General Motors has the support of fellow American automotive giant Ford, which wants to see the Andretti-GM union join them in Formula 1.

Key ally for Andretti and General Motors emerges in tricky F1 pursuit

On May 28, 2024, Mark Reuss, President of General Motors Co (NYSE:GM), executed a sale of 150,000 shares of the company.

Insider Sale: President Mark Reuss Sells 150,000 Shares of General Motors Co (GM)

Get the most recent info and news about General Motors on HackerNoon, where 10k+ technologists publish stories for 4M+ monthly readers.

General Motors Company designs, builds, and sells cars, trucks, crossovers, and automobile parts worldwide. The company operates through GM North America, GM International, Cruise, and GM Financial segments. It markets its vehicles primarily under the Buick, Cadillac, Chevrolet, GMC, Holden, Baojun, and Wuling brand names. The company also sells trucks, crossovers, and cars to dealers for consumer retail sales, as well as to fleet customers, including daily rental car companies, commercial fleet customers, leasing companies, and governments. In addition, it offers safety and security services for retail and fleet customers, including automatic crash response, emergency services, crisis assist, stolen vehicle assistance, roadside assistance, and turn-by-turn navigation, as well as connected services comprising mobile applications for owners to remotely control their vehicles and electric vehicle owners to locate charging stations, on-demand vehicle diagnostics, smart driver, marketplace in-vehicle commerce, connected navigation, SiriusXM with 360L, and 4G LTE wireless connectivity. Further, the company provides automotive financing services; and operates an online new vehicle store. General Motors Company was founded in 1908 and is headquartered in Detroit, Michigan.

General Motors

GM has made the much-overlooked claim that by mid-decade, it expects to commercialize the most exotic of the stretch futuristic batteries currently on electric vehicle drawing boards. In remarks yesterday at Barclay’s Automotive Conference, GM CEO Mary Barra announced an explicit campaign to capture the lead in EV development from Tesla and everyone else in the industry. That included a 35% bump in EV development investment to $27 billion by 2025 and the deployment of EVs that will go up to a whopping 450 miles on a charge, farther than any of its major competitors have discussed. Most coverage of the presentation focused on plus the of the electric Cadillac Lyriq to the first quarter of 2022, nine months early. But the reports that focused on these developments missed the big news. Doug Parks, one of Barra’s executive vice presidents, said that GM’s next-generation EV battery, contained in its mid-decade EVs, will have a metallic lithium anode. those announcements accelerated release This is a : Because of the gargantuan energy lift that metallic lithium can bring, it has been a holy grail of the battery world for almost a half-century. But no one has managed to figure out how to get past an unfortunate side reaction, which is that metallic lithium catches fire, grows dangerous spikes that short circuit batteries, and is generally a difficult beast to tame. huge deal In September, the high-spending Silicon Valley startup in a reverse merger solely on the strength of its claim to have solved metallic lithium. QuantumScape’s partner is VW, which has suggested it, too, will have metallic lithium anodes in its batteries by 2025. QuantumScape went public But when QuantumScape failed to publicly release any data, leading independent battery experts withheld any cheering until they could see the goods. Likewise now with GM. I asked for thoughts on GM’s announcement from a few of the battery experts. One emailed back to say, “That’s bullshit.” A second, on WhatsApp, said, “That’s just crazy.” Parks said the anode would reduce the cost of GM’s batteries well below $100 per kWh, which has been another battery industry holy grail threshold. The tenor of the battery race is driving companies to make big moves and big claims. Ford has said it is spending $11.5 billion on electric vehicles over a five-year period ending in 2022. VW says it will spend on EV and hybrid technology by mid-decade. Tesla says its battery and cars. $54.5 billion it’s entirely reengineering “Tesla’s got a good jump, and they’ve done great things,” Parks told reporters in a pre-briefing Wednesday evening. “They’re formidable competitors … and there’s a lot of startups and everyone else invading this space. We’re not going to cede leadership there.” What GM is saying specifically is that by the middle of the decade, it will produce a second generation of its new lithium-ion EV battery, which it calls the Ultium. This battery will include a lithium-metal anode. It has already taken these pretty far, it says — achieving an impressive 500 charge-and-discharge cycles. Usually, EV batteries are thought to be ready for showtime when they can reliably do 1,000 charge-and-discharge cycles while retaining more or less the same capacity. Getting hundreds means it’s not quite there but is seriously robust. To questions yesterday from Barclays managing director Brian Johnson, Parks said the anode would reduce the cost of GM’s batteries well below $100 per kWh, which has been another battery industry holy grail threshold. Parks seemed in fact to say that the cost of the entire battery pack, including all the weight of the various filler and junk that surrounds the individual cells, would be well below $100 per kWh, which, if true, would be stunning. In response to a question I asked in the pre-briefing about the battery’s composition, Parks said the battery’s energy density would be 1,200 watt-hours per liter, a feat that experts I later spoke with had a hard time believing. The GM battery would differ from QuantumScape’s in that it is not solid state — it uses a liquid electrolyte. Parks said there is much work left to do scaling up the battery but that “we think we are at the leading edge of this curve.” Parks did not give a precise year in the mid-decade when all this will happen nor how many vehicles will feature the anode. In a statement, a company spokesman said there would be a “phase-in of many new technology areas related to EVs, including battery chemistry.” When I queried Jeff Sakamoto, a lithium-metal researcher at the University of Michigan, he said that using a liquid electrolyte with metallic lithium would make it “tough” to achieve any more than a few hundred charge-discharge cycles — “insufficient for EVs.” He said the rough 2025 time frame seemed optimistic. “To me, most of the underlying physics works out on paper, but there are still some knowledge gaps that must be bridged before any lithium solid-state battery can be considered a viable solution, especially for EVs.” Paul Albertus, a professor at the University of Maryland, said that GM would need to start production slowly because it will probably have to change the process it’s using to manufacture its current lithium-ion chemistry. “I think there’s also an aspect of starting small to see if any surprise field failures or challenges arise — put out a small vehicle run and wait a year or two and make sure no surprises happen, that type of thing,” he said. Albertus said he wouldn’t be surprised if GM totally misses the deadline and never produces a metallic lithium battery at all. But he added that he also wouldn’t be astonished if GM succeeds. “What makes me a little more optimistic is that we know there are some very large efforts working on this now,” he said. “… With automotive batteries growing so much in the coming 10 years, there’s now motivation to tackle something of the complexity of [metallic lithium]. There’s also been a lot of really good science finally getting done in the past few years, and that does help people innovate.” Read behind a paywall at https://themobilist.medium.com/gm-claims-its-on-the-verge-of-commercializing-the-most-exotic-battery-chemistry-of-all-a310254fd1e2

