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Auvo

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Löydä koska ja missä Supertähtien yllätysremontit Suomi on katsottavissa. Tutustu tähän ja muihin televisio- sekä suoratoisto-ohjelmiin ja elokuviin sivustollamme Telkku.com!

Supertähtien yllätysremontit Suomi

Auvo auttaa Katria saamaan äänensä kuuluviin, mutta saa yllättäen itsekin kokea, millaista on olla pieni. julkaistu ke 24.8.2022 poistuu 7.9.2023 katselukerrat 6 014

Pientä ja suurta

Auvo, Manu ja Metti osuvat salaperäisen matkalaisen leirinuotiolle, ja saavat tältä merirosvoaarteen kartan. Monien mutkien jälkeen kartta johdattaa heidät satumaisen luontoaarteen ääreen. julkaistu su 5.6.2022 poistui 17.7.2023 katselukerrat 6 415

Viilumetsän aarre

Sepi lähtee hoidattamaan piikkejään ja pyytää Auvoa lastenvahdiksi. Tehtävä vaikuttaa helpolta, mutta kolmen vilkkaan pöllönpoikasen yhtäaikainen hoitaminen saa Auvon keksijänaivot raksuttamaan. julkaistu su 5.6.2022 poistui 10.7.2023 ...

Auvo lastenvahtina

Pilvi tekee valtavan, Vilma-kania esittävän patsaan, jonka siirtelymahdollisuus aiheuttaa tuhoa, kun opossumit keksivätkin lähteä koeajolle. julkaistu su 5.6.2022 poistuu 7.9.2023 katselukerrat 12 437

Vilman patsas

In 2021, hosted the first-ever awards!! HackerNoon Startup of the Year The goal of this campaign was to give recognition to the hardworking startups who pulled through the pandemic -- growing and thriving (despite all of the challenges). More than 44,000 startups were nominated all across the globe! They were selected from over 4,553 cities, with a total number of * cough * “4 billion” votes. Insane, right? To see who is a winner in each respective city, visit and pick a city. The name at the top of the list is the winner. (Awards, with compromised reviews, are under review.) startups.hackernoon.com The will announce the winners per region in their respective cities. Startups Winners Announcement Series Apparently, South America startups did incredibly well in that regard. With so many, until now, uncrowned jewels, it’s time we present to you... * drumroll* 🥳 The South America Startups of the Year Winners! Santiago, Chile Chile is known for being the most stable country to do business in Latin America, ranking 13th in The Economist’s Business Environment Index. Moreover, according to the Global Innovation Index, Chile ranks first in Latin America for innovation and is recognised as Chilean Silicon Valley with Santiago being the central city. The winner is…. QCObjects It is the 1st world class open-source framework made in Latin-America. São Paulo, Brazil And the winner of Brazil’s vibrant financial center is... Innvo Digital Their mission is to create the best ecosystem for a technology professional to work and undertake while sharing it with our customers and society. São Carlos The absolute leader of Sao Carlos seems to be: Predify Predify is an online software for optimizing pricing proccess and improving profits with AI and Big Data Analytics. Belo Horizonte, Brazil With a more than noble goal, Belo Horizone’s winner is: CM Tecnologia CM Tecnologia is an online scheduling system for healthcare providers in Brazil. Porto Alegre, Brazil With the focus on data protection, Porto Alegre, Brazil’s winner is…. Privacy Tools Privacy compliance platform, based on AI/Blockchain, which helps global companies to keep compliant with the data protection requirements. Brasil, Brazil With the focus on EdTech, Brasil is bringing us: Awari Awari is a YC-W21 company focusing on education for the Tech Industry. The company is growing at a fast pace and has gathered experts from all leading tech companies into a full education experience to prepare the next cohort of tech talent. Quito The capital of Ecuador, and also the closest capital to the actual equator, as the winner brings us: Agrodemanda The most comprehensive agricultural directory in Ecuador. Rio De Janeiro, Brazil Rio de Janeiro has more than 300 startups and is ranked 3rd in Brazil in terms of startup ecosystem development. The city is famous for the startups in the spheres of education, energy & environment and foodtech. Their winner is…. Bybrand Bybrand is the simplest way to save time and avoid frustration when managing your employee's email signatures. Buenos Aires, Argentina Buenos Aires, Argentina is not only the cosmopolitan capital with a vibrant history. It’s also Latin America’s most important port. Which makes their winner actually a very logical choice. Please meet: Liftit Liftit is the leading technology platform to automate and execute last-mile truck deliveries across Latin America. Curitiba, Brazil With a more noble mission and sustainability in mind, Curitiba, Brazil, as the winner brings us... Discrepante is the marketplace for a more human and sustainable economy Discrepante.com Florianópolis, Brazil Would you really be surprised one of the Brazil’s winners is related to football training? We neither. Please meet... Fútbol Fit Club Football FitClub® is a fitness training firm that gives fitness training to improve like a professional football player. Medellín, Colombia And the first Colombian Startup Winner is is a data intelligence solution powered by a high quality team. Meet... LogComex Market intelligence for international trade in Latin America Montevideo, Uruguay The host of inaugural FIFA world cup of 1930 comes through when it comes to innovation even in 2021. I’d like to introduce you to: Drop Technologies First electronic hand antiperspirant device Lima, Peru Lima, Peru, has in 2021 delivered some amazing results when it comes to blockchain technology. Their winner is very well deserved: RSK Labs RSK is the first open-source smart contract platform São Carlos Do Pinhal Well this is a unique case. The absolute unanimous winner of São Carlos Do Pinhal is: brain4care brain4care solution enables monitoring intracranial compliance noninvasively anywhere Blumenau, Brazil We’re sensing a theme here. Another stellar EdTech winner is coming from Blumenau, Brazil. Please meet... Uniasselvi Uniasselvi is a Brazilian distance-learning company that offers more than 60 undergraduate and postgraduate courses, in person and distance. Bogotá, Colombia Startups of Bogotá, Colombia definitely showed they’re not to be underestimated. With a very noble mission, let me introduce you to: Risü Risü is an online platform that links online consumption to donation to social institutions. Campina, Brazil If there’s one typo of business we’d single out as taking the biggest hit out of the pandemic, it would have to be small businesses. Thankfully, the winner of Campina, Brazil, operates with small businesses in mind Superlógica Tecnologias Ltda. Superlogica is a management system company created for small service businesses. Cerqueira César, Brazil Swooping half the points from the entire City, this year’s winners of Cerqueira César are InfoPrice INFOPRICE is a business intelligence company focused on physical retail pricing. Governador Valadares, Brazil The same can be said for the winners here. Let’s hear it for: Grupo KPG Saas, Payment, Manufacture, Pharmaceutical, E-commerce, Marketplace, Logistics Caxias Do Sul Known for wine making and Italian cultural influence, Caxias Do Sul brings as an interesting winner. Studio 360 Studio 360 is a proptech focused on easing the purchase experience for real state companies, brokers, and costumers. Sorocaba From Sorocaba, we have an interesting concept coming. Looking forward to seeing it further develop. Let’s meet: Quinto Quinto is a social network that represents the collective opinion of society—to help democracy evolve through technology Viña Del Mar To be a venue-oriented business and survive through the pandemic definitely deserves some recognition. In the name of that, let’s meet: SonicPollen SonicPollen, based in Chile, is a web-based booking platform that connects bands with venues and organizers. Lages Learn more about high-growth startups in Lages, South America. and the winner is…. Qualiall Qualiall develops technologies for training in operational processes such as work security, equipment maintenance, and more. Córdoba, Argentina Another gem from Argentina brings us unique agricultural solutions. Please meet: Kilimo Kilimo is a Big Data solution for water management in agriculture. São José Dos Campos, Brazil Another great agricultural solution comes this time from Brasil. Meet São José Dos Campos Startup winner: TerraMagna TerraMagna is a fintech company that focuses on risk mitigation for agribusinesses. São Leopoldo, Brazil Event-oriented businesses that have survived in the previous years should definitely bottle their perseverance and start selling it. One of those is definitely this winner: Wedy Wedy offers a one-stop-social marketplace for weddings. Pelotas, Brazil Medical scheduling platforms are definitely something we need to see more of. So glad Pelotos winner comes from this particular niche. Fácil Consulta Fácil Consulta is a booking platform that allows people to schedule an appointment with a Specialist Physician in their area. Santa Maria Could you believe how versatile the startups of South America are when we are only now coming across a game development studio? Let’s meet them Imgnation Studios Imgnation Studios is a game development studio that focuses on virtual reality. Asunción, Paraguay A FinTech solution coming from Paraguay. Meet Asunción Startup of the Year winner Vision Banco Vision Banco is a solid and growing financial institution serving the financial needs of the micro, small and medium enterprises. Caracas Venezualan food delivery app that seemed to have your heart. Meet Caracas Startup of the Year winner Yummy Inc. First Food Delivery App in Venezuela Recife A suitable combo between gaming and education, Recife Startup of the Year winner brings revolutionary solutions. Let’s meet them. Joy Street Joy Street offers develops video games focused on educational needs. Rosario, Argentina The winner of Rosario Startup of the Year award for 2021 is... Gbot Gbot is an autonomous robot that controls weeds without using agrochemicals. We also collect data in fields and generate precision maps. Vila Olímpia, Brazil The winner of Vila Olímpia Startup of the Year award for 2021 is... Seekr Seekr operates in the Information Technology sector, providing tools for managing and monitoring of brands. Barueri The winner of Barueri Startup of the Year award for 2021 is.... Glic Brazilian mhealth (web/app) platform for diabetes management Brasília The winner of Brasília Startup of the Year award for 2021 is... Ticies Ticies is a social network that helps people plan where to go with their friends, relatives and acquaintances, in all cities of the world. Juiz De Fora The winner of Juiz De Fora Startup of the Year award for 2021 is... Qranio Qranio is a gamified mobile learning platform for enterprises that rewards learning and stimulates users to engage with educational content. La Paz The winner of La Paz Startup of the Year award for 2021 is... Jukumari Advice 360 photo based market place for responsible tour operators Temuco The winner of Temuco Startup of the Year award for 2021 is... GoFeels GoFeels is the first independent hospitality network in the world. We promote collaborative work among hotels around the world. Vitacura The winner of Vitacura Startup of the Year award for 2021 is... MeatMe MeatMe delivers meats and accessories to have a culinary experience. Barranquilla The winner of Barranquilla Startup of the Year award for 2021 is... Mandaos Mandaos is a digital platform that connects companies and people with a network of independent messengers Ceará The winner of Ceará Startup of the Year award for 2021 is... Caroá Caroá is a cooperative that sells 34 varieties of vegetables, organic fruits, and vegetables. Chapecó The winner of Chapecó Startup of the Year award for 2021 is... HortiAgro HortiAgro is a seed and fertilizer market. Chile Chico The winner of Chile Chico Startup of the Year award for 2021 is... Gleengo Gleengo connects children with their favorite toys, through a tiny device and an application based on text-to-voice. Goiânia The winner of Goiânia Startup of the Year award for 2021 is... Auvo Auvo is an external teams management software. Itá The winner of Itá Startup of the Year award for 2021 is... TISSUELAB Tissuelab is a research and development center focused on biotechnology, and systems for reconstructive musculoskeletal surgery. Jardim Paulista The winner of Jardim Paulista Startup of the Year award for 2021 is... DiviCom DiviCom is a digital video networking company that designs, integrates, and markets complete systems. João Pessoa The winner of João Pessoa Startup of the Year award for 2021 is... YupiPlay YupiPlay develops and offers an educational gaming platform. La Reina The winner of La Reina Startup of the Year award for 2021 is... INTI-TECH INTI-TECH is the fastest growing renewable energy in Chile and the world's leading unconventional source. Leme The winner of Leme Startup of the Year award for 2021 is... EcoStart EcoStart focuses on manufacturing sustainable electric bicycles, fans, and accessories. Loja The winner of Loja Startup of the Year award for 2021 is... Enerwi Corporation Keeping people connected and secured through wireless charging novel solutions Manizales The winner of Manizales Startup of the Year award for 2021 is... Porter Metrics Porter Metrics has developed an automated solution for generating social media reports in minutes. Nova Lima The winner of Nova Lima Startup of the Year award for 2021 is... Biomimetic Solutions Biomimetic Solutions is an innovative tissue engineering company. Paraná The winner of Paraná Startup of the Year award for 2021 is... Laura Laura is a provider of artificial intelligence and predictive analytics platform for hospitala and clinics to prevent damage & reduce costs. Salvador The winner of Salvador Startup of the Year award for 2021 is... Sanar Sanar is a platform that focuses on being the doctor's guide and support, offering specialized content that helps in their development. Santa Catarina The winner of Santa Catarina Startup of the Year award for 2021 is... DrPOCKET Sunchales The winner of Sunchales Startup of the Year award for 2021 is... Eolo Pharma Eolo Pharma focuses on developing drugs targeting the molecular and cellular basis of inflammation-related diseases Valparaiso The winner of Valparaiso Startup of the Year award for 2021 is... Invitta Invitta transforms plastic waste into a high-performance construction material for manufacturing sustainable urban furniture. Vila Velha The winner of Vila Velha Startup of the Year award for 2021 is... OneGrid OneGrid is a developer of hardware and software solutions focused on energy management. Indaiatuba The winner of Indaiatuba Startup of the Year award for 2021 is... DWUL Energy Renewable Energy Campinas, Brazil The winner of Campinas Startup of the Year award for 2021 is... Ativy Ativy is an IT company specialized in cloud computing, data centers, IT outsourcing, service desk, and IT management. Las Condes, Chile The winner of Las Condes Startup of the Year award for 2021 is... # MuvPass Subscription services that allows it's users unlimited access to fitness classes in the best gyms and studios in Latin America. Pinheiros, Brazil The winner of Pinheiros Startup of the Year award for 2021 is... GuiaBolso GuiaBolso is a personal finance platform that empowers people to take control of financial well-being. Rio, Brazil The winner of Rio Startup of the Year award for 2021 is... EmCasa EmCasa is a vertically-integrated platform that helps customers buy and sell a home in Latin America, starting in Brazil. Guayaquil The winner of Guayaquil Startup of the Year award for 2021 is... Sabroso Online platform for LatAm's enterprises to order low-cost meals Jundiaí The winner of Jundiaí Startup of the Year award for 2021 is... Sintecsys Sintecsys offers a fast and effective detection, reducing losses and costs with fire brigades. Minas Gerais The winner of Minas Gerais Startup of the Year award for 2021 is... Rizoflora Biotechnology Rizoflora Biotechnology is a producer of biological product designed for nematodes control. São Caetano Do Sul The winner of São Caetano Do Sul Startup of the Year award for 2021 is... Wevo 1 iPaaS in Latam - Enabling companies to be truly digital companies with APIs, systems and data integrated into a single platform Canoas The winner of Canoas Startup of the Year award for 2021 is... ContaÁgil ContaÁgil facilitates and reduces bureaucracy in accounting routines of companies. Chile The winner of Chile Startup of the Year award for 2021 is... Unight Unight is the future of nightlife social media. Israelândia The winner of Israelândia Startup of the Year award for 2021 is... Matchpool Matchpool is a platform that creates human connections. La Plata The winner of La Plata Startup of the Year award for 2021 is... Pixivus Pixivus offers a community-based application that enables its users to collect, store, and share pictures of events. Novo Hamburgo The winner of Novo Hamburgo Startup of the Year award for 2021 is... Devorando Devorando offers an online food ordering system and menus available in restaurants based in Brazil. Pernambuco The winner of Pernambuco Startup of the Year award for 2021 is... Redu Redu is a distant learning platform which is built on a social networking structure. Providencia The winner of Providencia Startup of the Year award for 2021 is... # TrainFES TrainFES is a medical Devices for the rehabilitation of people with paralysis. Ribeirão Prêto The winner of Ribeirão Prêto Startup of the Year award for 2021 is... AllyHub Ally is a sales distribution channel technology that connects Schools and Service Providers with Education Agencies. Santa Cruz De La Sierra The winner of Santa Cruz De La Sierra Startup of the Year award for 2021 is... Mobi Latam Mobi Latam is a clean energy startup that focuses on transportation systems based on electric mobility. Santana De Parnaíba The winner of Santana De Parnaíba Startup of the Year award for 2021 is... MercadoLivre.com is an e-commerce technology company providing solutions to individuals and companies buying, selling, and more online. MercandoLivre.com More Information If you’re a winner, please answer ! If you’re a reader, participant, just a curious individual, consider answering one of our interviews . this interview here Read the FAQs about or . Startups here here To see all of the winners you may visit , pick any city and view the name that appears at the top. this site If you’re interested in partnering with us, we invite you to look through some of these or reach out to our business department. unique sponsorship options Perhaps creating your very own tickles your fancy or maybe you have a whole new idea for how we can partner! writing contest Thank you to all of our sponsors! We truly appreciate your contribution to the tech world and thus the future! The Startups Awards are sponsored by: , , , , , , , , , , Blockster Brex FreeCodeCamp LaunchDarkly Coil Udacity Udemy Vercel Asia Pacific Transgender Network Bybit Everscale Network This post was written/compiled by Shreya Kundu and Katarina Andrejević! If you have any questions or concerns or would like to report a discrepancy please email ellen@hackernoon.com.

