The legacy tech that is available, for trading and portfolio management, to hedge funds is broken

Pinnakl

pinnakl

The Pinnacle of Blockchain Technology: The Decentralized Man

Supernormal Stimuli: Are We Amusing Ourselves to Death?

অতিপ্রাকৃতিক উদ্দীপনা: আমরা কি মৃত্যুর জন্য মজা করছি?

Übernatürliche Reize: Amüsieren wir uns zu Tode?

Estímulos sobrenaturales: ¿nos estamos divirtiendo hasta la muerte?

Stimuli supranormaux : sommes-nous en train de nous amuser à mourir ?

अलौकिक उत्तेजनाएँ: क्या हम अपने आप को मौत तक मनोरंजन कर रहे हैं?

超常刺激：私たちは死ぬほど楽しんでいるのか？

초자연적 자극: 우리는 죽을 때까지 즐겁게 지내고 있나요?

Estímulos supranormais: estamos nos divertindo até a morte?

Сверхъестественные стимулы: развлекаемся ли мы до смерти?

Olağanüstü Uyarıcılar: Kendimizi Ölesiye Eğlendiriyor muyuz?

Kích thích siêu thường: Chúng ta có đang vui vẻ đến chết không?

超常刺激：我们是不是正在自娱自乐？

Heavy is the head that wears the crown during a Wall Street bull run, and Nvidia’s status as the king of the ongoing generative AI boom means that the stock can be especially vulnerable to the prospect of weakening investor appetite. Nvidia (NASDAQ:NVDA) became Wall Street’s most exciting story in 2023. The chipmaker rallied some 239% throughout the year, smashing through the $1 trillion market capitalization glass ceiling before peaking at a cap in excess of $2tn. The optimism, fuelled by the ongoing generative AI market rally, continued to make waves in the first-quarter of 2024. NVDA rallied almost 90% in the opening three months of the year to continue its strong spell of dominance. But finally, at the beginning of April, the stock took its biggest tumble in four years. The wider implications of the Nvidia sell-off are likely to shake up the entire generative AI market. Counting the Cost of NVDA Sell-Offs The first three weeks of April 2024 saw NVDA shed more than 15% of its value amid widespread investor sell-offs. Although greater degrees of volatility can be expected for stocks caught up in the hype phase of an emerging market, Nvidia’s April downturn has been the company’s biggest dip since the initial outbreak of the Covid-19 pandemic in March 2020. For Nvidia, the pandemic era must seem like a lifetime ago. Its March 2020 sell-offs saw the stock tumble to $51.44. The stock’s peak closing price in March 2024 was $950.02. But after four years of meteoric growth, Nvidia’s recent market downturn is particularly concerning–particularly now the stock’s gained the status as a tech pioneer that’s been driving the S&P 500 higher on a consistent basis. Nvidia’s position at the pinnacle of the generative AI boom could mean that the stock would always be the first to absorb the impact of a change in investor sentiment, and the fears are that NVDA would be the canary in the mine should we suddenly discover that generative AI has been in a hype bubble that’s ready to burst for some time. Although Nvidia’s recent difficulties have coincided with the news that US inflation rates have continued to defy expectations, placing the prospect of interest rate cuts on hold for the foreseeable future, analysts suggest that fears over a cooling generative AI boom remain the key cause of investor concern in the wake of the underperformance. “The stock pullback has very little to do with rates,” explained Parag Thatte, strategist at Deutsche Bank, in a recent Financial Times interview. “It’s more to do with investors pricing in slower earnings growth.” There’s also a growing concern that generative AI has begun to transform from its hype phase towards the question of implementation. While Large Language Models (LLMs) like ChatGPT have been excellent tools in showcasing the sheer power and potential of generative AI, they’ve also been largely error-prone in a manner that could cause more hesitancy among enterprises seeking to embrace the technology. It’s for this reason that Gil Luria, analyst at DA Davidson, warned that a 20% slide in Nvidia stock could take place by the end of 2024. Luria suggests that the implementation phase of Nvidia’s products, which are currently being bought en masse by the likes of Microsoft and Amazon, could run into trouble when key buyers reach capacity and demand begins to fall. However, other analysts suggest that the end of the generative AI hype phase will be met by a more strategic implementation phase which could keep the industry growing. “Concerns about GenAI failing to meet expectations in 2024 should be seen in the context of the natural cycle of hype and maturity of new technologies,” said Maxim Manturov, head of investment research at Freedom Finance Europe. “While some industry leaders have expressed frustration with the speed of progress in artificial intelligence and GenAI in their organisations, others remain optimistic about its potential.” “Companies heavily invested in GenAI are under pressure to prove profitability, and industry reports indicate a shift to more strategic AI initiatives in the coming years,” Manturov added. Is the Generative AI an Exceptional Case? Here, it’s worth reminding ourselves that we’re talking about the generative AI boom. This is an industry that Bloomberg Intelligence last year suggested would grow at a CAGR of 42% into a $1.3 trillion industry by 2032. Fresh demand for generative AI products was predicted to add around $280 million in new software revenue. Could we be talking about an emerging technology that’s market impact is without precedent? Morgan Stanley analysts have underlined the indecisive outlook for Nvidia and the wider generative AI market in Q2 2024 by proposing a more bullish outlook for NVDA as well as the generative AI-focused Amazon (AMZN). As Nvidia reported Q4 2023 revenue increases of 265% to $22 billion, CEO Jensen Huang claimed that "accelerated computing and generative AI have hit the tipping point," before adding that "demand is surging worldwide across companies, industries and nations." The company’s innovations in recent weeks have included the text-to-3D tool, LATTE3D, which Nvidia described as a ‘virtual 3D printer’, as well as a collaboration with Hipocratic AI to develop a network of generative AI nurses that could cost $9 an hour. Nvidia’s quick 5% rebound following its record sell-offs should serve as a wider vote of confidence in the innovation that the company is capable of. Regardless of whether its success has made it the GenAI canary, NVDA is certainly an innovative stock that’s capable of sustaining its impressive growth. Bubble or Boom? Ultimately, Nvidia’s performance over the past 12 months means that the stock has become a live litmus test for investor sentiment in generative AI. Whether that’s a good thing or not depends wholly on the capabilities of the industry itself. Nvidia’s rapid growth has seen the stock rub shoulders with the likes of Microsoft, Apple, and Alphabet, and the company will be keen to follow the lead of other industry giants to continue sustaining its growth through innovation. As a semiconductor leader, the possibilities are nearly endless for Nvidia. As long as the capabilities of generative AI continue to astound clients and deliver on its promises, NVDA remains a stock that can continue to grow. If investors get a whiff of saturation or inefficiency, the Nvidia litmus test could face some further challenges ahead. Heavy is the head that wears the crown during a Wall Street bull run, and Nvidia’s status as the king of the ongoing generative AI boom means that the stock can be especially vulnerable to the prospect of weakening investor appetite. Nvidia (NASDAQ:NVDA) became Wall Street’s most exciting story in 2023. The chipmaker rallied some 239% throughout the year, smashing through the $1 trillion market capitalization glass ceiling before peaking at a cap in excess of $2tn. Nvidia The optimism, fuelled by the ongoing generative AI market rally, continued to make waves in the first-quarter of 2024. NVDA rallied almost 90% in the opening three months of the year to continue its strong spell of dominance. But finally, at the beginning of April, the stock took its biggest tumble in four years. The wider implications of the Nvidia sell-off are likely to shake up the entire generative AI market. Counting the Cost of NVDA Sell-Offs The first three weeks of April 2024 saw NVDA shed more than 15% of its value amid widespread investor sell-offs. Although greater degrees of volatility can be expected for stocks caught up in the hype phase of an emerging market, Nvidia’s April downturn has been the company’s biggest dip since the initial outbreak of the Covid-19 pandemic in March 2020. For Nvidia, the pandemic era must seem like a lifetime ago. Its March 2020 sell-offs saw the stock tumble to $51.44. The stock’s peak closing price in March 2024 was $950.02. But after four years of meteoric growth, Nvidia’s recent market downturn is particularly concerning–particularly now the stock’s gained the status as a tech pioneer that’s been driving the S&P 500 higher on a consistent basis. Nvidia’s position at the pinnacle of the generative AI boom could mean that the stock would always be the first to absorb the impact of a change in investor sentiment, and the fears are that NVDA would be the canary in the mine should we suddenly discover that generative AI has been in a hype bubble that’s ready to burst for some time. Although Nvidia’s recent difficulties have coincided with the news that US inflation rates have continued to defy expectations, placing the prospect of interest rate cuts on hold for the foreseeable future, analysts suggest that fears over a cooling generative AI boom remain the key cause of investor concern in the wake of the underperformance. prospect of interest rate cuts on hold prospect of interest rate cuts on hold “The stock pullback has very little to do with rates,” explained Parag Thatte , strategist at Deutsche Bank, in a recent Financial Times interview. “It’s more to do with investors pricing in slower earnings growth.” explained Parag Thatte explained Parag Thatte There’s also a growing concern that generative AI has begun to transform from its hype phase towards the question of implementation. While Large Language Models (LLMs) like ChatGPT have been excellent tools in showcasing the sheer power and potential of generative AI, they’ve also been largely error-prone in a manner that could cause more hesitancy among enterprises seeking to embrace the technology. largely error-prone largely error-prone It’s for this reason that Gil Luria, analyst at DA Davidson, warned that a 20% slide in Nvidia stock could take place by the end of 2024. Luria suggests that the implementation phase of Nvidia’s products, which are currently being bought en masse by the likes of Microsoft and Amazon, could run into trouble when key buyers reach capacity and demand begins to fall. 20% slide in Nvidia stock 20% slide in Nvidia stock However, other analysts suggest that the end of the generative AI hype phase will be met by a more strategic implementation phase which could keep the industry growing. “Concerns about GenAI failing to meet expectations in 2024 should be seen in the context of the natural cycle of hype and maturity of new technologies,” said Maxim Manturov, head of investment research at Freedom Finance Europe . “While some industry leaders have expressed frustration with the speed of progress in artificial intelligence and GenAI in their organisations, others remain optimistic about its potential.” Freedom Finance Europe Freedom Finance Europe “Companies heavily invested in GenAI are under pressure to prove profitability, and industry reports indicate a shift to more strategic AI initiatives in the coming years,” Manturov added. Is the Generative AI an Exceptional Case? Here, it’s worth reminding ourselves that we’re talking about the generative AI boom. This is an industry that Bloomberg Intelligence last year suggested would grow at a CAGR of 42% into a $1.3 trillion industry by 2032 . $1.3 trillion industry by 2032 $1.3 trillion industry by 2032 Fresh demand for generative AI products was predicted to add around $280 million in new software revenue. Could we be talking about an emerging technology that’s market impact is without precedent? Morgan Stanley analysts have underlined the indecisive outlook for Nvidia and the wider generative AI market in Q2 2024 by proposing a more bullish outlook for NVDA as well as the generative AI-focused Amazon (AMZN). more bullish outlook more bullish outlook As Nvidia reported Q4 2023 revenue increases of 265% to $22 billion, CEO Jensen Huang claimed that "accelerated computing and generative AI have hit the tipping point," before adding that "demand is surging worldwide across companies, industries and nations." claimed that claimed that The company’s innovations in recent weeks have included the text-to-3D tool , LATTE3D, which Nvidia described as a ‘virtual 3D printer’, as well as a collaboration with Hipocratic AI to develop a network of generative AI nurses that could cost $9 an hour. text-to-3D tool text-to-3D tool collaboration with Hipocratic AI collaboration with Hipocratic AI Nvidia’s quick 5% rebound following its record sell-offs should serve as a wider vote of confidence in the innovation that the company is capable of. Regardless of whether its success has made it the GenAI canary, NVDA is certainly an innovative stock that’s capable of sustaining its impressive growth. Bubble or Boom? Ultimately, Nvidia’s performance over the past 12 months means that the stock has become a live litmus test for investor sentiment in generative AI. Whether that’s a good thing or not depends wholly on the capabilities of the industry itself. Nvidia’s rapid growth has seen the stock rub shoulders with the likes of Microsoft, Apple, and Alphabet, and the company will be keen to follow the lead of other industry giants to continue sustaining its growth through innovation. As a semiconductor leader, the possibilities are nearly endless for Nvidia. As long as the capabilities of generative AI continue to astound clients and deliver on its promises, NVDA remains a stock that can continue to grow. If investors get a whiff of saturation or inefficiency, the Nvidia litmus test could face some further challenges ahead.

