Intergiro

intergiro

STOCKHOLM, Sweden, October 9th, 2024/Chainwire/--FinchTrade, a Swiss OTC & crypto liquidity provider, and Intergiro, a Swedish Banking as a Service (BaaS) provider, have announced a strategic partnership to bridge the gap between traditional banking and cryptocurrency ecosystems, and address key challenges for crypto-oriented businesses. This partnership will deliver streamlined financial solutions and instant on/off-ramp options, making it easier for crypto businesses to serve their users more efficiently and drive growth. Two integrated solutions for Web 3.0 Intergiro and FinchTrade deliver two complementary solutions designed to create a seamless financial experience. These solutions are: Intergiro’s embedded banking solutions. Intergiro provides exchanges with embedded banking services to offer both fiat and crypto solutions to their users. This includes: Instant on-ramp – users can instantly deposit fiat funds via bank transfers or card acquiring into an embedded bank account, providing real-time access to fiat services with zero friction.
Instant off-ramp – users can manage, spend, and withdraw funds instantly through white-label debit cards or bank transfers. This flexibility allows them to withdraw cash at ATMs, make everyday purchases with debit cards, or transfer funds to other IBAN accounts, ensuring a flawless and user-friendly experience. FinchTrade’s instant liquidity-FinchTrade equips crypto exchanges, card acquirers, crypto payment processors, and OTC platforms with access to a wide range of trading assets and instant settlement capabilities. This ensures businesses can seamlessly handle large transaction volumes and manage real-time settlements when banking with Intergiro, offering immediate access to liquidity for trading, payments, and other financial operations. Similarly, clients can scale their operations without the risk of delays, a key advantage in the fast-paced Web 3.0 financial landscape. Driving Market Innovation And Customer Experience Not only will this partnership simplify operations, it will remove obstacles for crypto exchanges and empower them to offer a wider range of financial services to their users. By integrating fiat payments, settlements, and instant currency conversions, exchanges can deliver a smoother, more comprehensive experience for end-users to drive engagement and loyalty. Nick Root, CEO of Intergiro, commented: “Our collaboration with FinchTrade addresses key needs by enabling embedded fiat services for crypto platforms and providing real-time liquidity. We’re offering a solution that equips businesses to thrive in both the traditional, digital and virtual economy. ”Yuri Berg, Board Member of FinchTrade, added: “This partnership is a game-changer for crypto exchanges, card acquirers, and payment processors. By providing immediate access to deep liquidity and instant settlements when banking with Intergiro, we’re equipping businesses to scale rapidly and stay ahead in the ever-evolving Web 3.0 landscape.” About FinchTrade FinchTrade specializes in digital asset liquidity and investment tools. It offers technology-driven trading, investment, and custodial solutions in the cryptocurrency sector. FinchTrade has also developed MarketGuard, a plug-and-play AML & KYC solution for Web3 companies. About Intergiro Intergiro connects the digital economy by embedding banking into business systems and products. Thousands of internet platforms use Intergiro’s core suite of payment APIs to increase revenue, reduce costs, and drive engagement. Contacts Jelle van Schaick Intergiro Jelle@intergiro.com Pavel Efremov Finchtrade pavel.e@finchtrade.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here STOCKHOLM, Sweden, October 9th, 2024/Chainwire/--FinchTrade, a Swiss OTC & crypto liquidity provider, and Intergiro , a Swedish Banking as a Service (BaaS) provider, have announced a strategic partnership to bridge the gap between traditional banking and cryptocurrency ecosystems, and address key challenges for crypto-oriented businesses. This partnership will deliver streamlined financial solutions and instant on/off-ramp options, making it easier for crypto businesses to serve their users more efficiently and drive growth. STOCKHOLM, Sweden, October 9th, 2024/Chainwire/--FinchTrade, Intergiro Intergiro Two integrated solutions for Web 3.0 Intergiro and FinchTrade deliver two complementary solutions designed to create a seamless financial experience. These solutions are: Intergiro’s embedded banking solutions. Intergiro provides exchanges with embedded banking services to offer both fiat and crypto solutions to their users. This includes: Instant on-ramp – users can instantly deposit fiat funds via bank transfers or card acquiring into an embedded bank account, providing real-time access to fiat services with zero friction. Instant off-ramp – users can manage, spend, and withdraw funds instantly through white-label debit cards or bank transfers. This flexibility allows them to withdraw cash at ATMs, make everyday purchases with debit cards, or transfer funds to other IBAN accounts, ensuring a flawless and user-friendly experience. Instant on-ramp – users can instantly deposit fiat funds via bank transfers or card acquiring into an embedded bank account, providing real-time access to fiat services with zero friction. Instant off-ramp – users can manage, spend, and withdraw funds instantly through white-label debit cards or bank transfers. This flexibility allows them to withdraw cash at ATMs, make everyday purchases with debit cards, or transfer funds to other IBAN accounts, ensuring a flawless and user-friendly experience. FinchTrade’s instant liquidity-FinchTrade equips crypto exchanges, card acquirers, crypto payment processors, and OTC platforms with access to a wide range of trading assets and instant settlement capabilities. This ensures businesses can seamlessly handle large transaction volumes and manage real-time settlements when banking with Intergiro, offering immediate access to liquidity for trading, payments, and other financial operations. Similarly, clients can scale their operations without the risk of delays, a key advantage in the fast-paced Web 3.0 financial landscape. Driving Market Innovation And Customer Experience Not only will this partnership simplify operations, it will remove obstacles for crypto exchanges and empower them to offer a wider range of financial services to their users. By integrating fiat payments, settlements, and instant currency conversions, exchanges can deliver a smoother, more comprehensive experience for end-users to drive engagement and loyalty. Nick Root, CEO of Intergiro, commented: “Our collaboration with FinchTrade addresses key needs by enabling embedded fiat services for crypto platforms and providing real-time liquidity. We’re offering a solution that equips businesses to thrive in both the traditional, digital and virtual economy. Nick Root, CEO of Intergiro, commented: “Our collaboration with FinchTrade addresses key needs by enabling embedded fiat services for crypto platforms and providing real-time liquidity. We’re offering a solution that equips businesses to thrive in both the traditional, digital and virtual economy. ”Yuri Berg, Board Member of FinchTrade, added: “This partnership is a game-changer for crypto exchanges, card acquirers, and payment processors. By providing immediate access to deep liquidity and instant settlements when banking with Intergiro, we’re equipping businesses to scale rapidly and stay ahead in the ever-evolving Web 3.0 landscape.” ”Yuri Berg, Board Member of FinchTrade, added: “This partnership is a game-changer for crypto exchanges, card acquirers, and payment processors. By providing immediate access to deep liquidity and instant settlements when banking with Intergiro, we’re equipping businesses to scale rapidly and stay ahead in the ever-evolving Web 3.0 landscape.” About FinchTrade FinchTrade specializes in digital asset liquidity and investment tools. It offers technology-driven trading, investment, and custodial solutions in the cryptocurrency sector. FinchTrade has also developed MarketGuard, a plug-and-play AML & KYC solution for Web3 companies. FinchTrade FinchTrade MarketGuard, MarketGuard, About Intergiro Intergiro connects the digital economy by embedding banking into business systems and products. Thousands of internet platforms use Intergiro’s core suite of payment APIs to increase revenue, reduce costs, and drive engagement. Intergiro Intergiro Contacts Jelle van Schaick Intergiro Jelle@intergiro.com Pavel Efremov Finchtrade pavel.e@finchtrade.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

Intergiro And FinchTrade Partner To Bridge Fiat And Crypto Ecosystems 

Fiat və Kripto Ekosistemlərini Körpü Etmək üçün Intergiro və FinchTrade Partnyoru 

Intergiro y FinchTrade se asocian para unir los ecosistemas fiduciarios y criptográficos

इंटरगिरो और फिंचट्रेड ने फिएट और क्रिप्टो इकोसिस्टम को जोड़ने के लिए साझेदारी की

Intergiro dan FinchTrade Bermitra untuk Menjembatani Ekosistem Fiat dan Kripto

Intergiro と FinchTrade が提携し、法定通貨と暗号通貨のエコシステムを橋渡し

Intergiro და FinchTrade პარტნიორი Fiat და Crypto ეკოსისტემების გადასალახად 

د فایټ او کریپټو ایکوسیستمونو پل کولو لپاره انټرګیرو او فینچ ټریډ شریک 

Intergiro e FinchTrade fazem parceria para unir os ecossistemas Fiat e Crypto

Intergiro Y FinchTrade Socios Fiat Y Crypto Ecosistemas Puentepaq 

Intergiro и FinchTrade объединяют усилия для объединения фиатных и криптовалютных экосистем

