Qonto

qonto

Authors:
(1) Aviad Rom, The Data Science Institute, Reichman University, Herzliya, Israel;
(2) Kfir Bar, The Data Science Institute, Reichman University, Herzliya, Israel. Table of Links Abstract and Introduction Related Work Methodology Experimental Settings Results Conclusion and Limitations Bibliographical References 5. Results The results are summarized in Table 1. The table rows are organized into three distinct groups. The first group features combinations of various baseline models. The second group showcases combinations incorporating our proprietary HeArBERT model, while the third group highlights combinations involving the contrasting HeArBERTNT model. We train multiple machine-translation combinations, in both directions Arabic-to-Hebrew and Hebrewto-Arabic, based on the same encoder-decoder architecture, initialized with different language model combinations. We assign various combinations of language models to the encoder and decoder components, ensuring that the selected models align with the respective source and target languages. In other words, we make sure that the language model which we use to initialize the encoder, is compatible with the system’s source language. Since mBERT and CAMeLBERTET , as well as our two models HeArBERT and HeArBERTNT , can potentially handle both languages, we experiment with combinations where each of them is assigned to both, the encoder and decoder components at the same time. The results demonstrate that the second group, which utilizes our HeArBERT to initialize a Hebrew decoder, surpasses the performance of all other combinations. Notably, the pairing of GigaBERT with HeArBERT is the standout performer across both directions. It surpasses the performance of using HeArBERT for initializing the Arabic encoder by only a few minor points. The performance of HeArBERTNT , as reported in the third group is consistently lower than HeArBERT in both directions. The difference seems to be more significant in the Arabic-to-Hebrew direction. Overall, the results show that transliterating the Arabic training texts into the Hebrew script as a pre-processing step is beneficial for an Arabic-to-Hebrew machine translation system. The impact of the transliteration proves to be less pronounced in the reverse translation direction. Using the extended (ET) version of CAMeLBERT has a reasonable performance. However, it performs much worse than the best model in both directions, indicating that extending the vocabulary with transliterated Arabic tokens does contribute to better capturing the meaning of Hebrew tokens in context. This implies that joint pre-training on both languages is essential for achieving a more robust language representation. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Aviad Rom, The Data Science Institute, Reichman University, Herzliya, Israel; (2) Kfir Bar, The Data Science Institute, Reichman University, Herzliya, Israel. Authors: Authors: (1) Aviad Rom, The Data Science Institute, Reichman University, Herzliya, Israel; (2) Kfir Bar, The Data Science Institute, Reichman University, Herzliya, Israel. Table of Links Abstract and Introduction Abstract and Introduction Related Work Related Work Methodology Methodology Experimental Settings Experimental Settings Results Results Conclusion and Limitations Conclusion and Limitations Bibliographical References Bibliographical References 5. Results The results are summarized in Table 1. The table rows are organized into three distinct groups. The first group features combinations of various baseline models. The second group showcases combinations incorporating our proprietary HeArBERT model, while the third group highlights combinations involving the contrasting HeArBERTNT model. We train multiple machine-translation combinations, in both directions Arabic-to-Hebrew and Hebrewto-Arabic, based on the same encoder-decoder architecture, initialized with different language model combinations. We assign various combinations of language models to the encoder and decoder components, ensuring that the selected models align with the respective source and target languages. In other words, we make sure that the language model which we use to initialize the encoder, is compatible with the system’s source language. Since mBERT and CAMeLBERTET , as well as our two models HeArBERT and HeArBERTNT , can potentially handle both languages, we experiment with combinations where each of them is assigned to both, the encoder and decoder components at the same time. The results demonstrate that the second group, which utilizes our HeArBERT to initialize a Hebrew decoder, surpasses the performance of all other combinations. Notably, the pairing of GigaBERT with HeArBERT is the standout performer across both directions. It surpasses the performance of using HeArBERT for initializing the Arabic encoder by only a few minor points. The performance of HeArBERTNT , as reported in the third group is consistently lower than HeArBERT in both directions. The difference seems to be more significant in the Arabic-to-Hebrew direction. Overall, the results show that transliterating the Arabic training texts into the Hebrew script as a pre-processing step is beneficial for an Arabic-to-Hebrew machine translation system. The impact of the transliteration proves to be less pronounced in the reverse translation direction. Using the extended (ET) version of CAMeLBERT has a reasonable performance. However, it performs much worse than the best model in both directions, indicating that extending the vocabulary with transliterated Arabic tokens does contribute to better capturing the meaning of Hebrew tokens in context. This implies that joint pre-training on both languages is essential for achieving a more robust language representation. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Training a Bilingual Language Model by Mapping Tokens onto a Shared Character Space: Results

Authors:
(1) Aviad Rom, The Data Science Institute, Reichman University, Herzliya, Israel;