Nvidia’s Shaky Q2 Underlines the Stock as Generative AI’s Sentiment Litmus Test

এনভিডিয়ার শ্যাকি Q2 জেনারেটিভ এআই এর সেন্টিমেন্ট লিটমাস টেস্ট হিসাবে স্টককে আন্ডারলাইন করেছে

Nvidias wackeliges zweites Quartal unterstreicht die Aktie als Lackmustest für die Stimmung von Generative AI

El inestable segundo trimestre de Nvidia subraya la acción como prueba de fuego del sentimiento de la IA generativa

Le Shaky Q2 de Nvidia souligne le titre en tant que test décisif du sentiment de Generative AI

एनवीडिया की अस्थिर Q2 स्टॉक को जेनरेटिव एआई के सेंटीमेंट लिटमस टेस्ट के रूप में रेखांकित करती है

エヌビディアの第2四半期の不安定さは、ジェネレーティブAIの感情リトマス試験として株価を浮き彫りにする

Nvidia의 Shaky Q2는 Generative AI의 감정 리트머스 테스트로서 주식을 강조합니다.

Shaky Q2 da Nvidia destaca o estoque como teste decisivo de sentimento da IA generativa

Нестабильный второй квартал Nvidia подчеркивает, что акции являются лакмусовой бумажкой настроений генеративного ИИ

Nvidia'nın Titrek 2. Çeyreği Hisse Senedinin Üretken Yapay Zekanın Duyarlılık Turnusol Testi Olduğunu Vurguluyor

Q2 run rẩy của Nvidia nhấn mạnh cổ phiếu là bài kiểm tra tình cảm của AI sáng tạo

Nvidia 第二季度业绩表现不佳，凸显其股票是生成式 AI 情绪的试金石

This paper is under CC 4.0 license. available on arxiv Authors: (1) Ryen W. White, Microsoft Research, Redmond, WA, USA. Table of Links Abstract and Taking Search to task AI Copilots Challenges Opportunities The Undiscovered Country and References 2 AI COPILOTS Copilots[6] are applications of modern AI (foundation models, etc.) to help people with complex cognitive tasks. Copilots have conversational user interfaces and their users engage with them via natural language, they are powered by foundation models such as GPT-4, they are extensible with skills/tools/plugins, and they are scoped to specialized domains or applications (including search). Copilots are designed to to keep humans at the center of the task completion process and augment human capabilities to help them complete a broader range of tasks in less time. The Microsoft copilot stack (Figure 2) contains four main layers: (1) The copilot frontend, covering the user experience and extensibility with plugins, enabling developers to provide additional visible tools to the copilot; (2) The orchestration layer that handles the internal information flows, prompting, grounding, executing the plugins and processing their responses, among other things; (3) Copilots leverage the power of large foundation models that can be provided to the developer as is, specialized to specific tasks, domains, or applications, or developers can bring their own models to use to power copilot functionality, and; (4) This all runs on top of massive scale AI infrastructure hosted in the cloud on Azure. Underpinning all of this is Microsoft’s strong commitment to responsible AI, which ensures that copilots are safe, secure, and transparent, via an iterative, layered approach with mitigations spanning the model, prompts, grounding, and the user experience. Examples of copilots include GitHub Copilot,[7] an AI pair programmer that has been shown to reduce developer effort, enable more task success, and significantly expedite task completion [5]. Copilots are also emerging in search. Popular Web search engines such as Bing and Google are adding copilot functionalities to their search engines in the form of conversational agents such as Bing Chat and Google BARD. In search, copilots can help searchers to tackle a broader range of tasks than information finding and go deeper than surface (SERP-level) interactions with content by synthesizing answers on the searcher’s behalf. They also enable searchers to communicate their intents and goals more directly. Returning to the task tree (Figure 1), the focus on engaging copilots via natural language interactions allows both searchers and systems to consider higher-level task representations (macrotasks, subtasks) in addition to the more granular actions (queries, result clicks, pagination, and so on) that searchers already perform when engaging with traditional search engines. 2.1 Copilots in Search Copilots and chat experiences are a complement not a replacement to traditional search engines. Search engines have been around for decades and serve a valuable purpose: providing near instantaneous access to answers and resources for a broad range of search requests. These existing and emerging modalities can and should work well together to help searchers tackle a wider range of tasks. The capabilities of copilots to better understand intentions and provide assistance beyond fact finding and basic learning/investigation will expand the task frontier, broadening the range of complex search tasks that can be completed, e.g., direct support for tasks requiring creative inspiration (Figure 3). This all moves us a step closer toward intelligent search systems that can help with all-task completion, covering the full universe of tasks for which people might need search support. One way to define the range of tasks that copilots can support is though Bloom’s taxonomy of learning objectives [27]. Creation is at the pinnacle of that taxonomy and we have only scratched the surface in creativity support with next-word prediction through transformer models [16]. We are already seeing expansions into modalities beyond text (images, video, audio, and so on) and could consider support for other creative tasks including planning, analysis, and invention. There are also many other layers in Bloom’s taxonomy (e.g., evaluation - help searchers make judgments and decisions, application - help searchers complete new tasks, understanding - explain ideas and concepts to accelerate learning) that could form future task frontiers. Beyond greater capabilities, the introduction of copilots into search will also change how people will engage with search systems. In copilots, the mode of interaction is primarily natural language input, with some recent expansion toward multimodal inputs and outputs via the introduction of diffusion models such as DALL·E. Copilots can generate/synthesize direct answers, with source attribution for provenance, to build trust with users, and to drive traffic back to content creators, which is important to incentivize further content creation that fuels future foundation models. The overall search interaction flow is also different between search engines and copilots. When using copilots, searchers do not need to decompose their goal into sub-goals or sub-queries, examine SERPs and landing pages, and aggregate/synthesize relevant knowledge from retrieved information. Figure 4 has a comparison of the information seeking processes in the two modalities. In copilots, the responsibility for generating answers is delegated by the searcher to the system, which is not without its challenges in terms of human agency and human learning, as we discuss later in this article. 2.2 Adding Copilots to Search Engines It is not practical nor necessary to deploy copilots for all search tasks. Foundation model inference is expensive at massive scale and search engine algorithms have been honed over decades to provide relevant results for a broad range of tasks (e.g., navigation, factfinding). Conversational interfaces are less familiar for searchers and it will take time for searchers to adapt to this way of searching. Traditional search engines are sufficient when searchers know exactly what they want. Copilots are helpful for more complex search tasks or in situations where searchers may be struggling to find relevant information. Task complexity can be estimated using aggregate metrics such as the amount of engagement with the search engine (e.g., number of query reformulations) for similar tasks historically. As generative AI appears in more applications and searchers better understand search copilot capabilities, the tasks that searchers bring to search copilots will likely evolve. We will also see a growth in search experiences that unify traditional search and copilots. In a step towards this, search engines such as Bing and Google are already integrating dynamic answers from foundation models into their SERPs for some informational queries. Also, in the Google copilot (BARD), search results are displayed immediately below the copilot responses, allowing searchers to easily engage with them as desired. In the Bing copilot (Bing Chat), the search and copilot experiences are more separated and searchers must select a specific modality based on their task and personal preferences. Bing also provides searchers with control over other aspects, such as conversation style and tone, although it is not clear that searchers are sufficiently familiar with AI copilots at this time to use these nuanced controls effectively. Search copilots such as Bing Chat use retrieval augmented generation (RAG) [28] to ground copilot responses via timely and relevant results. This has many advantages, including: (1) There is no need to retrain the massive foundation model over time; (2) Search results provide relevant and fresh information to foundation models, and; (3) It provides a provenance signal linking generated content with online sources. In response to a searcher prompt, the foundation model generates internal queries iteratively that are used to retrieve the results that form context for the copilot answers created using generative AI. Displaying these queries to searchers inline in the dialog (as in Bing Chat) creates greater transparency and helps build trust with searchers that the system is understanding their tasks and goals. The Bing orchestrator can also pull in relevant instant answers from the search engine such as weather, stock, sports, and so on, and display those in copilot responses instead of or in addition to the answers generated by the foundation model. Figure 5 shows the high-level search process from query (+ conversation context) to the answer, and the role of various key system components. Copilots also enable search engines to support more complex search tasks. Using search alone would require more searcher effort to examine search results and manually generate answers or insights (see recent work on the Delphic costs and benefits of search [9]). Of course, there are different perspectives on task complexity, e.g., the copilot perspective (denoting the amount of computation, requests, etc. required for the system to complete the task) and the searcher perspective (denoting the amount of manual effort required for the human searcher to generate an answer and complete a task). Table 1 considers the task complexity from these two different perspectives and (again drawing from Bloom’s taxonomy) provides some current, anecdotal examples of the types of tasks that both searcher and systems may find to be more or less complex. Assuming that foundational model costs will drop and sophistication will increase, we focus here on the task complexity for searchers. [6] Copilots is the Microsoft terminology for an AI-powered digital assistant designed to  work alongside humans to augment their cognitive capabilities. [7] https://github.com/features/copilot

