General Motors issued a sweeping recall for trucks and SUVs with the 6.2-liter V-8 engine. But the problem is far more widespread than people realize.

Over 28,000 People Complained Before GM Recalled Its 6.2-Liter V-8

General Motors is recalling nearly 600,000 trucks and SUVs from several brands because they might suffer a catastrophic engine failure. Here are the details.

GM Recalls Over Half a Million Trucks and SUVs Over Engine Defects

Ford and General Motors are just two of the automakers who've done away with gas caps in favor of capless fuel systems in many models.

Why Don't Some New Cars Come With Gas Caps Anymore?

In a filing to dismiss a lawsuit, General Motors argued that it can sell your driving data because you drive on public roads. Here are the details.

GM Argues It Can Sell Your Data Because You Drive on Public Roads

The General Motors CAMI Assembly plant in Ingersoll, Ont., will shut down next month with plans to reopen in the fall at half capacity.

GM halts production, lays off hundreds at Ingersoll, Ont., assembly plant, to reopen in October

DETROIT — General Motors is cutting production of its all-electric BrightDrop delivery vans at a plant in Canada and will idle the facility through much of this year.

GM cutting jobs, idling Canadian electric van plant due to 'market demand'

General Motors is laying off 500 workers at a factory in Canada because of weak demand for its all-electric BrightDrop vans, according to CNBC. GM told

GM cuts 500 jobs because of weak demand for BrightDrop electric vans

General Motors is cutting production of its all-electric delivery vans at a plant in Canada ahead of idling the facility through much of this year.

GM cutting jobs, idling Canadian electric van plant

CBC News has learned General Motors's CAMI Assembly Plant in Ingersoll, Ont., will shut down next month, and re-open months later at half capacity.

Hundreds of workers laid off at Ingersoll, Ont. assembly plant as GM halts production

General Motors has cancelled shifts at its Oshawa plant until Monday amid a part shortage, the union representing workers at the assembly plant confirmed to CTV News Toronto.