(2) Kfir Bar, The Data Science Institute, Reichman University, Herzliya, Israel. Table of Links Abstract and Introduction Related Work Methodology Experimental Settings Results Conclusion and Limitations Bibliographical References Abstract We train a bilingual Arabic-Hebrew language model using a transliterated version of Arabic texts in Hebrew, to ensure both languages are represented in the same script. Given the morphological, structural similarities, and the extensive number of cognates shared among Arabic and Hebrew, we assess the performance of a language model that employs a unified script for both languages, on machine translation which requires cross-lingual knowledge. The results are promising: our model outperforms a contrasting model which keeps the Arabic texts in the Arabic script, demonstrating the efficacy of the transliteration step. Despite being trained on a dataset approximately 60% smaller than that of other existing language models, our model appears to deliver comparable performance in machine translation across both translation directions. Keywords: bilingual language model, transliteration, Arabic, Hebrew 1. Introduction Pre-trained language models have become essential for state-of-the-art performance in mono and multilingual natural language processing (NLP) tasks. They are pre-trained once and can then be fine-tuned for various downstream NLP tasks. It has been shown that language models generalize better on multilingual tasks when the target languages share structural similarity, possibly due to script similarity (K et al., 2020). Typically, language models are trained on sequences of tokens that often correspond to words and subword components. Arabic and Hebrew are two Semitic languages that share similar morphological structures in the composition of their words, but use distinct scripts for their written forms. The Hebrew script primarily serves Hebrew, but is also employed in various other languages used by the Jewish population. These languages include Yiddish (or “JudeoGerman”), Ladino (or “Judeo-Spanish”), and JudeoArabic, which comprises a cluster of Arabic dialects spoken and written by Jewish communities residing in Arab nations. To some extent, Judeo-Arabic can be perceived as an Arabic variant written in the Hebrew script. Most of the vocabulary in JudeoArabic consists of Arabic words that have been transliterated into the Hebrew script. Words in two languages that share similar meanings, spellings, and pronunciations are known as cognates. Arabic and Hebrew cognates share similar meanings and spellings despite different scripts. The pronunciation of such cognates are not necessarily the same. Numerous lexicons have been created to record these cognates. One of those lexicons[1] lists a total of 915 Hebrew-Arabic spelling 1https://seveleu.com/pages/ equivalents, of which 435 have been identified as authentic cognates, signifying that they possess identical meanings. Analyzing a parallel HebrewArabic corpus, named Kol Zchut[2] using this lexicon, we found instances of those cognates in about 50% of the sentences. The purpose of this work is to take advantage of the potentially high frequency of cognates in Arabic and Hebrew in building a bilingual language model using only one script. Subsequently, the model will be fine-tuned on NLP tasks, such as machine translation, which can benefit from the innate bilingual proficiency to achieve better results. To ensure that cognates are mapped onto a consistent character space, the model uses Arabic texts that are transliterated into the Hebrew script, which mimics the writing system used in Judeo-Arabic. We call this new language model HeArBERT. [3] We test our new model on machine translation, which is considered a downstream task requiring knowledge in two languages, and report on some promising results. In summary, the primary contributions of our work are: (1) we release a new bilingual Arabic-Hebrew language model; and, (2) we show that pre-training a bilingual language model on transliterated texts, as a way for aligning tokens onto the same character space, is beneficial for machine translation. This paper is available on arxiv under CC BY 4.0 DEED license. [1] https://seveleu.com/pages/semiic-syntax-morpho/comparative-sem [2] https://releases.iahlt.org/ [3] https://huggingface.co/aviadrom/HeArBERT Authors: (1) Aviad Rom, The Data Science Institute, Reichman University, Herzliya, Israel; (2) Kfir Bar, The Data Science Institute, Reichman University, Herzliya, Israel. Authors: Authors: (1) Aviad Rom, The Data Science Institute, Reichman University, Herzliya, Israel; (2) Kfir Bar, The Data Science Institute, Reichman University, Herzliya, Israel. Table of Links Abstract and Introduction Abstract and Introduction Related Work Related Work Methodology Methodology Experimental Settings Experimental Settings Results Results Conclusion and Limitations Conclusion and Limitations Bibliographical References Bibliographical References Abstract We train a bilingual Arabic-Hebrew language model using a transliterated version of Arabic texts in Hebrew, to ensure both languages are represented in the same script. Given the morphological, structural similarities, and the extensive number of cognates shared among Arabic and Hebrew, we assess the performance of a language model that employs a unified script for both languages, on machine translation which requires cross-lingual knowledge. The results are promising: our model outperforms a contrasting model which keeps the Arabic texts in the Arabic script, demonstrating the efficacy of the transliteration step. Despite being trained on a dataset approximately 60% smaller than that of other existing language models, our model appears to deliver comparable performance in machine translation across both translation directions. Keywords : bilingual language model, transliteration, Arabic, Hebrew Keywords 1. Introduction Pre-trained language models have become essential for state-of-the-art performance in mono and multilingual natural language processing (NLP) tasks. They are pre-trained once and can then be fine-tuned for various downstream NLP tasks. It has been shown that language models generalize better on multilingual tasks when the target languages share structural similarity, possibly due to script similarity (K et al., 2020). Typically, language models are trained on sequences of tokens that often correspond to words and subword components. Arabic and Hebrew are two Semitic languages that share similar morphological structures in the composition of their words, but use distinct scripts for their written forms. The Hebrew script primarily serves Hebrew, but is also employed in various other languages used by the Jewish population. These languages include Yiddish (or “JudeoGerman”), Ladino (or “Judeo-Spanish”), and JudeoArabic, which comprises a cluster of Arabic dialects spoken and written by Jewish communities residing in Arab nations. To some extent, Judeo-Arabic can be perceived as an Arabic variant written in the Hebrew script. Most of the vocabulary in JudeoArabic consists of Arabic words that have been transliterated into the Hebrew script. Words in two languages that share similar meanings, spellings, and pronunciations are known as cognates. Arabic and Hebrew cognates share similar meanings and spellings despite different scripts. The