Navigating Complex Search Tasks with AI Copilots: AI Copilots

With the development of generative artificial intelligence and its practical applications, creating servers for artificial intelligence has become critical for various industries - from automotive manufacturing to medicine, as well as for educational and governmental institutions. Let's consider the most important components that affect the selection of a server for artificial intelligence: the central processing unit (CPU) and graphics processing unit (GPU). Selecting appropriate processors and graphics cards will enable you to set up a high-performance platform and significantly accelerate calculations related to artificial intelligence on a dedicated or virtual (VPS) server. Rent GPU servers with instant deployment or a server with a custom configuration with professional-grade NVIDIA Tesla H100 / H100 80Gb or RTX A5000 / A4000 cards. GPU servers with game RTX4090 cards are also available. How do you choose the right processor for your AI server? The processor is the main "calculator" that receives commands from users and performs "command cycles," which will yield the desired results. Therefore, a big part of what makes an AI server so powerful is its CPU. You might expect a comparison between AMD and Intel processors. Yes, these two industry leaders are at the forefront of processor manufacturing, with the lineup of Intel 5th generation Intel® Xeon® ( and already announced 6th generation) and AMD EPYC™ 8004/9004 representing the pinnacle of x86-based CISC processors. If you are looking for excellent performance combined with a mature and proven ecosystem, selecting top-of-the-line products from these chip manufacturers would be the right choice. If budget is a concern, consider older versions of Intel® Xeon® and AMD EPYC™ processors. Even desktop CPUs from AMD or Nvidia's higher-end models would be a good starting point for working with AI if your workload does not require a large number of cores and multithreading capabilities. In practice, when it comes to language models, the choice of graphics accelerator or the amount of RAM installed in the server will have a greater impact than the choice between CPU types. While some models, such as the 8x7B from Mixtral, can produce results comparable to the computational power of tensor cores found in video cards when run on a CPU, they also require 2-3 times more RAM than a CPU + GPU bundle. For instance, a model that runs on 16 GB of RAM and 24 GB of GPU video memory may require up to 64 GB of RAM when running solely on the CPU. In addition to AMD and Intel, there are other options available. These can be solutions based on ARM architecture, such as NVIDIA Grace™, which combines ARM cores with patented NVIDIA features, or Ampere Altra™. How do you choose the right graphics processing unit (GPU) for your AI server? The GPU plays an increasingly important role in AI server operations today. It serves as an accelerator that helps the CPU process requests to neural networks much faster and more efficiently. The GPU can break down tasks into smaller segments and perform them simultaneously using parallel computing or specialized cores. For example, NVIDIA's tensor cores provide orders of magnitude higher performance in 8-bit floating-point (FP8) calculations with Transformer Engine, Tensor Float 32 (TF32), and FP16, showing excellent results in high-performance computing (HPC). This is particularly noticeable not during inference (the operation of the neural network) but during training, as for example, for models with FP32, this process can take several weeks or even months. To narrow down your search criteria, consider the following questions: Will the nature of your AI server's workload change over time? Most modern GPUs are designed for very specific tasks. The architecture of their chips may be suitable for certain areas of AI development or application, and new hardware and software solutions can render previous generations of GPU obsolete in just a few years (1-2-3).
Will you mainly focus on training AI or inference (usage)? These two processes are the foundation of all modern AI iterations with limited memory budgets. During training, the AI model processes a large amount of data with billions or even trillions of parameters. It adjusts the "weights" of its algorithms until it can consistently generate correct results. In inference mode, the AI relies on the "memory" of its training to respond to new input data in the real world. Both processes require significant computational resources, so GPUs and expansion modules are installed for acceleration. Graphic processing units (GPUs) are designed specifically for training deep learning models with specialized cores and mechanisms that can optimize this process. For example, NVIDIA's H100 with 8 GPU cores provides more than 32 petaflops of performance in FP8 deep learning. Each H100 contains fourth-generation tensor cores using a new type of data called FP8 and a "Transformer Engine" for optimization. Recently, NVIDIA introduced the next generation of their GPUs, the B200, which will be even more powerful. A strong alternative to AMD solutions is the AMD Instinct™ MI300X. Its feature is a large memory capacity and high data bandwidth, which is important for inferencing-based generative AI applications, such as large language models (LLM). AMD claims that their GPUs are 30% more efficient than NVIDIA's solutions but have less mature software. If you need to sacrifice a bit of performance to fit within budget constraints or if your dataset for training the AI is not too large, you can consider other options from AMD and NVIDIA. For inferencing tasks or when continuous operation in 24/7 mode for training is not required, "consumer" solutions based on Nvidia RTX 4090 or RTX 3090 may be suitable. If you are looking for stability in long-term calculations for model training, you can consider NVIDIA's RTX A4000 or A5000 cards. Although the H100 with PCIe bus may offer a more powerful solution with 60-80% performance depending on the tasks, the RTX A5000 is a more accessible option and could be an optimal choice for certain tasks (such as working with models like 8x7B). For more exotic inference solutions, you can consider cards like AMD Alveo™ V70, NVIDIA A2/L4 Tensor Core, and Qualcomm® Cloud AI 100. In the near future, AMD and NVIDIA plan to outperform Intel's GPU Gaudi 3 in the AI training market. Considering all these factors and taking into account software optimization for HPC and AI, we recommend servers with Intel Xeon or AMD Epyc processors and GPUs from NVIDIA. For AI inferencing tasks, you can use GPUs from RTX A4000/A5000 to RTX 3090, while for training and working on multi-modal neural networks, it's advisable to allocate budgets for solutions from RTX 4090 to A100/H100. Rent GPU servers with instant deployment or a server with a custom configuration with professional-grade NVIDIA Tesla H100 / H100 80Gb or RTX A5000 / A4000 cards. GPU servers with game RTX4090 cards are also available. This story was distributed under HackerNoon’s Business Blogging Program. Learn more about the program here. With the development of generative artificial intelligence and its practical applications, creating servers for artificial intelligence has become critical for various industries - from automotive manufacturing to medicine, as well as for educational and governmental institutions. Let's consider the most important components that affect the selection of a server for artificial intelligence: the central processing unit (CPU) and graphics processing unit (GPU). Selecting appropriate processors and graphics cards will enable you to set up a high-performance platform and significantly accelerate calculations related to artificial intelligence on a dedicated or virtual (VPS) server. Rent GPU servers with instant deployment or a server with a custom configuration with professional-grade NVIDIA Tesla H100 / H100 80Gb or RTX A5000 / A4000 cards. GPU servers with game RTX4090 cards are also available. Rent GPU servers with instant deployment or a server with a custom configuration with professional-grade NVIDIA Tesla H100 / H100 80Gb or RTX A5000 / A4000 cards. GPU servers with game RTX4090 cards are also available. Rent GPU servers with instant deployment Rent GPU servers with instant deployment custom configuration custom configuration GPU servers with game RTX4090 cards GPU servers with game RTX4090 cards How do you choose the right processor for your AI server? The processor is the main "calculator" that receives commands from users and performs "command cycles," which will yield the desired results. Therefore, a big part of what makes an AI server so powerful is its CPU. You might expect a comparison between AMD and Intel processors. Yes, these two industry leaders are at the forefront of processor manufacturing, with the lineup of Intel 5th generation Intel® Xeon® ( and already announced 6th generation) and AMD EPYC™ 8004/9004 representing the pinnacle of x86-based CISC processors. If you are looking for excellent performance combined with a mature and proven ecosystem, selecting top-of-the-line products from these chip manufacturers would be the right choice. If budget is a concern, consider older versions of Intel® Xeon® and AMD EPYC™ processors. Even desktop CPUs from AMD or Nvidia's higher-end models would be a good starting point for working with AI if your workload does not require a large number of cores and multithreading capabilities. In practice, when it comes to language models, the choice of graphics accelerator or the amount of RAM installed in the server will have a greater impact than the choice between CPU types. While some models, such as the 8x7B from Mixtral, can produce results comparable to the computational power of tensor cores found in video cards when run on a CPU, they also require 2-3 times more RAM than a CPU + GPU bundle. For instance, a model that runs on 16 GB of RAM and 24 GB of GPU video memory may require up to 64 GB of RAM when running solely on the CPU. In addition to AMD and Intel, there are other options available. These can be solutions based on ARM architecture, such as NVIDIA Grace™, which combines ARM cores with patented NVIDIA features, or Ampere Altra™. How do you choose the right graphics processing unit (GPU) for your AI server? The GPU plays an increasingly important role in AI server operations today. It serves as an accelerator that helps the CPU process requests to neural networks much faster and more efficiently. The GPU can break down tasks into smaller segments and perform them simultaneously using parallel computing or specialized cores. For example, NVIDIA's tensor cores provide orders of magnitude higher performance in 8-bit floating-point (FP8) calculations with Transformer Engine, Tensor Float 32 (TF32), and FP16, showing excellent results in high-performance computing (HPC). This is particularly noticeable not during inference (the operation of the neural network) but during training, as for example, for models with FP32, this process can take several weeks or even months. To narrow down your search criteria, consider the following questions: Will the nature of your AI server's workload change over time? Most modern GPUs are designed for very specific tasks. The architecture of their chips may be suitable for certain areas of AI development or application, and new hardware and software solutions can render previous generations of GPU obsolete in just a few years (1-2-3). Will you mainly focus on training AI or inference (usage)? These two processes are the foundation of all modern AI iterations with limited memory budgets. Will the nature of your AI server's workload change over time? Most modern GPUs are designed for very specific tasks. The architecture of their chips may be suitable for certain areas of AI development or application, and new hardware and software solutions can render previous generations of GPU obsolete in just a few years (1-2-3). Will you mainly focus on training AI or inference (usage)? These two processes are the foundation of all modern AI iterations with limited memory budgets. During training, the AI model processes a large amount of data with billions or even trillions of parameters. It adjusts the "weights" of its algorithms until it can consistently generate correct results. In inference mode, the AI relies on the "memory" of its training to respond to new input data in the real world. Both processes require significant computational resources, so GPUs and expansion modules are installed for acceleration. Graphic processing units (GPUs) are designed specifically for training deep learning models with specialized cores and mechanisms that can optimize this process. For example, NVIDIA's H100 with 8 GPU cores provides more than 32 petaflops of performance in FP8 deep learning. Each H100 contains fourth-generation tensor cores using a new type of data called FP8 and a "Transformer Engine" for optimization. Recently, NVIDIA introduced the next generation of their GPUs, the B200, which will be even more powerful. A strong alternative to AMD solutions is the AMD Instinct™ MI300X. Its feature is a large memory capacity and high data bandwidth, which is important for inferencing-based generative AI applications, such as large language models (LLM). AMD claims that their GPUs are 30% more efficient than NVIDIA's solutions but have less mature software. If you need to sacrifice a bit of performance to fit within budget constraints or if your dataset for training the AI is not too large, you can consider other options from AMD and NVIDIA. For inferencing tasks or when continuous operation in 24/7 mode for training is not required, "consumer" solutions based on Nvidia RTX 4090 or RTX 3090 may be suitable. If you are looking for stability in long-term calculations for model training, you can consider NVIDIA's RTX A4000 or A5000 cards. Although the H100 with PCIe bus may offer a more powerful solution with 60-80% performance depending on the tasks, the RTX A5000 is a more accessible option and could be an optimal choice for certain tasks (such as working with models like 8x7B). For more exotic inference solutions, you can consider cards like AMD Alveo™ V70, NVIDIA A2/L4 Tensor Core, and Qualcomm® Cloud AI 100. In the near future, AMD and NVIDIA plan to outperform Intel's GPU Gaudi 3 in the AI training market. Considering all these factors and taking into account software optimization for HPC and AI, we recommend servers with Intel Xeon or AMD Epyc processors and GPUs from NVIDIA. For AI inferencing tasks, you can use GPUs from RTX A4000/A5000 to RTX 3090, while for training and working on multi-modal neural networks, it's advisable to allocate budgets for solutions from RTX 4090 to A100/H100. Rent GPU servers with instant deployment or a server with a custom configuration with professional-grade NVIDIA Tesla H100 / H100 80Gb or RTX A5000 / A4000 cards. GPU servers with game RTX4090 cards are also available. Rent GPU servers with instant deployment or a server with a custom configuration with professional-grade NVIDIA Tesla H100 / H100 80Gb or RTX A5000 / A4000 cards. GPU servers with game RTX4090 cards are also available. Rent GPU servers with instant deployment Rent GPU servers with instant deployment custom configuration custom configuration GPU servers with game RTX4090 cards GPU servers with game RTX4090 cards This story was distributed under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed under HackerNoon’s Business Blogging Program. Learn more about the program here . here here

How Do You Choose the Best Server, CPU, and GPU for Your AI?

কিভাবে আপনি আপনার AI এর জন্য সেরা সার্ভার, CPU এবং GPU চয়ন করবেন?

Wie wählen Sie den besten Server, die beste CPU und die beste GPU für Ihre KI aus?

¿Cómo se elige el mejor servidor, CPU y GPU para su IA?

Comment choisir le meilleur serveur, CPU et GPU pour votre IA ?

आप अपने AI के लिए सर्वोत्तम सर्वर, CPU और GPU का चयन कैसे करते हैं?

AI に最適なサーバー、CPU、GPU をどのように選択すればよいでしょうか?

AI에 가장 적합한 서버, CPU 및 GPU를 어떻게 선택합니까?

Como você escolhe o melhor servidor, CPU e GPU para sua IA?

Как выбрать лучший сервер, процессор и графический процессор для вашего ИИ?

Yapay Zekanız için En İyi Sunucuyu, CPU'yu ve GPU'yu Nasıl Seçersiniz?

Làm cách nào để bạn chọn Máy chủ, CPU và GPU tốt nhất cho AI của mình?