Meet the Winners of Startups in South America

Authors: (1) Renato P. dos Santos, CIAGE – Centre for Generative Artificial Intelligence in Cognition and Education. Table of Links Abstract and Introduction Materials And Methods Results and Analyses Prompts and generated texts Conceptualizing chemical reactions Deepening on understanding of chemical reactions Question about combustion Question about a graph of gases turning into water over time Question about the difference between atoms, molecules, and moles Deepening on the concept of mole Question about changing of state Question about an animated representation of water molecules undergoing phase changes Question about plasma, a state of matter Question about chemical bondings Question about illustration of chemical bonds Question about the essence of the type of chemical bonding Further analysis Conclusions Limitations of the study and possible future studies Author Contributions, Conflicts of interest, Acknowledgements, and References Deepening on the concept of mole P1: so can you talk about a mole of beer bottles? ChatGPT: Absolutely! The concept of a "mole" isn't exclusive to atoms or molecules; it's simply a specific quantity. In the realm of chemistry, one mole always refers to 6.022 x 10[23] of some entity, whether that's atoms, molecules, electrons, or even beer bottles. So, when we say "a mole of beer bottles," we're referring to 6.022 x 10[23] beer bottles. That's aLOT of beer bottles! However, it's worth noting that outside of a chemistry context, using "mole" to refer to quantities of everyday items might be confusing. In everyday language, we have other terms for large quantities, like "dozen" (for 12 of something) or "gross" (for 144 of something). But theoretically, yes, you can have a mole of anything, including beer bottles. Just to give you a sense of how large this number is: If you were to try and count to Avogadro's number at a rate of one number per second, it would take you more than 19 billion years! Would you like to explore more about the implications of having a mole of beer bottles or any other fun examples? *: Yes, I can talk about a mole of beer bottles, but it might not be very practical. A mole of beer bottles is a very large number of beer bottles, much more than you can ever drink or see in your lifetime. Let me explain why.* BingChat A mole of beer bottles is the amount of beer bottles that contains as many bottles as there are atoms in 12 grams of carbon-12. This number is approximately 6.022 x 10[23], which is a very big number. To give you some idea of how big this number is, here are some comparisons: • If you had a mole of beer bottles, you could cover the entire surface of the Earth with about 100 layers of beer bottles. • If you had a mole of beer bottles, you could fill up the Pacific Ocean with beer bottles about 30 times. • If you had a mole of beer bottles, you could stack them up to reach the moon and back about 2000 times. As you can see, a mole of beer bottles is a huge amount of beer bottles that is impossible to handle or imagine. That’s why chemists use the mole as a unit of measurement for atoms and molecules, which are very small and numerous, rather than for everyday objects like beer bottles. A mole of atoms or molecules has a mass that is easy to measure and compare with other substances. For example, one mole of oxygen gas has a mass of 32 grams, and one mole of water has a mass of 18 grams. So, to summarize, a mole of beer bottles is a hypothetical amount of beer bottles that contains 6.022 x 10[23] bottles. This number is so large that it is not useful or meaningful for describing real-world situations. A mole is more suitable for measuring the amount of atoms or molecules in a substance. I hope this helps you understand what a mole of beer bottles means. *: Yes, I can talk about a mole of beer bottles.* Bard A mole of beer bottles is a unit of measurement that is used to count beer bottles. One mole of beer bottles contains 6.02214076×10^23 beer bottles. This number is known as Avogadro's constant. To put this into perspective, a mole of beer bottles is a lot of beer bottles! It would take over 100 billion years to count them all, at a rate of one bottle per second. Of course, no one has ever seen a mole of beer bottles in real life. It is a theoretical concept that is used by chemists to count atoms and molecules. But that doesn't mean that we can't have fun thinking about it. What would a mole of beer bottles look like? How much would it weigh? How much beer would it contain? If we assume that each beer bottle is 12 ounces, then a mole of beer bottles would weigh over 100 million pounds. And it would contain over 700 million gallons of beer! That's a lot of beer! So, next time you're enjoying a cold beer with your friends, take a moment to think about themole of beer bottles that it represents. It's a truly mind-boggling concept. *: Sure, I'm happy to explain the concept of a mole using beer bottles as an example.* Claude A mole represents a specific number - 6.02 x 10^23 - of anything, whether atoms, molecules, or objects like beer bottles. Let's imagine a mole of beer bottles. That would be 6.02 x 10^23 individual bottles. That's a huge number of bottles! To put it in perspective: • If you had 1 mole of beer bottles and stacked them on top of each other, the stack would reach about 1.5 billion light years tall - that's halfway across the observable universe! • If you lined up 1 mole of beer bottles side by side, the line would stretch for 9 trillion kilometers - over 200 times the distance between Earth and Pluto. • If you purchased 1 mole of beer bottles at $10 per bottle, it would cost 60.2 sextillion dollars - way more than the GDP of Earth! So a mole represents an enormous number of things. It's very useful in chemistry for relating atomic/molecular masses to measurable macroscopic masses. For example, if you know the mass of 1 mole of carbon atoms, you can use that to calculate how many carbon atoms are in a certain macroscopic sample. Hope this silly example with beer bottles helps give you an idea of just how huge a mole really is! Let me know if you need any clarification or want to go through more practice problems applying the mole concept. starts with “Absolutely!” offering . It then discusses the "mole" concept using by noting that while it is a "specific quantity" in the "realm of chemistry," it is not exclusively tied to "atoms" or "molecules" and enters a Meta-discussion & Perspective Framing by conceding that "outside of a chemistry context," the general use of the word ‘mole’ “might be confusing”. Proceeding further, the explanation by making analogies with everyday terms like "dozen" and "gross", also with the . The perspective on counting to Avogadro's number “to give a sense” of how large this number is and the final invitation to discuss "implications" or "any other fun examples" leans into . also starts by offering , conceding that “Yes”, one “can talk about a mole of beer bottles” but right after turns to , by referring that “it might not be very practical” and later that “it is not useful or meaningful for describing real-world situations”. It then provides a detailed explanation of the mole concept, highlighting the enormousness of "6.022 x 10^23" through real-world scenarios like covering the "Earth" or filling the "Pacific Ocean", which emphasises the . Categorisation of the mole as primarily for counting "atoms/molecules" again showcases , while providing an example of the application of the concept to oxygen gas aligns with the . The closing “I hope this helps” followed by an emoji implies a touch of . likewise starts with “Yes, I can talk about a mole of beer bottles,” offering . It then engages the reader with the enormity of "6.02214076×10^23 beer bottles", by undergoing a detailed calculation of “What would a mole of beer bottles? look like”, offering perspectives on time ("100 billion years") and volume ("700 million gallons of visualisation and “To put this into perspective”, aligning with . Highlighting it as a "theoretical concept" "used by chemists emphasises and the jovial remark about enjoying beer with friendly tone emphasising the mind-bogglingness of the indicates . Claude starts with , accepting beer bottles as an example to breakdown of the mole concept and its application as a “specific number,” “very useful in chemistry,” that may be applied to count “anything”. It then invites one to “imagine a mole of beer bottles”, which would be “a ,huge number” and puts “it in perspective”, drawing vivid pictures such as a stack reaching "1.5 billion light years", a line stretching "200 times the distance between Earth and Pluto," and the cost of “60.2 sextillion dollars,” underscoring the . The closing “Hope this silly example” and followed by invitation for more "practice problems" or “clarifications” hints at . Nevertheless, while the use of the word "silly" by Claude might intend to suggest that the example provided is not to be taken too seriously or is trivial in nature, it also could be perceived as a bit demeaning or dismissive, negatively impacting the category. ChatGPT Personalization & Personable Touch Instructional Strategies Use of Analogy & Comparative Illustrations Engagement & Interactivity BingChat Personalization & Personable Touch Instructional Strategies Use of Analogy & Comparative Illustrations Instructional Strategies Use of Analogy & Comparative Illustrations Personalization & Personable Touch Bard Personalization & Personable Touch Use of Analogy & Comparative Illustrations Instructional Strategies Personalization & Personable Touch Instructional Strategies Use of Analogy & Comparative Illustrations Reinforcement & Feedback Personalization & Personable Touch This paper is under CC BY-SA 4.0 DEED license. available on arxiv

How GenAIbots Demystify Mole Concept, Avogadro’s Number, Atoms, and Molecules

কিভাবে GenAIbots মোল ধারণা, অ্যাভোগাড্রোর সংখ্যা, পরমাণু এবং অণুগুলিকে ডিমিস্টিফাই করে

Wie GenAIbots das Mole-Konzept, die Avogadro-Zahl, Atome und Moleküle entmystifizieren

Cómo los GenAIbots desmitifican el concepto de mole, el número de Avogadro, los átomos y las moléculas

Comment les GenAIbots démystifient le concept de taupe, le nombre d'Avogadro, les atomes et les molécules

GenAIbots ने मोल अवधारणा, एवोगैड्रो संख्या, परमाणु और अणुओं के रहस्य को कैसे उजागर किया

GenAIbots がモル概念、アボガドロ数、原子、分子を解明する方法

GenAIbot이 두더지 개념, 아보가드로의 수, 원자 및 분자를 이해하는 방법

Como GenAIbots desmistificam o conceito de toupeira, número de Avogadro, átomos e moléculas

Как GenAIbots демистифицируют концепцию кротов, число Авогадро, атомы и молекулы

GenAIbotlar Köstebek Kavramını, Avogadro Sayısını, Atomları ve Molekülleri Nasıl Aydınlatıyor?

Cách GenAIbots làm sáng tỏ khái niệm nốt ruồi, số lượng, nguyên tử và phân tử của Avogadro