GM cancels some shifts at Oshawa assembly plant

General Motors

generalmotors

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

Meta sure knows how to roil an entire industry. Its $14.3 billion investment in Scale AI has intensified an ongoing discussion about AI data quality and trust—sometimes in ways that reflect poorly on Meta and Scale, but undeniably in ways that matter. The investment, announced in June 2025, granted Meta a 49% non-voting stake in the AI data labeling startup while hiring away its CEO, Alexandr Wang, to lead a new “superintelligence” division. What followed was nothing short of a supply chain catastrophe that exposed fundamental vulnerabilities in the entire AI ecosystem. hiring away its CEO, Alexandr Wang hiring away its CEO, Alexandr Wang Within days, major clients including Google, OpenAI, and xAI began severing ties with Scale AI, triggering what one competitor described as “the equivalent of an oil pipeline exploding between Russia and Europe.” the equivalent of an oil pipeline exploding between Russia and Europe the equivalent of an oil pipeline exploding between Russia and Europe The fallout has brought renewed focus to two critical areas shaping the future of AI development: the trust infrastructure that supports partnerships and the growing need for high-quality training data. An Imperative for Trust in AI Development Scale had built its valuation on a simple but powerful proposition: serve as a neutral arbiter in the data labeling market, providing services to virtually every major AI lab without playing favorites. That neutrality was Scale’s most valuable asset, allowing companies like Google, OpenAI, and Microsoft to outsource critical data preparation work without worrying about competitive intelligence leaking to rivals. Meta’s investment shattered that trust overnight. As Garrett Lord, CEO of Scale competitor Handshake, explained: “The labs don’t want the other labs to figure out what data they’re using to make their models better. If you’re General Motors or Toyota, you don’t want your competitors coming into your manufacturing plant and seeing how you run your processes.” explained explained A client exodus was swift and decisive. Google, Scale’s largest customer with plans to spend approximately $200 million on Scale’s services in 2025, immediately began planning to sever ties. OpenAI confirmed it was winding down relationships that had been months in the making. xAI put projects on hold. immediately began planning to sever ties immediately began planning to sever ties But the trust crisis ran deeper than competitive concerns. Business Insider’s subsequent investigation revealed that Scale AI had been using public Google Docs to track work for high-profile customers, leaving thousands of pages of confidential project documents accessible to anyone with a link. The exposed materials included sensitive details about how Google used ChatGPT to improve its struggling Bard chatbot, training documents for xAI’s Project Xylophone, and Meta’s own confidential AI training materials. had been using public Google Docs had been using public Google Docs The security lapses extended to Scale’s workforce, with public documents containing private email addresses of thousands of contractors, wage information, and performance evaluations—including lists of workers suspected of “cheating.” Cybersecurity experts described Scale’s practices as “extremely unreliable,” warning that such vulnerabilities could expose both the company and its clients to various forms of cyberattacks. Scale responded by vowing to conduct a thorough investigation and disable public document sharing, but the damage had been done. but the damage had been done The Data Quality Challenge While trust dominated headlines, the Meta-Scale deal spotlighted an even more fundamental challenge: the growing scarcity of high-quality training data that threatens to constrain AI development. Meta’s willingness to pay $14.3 billion for Scale was about securing access to what has become AI’s most precious resource. The data quality crisis is both quantitative and qualitative. Research by Epoch AI indicates that the entire stock of human-generated public text data, estimated at around 300 trillion tokens, could be exhausted between 2026 and 2032. But the problem runs deeper than simple scarcity. An Amazon/AWS/UC Santa Barbara study estimated that 57% of online content is now AI-generated, creating an “authenticity crisis” that undermines the quality of training data. entire stock of human-generated public text data entire stock of human-generated public text data 57% of online content is now AI-generated 57% of online content is now AI-generated The proliferation of synthetic content creates a vicious cycle. AI models trained on AI-generated data suffer from what researchers call model collapse, a phenomenon where successive generations of models lose their ability to capture the full complexity and variability of real-world data. Early model collapse affects minority data and edge cases, while late model collapse can render models nearly useless as they lose most of their variance and begin confusing basic concepts. model collapse model collapse The solution is to rely on subject matter experts who apply their knowledge to train and quality check AI applications. For example, AI models in healthcare need the deep insights that reside inside the minds of industry practitioners. Those practitioners, in turn, need to be taught how to prompt large language models in order to train them. You just don’t find experts off the shelf. They must be sourced. It’s no wonder that 81% of businesses say that they have significant data quality issues. It’s no wonder that 81% of businesses say that they have significant data quality issues It’s no wonder that 81% of businesses say that they have significant data quality issues Scale AI’s business model was built on solving these challenges through a global network of over 240,000 contractors who manually annotate images, texts, and videos. But the company’s internal documents revealed quality control problems that extended beyond security breaches. Scale struggled with “spammy behavior” from unqualified contributors, with project logs showing efforts to clamp down on contractors who submitted “transparently shoddy work that managed to evade detection.” transparently shoddy work that managed to evade detection transparently shoddy work that managed to evade detection The pressure to serve major clients during the post-ChatGPT AI boom led to compromises in quality control. Programs meant to be staffed exclusively by experts became “flooded with spam,” according to internal documents. Even when projects were meant to be anonymized, contractors could easily identify clients from the nature of tasks or instruction phrasing, sometimes simply by prompting models directly. Ripple Effects Across the AI Ecosystem The Meta-Scale controversy has accelerated market fragmentation as companies scramble to reduce dependency on single providers. Scale competitors report dramatic increases in demand. This, by the way, is not a bad thing. Competition is good. This fragmentation also reflects a broader recognition that businesses need to vet their data providers carefully, especially because one lapse can compromise the AI infrastructure. AI development hinges on a complex web of relationships. Data integrity, vendor neutrality, and competitive intelligence intersect in ways that can quickly destabilize entire supply chains. AI infrastructure decisions carry risks that extend far beyond technical performance metrics. On the other hand, enterprises and data foundries that collaborate on training AI with subject matter expertise wield an enormous advantage right now. Data foundries that build trust and possess proven processes for ensuring data quality will emerge as the AI darlings. Scale competitors report dramatic increases in demand Scale competitors report dramatic increases in demand

Meta’s $14B Bet on Scale AI Backfires, Triggers AI Trust Crisis

Meta's $ 14B Bet na Scale AI Backfires, Triggers AI Trust Crisis

Meta vsadí 14B dolarů na škálu AI Backfires, spouští krizi AI důvěry

Το στοίχημα $ 14B της Meta στις πυρκαγιές κλίμακας AI, προκαλεί την κρίση εμπιστοσύνης AI

La apuesta de $ 14B de Meta en los incendios de inteligencia artificial de escala, desencadenan la crisis de confianza de la inteligencia artificial