pronunciation of such cognates are not necessarily the same. Numerous lexicons have been created to record these cognates. One of those lexicons[1] lists a total of 915 Hebrew-Arabic spelling 1https://seveleu.com/pages/ equivalents, of which 435 have been identified as authentic cognates, signifying that they possess identical meanings. Analyzing a parallel HebrewArabic corpus, named Kol Zchut[2] using this lexicon, we found instances of those cognates in about 50% of the sentences. The purpose of this work is to take advantage of the potentially high frequency of cognates in Arabic and Hebrew in building a bilingual language model using only one script. Subsequently, the model will be fine-tuned on NLP tasks, such as machine translation, which can benefit from the innate bilingual proficiency to achieve better results. To ensure that cognates are mapped onto a consistent character space, the model uses Arabic texts that are transliterated into the Hebrew script, which mimics the writing system used in Judeo-Arabic. We call this new language model HeArBERT. [3] We test our new model on machine translation, which is considered a downstream task requiring knowledge in two languages, and report on some promising results. In summary, the primary contributions of our work are: (1) we release a new bilingual Arabic-Hebrew language model; and, (2) we show that pre-training a bilingual language model on transliterated texts, as a way for aligning tokens onto the same character space, is beneficial for machine translation. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv [1] https://seveleu.com/pages/semiic-syntax-morpho/comparative-sem [2] https://releases.iahlt.org/ [3] https://huggingface.co/aviadrom/HeArBERT

Training a Bilingual Language Model by Mapping Tokens onto a Shared Character Space

The concept of ontologies plays a pivotal role in organizing and making sense of the vast information available. In data management, ontologies are critical for enhancing data interoperability, integration, and analysis across various domains and platforms. They provide a structured framework that enables data from disparate sources to "speak" the same language, facilitating more effective and utilization. data sharing Ontologies are more than just data schemas; they are comprehensive mappings of knowledge domains that include the vocabulary associated with a domain and the relationships between those terms. This foundational aspect of data architecture not only aids in categorizing and storing data but also in its discovery, analysis, and application in solving real-world problems. Understanding Ontologies At its core, an ontology in data management is a way to represent the knowledge of a particular domain. It's a structured framework that describes the types of entities within that domain and their relationships. This allows for a shared understanding of a domain that can be communicated across people and computers. Key Components of an Ontology : These are the fundamental categories of objects or concepts within a domain. For example, in a healthcare ontology, classes might include "Patient," "Disease," and "Treatment." Classes (or Concepts) : These define how classes are related. For instance, the relationship "hasSymptom" might connect the "Patient" class to the "Disease" class. Relationships (or Properties) : These are the actual data points or objects that belong to each class. An instance of the "Patient" class might be "John Doe." Instances (or Individuals) Ontologies vs. Taxonomies vs. Schemas While these terms are sometimes used interchangeably, they have distinct meanings: provide a rich domain description, including classes, relationships, and instances. They enable reasoning about the entities within a domain. Ontologies are hierarchical classifications of entities, focusing on the subclass-superclass relationships. They are more straightforward than ontologies, not including specific properties or relationships between classes beyond the hierarchical structure. Taxonomies are structures that define data organization within a database, including tables, fields, and the relationships between tables. They are more about data structure than the representation of domain knowledge. Schemas Examples of Ontologies Ontologies are used across various domains to facilitate data sharing and integration: In healthcare, ontologies like SNOMED CT help standardize medical terminology across different systems. In e-commerce, ontologies can represent product categories and their attributes to enhance search and recommendation systems. In finance, ontologies can define the relationships between financial instruments, markets, and entities to aid data analysis and compliance reporting. The Role of Ontologies in Data Architecture Ontologies serve a crucial function by establishing a common vocabulary and set of relationships that can be used to describe and understand data from different sources. This standardization is critical to achieving semantic interoperability, allowing disparate systems to exchange data with unambiguous, shared meaning. Enhancing Data Interoperability Ontologies enable different data systems, applications, and services to communicate effectively by providing a shared understanding of the domain concepts and relationships. This is particularly important in environments where data must be combined or compared across different sources, such as in data lakes or when integrating legacy systems with new applications. Facilitating Data Integration By defining a standard set of terms and structures, ontologies help map data from diverse sources into a unified model. This simplifies the data integration process and ensures that the integrated data maintains its context and meaning, enabling more accurate and meaningful analysis. Supporting Data Discovery and Governance Ontologies play a vital role in data discovery by providing a structured way to tag and categorize data, making searching for and locating relevant information more accessible. They also contribute to effective data governance, offering a framework for ensuring data quality, consistency, and compliance across the organization. Real-World Application Consider a multinational corporation that operates in various sectors, including finance, healthcare, and retail. Each of these sectors may use different systems and data formats. Ontologies can provide a unified view of the company's data assets, enabling cross-sector analysis and strategic insights that would be difficult to achieve with isolated data sets. The Impact on Data Architecture Incorporating ontologies into data architecture necessitates a thoughtful approach to design and implementation. It