GenAIbots 如何揭开摩尔概念、阿伏伽德罗常数、原子和分子的神秘面纱

Starting my job hunt in a new country felt like stepping into a massive library with no idea where to find the book I needed. Coming right after the neighboring country's violent invasion of Ukraine in 2022, I arrived with high hopes but quickly encountered a wall. The job market here was a whole new world, and my usual approach just wasn't cutting it. Friends and settlement organizations suggested trying online job boards such as Job Bank Canada and Indeed. The concept of Job Application Automation with a Human Touch I wasn't aiming to spam companies with my resume. Instead, I sought to automate certain aspects of the process to save time and target suitable jobs for me in a more personalized manner. Moreover, it wasn’t solely about finding a job; it was also about learning how to use technology and AI, in particular. In sharing this story, I hope to inspire others to look at their challenges differently and maybe even find new ways to tackle them. Here's how I pieced it together, combining RSS feeds, server-side scripts, custom Chrome extensions, and the innovative use of AI to enrich data and personalize applications: Step 1: RSS for Job Listings Daily, a script on a Linux server I set up would fetch RSS feeds from Job Bank Canada, filtering opportunities based on my predefined keywords. It would store the data gathered into a cache file that another part of the system would work with later. This step appreciates the enduring value of RSS, allowing me and other old-school guys to stay updated with minimal effort. So a big special thank you to the services and platforms like Job Bank for maintaining it. Technologies used PHP
PHP Simple XML
Linux Bash Scripting
Crontabs Step 2: The Manual Touch: Reviewing Matches Despite the automation intention, I chose to review the matches manually, ensuring that only the most relevant opportunities were considered, at least at the beginning. This blend of automation and personal oversight was enough to maintain quality in the job hunt. I figured out there were too many vague jobs that didn't sound legit to me. Technically, the script reads the cached file and displays the number of items found for each keyword. At first, I was copying job posting details manually, but I gave up doing so for each listing. Even so, sometimes the same position can be seen twice or more if it contains different keywords, so… Technologies used PHP Step 3: Crafting a Custom Chrome Extension Screenshot of the extracted data (link) ..to expedite the review process and eliminate duplicates, I developed a simple parsing Chrome extension. The details it captured included the position name, salary range, contact info, and other essential data. I opted to automate this aspect in later versions of the system and either discard or repurpose the extension. Once I obtained the data, if I deemed the listing worthwhile, a click would save it directly to a Google Spreadsheet. Initially, I didn't want to set up a separate database for this project. Therefore, this convenient and widely used Google product suited my needs, allowing me to maintain simplicity The extension: Install the extension View Github source code Technologies used Google Spreadsheets API
Google Chrome’s extension
JavaScript Step 4: Enriching Data with Server-Side Scripts and AI I then enhanced my spreadsheet's listings using a server-side script to extract insights from employer websites, such as career page addresses, additional contact details, and social media links. More technical details were also gathered, like the server software and programming languages used. The major breakthrough came from integrating OpenAI's API, which summarized websites and classified industries, enriching each saved job listing with valuable context. To optimize OpenAI token usage, I trimmed the content to focus on the most relevant sections for analysis, avoiding excess from footers or irrelevant links. Technologies used PHP cURL
Prompt Engineering
OpenAI API
Google Spreadsheets API Step 5: Personalizing Cover Letters and resumes objectives To avoid the impersonal nature of mass applications, I used OpenAI's API selectively to tailor cover letters, inserting placeholders for the company name, industry, and the software they utilize. When you know the product or service the company produces, you understand the value you may provide to it. That was the only way to stand out among other applicants who used the semi-cold outreach approach. This strategy demonstrated a genuine interest in each employer even if I visited the website automatically in less than a second. Technologies used OpenAI API
PHP Step 6: Generating PDFs for Applications Aware of the limitations and risks associated with direct links in emails, I opted for a more conservative approach: converting resumes and cover letters into PDFs. Despite my preference for digital simplicity, the goal was to ensure my applications reached their destination with a reduced chance of being sidelined into spam folders or worse. The generated PDF files were cached on the server side and attached to my emails. Technologies used PHP
DomPDF
Ubuntu Linux
JavaScript Step 7: Enhancing Communication and Email Dispatch After the initial steps of data enrichment and personalization, it is now time for the critical phase — communication. Upon selecting a potential opportunity, a predefined email template is loaded into a text area, ready for any final, personal touches. The interface was similar to Gmail's one, where email addresses and subject lines were populated automatically. I used the position name, my name, and the company name in the subject. Sometimes I had to visit the website of a company of my interest again to ensure I don’t like or sound mistaken. Anyway, I reviewed and, if necessary, customized each message before proceeding. This part can be automated later too. At least I thought I would do so. To further avoid the stigma of automated spam, emails were dispatched at random intervals. This careful timing was designed to mimic human irregularity, reducing the likelihood of triggering spam filters and increasing the chance of email deliverability. Technologies used Gmail’s SMTP
CronJobs
Water.css
PHP Step 8: Refining Email opening Tracking Another gem of my job application toolkit was an email tracking system. I implemented a technique to outsmart tracker caching, subtly changing the tracking image's color scheme embedded in my email signature with each dispatch. This ensured each email open was uniquely recorded, triggering a script to log the activity directly into my Google Spreadsheet against the respective employer. This method provided precise insights into who engaged with my emails and how many times, significantly informing the effectiveness of my approach. While this tactic faced limitations with Gmail, it proved highly effective with other providers, particularly corporate ones. Technologies used cURL
Google Spreadsheets API
PHP Conclusion Sending each resume out of over 400 felt like giving away a great book to someone who's already received 400 others. The rare responses  I got were like small beams of light—rare but motivating. Adding a tracking system helped me see if my book even stood out in the pile. It was a journey of patience, perseverance, and a personal test showing that job platforms like Job Bank might not be the best for IT jobs. It seems employers prefer traditional methods like recruiters, LinkedIn, or employee referrals. Automating my job search was a huge learning curve. It was more than just speeding up applications; it was about making technology an ally in a personal quest. By blending RSS feeds, a Chrome extension, AI, and clever email strategies, I made my job hunt more efficient yet personal. The experience taught me valuable lessons about using technology wisely and staying true to myself. It's not about replacing the personal touch with technology but enhancing it, showing that with the right approach, we can push beyond what we thought was possible. Starting my job hunt in a new country felt like stepping into a massive library with no idea where to find the book I needed. Coming right after the neighboring country's violent invasion of Ukraine in 2022, I arrived with high hopes but quickly encountered a wall. The job market here was a whole new world, and my usual approach just wasn't cutting it. Friends and settlement organizations suggested trying online job boards such as Job Bank Canada and Indeed. The concept of Job Application Automation with a Human Touch I wasn't aiming to spam companies with my resume. Instead, I sought to automate certain aspects of the process to save time and target suitable jobs for me in a more personalized manner. Moreover, it wasn’t solely about finding a job; it was also about learning how to use technology and AI, in particular. In sharing this story, I hope to inspire others to look at their challenges differently and maybe even find new ways to tackle them. Here's how I pieced it together, combining RSS feeds, server-side scripts, custom Chrome extensions, and the innovative use of AI to enrich data and personalize applications: Step 1: RSS for Job Listings Daily, a script on a Linux server I set up would fetch RSS feeds from Job Bank Canada, filtering opportunities based on my predefined keywords. It would store the data gathered into a cache file that another part of the system would work with later. This step appreciates the enduring value of RSS, allowing me and other old-school guys to stay updated with minimal effort. So a big special thank you to the services and platforms like Job Bank for maintaining it. Technologies used PHP PHP Simple XML Linux Bash Scripting Crontabs PHP PHP Simple XML Linux Bash Scripting Crontabs Step 2: The Manual Touch: Reviewing Matches Despite the automation intention, I chose to review the matches manually, ensuring that only the most relevant opportunities were considered, at least at the beginning. This blend of automation and personal oversight was enough to maintain quality in the job hunt. I figured out there were too many vague jobs that didn't sound legit to me. Technically, the script reads the cached file and displays the number of items found for each keyword. At first, I was copying job posting details manually, but I gave up doing so for each listing. Even so, sometimes the same position can be seen twice or more if it contains different keywords, so… Technologies used PHP PHP Step 3: Crafting a Custom Chrome Extension Screenshot of the extracted data (link) Screenshot of the extracted data ( link ) link ..to expedite the review process and eliminate duplicates, I developed a simple parsing Chrome extension. The details it captured included the position name, salary range, contact info, and other essential data. I opted to automate this aspect in later versions of the system and either discard or repurpose the extension. Once I obtained the data, if I deemed the listing worthwhile, a click would save it directly to a Google Spreadsheet. Initially, I didn't want to set up a separate database for this project. Therefore, this convenient and widely used Google product suited my needs, allowing me to maintain simplicity The extension: Install the extension Install the extension View Github source code View Github source code Technologies used Google Spreadsheets API Google Chrome’s extension JavaScript Google Spreadsheets API Google Chrome’s extension JavaScript Step 4: Enriching Data with Server-Side Scripts and AI I then enhanced my spreadsheet's listings using a server-side script to extract insights from employer websites, such as career page addresses, additional contact details, and social media links. More technical details were also gathered, like the server software and programming languages used. The major breakthrough came from integrating OpenAI's API, which summarized websites and classified industries, enriching each saved job listing with valuable context. To optimize OpenAI token usage, I trimmed the content to focus on the most relevant sections for analysis, avoiding excess from footers or irrelevant links. Technologies used PHP cURL Prompt Engineering OpenAI API Google Spreadsheets API PHP cURL Prompt Engineering OpenAI API Google Spreadsheets API Step 5: Personalizing Cover Letters and resumes objectives To avoid the impersonal nature of mass applications, I used OpenAI's API selectively to tailor cover letters, inserting placeholders for the company name, industry, and the software they utilize. When you know the product or service the company produces, you understand the value you may provide to it. That was the only way to stand out among other applicants who used the semi-cold outreach approach. This strategy demonstrated a genuine interest in each employer even if I visited the website automatically in less than a second. Technologies used OpenAI API PHP OpenAI API PHP Step 6: Generating PDFs for Applications Aware of the limitations and risks associated with direct links in emails, I opted for a more conservative approach: converting resumes and cover letters into PDFs. Despite my preference for digital simplicity, the goal was to ensure my applications reached their destination with a reduced chance of being sidelined into spam folders or worse. The generated PDF files were cached on the server side and attached to my emails. Technologies used PHP DomPDF Ubuntu Linux JavaScript PHP DomPDF Ubuntu Linux JavaScript Step 7: Enhancing Communication and Email Dispatch After the initial steps of data enrichment and personalization, it is now time for the critical phase — communication. Upon selecting a potential opportunity, a predefined email template is loaded into a text area, ready for any final, personal touches. The interface was similar to Gmail's one, where email addresses and subject lines were populated automatically. I used the position name, my name, and the company name in the subject. Sometimes I had to visit the website of a company of my interest again to ensure I don’t like or sound mistaken. Anyway, I reviewed and, if necessary, customized each message before proceeding. This part can be automated later too. At least I thought I would do so. To further avoid the stigma of automated spam, emails were dispatched at random intervals. This careful timing was designed to mimic human irregularity, reducing the likelihood of triggering spam filters and increasing the chance of email deliverability. Technologies used Gmail’s SMTP CronJobs Water.css PHP Gmail’s SMTP CronJobs Water.css PHP Step 8: Refining Email opening Tracking Another gem of my job application toolkit was an email tracking system. I implemented a technique to outsmart tracker caching, subtly changing the tracking image's color scheme embedded in my email signature with each dispatch. This ensured each email open was uniquely recorded, triggering a script to log the activity directly into my Google Spreadsheet against the respective employer. This method provided precise insights into who engaged with my emails and how many times, significantly informing the effectiveness of my approach. While this tactic faced limitations with Gmail, it proved highly effective with other providers, particularly corporate ones. Technologies used cURL Google Spreadsheets API PHP cURL Google Spreadsheets API PHP Conclusion Sending each resume out of over 400 felt like giving away a great book to someone who's already received 400 others. The rare responses  I got were like small beams of light—rare but motivating. Adding a tracking system helped me see if my book even stood out in the pile. It was a journey of patience, perseverance, and a personal test showing that job platforms like Job Bank might not be the best for IT jobs. It seems employers prefer traditional methods like recruiters, LinkedIn, or employee referrals. Automating my job search was a huge learning curve. It was more than just speeding up applications; it was about making technology an ally in a personal quest. By blending RSS feeds, a Chrome extension, AI, and clever email strategies, I made my job hunt more efficient yet personal. The experience taught me valuable lessons about using technology wisely and staying true to myself. It's not about replacing the personal touch with technology but enhancing it, showing that with the right approach, we can push beyond what we thought was possible.