如何为您的 AI 选择最佳的服务器、CPU 和 GPU？

The game industry is on the verge of undergoing a dramatic transition as a result of the launch of Wilder World, a massively multiplayer online metaverse of the next generation that combines old knowledge with cutting-edge technology in a seamless manner. It was just announced that this virtual world would be available for wishlisting on the Epic Games Store. The enormous city of Wiami is home to this attraction, which boasts an immersive experience unlike any other. In Wilder World, players have the chance to shape their own future by using a decentralised environment that encourages freedom and innovation. They must rebel against the authoritarian Government and forge their own path within this setting. Epic Games Store Listing: A Strategic Leap The decision to list Wilder World on the Epic Games Store, a platform boasting over 270 million total users and 75 million monthly active users, is a testament to the game's ambitious vision,  Frank Wilder, Co-founder of Wilder World, articulates this strategic move as "setting the pace for next-gen gaming in the metaverse." Epic Games and Wilder World have joined forces because they share the same goals and values: to change the status quo, especially the unfair middlemen that have been holding back the video game industry for a long time. The fact that Wilder World works with Nvidia's GeForce Now and SuperVerse shows how far it can spread. These deals make it possible for Wilder World to reach gamers on both the Web2 and Web3 networks. The company shows its commitment to keeping prices low while still supporting a huge world full of games by focusing on growth, which is made possible by relationships with Polygon and Celestia. Working with Metagravity also means that the virtual worlds can hold thousands of people, which makes it feel like you're really in the game. Coming of AAA Blockchain Games The game community is holding its breath as Wilder World gets ready for its wider release in 2024. The alpha testing process has already shown signs that this metaverse could change the rules of online games. Wilder World is more than just a game. It's a gateway to a new era of digital travel and connection, with its unique mix of old knowledge and cutting-edge technology from the future. Getting together with Epic Games Store is just the start of Wilder World's journey. The vast world of Wiami has a lot of room for growth and change, and that can only happen as more players explore it. This is what Frank Wilder put it: "to create a novel experience using cutting-edge technology, offering players a virtual space for gaming, socialising, and earning." With this kind of vision driving the project, Wilder World is set to become a model of freedom and new ideas in the metaverse. A Deep Dive into Wiami's Expansive Universe A competitive racing game that is presently in alpha testing and is scheduled for a wider release in 2024, Wilder World's first experience takes place in the city, which serves as the background for the game. Every region of the cosmos has a tale to tell, every race is a challenge, and every contact is an opportunity to create friendships that will last a lifetime. This is only the beginning of what promises to be an ever-evolving world. Not only is Wilder World a video game, but it is also an AAA enterprise of the future generation that showcases the pinnacle of photorealistic images and is equipped with complete artificial intelligence inside the game itself. The team that was in charge of creating the engine itself built it, and they used the cutting-edge Unreal Engine all throughout the process. Participating in a full economy in which everything from items and equipment to land and avatars exists as transferable digital assets is the goal of this metaverse. The objective of this metaverse is not restricted to just playing; rather, it is to actively participate in this economy. The fact that players have the capacity to earn genuine income makes it conceivable for a new paradigm to emerge in which gaming, socialising, and economic participation are all combined. Visit Wilder World's website and follow their X account to find out what's going on with them. Don't miss your chance to be a part of this groundbreaking project. Put Wilder World on your request on the Epic Games Store right now and be the first to create a new era of freedom and creation in the metaverse. Don’t forget to like and share the story! This author is an independent contributor publishing via our brand-as-author program. Be it through direct compensation, media partnerships, or networking, the author has a vested interest in the company/ies mentioned in this story. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Vested Interest Disclosure:

Wilder World on Epic Games Store: Where Ancient Wisdom Meets the Future of Gaming

এপিক গেম স্টোরে ওয়াইল্ডার ওয়ার্ল্ড: যেখানে প্রাচীন জ্ঞান গেমিংয়ের ভবিষ্যত পূরণ করে

Wilder World im Epic Games Store: Wo alte Weisheit auf die Zukunft des Gamings trifft

Wilder World en Epic Games Store: donde la sabiduría antigua se encuentra con el futuro de los juegos

Wilder World sur Epic Games Store : là où la sagesse ancienne rencontre l'avenir du jeu

एपिक गेम्स स्टोर पर वाइल्डर वर्ल्ड: जहां प्राचीन ज्ञान गेमिंग के भविष्य से मिलता है

Epic Games ストアの Wilder World: 古代の知恵とゲームの未来が出会う場所

Epic Games Store의 Wilder World: 고대의 지혜와 게임의 미래가 만나는 곳

Wilder World na Epic Games Store: onde a sabedoria antiga encontra o futuro dos jogos

Wilder World в магазине Epic Games: где древняя мудрость встречается с будущим игр

Wilder World Epic Games Store'da: Kadim Bilgeliğin Oyunun Geleceğiyle Buluştuğu Yer

Wilder World trên Epic Games Store: Nơi trí tuệ cổ xưa gặp gỡ tương lai của trò chơi

Epic Games Store 上的 Wilder World：古老智慧与游戏未来的邂逅

Virtual Reality (VR) and Augmented Reality (AR) are at the forefront of technological innovation, transforming how we interact with the digital world. Meanwhile, Enterprise Resource Planning (ERP) systems are the backbone of modern business operations, integrating various processes into a unified system. Integrating VR/AR with ERP is not just a possibility; it's a game-changer in business technology. Integrating VR/AR with ERP systems opens up business possibilities. This fusion enhances data visualization, immerses training, and streamlines decision-making processes. Leading this innovative journey is Technologies, a company at the pinnacle of the SaaS, ERP, AI, and AR/VR sectors. AvanSaber Understanding VR and AR Virtual Reality immerses users in a completely virtual environment, often used in gaming and simulations. Augmented Reality, on the other hand, overlays digital information onto the real world, as seen in applications like Pokémon GO and IKEA's AR furniture app. These technologies are revolutionizing industries from healthcare, where AR assists in complex surgeries, to education, where VR creates immersive learning environments. The potential for VR and AR is vast. With the backing of visionary leaders like , founder and CEO of AvanSaber Technologies, these technologies are set to redefine how businesses operate. Nikhil Jathar Overview of ERP Systems Enterprise Resource Planning systems integrate various business processes, from inventory management to human resources, into a single, cohesive system. They are essential for streamlined operations and data management. An ERP system typically includes finance, HR, supply chain management modules, and more, allowing seamless data flow across departments. With the advent of AI and machine learning, ERP systems are becoming more intelligent and predictive. Companies like AvanSaber Technologies are at the forefront of this evolution, integrating cutting-edge technologies into ERP solutions. Integration of VR/AR With ERP Fusing VR/AR with ERP systems can lead to unparalleled enhancements in data visualization, employee training, and customer engagement. Imagine analyzing complex data sets through immersive VR experiences or training employees in virtual environments that simulate real-world scenarios. The industry's advancement is propelled by leading manufacturers like Meta VR and Apple's Vision Pro, which have set high standards in the VR/AR realm. Their innovations lay the groundwork for widespread adoption and integration into business systems. Amidst these developments, AvanSaber Technologies, under the leadership of Nikhil Jathar, stands out for its ability to integrate these advanced VR/AR solutions with robust ERP systems. By harnessing the capabilities of industry giants like Meta and Apple, AvanSaber tailors these technologies to meet specific business needs, ensuring seamless integration and maximum efficiency. "In the near future, the fusion of VR, AR, and ERP will not only transform how we conduct business but will redefine the very essence of interaction within our workspaces. Imagine a world where training sessions are conducted in virtual environments that are indistinguishable from reality, where augmented overlays provide real-time data and insights right before our eyes, and where mixed reality allows us to manipulate and interact with 3D models of our products, systems, and processes. This is not just a dream; it's the inevitable next step in our technological evolution. At AvanSaber Technologies, we are not just chasing this future; we are actively crafting it, ensuring that businesses can not only keep pace with these changes but thrive in the new realms of possibility they unlock." - Nikhil Jathar, Founder, AvanSaber Technologies. Real-World Examples of VR/AR in ERP Systems Incorporating VR/AR technology into ERP systems is more than just theoretical. For instance, using Meta VR's immersive environments for employee training or Apple's Vision Pro AR tools for enhanced data visualization in real-time can revolutionize business operations. While the potential is immense, integrating these sophisticated technologies into ERP systems comes with challenges. Issues like cost-effectiveness, technological compatibility, and user acceptance are at the forefront. This is where AvanSaber Technologies excels, offering customized solutions that mitigate these challenges and maximize the benefits of VR/AR-ERP integration. Case Studies - Enhanced Training with Meta VR: AvanSaber Technologies collaborated with a manufacturing firm to implement Meta VR in their ERP system for training purposes. This integration allowed employees to navigate a virtual replica of their workplace, significantly improving their understanding of complex machinery and safety procedures. The result was a 40% reduction in training time and a noticeable decrease in on-site accidents. Case Study 1 - Inventory Management with OTC VR headset: AvanSaber Technologies leveraged Android technology to enhance the ERP system's inventory management module in a retail scenario. Employees used AR glasses to locate items in the warehouse quickly, view product details, and update inventory levels in real-time. This solution increased inventory accuracy by 35% and streamlined the supply chain process. Case Study 2 Real-World Examples of VR/AR/MR in Industries Electric Utilities GE Renewable Energy experimented with equipping their engineering team with smart AR glasses for wiring insertion tasks. The AR glasses team completed the work twice as fast, with greater accuracy and fewer mistakes than paper manuals. Augmented Reality for Maintenance and Safety: AR can also be used for safety training, where it overlays 3D models onto equipment, allowing engineers to see problem areas in real-time and reducing the risk of injury. Water Utilities Toms River Municipal Utilities Authority invested in MR holographic headsets to overlay a digital copy of their underground assets. This technology allows field technicians to see through the dirt and locate assets more accurately, increasing productivity and preventing costly errors due to inaccurate above-ground markings. Mixed Reality for Asset Management: Manufacturing AR glasses can guide workers through warehouse areas to quickly find and select necessary items among millions. For example, researchers from MIT presented an AR headset that enables users to pick up the correct item with 96% accuracy by localizing hidden items within a mock warehouse environment. Augmented Reality in Logistics and Warehouse Management: AR devices and applications can simulate real-life activities in a semi-virtual environment, allowing workers to train safely and effectively. For instance, a B2B skilling startup offers AR-enabled solutions for training employees, boosting retention rates and on-the-job performance. Augmented Reality for Employee Training: AR allows manufacturing designers and technicians to visualize and interact with the product in detail, working on its features before the actual production begins. This enhances the design process and helps identify potential issues early on. Augmented Reality for Product Design and Assembly: These examples demonstrate how VR, AR, and MR technologies can be applied across various industries to improve efficiency, safety, and training and enhance the overall quality of operations. Integrating these technologies into ERP systems can significantly advance how businesses manage their resources and interact with their environments. Future Trends and Predictions The future of VR/AR in ERP systems is promising and filled with potential. We expect more immersive training environments, enhanced data visualization tools, and real-time interactive decision-making processes. These technologies will improve efficiency and open new remote collaboration and customer engagement avenues. Emerging technologies and their potential impact, such as 5G, AI, and the Internet of Things (IoT), are set to enhance VR/AR capabilities further. 5G will enable faster data transmission, making VR/AR experiences more seamless. AI and IoT, when integrated with VR/AR, can offer more personalized and intelligent solutions. AvanSaber Technologies, with its focus on innovation, is well-positioned to harness these advancements for more sophisticated ERP solutions. To stay ahead in this rapidly evolving landscape, businesses must be proactive. Partnering with technology leaders like AvanSaber Technologies can provide them with the necessary expertise and tools to integrate these advanced technologies into their existing ERP systems. Staying informed and adaptable is critical to leveraging these innovations for business growth and success. Integrating VR/AR with ERP systems represents a significant leap forward in how businesses operate and make decisions. AvanSaber Technologies, led by the visionary Nikhil Jathar, is at the forefront of this revolution. By combining the power of VR/AR with the efficiency of ERP, AvanSaber is not just predicting the future; they are actively creating it. In Conclusion, As we embrace this new era of technological advancement, businesses can transform operations, increase efficiency, and provide unparalleled experiences to their employees and customers. The collaboration between giants like Meta and Apple with innovative companies like AvanSaber Technologies is a testament to the endless possibilities that await. The journey into the future of VR/AR and ERP is just beginning, and it's an exciting time for businesses to jump on board. With the guidance and expertise of AvanSaber Technologies, the path toward a more interactive, efficient, and immersive business world is clearer than ever.