General Motor's Claims To Commercialise the Most Innovative Battery Technology

As the world becomes increasingly digital, businesses across industries—from retail to finance and automotive—must handle massive volumes of data. Whether managing product catalogs, processing telemetry data, or handling financial transactions, the ability to manage and scale real-time data across multiple regions is critical for global operations. One expert leading the way in solving these data management challenges is Amey Banarse, a solutions engineer with over a decade of experience in distributed database technologies. Amey uses his expertise to solve complex data challenges, reduce costs, and improve performance. His work has led to successful collaborations with Fortune 500 enterprises, including global financial institutions like Wells Fargo and Fiserv, large retailers like Kroger, and automotive companies including General Motors, making him a prominent figure in the field. The Challenges of Data Management in the Digital Age Coordinating large amounts of information presents a unique challenge in the modern world. Technological breakthroughs like real-time analytics and machine learning have provided immense benefits, but these advancements also introduce new obstacles, such as the need for low-latency access and maintaining consistent data availability across regions. Amey Banarse shares his insights into overcoming these challenges and the journey that led him to become an expert in the field. Q: Amey, what inspired you to pursue a career in technical solutions engineering? A: My journey began with a Master’s degree from the University of Pennsylvania in 2010. I started my career as a Senior Data Analytics Consultant at Gemini Systems, New York, where I focused on data analytics platforms in the financial services sector. Collaborating with high-profile clients like the New York Stock Exchange (NYSE) to develop solutions and working on high-impact projects sparked my passion for data management. It was during my collaboration with the Financial Industry Regulatory Authority (FINRA) that I truly recognized the critical importance of robust data architecture. Today, as a recognized thought leader in the data industry, I combine deep expertise in distributed systems and cloud-native transactional applications with a commitment to advancing enterprise data innovation. I am honored to contribute to the leading forums like Forbes Technology Council sharing insights through writing, presentations, and thought leadership. My speaking engagements at premier conferences like AWS ReInvent, VMWare Explore, and SpringOne reflect my dedication to shaping industry discourse, from modern data architectures to enterprise data strategies, helping organizations unlock the full potential of their data assets. Q: As industries face the increasing demand for real-time data processing, what challenges are they encountering? A: Today’s businesses, especially in sectors and developing industries like retail, automotive and finance, must manage massive volumes of real-time data while ensuring security and scalability. Challenges include handling traffic surges during peak periods, maintaining data integrity across geographically distributed systems, and providing consistent performance. Failure to address these issues can lead to bottlenecks, data inconsistencies, and operational inefficiencies that significantly impact customer experience and revenue. Q: Can you share your approach to solving these challenges using distributed database technologies? A: Absolutely. I leverage technologies like YugabyteDB, which provides the capability to handle high-throughput transactions, maintain strong consistency, and support multi-region deployments. My focus is on designing data architecture that integrates distributed databases with cloud-native platforms, ensuring real-time data is accessible and reliable across different locations. Collaborating with engineering teams, I implement rigorous testing under simulated high-traffic conditions and fine-tune systems for optimal performance. Q: Could you elaborate on a specific project where you implemented these solutions? A: One notable project was with a global retailer managing over 300 million products. They needed a scalable system to ensure real-time data access during peak shopping periods and ensure there was no downtime during the deals and product launch days. I led the team and implemented a scalable architecture solution using YugabyteDB, which not only reduced their total cost of ownership by over $10 million but also ensured consistent high-performance operations during critical shopping seasons. This solution provides a seamless end-customer experience during the peak holiday season, which increases customer loyalty and the retailer’s brand recognition. Q: Could you elaborate on a specific project where you implemented these solutions? A: One notable project was with a global retailer managing over 300 million products. They needed a scalable system to ensure real-time data access during peak shopping periods and ensure there was no downtime during the deals and product launch days. I led the team and implemented a scalable architecture solution using YugabyteDB, which not only reduced their total cost of ownership by over $10 million but also ensured consistent high-performance operations during critical shopping seasons. This solution provides a seamless end-customer experience during the peak holiday season, which increases customer loyalty and the retailer’s brand recognition. Q: You also worked with General Motors. What challenges did they face, and how did you help them? A: General Motors’ connected car platform plays a critical role in supporting its new services by collecting and leveraging data from over 20 million connected vehicles. The platform powers features such as vehicle health tracking, remote starts, and road condition reporting through GM mobile apps and OnStar. However, the existing database powering this platform, Apache Cassandra, was becoming a bottleneck. It resulted in high operational costs, limited scalability, and performance issues, especially during peak demand. I worked closely with their technical leadership to redesign the system architecture, which can process vast amounts of telemetry data in real-time, maintain consistency, and scale efficiently. I also led the migration from Apache Cassandra to YugabyteDB. This migration resulted in a tenfold improvement in scalability and performance, enabling the system to handle up to three million writes per second while significantly reducing hardware footprint and operational costs. This transformation allowed GM to enhance its connected vehicle services and deliver a superior customer experience. Q: Can you provide an example of how you helped a financial services customer modernize their data systems? A: A significant case was when I collaborated with senior technical leadership of a large financial services company in migrating from a legacy IBM DB2 mainframe to a modern, scalable system to support a retail portfolio dashboard. By leading the transition to YugabyteDB, I established a resilient architecture that not only reduced the total cost of ownership but also improved the end customer experience. The successful migration allowed live user onboarding, creating a flexible and reliable system supporting future growth. This enabled the company to meet its primary goals of migrating away from the mainframe and leveraging cloud-native databases, which can be deployed on cloud and hybrid commodity architectures. It also helped with improved productivity—rather than being slowed down by waiting for provisioning and maintenance of DB2 on the mainframe, the application team can quickly deploy YugabyteDB anywhere, and it automatically scales as needed. Q: Your achievements in this field are impressive. What drives you to continue pushing boundaries? A: I’m motivated by the impact that scalable data platform solutions can have on mission-critical applications that power businesses and society. Leading high-profile projects on behalf of YugabyteDB, like designing a scalable platform for the Super Bowl 2024 streaming event, has been incredibly rewarding. The ability to deliver a high-quality streaming experience to over 125 million viewers underscores the importance of scalable, resilient systems in today’s interconnected world. Q: What is your vision for the future of data management? A: I believe the ingenuity of technical solutions engineers like myself is crucial for a better-connected world. As industries evolve and the demand for real-time data continues to surge, developing innovative solutions that enable organizations to scale effectively is essential. I aim to lead this charge, ensuring businesses can meet their data challenges head-on and drive success through intelligent data management strategies. Amey Banarse’s ability to architect scalable, resilient systems has allowed these businesses to modernize and handle real-time data at unprecedented scales. His expertise in distributed database technologies is shaping the future of data management and paving the way for organizations to thrive in an increasingly data-driven landscape. As the world becomes increasingly digital, businesses across industries—from retail to finance and automotive—must handle massive volumes of data. Whether managing product catalogs, processing telemetry data, or handling financial transactions, the ability to manage and scale real-time data across multiple regions is critical for global operations. One expert leading the way in solving these data management challenges is Amey Banarse, a solutions engineer