Meta- ի $ 14B- ի բաղադրիչը Scale AI Backfires- ում, AI Trust Crisis- ում

メタの14BドルのスケールAIバックファイアに賭け、AI信頼危機を引き起こす

Meta-ийн $ 14B Scale AI Backfires дээр тавтай морилно уу, AI Trust Crisis

Taruhan $ 14B Meta pada Scale AI Backfires, Memicu Krisis Kepercayaan AI

Qalabka 14B ee Meta on Scale AI Backfires, Qalabka AI Trust Crisis

Мета $ 14Б опклада на скалу АИ Бацкфариес, изазива кризу поверења АИ

I-Meta's $ 14B Bet kwi-Scale AI Backfires, I-AI Trust Crisis

The world has been waiting for Nvidia's GTC 2025 conference (GPU Technology Conference) held in San Jose, California with a bundle of anticipation. This year, the giant chipmaker showcased the company's latest advancements in artificial intelligence (AI) and computing technologies. CEO Jensen Huang delivered a keynote address highlighting several significant announcements.​ Nvidia's GTC 2025 conference Nvidia's GTC 2025 conference Huang introduced its upcoming AI chip architectures, Blackwell Ultra, Vera Rubin, and Vera Rubin Ultra. Blackwell Ultra is slated for release later this year, offering enhanced memory capacity. Vera Rubin is expected in late 2026, featuring increased processing speeds, with Vera Rubin Ultra following in 2027. introduced introduced We spoke to Liam Galin of data analytics company SQream, a first timer at the annual conference, who thinks this is probably the most important conference when it comes to AI. Liam Galin Liam Galin SQream SQream “It's the combination of theoretical and practical AI and that is something that I really, really liked,” he says. This year’s event is expected to attract approximately 25,000 in-person attendees and a virtual participation of 300,000 attendees. As always, the event is attracting a diverse audience, including developers and AI engineers, business executives and investors, academia and researchers, automotive and robotics industry experts, healthcare and scientific computing specialists, and media and gaming enthusiasts. attract attract “It's a great place to get to know the industry,” says Galin. “You see the ecosystem, you see the partners, you see customers,” he gushes. Tech industry leaders, CEOs, and venture capitalists are present at the conference to explore investment opportunities and partnerships. “I'm looking at it from a business perspective, but the beauty of this conference is that there are many different opportunities for people from the academia, for programmers and for business people to meet their peers. It's very unique in the sense that it covers all the layers and all the fronts of the AI world,” he says. He points out that software developers working on AI, deep learning, and high-performance computing are of course attending the conference to learn about new tools, SDKs, and frameworks, and have a lot to gain. “I think that one of the key benefits of the conference, especially for developers, is the endless amount of workshops where they really touch the basics and the advanced capabilities that are relevant for different types of industries, different types of applications, and different type of technologies. “So, developers gain a lot in this conference and that's why in terms of demographics, you see a lot of developers coming to this conference. They come to this conference to absorb what is new and what is next in the world of GPUs and AI.” Galin has been in the AI industry for eight years and is happy to see that “AI got to the stage of practicality.” The company he works for uses GPUs to make advanced data queries more cost effective for companies. “You see how you can really harness AI and not just generate funny pictures, but harness AI for business values and addressing business pains. This conference covers it all, starting from the very minor details of technology,” he explains. The conference has been highlighting Nvidia's focus on robotics too, introducing "Blue," a robot developed in collaboration with Disney Research and Google DeepMind. This initiative aims to advance robotics technologies and includes the new physics engine, Newton, designed to enhance simulation capabilities. https://www.youtube.com/watch?v=SqgpsquIcUo&embedable=true https://www.youtube.com/watch?v=SqgpsquIcUo&embedable=true Additionally, Nvidia unveiled Isaac GR00T N1, an open-source model for developing humanoid robots, and the evolving Cosmos AI model for creating synthetic training data, aiming to lower costs and improve efficiency in AI and robotics training. ​ Isaac GR00T N1, an open-source model Isaac GR00T N1, an open-source model Nvidia announced Dynamo, an open-source inference software system designed to scale AI models effectively. The company also revealed a partnership with General Motors to integrate custom AI systems into self-driving cars, reflecting Nvidia's strategic move into the automotive sector. ​ announced announced a partnership with General Motors a partnership with General Motors “If you look at the roadmap of Nvidia, with both its hardware and software suite, the company is covering so many industries and so many directions in the future of, I would say, even mankind,” he says. For software developers, GTC remains essential, offering insights into cutting-edge AI frameworks, GPU advancements, and open-source tools that shape the future of AI development. As Nvidia continues pushing technological boundaries, GTC serves as a crucial hub for learning, networking, and driving the next wave of AI-driven innovation. Navanwita Sachdev, Editor, The Tech Panda Navanwita Sachdev, Editor, The Tech Panda Navanwita Sachdev, Editor, The Tech Panda Navanwita Sachdev The Tech Panda

Nvidia GTC 2025: AI Goes Big, Robots Get Smarter, and GPUs Rule the World

Nvidia GTC 2025: الذكاء الاصطناعي يتطور بشكل كبير، والروبوتات تصبح أكثر ذكاءً، ووحدات معالجة الرسومات تحكم العالم

Nvidia GTC 2025: la IA es fa gran, els robots són més intel·ligents i les GPU governen el món

Nvidia GTC 2025: AI se rozrůstá, roboti jsou chytřejší a GPU vládnou světu

Nvidia GTC 2025: AI bliver stor, robotter bliver klogere, og GPU'er styrer verden

Nvidia GTC 2025: KI wird groß, Roboter werden intelligenter und GPUs beherrschen die Welt

Nvidia GTC 2025: La IA crece, los robots se vuelven más inteligentes y las GPU dominan el mundo

Nvidia GTC 2025: AI הולך בגדול, רובוטים נעשים חכמים יותר ו-GPUs שולטים בעולם

Nvidia GTC 2025: AI が巨大化し、ロボットが賢くなり、GPU が世界を支配する

Nvidia GTC 2025: مصنوعي ذهانت لوی کیږي، روباټونه هوښیار کیږي، او GPUs په نړۍ واکمني کوي

Nvidia GTC 2025: AI Goes Big, Robots Smarter, iyo GPUs ayaa xukuma aduunka

Nvidia GTC 2025: AI rritet, robotët bëhen më të zgjuar dhe GPU-të sundojnë botën

Nvidia GTC 2025：人工智能发展迅猛，机器人更加智能，GPU 统治世界

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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