Virtual Reality, Augmented Reality, and ERP

ভার্চুয়াল রিয়েলিটি, অগমেন্টেড রিয়েলিটি এবং ইআরপি

Virtual Reality, Augmented Reality und ERP

Realidad virtual, realidad aumentada y ERP

Réalité virtuelle, réalité augmentée et ERP

आभासी वास्तविकता, संवर्धित वास्तविकता और ईआरपी

仮想現実、拡張現実、ERP

가상 현실, 증강 현실 및 ERP

Realidade Virtual, Realidade Aumentada e ERP

Виртуальная реальность, дополненная реальность и ERP

Sanal Gerçeklik, Artırılmış Gerçeklik ve ERP

Thực tế ảo, thực tế tăng cường và ERP

虚拟现实、增强现实和 ERP

86 Stories To Learn About Product Strategy

266 Stories To Learn About Product Development

Authors: (1) Ruohan Zhang, Department of Computer Science, Stanford University, Institute for Human-Centered AI (HAI), Stanford University & Equally contributed; zharu@stanford.edu; (2) Sharon Lee, Department of Computer Science, Stanford University & Equally contributed; zharu@stanford.edu; (3) Minjune Hwang, Department of Computer Science, Stanford University & Equally contributed; zharu@stanford.edu; (4) Ayano Hiranaka, Department of Mechanical Engineering, Stanford University & Equally contributed; zharu@stanford.edu; (5) Chen Wang, Department of Computer Science, Stanford University; (6) Wensi Ai, Department of Computer Science, Stanford University; (7) Jin Jie Ryan Tan, Department of Computer Science, Stanford University; (8) Shreya Gupta, Department of Computer Science, Stanford University; (9) Yilun Hao, Department of Computer Science, Stanford University; (10) Ruohan Gao, Department of Computer Science, Stanford University; (11) Anthony Norcia, Department of Psychology, Stanford University (12) Li Fei-Fei, 1Department of Computer Science, Stanford University & Institute for Human-Centered AI (HAI), Stanford University; (13) Jiajun Wu, Department of Computer Science, Stanford University & Institute for Human-Centered AI (HAI), Stanford University. Table of Links Abstract & Introduction Brain-Robot Interface (BRI): Background The NOIR System Experiments Results Conclusion, Limitations, and Ethical Concerns Acknowledgments & References Appendix 1: Questions and Answers about NOIR Appendix 2: Comparison between Different Brain Recording Devices Appendix 3: System Setup Appendix 4: Task Definitions Appendix 5: Experimental Procedure Appendix 6: Decoding Algorithms Details Appendix 7: Robot Learning Algorithm Details Abstract We present Neural Signal Operated Intelligent Robots (NOIR), a general-purpose, intelligent brain-robot interface system that enables humans to command robots to perform everyday activities through brain signals. Through this interface, humans communicate their intended objects of interest and actions to the robots using electroencephalography (EEG). Our novel system demonstrates success in an expansive array of 20 challenging, everyday household activities, including cooking, cleaning, personal care, and entertainment. The effectiveness of the system is improved by its synergistic integration of robot learning algorithms, allowing for NOIR to adapt to individual users and predict their intentions. Our work enhances the way humans interact with robots, replacing traditional channels of interaction with direct, neural communication. Project website: https://noir-corl.github.io/ Brain-Robot Interface; Human-Robot Interaction Keywords: 1 Introduction Brain-robot interfaces (BRIs) are a pinnacle achievement in the realm of art, science, and engineering. This aspiration, which features prominently in speculative fiction, innovative artwork, and groundbreaking scientific studies, entails creating robotic systems that operate in perfect synergy with humans. A critical component of such systems is their ability to communicate with humans. In human-robot collaboration and robot learning, humans communicate their intents through actions [1], button presses [2, 3], gaze [4–7], facial expression [8], language [9, 10], etc [11, 12]. However, the prospect of direct communication through neural signals stands out to be the most thrilling but challenging medium. We present Neural Signal Operated Intelligent Robots (NOIR), a general-purpose, intelligent BRI system with non-invasive electroencephalography (EEG). The primary principle of this system is hierarchical shared autonomy, where humans define high-level goals while the robot actualizes the goals through the execution of low-level motor commands. Taking advantage of the progress in neuroscience, robotics, and machine learning, our system distinguishes itself by extending beyond previous attempts to make the following contributions. First, NOIR is general-purpose in its diversity of tasks and accessibility. We show that humans can accomplish an expansive array of 20 daily everyday activities, in contrast to existing BRI systems that are typically specialized at one or a few tasks or exist solely in simulation [13–22]. Additionally, the system can be used by the general population, with a minimum amount of training. Second, the “I” in NOIR means that our robots are intelligent and adaptive. The robots are equipped with a library of diverse skills, allowing them to perform low-level actions without dense human supervision. Human behavioral goals can naturally be communicated, interpreted, and executed by the robots with parameterized primitive skills, such as Pick(obj-A) or MoveTo(x,y). Additionally, our robots are capable of learning human intended goals during their collaboration. We show that by leveraging the recent progress in foundation models, we can make such a system more adaptive with limited data. We show that this can significantly increase the efficiency of the system. The key technical contributions of NOIR include a modular neural signal decoding pipeline for human intentions. Decoding human intended goals (e.g., “pick up the mug from the handle”) from neural signals is extremely challenging. We decompose human intention into three components: What object to manipulate, How to interact with the object, and Where to interact, and show that such signals can be decoded from different types of neural data. These decomposed signals naturally correspond to parameterized robot skills and can be communicated effectively to the robots. In 20 household activities involving tabletop or mobile manipulations, three human subjects successfully used our system to accomplish these tasks with their brain signals. We demonstrate that few-shot robot learning from humans can significantly improve the efficiency of our system. This approach to building intelligent robotic systems, which utilizes human brain signals for collaboration, holds immense potential for the development of critical assistive technologies for individuals with or without disabilities and to improve the quality of their life. This paper is under CC 4.0 license. available on arxiv

NOIR: Neural Signal Operated Intelligent Robots for Everyday Activities

NOIR: দৈনন্দিন কার্যকলাপের জন্য নিউরাল সিগন্যাল পরিচালিত বুদ্ধিমান রোবট

NOIR: Mit neuronalen Signalen betriebene intelligente Roboter für alltägliche Aktivitäten

NOIR: Robots inteligentes operados por señales neuronales para actividades cotidianas

NOIR : robots intelligents actionnés par des signaux neuronaux pour les activités quotidiennes

NOIR: रोजमर्रा की गतिविधियों के लिए न्यूरल सिग्नल संचालित इंटेलिजेंट रोबोट

NOIR: 日常活動のための神経信号で動作するインテリジェント ロボット

느와르(NOIR): 일상 활동을 위한 신경 신호로 작동되는 지능형 로봇

NOIR: Robôs Inteligentes Operados por Sinais Neurais para Atividades Diárias

NOIR: интеллектуальные роботы, управляемые нейронными сигналами, для повседневной деятельности

NOIR: Günlük Faaliyetler için Nöral Sinyalle Çalışan Akıllı Robotlar

NOIR: Robot thông minh vận hành bằng tín hiệu thần kinh cho các hoạt động hàng ngày

NOIR：用于日常活动的神经信号操作智能机器人

ERIK ADOLPH vs. Uber Court Filing, retrieved on July 17, 2023, is part of . You can jump to any part in this filing . This is part 14 of 15. HackerNoon’s Legal PDF Series here G. Finally, Uber and amicus curiae United States Chamber of Commerce suggest that a PAGA plaintiff subject to an arbitration agreement breaches that agreement by filing suit in court. But if a defendant believes arbitration is required, it is “[t]he party seeking arbitration [that] bears the burden of proving the existence of an arbitration agreement” in court. (Pinnacle Museum Tower Assn. v. Pinnacle Market Development (US), LLC (2012) 55 Cal.4th 223, 236.) And even where a plaintiff concedes the applicability of an arbitration agreement, the plaintiff does not breach the agreement by alleging in a complaint that one or more violations were committed against the plaintiff for the purpose of meeting PAGA’s standing requirements. Several amici curiae have also argued that we should narrow the statute’s standing requirements in order to curb alleged abuses of PAGA. These arguments are best directed to the Legislature, which may amend the statute to limit PAGA enforcement if it chooses. Our task is to give effect to the statute as we find it. Under the statute, a plaintiff who files a PAGA action with individual and non-individual claims does not lose standing to litigate the non-individual claims in court simply because the individual claims have been ordered to arbitration. Continue Reading . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case S274671 retrieved on September 22, 2023, from is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. courts.ca.gov

When Can a PAGA Plaintiff Lose Standing?

2023 might have been India’s lowest-funded year in last 5 years as the funding winter continues, but the decade shows good progress. A tech-savvy young population, Internet penetration, and an expanding middle class have led to an explosion of startups in India in the past decade On National Startup Day, that India witnessed an unprecedented surge in the creation and funding of startups in the last ten years. The country has solidified its position as a major global center for innovation and entrepreneurship with a rise in the number of tech companies in India to 122k to date, with a peak in 2020, witnessing more than 16K new additions. The funding landscape has also experienced a monumental shift, with 2020 and 2021 being the highest investment years over the past decade. Tracxn reported Despite the challenges faced in the funding landscape in 2023, investors, including venture capitalists, angel investors, and private equity firms, have shown remarkable confidence in Indian startups. The funding scenario, both from Indian and global investors, has been encouraging, with a total funding of $8.4 billion in India in 2023, demonstrating resilience amid market uncertainties. The startup ecosystem is ready to lead in bringing more innovation, creating jobs, and boosting economic prosperity. We are on the verge of a new era of success, supported by technology, business-friendly regulations, and the strong dedication of our entrepreneurs Neha Singh, co-founder of Tracxn Neha Singh, co-founder of Tracxn, said, “Over the past ten years, India’s startup sector has grown a lot, becoming a global hub for new ideas and economic growth. Entrepreneurs have shown great determination and creativity, contributing significantly to the country’s economic development. Looking ahead, we are optimistic about the future. The startup ecosystem is ready to lead in bringing more innovation, creating jobs, and boosting economic prosperity. We are on the verge of a new era of success, supported by technology, business-friendly regulations, and the strong dedication of our entrepreneurs.” Key Factors to Growth The upsurge in the number of startups in the country can be attributed to several key factors. These include a tech-savvy young population resulting from widespread mobile internet access, the expansion of the middle class, and a favorable regulatory landscape. These elements collectively have fostered a conducive environment for entrepreneurial growth and innovation in the startup ecosystem. Top Performing Sectors Top-funded sectors from 2014 to 2023 include retail, enterprise applications, FinTech, transportation & logistics tech, food and agriculture tech, auto tech, travel & hospitality tech, and EdTech. Factors like increased internet penetration, digitization, and government initiatives have fueled the growth of these sectors. Startups have played a pivotal role in driving Research and Development (R&D), especially in knowledge-intensive sectors. India’s investment in Deep Tech (R&D oriented) has been increasing steadily over the years with total funding of US$6.73B, and in a decade, it received US$6.23 billion, reflecting the country’s steady growth and commitment to science and technology, particularly in Deep Tech, funding has witnessed a substantial increase, reaching $753.7 million in 2023. This trend emphasizes the importance of technology-driven innovation in shaping India’s future. India’s online commerce has grown tremendously, with sectors like Retail Tech receiving substantial funding. The funding trends in retail tech showcase the sector’s resilience, with US$2.3 billion invested in 2023, indicating sustained growth. New Emerging Sectors The addition of new sectors such as DeepTech, SpaceTech, AI, and EVs has broadened the startup landscape. Noteworthy sectors like DeepTech have seen a rise in funding, reaching $2.1 billion in 2022, underlining the market’s confidence in innovative technologies. The EV industry, which attracted investor interest and is one of the booming sectors, has seen $4.8 billion in funding in the past ten years. Even Space Tech has been in demand after its transformation which is credited to privatisation. India ranks seventh in terms of funding within the International SpaceTech landscape to date. The funding reached $114.9 million in 2022 and $122.2 million in 2023 and will continue to grow. Job Creation One of the most significant achievements of the Indian startup ecosystem has been job creation, with approximately 10 lakh jobs generated by startups till 2023, as per Economic Survey 2023. Furthermore, startups focusing on continued learning and professional upskilling have attracted significant investments, reflecting the industry’s commitment to employee empowerment. Top Favorable Cities for Startups City-wise, Bengaluru remains the startup hub, with a total funding of $70.4 billion in a decade, followed by Delhi-NCR and Mumbai. The government’s focus on promoting entrepreneurship is evident, with increased funding across metros and non-metros alike. Several successful startups have emerged in non-metropolitan areas, driving innovation and transforming their regions, such as Jaipur, which has notably secured its position among the top cities in FinTech funding, garnering an impressive $214 million in 2023. Unicorns, Soonicorns, & Minicorns In a significant achievement for the Indian startup ecosystem, it marked a major milestone by welcoming the 100th Indian startup into the unicorn club in 2022. As of now, a total of 111 unicorns have been created in India, with 355 promising ones poised to join the ranks soon. Similarly, the rise of Soonicorns began in 2019, and the year 2022 witnessed an unprecedented surge with the creation of 96 Soonicorns. Additionally, the Minicorn trend, which also boomed in 2019, experienced substantial growth, reaching its pinnacle in 2022 with the emergence of 264 Minicorns. This showcases the dynamic and flourishing nature of the Indian startup ecosystem, with both Soonicorns and Minicorns contributing to the nation’s entrepreneurial landscape. Women-Led Startups Over the past decade, entrepreneurial advancements have transformed India significantly, with women playing a significant role in driving this progress. The addition of women-led startups has also witnessed growth, with total funding reaching $1.1 billion in 2023 from $0.456B in 2014. This trend is exemplified by the fact that twenty out of the total unicorns created to date are women-led startups, highlighting women’s impactful and growing role in shaping the nation’s startup ecosystem. Future Outlook India’s economic growth is intricately tied to the success of its startup ecosystem. India’s economic outlook remains optimistic, with a projected annual growth rate of 6.3% (as per IMF) in 2023–24. The government’s ongoing support, as seen in the 2023 budget with initiatives like tax concessions and the Drone Shakti Program, reinforces the commitment to nurturing the tech ecosystem. Investor confidence in the Indian startup ecosystem has seen a substantial increase over the last decade. Despite the recent funding winter, the number of investors has increased from 2.45k in 2014 to 16.3k in 2021.  The long-term prospects remain favorable, with India poised to become the third-largest economy in the world by 2027, according to the IMF. As the global situation stabilizes, a revival of investment activity is expected in the coming years, reaffirming India’s position as a top destination for investors. This article was originally published by on Navanwita Bora Sachdev The Tech Panda.