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Every great innovation comes with some unique challenges. Automation can simplify your marketing processes, save time, and give insight you may otherwise miss. As helpful — even necessary — as automating content is, it carries some cybersecurity risks you should know about. That doesn’t mean these tools are strictly unsafe, but it does warrant attention. You need to ensure you’re using AI safely if you hope to experience its benefits to the fullest. You can do so by following these five content automation cybersecurity steps. 1. Determine Your Risk Level The first step to more secure content automation is deciding exactly what and how serious your cyber risks are. A more detailed picture of your risk landscape will make it easier to determine what kind of protections are necessary. Some generative AI models may require legal permission to use customers’ data. Other marketing automation tools don’t store any customer information, so breaches won’t be quite so severe. Review your tools and the data they access to see what type of risk you’re dealing with. In general, the more sensitive information you use in content automation, the more advanced security measures you’ll need. Your specific market may influence these decisions, too. At least 14 states have data privacy laws that may introduce unique requirements. Consider all these factors when determining your risk level and err on the side of going further than required to be safe. 2. Encrypt Everything Regardless of your risk level, you’ll want to encrypt all your content automation data. Encryption scrambles information so it doesn’t reveal any actual details even if a hacker accesses it. That should apply to everything from customer information to social media logins to AI training data. You may already encrypt data at rest, but it’s important to encrypt it in transit, too. You might even be able to encrypt it during use. That wasn’t always possible, but new standards like homomorphic encryption let you analyze data with AI without decrypting it. Refer to any applicable laws or industry standards to see if there’s a specific level of encryption you must use. The government has recently recognized quantum-proof encryption methods you could use if you want to go the extra mile. 3. Restrict Access Access controls are another big part of content automation cybersecurity. When fewer people or programs can access your content tools, scheduling software, and social media accounts, hackers have fewer options to break into. Zero-trust architecture takes access restrictions and user authentication to extremes, so it’s the safest way forward. While this approach takes a while to set up, it increases visibility and reduces downtime by double-checking everything before trusting it. Even if you don’t opt for the zero-trust model, you should restrict content automation tools so only the employees who need it for their daily work can access it. Strict access controls only work when you can reliably tell who’s who. Simple username-password combinations are too simple and easy to break past. Use multi-factor authentication (MFA) — ideally through SMS or cryptographic keys and not email — instead. 4. Choose Your Tools Carefully Next, you’ll want to take a closer look at the software you use to automate your content strategy. These tools could potentially have access to some sensitive information — including customer data and your social media accounts — so you should ensure they’re safe before trusting them. The first step in reducing these third-party risks is to consolidate your content automation tools as much as possible. Businesses use more than a hundred apps on average, and any one of these could have security flaws that jeopardize your data. Opting for a single provider to cover more services makes it easier to ensure you’re using a reliable, safe solution. You should also have a formal process for determining if a software provider meets your security needs. The Cybersecurity and Information Security Agency outlines software supply chain security steps you can follow to create such a system. 5. Train Everyone Make sure everyone on your content team understands security best practices. These include aspects like strong password management, why to use MFA, and how to spot phishing attempts. As many as 95% of cybersecurity issues stem in part from human error. Consequently, if you train everyone on your team to practice safer, more mindful practices, you’ll prevent a lot of damage. Of course, even well-trained employees can still make mistakes, but the more prepared your workforce is for these risks, the less likely those errors will become. Because people tend to forget things, periodic refresher training is a good idea. Retrain everyone in security best practices at least once annually to keep safety at the top of everybody’s mind. Cybersecurity Is Crucial for Content Automation Without reliable cybersecurity, your content automation tools could become a huge risk before you realize it. By contrast, you can use this technology to its fullest extent if you protect it first. These five steps will help you create a cybersecurity plan that works for your content automation strategy. Be sure to review your system regularly to ensure you’re on top of any developing security trends or new risks. Sources: https://hackernoon.com/what-are-the-top-challenges-facing-cybersecurity-automation-adoption https://iapp.org/resources/article/us-state-privacy-legislation-tracker/ https://www.nist.gov/news-events/news/2022/07/nist-announces-first-four-quantum-resistant-cryptographic-algorithms https://morefield.com/blog/implementing-zero-trust-architecture/ https://www.statista.com/statistics/1233538/average-number-saas-apps-yearly/ https://www.cisa.gov/sites/default/files/publications/defending_against_software_supply_chain_attacks_508_1.pdf https://www3.weforum.org/docs/WEF_The_Global_Risks_Report_2022.pdf Every great innovation comes with some unique challenges. Automation can simplify your marketing processes, save time, and give insight you may otherwise miss. As helpful — even necessary — as automating content is, it carries some cybersecurity risks you should know about . That doesn’t mean these tools are strictly unsafe, but it does warrant attention. you should know about You need to ensure you’re using AI safely if you hope to experience its benefits to the fullest. You can do so by following these five content automation cybersecurity steps. 1. Determine Your Risk Level The first step to more secure content automation is deciding exactly what and how serious your cyber risks are. A more detailed picture of your risk landscape will make it easier to determine what kind of protections are necessary. Some generative AI models may require legal permission to use customers’ data. Other marketing automation tools don’t store any customer information, so breaches won’t be quite so severe. Review your tools and the data they access to see what type of risk you’re dealing with. In general, the more sensitive information you use in content automation, the more advanced security measures you’ll need. Your specific market may influence these decisions, too. At least 14 states have data privacy laws that may introduce unique requirements. Consider all these factors when determining your risk level and err on the side of going further than required to be safe. 14 states have data privacy laws 2. Encrypt Everything Regardless of your risk level, you’ll want to encrypt all your content automation data. Encryption scrambles information so it doesn’t reveal any actual details even if a hacker accesses it. That should apply to everything from customer information to social media logins to AI training data. You may already encrypt data at rest, but it’s important to encrypt it in transit, too. You might even be able to encrypt it during use. That wasn’t always possible, but new standards like homomorphic encryption let you analyze data with AI without decrypting it. Refer to any applicable laws or industry standards to see if there’s a specific level of encryption you must use. The government has recently recognized quantum-proof encryption methods you could use if you want to go the extra mile. recognized quantum-proof encryption methods 3. Restrict Access Access controls are another big part of content automation cybersecurity. When fewer people or programs can access your content tools, scheduling software, and social media accounts, hackers have fewer options to break into. Zero-trust architecture takes access restrictions and user authentication to extremes, so it’s the safest way forward. While this approach takes a while to set up, it increases visibility and reduces downtime by double-checking everything before trusting it. Even if you don’t opt for the zero-trust model, you should restrict content automation tools so only the employees who need it for their daily work can access it. it increases visibility and reduces downtime Strict access controls only work when you can reliably tell who’s who. Simple username-password combinations are too simple and easy to break past. Use multi-factor authentication (MFA) — ideally through SMS or cryptographic keys and not email — instead. 4. Choose Your Tools Carefully Next, you’ll want to take a closer look at the software you use to automate your content strategy. These tools could potentially have access to some sensitive information — including customer data and your social media accounts — so you should ensure they’re safe before trusting them. The first step in reducing these third-party risks is to consolidate your content automation tools as much as possible. Businesses use more than a hundred apps on average, and any one of these could have security flaws that jeopardize your data. Opting for a single provider to cover more services makes it easier to ensure you’re using a reliable, safe solution. more than a hundred apps You should also have a formal process for determining if a software provider meets your security needs. The Cybersecurity and Information Security Agency outlines software supply chain security steps you can follow to create such a system. outlines software supply chain security steps 5. Train Everyone Make sure everyone on your content team understands security best practices. These include aspects like strong password management, why to use MFA, and how to spot phishing attempts. As many as 95% of cybersecurity issues stem in part from human error. Consequently, if you train everyone on your team to practice safer, more mindful practices, you’ll prevent a lot of damage. Of course, even well-trained employees can still make mistakes, but the more prepared your workforce is for these risks, the less likely those errors will become. 95% of cybersecurity issues Because people tend to forget things, periodic refresher training is a good idea. Retrain everyone in security best practices at least once annually to keep safety at the top of everybody’s mind. Cybersecurity Is Crucial for Content Automation Without reliable cybersecurity, your content automation tools could become a huge risk before you realize it. By contrast, you can use this technology to its fullest extent if you protect it first. These five steps will help you create a cybersecurity plan that works for your content automation strategy. Be sure to review your system regularly to ensure you’re on top of any developing security trends or new risks. Sources: Sources: https://hackernoon.com/what-are-the-top-challenges-facing-cybersecurity-automation-adoption https://hackernoon.com/what-are-the-top-challenges-facing-cybersecurity-automation-adoption https://iapp.org/resources/article/us-state-privacy-legislation-tracker/ https://iapp.org/resources/article/us-state-privacy-legislation-tracker/ https://www.nist.gov/news-events/news/2022/07/nist-announces-first-four-quantum-resistant-cryptographic-algorithms https://www.nist.gov/news-events/news/2022/07/nist-announces-first-four-quantum-resistant-cryptographic-algorithms https://morefield.com/blog/implementing-zero-trust-architecture/ https://morefield.com/blog/implementing-zero-trust-architecture/ https://www.statista.com/statistics/1233538/average-number-saas-apps-yearly/ https://www.statista.com/statistics/1233538/average-number-saas-apps-yearly/ https://www.cisa.gov/sites/default/files/publications/defending_against_software_supply_chain_attacks_508_1.pdf https://www.cisa.gov/sites/default/files/publications/defending_against_software_supply_chain_attacks_508_1.pdf https://www3.weforum.org/docs/WEF_The_Global_Risks_Report_2022.pdf https://www3.weforum.org/docs/WEF_The_Global_Risks_Report_2022.pdf

How Can You Integrate Cybersecurity Into Your Content Automation Process?

(AI) has emerged as a game-changer in many industries. Predictive AI technology has helped reshape age-old processes, saving organizations time, money, and frustration. A litany of different organizations have leveraged AI solutions to streamline their operations and ultimately drive productivity, changing industries ranging from art to finance. Artificial intelligence While some have bemoaned the rise of AI, wringing their hands over a possible AI takeover, others have recognized the power of machine learning, algorithms, and computer vision to transform their jobs and allow them to get back to the innovating that truly moves the needle towards progress. AI for Boosting Productivity AI has proven to be a boon for productivity within many organizations. By automating repetitive tasks and allowing employees to focus on the parts of their jobs that carry higher value, AI paves the way for more creativity, innovation, and problem-solving. “By analyzing trends in task completion times and quality and assigning specific team members based on past performance and partnerships, AI encourages a culture of recognition and motivation,” says Brianna Van Zanten, Customer Success Manager at InCheq. “When workers' achievements are acknowledged through data-driven insights, it provides a tangible record of their individualistic abilities.” With more workers taking heed of the message of the Great Resignation and seeking work that is personally and professionally rewarding, utilizing AI to create a better work culture can help companies with talent retention and employee satisfaction, not to mention productivity. With actionable insights from performance data, AI can not only make the workplace better for the employees but also significantly improve the overall performance of the organization as a whole. AI for Content Creation With the rise of AI, many in creative fields have feared that predictive AI creations signal the end of human creativity. However, several organizations have learned to use AI not only to help them be more productive but also to give them the space they need to be more creative. “As a podcast host, I can attest to AI's transformative potential for the podcasting industry,” explains Heidi Moore, host of Wine Crush Podcast. “When we integrate AI into existing podcast workflows, it's not merely an additive process — it’s a paradigm shift. The capability of AI to sift through vast datasets allows it to predict and generate engaging topics that resonate deeply with the audience's preferences and current trends. This predictive prowess is instrumental in crafting content strategies that captivate listeners and foster a loyal audience base.” By automating the routines that take away from the creative process, creative entrepreneurs and leaders can focus more on experimenting and enjoying the fun parts of working within a creative space. If they view AI as a collaborative partner in their creative work rather than a hindrance, they may come by a more efficient process. AI for the Supply Chain As we saw laid bare during the pandemic, the efficiency and health of the global supply chain are critical for business. The introduction of AI has allowed those in the supply chain space to optimize traditional processes and explore technology-informed ways of making things run smoothly worldwide. “AI and RFID technology can be pivotal in transforming supply chain operations, particularly in enhancing the efficiency and sustainability of eco-friendly products,” says Joseph Schlossberg, VP of Global Sales at Sourcing Solutions International. “By integrating RFID's real-time tracking capabilities with AI's analytical prowess, we could significantly streamline supply chain processes, ensuring accurate inventory management and reducing unnecessary waste. This synergy is vital for adapting to rapid market changes and managing resources more effectively.” AI use within the supply chain significantly impacts the recent push for sustainability. “By evaluating extensive data on lifecycle impacts, supply chain practices, and product usage, AI facilitates more sustainable decision-making and innovation,” Schlossberg explains. “It helps us improve product design and predicts the environmental impact of materials and processes that we use.” AI in Finance AI also has a significant role to play in an industry like finance, where accuracy is paramount. “As more and more financial advisors embrace advancements in technology, AI continues to play a larger role,” says Aaron Cirksena of MDRN Capital. With the power to analyze vast data sets and uncover valuable insights for investors, AI can unlock new opportunities within the complex world of finance. “We can use AI in a multitude of ways, whether it is used to manage investments, give predictive AI-driven recommendations, or assist advisors in the day-to-day operations of their firms,” Cirksena notes. “Additionally, more advisors are embracing AI for client communication and assisting them with finding new lead sources.” Financial entities that embrace these technologies within the financial space stand to gain an advantage over those competitors who choose not to evolve or even bend with the changing times. As AI becomes more a part of everyday life, more investors and everyday banking consumers will come to expect the value and efficiency that AI can bring, gravitating towards those organizations that can offer it. AI automation has left its fingerprints in nearly every industry. As we head into the future, the potential for AI in areas from healthcare to the arts promises to reshape the way we work, produce, and innovate. Lead image source