with over a decade of experience in distributed database technologies. Amey uses his expertise to solve complex data challenges, reduce costs, and improve performance. His work has led to successful collaborations with Fortune 500 enterprises, including global financial institutions like Wells Fargo and Fiserv, large retailers like Kroger, and automotive companies including General Motors, making him a prominent figure in the field. The Challenges of Data Management in the Digital Age The Challenges of Data Management in the Digital Age Coordinating large amounts of information presents a unique challenge in the modern world. Technological breakthroughs like real-time analytics and machine learning have provided immense benefits, but these advancements also introduce new obstacles, such as the need for low-latency access and maintaining consistent data availability across regions. Amey Banarse shares his insights into overcoming these challenges and the journey that led him to become an expert in the field. Q: Amey, what inspired you to pursue a career in technical solutions engineering? Q: Amey, what inspired you to pursue a career in technical solutions engineering? A : My journey began with a Master’s degree from the University of Pennsylvania in 2010. I started my career as a Senior Data Analytics Consultant at Gemini Systems, New York, where I focused on data analytics platforms in the financial services sector. Collaborating with high-profile clients like the New York Stock Exchange (NYSE) to develop solutions and working on high-impact projects sparked my passion for data management. It was during my collaboration with the Financial Industry Regulatory Authority (FINRA) that I truly recognized the critical importance of robust data architecture. Today, as a recognized thought leader in the data industry, I combine deep expertise in distributed systems and cloud-native transactional applications with a commitment to advancing enterprise data innovation. I am honored to contribute to the leading forums like Forbes Technology Council sharing insights through writing, presentations, and thought leadership. My speaking engagements at premier conferences like AWS ReInvent, VMWare Explore, and SpringOne reflect my dedication to shaping industry discourse, from modern data architectures to enterprise data strategies, helping organizations unlock the full potential of their data assets. A Q: As industries face the increasing demand for real-time data processing, what challenges are they encountering? Q: As industries face the increasing demand for real-time data processing, what challenges are they encountering? A : Today’s businesses, especially in sectors and developing industries like retail, automotive and finance, must manage massive volumes of real-time data while ensuring security and scalability. Challenges include handling traffic surges during peak periods, maintaining data integrity across geographically distributed systems, and providing consistent performance. Failure to address these issues can lead to bottlenecks, data inconsistencies, and operational inefficiencies that significantly impact customer experience and revenue. A Q: Can you share your approach to solving these challenges using distributed database technologies? Q: Can you share your approach to solving these challenges using distributed database technologies? A : Absolutely. I leverage technologies like YugabyteDB , which provides the capability to handle high-throughput transactions, maintain strong consistency, and support multi-region deployments. My focus is on designing data architecture that integrates distributed databases with cloud-native platforms, ensuring real-time data is accessible and reliable across different locations. Collaborating with engineering teams, I implement rigorous testing under simulated high-traffic conditions and fine-tune systems for optimal performance. A YugabyteDB YugabyteDB Q: Could you elaborate on a specific project where you implemented these solutions? Q: Could you elaborate on a specific project where you implemented these solutions? A : One notable project was with a global retailer managing over 300 million products. They needed a scalable system to ensure real-time data access during peak shopping periods and ensure there was no downtime during the deals and product launch days. I led the team and implemented a scalable architecture solution using YugabyteDB, which not only reduced their total cost of ownership by over $10 million but also ensured consistent high-performance operations during critical shopping seasons. This solution provides a seamless end-customer experience during the peak holiday season, which increases customer loyalty and the retailer’s brand recognition. A Q: Could you elaborate on a specific project where you implemented these solutions? Q: Could you elaborate on a specific project where you implemented these solutions? A : One notable project was with a global retailer managing over 300 million products. They needed a scalable system to ensure real-time data access during peak shopping periods and ensure there was no downtime during the deals and product launch days. I led the team and implemented a scalable architecture solution using YugabyteDB, which not only reduced their total cost of ownership by over $10 million but also ensured consistent high-performance operations during critical shopping seasons. This solution provides a seamless end-customer experience during the peak holiday season, which increases customer loyalty and the retailer’s brand recognition. A Q: You also worked with General Motors. What challenges did they face, and how did you help them? Q: You also worked with General Motors. What challenges did they face, and how did you help them? A: General Motors’ connected car platform plays a critical role in supporting its new services by collecting and leveraging data from over 20 million connected vehicles . The platform powers features such as vehicle health tracking, remote starts, and road condition reporting through GM mobile apps and OnStar. However, the existing database powering this platform, Apache Cassandra, was becoming a bottleneck. It resulted in high operational costs, limited scalability, and performance issues, especially during peak demand. I worked closely with their technical leadership to redesign the system architecture, which can process vast amounts of telemetry data in real-time, maintain consistency, and scale efficiently. I also led the migration from Apache Cassandra to YugabyteDB. This migration resulted in a tenfold improvement in scalability and performance, enabling the system to handle up to three million writes per second while significantly reducing hardware footprint and operational costs. This transformation allowed GM to enhance its connected vehicle services and deliver a superior customer experience. A: 20 million connected vehicles 20 million connected vehicles Q: Can you provide an example of how you helped a financial services customer modernize their data systems? Q: Can you provide an example of how you helped a financial services customer modernize their data systems? A : A significant case was when I collaborated with senior technical leadership of a large financial services company in migrating from a legacy IBM DB2 mainframe to a modern, scalable system to support a retail portfolio dashboard. By leading the transition to YugabyteDB, I established a resilient architecture that not only reduced the total cost of ownership but also improved the end customer experience. The successful migration allowed live user onboarding, creating a flexible and reliable system supporting future growth. This enabled the company to meet its primary goals of migrating away from the mainframe and leveraging cloud-native databases, which can be deployed on cloud and hybrid commodity architectures. It also helped with improved productivity—rather than being slowed down by waiting for provisioning and maintenance of DB2 on the mainframe, the application team can quickly deploy YugabyteDB anywhere, and it automatically scales as needed. A Q: Your achievements in this field are impressive. What drives you to continue pushing boundaries? Q: Your achievements in this field are impressive. What drives you to continue pushing boundaries? A : I’m motivated by the impact that scalable data platform solutions can have on mission-critical applications that power businesses and society. Leading high-profile projects on behalf of YugabyteDB, like designing a scalable platform for the Super Bowl 2024 streaming event, has been incredibly rewarding. The ability to deliver a high-quality streaming experience to over 125 million viewers underscores the importance of scalable, resilient systems in today’s interconnected world. A Q: What is your vision for the future of data management? Q: What is your vision for the future of data management? A : I believe the ingenuity of technical solutions engineers like myself is crucial for a better-connected world. As industries evolve and the demand for real-time data continues to surge, developing innovative solutions that enable organizations to scale effectively is essential. I aim to lead this charge, ensuring businesses can meet their data challenges head-on and drive success through intelligent data management strategies. A Amey Banarse’s ability to architect scalable, resilient systems has allowed these businesses to modernize and handle real-time data at unprecedented scales. His expertise in distributed database technologies is shaping the future of data management and paving the way for organizations to thrive in an increasingly data-driven landscape.