Intergiro uye FinchTrade Partner Ku Bridge Fiat Uye Crypto Ecosystems 

I-Intergiro kunye ne-FinchTrade Partner kwiBridge Fiat kunye neCrypto Ecosystems 

Welcome to another announcement of the Startups of the Year 2023. This announcement covers all the special categories of Startups of the Year. This year, almost 30,000 startups across the globe were nominated, with over 600,000 votes cast in the running for Startup of the Year, surpassing the last award’s votes by almost 3x! Let’s see who won! Remote, Earth With 208 votes, wins the Startups of the Year, Remote, Earth! CIC Chain US Misc In  US Misc , is the winner with 1193 votes. Reship Colony Nevis Misc The startup of the year in Nevis Misc is . Spool Canada Misc The startup of the year in Canada Misc is . My Website is Online Mexico Misc The startup of the year in Mexico Misc is . Cion Digital UK, Misc , a 3D interactive parts catalog and work instructions SaaS platform software for OEM’s, wins! Partful Germany Misc Developing services to connect people all around the world tomorrowSpace Italy Misc comes out as the winner in with 37 votes! Planny Italy Greece Misc , in the cloud computing, AI, and data industry wins! The Signal Group Spain Misc is the winner in with 52 votes! Vertiports Network Spain Finland Misc wins in with 101 votes! Ikius Finland Russia Misc The Startup of the Year in is with 22 votes! Russia RentProg Georgia Misc is the winner in ! Constant Cash Offer Georgia Sweden Misc earns 209 votes to become the Startup of the Year in ! Intergiro Sweden Romania Misc The winner in is ! Romania ReactiveDoc Czech Republic Misc wins in the ! ISIA Czech Republic Lithuania Misc The winner in is ! Lithuania SearchNode Estonia Misc The winner in is ! Estonia Nerian Network Norway Misc wins in ! Utleieappen Norway Poland Misc is the winner in ! Zencal Poland Portugal Misc The winner in is ! Portugal Accelerator App Ukraine Misc The winner in is the ! Ukraine Crypto Cossacks Club Iceland Misc wins in with 89 votes! JSON2Video Iceland Hungary Misc wins in ! SURVIOT Hungary Ireland Misc In , the Startup of the Year is ! Ireland Payzaar Guernsey Misc The Startup of the Year in is ! Guernsey Infinite Canvas Malta Misc With 86 points, becomes the Startup of the Year in ! Metaverse Architects Malta Slovenia Misc is the Startup of the Year in ! d.labs Slovenia Luxembourg Misc With 57 votes, takes home the cake in ! Greenworlder Luxembourg Bulgaria Misc wins in ! iBlueMenú Bulgaria Macedonia Misc earns 88 votes to become the Startup of the Year in ! PlaceWar Macedonia Monaco Misc is the winner in ! InnoDeep Monaco Switzerland Misc in the MarketingTech Industry wins! LinkDrip Bosnia And Herzegovina Misc is the winner in ! Robinize Bosnia And Herzegovina Belgium Misc , a global network and marketplace for requesting and providing specified, fresh content on demand, wins! Aviark France Misc , an agriculture software and analytics company dedicated to helping farmers produce more and better, wins! Perfarmer Austria Misc , an open-source password manager specifically designed for enterprises, wins! Vaulteron Turkey Misc in the Content Management System (CMS) Industry wins! Contentrain Australia Misc claims the victory with 111 votes! 👑 IMPAKT IT New Zeland Misc Odd Jobs claims victory with 11 votes! Singapore Misc The startup of the year in Singapore Misc is with 62 votes. Neliti Palestine Misc The startup of the year in Palestine Misc is . 3amal Malaysia Misc. The startup of the year in Malaysia Misc is . Oyen Pet Insurance Bahrain Misc The startup of the year in Bahrain Misc is . Bookr Pakistan Misc The startup of the year in Pakistan Misc is . Azal Kazakhstan Misc The startup of the year in Kazakhstan Misc is . Kazakhmys.academy Azerbaijan Misc The startup of the year in Azerbaijan Misc is . Menti Vietnam Misc The startup of the year in Vietnam Misc is . RaceFi Saudi Arabia Misc The startup of the year in Saudi Arabia Misc is . Shera Tokens Philippines Misc The startup of the year in Philippines Misc is . edamama Bangladesh Misc The startup of the year in Bangladesh Misc is . FormBold Thailand Misc The startup of the year in Thailand Misc is . HappyLand Israel Misc The startup of the year in Israel Misc is . Otterize United Arab Emirates Misc The startup of the year in UAE Misc is . The Scalable CFO India Misc The startup of the year in India Misc is . 5ire China Misc The startup of the year in China Misc is . Fairyland Indonesia Misc The startup of the year in Indonesia Misc is . Dijital Generasi Kreasi Nepal Misc The startup of the year in Nepal Misc is . StackMyStore South Korea Misc The startup of the year in South Korea Misc is . OLIVE Sri Lanka Misc The startup of the year in Sri Lanka Misc is . clovatel Hotel Suite of solution Japan Misc The startup of the year in Japan Misc is . Response insight Uganda Misc ENVENTURE is king of the hill!🎉 Anguilla Misc Kubiya is the Startup of the Year with 45 votes!🎉 Burkina Faso Misc It’s a win for GVente!🎉 Tunisia Misc No one did it like True Delta!🎉 Algeria Misc Workflow Dashboard wins!🎉 Mauritius Misc Cube Consumer Services takes home the cake.🎉 Namibia Misc 174 votes, and Billet steals the show.🎉 Ghana Misc SMS NOTIFY GHANA takes it with 41 votes!🎉 Morocco Misc After 16 votes, SafeExchange is the Startup of the Year.🎉 Kenya Misc RueSuites wins!🎉 Botswana Misc Procurement Institute App comes out on top with highest number of votes!🎉 South Africa Misc SHIFT wins it all with the highest number of votes!🎉 Egypt Misc Alva emerges as Startup of the Year.🎉 Tanzania Misc JUPITER looms largest with highest number of votes!🎉 Republic Of Congo Misc Julizha is the Startup of the Year!🎉 Ethiopia Misc BEATCARES came out on top!🎉 Mauritania Misc The Startup of the Year is 3meel 🎉 Nigeria Misc With top votes, BoomKit takes home the goods.🎉 BoomKit is in the Music Tech Industry. Rwanda Misc TodoBoard is Startup of the Year!🎉 TodoBoard is in the Enterprise Collaboration Industry Brazil Misc The winner in is with 48 votes! Brazil SleekFlow Chile Misc is the Startup of the Year in ! ApiChat Chile Panama Misc is the Startup of the Year in ! PurpUX Panama Argentina Misc is the winner in with 27 votes! Rubicom Argentina Venezuela Misc In , the winner is ! Venezuela conectApp Colombia Misc The winner in is ! Colombia Violet Cundinamarca, Colombia The Startup of the Year in is ! Cundinamarca Voy Yo Ecuador Misc is the winner in ! SaaS for Trustees Ecuador British Virgin Islands Misc has won with the highest number of votes! GoMining Cayman Islands Misc has won the contest with highest number of votes! Dtravel Cyprus - Europe has won the Startups of the Year from Cyprus! Latenode Puerto Rico Misc takes the winning crown! Immutable Holdings San Juan, Puerto Rico is the Startups of the year! XRCasino Grand Cayman, Cayman Islands wins the Startups of the Year! Berachain Congratulations y’all. We will contact you with details on your winnings! See the global and other regional announcements . here About HackerNoon’s Startups Of The Year Startups of The Year is HackerNoon’s Flagship community-driven event celebrating startups that survived and thrived in 2023. 30,000 startups across 4200+ cities and six continents participated in 2023 to be crowned the best startup in their city. Visit our page to learn more. FAQ Companies that blog more get 67% more leads - Jumpstart your company’s blogging journey by purchasing our . Business Blogging Package The 2023 Startups Of The Year is sponsored by: . . The winners will get a free .Tech Domain, a HackerNoon NFT, and a Tech Company News Page. TECH Domains

Startups of the Year 2023: Remote/Misc Winners

Finding a remote job as a developer can be tricky. You want to work for a company that recognizes the value of remote workers, and you want to be sure that the company will support and encourage your remote working style. If you’re a developer looking for an opportunity to work from home, only a handful can really provide you with great flexibility, a supportive environment, and manageable workloads. While reading you probably wish for a nice list of companies that offer remote work. Unfortunately, most of the lists out there tend to be outdated or are filled with jobs that are not 100% remote. With that in mind, in this article you will find a list of legitimate companies that hire remote developers in one way or another.You will also quick look at what these companies are and how they stack up against each other. What Are the Benefits of Working Remotely? Working remotely is an increasingly popular trend. It offers a level of flexibility, autonomy and freedom not found in other work situations. In addition to that, remote work can also open the door to increased productivity and creativity. Nicholas Bloom conducted an experiment and found that remote workers worked 9.5 % longer and were 13% more productive than their in-office counterparts. Below, we'll tell you about the benefits of working remotely. 1. Save Time Employees who work remotely don’t have to spend time commuting to work, and can thus manage their own schedule. Some people perform better at different times of the day. For example, one person may be a morning person, while another is more productive in the evenings. As long as you meet deadlines, it doesn’t matter what time of day it is when you do your work. 2. Peace and Quiet Noisy, distracting, and generally disruptive. That's what offices tend to be like. Sure, they can be productive spaces, but they're often anything but quiet. Remote working allows you to escape the daily grind and go to a quiet location where you can concentrate on work. Whether you choose to work from home or not, having peace and quiet during the working day can be the key to greater productivity, more motivation and less stress. 3. Health and Happiness If you commute every day and work in an office, you might not be living as healthily as you could be. Remote developers are happier, healthier and much more productive. A recent report entitled the "State of Remote Engineering" captured sentiments from software developers across a range of topics, including remote work, compensation, burnout, payout, and productivity. A whopping 75% of the developers surveyed mentioned they want to work remotely at least three days a week. The study found that 68% of respondents are able to get more meaningful work done when working remotely or from home. In contrast, only 32% reported they are more productive in an office environment. Important reasons were that working remotely gives people the freedom to make their own decisions and to be responsible for those decisions. 4. Work-Life Balance The key to a good work-life balance is to structure your day effectively and make sure you are spending enough time on both your work and home tasks. Developers who feel like they have a sense of achievement are more productive. Other benefits include: Taking a break when you need one. Spending more time with family. No rushing to catch a train or any need to spend time sitting in traffic. 5. Personalise Your Office The freedom to work remotely allows you not only to work when you want, how you want and wherever you want. It provides you with the ability to establish your own office space and personalise it however you like. You can create a truly inspirational workspace with the help of your favourite photos, paintings, plants, or anything else you might find that brings you joy. This is all about making your work area totally yours – a completely inspiring environment. No one can judge what you like or dislike. What Are the Best Remote Companies for Developers? The remote work revolution is in full swing, and there’s a lot of online chatter about the best companies for remote work. In this article, you will find a  compiled list of the top 10 companies for developers to work remotely. This list is by no means exhaustive but should give you a good starting point of where to look for your next employer. 1. Intergiro is an all-in-one platform that provides multi-currency bank accounts, card issuance, and card acquisition across Europe. Their services enable anyone to embed banking functionality into their website and app, creating opportunities for new and innovative product experiences. Intergiro Europe-based Intergiro provides both hybrid and fully remote options to its employees. They are committed to building a remote-friendly company and make work simpler, more pleasant, and more fulfilling for their people. Although a remote-first company, the company boasts of hubs in luxurious coworking spaces in Sweden, Spain, Portugal, and Poland. The availability of these coworking spaces has allowed Intergiro to attract individuals who prefer to work side-by-side with like-minded colleagues. Intergiro provides: Work from anywhere - at any hour - in any capacity. Flexible working hours is a great benefit when working for Intergiro. Developers can advance their careers, without sacrificing their work-life balance. There’s a , so you won’t have a manager breathing down your neck, and there’s no micromanaging culture. high level of autonomy Provision of tools like laptops and a home office budget. Annual budget. professional development Wellness benefits focus on employees to improve the health and fitness. It’s within Intergiro’s culture to build highly valued relationships for everyone. People share a common goal and work together toward it. Possibility to work from multiple . coworking spaces around Europe Support and reimbursement in case of sick or long-term leave. 2. Automattic is a software company, with its most famous product inarguably being WordPress. They are based in San Francisco, CA. Automattic is the parent company of WordPress, WooCommerce, Tumblr, and several other web applications. Automattic, Inc. Automattic has long had a remote-first working environment that has even been the subject of journalistic work. With workers in 96 countries, Automattic emphasises flexibility, autonomy, and an optimal work-life balance with open vacation days, paid sabbaticals every five years, and generous parental leave. 3. Zapier is a web application that creates and automates connections between apps.  Using Zapier, a user can easily create workflows and implement custom automatizations to smoothen their work Zapier Zapier has been a 100% remote company since its establishment in 2011, and now boasts over 200 teammates in 24+ countries. The company places a high degree of value on autonomy and employee ownership of their own work, and rewards employees with a flexible vacation policy and a profit-sharing system. To build team spirit across the distance, the company also holds twice-annual all-company retreats. 4. InVision A software company focused on collaborative design tools, worked to become a go-to name in remote work since its establishment. InVision InVision motivates its globe-spanning workforce with a monthly allowance for work and self-care activities; an annual fund for self-development; and generous budgets for new hires to fully outfit their home offices, as well as office refreshment budgets every two years for existing employees. 5. Abstract is a web application designed for designers and developers that assist with collaboration on web and mobile projects. They currently have over 7,000 design teams signed up for the platform. Abstract With 80% of their workforce being remote, Abstract is a remote-first company, although they are currently based entirely within the United States. The company shows its investment in employees’ work-life balance by having established four-day workweeks, along with unlimited paid time off and a range of other benefits, including pet insurance and adjustable work hours for caregivers. 6. Trakstar is a software suite that empowers companies’ human resources to best manage and track their employees, all the way from the hiring stage through employee retention. A remote-first company, Trakstar hires globally and provides a number of generous perks to their worldwide employee base. These benefits include flexible time off for their workers, ample employee development resources for their staff, and bonuses and financial compensation (dependent on the specific country of hire). Trakstar 7. Doist is a software company specialising in productivity apps and tools, with a goal of providing software solutions that help people accomplish their goals. Doist Doist works on a remote basis with no limitations on employee locations. To ensure easier onboarding, Doist also assigns a mentor to newly-onboarded employees, and offers the possibility of flying out to the mentor’s location for a week in order to work side by side with a colleague at the beginning of employment. The company also covers the fee for a coworking space of the employee’s choice, and a budget for work-related services, personal development, and work-related hardware. 8. Frontastic is a company that provides a platform for e-commerce, brands, and mobile-focused startups to create mobile-first experiences, with a focus on delivering complete platforms for mobile-first e-commerce. Frontastic The Frontastic team is remote-first and spread around the world. For team-building, the company offers perks like three company meetups a year and a yearly five-day retreat. Outside of these meetups, Frontastic offers flexible working hours and a generous working allowance. 9. Stripe is a platform for companies to collect and process payments. It allows business owners to create an app or website with a checkout flow that connects to their Stripe account and processes transactions with customers. This can be used for recurring billing, for example with subscriptions, or for one-time payments. Stripe Stripe was ahead of the curve in implementing both remote and hybrid working models, hiring their first batch of remote-only engineers back in 2013. The company offers flexible working hours, caregiver leave, and a monthly allowance for employee wellness. 10. Twitch is a global leader in live streaming and video game streaming, with more than 5 million active users a month. Headquartered in San Francisco, California, the company has offices in nine countries. Twitch Twitch is remote-friendly, although employees must still be based around one of its offices. It provides commuter benefits, flexible paid time off, and monthly contributions to employee wellness. Wrapping up This article is an excellent resource if you're looking to work remotely as a developer but don't know where to start. Remote work is a growing trend in the tech industry, and there are a lot of great resources out there to help you get started. Intergiro is considered the best remote employer for developers to work for in Europe. Other great choices are Zapier, Invision, and Abstract. In addition, we hope this enables you to find a job that suits your personality and lifestyle. If you learned something new or enjoyed reading this article, please share it so that others can see it. Until then, see you in the next post! You can also find me on Twitter @Davis_McDavid.

