Ameren Illinois’ work in restoring power to nearly 200,000 customers in the wake of the June Derecho was recognized by the nation’s premier energy industry association. The company was presented

National Energy Association recognizes Ameren Illinois for power rstoration during late June derecho | Community | effinghamdailynews.com

As of 12 o'clock this afternoon, Ameren reported nearly 200 customers without power in Putnam County. That included more than 50 Hennepin Ameren customers and more than 100 outages in Mark. However by 1 o'clock, those outages were no longer being reported.

Icy Conditions Leads To Morning Power Outages | Classic Hits 103.9 WLPO | starvedrock.media

Ameren Missouri has a program going on that will support non-profit organizations that provide services for kids...

Ameren Missouri St Louis Blues Power Play for Kids - My Ozarks Online

Ameren Missouri electric customers will be seeing a change in their monthly bill.

Ameren Missouri electric customers to see electric rates drop

(KFVS) - Natural gas customers with Ameren Missouri will be seeing gas rates change. Starting November 1, the rate will drop to $.60 per every 100 cubic feet. Before this, customers paid $.63.

Ameren Missouri natural gas rates to drop

A former employee is suing to force a Mississippi utility to repay $382 million to the federal government. The money was meant to aid a failed coal-fueled power plant.

Whistleblower says utility should repay $382 million in federal aid given to failed clean coal plant

A subsidiary of St. Louis-based Ameren Corporation, in partnership with the Missouri Electric Commission, has been selected by the regional grid operator, the Midcontinent Independent System Operator .

Ameren subsidiary selected to develop important grid reliability project in northwest Missouri

accounting for about 18% of your utility bill. "There are many ways to keep our homes comfortable without blasting the heat this time of year," said Tony Lozano, director of energy solutions at Ameren Missouri. "For example, using throw rugs on bare ...

Don't be a zombie when it comes to energy savings this Halloween season

Avoid running hot water to thaw food. Meal prep ahead of time by defrosting frozen foods in the fridge overnight to save on energy and water. Water heating is the second largest energy expense in your home, accounting for about 18% of your utility bill.

Don't Be A Zombie When It Comes To Energy Savings This Halloween Season

The tanks near Spencer Butte Middle School are expected to be operational by the end of the year, two years after EWEB began construction.