How Distributed Databases Power Mission-Critical Business Apps: A Case Study with Amey Banarse

Hoe verspreide databasisse missie-kritiese besigheidsprogramme bemagtig: 'n gevallestudie met Amey Banarse

Hvordan distribuerede databaser driver Mission-Critical Business Apps: A Case Study med Amey Banarse

Cómo las bases de datos distribuidas potencian las aplicaciones empresariales de misión crítica: un estudio de caso con Amey Banarse

分散データベースがミッションクリティカルなビジネスアプリを強化する方法: Amey Banarse のケーススタディ

Како се дистрибуирани базите на податоци моќни мисија-критични деловни апликации: студија на случај со Ејми Банарсе

Bagaimana Pangkalan Data Teragih Menguasakan Aplikasi Perniagaan Misi-Kritis: Kajian Kes dengan Amey Banarse

Jak rozproszone bazy danych zasilają aplikacje biznesowe o znaczeniu krytycznym: studium przypadku z Amey Banarse

Как распределенные базы данных поддерживают критически важные бизнес-приложения: пример с Эми Банарсе

Mafambisirwo Akaitwa Databases Power Mission-Critical Business Apps: Nyaya Yekudzidza naAmey Banarse

எப்படி விநியோகிக்கப்படும் தரவுத்தளங்கள் பவர் மிஷன்-கிரிட்டிகல் பிசினஸ் ஆப்ஸ்: அமே பனார்ஸுடன் ஒரு வழக்கு ஆய்வு

Чӣ гуна пойгоҳи додаҳои тақсимшуда Барномаҳои миссияи муҳими тиҷоратӣ: Омӯзиши мисол бо Ами Банарсе

ከመይ ጌሮም ዝተዘርግሑ ዳታቤዛት ንተልእኾ-ወሳኒ ንግዳዊ ኣፕስ ሓይሊ ይህቡ: መጽናዕቲ ፍጻመ ምስ ኣመይ ባናርሰ

Driving Innovation: Patents and Projects Shaping the Automotive Industry

ড্রাইভিং ইনোভেশন: পেটেন্ট এবং প্রকল্পগুলি মোটরগাড়ি শিল্পকে আকার দেয়

Impulsando la innovación: patentes y proyectos que configuran la industria automotriz

Poticanje inovacija: patenti i projekti koji oblikuju automobilsku industriju

イノベーションの推進: 自動車産業を形成する特許とプロジェクト

ນະວັດຕະກໍາການຂັບລົດ: ສິດທິບັດ ແລະໂຄງການສ້າງອຸດສາຫະກໍາລົດຍົນ

Go Kganna Boitlhamelo: Dipatente le Diprotšeke tše di Bopago Intasteri ya Dikoloi

Продвижение инноваций: патенты и проекты, формирующие автомобильную промышленность

Kutyaira Innovation: Patents uye Mapurojekiti Kugadzira Iyo Indasitiri Yemotokari

ขับเคลื่อนการสร้างสรรค์นวัตกรรม: สิทธิบัตรและโครงการที่กำหนดรูปลักษณ์ของอุตสาหกรรมยานยนต์

Pagmamaneho ng Innovation: Mga Patent at Proyekto na Humuhubog sa Industriya ng Automotive

Innovatsiyalarni qo'zg'atuvchi: avtomobil sanoatini shakllantiruvchi patentlar va loyihalar

I-Driving Innovation: Amalungelo Obunikazi Nemiklamo Ebumba Imboni Yezimoto

You’ve probably heard about crypto exchanges like Binance, KuCoin, Crypto.com, HTX , and others that sell tokens (e.g., BNB, KCS, CRO, and HT). Are these tokens worth buying? Can you make money from them? Depends on what you want to do These tokens offer perks like discounts on trading fees and access to exclusive benefits or promotions. On top of that, their prices sometimes go up a lot, giving you opportunities to sell your tokens for a profit. At the same time, exchanges control those tokens outright. Unlike Bitcoin or other cryptocurrencies that are governed by immutable blockchains and computer protocols that no single entity can change, exchange tokens function more like frequent flier miles or cash-back rewards. Exchanges can change the terms, supply, function, and benefits at any time without notice. Some exchanges sell those tokens to fund operations, cover liabilities, and line executives’ pockets. It’s a cheap way to raise money and easier than generating profit from operations. When you buy those tokens, you’re essentially subsidizing the exchange. All that glitters . . . Many exchanges hold some tokens as reserves or collateral for loans. FTX did this with its FTT token. In fact, FTX counted its token as an asset on its balance sheet while using its trading arm, Alameda, to prop up the price. Imagine General Motors creating one billion GM tokens, selling a single token to a Buick dealer for $100, then claiming to have $100 billion in assets. How do you know your exchange isn’t doing the same thing? With DeFi protocols, you don’t even need a trading arm. You simply need to cover your tracks. With Bitcoin, you can run a node and trace everything to the genesis block. Exchanges can’t even verify the number of circulating tokens, much less unravel the maze of transactions you’ll find on their blockchains. Don’t buy the BS While you can benefit from token rewards and make money trading these tokens, set realistic expectations — even if the exchange touts a lofty ambition. For example, Bitfinex frames its LEO token as “a token designed to empower the Bitfinex community.” Crypto.com says CRO is the “trading, payment, and financial services token for a cross-asset intermediary settlement layer.” Gate and Binance built decentralized financial platforms around their tokens, ostensibly with community governance. In reality, the exchanges still dictate the issuance, function, and utility. You get no equity in the business nor any claim on its assets. You have to trust that the exchanges make good on their word and stay in business. Crypto doesn’t have the best track record on any of those things. Bottom line Exchange tokens are crypto’s version of Dave and Buster’s tokens. Treat them as such. Few buy Dave & Buster’s tokens because they expect to make money. They enjoy playing games and winning prizes. They don’t treat the tokens as investments. Neither should you. Sure, if the token price goes up, you can sell for profit — but you can say that for every cryptocurrency. There’s nothing special about any token simply because an exchange slaps its logo on the ticker. Enjoy whatever perks the exchange gives you. Trade the tokens if you’d like. I won’t be joining you. Mark Helfman publishes the Crypto is Easy newsletter. He is also the author of three books and a top Bitcoin writer on Medium and Hacker Noon. Learn more about him in his bio and connect with him on Tealfeed. You’ve probably heard about crypto exchanges like Binance, KuCoin, Crypto.com, HTX , and others that sell tokens (e.g., BNB, KCS, CRO, and HT). Are these tokens worth buying? Can you make money from them? Depends on what you want to do These tokens offer perks like discounts on trading fees and access to exclusive benefits or promotions. On top of that, their prices sometimes go up a lot, giving you opportunities to sell your tokens for a profit. At the same time, exchanges control those tokens outright. Unlike Bitcoin or other cryptocurrencies that are governed by immutable blockchains and computer protocols that no single entity can change, exchange tokens function more like frequent flier miles or cash-back rewards. Exchanges can change the terms, supply, function, and benefits at any time without notice. Some exchanges sell those tokens to fund operations, cover liabilities, and line executives’ pockets. It’s a cheap way to raise money and easier than generating profit from operations. When you buy those tokens, you’re essentially subsidizing the exchange. All that glitters . . . Many exchanges hold some tokens as reserves or collateral for loans. FTX did this with its FTT token. In fact, FTX counted its token as an asset on its balance sheet while using its trading arm, Alameda, to prop up the price. Imagine General Motors creating one billion GM tokens, selling a single token to a Buick dealer for $100, then claiming to have $100 billion in assets. How do you know your exchange isn’t doing the same thing? your With DeFi protocols, you don’t even need a trading arm. You simply need to cover your tracks. With Bitcoin, you can run a node and trace everything to the genesis block. Exchanges can’t even verify the number of circulating tokens, much less unravel the maze of transactions you’ll find on their blockchains. Don’t buy the BS While you can benefit from token rewards and make money trading these tokens, set realistic expectations — even if the exchange touts a lofty ambition. For example, Bitfinex frames its LEO token as “a token designed to empower the Bitfinex community.” Crypto.com says CRO is the “trading, payment, and financial services token for a cross-asset intermediary settlement layer.” Gate and Binance built decentralized financial platforms around their tokens, ostensibly with community governance. In reality, the exchanges still dictate the issuance, function, and utility. You get no equity in the business nor any claim on its assets. You have to trust that the exchanges make good on their word and stay in business. Crypto doesn’t have the best track record on any of those things. Bottom line Exchange tokens are crypto’s version of Dave and Buster’s tokens. Treat them as such. Few buy Dave & Buster’s tokens because they expect to make money. They enjoy playing games and winning prizes. They don’t treat the tokens as investments. Neither should you. Sure, if the token price goes up, you can sell for profit — but you can say that for every cryptocurrency. but you can say that for every cryptocurrency. There’s nothing special about any token simply because an exchange slaps its logo on the ticker. Enjoy whatever perks the exchange gives you. Trade the tokens if you’d like. I won’t be joining you. Mark Helfman publishes the Crypto is Easy newsletter. He is also the author of three books and a top Bitcoin writer on Medium and Hacker Noon . Learn more about him in his bio and connect with him on Tealfeed . Crypto is Easy Crypto is Easy Medium Medium Hacker Noon Hacker Noon bio Tealfeed

Are Crypto Exchange Tokens a Good Investment? 