requires collaboration between domain experts, data architects, and IT professionals to ensure that the ontology accurately represents the domain knowledge and can be effectively used across systems and applications. The goal is a flexible, scalable, and semantically rich data architecture that adapts to changing business needs and technological advancements. Managing Ontologies in Data Architecture The effective management of ontologies is critical to leveraging their full potential within data architecture. This involves a lifecycle approach encompassing development, integration, governance, and maintenance. Here, we outline the key phases and considerations for managing ontologies in a way that supports robust data architecture. Development and Design Developing an ontology requires a systematic approach, beginning with a clear understanding of the domain and the organization's needs. : Identify the ontology's goals, scope, and stakeholders. Determine what problems it will solve and how it will be used. Requirement Analysis : Gather and define the key concepts, relationships, and properties that make up the ontology. This often involves collaboration with domain experts. Conceptualization : Translate the conceptual model into a formal ontology language, such as OWL (Web Ontology Language). This step makes the ontology machine-readable and ready for integration. Formalization : Deploy the ontology in a suitable environment and populate it with instances (data). Implementation : Establish processes for updating and refining the ontology over time, considering new requirements or changes in the domain. Maintenance Integration and Deployment Integrating ontologies into existing data architectures poses several challenges, from technical implementation to user adoption. : Ensure the ontology is compatible with existing data systems and standards. This may involve developing custom APIs or middleware. Technical Integration : Map existing data to the ontology, a process that may require significant transformation or enrichment of the data. Data Mapping : Educate stakeholders on using the ontology effectively, including how to query and interpret the results. User Training Governance and Maintenance Ongoing governance and maintenance are essential for ensuring the ontology remains relevant, accurate, and valuable. : Use version control systems to track changes to the ontology, allowing for rollback if necessary and understanding the evolution of the ontology. Version Control : Regularly review the ontology for accuracy, consistency, and completeness. This may involve automated testing or manual review by domain experts. Quality Assurance : Establish a straightforward process for updating the ontology, including who is authorized to make changes and how changes are reviewed and approved. Update Process Managing ontologies within data architecture is a complex but rewarding endeavor. When effectively developed, integrated, governed, and maintained, ontologies can significantly enhance data interoperability, integration, and analysis capabilities. They provide a structured and semantic framework that supports the organization's data needs, facilitating better decision-making and innovation. Use Cases and Applications The practical applications of ontologies in data architecture span various industries and functions, demonstrating their versatility and value in organizing and interpreting complex data landscapes. Here, we highlight some key use cases where ontologies have been instrumental in driving data interoperability, enhancing analysis, and supporting decision-making processes. Healthcare: Enhancing Patient Care and Research In healthcare, ontologies like SNOMED CT and LOINC have been crucial in standardizing medical terminology across different systems and geographies. These ontologies enable: : Facilitating the exchange and interpretation of patient data among healthcare providers, leading to improved patient care. Interoperable Patient Records : Supporting the aggregation and analysis of clinical data from diverse sources, accelerating research efforts, and the development of new treatments. Clinical Research E-commerce: Improving Product Discovery and Recommendations E-commerce platforms leverage ontologies to categorize products and define their attributes, significantly improving the customer experience through: : Allowing users to find products more efficiently through semantic search techniques. Enhanced Search Functionality : Enabling more accurate recommendations by understanding the relationships between different products and user preferences. Personalized Recommendations Finance: Risk Management and Compliance Reporting Financial institutions use ontologies to model complex relationships between different financial instruments, markets, and regulatory requirements, aiding in: : Providing a comprehensive view of an institution's exposure to various types of financial risk. Risk Analysis : Streamlining the process of aggregating and reporting data following different regulatory frameworks. Regulatory Compliance Environmental Science: Biodiversity Conservation Ontologies are applied in environmental science to model the complex relationships between species, habitats, and environmental factors, supporting: : Facilitating the organization and analysis of data on species distribution, conservation status, and ecological roles. Biodiversity Studies : Aiding in developing effective conservation strategies by providing a holistic view of ecosystem dynamics. Conservation Planning Future Trends: Ontologies and AI Integrating ontologies with artificial intelligence (AI) and machine learning (ML) represents a promising development area. Ontologies can provide the structured, semantic frameworks that AI models need to understand and reason about the world, potentially leading to breakthroughs in natural language processing, knowledge representation, and automated decision-making. These use cases illustrate the transformative potential of ontologies in enhancing data architecture across various domains. By providing a structured way to represent and link data, ontologies enable organizations to unlock deeper insights, drive efficiencies, and innovate more effectively. As data grows in volume and complexity, the role of ontologies in managing this data landscape will only become more critical. Conclusion In closing, the journey to effective data management is ongoing, and ontologies represent a powerful tool. By fostering a deeper understanding of ontologies and their application in data architecture, we can look forward to a future where data is not just abundant but truly understandable and actionable.