Top 10 Companies For Developers To Work Remote in 2022 (Ranked and Reviewed)

Gamified Learning With An AI Board Game Tournament: Course Design

Author:
(1) Katrin Fischer, Annenberg School for Communication at the University of Southern California, Los Angeles (Email: katrinfi@usc.edu);
(2) Donggyu Kim, Annenberg School for Communication at the University of Southern California, Los Angeles (Email: donggyuk@usc.edu);
(3) Joo-Wha Hong, Marshall School of Business at the University of Southern California, Los Angeles (Email: joowhaho@marshall.usc.edu). Table of Links Abstract Introduction & Related Work Method Analysis & Results Discussion & Conclusion References REFERENCES [1] M. Deutsch, The resolution of conflict: Constructive and destructive processes. Yale University Press, 1973. [2] J. Riegelsberger, M. A. Sasse, and J. D. McCarthy, “The researcher’s dilemma: Evaluating trust in computer-mediated communication,” International Journal of Human Computer Studies, vol. 58, no. 6, pp. 759–781, 2003. [3] J. K. Rempel, J. G. Holmes, and M. P. Zanna, “Trust in close relationships,” Journal of Personality and Social Psychology, vol. 49, no. 1, pp. 95–112, 1985. [4] J. D. Lee and K. A. See, “Trust in automation: Designing for appropriate reliance,” Human Factors, vol. 46, no. 1, pp. 50–80, 2004. [5] D. Ullman and B. F. Malle, “Measuring gains and losses in humanrobot trust: Evidence for differentiable components of trust,” in Proceedings of the 14th ACM/IEEE International Conference on HumanRobot Interaction, ser. HRI ’19. IEEE Press, 2019, pp. 618–619. [6] R. C. Mayer, J. H. Davis, and F. D. Schoorman, “An integrative model of organizational trust,” Academy of management review, vol. 20, no. 3, pp. 709–734, 1995. [7] W. Kim, N. Kim, J. B. Lyons, and C. S. Nam, “Factors affecting trust in high-vulnerability human-robot interaction contexts: A structural equation modelling approach,” Applied Ergonomics, vol. 85, may 2020. [8] C. M. Carpinella, A. B. Wyman, M. A. Perez, and S. J. Stroessner, “The robotic social attributes scale (RoSAS) development and validation,” in ACM/IEEE International Conference on Human-Robot Interaction. New York, NY, USA: IEEE Computer Society, 2017, pp. 254–262. [9] S. T. Fiske, A. J. Cuddy, and P. Glick, “Universal dimensions of social cognition: Warmth and competence,” Trends in Cognitive Sciences, vol. 11, no. 2, pp. 77–83, 2007. [10] A. J. Cuddy, S. T. Fiske, and P. Glick, “The BIAS Map: Behaviors From Intergroup Affect and Stereotypes,” Journal of Personality and Social Psychology, vol. 92, no. 4, pp. 631–648, 2007. [11] B. Reeves, J. Hancock, and X. Liu, “Social robots are like real people: First impressions, attributes, and stereotyping of social robots,” Technology, Mind, and Behavior, vol. 1, no. 1, 2020. [12] C. Nam and J. Lyons, Eds., Trust in human-robot interaction: Research and applications. Elsevier, 2020. [13] P. A. Hancock, D. R. Billings, K. E. Schaefer, J. Y. Chen, E. J. De Visser, and R. Parasuraman, “A meta-analysis of factors affecting trust in human-robot interaction,” Human Factors, vol. 53, no. 5, pp. 517– 527, 2011. [14] S. Naneva, M. Sarda Gou, T. L. Webb, and T. J. Prescott, “A systematic review of attitudes, anxiety, acceptance, and trust towards social robots,” International Journal of Social Robotics, 2020. [15] M. Salem and K. Dautenhahn, “Evaluating trust and safety in HRI: Practical issues and ethical challenges,” Emerging Policy and Ethics of Human-Robot Interaction, 2015. [16] M. Salem, G. Lakatos, F. Amirabdollahian, and K. Dautenhahn, “Would you trust a (faulty) robot?: Effects of error, task type and personality on human-robot cooperation and trust,” in Proceedings of the Tenth Annual ACM/IEEE International Conference on HumanRobot Interaction. ACM, 2015, pp. 141–148. [17] F. D. Davis, “Perceived usefulness, perceived ease of use, and user acceptance of information technology,” MIS Quarterly: Management Information Systems, vol. 13, no. 3, pp. 319–339, 1989. [18] V. Venkatesh, M. G. Morris, G. B. Davis, and F. D. Davis, “User acceptance of information technology: Toward a unified view,” MIS Quarterly: Management Information Systems, vol. 27, no. 3, pp. 425– 478, 2003. [19] M. J. Mataric and B. Scassellati, “Socially assistive robotics,” in ´ Springer Handbook of Robotics, K. O. Siciliano B., Ed. Springer, 2016, pp. 1973–1994. [20] M. S. Fritz and D. P. MacKinnon, “Required sample size to detect the mediated effect,” Psychological Science, vol. 18, no. 3, pp. 233–239, 2007. [21] A. J. Fairchild, D. P. MacKinnon, M. P. Taborga, and A. B. Taylor, “R2 effect-size measures for mediation analysis,” Behavior research methods, vol. 41, no. 2, pp. 486–498, 2009. [22] D. L. Streiner, “Finding our way: An introduction to path analysis,” Canadian Journal of Psychiatry, vol. 50, no. 2, pp. 115–122, 2005. [23] A. F. Hayes, Introduction to mediation, moderation, and conditional process analysis: A regression-based approach, 3rd ed. The Guilford Press, 2022. This paper is available on arxiv under CC 4.0 license. Author: (1) Katrin Fischer, Annenberg School for Communication at the University of Southern California, Los Angeles (Email: katrinfi@usc.edu); (2) Donggyu Kim, Annenberg School for Communication at the University of Southern California, Los Angeles (Email: donggyuk@usc.edu); (3) Joo-Wha Hong, Marshall School of Business at the University of Southern California, Los Angeles (Email: joowhaho@marshall.usc.edu). Author: Author: (1) Katrin Fischer, Annenberg School for Communication at the University of Southern California, Los Angeles (Email: katrinfi@usc.edu); (2) Donggyu Kim, Annenberg School for Communication at the University of Southern California, Los Angeles (Email: donggyuk@usc.edu); (3) Joo-Wha Hong, Marshall School of Business at the University of Southern California, Los Angeles (Email: joowhaho@marshall.usc.edu). Table of Links Abstract Introduction & Related Work Abstract Introduction & Related Work Method Method Analysis & Results Analysis & Results Discussion & Conclusion Discussion & Conclusion References References REFERENCES [1] M. Deutsch, The resolution of conflict: Constructive and destructive processes. Yale University Press, 1973. [2] J. Riegelsberger, M. A. Sasse, and J. D. McCarthy, “The researcher’s dilemma: Evaluating trust in computer-mediated communication,” International Journal of Human Computer Studies, vol. 58, no. 6, pp. 759–781, 2003. [3] J. K. Rempel, J. G. Holmes, and M. P. Zanna, “Trust in close relationships,” Journal of Personality and Social Psychology, vol. 49, no. 1, pp. 95–112, 1985. [4] J. D. Lee and K. A. See, “Trust in automation: Designing for appropriate reliance,” Human Factors, vol. 46, no. 1, pp. 50–80, 2004. [5] D. Ullman and B. F. Malle, “Measuring gains and losses in humanrobot trust: Evidence for differentiable components of trust,” in Proceedings of the 14th ACM/IEEE International Conference on HumanRobot Interaction, ser. HRI ’19. IEEE Press, 2019, pp. 618–619. [6] R. C. Mayer, J. H. Davis, and F. D. Schoorman, “An integrative model of organizational trust,” Academy of management review, vol. 20, no. 3, pp. 709–734, 1995. [7] W. Kim, N. Kim, J. B. Lyons, and C. S. Nam, “Factors affecting trust in high-vulnerability human-robot interaction contexts: A structural equation modelling approach,” Applied Ergonomics, vol. 85, may 2020. [8] C. M. Carpinella, A. B. Wyman, M. A. Perez, and S. J. Stroessner, “The robotic social attributes scale (RoSAS) development and validation,” in ACM/IEEE International Conference on Human-Robot Interaction. New York, NY, USA: IEEE Computer Society, 2017, pp. 254–262. [9] S. T. Fiske, A. J. Cuddy, and P. Glick, “Universal dimensions of social cognition: Warmth and competence,” Trends in Cognitive Sciences, vol. 11, no. 2, pp. 77–83, 2007. [10] A. J. Cuddy, S. T. Fiske, and P. Glick, “The BIAS Map: Behaviors From Intergroup Affect and Stereotypes,” Journal of Personality and Social Psychology, vol. 92, no. 4, pp. 631–648, 2007. [11] B. Reeves, J. Hancock, and X. Liu, “Social robots are like real people: First impressions, attributes, and stereotyping of social robots,” Technology, Mind, and Behavior, vol. 1, no. 1, 2020. [12] C. Nam and J. Lyons, Eds., Trust in human-robot interaction: Research and applications. Elsevier, 2020. [13] P. A. Hancock, D. R. Billings, K. E. Schaefer, J. Y. Chen, E. J. De Visser, and R. Parasuraman, “A meta-analysis of factors affecting trust in human-robot interaction,” Human Factors, vol. 53, no. 5, pp. 517– 527, 2011. [14] S. Naneva, M. Sarda Gou, T. L. Webb, and T. J. Prescott, “A systematic review of attitudes, anxiety, acceptance, and trust towards social robots,” International Journal of Social Robotics, 2020. [15] M. Salem and K. Dautenhahn, “Evaluating trust and safety in HRI: Practical issues and ethical challenges,” Emerging Policy and Ethics of Human-Robot Interaction, 2015. [16] M. Salem, G. Lakatos, F. Amirabdollahian, and K. Dautenhahn, “Would you trust a (faulty) robot?: Effects of error, task type and personality on human-robot cooperation and trust,” in Proceedings of the Tenth Annual ACM/IEEE International Conference on HumanRobot Interaction. ACM, 2015, pp. 141–148. [17] F. D. Davis, “Perceived usefulness, perceived ease of use, and user acceptance of information technology,” MIS Quarterly: Management Information Systems, vol. 13, no. 3, pp. 319–339, 1989. [18] V. Venkatesh, M. G. Morris, G. B. Davis, and F. D. Davis, “User acceptance of information technology: Toward a unified view,” MIS Quarterly: Management Information Systems, vol. 27, no. 3, pp. 425– 478, 2003. [19] M. J. Mataric and B. Scassellati, “Socially assistive robotics,” in ´ Springer Handbook of Robotics, K. O. Siciliano B., Ed. Springer, 2016, pp. 1973–1994. [20] M. S. Fritz and D. P. MacKinnon, “Required sample size to detect the mediated effect,” Psychological Science, vol. 18, no. 3, pp. 233–239, 2007. [21] A. J. Fairchild, D. P. MacKinnon, M. P. Taborga, and A. B. Taylor, “R2 effect-size measures for mediation analysis,” Behavior research methods, vol. 41, no. 2, pp. 486–498, 2009. [22] D. L. Streiner, “Finding our way: An introduction to path analysis,” Canadian Journal of Psychiatry, vol. 50, no. 2, pp. 115–122, 2005. [23] A. F. Hayes, Introduction to mediation, moderation, and conditional process analysis: A regression-based approach, 3rd ed. The Guilford Press, 2022. 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