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Unlike most prognosticators in the cyber industry, I will try not to inflame the hype of yet another doomsday, the sky is falling discussion on the newest threat vector. Over the past quarter in particular, that has been Generative AI (GenAI). This technology has even captured the minds (and fears) of the C-Suite and Boardrooms, quite a bit differently than when security practitioners started talking about machine learning many years ago. Most (though not all) of the GenAI conversation has seemed to revolve around its ability to amplify productivity and its potential to continue the trend of automating away tasks that shouldn’t require humans. Lots of technology companies have been touting their new “AIs”, which predominantly have meant chatbots and interactive prompts. AI-powered companies have begun marketing many of GenAI’s positive expectations: predictive analytics, phishing detection & prevention, automated security patch generation, adaptive threat detection, enhanced biometrics, anomaly detection, threat simulation & training, malware generation & analysis. And Gartner recently found that one-third (34%) of organizations planned to deploy GenAI in the next 12 months. But Crowdstrike’s 2023 Global Threat report only mentions AI once, in its CEO’s opening letter about future technology. This is a strong indicator that we aren’t yet seeing it as an attack vector or amplifier in the wild. We surmise that the 2024 Global Threat report will be similar. In January, The National Cyber Security Center in the United Kingdom published a report entitled "The Near-Term Impact of AI on the Cyber Threat" detailing the potential way AI tools could be used offensively from increasing the sophistication of phishing attacks to malware and exploit development. Microsoft just corroborated that assertion with specific examples of ChatGPT-assisted phishing. This is a welcomed addition to a body of information highly focused on the new benefits of AI rather than its risks. We’ve heard and read that “soon” state-sponsored adversaries will be using GenAI to target critical infrastructure putting lives at risk. Such statements are not useful, as those adversaries have been focused on those targets forever. What matters is whether GenAI will provide useful new capabilities to compromise them. Gartner forecasts that in the next 2 years, GenAI will cause enough additional offensive capability that companies will end up spending 15% more on application and data security. Google’s recent “Cloud Cybersecurity Forecast” also indicated that Google expects GenAI to support phishing and other social engineering attacks. And CISA, which has become the US Government’s harbinger of all things cybersecurity, released a “Roadmap for Artificial Intelligence” this past November recommending developer accountability. This followed an October Executive Order by President Biden directing a variety of federal agencies to grapple with and protect against AI-enabled threats, including, for example, content authentication and watermarking by the Commerce Department and AI safety in drug discovery by the Department of Health and Human Services. So Which Risks Are Currently Accelerating? AI will increase the speed at which relatively low-quality phishing emails will be created, and likely even the sophistication of what happens after a link is clicked (chat boxes may start to replace web forms to solicit your information and bank account details). Phishing has been the overwhelmingly preferred and successful attack to establish initial access, whether through malware or password theft. The NCSC predicts that by 2025, GenAI will substantially improve the quality of Phishing emails (particularly the spelling and grammar of those emails), and thereby, increase the difficulty in identifying such malicious requests. The NCSC also predicted that the language models will be rapidly improved as exfiltrated data is reincorporated into the training models. Given its preference and preponderance, this is certainly the vector that will be impacted the soonest – automating dumb attacks. Anti-phishing training is going to have to move far beyond spotting typos to context-based evaluation. And humans are going to have to be retrained to use password managers and not re-sign into websites without careful and meticulous evaluation and contemplation. Several new anti-phishing tools seek to mitigate these risks (pixmsecurity, agari, etc.). The NCSC’s primary conclusion is that GenAI will enhance existing tactics, techniques, and procedures (TTPs) unevenly among categories of threat actors. We believe that in the near term, most of the benefit accrues to the unsophisticated as AI provides capability uplift in reconnaissance and social engineering. The NCSC states “More sophisticated uses of AI in cyber operations are highly likely to be restricted to threat actors with access to quality training data, significant expertise (in both AI and cyber), and resources. Such advanced uses are unlikely to be realized before 2025…AI lowers the barrier for novice cyber criminals, hackers-for-hire and hacktivists to carry out effective access and information gathering operations. This enhanced access will likely contribute to the global ransomware threat over the next two years.” While we appreciate the zealousness, we are skeptical of the timeframe. Over the next two years, we expect phishing emails to get more sophisticated and nation-states to continue training AIs on their incredibly large data sets. For emphasis, we’ll repeat – the near-term benefits accrue to the lowest-skilled hackers with limited resources executing TTPs in social engineering, phishing, passwords, and data exfiltration. We agree strongly that AI will also improve the efficiency of vulnerability scanning (“find me a server that is vulnerable to <fill in the blank>”). The NCSC stated that  "AI is highly likely to accelerate this challenge as reconnaissance to identify vulnerable devices becomes quicker and more precise.” To reference Alan Turing’s original formulation of Artificial Intelligence, GenAI dumb chatbots are likely to be perceived as real people relatively quickly. That will greatly enhance the ability of foreign criminals to sound like natives in their emailed requests for passwords, fund transfers, etc. Darktrace notes that GenAI has already “opened the door to providing offensive tools to more novice threat actors. But the efficacy of these tools will only be as good as those directing them. In the near term, we expect to see an increase in the speed and scale of familiar attack methodology.” The NCSC also suggests that GenAI will offer improvement in malware and exploit development and assistance in lateral movement, though we remain doubtful that in the next 5 years, there will be sufficient training models to enable vulnerability discovery available to non-nation-states or the largest 50 technology companies. The NCSC report essentially argues both sides, vacillating between two-year time frames and agreeing with this notion, by noting that GenAI tools would provide only minimal uplift for malware and exploit development for nation-states and organized cybercrime groups. But we are not saying that it’s impossible to happen relatively quickly; indeed, early last year HYAS researchers developed a proof-of-concept malware that leverages OpenAI to enable polymorphic code that can evade current cybersecurity products. And eventually (though we think it is at least 5 years out), GenAI systems themselves will be subject to data integrity attacks. What reasonable steps should we start to consider? We’ve reviewed a tremendous corpus of available literature looking for wisdom and practical advice on how to deal with these emergent threats (both the expected, near-term dumb ones and the hypothetical far away scary ones). As usual, the best tactical recommendations are the same recommendations that security teams have sought to implement since their establishment. These have become old saws by now: ● Establish governance approaches and workflow monitoring to ensure compliance with policies, standards, and procedures (whether by a human or an AI). ● Update and patch regularly against an asset registry or CMDB that consistently grows in the amount of coverage it represents (we hate that this still needs to be said). ● Monitor and block inappropriate access.  Better yet, develop a zero-trust architecture that inherently invalidates unexpected access. ● Develop continuous monitoring programs that ensure you have done all of the above (and alert you when something is out of the ordinary). ● Build strong security behavior programs to train employees toward secure, informed, and intentional behavior (no more than once a year, check the box security compliance videos). ● Monitor robust existing security control programs and quickly identify anomalous activity. ● Create relevant legal and compliance guidelines that detail appropriate use and security approvals for any GenAI activities. ● Create, disseminate, communicate, and evangelize acceptable use policies. In summary, over the next 5 years, we are most concerned about AI helping dumb attacks get faster and more automated (and minorly less dumb).  Darktrace agrees, claiming that we are “at the beginning of malicious actors’ applying AI techniques to automate more laborious aspects of their attacks.” All the current approaches to cybersecurity still hold – which is good news as most cyber teams remain highly taxed, being asked to do too much with too little. Unlike most prognosticators in the cyber industry, I will try not to inflame the hype of yet another doomsday, the sky is falling discussion on the newest threat vector. Over the past quarter in particular, that has been Generative AI (GenAI). This technology has even captured the minds (and fears) of the C-Suite and Boardrooms, quite a bit differently than when security practitioners started talking about machine learning many years ago. Most (though not all) of the GenAI conversation has seemed to revolve around its ability to amplify productivity and its potential to continue the trend of automating away tasks that shouldn’t require humans. Lots of technology companies have been touting their new “AIs”, which predominantly have meant chatbots and interactive prompts. AI-powered companies have begun marketing many of GenAI’s positive expectations: predictive analytics, phishing detection & prevention, automated security patch generation, adaptive threat detection, enhanced biometrics, anomaly detection, threat simulation & training, malware generation & analysis. And Gartner recently found that one-third (34%) of organizations planned to deploy GenAI in the next 12 months. But Crowdstrike’s 2023 Global Threat report only mentions AI once, in its CEO’s opening letter about future technology. This is a strong indicator that we aren’t yet seeing it as an attack vector or amplifier in the wild. We surmise that the 2024 Global Threat report will be similar. In January, The National Cyber Security Center in the United Kingdom published a report entitled "The Near-Term Impact of AI on the Cyber Threat" detailing the potential way AI tools could be used offensively from increasing the sophistication of phishing attacks to malware and exploit development. Microsoft just corroborated that assertion with specific examples of ChatGPT-assisted phishing. This is a welcomed addition to a body of information highly focused on the new benefits of AI rather than its risks. We’ve heard and read that “soon” state-sponsored adversaries will be using GenAI to target critical infrastructure putting lives at risk. Such statements are not useful, as those adversaries have been focused on those targets forever. What matters is whether GenAI will provide useful new capabilities to compromise them. Gartner forecasts that in the next 2 years, GenAI will cause enough additional offensive capability that companies will end up spending 15% more on application and data security. Google’s recent “Cloud Cybersecurity Forecast” also indicated that Google expects GenAI to support phishing and other social engineering attacks. And CISA, which has become the US Government’s harbinger of all things cybersecurity, released a “Roadmap for Artificial Intelligence” this past November recommending developer accountability. This followed an October Executive Order by President Biden directing a variety of federal agencies to grapple with and protect against AI-enabled threats, including, for example, content authentication and watermarking by the Commerce Department and AI safety in drug discovery by the Department of Health and Human Services. So Which Risks Are Currently Accelerating? So Which Risks Are Currently Accelerating? AI will increase the speed at which relatively low-quality phishing emails will be created, and likely even the sophistication of what happens after a link is clicked (chat boxes may start to replace web forms to solicit your information and bank account details). Phishing has been the overwhelmingly preferred and successful attack to establish initial access, whether through malware or password theft. The NCSC predicts that by 2025, GenAI will substantially improve the quality of Phishing emails (particularly the spelling and grammar of those emails), and thereby, increase the difficulty in identifying such malicious requests. The NCSC also predicted that the language models will be rapidly improved as exfiltrated data is reincorporated into the training models. Given its preference and preponderance, this is certainly the vector that will be impacted the soonest – automating dumb attacks. Anti-phishing training is going to have to move far beyond spotting typos to context-based evaluation. And humans are going to have to be retrained to use password managers and not re-sign into websites without careful and meticulous evaluation and contemplation. Several new anti-phishing tools seek to mitigate these risks (pixmsecurity, agari, etc.). The NCSC’s primary conclusion is that GenAI will enhance existing tactics, techniques, and procedures (TTPs) unevenly among categories of threat actors. We believe that in the near term, most of the benefit accrues to the unsophisticated as AI provides capability uplift in reconnaissance and social engineering. The NCSC states “More sophisticated uses of AI in cyber operations are highly likely to be restricted to threat actors with access to quality training data, significant expertise (in both AI and cyber), and resources. Such advanced uses are unlikely to be realized before 2025…AI lowers the barrier for novice cyber criminals, hackers-for-hire and hacktivists to carry out effective access and information gathering operations. This enhanced access will likely contribute to the global ransomware threat over the next two years.” While we appreciate the zealousness, we are skeptical of the timeframe. Over the next two years, we expect phishing emails to get more sophisticated and nation-states to continue training AIs on their incredibly large data sets. For emphasis, we’ll repeat – the near-term benefits accrue to the lowest-skilled hackers with limited resources executing TTPs in social engineering, phishing, passwords, and data exfiltration. We agree strongly that AI will also improve the efficiency of vulnerability scanning (“find me a server that is vulnerable to <fill in the blank>”). The NCSC stated that  "AI is highly likely to accelerate this challenge as reconnaissance to identify vulnerable devices becomes quicker and more precise.” To reference Alan Turing’s original formulation of Artificial Intelligence, GenAI dumb chatbots are likely to be perceived as real people relatively quickly. That will greatly enhance the ability of foreign criminals to sound like natives in their emailed requests for passwords, fund transfers, etc. Darktrace notes that GenAI has already “opened the door to providing offensive tools to more novice threat actors. But the efficacy of these tools will only be as good as those directing them. In the near term, we expect to see an increase in the speed and scale of familiar attack methodology.” The NCSC also suggests that GenAI will offer improvement in malware and exploit development and assistance in lateral movement, though we remain doubtful that in the next 5 years, there will be sufficient training models to enable vulnerability discovery available to non-nation-states or the largest 50 technology companies. The NCSC report essentially argues both sides, vacillating between two-year time frames and agreeing with this notion, by noting that GenAI tools would provide only minimal uplift for malware and exploit development for nation-states and organized cybercrime groups. But we are not saying that it’s impossible to happen relatively quickly; indeed, early last year HYAS researchers developed a proof-of-concept malware that leverages OpenAI to enable polymorphic code that can evade current cybersecurity products. And eventually (though we think it is at least 5 years out), GenAI systems themselves will be subject to data integrity attacks. What reasonable steps should we start to consider? What reasonable steps should we start to consider? We’ve reviewed a tremendous corpus of available literature looking for wisdom and practical advice on how to deal with these emergent threats (both the expected, near-term dumb ones and the hypothetical far away scary ones). As usual, the best tactical recommendations are the same recommendations that security teams have sought to implement since their establishment. These have become old saws by now: ● Establish governance approaches and workflow monitoring to ensure compliance with policies, standards, and procedures (whether by a human or an AI). ● Update and patch regularly against an asset registry or CMDB that consistently grows in the amount of coverage it represents (we hate that this still needs to be said). ● Monitor and block inappropriate access.  Better yet, develop a zero-trust architecture that inherently invalidates unexpected access. ● Develop continuous monitoring programs that ensure you have done all of the above (and alert you when something is out of the ordinary). ● Build strong security behavior programs to train employees toward secure, informed, and intentional behavior (no more than once a year, check the box security compliance videos). ● Monitor robust existing security control programs and quickly identify anomalous activity. ● Create relevant legal and compliance guidelines that detail appropriate use and security approvals for any GenAI activities. ● Create, disseminate, communicate, and evangelize acceptable use policies. In summary, over the next 5 years, we are most concerned about AI helping dumb attacks get faster and more automated (and minorly less dumb).  Darktrace agrees, claiming that we are “at the beginning of malicious actors’ applying AI techniques to automate more laborious aspects of their attacks.” All the current approaches to cybersecurity still hold – which is good news as most cyber teams remain highly taxed, being asked to do too much with too little.

GenAI - Soon to Be Great for Automating Dumb Attacks

Automatic Brand Sentiment Analysis with Generative AI

Get to know RemotiveBroker’s gRPC API and how it simplifies prototyping, building & testing automotive software. Build software the way you want and in the programming language of your choice. We make things easier with a Python wrapper and the gRPC stubs available in a public repo on our GitHub. Get stuff done with our gRPC API Want to make a quick prototype using Python? Interested in Rust? The RemotiveBroker gRPC API can be used to read and write vehicle signal data in your language of choice. This means that you can use the tools you prefer to quickly build applications that depend on real vehicle signal data. Given a simple configuration and a signal database file, you can get to work right away with recorded data or inside a physical vehicle with live data. Use the same API regardless if you are working with recorded or live data, write your application once and test it on your desktop and inside a vehicle! You can reuse previously recorded drive cycles, e.g. in a deterministic ECU testing setup or when prototyping graphical user interfaces. Once you are satisfied with your work you can, in a true shift-left spirit, bring it to a test rig or physical vehicle knowing that the code has been tested. Paired with our collaboration platform, RemotiveCloud, you can quickly scale up your development efforts with multiple virtualized RemotiveBrokers! Get started with three lines of code Start a RemotiveBroker with three lines of code: $ git clone https://github.com/remotivelabs/remotivebroker-bootstrap.git
$ cd remotivebroker-bootstrap
$ docker-compose up -d Try our free 3-minute demo or check out some of our stuff on Github, e.g the APIs, simple samples, ProtoPie integration) and go to the repo for running the bootstrap RemotiveBroker on your desktop. What is a RemotiveBroker? The RemotiveBroker is a flexible developer tool that fit into most setups. it exposes vehicle signal data via a pub/sub gRPC API. We support the most common vehicle networks like CAN, Flexray, LIN, and Automotive Ethernet – enabled by .dbx, .xml (fibex), .ldf and .arxml. We also proved E2E Protection. Make it even easier with API wrappers and gRPC stubs We have a public repo for our wrappers and stubs. Anyone is welcome to submit a pull request with a wrapper in their favorite language, get started with our gRPC proto files. Our ambition is to have wrappers and stubs/clients available for everyone. Advantages of gRPC Given a simple configuration, the RemotiveBroker will expose a gRPC API that puts you in control. With the broad language support that gRPC has you can use what you prefer, e.g Node.js, Ruby, C# or Kotlin. gRPC leverages HTTP/2 and a binary message format, this helps to reduce network bandwidth and decrease latency. This allows our RemotiveBroker to send large amounts of data in real-time, like in the case of high-speed CAN & CAN-FD or FlexRay. Python wrapper We have a Python wrapper API for a subset of the RemotiveBroker API to make it even easier to build applications. It is also available for easy installation through the Python Package Index. This wrapper API makes it easier to perform common operations like connecting to a RemotiveBroker and setting up vehicle signal data subscriptions. Connect to a RemotiveBroker and set up a signal subscription: # Connect to RemotiveBroker
client = Client()
client.on_connect = lambda c: print(f"Yay! - connected as {c.client_id} to {c.url}")
client.on_signals = lambda signals: print(signals.to_json())
client.connect(url="http://localhost:50051")

subscription = client.subscribe(
signal_names=["Vehicle.Speed"],
namespaces=["vss"],
changed_values_only=False) Typescript and Javascript stubs/clients Since we are working with gRPC-web in our web clients, we have both Typescript and Javascript stubs/clients available for grpc-web. It is also available via npm/yarn. While not as useful as a proper wrapper API, the stubs/clients are required when working with gRPC. However, just looking at the stubs is helpful when exploring the API. Subscribe to frame frequency distribution: // Setup request
const config = new FramesDistributionConfig()
const namespace = new NameSpace()namespace.setName(namespaceName)
config.setNamespace(namespace)