India’s Decade of Tech Startup Growth

Looks like it’s part 2 of my series on Data Quality! In a , I explored Airbnb’s strategy for enhancing data quality through incentives. They implemented a single score and clear scoring criteria to establish a common understanding between data producers and consumers, fostering a genuine sense of ownership. previous post https://hackernoon.com/data-quality-score-one-score-to-rule-them-all?embedable=true Now, Lyft is taking a distinct approach, not attempting the same thing differently, but rather focusing on different aspects of data quality. And, Lyft’s strategy complements Airbnb’s efforts. While I regard Airbnb’s DQ score (or any similar score) as an effective means to consolidate various attempts at elevating data quality, Lyft is tackling this challenge from a different angle. The Airbnb’s DQ score serves as a valuable tool for providing a concrete visualization of data quality. In essence, any initiative to enhance data quality should manifest a discernible impact on this score. On the other hand, Lyft presents one possible initiative to proactively enhance quality by testing and validating data against specific quality criteria. https://eng.lyft.com/from-big-data-to-better-data-ensuring-data-quality-with-verity-a996b49343f6?source=post_page-----415baad6c95b--------------------------------&embedable=true Fundamentally, it’s a different point in the data quality lifecycle. Introducing a mechanism to improve quality necessitates the ability to measure it initially. So, while ’s focus lies on and data quality, relying on the producer’s interest to enhance this quality and “look good,” Lyft takes a different approach. Lyft places emphasis on actively and data quality, providing both producers and consumers with the means to effectively improve and control the quality. Airbnb measuring observing testing validating Collectively, these approaches provide a comprehensive strategy to address and enhance throughout its . data quality lifecycle For this reason, I was particularly interested in taking a closer look at Lyft’s approach. Another factor that intrigued me is testing, more specifically, contract testing, which has been used for many years now in basic software engineering with the emergence of microservice architecture. Data contracts, however, are something more recent in the domain of data engineering and are seen as the pinnacle or one of the ultimate steps to put in place on the path to building high-quality data pipelines. This is why I wanted to examine Lyft’s approach in more detail and explore some potential parallels. As mentioned, the approaches taken by Airbnb and Lyft are complementary and aim to achieve the same goal: improving data quality. has developed the DQ score, which focuses on measuring and enhancing 4 distinct : Airbnb aspects of data quality DQ Score has guiding principles, including full coverage, automation, actionability, multi-dimensionality, and evolvability. It has dimensions like Accuracy, Reliability, Stewardship, and Usability. Lyft’s Verity is a platform designed to enhance data quality across 5 dimensions Defines data quality as the measure of how well data can be used as intended, covering aspects like . semantic correctness, consistency, completeness, uniqueness, well-formedness, and timeliness It’s easy to draw parallels between the 5 data quality aspects improved by Lyft’s Verity and the 4 data quality dimensions measured by the Airbnb’s DQ score. For example, aspects like Timeliness would certainly contribute to the DQ score’s Reliability, while Accuracy would be dependent on semantic correctness, completeness, and uniqueness of the data. The Usability score, on the other hand, is influenced by the consistency of the data among other factors. Lyft’s Verity focuses on defining related to . It follows a test-first and validation approach, whereas Airbnb’s unified DQ score emphasizes evaluating data quality through various dimensions. checks semantic correctness, consistency, completeness, uniqueness, well-formedness, and timeliness If we wanted to incorporate the DQ score into this last schema, it would be on the side of the Alert/Debug steps. Airbnb’s DQ score uses different signals to assess data quality across the . Accuracy, Reliability, Stewardship, and Usability aspects We also had a set of input signals that more directly measure quality (Midas certification, data validation, bugs, SLAs, automated DQ checks, etc.), whereas others were more like proxies for quality (e.g., valid ownership, good governance hygiene, the use of paved path tooling). As discussed earlier, there are likely overlaps between Airbnb’s DQ score and Verity. While Airbnb focuses on pushing data quality to the right, emphasizing measurement and scoring, Lyft’s Verity takes a proactive approach by shifting the check definition configurations, testing, and validation processes to the left, emphasizing proactive improvement of data quality. Now, my primary interest lies to the left, in the check definition configurations, testing, and validation aspects. How Lyft integrates data quality testing into its processes? Let’s first examine the execution paths. Currently, Lyft’s Verity is primarily focused on ensuring the quality of data stored in its Hive data warehouse. However, there are plans to expand its capabilities to support other data sources in the future. Note that while they refer to Hive as a data workhouse, they actually utilize it as a hybrid data storage solution, operating as a data warehouse for structured, processed, and cleansed data (silver layer) while also serving as a data lake for raw event data (bronze layer). Poor data quality in Hive caused tainted experimentation metrics, inaccurate machine learning features, and flawed executive dashboards. Verity’s checks can be integrated into an Airflow DAG to ensure that only high-quality raw data is processed and stored in Hive as derived data. Data producers and consumers can define their data quality checks and verify the data when it is produced or before it is consumed inside or . Airflow Flyte … The VerityAirflowOperator can be used in a blocking fashion to halt a DAG upon a check failure, preventing bad data from ever reaching production. This utilizes the “ ” pattern: we create data in a staged schema, verify the data with a blocking operator, then promote it to production if it passes quality checks. Stage-Check-Exchange Checks can also be performed manually or scheduled automatically to verify both raw and derived data. Verity Scheduled Checks are isolated from any data orchestration engine, so they still run even if Airflow or Flyte are completely down. This remedies a common problem of checks not alerting because the Airflow Task never ran. So, there are essentially 3 primary ways to trigger a check: as part of an Airflow DAG, manually, or scheduled through the Verity platform/UI. I don’t believe that current checks can be integrated into real-time streaming pipelines (such as Flink + Kafka) to validate for example data as it enters Hive (or even earlier). Implementing this type of real-time check would enable prompt detection of discrepancies, leading to reduced storage and processing costs and overall data quality enhancement. Well, to be thorough, Verity checks are managed through an API server, which could be used to trigger checks programmatically through some APIs. Verity API Server — This service handles all the external APIs regarding running checks as well as persisting and retrieving their results. The API Server does not execute any checks, but rather writes a message to our Check Queue, which utilizes (SQS). SimpleQueueService So, maybe, you could potentially trigger these jobs in a more real-time fashion, such as from a streaming job or even, as a long stretch, by integrating with Hive change data capture (CDC). However, when executed outside of Airflow, these jobs would not be able to block the data processing job; instead, they would generate asynchronous alerts pushed to the Check Queue. Some consumers would prefer to have data processing delayed when a check fails, while others would rather proceed and receive an alert. Now, let’s look at these data quality tests. Here’s an example that checks whether is never null. This is accomplished through a combination of query and condition components. rider_events.session_id core rider events session_id is not null: # check name
  metadata:
    id: 90bde4fa-148b-4f06-bd5f-f15b3d2ad759
    ownership_slack: #dispatch-service-dev
    tags: [rides, core-data, high-priority]
  query:
    type: dsl
    data_source_id: hive.core.rider_events
    filters:
      - session_id = null
  condition:
    type: fixed_threshold
    max: 0
  notifier_group:
    pagerduty_policy: dispatch-service
    email: dispatch-service@lyft.pagerduty.com Verity is primarily focused on defining and enforcing data quality checks rather than defining complete data schemas. Schema validation is not a novel concept. Several methods exist for defining event data schemas in event-based systems, such as JSON Schema, Protocol Buffers, Avro, or storage formats like Parquet. The optimal choice depends on your tech stack, usage, and specific requirements. While data schemas are valuable for defining the overall structure of data objects or table rows, they fall short in capturing more sophisticated validation checks specific to consumers, such as data distribution, business rules, SLAs, and thresholds. Data contracts go beyond schema validation, which focuses on identifying . I personally find that JSON Schema offers here a more suitable and readable option, effectively separating these structural and syntactical validation capabilities from serialization or storage concerns. syntactical errors However, to address and enhance data accuracy, having an effective means of defining data checks allows for defining the other facet of data contracts. semantic errors This is where Verity DSL comes into play. Beyond semantical validation, data contracts offer another crucial aspect that deserves attention. From a syntactical standpoint, validation checks remain consistent regardless of the consumer or producer involved. The set of validation rules is not tied to any specific consumer or producer and can be defined once and for all as a single schema. However, the Verity data contract DSL offers a finer granularity defining small independent rules, which is particularly well-suited for this context: the semantic meaning and usage of the data vary depending on the specific consumer. Additionally, not all consumers need to utilize all of the properties of an object. Their expectations differ. This doesn’t imply they are contradictory (which would certainly be an issue), but rather complementary and distinct ones. Therefore, allowing all consumers to establish unique rules that, when combined collaboratively, can provide a comprehensive understanding of the semantic significance of data quality is quite crucial. And it’s this aspect that particularly resonates with me. Bear with me, this may seem like a stretch, but from my perspective, it’s worth mentioning. :) collaborative Data exchange enables different teams (producers and consumers) to collaborate effectively. Establishing a shared understanding of these data exchanges is paramount, just like APIs in traditional software development. In microservice architectures, a known as consumer-driven contracts (CDC) has emerged, where consumers define the expected behavior of APIs provided by producers. Producers are responsible for verifying these contracts before releasing new versions. collaborative testing approach I think data contracts share a similar collaborative spirit. Even though data validation is performed on real data, rather than at release time, and does not block releases, it’s based on cooperation and encourages teamwork between data producers and consumers. I strongly believe that this collaborative approach is key to improving data quality and should be further integrated into the process. Well, I’m a big fan of drawing parallels… Notice actually that this collaborative aspect is something mentioned also as part of Lyft’s verity charter. The VerityUI provides a streamlined data discovery experience via the Verity Homepage. Our full-text search on the Check Definition Metadata lets users see all the checks currently being enforced and their Check Results. This has useful aggregations like owning team, table name, and tags. I’m not entirely clear on how data contract issues are shared between consumers and producers through the Verity platform UI, but I definitely recognize the significance of collaboration through the dashboards: The producer of a data product interface can confidently ensure that they’re not inadvertently causing downstream breakages that they hadn’t anticipated. The consumer of the interface can rest assured that their reliance on the interface is not and will not be compromised. While Verity is a remarkable tool for defining data quality checks, it’s unfortunately not open-source. Fortunately, there’s another open-source data quality framework called Soda Core that provides similar functionality. Soda Core is a free and open-source command-line tool and Python library that enables data engineers to test data quality. It utilizes user-defined input to generate SQL queries that run checks on datasets in a data source to find invalid, missing or unexpected data. When checks fail, they surface the data that you defined as “bad” in the check. https://medium.com/@seckindinc/soda-core-the-simplest-open-source-data-reliability-tool-f35ae5b576d9?source=post_page-----415baad6c95b--------------------------------&embedable=true During a scan of a dataset, Soda Core evaluates the predefined checks to identify invalid, missing, or unexpected data. Here’s the equivalent check using Soda.core syntax for the Verity DSL test that was previously defined. name: rider_events_session_id_check
source: hive
query: SELECT * FROM rider_events WHERE session_id IS NULL;
raise_alert: true
threshold: 10
action: slack
message: "There are more than 10 rows that are null for the 'session_id' property in the 'rider_events' table. Please investigate this issue." soda run --check checks/rider_events_session_id_check.yaml Soda Core is a powerful tool for ensuring the quality of your data. It can help you to identify and fix data problems early, before they can cause issues for your business. It’s worth noting that Soda Core can also facilitate data quality checks for streaming data by seamlessly integrating with Spark DataFrames. While Verity’s data quality checks for Hive are applied to static datasets, the checks for streaming data need to be more lightweight and efficient. Data would typically be processed in small batches of events, with very low latency, making them suitable for real-time checks and specific use cases like anomaly detection. Finally, let’s mention that there are other data validation tools available, such as DeeQu, Great Expectations, … As we wrap up, I hope you have a clearer understanding of the steps you can take to enhance your data quality journey. We’ve seen two distinct approaches to data quality improvement, each with its own strengths and methodologies. One focuses on increasing visibility and observability, motivating data producers to raise the quality bar. The other prioritizes elevating the quality standard through a testing and validation-first approach. Both are complementary. Verity is not merely a domain-specific language (DSL) for defining data checks; it’s a centralized platform that empowers data practitioners to collaborate effectively. This platform helps producers and consumers align on data quality expectations, including format, structure, and accuracy. Verity’s data contract management capabilities could (is?) further enhanced by integrating with a broader set of features, such as metadata management and discovery, to address more sophisticated data quality needs. Similar to Airbnb’s DQ score, Lyft’s Verity fosters a collaborative feedback loop between data producers and consumers. By incentivizing and empowering each team to take ownership of data quality, Verity cultivates a supportive environment where data quality continuously improves. , Found this article useful? Follow me on Linkedin , and ! Please 👏 this article to share it! Hackernoon Medium Also published . here