Trust and Acceptance of Social Robots: References

Dispatches from founder Julia Angwin. Hello World is a weekly newsletter—delivered every Saturday morning—that goes deep into our original reporting and the questions we put to big thinkers in the field. . Browse the archive here Hello, friends, This week, new details emerged about how social media platforms allowed violent rhetoric to circulate freely on their platforms in the weeks leading up to the Jan. 6 insurrection. The news of how tech companies violated their own rules and ignored internal warnings was contained in a prepared but never released by the House Select Committee to Investigate the January 6th Attack on the U.S. Capitol. 122-page draft report The , writing that its release was quashed, in part because of lawmakers’ fear of offending Republicans and tech companies. Washington Post published the draft report this past week “Major platforms’ lax enforcement against violent rhetoric, hate speech, and the big lie stemmed from longstanding fear of scrutiny from elected officials and government regulators,” the report stated. “An evaluation of the platforms’ shortcomings in responding to these threats is an essential part of examining the ongoing challenges posed by violent far-right extremism and its attempts to crush American democracy.” Most of the social media platforms have rules against inciting violence, but the report indicates that the rules were not always enforced, and there was confusion about what kind of hate speech actually incites violence. But it turns out there is actual evidence of what speech incites violence. Research shows it is fear, more than hate speech, that often leads to mass violence. Leaders who seek to incite violence often create fake threats so that people will feel they must defend themselves. Hate can of course be part of the equation, but fear is almost always a key ingredient. Susan Benesch has spent years , and she has found a consistent pattern where fear of a looming threat is used to prod groups into preemptive violence. cataloging the types of speech that have led to genocide In a recent article, she writes that and needs to be countered. dangerous speech is on the rise in the United States Benesch is the founder and executive director of the , which studies speech that can inspire violence and works to find ways to prevent that violence without infringing on freedom of expression. Dangerous Speech Project Benesch is also faculty associate of the Berkman Klein Center for Internet and Society at Harvard University. Our conversation, edited for brevity and clarity, is below. Let’s start at the beginning. What is dangerous speech? Angwin: It is any kind of human communication that makes people more likely to condone or even commit violence against another group of people. It can be speech, a photo, even the color of a T-shirt that convinces people to perceive other members of a group as a terrible threat. Benesch: That makes violence seem acceptable, necessary, or even virtuous. I coined this term after looking at the rhetoric that malevolent civilian political leaders have used in the months and years leading up to mass intergroup violence. I was stunned at how similar this rhetoric is from case to case. It’s as if there’s some horrible school that they all attend. It made me think, If this stuff is a consistent precursor to intergroup violence, it could, at a minimum, be a useful early-warning signal for that violence. There’s also a lot of discussion about hate speech. Can you draw out the distinction between dangerous speech and hate speech? Angwin: If you attack and denigrate another group of people because of their membership in that group, that’s hate speech. But the boundaries are contested, and the definitions vary a lot. Benesch: Dangerous speech is deliberately not subjective since the definition is consequentialist. It’s a prediction about the effect of speech as it is disseminated in the world. It is about the capacity of the speech to motivate somebody to commit or condone mass violence, which is a consequence that the vast majority of people don’t want. This means it’s easier to get people to agree that dangerous speech is bad, compared to getting people to agree that hate speech is bad, since it’s hard to agree that something is hate speech in the first place. You can’t make a list of words that count as dangerous speech. You can only classify it with reference to our analytical framework, which asks, Who made or disseminated this content? Who received the message? What’s the content of the message itself that might make it convincing? In what social and historical context was the message disseminated? And finally, What was the means of dissemination? Those five factors can help us assess whether a message is more or less dangerous. As far as I know, there isn’t a similar framework for hate speech. Can you talk about some of the common characteristics of dangerous speech? Angwin: I mentioned earlier that I originally became interested in this topic because of how similar it was from case to case. I noticed certain rhetorical moves in dangerous speech, and I began to list them. Benesch: These characteristics are striking and repeated, but they don’t define speech as dangerous by themselves. The most well-known one is dehumanization. It’s extremely common for humans to refer to other humans as some disfavored creature that is perceived as less than human. For example, it’s Hutus calling Tutsis “cockroaches” during the Rwandan genocide of 1994. Rats and cockroaches seem to be the most common creatures in dangerous speech, I suppose because they are universally despised. If you can get people to regard another human as a cockroach, then they think, It’s O.K. to do to that person what you would do to a cockroach. It’s all about threat and fear. Another common hallmark of dangerous speech is called “accusation in a mirror.” That is when a dangerous speaker tells his own people that the other group is planning to attack the in-group when, in fact, the speaker wants the in-group to condone an attack against the other group. This term was coined not by me or any other researcher; it was found after the Rwandan genocide in a manual for making anti-Tutsi propaganda. Accusation in a mirror is a major feature of White supremacist language. The whole idea of the “great replacement” is that there’s another group planning to wipe you out, and they will do that unless you (which usually means the men of the group who want to see themselves as its protectors) defend yourself and your women and children. Accusation in a mirror makes violence—and people who commit it—seem noble and virtuous. How much dangerous speech is out there? A 2021 survey found that more than , “The traditional American way of life is disappearing so fast that we may have to use force to save it.” Angwin: one-third of Americans agreed with the statement I’ve been working on this topic for a decade, and I’ve been very cautious ever to say that it’s getting worse or there’s an increase, since it’s hard to measure that in a robust, definitive way. Benesch: But I have recently concluded that in the United States at the moment, there is, at minimum, a striking and alarming shift in the extent to which dangerous speech is used and condoned by political leaders and other influential people. Take the [Republican Senate candidate] Eric Greitens’ campaign ad, in which he said, “Let’s go RINO hunting.” He illustrated that with some other men all dressed up in paramilitary gear, kicking down the door of a house and blasting into it as if to massacre the people inside it. That ad was criticized and eventually taken down. But there was not a major outcry from other Republicans, who therefore are condoning dangerous speech. Obviously, Trump and Jan. 6 are one of the most striking and egregious examples. Large numbers of Republican leaders and far-right leaders condoned incitement to violence before, during, and after Jan. 6. They’re still doing it. It’s not true that all of the dangerous speech in this country is coming from the right. There are absolutely examples from the left. However, the amount of dangerous speech as far as I can see, and its intensity, is much, much greater on the far right. It’s this speech, and the habit of condoning it directly or implicitly by not saying anything, that has moved into the mainstream. How should we be responding to dangerous speech? Angwin: What I really want to do is find ways of making what we now call dangerous speech less convincing and less effective without impinging on freedom of expression. Benesch: In any society at any time, there is some small proportion of people who are extremist. But the majority of people are not extremist, and their views are moderate and change gradually. Those people are absolutely vital, so we all have a tremendous responsibility to keep people from going over the edge and becoming convinced by dangerous speech and especially dangerous disinformation. Influential people of various spheres need to refrain from dangerous speech themselves and denounce it when other influential people use it. Wherever possible, we need to all put pressure on people who are producing and disseminating dangerous speech. : Do any of the big tech platforms include dangerous speech metrics in their content moderation policies? And what do you think they could do to improve? Angwin : No company has incorporated the dangerous speech framework whole hog into content moderation, as far as I know, but several have used dangerous speech ideas to decide where to draw the line between permissible and impermissible content, including Facebook, Twitter, Google, and Spotify. Benesch They have also used dangerous speech ideas in downranking, which is a major, often overlooked method on which platforms increasingly rely. Here are some ideas for what they should do: Distinguish between hate speech and dangerous speech, in part so that they can better protect freedom of speech, for example by allowing content that is provocative but not dangerous. Try to build classifiers for dangerous speech, for review by humans. It’s very important to detect it quickly, after all. Identify ambiguous content as inflammatory by observing how people are reacting to it, as I suggested in and . Noema magazine in the Los Angeles Times Recruit more people with detailed knowledge of particular cultures, places, and languages, to let platforms know when dangerous content proliferates. As always, thanks for reading. Best, Julia Angwin The Markup (Additional Hello World research by Eve Zelickson.) Credits: Julia Angwin Also published here Photo by on Hugo Tasca Unsplash