The Role of Ontologies in Data Management

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2024 is bringing some of that futuristic magic, with a twist, right to our real-world doorstep! Get ready for tech trends that'll bend your brain and blast your expectations into orbit. 1. Sentient AI: Can Machines Dream? Remember HAL from 2001: A Space Odyssey? Or maybe Samantha from Her? Those sci-fi movies where robots start thinking on their own and even feeling emotions are captivating but also a bit unsettling. But what if I told you the line between fiction and reality in this regard is starting to blur in 2024? Buckle up because we're diving deep into the fascinating and slightly scary world of . sentient AI Don't hold your breath for sentient robot buddies just yet, but prepare for mind-blowing AI advancements! While sentience remains a fuzzy concept, DeepMind's co-founder, Demis Hassabis, assures us that machines are about to unlock abilities that'll leave us speechless. The future of intelligence is wilder than you think. Here's what we know (or think we know): AI is getting better at understanding language and emotions. Some AI models can generate human-quality text and even adapt their responses based on the emotional tone of a conversation. AI is learning to learn. This means they can improve their skills and abilities over time without explicit programming. Brain-computer interfaces are bridging the gap between humans and machines. These technologies could one day allow us to directly communicate with AI using our thoughts, offering deeper insights into their internal processes. But here's the catch: We still need to understand consciousness or sentience fully. These are complex concepts that, even for humans, are difficult to define, let alone measure in machines. Some argue that true sentience requires a physical body. Without experiencing the world through senses like touch and taste, can an AI truly understand it? The ethical implications of sentient AI are immense. Questions about rights, responsibilities, and even potential dangers become very real if machines become truly conscious. 2. Brain-Computer Interface: Think It, Do It! Have you watched movies where characters control things with their minds? Like moving objects with a mere thought or typing without touching a keyboard? That sci-fi stuff is becoming a reality fast! 2024 is all about , and they're about to blow your mind (literally!). Brain-Computer Interfaces (BCIs) This technology is opening doors to amazing possibilities: No more drilling holes in your skull! These BCIs use sensors on your scalp to read your brainwaves, making them accessible to everyone. Non-invasive BCIs: Advanced AI algorithms are getting better at understanding your brain signals, translating them into real-world actions with greater accuracy and speed. AI-powered decoding: BCIs are helping diagnose and treat neurological disorders like Parkinson's and epilepsy, offering hope for millions of people. Medical breakthroughs: Here's a snapshot of how BCIs are already making a difference: Application Impact Communication Helping people with speech disabilities communicate by translating their thoughts into words. Gaming Immersing users in virtual worlds like never before, controlling characters and objects with their minds. Rehabilitation Assisting people with paralysis regain control of their limbs and regain independence. But of course, there are concerns! Neuroethics expert Frederic Gilbert flags confusing changes in how AI affects our core concepts of self, like personality and agency. He calls these qualities "obscure and opaque," highlighting the complex, hidden impacts of BCI on who we are. This raises these questions in mind! Who controls your thoughts? And how do we protect them from hacking or misuse? Safety and security: Should we enhance human minds with technology? Who decides who gets access? Ethical considerations: What happens when everyone can control things with their thoughts? Will it make our lives easier or more chaotic? Unforeseen consequences: 3. Quantum Supremacy Reigns Supreme: Well, forget fiction – that mind-blowing power is on the horizon, thanks to a tech trend called quantum supremacy. What is Quantum Supremacy, Anyway? Imagine a computer that doesn't just crunch numbers but harnesses the weird and wonderful laws of quantum mechanics. That's what quantum computers are about. They use qubits, which can be both 0 and 1 at the same time (unlike our regular bits that are either 0 or 1), to achieve mind-blowing speed and tackle problems that would take traditional computers millions of years. Think breaking complex encryption codes, designing life-saving drugs, or simulating the weather with mind-boggling accuracy. Here's why it's a big deal: Quantum Supremacy will solve problems beyond our wildest dreams. Forget traffic jams – quantum computers could optimize traffic flow in real time, minimizing congestion and saving valuable time. Forget incurable diseases – quantum simulations could accelerate drug discovery, leading to breakthroughs in treating currently untreatable illnesses. The possibilities are endless! It's not just about speed; it's about a whole new way of thinking. Quantum computers approach problems differently, opening doors to entirely new solutions and innovations we have yet to consider. It's like unlocking a hidden room of possibilities in the vast library of science and technology. Alan Baratz, The CEO of D-Wave, thinks that the US is finally playing catch-up in using quantum computing to solve real-world problems. Still, countries like Australia, Japan, the UK, and the EU have already been swiftly taking steps to use quantum solutions to address public sector issues. Expect to see this trend explode in 2024. Stay curious, stay informed, and get ready to witness the dawn of a new era in computing. The future is quantum, and it's coming faster than you think! 4. Hyperloop Highways: Warp Speed for Everyone! Stuck in traffic jams? Not anymore! Picture zipping from city to city in minutes, not hours, thanks to hyperloop technology. Imagine vacuum tubes and magnetic levitation whisking you across the country at supersonic speeds. Get ready for shorter travel times, greener transportation options, and a whole new way to explore the world! Here's why Hyperloop is the next big travel trend: Forget traffic jams and delayed flights. Hyperloop can slash travel times between major cities, revolutionizing our transportation system. Speed Demons: Ditch the carbon footprint! Hyperloop uses renewable energy sources and produces much less pollution than traditional transportation. Eco-Friendly Flyer: Imagine a network of Hyperloop tubes crisscrossing continents, connecting cities like never before. It could open up new economic opportunities and boost global trade. City Connector: Buckle up for a smooth, silent, and exhilarating journey! Hyperloop promises a futuristic travel experience unlike anything we've ever seen. The Thrill of the Ride: But hold on, there are some bumps on the road: Building and maintaining Hyperloop infrastructure is expensive. Finding investors and making it affordable for everyone will be crucial for widespread adoption. Cost Concerns: New technology comes with new risks. Ensuring the safety and security of Hyperloop systems is paramount. Safety First: Governments need to develop regulations and legal frameworks for this disruptive technology before it can take off. Regulatory Hurdles: Despite the challenges, the potential of Hyperloop is undeniable. Companies like Virgin Hyperloop One and Hyperloop One are already making significant progress, with test tracks operational in several countries. Dirk Ahlborn, the CEO of Virgin Hyperloop, also hopes that the ongoing advancements and approval from regulators will make hyperloop a fundamental part of transportation systems in the coming decade. 