// Subscribe
this._frameDistributionStream =  
this._networkServiceClient.subscribeToFramesDistribution(config) Our tooling and APIs are constantly evolving. Follow us on GitHub and LinkedIn to not miss out on more helpful developer content. If you have wishes or thoughts for what we should develop next don’t be a stranger! Get to know RemotiveBroker’s gRPC API and how it simplifies prototyping, building & testing automotive software. Build software the way you want and in the programming language of your choice. We make things easier with a Python wrapper and the gRPC stubs available in a public repo on our GitHub. programming language Get stuff done with our gRPC API Want to make a quick prototype using Python? Interested in Rust? The RemotiveBroker gRPC API can be used to read and write vehicle signal data in your language of choice. This means that you can use the tools you prefer to quickly build applications that depend on real vehicle signal data. Given a simple configuration and a signal database file, you can get to work right away with recorded data or inside a physical vehicle with live data. RemotiveBroker Use the same API regardless if you are working with recorded or live data, write your application once and test it on your desktop and inside a vehicle! You can reuse previously recorded drive cycles, e.g. in a deterministic ECU testing setup or when prototyping graphical user interfaces. Once you are satisfied with your work you can, in a true shift-left spirit, bring it to a test rig or physical vehicle knowing that the code has been tested. Paired with our collaboration platform, RemotiveCloud , you can quickly scale up your development efforts with multiple virtualized RemotiveBrokers! RemotiveCloud Get started with three lines of code Get started with three lines of code Start a RemotiveBroker with three lines of code: $ git clone https://github.com/remotivelabs/remotivebroker-bootstrap.git
$ cd remotivebroker-bootstrap
$ docker-compose up -d $ git clone https://github.com/remotivelabs/remotivebroker-bootstrap.git
$ cd remotivebroker-bootstrap
$ docker-compose up -d Try our free 3-minute demo or check out some of our stuff on Github, e.g the APIs , simple samples , ProtoPie integration ) and go to the repo for running the bootstrap RemotiveBroker on your desktop . free 3-minute demo APIs simple samples ProtoPie integration bootstrap RemotiveBroker on your desktop What is a RemotiveBroker? What is a RemotiveBroker? The RemotiveBroker is a flexible developer tool that fit into most setups. it exposes vehicle signal data via a pub/sub gRPC API. We support the most common vehicle networks like CAN, Flexray, LIN, and Automotive Ethernet – enabled by .dbx, .xml (fibex), .ldf and .arxml. We also proved E2E Protection. Make it even easier with API wrappers and gRPC stubs Make it even easier with API wrappers and gRPC stubs We have a public repo for our wrappers and stubs. Anyone is welcome to submit a pull request with a wrapper in their favorite language, get started with our gRPC proto files. Our ambition is to have wrappers and stubs/clients available for everyone. public repo Advantages of gRPC Advantages of gRPC Given a simple configuration, the RemotiveBroker will expose a gRPC API that puts you in control. With the broad language support that gRPC has you can use what you prefer, e.g Node.js, Ruby, C# or Kotlin. gRPC leverages HTTP/2 and a binary message format, this helps to reduce network bandwidth and decrease latency. This allows our RemotiveBroker to send large amounts of data in real-time, like in the case of high-speed CAN & CAN-FD or FlexRay. Python wrapper Python wrapper We have a Python wrapper API for a subset of the RemotiveBroker API to make it even easier to build applications. It is also available for easy installation through the Python Package Index. This wrapper API makes it easier to perform common operations like connecting to a RemotiveBroker and setting up vehicle signal data subscriptions. RemotiveBroker Python Package Index. Connect to a RemotiveBroker and set up a signal subscription: Connect to a RemotiveBroker and set up a signal subscription: # Connect to RemotiveBroker
client = Client()
client.on_connect = lambda c: print(f"Yay! - connected as {c.client_id} to {c.url}")
client.on_signals = lambda signals: print(signals.to_json())
client.connect(url="http://localhost:50051")

subscription = client.subscribe(
signal_names=["Vehicle.Speed"],
namespaces=["vss"],
changed_values_only=False) # Connect to RemotiveBroker
client = Client()
client.on_connect = lambda c: print(f"Yay! - connected as {c.client_id} to {c.url}")
client.on_signals = lambda signals: print(signals.to_json())
client.connect(url="http://localhost:50051")

subscription = client.subscribe(
signal_names=["Vehicle.Speed"],
namespaces=["vss"],
changed_values_only=False) Typescript and Javascript stubs/clients Typescript and Javascript stubs/clients Since we are working with gRPC-web in our web clients, we have both Typescript and Javascript stubs/clients available for grpc-web. It is also available via npm/yarn . While not as useful as a proper wrapper API, the stubs/clients are required when working with gRPC. However, just looking at the stubs is helpful when exploring the API. npm/yarn Subscribe to frame frequency distribution: Subscribe to frame frequency distribution: // Setup request
const config = new FramesDistributionConfig()
const namespace = new NameSpace()namespace.setName(namespaceName)
config.setNamespace(namespace)

// Subscribe
this._frameDistributionStream =  
this._networkServiceClient.subscribeToFramesDistribution(config) // Setup request
const config = new FramesDistributionConfig()
const namespace = new NameSpace()namespace.setName(namespaceName)
config.setNamespace(namespace)

// Subscribe
this._frameDistributionStream =  
this._networkServiceClient.subscribeToFramesDistribution(config) Our tooling and APIs are constantly evolving. Follow us on GitHub and LinkedIn to not miss out on more helpful developer content. GitHub LinkedIn If you have wishes or thoughts for what we should develop next don’t be a stranger!

Revolutionize Automotive Software Development with RemotiveBroker's gRPC API

RemotiveBroker-এর gRPC API-এর সাহায্যে অটোমোটিভ সফটওয়্যার ডেভেলপমেন্টে বিপ্লব ঘটান

Revolutionieren Sie die Entwicklung von Automobilsoftware mit der gRPC-API von RemotiveBroker

Revolucione el desarrollo de software automotriz con la API gRPC de RemotiveBroker

Révolutionnez le développement de logiciels automobiles avec l'API gRPC de RemotiveBroker

रिमोटिवब्रोकर के gRPC API के साथ ऑटोमोटिव सॉफ्टवेयर विकास में क्रांतिकारी बदलाव लाएँ

RemotiveBroker の gRPC API で自動車ソフトウェア開発に革命を起こす

RemotiveBroker의 gRPC API로 자동차 소프트웨어 개발에 혁명을 일으키세요

Revolucione o desenvolvimento de software automotivo com a API gRPC da RemotiveBroker

Произведите революцию в разработке автомобильного программного обеспечения с помощью gRPC API RemotiveBroker

RemotiveBroker'ın gRPC API'si ile Otomotiv Yazılım Geliştirmede Devrim Yapın

Cách mạng hóa việc phát triển phần mềm ô tô với API gRPC của RemotiveBroker

利用 RemotiveBroker 的 gRPC API 彻底改变汽车软件开发

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Sam Bowyer, Equal contribution, Department of Mathematics and sam.bowyer@bristol.ac.uk;
(2)  Thomas Heap, Equal contribution, Department of Computer Science University of Bristol and thomas.heap@bristol.ac.uk;
(3) Laurence Aitchison, Department of Computer Science University of Bristol and laurence.aitchison@bristol.ac.uk. Table of Links Abstract & Introduction
Related work
Background
Methods
Experiments
"Conclusion, Limitations, and References"
Derivations
Algorithms
Experimental Datasets And Model Experimental Datasets And Models MovieLens Dataset The MovieLens100K1 (Harper and Konstan 2015) dataset contains 100k ratings of N=1682 films from among M=943 users. The original user ratings run from 0 to 5. Following Geffner and Domke (2022), we binarise the ratings into just likes/dislikes, by taking user-ratings of {0, 1, 2, 3} as a binarised-rating of 0 (dislikes) and user-ratings of {4, 5} as a binarised-rating of 1 (likes). We assume binarised ratings of 0 for films which users have not previously rated. The probabilistic graphical model is given in Fig 3. We use n to index films, and m to index users. Each film has a known feature vector, xn, while each user has a latent weight-vector, zm, of the same length, describing whether or not they like any given feature. There are 18 features, indicating which genre tags the film has (Action, Adventure, Animation, Childrens,...). Each film may have more than one tag. The probability of the user liking a film is given by taking the dot-product of the film’s feature vector with the latent weight-vector, and applying a sigmoid (σ(·)) (final line) Additionally, we have latent vectors for the global mean, µ, and variance, ψ, of the weight vectors. We use a random subset of N = 20 films and M = 450 users for our experiment to ensure high levels of uncertainty. We use equally sized but disjoint subset of users held aside for calculation of the predictive log-likelihood. In Fig. 1c,d we consider the variance and MSE of estimators of zm. Bus Delay Datase In our second experiment, we model the length of delays to New York school bus journeys,2 working with a dataset supplied by the City of New York (DOE 2023). Our goal is to predict the length of a delay, based on the year, y, borough, b, bus company, c and journey type, j. Specifically, our data includes the years 2015 − 2022 inclusive and covers the five New York boroughs (Brooklyn, Manhatten, The Bronx, Queens, Staten Island) as well as some surrounding areas (Nassau County, New Jersey, Connecticut, Rockland County, Westchester). There are 57 bus companies, and 6 different journey types (e.g. pre-K/elementary school route, general education AM/PM route etc.) We take I = 60 delayed buses in each borough and year, and take Y = 3 years and B = 3 boroughs. We then split along the I dimension to get two equally sized train and test sets. Thus, each delay is uniquely identified by the year, y, the borough, b, and the index, i, giving delayybi The delays are recorded as an integer number of minutes and we discard any entries greater than 130 minutes. We have a hierarchical latent variable model describing the impact of each of the features (year, borough, bus company and journey type) on the length of delays. Specifically, the integer delay is modelled with a Negative Binomial distribution, with fixed total count of 130, as we discard entries with delays longer than this. The expected delay length is controlled by a logits latent variable, logitsybi, with one logits for each delayed bus. The logits is a sum of three terms: one for the borough and year jointly, one for the bus company and one for the journey type. Each of these three terms is themselves a latent variable that must be inferred. First, we have a term for the year and borough, YearBoroughWeightyb, which has a hierarchical prior. Specifically, we begin by sampling a global mean and variance, GlobalMean and GlobalVariance. Then for each year, we use GlobalMean and GlobalVariance to sample a mean for each year, YearMeany. Additionally, we sample a variance for each borough, BoroughVarianceb. Then we sample a YearBoroughWeightyb from a Gaussian distribution with a year-dependent mean, YearMeany, and a borough-dependent variance, BoroughVarianceb. Next, the weights for the bus company and journey type are very similar. Specifically, we have one latent weight for each bus company, CompanyWeightc, with c ∈ {1, . . . , 57}, and for each journey type, JourneyTypeWeightj, with j ∈ {1, . . . , 6}. We have a table identifying the bus company, bybi, and journey type, jybi, for each delayed bus journey (remember that a particular delayed bus journey is uniquely identified by the year, y, borough, b and index i). In logitsybi we use these tables to pick out the right company and journey type weight for that particular delayed bus journey, CompanyWeightcybi and JourneyTypeWeight cybi. The final generative model is, and the graphical model is given in Fig. 4 Experiment Details We ran all experiments on an 80GB NVIDIA A100 GPU and averaged the results over three different dataset splits generated using different random seeds. On each dataset split, the MovieLens experiments required a maximum 26.9 GB of memory at any one time. For the NYC Bus Breakdown experiments this value was 0.9 GB. For each experiment using a single dataset  split, we used global IS, MP IS, VI and HMC methods to obtain (for each value of K in the IS methods, or each iteration of VI/HMC) 1000 values of the ELBO, predictive log-likelihood (on a test set that was disjoint from the training set), and posterior mean estimates for each latent variable in the model. These 1000 values were averaged for the ELBO and predictive log-likelihood, whilst the 1000 mean estimates were used to calculate mean squared error or variance per variable, based on whether the observed data had been sampled from the model itself—in which case the true variable values were known—or not. We present error bars in the top rows of Fig. 1 and Fig. 2 representing the standard deviation over the three dataset splits. For VI, we optimize our approximate posterior using Adam with learning rates of 10−2, 10−3 and 10−4 (we found that learning rates faster than 10−2 were unstable), and ran 100 optimization steps for both for the MovieLens and Bus Breakdown models. For HMC, we ran the No U-Turn Sampler on our GPU using JAX (Bradbury et al. 2018) to obtain 10 consecutive samples after a single step with PyMC’s (Salvatier, Wiecki, and Fonnesbeck 2016) pre-tuning and with all other parameters set to their default values. We present results of the first 5 samples (not including the tuning sample) in order to align as best we can with the small time-scale of the IS methods. This paper is available on arxiv under CC 4.0 license. Authors: (1) Sam Bowyer, Equal contribution, Department of Mathematics and sam.bowyer@bristol.ac.uk; (2)  Thomas Heap, Equal contribution, Department of Computer Science University of Bristol and thomas.heap@bristol.ac.uk; (3) Laurence Aitchison, Department of Computer Science University of Bristol and laurence.aitchison@bristol.ac.uk. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Sam Bowyer, Equal contribution, Department of Mathematics and sam.bowyer@bristol.ac.uk; (2)  Thomas Heap, Equal contribution, Department of Computer Science University of Bristol and thomas.heap@bristol.ac.uk; (3) Laurence Aitchison, Department of Computer Science University of Bristol and laurence.aitchison@bristol.ac.uk. Table of Links Abstract & Introduction Related work Background Methods Experiments "Conclusion, Limitations, and References" Derivations Algorithms Experimental Datasets And Model Abstract & Introduction Abstract & Introduction Related work Related work Background Background Methods Methods Experiments Experiments "Conclusion, Limitations, and References" "Conclusion, Limitations, and References" Derivations Derivations Algorithms Algorithms Experimental Datasets And Model Experimental Datasets And Model Experimental Datasets And Models MovieLens Dataset The MovieLens100K1 (Harper and Konstan 2015) dataset contains 100k ratings of N=1682 films from among M=943 users. The original user ratings run from 0 to 5. Following Geffner and Domke (2022), we binarise the ratings into just likes/dislikes, by taking user-ratings of {0, 1, 2, 3} as a binarised-rating of 0 (dislikes) and user-ratings of {4, 5} as a binarised-rating of 1 (likes). We assume binarised ratings of 0 for films which users have not previously rated. The probabilistic graphical model is given in Fig 3. We use n to index films, and m to index users. Each film has a known feature vector, xn, while each user has a latent weight-vector, zm, of the same length, describing whether or not they like any given feature. There are 18 features, indicating which genre tags the film has (Action, Adventure, Animation, Childrens,...). Each film may have more than one tag. The probability of the user liking a film is given by taking the dot-product of the film’s feature vector with the latent weight-vector, and applying a sigmoid (σ(·)) (final line) Additionally, we have latent vectors for the global mean, µ, and variance, ψ, of the weight vectors. We use a random subset of N = 20 films and M = 450 users for our experiment to ensure high levels of uncertainty. We use equally sized but disjoint subset of users held aside for calculation of the predictive log-likelihood. In Fig. 1c,d we consider the variance and MSE of estimators of zm. Bus Delay Datase In our second experiment, we model the length of delays to New York school bus journeys,2 working with a dataset supplied by the City of New York (DOE 2023). Our goal is to predict the length of a delay, based on the year, y, borough, b, bus company, c and journey type, j. Specifically, our data includes the years 2015 − 2022 inclusive and covers the five New York boroughs (Brooklyn, Manhatten, The Bronx, Queens, Staten Island) as well as some surrounding areas (Nassau County, New Jersey, Connecticut, Rockland County, Westchester). There are 57 bus companies, and 6 different journey types (e.g. pre-K/elementary school route, general education AM/PM route etc.) We take I = 60 delayed buses in each borough and year, and take Y = 3 years and B = 3 boroughs. We then split along the I dimension to get two equally sized train and test sets. Thus, each delay is uniquely identified by the year, y, the borough, b, and the index, i, giving delayybi The delays are recorded as an integer number of minutes and we discard any entries greater than 130 minutes. We have a hierarchical latent variable model describing the impact of each of the features (year, borough, bus company and journey type) on the length of delays. Specifically, the integer delay is modelled with a Negative Binomial distribution, with fixed total count of 130, as we discard entries with delays longer than this. The expected delay length is controlled by a logits latent variable, logitsybi, with one logits for each delayed bus. The logits is a sum of three terms: one for the borough and year jointly, one for the bus company and one for the journey type. Each of these three terms is themselves a latent variable that must be inferred. First, we have a term for the year and borough, YearBoroughWeightyb, which has a hierarchical prior. Specifically, we begin by sampling a global mean and variance, GlobalMean and GlobalVariance. Then for each year, we use GlobalMean and GlobalVariance to sample a mean for each year, YearMeany. Additionally, we sample a variance for each borough, BoroughVarianceb. Then we sample a YearBoroughWeightyb from a Gaussian distribution with a year-dependent mean, YearMeany, and a borough-dependent variance, BoroughVarianceb. Next, the weights for the bus company and journey type are very similar. Specifically, we have one latent weight for each bus company, CompanyWeightc, with c ∈ {1, . . . , 57}, and for each journey type, JourneyTypeWeightj, with j ∈ {1, . . . , 6}. We have a table identifying the bus company, bybi, and journey type, jybi, for each delayed bus journey (remember that a particular delayed bus journey is uniquely identified by the year, y, borough, b and index i). In logitsybi we use these tables to pick out the right company and journey type weight for that particular delayed bus journey, CompanyWeightcybi and JourneyTypeWeight cybi. The final generative model is, and the graphical model is given in Fig. 4 Experiment Details We ran all experiments on an 80GB NVIDIA A100 GPU and averaged the results over three different dataset splits generated using different random seeds. On each dataset split, the MovieLens experiments required a maximum 26.9 GB of memory at any one time. For the NYC Bus Breakdown experiments this value was 0.9 GB. For each experiment using a single dataset  split, we used global IS, MP IS, VI and HMC methods to obtain (for each value of K in the IS methods, or each iteration of VI/HMC) 1000 values of the ELBO, predictive log-likelihood (on a test set that was disjoint from the training set), and posterior mean estimates for each latent variable in the model. These 1000 values were averaged for the ELBO and predictive log-likelihood, whilst the 1000 mean estimates were used to calculate mean squared error or variance per variable, based on whether the observed data had been sampled from the model itself—in which case the true variable values were known—or not. We present error bars in the top rows of Fig. 1 and Fig. 2 representing the standard deviation over the three dataset splits. For VI, we optimize our approximate posterior using Adam with learning rates of 10−2, 10−3 and 10−4 (we found that learning rates faster than 10−2 were unstable), and ran 100 optimization steps for both for the MovieLens and Bus Breakdown models. For HMC, we ran the No U-Turn Sampler on our GPU using JAX (Bradbury et al. 2018) to obtain 10 consecutive samples after a single step with PyMC’s (Salvatier, Wiecki, and Fonnesbeck 2016) pre-tuning and with all other parameters set to their default values. We present results of the first 5 samples (not including the tuning sample) in order to align as best we can with the small time-scale of the IS methods.