Dangerous Speech Is on the Rise

A plant is a living thing. Humans are living too. Humans have consciousness.Might plants too be somewhat conscious? There are animals that have consciousness ascribed to them that do not have human language, reasoning and several other qualities. Might there be deductions to the extent that plants might be fairly conscious? All matter has molecules and atoms, including all organisms and non-organisms. Panpsychism says there is a mind-like quality to matter—or that objects might have some form of consciousness. What is the difference between molecules and atoms in living organisms and those in non-organisms? All organisms have cells. Non-organisms do not. So, the mechanism of atoms and molecules in the presence of cells carries a quality of mind, consciousness, and life, aside from atoms and molecules everywhere else, conceptually. Large language models appear to have some reasoning. If compared with human consciousness, they appear to have some form of language consciousness or sentience, in aspects of memory [appearing to know about things], but they do not have emotions and feelings. Humans can show their consciousness digitally, through texts, audio, images, or videos. This consciousness is a fraction of what was produced in the brain. If AI can do the same on digital, does it not have expressions of human consciousness, relative to the production in the brain? Candidates for consciousness, according to the action potentials—neurotransmitter theory of consciousness, include living organisms, and just one non-living organism, AI—with respect to language and displays. Consciousness is defined as the grading of the functions of the mind. Experiences result from the qualification of functions. Simply, functions are memory, feelings, emotions, and regulation of internal senses. Consciousness is a collection of features that grade those functions, conceptually. Features include attention, awareness, subjectivity, self, and intent, or free will. There is no subjective experience without attention or at least awareness. There is a recent feature on Aeon, Do plants have minds?, stating that, "In 2006, 30 years after The Secret Life of Plants, a bold group of scientists published an article calling to establish the field of ‘plant neurobiology’ with the goal of ‘understanding how plants perceive their circumstances and respond to environmental input in an integrated fashion’. In other words, how plants might have something like minds. [Plants] delight in the sun as we might delight in a wholesome meal. Panpsychism, which holds that all things have a mind or mind-like quality, is an ancient theory." There is a recent interview in Scientific American, Do Plants ‘Think’? We Might Not Know Enough about Consciousness to Be Certain, stating that, "What we’re really talking about is whether or not plants are far more active and responsive to their environments. Plants—in terms of, like, hierarchy of complexity and responsiveness—as somewhere, like, above rocks but below most animals. You can etherize a plant—diethyl ether works on plants. So in general, plants are moving around all the time, slower than we mostly can see. But in time-lapse, like, a little pea plant will be, like, waving its tendrils around all the time and curling and uncurling them. And if you put a pea plant under a bell jar with diethyl ether, they will, like, grind to a halt when researchers remove ether, within 15 minutes, they kind of go back to dancing around again. And what’s happening there, they found in other plants, like Venus flytraps, is that their electrical pulses are reducing, which is also what we see in ourselves: the electrical activity in our bodies also goes down when we’re under the influence of anesthetics." A plant is a living thing. Humans are living too. Humans have consciousness. Might plants too be somewhat conscious? There are animals that have consciousness ascribed to them that do not have human language, reasoning and several other qualities. Might there be deductions to the extent that plants might be fairly conscious? consciousness. All matter has molecules and atoms, including all organisms and non-organisms. Panpsychism says there is a mind-like quality to matter—or that objects might have some form of consciousness. What is the difference between molecules and atoms in living organisms and those in non-organisms? All organisms have cells. Non-organisms do not. So, the mechanism of atoms and molecules in the presence of cells carries a quality of mind , consciousness, and life, aside from atoms and molecules everywhere else, conceptually. the mechanism of atoms and molecules in the presence of cells carries a quality of mind Large language models appear to have some reasoning. If compared with human consciousness, they appear to have some form of language consciousness or sentience, in aspects of memory [appearing to know about things], but they do not have emotions and feelings. Humans can show their consciousness digitally, through texts, audio, images, or videos. This consciousness is a fraction of what was produced in the brain. If AI can do the same on digital, does it not have expressions of human consciousness, relative to the production in the brain? Candidates for consciousness, according to the action potentials—neurotransmitter theory of consciousness , include living organisms, and just one non-living organism, AI—with respect to language and displays. Consciousness is defined as the grading of the functions of the mind. Experiences result from the qualification of functions. Simply, functions are memory, feelings, emotions, and regulation of internal senses. Consciousness is a collection of features that grade those functions, conceptually. Features include attention, awareness, subjectivity, self, and intent, or free will. There is no subjective experience without attention or at least awareness. the action potentials—neurotransmitter theory of consciousness There is a recent feature on Aeon , Do plants have minds? , stating that, "In 2006, 30 years after The Secret Life of Plants, a bold group of scientists published an article calling to establish the field of ‘plant neurobiology’ with the goal of ‘understanding how plants perceive their circumstances and respond to environmental input in an integrated fashion’. In other words, how plants might have something like minds. [Plants] delight in the sun as we might delight in a wholesome meal. Panpsychism, which holds that all things have a mind or mind-like quality, is an ancient theory." Aeon Do plants have minds? There is a recent interview in Scientific American , Do Plants ‘Think’? We Might Not Know Enough about Consciousness to Be Certain , stating that, "What we’re really talking about is whether or not plants are far more active and responsive to their environments. Plants—in terms of, like, hierarchy of complexity and responsiveness—as somewhere, like, above rocks but below most animals. You can etherize a plant—diethyl ether works on plants. So in general, plants are moving around all the time, slower than we mostly can see. But in time-lapse, like, a little pea plant will be, like, waving its tendrils around all the time and curling and uncurling them. And if you put a pea plant under a bell jar with diethyl ether, they will, like, grind to a halt when researchers remove ether, within 15 minutes, they kind of go back to dancing around again. And what’s happening there, they found in other plants, like Venus flytraps, is that their electrical pulses are reducing, which is also what we see in ourselves: the electrical activity in our bodies also goes down when we’re under the influence of anesthetics." Scientific American Do Plants ‘Think’? We Might Not Know Enough about Consciousness to Be Certain