Advancing Data Quality: Exploring Data Contracts with Lyft

ডেটা গুণমান উন্নত করা: Lyft-এর সাথে ডেটা চুক্তিগুলি অন্বেষণ করা

Verbesserung der Datenqualität: Erkundung von Datenverträgen mit Lyft

Mejora de la calidad de los datos: exploración de los contratos de datos con Lyft

Améliorer la qualité des données : explorer les contrats de données avec Lyft

डेटा गुणवत्ता को आगे बढ़ाना: Lyft के साथ डेटा अनुबंध की खोज करना

データ品質の向上: Lyft とのデータ契約の検討

데이터 품질 향상: Lyft를 통한 데이터 계약 탐색

Avançando na qualidade dos dados: explorando contratos de dados com Lyft

Повышение качества данных: изучение контрактов на передачу данных с Lyft

Veri Kalitesini Geliştirme: Lyft ile Veri Sözleşmelerini Keşfetmek

Nâng cao chất lượng dữ liệu: Khám phá hợp đồng dữ liệu với Lyft

提高数据质量：与 Lyft 探索数据合同

Crypto Hub is the First Stake-to-Own (S2O) Launchpad in the market, which is powered by the community and tailored to empower Projects. We provide the resources, tools, and guidance you need to go to market faster. Web3 Crypto Hub has crafted a variety of Web3 solutions that enable the rapid creation of cryptocurrency projects in under an hour. Here is our current set of solutions: 🪙 Token Maker Simplify the process of creating tokens on blockchain platforms using our Token Factory. Customize essential features like token names and supplies, empowering users to design tokens tailored to their specific needs and use cases. 🚀 Token Sales Serve as a catalyst for emerging projects by providing a platform for early investors to access tokens before their public release. Beyond funding, our launchpad cultivates a supportive community that contributes to the project’s success and growth. 🔒 Token Vesting In the dynamic realm of DeFi, vaults act as secure management hubs. Users can effortlessly navigate through dashboards, creating allocations and adhering to vesting schedules. This ensures a safe and strategic approach to handling digital assets within the decentralized ecosystem. 🎁 Token Airdrops Facilitate widespread token distribution with ease, fostering community engagement and awareness. The emphasis on low gas fees ensures a cost-effective method for project promotion and user involvement, making airdrops an efficient and accessible strategy. 💥 Multisender Optimize the token distribution process by consolidating transactions. Multisenders enhance efficiency, making them a valuable tool for managing tasks like reward distribution and executing small-scale airdrop campaigns. Why are we the Ultimate Web3 Solution? In a crowded landscape of options, we are the definitive choice for your crypto and blockchain needs. Our platform features a simple and pleasant UI/UX design, ensuring a user-friendly experience. 🌟 Designful: We offer adaptable and embeddable solutions tailored to your unique requirements, which transform basic WordPress websites into powerful Dapps. 🌟 Adaptable: We’re dedicated to providing the most cost-effective option in the market, making blockchain technology accessible to all. 🌟 Affordable: Thanks to our community you will be able to expand your audience for free and receive support from our dedicated Growth Hackers. After you raise the necessary funds, we can help you build custom developments with our in-house developer team. Token Details & Tokenomics $HUB is an Ethereum ERC20 Token that has a 2% fee on each buy and sell, which fuels our ecosystem wallet. In order to keep the token economics fair and balanced, we decided to burn 77% of its total supply since we haven’t sold the desired allocation in our token sales. This makes $HUB Token an extremely low market cap gem with tons of room to grow as we keep building the ecosystem, currently with less than $80,000 valuation and an Initial Market Cap under $10,000. Additionally, our smart contract underwent a meticulous audit by , a renowned name in the crypto security space, and the founders of Crypto Hub have gone through a rigorous KYC (Know Your Customer) process conducted by . Coinsult Assure Defi Since 77% of the total supply had been burned, $HUB now has 5 different allocations which respect the following vesting schedule: $HUB Ranks & Benefits In the Crypto Hub Ecosystem, the $HUB Tokens play a pivotal role in empowering our community through the Crypto Hub Ranks system. These ranks are determined by the amount of $HUB Tokens , unlocking a range of benefits tailored to enhance the experience for our valued members. staked in our smart contracts Crypto Hub Ranks serve as a recognition of your commitment to our ecosystem, with each rank offering distinct advantages based on the quantity of $HUB Tokens staked. By staking 1,000 HUB, you earn a 5% Annual Percentage Yield (APY), enjoy a 10% commission on transactions, receive a 5% discount on our in-house marketing services, and gain ownership privileges. Starter Rank: By staking 15,000 HUB, you earn a 15% Annual Percentage Yield (APY), enjoy a 20% commission on transactions, receive a 15% discount on our in-house marketing services, and gain ownership privileges. Hero Rank: Stepping up to 50,000 HUB not only retains Hero Rank benefits but also boosts your APY to 25%, increases transaction commission to 30%, and unlocks a 30% discount on our in-house marketing services. Additionally, you gain both ownership privileges and exclusive Diamond Access. Veteran Rank: At the pinnacle, staking 100,000 HUB elevates your APY to an impressive 40%, raises transaction commission and in-house marketing services discounts to 40%, and secures ownership privileges. Furthermore, Legend Rank offers exclusive Diamond Access and grants you the distinguished status of an Exclusive Member. Legend Rank: As you progress through the ranks, you unlock a spectrum of benefits ranging from financial incentives to exclusive access. The more $HUB Tokens you stake, the richer your experience becomes within the Crypto Hub Ecosystem.. Join us on this exciting journey as we revolutionize the Web 3.0 ecosystem and empower the crypto community. Together, let’s shape the future of decentralized finance. | | | | Website Twitter Telegram Discord Whitepaper

Crypto Hub Launches The Ultimate Web3 Solution

ক্রিপ্টো হাব আলটিমেট ওয়েব3 সমাধান চালু করেছে

Crypto Hub bringt die ultimative Web3-Lösung auf den Markt

Crypto Hub lanza la solución Web3 definitiva

Crypto Hub lance la solution Web3 ultime

क्रिप्टो हब ने अल्टीमेट वेब3 समाधान लॉन्च किया

Crypto Hub が究極の Web3 ソリューションを発表

Crypto Hub, 최고의 Web3 솔루션 출시

Crypto Hub lança a solução Web3 definitiva

Crypto Hub запускает идеальное решение Web3

Crypto Hub En İyi Web3 Çözümünü Başlatıyor

Crypto Hub ra mắt giải pháp Web3 tối ưu

Crypto Hub 推出终极 Web3 解决方案

The ascent of Web3 as the next frontier in internet development has become a consensus, and the question now arises: how will these traditional Web2 giants play with NFTs, the cornerstone of Web3? Web3 has seen an influx of new players, with many brands that once battled in the Web2 arena now strategically positioning themselves in Web3. These seasoned Web2 brands, with their extensive experience, bring forth innovative marketing strategies upon entering Web3. The Increasing Trend of Web2 Brands Entering Web3 Can Be Attributed to Several Factors: Rising Consumer Expectations for Personalization Modern consumers are no longer satisfied with traditional goods and services; they seek personalized and unique experiences. NFTs, as representatives of digital assets, offer brands an excellent tool to achieve this level of personalization. Building Genuine, Multidimensional Business Experiences through Ownership Web3, built on blockchain technology, provides users with true ownership. This decentralized characteristic fosters diverse and highly interactive business experiences, deeply engaging users in a multidimensional manner. Acknowledging Web3 as the upcoming trend in internet development, Web2 brands understand the necessity of seizing this opportunity. Web3 presents a unique chance for these brands to evolve and stay relevant in the ever-changing digital landscape. Entering Web3 often starts with taking the first step, and for many, the preferred choice is through NFTs. Web3 Gateway: NFT NFT, as a crucial component of Web3 development, is considered the primary channel and vehicle for the future of the Web3 world. Several renowned brands have chosen NFT as the gateway to enter Web3, demonstrating its significance in this domain: Disney: Disney Pinnacle In November 2023, Disney announced a collaboration with NFT IP development company Dapper Labs to launch the NFT platform "Disney Pinnacle." Iconic characters from Star Wars and Pixar studios will be presented in the form of NFTs. Starbucks: Odyssey Program Starbucks launched the "Odyssey" initiative, utilizing NFTs to reshape its membership system and strengthen brand influence. Trip: Trekki NFT Project Backed by Trip, the NFT project Trekki signifies not only owning a digital travel companion but also enjoying Trip coins, premium memberships, travel AI assistants, and other benefits. From the current entry of Web2 brands into the news, it is evident that companies in the fast-moving consumer goods, content culture, and service sectors are actively embracing Web3 with a rapid response. Brands with native philosophies and audience alignment with Web3 culture, particularly those using NFT as an entry point, are more readily accepted. Ownership & Membership System Establishment Granting users true ownership of digital assets, brands leverage NFTs to authenticate and safeguard their digital content or offer exclusive services to users. The tangible form of NFTs makes it easy for brands to construct user identity and loyalty systems. Uniqueness and Scarcity The uniqueness and limited quantity of NFTs make users more receptive to and appreciative of these digital assets. This underscores and signifies the identity and status of each NFT holder, elevating user trust and a sense of belonging to the brand. Weak Financial Attributes of NFTs Compared to other cryptographic assets, NFTs primarily function as digital collectibles with relatively weak financial attributes. This characteristic avoids compliance issues, making NFTs more widely accepted by a diverse user base. Brand Innovation and Trend Embrace Introducing NFTs is not merely a business strategy but also a way for brands to demonstrate innovation and embrace new technologies and trends, showcasing their commitment to staying at the forefront of the industry. NFT Marketing Strategies Many industry giants have embraced Web3, starting with NFTs. How do they execute NFT marketing strategies? NFT & Philanthropy Integrating NFTs with philanthropy not only showcases a brand's social responsibility but also establishes a positive image, resonating with core values tied to charitable causes. Associating NFT projects with specific social issues helps attract target audiences, and philanthropic actions often gain widespread media attention. Importantly, philanthropic NFT projects tend to navigate regulatory scrutiny more compliantly, providing brands with a safer marketing space. Luxury skincare brand La Prairie collaborates with digital artist Carla Chan for its inaugural NFT art series, "Space Beyond." The NFTs, with 365+1 versions generated in real-time based on weather and population data, will be exhibited at FRIEZE in New York on May 18, with proceeds dedicated to glacier conservation worldwide. NFT & Metaverse The application of NFTs in the metaverse is a common strategy in NFT marketing. The metaverse offers a virtual world that provides brands with a broader display space, making NFT presentations more vivid. Social interactivity creates additional avenues for NFT sharing and promotion among users. Furthermore, the metaverse opens up innovative possibilities for brands, allowing them to expand NFT applications through virtual events, exhibitions, and other formats, bringing more creativity and imaginative space to digital marketing. Louis Vuitton introduces the commemorative game "Louis: The Game," featuring 30 NFT rewards, including 10 designs by digital artist Beeple. Cooperating with Native Web3 Entities From the above examples of NFT marketing, it can be observed that many major players prefer to collaborate with native Web3 entities when designing NFTs. These entities can be individual digital artists or projects. Trekki NFT, in order to expand its audience, launched a joint campaign with a Web3 growth platform. The benefits of collaborating with such platforms include: The channels are highly vertical, providing access to the Web3 market. Specialized Channels: Growth platforms have a substantial user base with a high level of awareness, reducing the need for extensive user education. Large User Base: Various gameplay options cover different stages of the user lifecycle, from acquiring users during the initial phase of the NFT project to user conversion and retention, accelerating the project's operational pace. Diverse Gameplay Across User Lifecycle: Trekki NFT, a project supported by a leading Web2 brand, has achieved tremendous success in the Web3 space, with a total issuance of 10,000 NFTs and a community subscription rate reaching 100%. Collaborating with Web3 projects in marketing strategies facilitates convenient user participation in the NFT market, fostering broader dissemination of NFTs. Brands can leverage the technological innovations of Web3 projects to enhance NFTs with unique features and gameplay, ultimately enriching the user experience. Challenges and Prospects While major players entering Web3 through NFTs have achieved initial success, deeper marketing efforts have exposed some issues: Limited Sustainability The NFT market is still in its early stages, lacking mature standards and regulations. Brands issuing NFTs often fail to implement long-term maintenance and empowerment plans, leading to the difficulty of sustaining the value and significance of NFTs. User interest and demand for NFTs may fluctuate over time, posing challenges to their circulation and usage. Reputation Challenges Brands emphasize the identity and cultural aspects of NFTs over their financial attributes, causing users to perceive NFTs as symbolic and virtual rather than practical. This perception diminishes the social value and influence of NFTs. While acknowledging the challenges in NFT marketing, it's undeniable that the strategies employed by Web2 brands entering Web3 have shown initial effectiveness. The integration of traditional brand marketing from the internet era with Web3 technology is gradually taking shape. Looking ahead, as more projects and users enter the space, the application scope of NFTs will expand, and initial marketing challenges will be addressed. In this evolving marketing battleground that combines Web3, brands, and projects deeply immersed in the field are actively exploring alternative marketing approaches to enhance market share. For projects outside this landscape, leveraging the marketing methods discussed earlier could be a strategic move, seizing opportunities before the marketing battlefield expands and securing a position. Also published here