EWEB's southeast Eugene water tanks near completion

Ameren

ameren

Swarnav and his team are enabling commercial property owners to go off-grid, allowing them to control and monetize electricity generation by harvesting energy from simple actions like people’s footsteps. Davis Baer : What’s your background, and what are you working on? My name is Swarnav Pujari, and I’m the founder of TouchLight Innovations based in Brooklyn, NY. We have opened up a new perspective for commercial property owners to take toward electricity generation on-site. Places like Hartsfield-Jackson Atlanta Airport and the New Lab @ Brooklyn Navy Yards have already started to take note of the opportunity TouchLight brings to the table with our unique energy capture system and methodology to enable properties to generate most, if not all, of their own electricity on-site from renewable solutions — and utilities like Ameren have taken notice and put up capital to support TouchLight in our venture. We focus on using our product Energy Tile to capture lost kinetic energy in heavy traffic locations on commercial properties. This system is a method of turning parking lots into power plants and generate a substantial amount of power to get properties closer to energy independence. Distributed generation is the future of how electricity will be generated, distributed and utilized. The Energy Tile is the first step to getting these largely untapped properties to a point where they can both contribute toward this larger market trend and capitalize on the benefits of generating most of their own electricity. The opportunity to turn energy into an asset which generates non-traditional forms of revenue for a property owner as opposed to just saving them on electricity is what makes TouchLight’s model so attractive to owners in the commercial space. What motivated you to get started with your company? My journey into this company started with my interest in the energy space starting back when I was 11 and designed a concept car for a Ford motor challenge. That car was fully powered off of green sources and it got me some time in a NY Times article. Since then I continued my love for energy and electricity until I got to High School where I started doing research in the Materials Field as I wanted to see how we could make kinetic energy capture a reality through piezoelectric like technologies. I spent years in that space learning and educating myself about the field. I eventually found myself in the commercial real estate space just shadowing mentors that did redevelopment work on distressed properties with a core focus on why they didn’t install solar panels when doing these projects. Really understanding the thought process of these individuals. It was the full experience of learning the technical side of developing a power generation solution and the business financial values from the real estate developers that really opened my eyes to a business opportunity in self-generation and distributed generation technologies. This was a long journey that started in high school and was inspired back when I was 11. I wanted to be part of the future and now working on trying to contribute to that renewable future that I know we will get to. How have you attracted users and grown your company? We are in a relationship business. It is all about who you know. Fortunately for us we had developed all of those connections early on in our careers and that opened up opportunities for us to directly call and get meetings with key decision makers. Once we started working on those meetings we were selling them the product we were building back when things were built out of electrical tape and wooden supports. It took multiple meetings of people not satisfied with our approach until we found something that got our first client to write us a check. This entire game is about who you know and in today’s world there are so many ways to connect with people and form that relationship. Social media, conferences, events, etc just get out there and talk to people about what you are doing and get introduced to the next person, but, always nurture every relationship you form through the process. What are your goals for the future? We are focused on really building out and completing projects that are in our pipeline. Our company is ready to launch and we want to show our clients the quantum leap advantage they get with TouchLight as opposed to traditional service providers. Self-generating your own electricity will be a game changer in opening up new opportunities for the next decade, we are now ready to start contributing to that trend. What are the biggest challenges you’ve faced and obstacles you’ve overcome? If you had to start over, what would you do differently? I have made many mistakes. I don’t regret any of them. They were painful then, but, fun stories now that crafted who I am today. I have made many hiring mistakes and still work on my recruiting skills today. Your company is the people, and if you get the wrong people and run the day-to-day operations with the wrong people — the company will get no where. Trying to change the wrong person into the right person will be a bit of a challenge in the early days, as you have a timeline to meet that doesn’t give you the flexibility to train and nurture a person into who you want to be, and there never is no guarantee to that either. Hire quick, fire quicker — it is something you acquire with time, but, you need to see signs of the person not working and part ways sooner as it will save that relationship should you decide to work together in the future. Have you found anything particularly helpful or advantageous? I learned over time from mentors and other who succeeded in the space I want to grow within. Learn from others — experiences are the best form of knowledge. What’s your advice for entrepreneurs who are just starting out? Entrepreneurship has become the ‘hip’ trend these days and many people are starting businesses. However, one common thing I see between many entrepreneurs is overlooking the fact they are indeed starting a business which needs to make money and attain profitability if it is to be sustainable long term. You are not starting a business to raise money or just IPO always as your exit strategy. Each business is different and there is no one size fits all answer of how to build your company. Don’t start your business because of money as you will quit after one or two hard hurdles. Once you have an internal mission for why the company will be founded do your homework by selling your product/service to your target client. You will learn the most about the right way to build your company when you ask people for money. Where can we go to learn more? You can find TouchLight on social media on the below platforms and I am happy to answer questions in the comments below. | | | | Website Facebook Instagram Twitter Linkedin This interview is brought to you by , a tool to schedule and automatically repeat your posts on Facebook, Instagram, Twitter, Pinterest, LinkedIn, and Google My Business OneUp