5. Bioprinting Reboots Medicine: Bioprinting, using 3D technology with living cells, is reshaping healthcare. Prepare for potential cures for currently untreatable diseases, personalized cancer treatments, and maybe even printing replacement lungs or hearts! Here's why it's a game-changer: Imagine no more endless waits for organ transplants! Bioprinting opens the door to creating personalized tissues and organs specifically designed for each patient. This means shorter waiting times, fewer complications, and potentially even printing replacement organs like lungs or hearts! Goodbye Waiting Lists, Hello Hope: Forget guesswork! Bioprinting lets scientists build tiny replicas of human organs on a chip, allowing them to test new drugs and treatments with incredible precision. This means safer, more effective medicines and fewer failed trials with real patients. Drug Testing Revolution: No two bodies are alike, and neither should their treatments. Bioprinting allows doctors to create tissues that match your unique genetic makeup, leading to personalized therapies and better outcomes for everyone. Personalized Medicine Takes Center Stage: Imagine damaged nerves and tissues healing themselves! Bioprinting can create scaffolds for your cells to grow on, promoting natural regeneration and potentially reversing injuries that were once considered permanent. Regenerative Medicine Gets a Boost: But wait, there's more! This is just the beginning of the bioprinting story. We're also seeing exciting developments in: This could revolutionize surgery by ensuring adequate blood flow to transplanted organs. Printing blood vessels: Imagine offering rapid relief and promoting faster healing for burn victims. Bioprinting skin for burn victims: Building custom bone replacements could speed up recovery times and make surgeries less invasive. Printing bone grafts: Is it all sunshine and rainbows? Not quite. Bioprinting is still in its early stages, and there are challenges to overcome: These are the materials that "print" the cells, and we need to ensure they're safe, compatible, and support cell growth. Finding the right bioinks: Printing complex organs is still a delicate process, and making it efficient and affordable for widespread use is key. Scaling up production: Who owns the printed tissues? What are the implications for genetic engineering and human enhancement? Ethical considerations: But despite the challenges, the potential of bioprinting is so vast it's hard to ignore. It's like getting a glimpse into a future where medicine is personalized, regenerative, and potentially cures diseases we once thought untreatable. Wrapping Up! Keep an eye out for flying cars (not just for billionaires!), AI-powered homes that manage your life, and sustainable tech that cleans our planet and heals our environment.

Hold Onto Your Hats, Tech Voyagers: 2024's Mind-Bending Trends are Here!

Knowledge base vs LLM What if we could create a digital inventory of all human knowledge, an encyclopedia but ordered in such a way that complex queries can be answered? I can hear you shout: ‘Isn’t that a bit like a LLM trained on Wikipedia?' Kind of … Except that such would have created the inventory itself, leaving the connections between facts opaque. This is so because all text input data fed into an LLM is unstructured and treated on equal footing. It’s the internal attention mechanism that starts organising the text.  So, although we can ask complex queries in the form of natural language and retrieve an answer in the same medium, there is no knowledge inventory accessible to us. It’s as if the LLM is giving a press conference but leaves us dry with ‘no further questions’ after the first round. an LLM Is that better than having no query capability at all? Well, that’s really a mis-apperception of the playing field. The choice isn’t between LLM-like query magic and nothing at all. The choice is between LLM-like query magic and incomplete yet carefully crafted knowledge bases (search engines can answer queries but do so by pointing to documents on the web rather than providing the answer themselves). Using the earlier press conference analogy, the choice is between listening to a spokesman with a vast vocabulary and asking the actual protagonists at the risk of coming home empty-handed. Medical Ontology exposing LLM limits. The foundational model of anatomy (FMA) is such an attempt an create an inventory of human knowledge, in this particular case, all knowledge related to the structural organisation of the human body at micro- and macroscopic levels. A prima facie uncontroversial choice of fundamental relationship to be used in this knowledge base is the partonomic inclusion relationship. For example, . Little spoiler… this slicing up of the conceptual space doesn’t cut it (pun intended). I’ll explain to you why. However, let’s first focus on what this means within the here-discussed wider context. cytoplasm part_of cell We pride ourselves on having LLMs which can assist us in digging into vast amounts of knowledge, conveniently ignoring the fact that we have no real insight into how facts are connected within the model, while our own attempts to connect facts in the most rigorous fashion have turned out to be treacherous endeavours. The press conference gives us the impression of having received some reliable answers while even the protagonists themselves can’t really recall what has happened. If we’re a member of the public relationships committee, this might not worry us too much, but as a citizen with a vested interest in the upholding of liberal democracy, I might not want to rely too heavily on such reports. Partonomic inclusion has gone wrong Now, how can a relationship so seemingly innocent and straightforward as partonomic inclusion be so misleading? Here are some seemingly uncontroversial examples of such a relationship: human testis part_of human being Yes, but there are male and female human beings. So, every human