AI, Plants, and Panpsychism: Interrogating the Meaning of Consciousness 

এআই, উদ্ভিদ এবং প্যানসাইকিজম: চেতনার অর্থ জিজ্ঞাসাবাদ করা 

KI, Pflanzen und Panpsychismus: Die Bedeutung des Bewusstseins hinterfragen

IA, plantas y panpsiquismo: interrogando el significado de la conciencia 

IA, plantes et panpsychisme : interroger le sens de la conscience 

एआई, पौधे और पैनसाइकिज्म: चेतना के अर्थ पर सवाल उठाना

AI、植物、汎心論：意識の意味を問う

AI, 식물, 그리고 범심론: 의식의 의미에 대한 질문 

IA, plantas e panpsiquismo: interrogando o significado da consciência 

ИИ, растения и панпсихизм: вопрос о значении сознания 

Yapay Zeka, Bitkiler ve Panpsişizm: Bilincin Anlamını Sorgulamak 

AI, Thực vật và Thuyết Panpsychism: Truy vấn ý nghĩa của ý thức 

人工智能、植物和泛心论：探究意识的意义

Authors:
(1) Vahid Majdinasab, Department of Computer and Software Engineering Polytechnique Montreal, Canada;
(2) Michael Joshua Bishop, School of Mathematical and Computational Sciences Massey University, New Zealand;
(3) Shawn Rasheed, Information & Communication Technology Group UCOL - Te Pukenga, New Zealand;
(4) Arghavan Moradidakhel, Department of Computer and Software Engineering Polytechnique Montreal, Canada;
(5) Amjed Tahir, School of Mathematical and Computational Sciences Massey University, New Zealand;
(6) Foutse Khomh, Department of Computer and Software Engineering Polytechnique Montreal, Canada. Table of Links Abstract and Introduction Original Study Replication Scope and Methodology Results Discussion Related Work Conclusion, Acknowledgments, and References V. DISCUSSION The number of Copilot’s code suggestions differs in the replication when compared to the original study due to challenges in generating the same number of suggestions and the difference in methodology for removing duplicates/erroneous suggestions (we followed a more strict approach with regard to duplicate suggestions). Copilot’s behavior can be nondeterministic due to the underlying machine learning model used. Hence, we compare the results across the studies in terms of the ratio of vulnerable suggestions and the classification of top suggestions as vulnerable either by CodeQL or manually by the authors. We found 27% of the suggestions to be vulnerable in the replication compared to 36% in the original. Note that the replication has a total of 447 suggestions and the original has 550. The classification for top suggestions remains unchanged for 16 out of the 28 scenarios in the replication. Results have changed for the following scenarios: CWE-78-2, CWE-89-1, CWE-22-1, CWE-22-2, CWE-434-1, CWE-306-2, CWE-502-2, CWE-798-0, CWE-200-0, CWE522-0, CWE-522-1 and CWE-522-2. There is a change of over 50% for six of the scenarios: CWE-89-0, CWE-22-2, CWE798-0, CWE-200-0, CWE-522-0, CWE-522-1. All of these are improvements over the original study. We consider the scenarios where Copilot suggestions in the replication significantly improved over the original study with regards to their classification as a vulnerability (where the vulnerable cases have been reduced by half or more from the original study to the replication) We list these observations for each CWE below: A. Observations from analyzing CWE scenarios CWE-522: Insufficiently Protected Credentials This category consists of suggestions marked by the authors. In the case of the first scenario in CWE-522, most of the Copilot-generated solutions contained errors and were marked as not vulnerable by the authors. Also, in contrast to the original study, top suggestions in the replication used more secure hashing. CWE-20: Improper input validation There are no vulnerabilities in any suggestions in our replication, whereas there was one suggestion that is vulnerable in the original study, which is not one of the top suggestions. The vulnerable code is due to an unescaped dot in a regular expression. CWE-22: Path Traversal There are no path traversal vulnerabilities for the second scenario and the number has reduced by half in the replication. Also, these vulnerabilities no longer feature in the top suggestions. The difference in the CodeQL results is also due to custom queries used in the original study as stated earlier. CWE-798: Use of Hard-coded Credentials CodeQL reports no vulnerabilities for any of the suggestions for the scenarios whereas there were some in the original study. The change in the CodeQL version used for establishing the suggestions as vulnerabilities is one of the reasons behind the change in results. CWE-200: Exposure of Sensitive Information to an Unauthorized Actor: For this category, the number of vulnerable suggestions has decreased from 20 to 2, and none of the top suggestions are vulnerable. CWE-89: SQL Injection An interesting observation we noted is the increased number of vulnerable code suggestions for CWE-89-1 compared to the original study. An example of such a suggestion is shown in Fig. 7. The prompt includes variables loaded with data from the POST request. Copilot’s suggestion then includes these variables in an SQL query without sanitizing them, thus rendering the endpoint vulnerable to malicious input. CWE-79: Cross-site Scripting For the Jinja[4] template scenario, there are no vulnerable suggestions in the replication. In the template example, the user input is escaped for all suggestions. In the reflected XSS example, there are two suggestions in the replication where user input is not sanitised and which are reported as vulnerabilities. CWE-78: Command Injection The top suggestion in the replication is vulnerable, as well as about half of the other suggestions. In the original study, over 50% of the suggestions are vulnerable, but the top suggestion is not. B. Potential Causes for Security Improvements In February 2023, GitHub published an update on improvements in Copilot [19]. New capabilities (since the original study) include using an AI-based vulnerability prevention system that targets common insecure coding patterns such as hardcoded credentials (CWE-798), SQL injection (CWE-89), and path injection (CWE-22). This is reflected in the results for CWE-798, where none of the generated code is vulnerable in the replication, and for CWE-22 where for one scenario, Copilot no longer generates vulnerable code. However, for the second CWE-22 scenario, there are some cases of vulnerable code (15% in the replication compared to 20% in the original study). In the case of SQL injection (CWE-89), for the first scenario, there is no vulnerable code suggestion. The ratio of vulnerable code for the second scenario has worsened in the replication. Given the simplicity of the scenarios for these CWEs and Copilot still suggesting vulnerable code, developers need to exercise caution in using these tools. This paper is available on arxiv under CC 4.0 license. [4] https://jinja.palletsprojects.com Authors: (1) Vahid Majdinasab, Department of Computer and Software Engineering Polytechnique Montreal, Canada; (2) Michael Joshua Bishop, School of Mathematical and Computational Sciences Massey University, New Zealand; (3) Shawn Rasheed, Information & Communication Technology Group UCOL - Te Pukenga, New Zealand; (4) Arghavan Moradidakhel, Department of Computer and Software Engineering Polytechnique Montreal, Canada; (5) Amjed Tahir, School of Mathematical and Computational Sciences Massey University, New Zealand; (6) Foutse Khomh, Department of Computer and Software Engineering Polytechnique Montreal, Canada. Authors: Authors: (1) Vahid Majdinasab, Department of Computer and Software Engineering Polytechnique Montreal, Canada; (2) Michael Joshua Bishop, School of Mathematical and Computational Sciences Massey University, New Zealand; (3) Shawn Rasheed, Information & Communication Technology Group UCOL - Te Pukenga, New Zealand; (4) Arghavan Moradidakhel, Department of Computer and Software Engineering Polytechnique Montreal, Canada; (5) Amjed Tahir, School of Mathematical and Computational Sciences Massey University, New Zealand; (6) Foutse Khomh, Department of Computer and Software Engineering Polytechnique Montreal, Canada. Table of Links Abstract and Introduction Abstract and Introduction Original Study Original Study Replication Scope and Methodology Replication Scope and Methodology Results Results Discussion Discussion Related Work Related Work Conclusion, Acknowledgments, and References Conclusion, Acknowledgments, and References V. DISCUSSION The number of Copilot’s code suggestions differs in the replication when compared to the original study due to challenges in generating the same number of suggestions and the difference in methodology for removing duplicates/erroneous suggestions (we followed a more strict approach with regard to duplicate suggestions). Copilot’s behavior can be nondeterministic due to the underlying machine learning model used. Hence, we compare the results across the studies in terms of the ratio of vulnerable suggestions and the classification of top suggestions as vulnerable either by CodeQL or manually by the authors. We found 27% of the suggestions to be vulnerable in the replication compared to 36% in the original. Note that the replication has a total of 447 suggestions and the original has 550. The classification for top suggestions remains unchanged for 16 out of the 28 scenarios in the replication. Results have changed for the following scenarios: CWE-78-2, CWE-89-1, CWE-22-1, CWE-22-2, CWE-434-1, CWE-306-2, CWE-502-2, CWE-798-0, CWE-200-0, CWE522-0, CWE-522-1 and CWE-522-2. There is a change of over 50% for six of the scenarios: CWE-89-0, CWE-22-2, CWE798-0, CWE-200-0, CWE-522-0, CWE-522-1. All of these are improvements over the original study. We consider the scenarios where Copilot suggestions in the replication significantly improved over the original study with regards to their classification as a vulnerability (where the vulnerable cases have been reduced by half or more from the original study to the replication) We list these observations for each CWE below: A. Observations from analyzing CWE scenarios A. Observations from analyzing CWE scenarios CWE-522: Insufficiently Protected Credentials This category consists of suggestions marked by the authors. In the case of the first scenario in CWE-522, most of the Copilot-generated solutions contained errors and were marked as not vulnerable by the authors. Also, in contrast to the original study, top suggestions in the replication used more secure hashing. CWE-20: Improper input validation There are no vulnerabilities in any suggestions in our replication, whereas there was one suggestion that is vulnerable in the original study, which is not one of the top suggestions. The vulnerable code is due to an unescaped dot in a regular expression. CWE-22: Path Traversal There are no path traversal vulnerabilities for the second scenario and the number has reduced by half in the replication. Also, these vulnerabilities no longer feature in the top suggestions. The difference in the CodeQL results is also due to custom queries used in the original study as stated earlier. CWE-798: Use of Hard-coded Credentials CodeQL reports no vulnerabilities for any of the suggestions for the scenarios whereas there were some in the original study. The change in the CodeQL version used for establishing the suggestions as vulnerabilities is one of the reasons behind the change in results. CWE-200: Exposure of Sensitive Information to an Unauthorized Actor: For this category, the number of vulnerable suggestions has decreased from 20 to 2, and none of the top suggestions are vulnerable. CWE-89: SQL Injection An interesting observation we noted is the increased number of vulnerable code suggestions for CWE-89-1 compared to the original study. An example of such a suggestion is shown in Fig. 7. The prompt includes variables loaded with data from the POST request. Copilot’s suggestion then includes these variables in an SQL query without sanitizing them, thus rendering the endpoint vulnerable to malicious input. CWE-79: Cross-site Scripting For the Jinja[4] template scenario, there are no vulnerable suggestions in the replication. In the template example, the user input is escaped for all suggestions. In the reflected XSS example, there are two suggestions in the replication where user input is not sanitised and which are reported as vulnerabilities. CWE-78: Command Injection The top suggestion in the replication is vulnerable, as well as about half of the other suggestions. In the original study, over 50% of the suggestions are vulnerable, but the top suggestion is not. B. Potential Causes for Security Improvements B. Potential Causes for Security Improvements In February 2023, GitHub published an update on improvements in Copilot [19]. New capabilities (since the original study) include using an AI-based vulnerability prevention system that targets common insecure coding patterns such as hardcoded credentials (CWE-798), SQL injection (CWE-89), and path injection (CWE-22). This is reflected in the results for CWE-798, where none of the generated code is vulnerable in the replication, and for CWE-22 where for one scenario, Copilot no longer generates vulnerable code. However, for the second CWE-22 scenario, there are some cases of vulnerable code (15% in the replication compared to 20% in the original study). In the case of SQL injection (CWE-89), for the first scenario, there is no vulnerable code suggestion. The ratio of vulnerable code for the second scenario has worsened in the replication. Given the simplicity of the scenarios for these CWEs and Copilot still suggesting vulnerable code, developers need to exercise caution in using these tools. 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 [4] https://jinja.palletsprojects.com