POS Financing vs. BNPL: Understanding the Differences and Evolution of Solutions

The electric vehicle (EV) market is heating up in the global arena with the US and China gearing up for yet another price war. While US car giants are slashing prices, China is negotiating hard to set up shop in Europe. In fact, the pending EV competition in Europe could mean job losses in the future. According to the International Energy Agency, environmentally speaking, 2030 will be a substantial year, because the world fossil fuel demand will peak by then as a larger number of EVs hit the road and the Chinese economy leans towards cleaner energy. Meanwhile, the debate about how effective EVs will actually be still continues. Last year, US automaker General Motors launched its luxury electric Cadillac Lyriq in France, an expansion of its market in Europe after returning last year and a test of whether online-only sales will attract wealthy car buyers. In the US, last year, Tesla slashed prices on its two most expensive EVs, after Tesla Chairman and CEO, Elon Musk realized that price cuts on other models had upped demand. The price cuts, one of many adjustments last year, ranged from 4% to 9%. In December, Musk unveiled Tesla’s new electric Semi truck, looking to reduce highway emissions with a battery power three times that of its diesel counterparts. Car companies are exploring all fronts to catch the consumer’s attention, leading to another price war between two super economies And it’s not just car makers who are into EVs. Foxconn, the largest electronics manufacturer and the supplier of Apple, took to producing EVs last year. Chip giant NVIDIA is helping them in their pursuit, alongside developing automated and autonomous vehicle platforms. Big tech is yet to fully enter this arena though. In February though, Apple canceled work on its electric car, according to Reuters, a decade after the tech giant started the project. Powerful partnerships are forming too. Honda and General Motors are planning to jointly develop affordable EVs. Hybrids & More Car companies are trying to sell more than just EVs as an environment-friendly solution by targeting tougher emissions standards. For example, in May, Toyota showcased next-generation engines for use in cars like hybrids and those running on biofuel. Volkswagen announced plans to cut down spend on combustion engines from 2025. It is working toward a target of 50% EV sales globally by 2030. Hybrid car models are likely to flood the market in the coming year. Fiat owner Stellantis is likely to announce a hybrid version of its 500e small electric car to be produced at its Mirafiori plant in Italy. In May, China’s Geely talked about launching a series of hybrid models in the first half of next year, which will boast of its next-generation, more fuel-efficient engine. Chinese are Gearing Up with EV China has been buckling down to make it even bigger than it is in the global EV market. Last June, China introduced a 520 billion yuan ($72.3 billion) package to increase EV sales and other green cars in the coming four years. Meanwhile, the debate about how effective EVs will actually be, still continues Chinese EV makers have been concentrating on competing with the US. Chinese EV maker Nio has made a deal with Chinese EV giant BYD as a source for its batteries. According to reports, Nio aims to compete with Tesla. A made-in-China EV is also expected to enter the US market this summer offering power and efficiency similar to the Tesla Model Y, but for about US$8,000 less. US carmakers could face stiff competition from China, looking at the EX30 from Volvo, the Swedish luxury brand owned by China’s Geely. Volvo Cars announced in February that it’s getting ready to convert to electric all its mainstay models, i.e., three SUVs and two sedans. The auto giant is also set to unveil a luxury electric van to improve sales in Asia. In March, BYD launched a new version of its Destroyer 07 hybrid car at a starting price that’s 11.3% lower than its predecessor, according to Reuters. Europe, a Veritable EV Playground Many events are coming into play on the European EV playground, and China is moving fast. The country is getting ready to make a deal with Europe as EV tariffs loom. In September, Beijing criticized a probe launched by the European Commission into China’s electric car subsidies as protectionist and warned it would damage relations. Ursula von der Leyen, President of the European Commission announced the investigation saying markets were getting flooded with EVs that had artificially low prices because of state subsidies. In fact, Stellantis is ready for a competition with Chinese rivals in the European market for EVs, which can be a harbinger of consequences for jobs and production according to the group’s Chief Executive Carlos Tavares as reported by Reuters. Already, last February, Ford announced axing one in nine jobs in product development and administration across Europe as it competes in the EV market. The US automaker announced plans to invest US$3.5 billion to build an EV battery plant in Michigan with Chinese partner CATL. Will This Price War Benefit the Consumer? Part of the charm of owning an EV is not just the environmental friendliness but also style and luxury, one of the prime reasons for EV adoption. Car companies are exploring all fronts to catch the consumer’s attention, leading to another price war between two super economies. This war of the EV makers could bring EV prices down globally. But it also means job cuts. The semiconductor chip war has already caused tumult. It remains to be seen how far it goes this time. Navanwita Bora Sachdev, Editor, The Tech Panda The electric vehicle (EV) market is heating up in the global arena with the US and China gearing up for yet another price war. While US car giants are slashing prices, China is negotiating hard to set up shop in Europe. In fact, the pending EV competition in Europe could mean job losses in the future. The electric vehicle (EV) According to the International Energy Agency , environmentally speaking, 2030 will be a substantial year, because the world fossil fuel demand will peak by then as a larger number of EVs hit the road and the Chinese economy leans towards cleaner energy. Meanwhile, the debate about how effective EVs will actually be still continues. International Energy Agency fossil fuel demand will peak Last year, US automaker General Motors launched its luxury electric Cadillac Lyriq in France, an expansion of its market in Europe after returning last year and a test of whether online-only sales will attract wealthy car buyers . General Motors launched online-only sales will attract wealthy car buyers In the US, last year, Tesla slashed prices on its two most expensive EVs, after Tesla Chairman and CEO, Elon Musk realized that price cuts on other models had upped demand. The price cuts, one of many adjustments last year, ranged from 4% to 9%. In December, Musk unveiled Tesla’s new electric Semi truck, looking to reduce highway emissions with a battery power three times that of its diesel counterparts. Tesla slashed prices unveiled Car companies are exploring all fronts to catch the consumer’s attention, leading to another price war between two super economies Car companies are exploring all fronts to catch the consumer’s attention, leading to another price war between two super economies Car companies are exploring all fronts to catch the consumer’s attention, leading to another price war between two super economies Car companies are exploring all fronts to catch the consumer’s attention, leading to another price war between two super economies Car companies are exploring all fronts to catch the consumer’s attention, leading to another price war between two super economies And it’s not just car makers who are into EVs. Foxconn , the largest electronics manufacturer and the supplier of Apple , took to producing EVs last year. Chip giant NVIDIA is helping them in their pursuit, alongside developing automated and autonomous vehicle platforms. Big tech is yet to fully enter this arena though. In February though, Apple canceled work on its electric car , according to Reuters, a decade after the tech giant started the project. Foxconn Apple took to producing NVIDIA Apple canceled work on its electric car Powerful partnerships are forming too. Honda and General Motors are planning to jointly develop affordable EVs. planning to jointly develop Hybrids & More Hybrids & More Hybrids & More Car companies are trying to sell more than just EVs as an environment-friendly solution by targeting tougher emissions standards. For example, in May, Toyota showcased next-generation engines for use in cars like hybrids and those running on biofuel. Volkswagen announced plans to cut down spend on combustion engines from 2025. It is working toward a target of 50% EV sales globally by 2030. Toyota showcased next-generation engines Volkswagen announced plans Hybrid car models are likely to flood the market in the coming year. Fiat owner Stellantis is likely to announce a hybrid version of its 500e small electric car to be produced at its Mirafiori plant in Italy. In May, China’s Geely talked about launch ing a series of hybrid models in the first half of next year, which will boast of its next-generation, more fuel-efficient engine. Fiat Stellantis hybrid version of its 500e small electric car China’s Geely talked about launch Chinese are Gearing Up with EV Chinese are Gearing Up with EV Chinese are Gearing Up with EV China has been buckling down to make it even bigger than it is in the global EV market. Last June, China introduced a 520 billion yuan ($72.3 billion) package to increase EV sales and other green cars in the coming four years. introduced Meanwhile, the debate about how effective EVs will actually be, still continues Meanwhile, the debate about how effective EVs will actually be, still continues Meanwhile, the debate about how effective EVs will actually be, still continues Meanwhile, the debate about how effective EVs will actually be, still continues Meanwhile, the debate about how effective EVs will actually be, still continues Chinese EV makers have been concentrating on competing with the US. Chinese EV maker Nio has made a deal with Chinese EV giant BYD as a source for its batteries. According to reports, Nio aims to compete with Tesla. A made-in-China EV is also expected to enter the US market this summer offering power and efficiency similar to the Tesla Model Y, but for about US$8,000 less. US carmakers could face stiff competition from China, looking at the EX30 from Volvo, the Swedish luxury brand owned by China’s Geely. Nio made a deal also expected Volvo Cars announced in February that it’s getting ready to convert to electric all its mainstay models, i.e., three SUVs and two sedans. The auto giant is also set to unveil a luxury electric van to improve sales in Asia. In March, BYD launched a new version of its Destroyer 07 hybrid car at a starting price that’s 11.3% lower than its predecessor, according to Reuters. announced in February BYD launched Europe, a Veritable EV Playground Europe, a Veritable EV Playground Europe, a Veritable EV Playground Many events are coming into play on the European EV playground, and China is moving fast. The country is getting ready to make a deal with Europe as EV tariffs loom. In September, Beijing criticized a probe launched by the European Commission into China’s electric car subsidies as protectionist and warned it would damage relations. Ursula von der Leyen, President of the European Commission announced the investigation saying markets were getting flooded with EVs that had artificially low prices because of state subsidies. make a deal In fact, Stellantis is ready for a competition with Chinese rivals in the European market for EVs, which can be a harbinger of consequences for jobs and production according to the group’s Chief Executive Carlos Tavares as reported by Reuters . Already, last February, Ford announced axing one in nine jobs in product development and administration across Europe as it competes in the EV market. The US automaker announced plans to invest US$3.5 billion to build an EV battery plant in Michigan with Chinese partner CATL. competition with Chinese rivals Reuters Ford announced Will This Price War Benefit the Consumer? Will This Price War Benefit the Consumer? Will This Price War Benefit the Consumer? Part of the charm of owning an EV is not just the environmental friendliness but also style and luxury, one of the prime reasons for EV adoption. Car companies are exploring all fronts to catch the consumer’s attention, leading to another price war between two super economies. This war of the EV makers could bring EV prices down globally. But it also means job cuts. The semiconductor chip war has already caused tumult. It remains to be seen how far it goes this time. semiconductor chip war Navanwita Bora Sachdev, Editor, The Tech Panda Navanwita Bora Sachdev, Editor, The Tech Panda Navanwita Bora Sachdev , Editor, The Tech Panda Navanwita Bora Sachdev