Using Autodiff to Estimate Posterior Moments, Marginals and Samples: Experimental Datasets and Model

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Sam Bowyer, Equal contribution, Department of Mathematics and sam.bowyer@bristol.ac.uk;
(2)  Thomas Heap, Equal contribution, Department of Computer Science University of Bristol and thomas.heap@bristol.ac.uk;
(3) Laurence Aitchison, Department of Computer Science University of Bristol and laurence.aitchison@bristol.ac.uk. Table of Links Abstract & Introduction
Related work
Background
Methods
Experiments
"Conclusion, Limitations, and References"
Derivations
Algorithms
Experimental Datasets And Model Algorithms This paper is available on arxiv under CC 4.0 license. Authors: (1) Sam Bowyer, Equal contribution, Department of Mathematics and sam.bowyer@bristol.ac.uk; (2)  Thomas Heap, Equal contribution, Department of Computer Science University of Bristol and thomas.heap@bristol.ac.uk; (3) Laurence Aitchison, Department of Computer Science University of Bristol and laurence.aitchison@bristol.ac.uk. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Sam Bowyer, Equal contribution, Department of Mathematics and sam.bowyer@bristol.ac.uk; (2)  Thomas Heap, Equal contribution, Department of Computer Science University of Bristol and thomas.heap@bristol.ac.uk; (3) Laurence Aitchison, Department of Computer Science University of Bristol and laurence.aitchison@bristol.ac.uk. Table of Links Abstract & Introduction Related work Background Methods Experiments "Conclusion, Limitations, and References" Derivations Algorithms Experimental Datasets And Model Abstract & Introduction Abstract & Introduction Related work Related work Background Background Methods Methods Experiments Experiments "Conclusion, Limitations, and References" "Conclusion, Limitations, and References" Derivations Derivations Algorithms Algorithms Experimental Datasets And Model Experimental Datasets And Model Algorithms