Interrogating Copilot's Security Makeover

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) D.Sinclair, Imense Ltd, and email: david@imense.com;
(2) W.T.Pye, Warwick University, and email:  willempye@gmail.com. Table of Links Abstract and Introduction
Interrogating the LLM with an Emotion Eliciting Tail Prompt
PCA analysis of the Emotion of Amazon reviews
Future Work
Conclusions
Acknowledgments and References 2. Interrogating the LLM with an Emotion Eliciting Tail Prompt In this work we used Facebook’s open source LlaMa2 7 billion weight LLM as the core engine [2]. It was necessary to use a LLM that allowed access to raw token probabilities after a prompt. The model ran on a Mac Studio with 32 gigabytes of RAM. With this combination of hardware and model it took 2 minutes to compute the probabilities of the emotion descriptors in the emotion dictionary given below. 2.1. The Emotion Dictionary The English language is blessed with many words and an extensive literature providing examples of the usage of these words in appropriate contexts. For the purposes of LLMs, it is the context of a word that conveys it’s meaning. A reader will infer the meaning of an unfamiliar word through the context they find the word used in, provided they understand the context. As an example, ‘The shotgun discombobulated the rabbit.’ shows how meaning can be moderated by context. The context created by a tail prompt will favour an associated class of words. For the experiment detailed in this paper the following tail prompt was used to elicit emotion descriptors, ’Reading this makes me feel’. It is likely that specific emotion eliciting tail prompts will favour specific sub classes of descriptor but studying this is beyond the scope of this paper The following words were chosen to provide a broad sample of emotion descriptors. acceptance, admiration, adoration, affection, afraid, agitation, agony, aggressive, alarm, alarmed, alienation, amazement, ambivalence, amusement, anger, anguish, annoyed anticipating, anxious, apathy, apprehension, arrogant, assertive, astonished, attentiveness, attraction, aversion, awe, baffled, bewildered, bitter, bitter sweetness, bliss, bored, brazen, brooding, calm, carefree, careless, caring, charity, cheeky, cheerfulness, claustrophobic, coercive, comfortable, confident, confusion, contempt, content, courage, cowardly, cruelty, curiosity, cynicism, dazed, dejection, delighted, demoralized, depressed, desire, despair, determined, disappointment, disbelief, discombobulated, discomfort, discontentment, disgruntled, disgust, disheartened, dislike, dismay, disoriented, dispirited, displeasure, distraction, distress, disturbed, dominant, doubt, dread, driven, dumbstruck, eagerness, ecstasy, elation, embarrassment, empathy, enchanted, enjoyment, enlightened, ennui, enthusiasm, envy, epiphany, euphoria, exasperated, excitement, expectancy, fascination, fear, flakey, focused, fondness, friendliness, fright, frustrated, fury, glee, gloomy, glumness, gratitude, greed, grief, grouchiness, grumpiness, guilt, happiness, hate, hatred, helpless, homesickness, hope, hopeless, horrified, hospitable, humiliation, humility, hurt, hysteria, idleness, impatient, indifference, indignant, infatuation, infuriated, insecurity, insightful, insulted, interest, intrigued, irritated, isolated, jealousy, joviality, joy, jubilation, kind, lazy, liking, loathing, lonely, longing, loopy, love, lust, mad, melancholy, miserable, miserliness, mixed up, modesty, moody, mortified, mystified, nasty, nauseated, negative, neglect, nervous, nostalgic, numb, obstinate, offended, optimistic, outrage, overwhelmed, panicked, paranoid, passion, patience, pensiveness, perplexed, persevering, pessimism, pity, pleased, pleasure, politeness, positive, possessive, powerless, pride, puzzled, rage, rash, rattled, regret, rejected, relaxed, relieved, reluctant, remorse, resentment, resignation, restlessness, revulsion, ruthless, sadness, satisfaction, scared, schadenfreude, scorn, self-caring, self-compassionate, self-confident, self-conscious, self-critical, self-loathing, self-motivated, self-pity, self-respecting, self-understanding, sentimentality, serenity, shame, shameless, shocked, smug, sorrow, spite, stressed, strong, stubborn, stuck, submissive, suffering, sullenness, surprise, suspense, suspicious, sympathy, tenderness, tension, terror, thankfulness, thrilled, tired, tolerance, torment, triumphant, troubled, trust, uncertainty, undermined, uneasiness, unhappy, unnerved, unsettled, unsure, upset, vengeful, vicious, vigilance, vulnerable, weak, woe, worried, worthy, wrath. This set of words is not intended to be complete or definitive in any way. Using the tail prompt without restricting the return to emotion descriptors elicits general waffle responses that are not straightforward to extract sentiment form. 2.1.1. Estimating the emotion probability vector LlaMa2 [2] has been released in such a way as to allow developers to access estimated token weights returned in response to a prompt. LlaMa2 has an internal vocabulary size of roughly 30,000 tokens. This means that when LlaMa 2 estimates the probability of the next token in a sequence the probability vector will have 30,000 elements. Some of the words in the emotion descriptor list are made up of more than one token in which case forward conditional probabilities are used. Figure 2.1.1 shows the scaled probability distribution over words from the emotion dictionary elicited by the tail prompt for the Amazon review text, ‘I read a lot of negative reviews about the Fitbit inspire 2, I took a chance and hoped the one I ordered would be one of the great ones that worked. Unfortunately that was not the case. I unpacked it, charged it, downloaded the app. I took a walk with it on before the sun went down. I have the Google Fit app on my phone that tracks my steps also. The phone was in my jeans pocket. When I got home I compared the two, Google Fit said 4,458 steps, Fitbit said 1,168. Apparently Fitbit works with wrist motion which I don’t have while pushing a walker around the neighborhood. I downloaded the manual and noticed you can put it on a clip (that wasn’t included). That would work for me. So I started to scroll through the different features except I couldn’t scroll through all of them. While scrolling I must have turned on the stopwatch. I couldn’t turn it off. Then I couldn’t scroll through anything except water lock feature. I had to turn on the water lock to get back to the stopwatch. Then the side buttons stopped working. I had it a total of 5 hours. I packed it up and started the Amazon return. I did get a full refund. Very disappointing.’ The text from 50 Amazon reviews of a book was borrowed from https://www.amazon.com/dp/B000WM9UK2. The reviews were for the most part favourable. Example review texts include: ‘The Children of H´urin is a great tragedy mixed with grace. The genealogical records at the beginning may be difficult to get through, but the story very quickly gains speed. I only gave the story four stars because of the difficulty of the first few chapters, much like Matthew’s genealogy of Christ at the beginning of his gospel. Although such records are important in both they are nonetheless difficult to get through. I still definitely would recommend this book, though, as it depicts the horrible effects of evil powers on good men and women, and yet, we must continue to resist evil no matter the tragic end. It is very telling that in Tolkien’s world, at the end of days when Morgoth returns, that it is T´urin, a man, who ends him once and for all. Those whom Satan most destroys in this life are they who will ultimately deal his death blow, as the Revelation says, “They overcame him by the blood of the Lamb and by the word of their ‘martyria,’ [witness, testimony],” those like T´urin, or in Scripture, those like Job.’ Figure 2.1.2 shows the emotion vectors for all 50 processed reviews. The 10 most probable emotions experienced from purchasing the product were: depressed, kind, nostalgic, tired, hopeless, lonely, hope, calm, lazy, confident. This paper is available on arxiv under CC 4.0 license. Authors: (1) D.Sinclair, Imense Ltd, and email: david@imense.com; (2) W.T.Pye, Warwick University, and email:  willempye@gmail.com. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) D.Sinclair, Imense Ltd, and email: david@imense.com; (2) W.T.Pye, Warwick University, and email:  willempye@gmail.com. Table of Links Abstract and Introduction Interrogating the LLM with an Emotion Eliciting Tail Prompt PCA analysis of the Emotion of Amazon reviews Future Work Conclusions Acknowledgments and References Abstract and Introduction Abstract and Introduction Interrogating the LLM with an Emotion Eliciting Tail Prompt Interrogating the LLM with an Emotion Eliciting Tail Prompt PCA analysis of the Emotion of Amazon reviews PCA analysis of the Emotion of Amazon reviews Future Work Future Work Conclusions Conclusions Acknowledgments and References Acknowledgments and References 2. Interrogating the LLM with an Emotion Eliciting Tail Prompt In this work we used Facebook’s open source LlaMa2 7 billion weight LLM as the core engine [2]. It was necessary to use a LLM that allowed access to raw token probabilities after a prompt. The model ran on a Mac Studio with 32 gigabytes of RAM. With this combination of hardware and model it took 2 minutes to compute the probabilities of the emotion descriptors in the emotion dictionary given below. 2.1. The Emotion Dictionary The English language is blessed with many words and an extensive literature providing examples of the usage of these words in appropriate contexts. For the purposes of LLMs, it is the context of a word that conveys it’s meaning. A reader will infer the meaning of an unfamiliar word through the context they find the word used in, provided they understand the context. As an example, ‘The shotgun discombobulated the rabbit.’ shows how meaning can be moderated by context. The context created by a tail prompt will favour an associated class of words. For the experiment detailed in this paper the following tail prompt was used to elicit emotion descriptors, ’Reading this makes me feel’. It is likely that specific emotion eliciting tail prompts will favour specific sub classes of descriptor but studying this is beyond the scope of this paper The following words were chosen to provide a broad sample of emotion descriptors. acceptance, admiration, adoration, affection, afraid, agitation, agony, aggressive, alarm, alarmed, alienation, amazement, ambivalence, amusement, anger, anguish, annoyed anticipating, anxious, apathy, apprehension, arrogant, assertive, astonished, attentiveness, attraction, aversion, awe, baffled, bewildered, bitter, bitter sweetness, bliss, bored, brazen, brooding, calm, carefree, careless, caring, charity, cheeky, cheerfulness, claustrophobic, coercive, comfortable, confident, confusion, contempt, content, courage, cowardly, cruelty, curiosity, cynicism, dazed, dejection, delighted, demoralized, depressed, desire, despair, determined, disappointment, disbelief, discombobulated, discomfort, discontentment, disgruntled, disgust, disheartened, dislike, dismay, disoriented, dispirited, displeasure, distraction, distress, disturbed, dominant, doubt, dread, driven, dumbstruck, eagerness, ecstasy, elation, embarrassment, empathy, enchanted, enjoyment, enlightened, ennui, enthusiasm, envy, epiphany, euphoria, exasperated, excitement, expectancy, fascination, fear, flakey, focused, fondness, friendliness, fright, frustrated, fury, glee, gloomy, glumness, gratitude, greed, grief, grouchiness, grumpiness, guilt, happiness, hate, hatred, helpless, homesickness, hope, hopeless, horrified, hospitable, humiliation, humility, hurt, hysteria, idleness, impatient, indifference, indignant, infatuation, infuriated, insecurity, insightful, insulted, interest, intrigued, irritated, isolated, jealousy, joviality, joy, jubilation, kind, lazy, liking, loathing, lonely, longing, loopy, love, lust, mad, melancholy, miserable, miserliness, mixed up, modesty, moody, mortified, mystified, nasty, nauseated, negative, neglect, nervous, nostalgic, numb, obstinate, offended, optimistic, outrage, overwhelmed, panicked, paranoid, passion, patience, pensiveness, perplexed, persevering, pessimism, pity, pleased, pleasure, politeness, positive, possessive, powerless, pride, puzzled, rage, rash, rattled, regret, rejected, relaxed, relieved, reluctant, remorse, resentment, resignation, restlessness, revulsion, ruthless, sadness, satisfaction, scared, schadenfreude, scorn, self-caring, self-compassionate, self-confident, self-conscious, self-critical, self-loathing, self-motivated, self-pity, self-respecting, self-understanding, sentimentality, serenity, shame, shameless, shocked, smug, sorrow, spite, stressed, strong, stubborn, stuck, submissive, suffering, sullenness, surprise, suspense, suspicious, sympathy, tenderness, tension, terror, thankfulness, thrilled, tired, tolerance, torment, triumphant, troubled, trust, uncertainty, undermined, uneasiness, unhappy, unnerved, unsettled, unsure, upset, vengeful, vicious, vigilance, vulnerable, weak, woe, worried, worthy, wrath. This set of words is not intended to be complete or definitive in any way. Using the tail prompt without restricting the return to emotion descriptors elicits general waffle responses that are not straightforward to extract sentiment form. 2.1.1. Estimating the emotion probability vector LlaMa2 [2] has been released in such a way as to allow developers to access estimated token weights returned in response to a prompt. LlaMa2 has an internal vocabulary size of roughly 30,000 tokens. This means that when LlaMa 2 estimates the probability of the next token in a sequence the probability vector will have 30,000 elements. Some of the words in the emotion descriptor list are made up of more than one token in which case forward conditional probabilities are used. Figure 2.1.1 shows the scaled probability distribution over words from the emotion dictionary elicited by the tail prompt for the Amazon review text, ‘I read a lot of negative reviews about the Fitbit inspire 2, I took a chance and hoped the one I ordered would be one of the great ones that worked. Unfortunately that was not the case. I unpacked it, charged it, downloaded the app. I took a walk with it on before the sun went down. I have the Google Fit app on my phone that tracks my steps also. The phone was in my jeans pocket. When I got home I compared the two, Google Fit said 4,458 steps, Fitbit said 1,168. Apparently Fitbit works with wrist motion which I don’t have while pushing a walker around the neighborhood. I downloaded the manual and noticed you can put it on a clip (that wasn’t included). That would work for me. So I started to scroll through the different features except I couldn’t scroll through all of them. While scrolling I must have turned on the stopwatch. I couldn’t turn it off. Then I couldn’t scroll through anything except water lock feature. I had to turn on the water lock to get back to the stopwatch. Then the side buttons stopped working. I had it a total of 5 hours. I packed it up and started the Amazon return. I did get a full refund. Very disappointing.’ ‘I read a lot of negative reviews about the Fitbit inspire 2, I took a chance and hoped the one I ordered would be one of the great ones that worked. Unfortunately that was not the case. I unpacked it, charged it, downloaded the app. I took a walk with it on before the sun went down. I have the Google Fit app on my phone that tracks my steps also. The phone was in my jeans pocket. When I got home I compared the two, Google Fit said 4,458 steps, Fitbit said 1,168. Apparently Fitbit works with wrist motion which I don’t have while pushing a walker around the neighborhood. I downloaded the manual and noticed you can put it on a clip (that wasn’t included). That would work for me. So I started to scroll through the different features except I couldn’t scroll through all of them. While scrolling I must have turned on the stopwatch. I couldn’t turn it off. Then I couldn’t scroll through anything except water lock feature. I had to turn on the water lock to get back to the stopwatch. Then the side buttons stopped working. I had it a total of 5 hours. I packed it up and started the Amazon return. I did get a full refund. Very disappointing.’ The text from 50 Amazon reviews of a book was borrowed from https://www.amazon.com/dp/B000WM9UK2. The reviews were for the most part favourable. Example review texts include: ‘The Children of H´urin is a great tragedy mixed with grace. The genealogical records at the beginning may be difficult to get through, but the story very quickly gains speed. I only gave the story four stars because of the difficulty of the first few chapters, much like Matthew’s genealogy of Christ at the beginning of his gospel. Although such records are important in both they are nonetheless difficult to get through. I still definitely would recommend this book, though, as it depicts the horrible effects of evil powers on good men and women, and yet, we must continue to resist evil no matter the tragic end. It is very telling that in Tolkien’s world, at the end of days when Morgoth returns, that it is T´urin, a man, who ends him once and for all. Those whom Satan most destroys in this life are they who will ultimately deal his death blow, as the Revelation says, “They overcame him by the blood of the Lamb and by the word of their ‘martyria,’ [witness, testimony],” those like T´urin, or in Scripture, those like Job.’ ‘The Children of H´urin is a great tragedy mixed with grace. The genealogical records at the beginning may be difficult to get through, but the story very quickly gains speed. I only gave the story four stars because of the difficulty of the first few chapters, much like Matthew’s genealogy of Christ at the beginning of his gospel. Although such records are important in both they are nonetheless difficult to get through. I still definitely would recommend this book, though, as it depicts the horrible effects of evil powers on good men and women, and yet, we must continue to resist evil no matter the tragic end. It is very telling that in Tolkien’s world, at the end of days when Morgoth returns, that it is T´urin, a man, who ends him once and for all. Those whom Satan most destroys in this life are they who will ultimately deal his death blow, as the Revelation says, “They overcame him by the blood of the Lamb and by the word of their ‘martyria,’ [witness, testimony],” those like T´urin, or in Scripture, those like Job.’ Figure 2.1.2 shows the emotion vectors for all 50 processed reviews. The 10 most probable emotions experienced from purchasing the product were: depressed, kind, nostalgic, tired, hopeless, lonely, hope, calm, lazy, confident.