Global EV Price War Looms as US and China Compete for Market Dominance 

Authors:
(1) Silei Xu, Computer Science Department, Stanford University Stanford, CA with equal contribution {silei@cs.stanford.edu};
(2) Shicheng Liu, Computer Science Department, Stanford University Stanford, CA with equal contribution {shicheng@cs.stanford.edu};
(3) Theo Culhane, Computer Science Department, Stanford University Stanford, CA {tculhane@cs.stanford.edu};
(4) Elizaveta Pertseva, Computer Science Department, Stanford University Stanford, CA, {pertseva@cs.stanford.edu};
(5) Meng-Hsi Wu, Computer Science Department, Stanford University Stanford, CA, Ailly.ai {jwu@ailly.ai};
(6) Sina J. Semnani, Computer Science Department, Stanford University Stanford, CA, {sinaj@cs.stanford.edu};
(7) Monica S. Lam, Computer Science Department, Stanford University Stanford, CA, {lam@cs.stanford.edu}. Table of Links Abstract and Introduction Related Work Semantic Parsing for Wikidata WikiWebQuestions (WWQ) Dataset Implementation Experiments Experiment with QALD-7 Conclusions, Limitations, Ethical Considerations, Acknowledgements, and References A. Examples of Recovering from Entity Linking Errors 3 Semantic Parsing for Wikidata Wikidata is the largest public knowledge base with over 12 billion facts represented by subjectpredicate-object triples using 100+ million entities and 10,000 properties. 3,000 of the properties are useful for answering natural language questions, whereas the rest are used to link data in Wikidata with external library catalogs and database IDs. Entities and properties are given unique identifiers, QIDs and PIDs, respectively. For example, the fact that Joe Biden is the president of the US can be represented as a triple (Q6279, P39, Q11696), where P39 is the PID for property position held, Q6279 and Q11696 are QIDs for Joe Biden and the president of the United States, respectively. 3.1 Query Format Unlike relational databases and Freebase, Wikidata has no predefined domains or types. Any entity can have an arbitrary set of properties. However, even though Wikidata is property-based, all named entities have one or more instance of properties to some domain entity; domain entities are organized into a hierarchy with the subclass of property. Note that the names of domain entities and properties are unique. Non-domain entities, on the other hand, can be ambiguous. For example, “Lincoln” can refer to the president, a car brand, a sparrow, an aircraft, and many different cities. We posit that it is impossible for LLMs to memorize the QIDs and PIDs for domains and properties. We modify the format of SPARQL queries to use the more mnemonic property name, instead of its PID. Similarly, we use entity names for domains. For example, the original SPARQL for the query “What car models does GM make?” is SELECT DISTINCT ?x WHERE { ?x wdt:P31/wdt:P279* wd:Q3231690. ?x wdt:P176 wd:Q81965. } This says that we are seeking x, where x is transitively either an instance of (wdt:P31) or a subclass of (wdt:P279) of an automobile model (wd:Q3231690), and x has General Motors (wd:Q81965) as the manufacturer (wdt:P176). Note wdt is the prefix for Wikidata property, and wd is for Wikidata entity With our modification, the query becomes: SELECT DISTINCT ?x WHERE { ?x wdt:instance_of/wdt:subclass_of* wd:automobile_model. ?x wdt:manufacturer wd:Q81965. } For non-domain entity QIDs, we also accept a string in lieu of a QID in case of entity linking errors. At inference time, we use simple heuristics to resolve the string to a QID before applying the query. For example, “wd:Q81965” in the query may be replaced with “wd:GM”. See Section 3.2.2 for more details. Normally, we refrain from changing standard query notations since LLMs have been pretrained on them. However, we posit that learning this new syntax is much easier than learning the PIDs and QIDs. Our experimentation with few-shot prompting suggests that LLMs can easily adjust to this format. 3.2 Entity Linking Linking entities for WikiWebQuestions is particularly difficult. First, since the dataset is collected from real-world questions without prompting the users for more information, users tend to refer to their entities of interest without using their full names. Second, the questions are generally short with very limited context, making it harder to disambiguate among entities with similar names. Lastly, many QIDs in Wikidata are used to represent terms not generally known as “named entities”. For example, domain entities are often ignored by entity linker models, as in “What is the biggest country in Europe by population?”, both “country” (Q6256) and “Europe” (Q46) are required to construct the correct SPARQL, but entity linkers only provide “Europe” and ignore “country”. 3.2.1 Semantic Parsing with Entity Linking To handle ambiguous entities, we use an entity linker to first find the domain names and QIDs of the entities mentioned in the text. We train a semantic parser that accepts users’ input along with the results produced by the entity linker. Formally, given a user input T, and a set of entity linker results ⟨e, q⟩, where e is the name (default label) Wikidata gives to an entity and q is its QID, the semantic parser produces the semantic parse of T in our modified SPARQL format. For the example above, the SOTA ReFinED entity linker (Ayoola et al., 2022) returns {⟨General Motors, Q81965⟩}. Unfortunately, it misses the entity automobile model (Q3231690), a term not usually considered to be an entity. 3.2.2 Recovering from Entity Linker Errors We want our semantic parser to be able to recover from mistakes by an entity linker. That is, the semantic parser should use entity linking when it is helpful, but it should still try to predict the right logical form when the linker fails. The semantic parser is trained to accept, along with the user query, an optional set of potentially useful QIDs from the entity linker. We include samples where some of the supplied linked entities are not used in the gold answer, as well as samples where there are missing linked entities. For the latter, we use mentions in the original query in lieu of the QIDs. At inference time, we use the mentions to look up the QIDs in Wikidata. If multiple matches exist, the most popular entity is returned. An example is shown in Appendix A. With the above example where the entity linker misses “automobile model”, the semantic parser is likely to predict “car model” by copying from the user query. We search “automobile model” among aliases in domains to find the correct QID. This design allows the model to potentially recover from entity-linking failures. This paper is available on arxiv under CC 4.0 license. Authors: (1) Silei Xu, Computer Science Department, Stanford University Stanford, CA with equal contribution {silei@cs.stanford.edu}; (2) Shicheng Liu, Computer Science Department, Stanford University Stanford, CA with equal contribution {shicheng@cs.stanford.edu}; (3) Theo Culhane, Computer Science Department, Stanford University Stanford, CA {tculhane@cs.stanford.edu}; (4) Elizaveta Pertseva, Computer Science Department, Stanford University Stanford, CA, {pertseva@cs.stanford.edu}; (5) Meng-Hsi Wu, Computer Science Department, Stanford