Founder Interviews: Swarnav Pujari of TouchLight Innovations

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Authors: (1) Dominic Petrak, UKP Lab, Department of Computer Science, Technical University of Darmstadt, Germany; (2) Nafise Sadat Moosavi, Department of Computer Science, The University of Sheffield, United Kingdom; (3) Ye Tian, Wluper, London, United Kingdom; (4) Nikolai Rozanov, Wluper, London, United Kingdom; (5) Iryna Gurevych, UKP Lab, Department of Computer Science, Technical University of Darmstadt, Germany. Table of Links Abstract & Introduction Related Work Datasets Examined Manual Error Type Analysis and Taxonomies Automatic Filtering for Potentially Relevant Dialogs Statistical Analysis Evaluation and Experiments Discussion Conclusion, Limitation, Acknowledgments, and References A Integrated Error Taxonomy – Details B Error-Indicating Sentences And Phrases C Automatic Filtering – Implementation D Automatic Filtering – Sentence-Level Analysis E Task-Oriented Dialogs – Examples F Effectiveness Of Automatic Filtering – A Detailed Analysis G Inter-Annotator Agreement – Detailed Analysis H Annotation Guidelines I Hyperparameters and Baseline Experiments J Human-Human Dialogs – Examples 9 Conclusion In this work, we examined the dialogs of six datasets from various types, including MultiWoZ, SGD, BABI, PersonaChat, Wizards-of-Wikipedia, and the human-bot split from the Self-Feeding Chatbot, for errors in system utterances and the types of subsequent user responses to assess their extendibility with annotations for learning from free-text human feedback. Our results show that this largely depends on whether the dialogs are human-human or human-bot, and whether they are task-oriented, open-domain, or knowledgegrounded. We found that human-bot dialogs, contain more errors in system utterances that are addressed with free-text human feedback in subsequent user responses, especially in the case of opendomain and knowledge-grounded dialogs. Therefore, it might be feasible to extend these datasets with the needed annotations to support research into methods for learning from free-text human feedback, e.g., by taking advantage of the recent developments in synthetic data generation. We also used the insights gained during this process to propose a new user response type taxonomy and a modified Integrated Error Taxonomy for the annotation of free-text human feedback. Our experiments show that including errors from system utterances and subsequent user responses has a positive impact in response generation. 10 Limitations The majority of our evaluation was done manually. Therefore, with respect to the original dataset sizes, we only consider a small fraction of the data in our study. It might be possible that our results would have been clearer when we would have considered more dialogs for the collection of error-indicating sentences. However, our analysis shows that errors found in the randomly selected dialogs are mostly ignored by the user, i.e., the user does not provide free-text human feedback that could be used for learning. Thus, as far as we are concerned, this does not limit the meaningfulness of our results. Regarding dataset selection, our corpus study (and its results) have only limited expressiveness for knowledge-grounded dialog datasets, since we only consider one of such datasets in our study, Wizards-of-Wikipedia (Dinan et al., 2019). However, this does not affect the relevance of our work, as there are already free-text human feedback annotated datasets available, e.g., FITS (Xu et al., 2023), and we considered a representative number of datasets from other dialog types for which there is a lack of publicly available feedback-annotated datasets, such as task-oriented dialogs. The taxonomies used in this work are also subject to limitations. In the case of the modified Integrated Error Taxonomy, our results show that it improves agreement across different dialog types. However, its abstract error types might limit application for specific use cases, e.g., for a more fine-grained consideration of different types of social errors. Moreover, it reflects only error types observed in the datasets examined. The same applies to the user response type taxonomy. 11 Acknowledgments This work has been funded by the LOEWE Distinguished Chair Ubiquitous Knowledge Processing (LOEWE initiative, Hesse, Germany) and the European Union under the Horizon Europe grant № 101070351 (SERMAS). References Antoine Bordes, Y-Lan Boureau, and Jason Weston. 2017. Learning end-to-end goal-oriented dialog. In International Conference on Learning Representations. Paweł Budzianowski, Tsung-Hsien Wen, Bo-Hsiang Tseng, Iñigo Casanueva, Stefan Ultes, Osman Ramadan, and Milica Gašic. 2018. ´ MultiWOZ - a largescale multi-domain Wizard-of-Oz dataset for taskoriented dialogue modelling. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 5016–5026, Brussels, Belgium. Association for Computational Linguistics. Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei. 2017. Deep Reinforcement Learning from Human Preferences. In Advances in Neural Information Processing Systems, volume 30. Curran Associates, Inc. Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, and Jason Wei. 2022. Scaling instruction-finetuned language models. Emily Dinan, Stephen Roller, Kurt Shuster, Angela Fan, Michael Auli, and Jason Weston. 2019. Wizard of wikipedia: Knowledge-powered conversational agents. In International Conference on Learning Representations. Laila Dybkjaer, Niels Ole Bernsen, and Hans Dybkjaer. 1996. Grice incorporated: Cooperativity in spoken dialogue. In COLING 1996 Volume 1: The 16th International Conference on Computational Linguistics. Braden Hancock, Antoine Bordes, Pierre-Emmanuel Mazare, and Jason Weston. 