Using Autodiff to Estimate Posterior Moments, Marginals and Samples: Algorithms

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Sam Bowyer, Equal contribution, Department of Mathematics and sam.bowyer@bristol.ac.uk;
(2)  Thomas Heap, Equal contribution, Department of Computer Science University of Bristol and thomas.heap@bristol.ac.uk;
(3) Laurence Aitchison, Department of Computer Science University of Bristol and laurence.aitchison@bristol.ac.uk. Table of Links Abstract & Introduction
Related work
Background
Methods
Experiments
"Conclusion, Limitations, and References"
Derivations
Algorithms
Experimental Datasets And Model Conclusion and Limitations We gave a new and far simpler method for computing posterior moments, marginals and samples in massively parallel importance sampling based on differentiating a slightly modified marginal likelihood estimator. The method has limitations, in that while it is considerably more effective than e.g. VI, HMC and global importance sampling, it is more complex. Additionally, at least a naive implementation may be quite costly in terms of memory consumption, limiting how the number of importance samples we can draw for each variable. That said, it should be possible to eliminate almost all of this overhead by careful optimizations to avoid allocating large intermediate tensors, following the strategy in KeOps (Charlier et al. 2021). References Aitchison, L. 2019. Tensor Monte Carlo: particle methods for the GPU era. Advances in Neural Information Processing Systems, 32. Albarghouthi, A.; D’Antoni, L.; Drews, S.; and Nori, A. V. 2017. Fairsquare: probabilistic verification of program fairness. Proceedings of the ACM on Programming Languages, 1(OOPSLA): 1–30. Andrieu, C.; Doucet, A.; and Holenstein, R. 2010. Particle markov chain monte carlo methods. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 72(3): 269–342. Bidyuk, B.; and Dechter, R. 2007. Cutset sampling for Bayesian networks. Journal of Artificial Intelligence Research, 28: 1–48. Bornschein, J.; and Bengio, Y. 2014. Reweighted wakesleep. arXiv preprint arXiv:1406.2751. Bradbury, J.; Frostig, R.; Hawkins, P.; Johnson, M. J.; Leary, C.; Maclaurin, D.; Necula, G.; Paszke, A.; VanderPlas, J.; Wanderman-Milne, S.; and Zhang, Q. 2018. JAX: composable transformations of Python+NumPy programs. Burda, Y.; Grosse, R.; and Salakhutdinov, R. 2015. Importance weighted autoencoders. arXiv preprint arXiv:1509.00519. Charlier, B.; Feydy, J.; Glaunes, J. A.; Collin, F.-D.; and Du- ` rif, G. 2021. Kernel Operations on the GPU, with Autodiff,  without Memory Overflows. Journal of Machine Learning Research. Chatterjee, S.; and Diaconis, P. 2018. The sample size required in importance sampling. The Annals of Applied Probability, 28(2): 1099–1135. Claret, G.; Rajamani, S. K.; Nori, A. V.; Gordon, A. D.; and Borgstrom, J. 2013. Bayesian inference using data flow anal- ¨ ysis. In Proceedings of the 2013 9th joint meeting on foundations of software engineering, 92–102. Corenflos, A.; Chopin, N.; and Sarkk ¨ a, S. 2022. De- ¨ Sequentialized Monte Carlo: a parallel-in time particle smoother. arXiv preprint arXiv:2202.02264. Crucinio, F. R.; and Johansen, A. M. 2023. Properties of Marginal Sequential Monte Carlo Methods. ArXiv:2303.03498 [math, stat]. Dawid, A. P. 1992. Applications of a general propagation algorithm for probabilistic expert systems. Statistics and computing, 2(1): 25–36. Dechter, R.; Broka, F.; Kask, K.; and Ihler, A. 2018. Abstraction Sampling in Graphical Models. AAAI. Del Moral, P.; Doucet, A.; and Jasra, A. 2006. Sequential Monte Carlo Samplers. Journal of the Royal Statistical Society Series B: Statistical Methodology, 68(3): 411–436. DOE. 2023. Bus Breakdown and Delays. url https://data.cityofnewyork.us/Transportation/BusBreakdown-and-Delays/ez4e-fazm Accessed on: 05.05.2023. Doucet, A.; Johansen, A. M.; et al. 2009. A tutorial on particle filtering and smoothing: Fifteen years later. Handbook of nonlinear filtering, 12(656-704): 3. Geffner, T.; and Domke, J. 2022. Variational Inference with Locally Enhanced Bounds for Hierarchical Models. arXiv preprint arXiv:2203.04432. Gehr, T.; Misailovic, S.; and Vechev, M. 2016. PSI: Exact symbolic inference for probabilistic programs. In International Conference on Computer Aided Verification, 62–83. Springer. Geldenhuys, J.; Dwyer, M. B.; and Visser, W. 2012. Probabilistic symbolic execution. In Proceedings of the 2012 International Symposium on Software Testing and Analysis, 166–176. Gogate, V.; and Dechter, R. 2012. Importance samplingbased estimation over and/or search spaces for graphical models. Artificial Intelligence, 184: 38–77. Goodman, N. D.; and Stuhlmuller, A. 2014. The design and implementation of probabilistic programming languages. Gordon, N. J.; Salmond, D. J.; and Smith, A. F. 1993. Novel approach to nonlinear/non-Gaussian Bayesian state estimation. In IEE proceedings F (radar and signal processing), volume 140, 107–113. IET. Harper, F. M.; and Konstan, J. A. 2015. The movielens datasets: History and context. Acm transactions on interactive intelligent systems (tiis), 5(4): 1–19. Heap, T.; and Laurence, A. 2023. Massively parallel reweighted wake-sleep. UAI. Hoffman, M. D.; Gelman, A.; et al. 2014. The No-UTurn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo. J. Mach. Learn. Res., 15(1): 1593–1623. Holtzen, S.; Van den Broeck, G.; and Millstein, T. 2020. Scaling exact inference for discrete probabilistic programs. Proceedings of the ACM on Programming Languages, 4(OOPSLA): 1–31. Jordan, M. I.; Ghahramani, Z.; Jaakkola, T. S.; and Saul, L. K. 1999. An introduction to variational methods for graphical models. Machine learning, 37(2): 183–233. Kingma, D. P.; Welling, M.; et al. 2019. An introduction to variational autoencoders. Foundations and Trends® in Machine Learning, 12(4): 307–392. Kuntz, J.; Crucinio, F. R.; and Johansen, A. M. 2023. The divide-and-conquer sequential Monte Carlo algorithm: theoretical properties and limit theorems. ArXiv:2110.15782 [math, stat]. Lai, J.; Domke, J.; and Sheldon, D. 2022. Variational Marginal Particle Filters. In International Conference on Artificial Intelligence and Statistics, 875–895. PMLR. Le, T. A.; Igl, M.; Rainforth, T.; Jin, T.; and Wood, F. 2017. Auto-encoding sequential monte carlo. arXiv preprint arXiv:1705.10306. Le, T. A.; Kosiorek, A. R.; Siddharth, N.; Teh, Y. W.; and Wood, F. 2020. Revisiting reweighted wake sleep for models with stochastic control flow. In Uncertainty in Artificial Intelligence, 1039–1049. PMLR. Lindsten, F.; Johansen, A. M.; Naesseth, C. A.; Kirkpatrick, B.; Schon, T. B.; Aston, J.; and Bouchard-Cotˆ e, A. 2017. ´ Divide-and-conquer with sequential Monte Carlo. Journal of Computational and Graphical Statistics, 26(2): 445–458. Maddison, C. J.; Lawson, J.; Tucker, G.; Heess, N.; Norouzi, M.; Mnih, A.; Doucet, A.; and Teh, Y. 2017. Filtering variational objectives. Advances in Neural Information Processing Systems, 30. Naesseth, C.; Linderman, S.; Ranganath, R.; and Blei, D. 2018. Variational sequential monte carlo. In International conference on artificial intelligence and statistics, 968–977. PMLR. Narayanan, P.; Carette, J.; Romano, W.; Shan, C.-c.; and Zinkov, R. 2016. Probabilistic inference by program transformation in Hakaru (system description). In International Symposium on Functional and Logic Programming, 62–79. Springer Obermeyer, F.; Bingham, E.; Jankowiak, M.; Pradhan, N.; Chiu, J.; Rush, A.; and Goodman, N. 2019. Tensor variable elimination for plated factor graphs. In International Conference on Machine Learning, 4871–4880. PMLR. Pfeffer, A. 2005. The design and implementation of IBAL: A generalpurpose probabilistic programming language. In Harvard Univesity. Citeseer. Salvatier, J.; Wiecki, T. V.; and Fonnesbeck, C. 2016. Probabilistic programming in Python using PyMC3. PeerJ Computer Science, 2: e55. Publisher: PeerJ Inc. Sankaranarayanan, S.; Chakarov, A.; and Gulwani, S. 2013. Static analysis for probabilistic programs: inferring whole program properties from finitely many paths. In Proceedings of the 34th ACM SIGPLAN conference on Programming language design and implementation, 447–458. Wang, D.; Hoffmann, J.; and Reps, T. 2018. PMAF: an algebraic framework for static analysis of probabilistic programs. ACM SIGPLAN Notices, 53(4): 513–528. Weinberg, S. 1995. The quantum theory of fields, volume 2. Cambridge university press. Zavatone-Veth, J.; Canatar, A.; Ruben, B.; and Pehlevan, C. 2021. Asymptotics of representation learning in finite Bayesian neural networks. Advances in neural information processing systems, 34: 24765–24777. This paper is available on arxiv under CC 4.0 license. Authors: (1) Sam Bowyer, Equal contribution, Department of Mathematics and sam.bowyer@bristol.ac.uk; (2)  Thomas Heap, Equal contribution, Department of Computer Science University of Bristol and thomas.heap@bristol.ac.uk; (3) Laurence Aitchison, Department of Computer Science University of Bristol and laurence.aitchison@bristol.ac.uk. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Sam Bowyer, Equal contribution, Department of Mathematics and sam.bowyer@bristol.ac.uk; (2)  Thomas Heap, Equal contribution, Department of Computer Science University of Bristol and thomas.heap@bristol.ac.uk; (3) Laurence Aitchison, Department of Computer Science University of Bristol and laurence.aitchison@bristol.ac.uk. Table of Links Abstract & Introduction Related work Background Methods Experiments "Conclusion, Limitations, and References" Derivations Algorithms Experimental Datasets And Model Abstract & Introduction Abstract & Introduction Related work Related work Background Background Methods Methods Experiments Experiments "Conclusion, Limitations, and References" "Conclusion, Limitations, and References" Derivations Derivations Algorithms Algorithms Experimental Datasets And Model Experimental Datasets And Model Conclusion and Limitations We gave a new and far simpler method for computing posterior moments, marginals and samples in massively parallel importance sampling based on differentiating a slightly modified marginal likelihood estimator. The method has limitations, in that while it is considerably more effective than e.g. VI, HMC and global importance sampling, it is more complex. Additionally, at least a naive implementation may be quite costly in terms of memory consumption, limiting how the number of importance samples we can draw for each variable. That said, it should be possible to eliminate almost all of this overhead by careful optimizations to avoid allocating large intermediate tensors, following the strategy in KeOps (Charlier et al. 2021). References Aitchison, L. 2019. Tensor Monte Carlo: particle methods for the GPU era. Advances in Neural Information Processing Systems, 32. Albarghouthi, A.; D’Antoni, L.; Drews, S.; and Nori, A. V. 2017. Fairsquare: probabilistic verification of program fairness. Proceedings of the ACM on Programming Languages, 1(OOPSLA): 1–30. Andrieu, C.; Doucet, A.; and Holenstein, R. 2010. Particle markov chain monte carlo methods. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 72(3): 269–342. Bidyuk, B.; and Dechter, R. 2007. Cutset sampling for Bayesian networks. Journal of Artificial Intelligence Research, 28: 1–48. Bornschein, J.; and Bengio, Y. 2014. Reweighted wakesleep. arXiv preprint arXiv:1406.2751. Bradbury, J.; Frostig, R.; Hawkins, P.; Johnson, M. J.; Leary, C.; Maclaurin, D.; Necula, G.; Paszke, A.; VanderPlas, J.; Wanderman-Milne, S.; and Zhang, Q. 2018. JAX: composable transformations of Python+NumPy programs. Burda, Y.; Grosse, R.; and Salakhutdinov, R. 2015. Importance weighted autoencoders. arXiv preprint arXiv:1509.00519. Charlier, B.; Feydy, J.; Glaunes, J. A.; Collin, F.-D.; and Du- ` rif, G. 2021. Kernel Operations on the GPU, with Autodiff,  without Memory Overflows. Journal of Machine Learning Research. Chatterjee, S.; and Diaconis, P. 2018. The sample size required in importance sampling. The Annals of Applied Probability, 28(2): 1099–1135. Claret, G.; Rajamani, S. K.; Nori, A. V.; Gordon, A. D.; and Borgstrom, J. 2013. Bayesian inference using data flow anal- ¨ ysis. In Proceedings of the 2013 9th joint meeting on foundations of software engineering, 92–102. Corenflos, A.; Chopin, N.; and Sarkk ¨ a, S. 2022. De- ¨ Sequentialized Monte Carlo: a parallel-in time particle smoother. arXiv preprint arXiv:2202.02264. Crucinio, F. R.; and Johansen, A. M. 2023. Properties of Marginal Sequential Monte Carlo Methods. ArXiv:2303.03498 [math, stat]. Dawid, A. P. 1992. Applications of a general propagation algorithm for probabilistic expert systems. Statistics and computing, 2(1): 25–36. Dechter, R.; Broka, F.; Kask, K.; and Ihler, A. 2018. Abstraction Sampling in Graphical Models. AAAI. Del Moral, P.; Doucet, A.; and Jasra, A. 2006. Sequential Monte Carlo Samplers. Journal of the Royal Statistical Society Series B: Statistical Methodology, 68(3): 411–436. DOE. 2023. Bus Breakdown and Delays. url https://data.cityofnewyork.us/Transportation/BusBreakdown-and-Delays/ez4e-fazm Accessed on: 05.05.2023. Doucet, A.; Johansen, A. M.; et al. 2009. A tutorial on particle filtering and smoothing: Fifteen years later. Handbook of nonlinear filtering, 12(656-704): 3. Geffner, T.; and Domke, J. 2022. Variational Inference with Locally Enhanced Bounds for Hierarchical Models. arXiv preprint arXiv:2203.04432. Gehr, T.; Misailovic, S.; and Vechev, M. 2016. PSI: Exact symbolic inference for probabilistic programs. In International Conference on Computer Aided Verification, 62–83. Springer. Geldenhuys, J.; Dwyer, M. B.; and Visser, W. 2012. Probabilistic symbolic execution. In Proceedings of the 2012 International Symposium on Software Testing and Analysis, 166–176. Gogate, V.; and Dechter, R. 2012. Importance samplingbased estimation over and/or search spaces for graphical models. Artificial Intelligence, 184: 38–77. Goodman, N. D.; and Stuhlmuller, A. 2014. The design and implementation of probabilistic programming languages. Gordon, N. J.; Salmond, D. J.; and Smith, A. F. 1993. Novel approach to nonlinear/non-Gaussian Bayesian state estimation. In IEE proceedings F (radar and signal processing), volume 140, 107–113. IET. Harper, F. M.; and Konstan, J. A. 2015. The movielens datasets: History and context. Acm transactions on interactive intelligent systems (tiis), 5(4): 1–19. Heap, T.; and Laurence, A. 2023. Massively parallel reweighted wake-sleep. UAI. Hoffman, M. D.; Gelman, A.; et al. 2014. The No-UTurn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo. J. Mach. Learn. Res., 15(1): 1593–1623. Holtzen, S.; Van den Broeck, G.; and Millstein, T. 2020. Scaling exact inference for discrete probabilistic programs. Proceedings of the ACM on Programming Languages, 4(OOPSLA): 1–31. Jordan, M. I.; Ghahramani, Z.; Jaakkola, T. S.; and Saul, L. K. 1999. An introduction to variational methods for graphical models. Machine learning, 37(2): 183–233. Kingma, D. P.; Welling, M.; et al. 2019. An introduction to variational autoencoders. Foundations and Trends® in Machine Learning, 12(4): 307–392. Kuntz, J.; Crucinio, F. R.; and Johansen, A. M. 2023. The divide-and-conquer sequential Monte Carlo algorithm: theoretical properties and limit theorems. ArXiv:2110.15782 [math, stat]. Lai, J.; Domke, J.; and Sheldon, D. 2022. Variational Marginal Particle Filters. In International Conference on Artificial Intelligence and Statistics, 875–895. PMLR. Le, T. A.; Igl, M.; Rainforth, T.; Jin, T.; and Wood, F. 2017. Auto-encoding sequential monte carlo. arXiv preprint arXiv:1705.10306. Le, T. A.; Kosiorek, A. R.; Siddharth, N.; Teh, Y. W.; and Wood, F. 2020. Revisiting reweighted wake sleep for models with stochastic control flow. In Uncertainty in Artificial Intelligence, 1039–1049. PMLR. Lindsten, F.; Johansen, A. M.; Naesseth, C. A.; Kirkpatrick, B.; Schon, T. B.; Aston, J.; and Bouchard-Cotˆ e, A. 2017. ´ Divide-and-conquer with sequential Monte Carlo. Journal of Computational and Graphical Statistics, 26(2): 445–458. Maddison, C. J.; Lawson, J.; Tucker, G.; Heess, N.; Norouzi, M.; Mnih, A.; Doucet, A.; and Teh, Y. 2017. Filtering variational objectives. Advances in Neural Information Processing Systems, 30. Naesseth, C.; Linderman, S.; Ranganath, R.; and Blei, D. 2018. Variational sequential monte carlo. In International conference on artificial intelligence and statistics, 968–977. PMLR. Narayanan, P.; Carette, J.; Romano, W.; Shan, C.-c.; and Zinkov, R. 2016. Probabilistic inference by program transformation in Hakaru (system description). In International Symposium on Functional and Logic Programming, 62–79. Springer Obermeyer, F.; Bingham, E.; Jankowiak, M.; Pradhan, N.; Chiu, J.; Rush, A.; and Goodman, N. 2019. Tensor variable elimination for plated factor graphs. In International Conference on Machine Learning, 4871–4880. PMLR. Pfeffer, A. 2005. The design and implementation of IBAL: A generalpurpose probabilistic programming language. In Harvard Univesity. Citeseer. Salvatier, J.; Wiecki, T. V.; and Fonnesbeck, C. 2016. Probabilistic programming in Python using PyMC3. PeerJ Computer Science, 2: e55. Publisher: PeerJ Inc. Sankaranarayanan, S.; Chakarov, A.; and Gulwani, S. 2013. Static analysis for probabilistic programs: inferring whole program properties from finitely many paths. In Proceedings of the 34th ACM SIGPLAN conference on Programming language design and implementation, 447–458. Wang, D.; Hoffmann, J.; and Reps, T. 2018. PMAF: an algebraic framework for static analysis of probabilistic programs. ACM SIGPLAN Notices, 53(4): 513–528. Weinberg, S. 1995. The quantum theory of fields, volume 2. Cambridge university press. Zavatone-Veth, J.; Canatar, A.; Ruben, B.; and Pehlevan, C. 2021. Asymptotics of representation learning in finite Bayesian neural networks. Advances in neural information processing systems, 34: 24765–24777.

Using Autodiff to Estimate Posterior Moments: Conclusion, Limitations, and References

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Sam Bowyer, Equal contribution, Department of Mathematics and sam.bowyer@bristol.ac.uk;
(2)  Thomas Heap, Equal contribution, Department of Computer Science University of Bristol and thomas.heap@bristol.ac.uk;
(3) Laurence Aitchison, Department of Computer Science University of Bristol and laurence.aitchison@bristol.ac.uk. Table of Links Abstract & Introduction
Related work
Background
Methods
Experiments
"Conclusion, Limitations, and References"
Derivations
Algorithms
Experimental Datasets And Model Derivations Global Importance Sampling Massively Parallel Importance Sampling This paper is available on arxiv under CC 4.0 license. Authors: (1) Sam Bowyer, Equal contribution, Department of Mathematics and sam.bowyer@bristol.ac.uk; (2)  Thomas Heap, Equal contribution, Department of Computer Science University of Bristol and thomas.heap@bristol.ac.uk; (3) Laurence Aitchison, Department of Computer Science University of Bristol and laurence.aitchison@bristol.ac.uk. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Sam Bowyer, Equal contribution, Department of Mathematics and sam.bowyer@bristol.ac.uk; (2)  Thomas Heap, Equal contribution, Department of Computer Science University of Bristol and thomas.heap@bristol.ac.uk; (3) Laurence Aitchison, Department of Computer Science University of Bristol and laurence.aitchison@bristol.ac.uk. Table of Links Abstract & Introduction Related work Background Methods Experiments "Conclusion, Limitations, and References" Derivations Algorithms Experimental Datasets And Model Abstract & Introduction Abstract & Introduction Related work Related work Background Background Methods Methods Experiments Experiments "Conclusion, Limitations, and References" "Conclusion, Limitations, and References" Derivations Derivations Algorithms Algorithms Experimental Datasets And Model Experimental Datasets And Model Derivations Global Importance Sampling Massively Parallel Importance Sampling

Using Autodiff to Estimate Posterior Moments, Marginals and Samples: Derivations

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Sam Bowyer, Equal contribution, Department of Mathematics and sam.bowyer@bristol.ac.uk;
(2)  Thomas Heap, Equal contribution, Department of Computer Science University of Bristol and thomas.heap@bristol.ac.uk;
(3) Laurence Aitchison, Department of Computer Science University of Bristol and laurence.aitchison@bristol.ac.uk. Table of Links Abstract & Introduction
Related work
Background
Methods
Experiments
"Conclusion, Limitations, and References"
Derivations
Algorithms
Experimental Datasets And Model Methods Of course, the contributions of this paper are not in computing the unbiased marginal likelihood estimator, which previously has been used in learning general probabilistic models, but instead our major contribution is a novel approach to computing key quantities of interest in Bayesian computation by applying the source term trick to the massively parallel marginal likelihood estimator. In particular, in the following sections, we outline in turn how to compute posterior expectations, marginals and samples. This paper is available on arxiv under CC 4.0 license. Authors: (1) Sam Bowyer, Equal contribution, Department of Mathematics and sam.bowyer@bristol.ac.uk; (2)  Thomas Heap, Equal contribution, Department of Computer Science University of Bristol and thomas.heap@bristol.ac.uk; (3) Laurence Aitchison, Department of Computer Science University of Bristol and laurence.aitchison@bristol.ac.uk. This paper is available on arxiv under CC 4.0 license. Authors : Authors (1) Sam Bowyer, Equal contribution, Department of Mathematics and sam.bowyer@bristol.ac.uk; (2)  Thomas Heap, Equal contribution, Department of Computer Science University of Bristol and thomas.heap@bristol.ac.uk; (3) Laurence Aitchison, Department of Computer Science University of Bristol and laurence.aitchison@bristol.ac.uk. Table of Links Abstract & Introduction Related work Background Methods Experiments "Conclusion, Limitations, and References" Derivations Algorithms Experimental Datasets And Model Abstract & Introduction Abstract & Introduction Related work Related work Background Background Methods Methods Experiments Experiments "Conclusion, Limitations, and References" "Conclusion, Limitations, and References" Derivations Derivations Algorithms Algorithms Experimental Datasets And Model Experimental Datasets And Model Methods Of course, the contributions of this paper are not in computing the unbiased marginal likelihood estimator, which previously has been used in learning general probabilistic models, but instead our major contribution is a novel approach to computing key quantities of interest in Bayesian computation by applying the source term trick to the massively parallel marginal likelihood estimator. In particular, in the following sections, we outline in turn how to compute posterior expectations, marginals and samples.

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