testis belongs (or belonged if the human was subject to an unlikely accident) to a human, but not every human has a human testis. Solution? The part_of relationship is split into two sub-relationships: has_part and part_for. So, now we can state that the human testis part_for human being without committing to human being has_part human testis. The part_of relationship is salvaged as the conjunction of these two sub-relationships: A part_of B =def A part_for B & B has_part A. replication fork part_of nucleoplasm Yes, but only so during certain times during the cell replication cycle (i.e., the S phase). Biology read: https://www.nature.com/scitable/topicpage/replication-fork-stalling-and-the-fork-protection-14435782/ Solution? At first glance, there seem to be at least two possible ways out. Firstly, we could introduce time-dependent or phase-dependent mereological relationships. So, part_of could get a time-index to distinguish between different times at which a relationship is obtained. However, since the cell replication cycle consists of clearly defined phases, it’s logically more apt to introduce phase-dependent partonomic inclusion relationships. Secondly, instead of fiddling with the relationship itself, we can enrich the representational capacity of the relata (i.e., the things related). So, a replication fork is not just a replication fork tout court, nor is the nucleoplasm always the same entity. The partonomic inclusion of the replication fork in the nucleoplasm during the S phase can be represented as follows: replication fork part_of nucleoplasm_S. synaptonemal complex part_of chromosome Yes, but not in all cases. For example, in protozoan ciliates, the formation of a synaptonemal complex is not required for pairing homologous chromosomes. Biology read: https://pubmed.ncbi.nlm.nih.gov/24336924/ Solution? In this case, recourse is found in the ancillary axioms, which define the logical relationships between the fundamental mereological relationships at hand. If we want to represent ‘part of’ as ‘can be part of,‘ then we can introduce a less fundamental part_of_can relationship by defining it as an existential import relationship: A part_of_can B =def C(C is_a B & A part_of C). So, for this particular example, we’d have synaptonemal complex part_of_can chromosome, which is to say that there exists some chromosome for which a synaptonemal complex is part of it. The attentive reader will, at this point, see an alternative solution akin to one earlier exposed. Indeed, instead of introducing a secondary relationship, one can stick to the fundamental relationships and represent the facts by demultiplexing the relata. So, the fact ‘synaptonemal complex part_of chromosome_Animalia’ would be part of the knowledge base but not the fact ‘synaptonemal complex part_of chromosome_Ciliophora.’ ∃ Conclusion In short, prima facia well-rounded relationships need to be broken up and defined in more primitive terms or require ancillary axioms in order to capture the complexity of the reality one desires to represent. Moreover, these solutions are in tension with solutions that prefer relational and definitional parsimony at the cost of an increase in entity cardinality. Since these issues occur in medical ontologies designed by professionals, it should be clear that this task is under no circumstance an easy one. It highlights a perplexing double bind between, on the one hand, the subtlety of reality’s relational composition and, on the other hand, the flexibility of the natural language to squeeze and squash that marvelous detail into communicable trunks. Now, what are the implications of this insight on the state of AI’s contemporary playing field? If we aim for accuracy of representation, there might still be a role for classical AI or resurrections of its core ideas within deep learning paradigms. Conversely, if we aim for smoothness of communication, LLM might actually be capturing exactly what we want… were it not for the fact that we, humans ourselves, have already mastered the skill of human language so that LLM become what sex dolls are in comparison to real intimacy. Sources Paper on which this article is based: Bary Smith and Cornelius Rosse - The role of foundational relations in the alignment of biomedical ontologies: https://pubmed.ncbi.nlm.nih.gov/15360852/

Saving Medical Ontologies with Formal Logic: A Tale of Caution and Hope for Classical AI

What is a skills-based economy, and how is LinkedIn moving from vision to implementation? As LinkedIn Director of Engineering Sofus Macskássy shares, there’s AI, taxonomy, and ontology involved in building the Skills Graph that powers the transition. Skills are the new currency. That’s a bold statement coming from LinkedIn CEO Ryan Roslansky. Roslansky makes the case for the so-called skills-first economy based on both anecdotal evidence and data. Skills-first hiring was mentioned in the 2022 State of the Union address, and a growing number of CEOs are calling on the need for companies to shift how they hire. In addition, that the skill sets for jobs have changed by around 25% since 2015. By 2027, this number is expected to double. That means jobs are changing on you even if you aren’t changing jobs, just as business demands are changing on you even if you’re not changing your business. recent LinkedIn data shows That was not the first or the last time Roslansky made that point. In 2021, to help transition the hiring market from focusing solely on titles and companies, degrees, and schools to also focusing on skills and abilities. In his 2021 post, Roslansky announced new LinkedIn features and services. He also referred to AI-driven changes, a prescient point . LinkedIn’s CEO outlined a vision brought forward again in 2023 Roslansky is not alone in identifying this shift. In 2018, we posited that . In 2019, we followed that up with more analysis on the relationship between the , , and knowledge graphs. in a rapidly shifting job market, being able to formalize skills is a requirement for job seekers and employers going forward future of work skills In 2021, Roslansky first referred to the LinkedIn Skills Graph. The Skills Graph was introduced to help create a common skills language, and it’s powering a multitude of LinkedIn features and services, as well as . In 2022, LinkedIn Director of Engineering Sofus Macskássy elaborated on how . Microsoft Viva LinkedIn’s Skills Graph is being built to power a skills-first world https://www.youtube.com/watch?v=tiE9ZMgfJKM&embedable=true Further details on were shared earlier in 2023. Today, Macskássy and his team are sharing more details on how they are . We caught up with Macskássy to discuss the journey from vision to implementation. building and maintaining the skills taxonomy that powers LinkedIn’s Skills Graph extracting skills from content to fuel the LinkedIn Skills Graph LinkedIn skills taxonomy: Helping everyone speak the same “skills