Web3 Battle Heats Up: How Web2 Giants Will Change Their Marketing Strategies in This New Game

In the realm of high fashion, Tom Ford stands as an icon, renowned for his exceptional talent and innovative contributions to the industry. Beyond the glamour of the runway, however, lies a complex narrative that intertwines with the broader discussion of mental health. This article delves into the life and career of Tom Ford, exploring the profound impact of his work on the fashion world and how his journey reflects aspects of mental health. I. Tom Ford: A Visionary in Fashion Tom Ford's name is synonymous with luxury, sophistication, and a distinctive design aesthetic. Born on August 27, 1961, in Austin, Texas, Ford's journey into the fashion world began with his studies at the Parsons School of Design in New York. His career trajectory saw significant milestones, including positions at renowned fashion houses such as Gucci and Yves Saint Laurent. Ford's groundbreaking work at Gucci, where he served as creative director from 1994 to 2004, revitalized the brand and positioned him as a trailblazer in the fashion industry. Ford's approach to fashion goes beyond creating clothing; it's about crafting a lifestyle. His keen eye for detail, emphasis on sensuality, and commitment to pushing boundaries have made him a transformative force in the industry. The intersection of creativity and mental well-being becomes apparent when examining the intense pressures and relentless pursuit of perfection that often characterize the world of high fashion. II. The Demands of the Fashion Industry and Mental Health The fashion industry, with its fast-paced nature and ever-evolving trends, is notorious for its demanding and competitive environment. Designers like Tom Ford, operating at the pinnacle of this world, face immense pressure to continuously innovate and deliver collections that captivate audiences. The creative process itself can be mentally taxing, requiring a delicate balance between artistic expression and commercial viability. The toll of such pressures on mental health is a pervasive issue in the fashion world. Designers often grapple with anxiety, burnout, and the constant quest for validation. Tom Ford, in his various interviews, has hinted at the challenges he faced during his tenure at Gucci, where the relentless pursuit of success sometimes overshadowed personal well-being. This reflection opens a window into the nuanced relationship between creative brilliance and the toll it can take on an individual's mental health. III. Tom Ford's Advocacy for Mental Health Awareness In recent years, there has been a growing awareness and destigmatization of mental health issues across various industries, including fashion. Tom Ford, with his characteristic candor, has been an advocate for mental health awareness. In interviews, he has discussed the importance of acknowledging the challenges faced by those in the creative fields and the need to prioritize mental well-being. Ford's openness about his own experiences navigating the pressures of the fashion industry contributes to a larger conversation about the intersection of creativity, success, and mental health. By sharing his story, Ford encourages a more compassionate and understanding approach to the struggles faced by individuals in the pursuit of their passions. IV. Creativity as a Double-Edged Sword While creativity is celebrated in the fashion world, it also poses a unique set of challenges for those who wield it. The intense creative process, coupled with the expectation of constant innovation, can lead to periods of self-doubt and vulnerability. Tom Ford's career trajectory, marked by both triumphs and moments of introspection, exemplifies the dual nature of creativity as a powerful force and a potential source of internal conflict. Exploring the intricacies of this duality, it becomes evident that fostering a supportive and empathetic environment within the fashion industry is crucial. Recognizing the humanity behind the creative genius is not only beneficial for individual mental health but also contributes to the overall sustainability of the industry. V. Conclusion: Navigating the Intersection of Creativity and Mental Health In conclusion, Tom Ford's journey through the world of high fashion serves as a compelling narrative that extends beyond the glamour of the runway. His impact on the industry is not only measured by the success of his designs but also by the candid discussions he fosters regarding mental health. As we celebrate the creative brilliance of individuals like Tom Ford, it is essential to acknowledge the complex relationship between creativity and mental well-being. By unraveling the layers of this intersection, we pave the way for a more compassionate and understanding approach to the challenges faced by those who shape the world of fashion and, by extension, the broader creative landscape. Also published . here

Tom Ford's Influence on Fashion, Creativity and Mental Health

In the heart of the mesmerizing desert jewel, Abu Dhabi, the soaring popularity of Formula 1 takes center stage, captivating the world with its high-speed drama, precision, and glamour. As the global craze for Formula 1 intensifies, Abu Dhabi emerges as the perfect host for the grand finale, with the Yas Marina Circuit aglow under the Arabian night, symbolizing luxury and speed. F1 a Global Craze & Abu Dhabi's Oasis of Luxury and Speed Formula 1, once a mere sport, has evolved into a global phenomenon, transcending borders and languages. Abu Dhabi's futuristic skyline provides a stunning backdrop to the Yas Marina Circuit, creating an unforgettable setting for the climax of the racing season. The city's embrace of the sport transforms it into a playground for enthusiasts and luxury seekers alike. The Yas Marina Circuit: Twilight Canvas for Racing Drama The Yas Viceroy, standing tall with its iconic LED-lit architecture, exemplifies the city's commitment to hosting the pinnacle of motorsport. The Grand Prix weekend becomes a celebration of speed, precision, and cultural exchange, with the buzzing energy of the fan village, glamorous after-parties, and the unmistakable scent of burnt rubber contributing to the carnival atmosphere. The Yas Marina Circuit, with its unique twilight race setting, becomes the canvas for unforgettable moments, telling stories of determination and skill. Formula 1 has evolved into more than a race; it's a global celebration of technology, human endurance, and the pursuit of victory. Binance's Crypto-Inspired Triumph: Gasly's Helmet Design Unveils As the engines roar and the crowd erupts in cheers, it's evident that Formula 1 has become an integral part of Abu Dhabi's identity. The city's skyline mirrors the stars of the racing world descending upon the Yas Marina Circuit. The Grand Prix weekend transforms into a pilgrimage for racing aficionados, leaving behind a legacy of speed, spectacle, and an undying craze echoing across the desert dunes. Pierre Gasly Experience: Binance's Exclusive Fan Engagement In a spectacular blend of cryptocurrency innovation and Formula 1 excitement, the global blockchain giant, unveils the winning design for BWT Alpine F1 Team driver Pierre Gasly's helmet at the upcoming Abu Dhabi Grand Prix on November 26, 2023. This thrilling reveal emerges from a global competition open to users and F1 enthusiasts worldwide. Binance, Binance From August 23 to September 8, 2023, participants were encouraged to unleash their creativity and submit helmet designs, with the judging panel, including Pierre Gasly and his team, seeking designs that embodied Binance's values and resonated with its innovative ethos. The global response was phenomenal, with over 700 submissions from more than 140 countries. The winning design, inspired by Binance's brand and Gasly's legacy, features a vibrant pink color to shine under the Abu Dhabi night lights. Paying homage to Arabic culture with a dedicated script, the design adds an extra layer of cultural significance. The lucky winner receives a replica of the helmet personally autographed by Pierre Gasly. As part of the partnership between Binance and Gasly, aimed at fostering creativity and fan engagement, Binance provides users and Gasly fans with the exclusive Pierre Gasly Experience. This includes race weekend hospitality, a personal meet-and-greet with Gasly, and signed mini-helmets, turning fans into an integral part of the F1 action. Rachel Conlan, Chief Marketing Officer at Binance, expresses delight at the global community's enthusiastic participation, highlighting the power of offering fans distinctive engagement opportunities. The partnership exemplifies a joint aim to connect the Web3 and sports realms, promising more innovative and engaging experiences for fans worldwide. As the world eagerly anticipates the Abu Dhabi Grand Prix, Binance's commitment to bringing fans closer to the action is a testament to the evolving landscape where cryptocurrency, sports, and creativity converge. For young enthusiasts navigating the realms of F1 and digital currencies, this is not just a helmet design; it's an invitation to be part of the thrilling intersection of innovation and adrenaline-fueled competition. Binance's Crypto-Creative Triumph Illuminates Abu Dhabi's Racing Legacy The crescendo of the Abu Dhabi Grand Prix, set against the dazzling backdrop of the Yas Marina Circuit, is not just a culmination of speed, spectacle, and luxury; it's a canvas where Formula 1's global allure meets the innovative spirit of cryptocurrency. Binance's groundbreaking partnership with F1, epitomized by the unveiling of Pierre Gasly's uniquely designed helmet, adds a new dimension to the racing experience. As the roaring engines fade into the night, Binance's commitment to fostering creativity and fan engagement stands as a shining example of the evolving synergy between the worlds of sports, technology, and passionate enthusiasts. The Abu Dhabi Grand Prix, now enriched by the crypto-inspired triumph, leaves a lasting imprint, echoing the spirit of innovation and inclusivity across the vast expanse of the desert and beyond. Don’t forget to like and share the story! : Vested Interest Disclosure This author is an independent contributor publishing via our . Be it through direct compensation, media partnerships, or networking, the author has a vested interest in the company/ies mentioned in this story. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR brand-as-author program

Binance to Unveil Winning Design for BWT Alpine F1 Team Driver Pierre Gasly

132 Stories To Learn About Information Security

Stories, News & Mentions about PINNAKL

Pinnakl’s Stories on HackerNoon