University Stanford, CA, Ailly.ai {jwu@ailly.ai}; (6) Sina J. Semnani, Computer Science Department, Stanford University Stanford, CA, {sinaj@cs.stanford.edu}; (7) Monica S. Lam, Computer Science Department, Stanford University Stanford, CA, {lam@cs.stanford.edu}. Authors: Authors: (1) Silei Xu, Computer Science Department, Stanford University Stanford, CA with equal contribution {silei@cs.stanford.edu}; (2) Shicheng Liu, Computer Science Department, Stanford University Stanford, CA with equal contribution {shicheng@cs.stanford.edu}; (3) Theo Culhane, Computer Science Department, Stanford University Stanford, CA {tculhane@cs.stanford.edu}; (4) Elizaveta Pertseva, Computer Science Department, Stanford University Stanford, CA, {pertseva@cs.stanford.edu}; (5) Meng-Hsi Wu, Computer Science Department, Stanford University Stanford, CA, Ailly.ai {jwu@ailly.ai}; (6) Sina J. Semnani, Computer Science Department, Stanford University Stanford, CA, {sinaj@cs.stanford.edu}; (7) Monica S. Lam, Computer Science Department, Stanford University Stanford, CA, {lam@cs.stanford.edu}. Table of Links Abstract and Introduction Abstract and Introduction Related Work Related Work Semantic Parsing for Wikidata Semantic Parsing for Wikidata WikiWebQuestions (WWQ) Dataset WikiWebQuestions (WWQ) Dataset Implementation Implementation Experiments Experiments Experiment with QALD-7 Experiment with QALD-7 Conclusions, Limitations, Ethical Considerations, Acknowledgements, and References Conclusions, Limitations, Ethical Considerations, Acknowledgements, and References A. Examples of Recovering from Entity Linking Errors A. Examples of Recovering from Entity Linking Errors 3 Semantic Parsing for Wikidata Wikidata is the largest public knowledge base with over 12 billion facts represented by subjectpredicate-object triples using 100+ million entities and 10,000 properties. 3,000 of the properties are useful for answering natural language questions, whereas the rest are used to link data in Wikidata with external library catalogs and database IDs. Entities and properties are given unique identifiers, QIDs and PIDs, respectively. For example, the fact that Joe Biden is the president of the US can be represented as a triple (Q6279, P39, Q11696), where P39 is the PID for property position held, Q6279 and Q11696 are QIDs for Joe Biden and the president of the United States, respectively. 3.1 Query Format Unlike relational databases and Freebase, Wikidata has no predefined domains or types. Any entity can have an arbitrary set of properties. However, even though Wikidata is property-based, all named entities have one or more instance of properties to some domain entity; domain entities are organized into a hierarchy with the subclass of property. Note that the names of domain entities and properties are unique. Non-domain entities, on the other hand, can be ambiguous. For example, “Lincoln” can refer to the president, a car brand, a sparrow, an aircraft, and many different cities. We posit that it is impossible for LLMs to memorize the QIDs and PIDs for domains and properties. We modify the format of SPARQL queries to use the more mnemonic property name, instead of its PID. Similarly, we use entity names for domains. For example, the original SPARQL for the query “What car models does GM make?” is SELECT DISTINCT ?x WHERE { ?x wdt:P31/wdt:P279* wd:Q3231690. ?x wdt:P176 wd:Q81965. } This says that we are seeking x, where x is transitively either an instance of (wdt:P31) or a subclass of (wdt:P279) of an automobile model (wd:Q3231690), and x has General Motors (wd:Q81965) as the manufacturer (wdt:P176). Note wdt is the prefix for Wikidata property, and wd is for Wikidata entity With our modification, the query becomes: SELECT DISTINCT ?x WHERE { ?x wdt:instance_of/wdt:subclass_of* wd:automobile_model. ?x wdt:manufacturer wd:Q81965. } For non-domain entity QIDs, we also accept a string in lieu of a QID in case of entity linking errors. At inference time, we use simple heuristics to resolve the string to a QID before applying the query. For example, “wd:Q81965” in the query may be replaced with “wd:GM”. See Section 3.2.2 for more details. Normally, we refrain from changing standard query notations since LLMs have been pretrained on them. However, we posit that learning this new syntax is much easier than learning the PIDs and QIDs. Our experimentation with few-shot prompting suggests that LLMs can easily adjust to this format. 3.2 Entity Linking Linking entities for WikiWebQuestions is particularly difficult. First, since the dataset is collected from real-world questions without prompting the users for more information, users tend to refer to their entities of interest without using their full names. Second, the questions are generally short with very limited context, making it harder to disambiguate among entities with similar names. Lastly, many QIDs in Wikidata are used to represent terms not generally known as “named entities”. For example, domain entities are often ignored by entity linker models, as in “What is the biggest country in Europe by population?”, both “country” (Q6256) and “Europe” (Q46) are required to construct the correct SPARQL, but entity linkers only provide “Europe” and ignore “country”. 3.2.1 Semantic Parsing with Entity Linking To handle ambiguous entities, we use an entity linker to first find the domain names and QIDs of the entities mentioned in the text. We train a semantic parser that accepts users’ input along with the results produced by the entity linker. Formally, given a user input T, and a set of entity linker results ⟨e, q⟩, where e is the name (default label) Wikidata gives to an entity and q is its QID, the semantic parser produces the semantic parse of T in our modified SPARQL format. For the example above, the SOTA ReFinED entity linker (Ayoola et al., 2022) returns {⟨General Motors, Q81965⟩}. Unfortunately, it misses the entity automobile model (Q3231690), a term not usually considered to be an entity. 3.2.2 Recovering from Entity Linker Errors We want our semantic parser to be able to recover from mistakes by an entity linker. That is, the semantic parser should use entity linking when it is helpful, but it should still try to predict the right logical form when the linker fails. The semantic parser is trained to accept, along with the user query, an optional set of potentially useful QIDs from the entity linker. We include samples where some of the supplied linked entities are not used in the gold answer, as well as samples where there are missing linked entities. For the latter, we use mentions in the original query in lieu of the QIDs. At inference time, we use the mentions to look up the QIDs in Wikidata. If multiple matches exist, the most popular entity is returned. An example is shown in Appendix A. With the above example where the entity linker misses “automobile model”, the semantic parser is likely to predict “car model” by copying from the user query. We search “automobile model” among aliases in domains to find the correct QID. This design allows the model to potentially recover from entity-linking failures. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Semantic Parsing for Wikidata: What You Need to Know