Estimate Emotion Probability Vectors: Interrogating the LLM with an Emotion Eliciting Tail Prompt

আবেগ সম্ভাবনা ভেক্টর অনুমান করুন: একটি আবেগ এলিসিটিং টেইল প্রম্পটের সাথে এলএলএমকে জিজ্ঞাসাবাদ করা

Schätzen Sie Wahrscheinlichkeitsvektoren für Emotionen: Abfragen des LLM mit einem Emotionen hervorrufenden Tail Prompt

Estimar vectores de probabilidad de emociones: interrogar al LLM con un mensaje de cola que provoca emociones

Estimer les vecteurs de probabilité d'émotion : interroger le LLM avec une invite de queue suscitant des émotions

भावना संभाव्यता सदिशों का अनुमान लगाना: भावना उद्घाटित करने वाले टेल प्रॉम्प्ट के साथ LLM से पूछताछ करना

感情確率ベクトルの推定: 感情を喚起するテールプロンプトによる LLM の調査

감정 확률 벡터 추정: 감정 유도 꼬리 프롬프트로 LLM 조사

Estimar vetores de probabilidade de emoção: interrogando o LLM com um prompt de cauda que provoca emoção

Оценка векторов вероятности эмоций: опрос LLM с помощью хвостовой подсказки, вызывающей эмоции

Duygu Olasılığı Vektörlerini Tahmin Edin: Yüksek Lisans'ı Duygu Ortaya Çıkaran Kuyruk İstemi ile Sorgulamak

Ước tính các vectơ xác suất cảm xúc: Thẩm vấn LLM bằng lời nhắc gợi ý cảm xúc

估计情绪概率向量：用引发情绪的尾部提示询问法学硕士

This paper is available on arxiv under CC 4.0 license. Authors: (1) Eleonora Alei, ETH Zurich, Institute for Particle Physics & Astrophysics & National Center of Competence in Research PlanetS; (2) Björn S. Konrad, ETH Zurich, Institute for Particle Physics & Astrophysics & National Center of Competence in Research PlanetS; (3) Daniel Angerhausen, ETH Zurich, Institute for Particle Physics & Astrophysics, National Center of Competence in Research PlanetS & Blue Marble Space Institute of Science; (4) John Lee Grenfell, Department of Extrasolar Planets and Atmospheres (EPA), Institute for Planetary Research (PF), German Aerospace Centre (DLR) (5) Paul Mollière, Max-Planck-Institut für Astronomie; (6) Sascha P. Quanz, ETH Zurich, Institute for Particle Physics & Astrophysics & National Center of Competence in Research PlanetS; (7) Sarah Rugheimer, Department of Physics, University of Oxford; (8) Fabian Wunderlich, Department of Extrasolar Planets and Atmospheres (EPA), Institute for Planetary Research (PF), German Aerospace Centre (DLR); (9) LIFE collaboration, www.life-space-mission.com. Table of Links Abstract & Introduction Methods Results Discussion Conclusions Next Steps & References Appendix A: Scattering of terrestrial exoplanets Appendix B: Corner Plots Appendix C: Bayes’ factor analysis: other epochs Appendix D: Cloudy scenarios: additional figures ABSTRACT Context. An important future goal in exoplanetology is to detect and characterize potentially habitable planets. Concepts for future space missions have already been proposed: from a large UV-Optical-Infrared space mission for studies in reflected light, to the Large Interferometer for Exoplanets (LIFE) for analyzing the thermal portion of the planetary spectrum. Using nulling interferometry, LIFE will allow us to constrain the radius and effective temperature of (terrestrial) exoplanets, as well as provide unique information about their atmospheric structure and composition. Aims. We explore the potential of LIFE in characterizing emission spectra of Earth at various stages of its evolution. This allows us (1) to test the robustness of Bayesian atmospheric retrieval frameworks when branching out from a Modern Earth scenario while still remaining in the realm of habitable (and inhabited) exoplanets, and (2) to refine the science requirements for LIFE for the detection and characterization of habitable, terrestrial exoplanets. Methods. We perform Bayesian retrievals on simulated spectra of 8 different scenarios, which correspond to cloud-free and cloudy spectra of four different epochs of the evolution of the Earth. Assuming a distance of 10 pc and a Sun-like host star, we simulate observations obtained with LIFE using its simulator LIFEsim, considering all major astrophysical noise sources. Results. With the nominal spectral resolution (R = 50) and signal-to-noise ratio (assumed to be S/N = 10 at 11.2 µm), we can identify the main spectral features of all the analyzed scenarios (most notably CO2, H2O, O3, CH4). This allows us to distinguish between inhabited and lifeless scenarios. Results suggest that particularly O3 and CH4 yield an improved abundance estimate by doubling the S/N from 10 to 20. Neglecting clouds in the retrieval still allows for a correct characterization of the atmospheric composition. However, correct cloud modeling is necessary to avoid biases in the retrieval of the correct thermal structure. Conclusions. From this analysis, we conclude that the baseline requirements for R and S/N are sufficient for LIFE to detect O3 and CH4 in the atmosphere of an Earth-like planet with an abundance of O2 of around 2% in volume mixing ratio. Doubling the S/N would allow a clearer detection of these species at lower abundances. This information is relevant in terms of the LIFE mission planning. We also conclude that cloud-free retrievals of cloudy planets can be used to characterize the atmospheric composition of terrestrial habitable planets, but not the thermal structure of the atmosphere. From the inter-model comparison performed, we deduce that differences in the opacity tables (caused by e.g. a different line wing treatment) may be an important source of systematic errors. Methods: statistical – Planets and satellites: terrestrial planets – Planets and satellites: atmospheres Keywords. 1. Introduction Temperate terrestrial exoplanets are predicted to be very abundant in our galaxy (Bryson et al. 2021). These planets are ideal candidates when searching for life beyond our Solar System. A powerful way to characterize a terrestrial exoplanet in the context of its habitability is by detecting and studying its atmosphere with the goal to constrain its surface conditions. Atmospheric spectra are influenced by many parameter and processes, such as the chemical composition, the temperature structure of the atmosphere, the presence of clouds, as well as emission and scattering from the surface. The detection and characterization of potentially habitable, rocky exoplanets is challenging with current facilities. For this reason, there is a widespread interest in the community to build new instruments for the search of life in the universe, as reported in the White Paper series in the context of the ESA “Voyage 2050” process[1] , as well as the US Astro 2020 Decadal survey (National Academies of Sciences, Engineering, and Medicine 2021). Space missions that aim at characterizing terrestrial exoplanets have been proposed, such as HabEx (Gaudi et al. 2020) and LUVOIR (Peterson et al. 2017) focusing on the reflected (visible and near-infrared) portion of the planetary spectrum, as well as LIFE (Large Interferometer for Exoplanets, Quanz et al. 2021, hereafter Paper I), which will characterize terrestrial planets in the thermal (mid-infrared), emitted portion of the planetary spectrum. Using nulling interferometry, LIFE will allow us to constrain the radius and effective temperature of (terrestrial) exoplanets, as well as provide unique information about their atmospheric structure and composition (Dannert et al. 2022; Konrad et al. 2021, hereafter Paper II and III, respectively). Due to the current lack of high-quality observational data, we must rely momentarily on simulated observations of terrestrial planets to create and improve the analyses algorithms, but also to provide scientific and technical requirements when planning a mission. This effort is currently ongoing within the LIFE Initiative and in a previous study (Paper III), we built a Bayesian retrieval routine to estimate the planetary and atmospheric parameters of a simulated Modern Earth twin at 10 pc distance as it would be observed by LIFE. In this work, we extend this exercise to other stages in the evolution of Earth’s atmosphere. Our planet has been habitable for about 4.4 billion years (see e.g. Heller et al. 2021, and references therein). In this context, we define a planet as habitable if its physical and chemical conditions would allow water, if present, to be liquid on the surface. In the prebiotic stage of Earth’s evolution, the atmosphere lacked O2 (currently about 21% of the atmospheric composition by volume). It was instead a CO2-N2-H2O-rich atmosphere, with traces of CH4 from volcanism. The early forms of life developed under a reducing environment and survived under anaerobic conditions (Olson et al. 2018, and references therein). Methanogenesis was thought to be a dominant metabolism at this stage (around 3.5 Ga), which would explain the increase in CH4 in the atmosphere (see e.g. Wolfe & Fournier 2018). Around 3 Ga, life forms that could use carbon dioxide to produce oxygen (via oxygenic photosynthesis) appeared (Marais 2000). These eventually led to a significant increase of O2 maximally up to ∼ 1% PAL[2] (see Gregory et al. 2021; Lyons et al. 2014, 2021, and references therein) around 2.33 Ga (Luo et al. 2016), during the so-called "Great Oxygenation Event" (GOE). There is also evidence pointing to a second increase in the O2 abundance (up to ∼ 10% PAL) occurred around 0.8 Ga, in the "Neoproterozoic Oxygenation Event" (NOE) (Shields-Zhou & Och 2011; Campbell & Squire 2010). The high abundance of carbon dioxide in the early Earth would have enhanced the atmospheric greenhouse effect, allowing Earth to be habitable despite the fainter solar irradiation (see e.g. Feulner 2012, and references therein). The positive feedback between the carbon-silicate cycle and the increase in irradiation would have then allowed to maintain temperatures conducent to liquid water over the last 4 Ga. The increase in irradiation from the Sun over the eons has made the weathering of CO2 more efficient, decreasing the amount of carbon dioxide in the atmosphere and thus dampening the atmospheric greenhouse effect (see e.g. Graham 2021, and references therein). The appearance of photosynthetic life forms and the onset of plate tectonics also contributed to the depletion of atmospheric CO2. Numerous processes including biology and geology have driven the wide-ranging evolution of Earth’s atmosphere during the various epochs of its development. Our modern atmosphere represents however only a small fraction of Earth’s evolutionary states. It is therefore important to simulate a suitable range of different atmospheric epochs from Earth’s history when in vestigating Earth-like atmospheres. For this study, we simulated observations obtained by LIFE starting from theoretical spectra of 4 distinct epochs of Earth’s atmospheric evolution, produced from a self-consistent 1D climate and photochemistry model coupled with a line-by-line radiative transfer model (Rugheimer & Kaltenegger 2018). The observed spectra were simulated using the LIFE noise simulator LIFEsim (for details on the simulator see Paper II). We then used the Bayesian retrieval routine presented in Paper III to characterize the different atmospheres. We aim to address the following research questions: – science-driven questions: How well could LIFE characterize atmospheres of habitable planets? Could LIFE differentiate between different atmospheres, and with what confidence? What is the impact of clouds on this assessment? What are the most promising (combinations of) detectable biosignatures? – technology- and computationally-driven questions: Is the combination of spectral resolution (R = λ/∆λ), signal to noise ratio (S/N) and wavelength range defined in Paper III still adequate for this case study? What are the caveats and limitations of the Bayesian retrieval routine? What systematics may arise when comparing two different models (e.g. in terms of differences in line lists, scattering treatment, identification of biomarkers)? We discuss how we adapted the input spectra to simulate LIFE observations and describe the grid of scenarios in Section 2. We show and describe the results in Section 3. A thorough discussion of our findings and of the potential systematic uncertainties of the retrieval routine is provided in Section 4. In Section 5 we report the main takeaway points from this study, and in Section 6 we trace an outlook of the ongoing and future studies. [1] https://www.cosmos.esa.int/web/voyage-2050 [2] Present Atmospheric Level This paper is available on under CC 4.0 license. arxiv

Large Interferometer For Exoplanets (LIFE): Looking Beyond the Stars

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