-term vision for Travala.com in this new era of travel and technology? Ishan Pandey: Finally, considering all the challenges and opportunities that have emerged, what is your long-term vision for Travala.com in this new era of travel and technology? Travala.com Juan Otero: We see Travala.com as becoming a hub for crypto travellers to create genuine connections with the world, themselves, and each other. Web3 is a community movement, and our community is constantly brimming with discussions about travel tips, destinations to explore, the broader web3 space, and suggestions for Travala.com . Juan Otero: Travala.com Travala.com We want Travala.com to be more than just a platform where travellers go to book their travel; in the long term, we envision Travala.com solidifying its position as the world's leading web3 travel ecosystem, where knowledge, creativity, and of course the act of travel itself is rewarded. Travala.com Travala.com Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program . HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Vested Interest Disclosure: business blogging program business blogging program

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