Edward Bernays, often referred to as the Father of Public Relations, or, more truthfully, the Father of Propaganda, left an indelible mark on the world of advertising. Born in 1891 in Vienna, Austria, a nephew of Sigmund Freud, Bernays learned from his uncle and applied never-before-considered concepts in advertising, primarily appealing to the unconscious, rather than the rational mind. Having moved to the United States in his youth, Bernays became famous after being involved with WWI efforts. He rebranded the war as a moral and patriotic cause, shaped public opinion, and disseminated pro-war messages. Some of the best-known campaigns by Bernays His influence is all over the marketing and advertising of today. Most of the beliefs and behaviors we still see were created by Bernays. Below are some of the popular campaigns. Women smoking In the 1920s, women smoking was seen as scandalous and taboo. Lucky Strike (You probably know it from ) wanted to increase their profits and half of the non-smoking population seemed like a good fit. Mad Men Edward Bernays orchestrated a staged event where fashionable debutants smoked cigarettes during a parade, branding them as “torches of freedom,” promoting Lucky Strike cigarettes. The rest is history. Example: A few examples 👇 Emotional marketing Edward Bernays understood that selling products was about evoking emotions and desires, not just listing utility. This was a revolutionary idea at the time. Another example. Instead of promoting cars based on features, he marketed them as symbols of male sexuality, focusing on emotional appeal. Before Bernays, it was seen as boujee and unnecessary to keep upgrading your car. Bernays advised car companies, like General Motors, to introduce annual design changes to stimulate demand, a strategy known as planned obsolescence. Ford refused to do so and GM remains a leading car manufacturer to this day. Interesting fact: Shift to consumerism Corporate America feared overproduction after World War I and needed to transition society from a needs-based culture to a wants-based culture. Bernays was like hold my beer and helped companies manufacture desires of products Americans didn’t need, leading to increased profits for corporations. Consumption shifted from needs to wants. Breakfast habits Bernays influenced American breakfast habits by promoting the idea that a heavier breakfast was healthier through manipulated expert opinions. He went to his physician and asked if he would agree that we expend a lot of calories through the night and, therefore wake up in a calorie deficit in the morning (which is technically true). His doctor agreed and Bernays asked if he could ask other physicians (5k of them) if they thought it was true. I believe with the right incentives ($), they agreed too. As a result, he convinced people that a substantial breakfast was essential, ultimately popularizing the concept of “the most important meal of the day.” Fashion consumerism Bernays played a role in popularizing department stores and modern fashion consumerism by hosting fashion shows with celebrities and emphasizing clothing as a form of individuality and self-expression. He hired celebrities (something never done before!) to showcase clothing as a means of conveying one’s unique personality and characteristics + influencing regular people to try to dress like their favourite celebrities. This is still very much alive today. Bernays’ continued influence Edward Bernays’ legacy continues to be seen in various aspects of modern society, particularly in the fields of advertising, public relations, and consumer culture: : The emphasis on emotional appeal, storytelling, and creating a desire for products remains central in advertising campaigns. Companies use celebrities, influencers, and lifestyle branding to connect with consumers on a personal and emotional level. Advertising and Marketing Techniques : The practice of designing products to become outdated or worn out quickly, is still employed by industries like technology and fashion to drive sales. Consumerism and Planned Obsolescence : Bernays is credited with coining the term “public relations,” and his influence in this field endures. Public Relations : Bernays’ work highlighted the power of persuasion and manipulation in shaping public opinion. Today, this legacy is evident in the realm of political communication, social media campaigns, and the dissemination of information where individuals and organizations use psychological tactics to influence public perception. Manipulation of Public Opinion In summary, Edward Bernays’ legacy is still very much alive today, as his ideas and techniques continue to shape how products are marketed, public opinion is influenced, and consumer culture operates. I don’t know how to feel about this man. On the one hand, he single-handedly re-invented 3 fields and created an influence alive today and as a psychologist, I find it interesting. On the other hand, his contribution to overconsumption and division within society which is slowly killing our world is painfully obvious. What is it about Austrians in the XX century? Also published . here This is from my newsletter, if you like to receive it straight to your inbox every Tuesday, sign up . here

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GENERAL MOTORS

EVERGREEN INDEX #1196

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GENERAL MOTORS

gm.com

10K+ employees

Since 1908

COMPANY RANKING

GENERAL MOTORS

EVERGREEN INDEX #1196

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STORIES ABOUT GENERAL MOTORS

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