2019. Learning from dialogue after deployment: Feed yourself, chatbot! In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3667– 3684, Florence, Italy. Association for Computational Linguistics. Matthew Henderson, Paweł Budzianowski, Iñigo Casanueva, Sam Coope, Daniela Gerz, Girish Kumar, Nikola Mrkšic, Georgios Spithourakis, Pei-Hao Su, ´ Ivan Vulic, and Tsung-Hsien Wen. 2019. ´ A repository of conversational datasets. In Proceedings of the First Workshop on NLP for Conversational AI, pages 1–10, Florence, Italy. Association for Computational Linguistics. Ryuichiro Higashinaka, Masahiro Araki, Hiroshi Tsukahara, and Masahiro Mizukami. 2021. Integrated taxonomy of errors in chat-oriented dialogue systems. In Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 89–98, Singapore and Online. Association for Computational Linguistics. Ryuichiro Higashinaka, Masahiro Araki, Hiroshi Tsukahara, and Masahiro Mizukami. 2021. Integrated taxonomy of errors in chat-oriented dialogue systems. In Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 89–98, Singapore and Online. Association for Computational Linguistics. Hyunwoo Kim, Jack Hessel, Liwei Jiang, Ximing Lu, Youngjae Yu, Pei Zhou, Ronan Le Bras, Malihe Alikhani, Gunhee Kim, Maarten Sap, and Yejin Choi. 2022. Soda: Million-scale dialogue distillation with social commonsense contextualization. Klaus Krippendorff. 2004. Reliability in content analysis. Human Communication Research, 30(3):411– 433. Ilya Loshchilov and Frank Hutter. 2019. Decoupled weight decay regularization. In International Conference on Learning Representations. Sebastian Möller, Klaus-Peter Engelbrecht, and Antti Oulasvirta. 2007. Analysis of communication failures for spoken dialogue systems. In INTERSPEECH 2007, International Speech Communication Association (ISCA). Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul F Christiano, Jan Leike, and Ryan Lowe. 2022. Training language models to follow instructions with human feedback. In Advances in Neural Information Processing Systems, volume 35, pages 27730–27744. Curran Associates, Inc. Kishore Papineni, Salim Roukos, Todd Ward, and WeiJing Zhu. 2002. Bleu: A method for automatic evaluation of machine translation. In Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, ACL ’02, page 311–318, USA. Association for Computational Linguistics. Sunghyun Park, Han Li, Ameen Patel, Sidharth Mudgal, Sungjin Lee, Young-Bum Kim, Spyros Matsoukas, and Ruhi Sarikaya. 2021. A scalable framework for learning from implicit user feedback to improve natural language understanding in large-scale conversational AI systems. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 6054–6063, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics. Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Yang, Zach DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019. Pytorch: An imperative style, high-performance deep learning library. Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019. Language models are unsupervised multitask learners. OpenAI blog, 1(8):9. Abhinav Rastogi, Xiaoxue Zang, Srinivas Sunkara, Raghav Gupta, and Pranav Khaitan. 2020. Towards scalable multi-domain conversational agents: The schema-guided dialogue dataset. Proceedings of the AAAI Conference on Artificial Intelligence, 34(05):8689–8696. Nils Reimers and Iryna Gurevych. 2019. SentenceBERT: Sentence embeddings using Siamese BERTnetworks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 3982–3992, Hong Kong, China. Association for Computational Linguistics. Abigail See and Christopher Manning. 2021. Understanding and predicting user dissatisfaction in a neural generative chatbot. In Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 1–12, Singapore and Online. Association for Computational Linguistics. Kurt Shuster, Jing Xu, Mojtaba Komeili, Da Ju, Eric Michael Smith, Stephen Roller, Megan Ung, Moya Chen, Kushal Arora, Joshua Lane, Morteza Behrooz, William Ngan, Spencer Poff, Naman Goyal, Arthur Szlam, Y-Lan Boureau, Melanie Kambadur, and Jason Weston. 2022. Blenderbot 3: a deployed conversational agent that continually learns to responsibly engage. Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, and TieYan Liu. 2020. MPNet: Masked and Permuted Pretraining for Language Understanding. In Advances in Neural Information Processing Systems, volume 33, pages 16857–16867. Curran Associates, Inc. Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023. Llama: Open and efficient foundation language models. Megan Ung, Jing Xu, and Y-Lan Boureau. 2022. SaFeRDialogues: Taking feedback gracefully after conversational safety failures. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6462– 6481, Dublin, Ireland. Association for Computational Linguistics. Mathilde Veron, Sophie Rosset, Olivier Galibert, and Guillaume Bernard. 2021. Evaluate on-the-job learning dialogue systems and a case study for natural language understanding. Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander Rush. 2020. Transformers: State-of-the-art natural language processing. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 38–45, Online. Association for Computational Linguistics. Jing Xu, Megan Ung, Mojtaba Komeili, Kushal Arora, Y-Lan Boureau, and Jason Weston. 2023. Learning new skills after deployment: Improving open-domain internet-driven dialogue with human feedback. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 13557–13572, Toronto, Canada. Association for Computational Linguistics. Saizheng Zhang, Emily Dinan, Jack Urbanek, Arthur Szlam, Douwe Kiela, and Jason Weston. 2018. Personalizing dialogue agents: I have a dog, do you have pets too? In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2204–2213, Melbourne, Australia. Association for Computational Linguistics. Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015. Character-level convolutional networks for text classification. In Advances in Neural Information Processing Systems, volume 28. Curran Associates, Inc. Chujie Zheng, Sahand Sabour, Jiaxin Wen, and Minlie Huang. 2022. Augesc: Large-scale data augmentation for emotional support conversation with pretrained language models. This paper is under CC BY-NC-SA 4.0 DEED license. available on arxiv

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