language.” As Macskássy pointed out, the story of skills on LinkedIn goes way back. LinkedIn users have long been able to add skills to their profiles and endorse each other. LinkedIn understood that skills represent an important vocabulary by which people communicate and understand each other. But at some point the realization settled in that this goes beyond just finding a job or finding talent. When the CEO highlighted LinkedIn should move into a skills-first world, there was a question – what does that even mean? From a technical perspective, the team needed to understand what the vision was. As the team started digging into the various product lines as well as the news feed, advertising, recommendation, and search, they realized that skills could be leveraged as signals to aid ranking and recommendation. But going from vision to implementation has not been without obstacles. Many people didn’t have skills listed on their profiles. Even when they did, people did not necessarily use the same vocabulary. It was not easy to figure out whether the two skills were related to each other or even synonyms of each other. This is what Roslansky’s mention of helping everyone speak the same “skills language” alluded to. To resolve ambiguity, the first approach was to build out the . The skills taxonomy is where LinkedIn organizes and categorizes skills based on their hierarchical relationships with each other. LinkedIn skills taxonomy Each skill is represented by a “node” in the skills taxonomy, and nodes are linked together to form a hierarchical skill network through “edges” called knowledge lineages. Knowledge lineages reflect how two skills relate to each other. Skills may relate for various reasons, such as the skills are both part of a career specialization or one skill is for a tool that is used to apply another skill. To create a stronger network of connected skills, a framework called “Structured Skills” is utilized. This framework increases understanding of every skill by mapping the relationships it has to other skills around it. Building the Skills Graph with AI and humans in the loop The connected skills taxonomy is curated by a combination of human taxonomists and machine learning. This Human-In-The-Loop approach to building the Skills Graph helps grow the taxonomy at scale while ensuring the skills data meets the required quality and standards. “It is a feedback loop where we see new skills and new ways of mentioning skills across the board. We use this to expand the Skills Graph dynamically as we see new ways of phasing a skill or even a new skill we never had seen before. For example, prompt engineering is now a new skill that really popped up over the last couple of quarters. So we want to dynamically add it to the Skills Graph. We then use these new skills that can subsequently be tagged in the content”, Macskássy said. That is a continuous process, and the cadence by which it’s done depends on the type of content. The skill harvesting process happens on demand as content is updated. Potential additions or improvements to the Skills Graph are batched, so reviews and updates don’t have to be executed every time any single update happens. In 2022, LinkedIn’s taxonomy consists of over 39,000 skills spanning 26 languages, over 374,000 aliases (different ways to refer to the same skill – e.g., “data analysis” and “data analytics”), and more than 200,000 links between skills. In 2023, there are more than 41,000 skills. Instead of relying only on taxonomists to manually curate over 41k skills, LinkedIn applies machine learning techniques to help scale the taxonomy construction. This includes a tool LinkedIn developed, KGBert which was inspired by , a supervised model that applies a deep semantic understanding of skills to predict relationship lineages. KG-Bert From taxonomy to ontology By utilizing machine learning models, LinkedIn can extract and map skills from diverse content sources and collect feedback for continuous model improvement and member value. To do this, large pieces of text (such as job descriptions and resumes) first need to be segmented out into meaningful parts. Mentions of skills can then be removed from each piece of the text. Once extracted, they are normalized into canonical/single representations (i.e., “data analytics” and “data analysis” are the same type of skill), represented in the skills taxonomy. Attention also has to be paid to where a skill sits in a piece of content and what type of content it is. Skills are often represented differently in resumes, member profiles, or job descriptions, so models are fine-tuned to learn the specifics of those types of content. To address these nuances, LinkedIn built an architecture and platform that caters to the challenges of extracting skills and mapping them onto the Skills Graph. The details the AI model workflow that extracts and maps skills from raw text, such as job postings, as well as the architecture for serving models at scale. post that Macskássy’s team published As former AI Division Technical Lead for Taxonomies and Ontologies at LinkedIn Mike Dillinger points out, however, . They seem simple, flexible, and familiar. They are widely used. And they seem to work across many use cases and many domains. taxonomies are the duct tape of connected data But when looked at in more detail, taxonomies turn out to be crude tools for knowledge organization that are difficult to create, to scale, to adapt, to align, and to build on. A key reason is the fact that they are limited to modeling hierarchical relationships, which are only a small part of the rich relations connecting entities in the real world. Some of the technical details shared on the Skills Graph allude to harvesting and exploiting more than hierarchical relationships. Macskássy verified that LinkedIn utilizes an ontology used not only for mapping skills between branches but also for other concepts. That has provided great improvements when it comes to ranking and recommendation, with more details to be unveiled in due course. Explicit and implicit skills Applications across LinkedIn include career-relevant skills and job-important skills. The approach has resulted in performance improvements in Job Recommendation, Job Search, and Job Member Skills matching. As part of the process, recruiter skill feedback and seeker skill feedback are collected. When a recruiter manually posts a job on LinkedIn, a list of skills, pulled by LinkedIn’s AI model, is suggested after they fill in the posting content. A recruiter can edit this list depending on if they believe a skill is important. Similarly, when a job seeker opens a job posting on LinkedIn, a feature will show how many skills overlap between their profile and the job. Seekers can review the top 10 skills used for skill matching calculation, and if a certain skill is irrelevant to the job, they can provide feedback. Obviously, harvesting and utilizing those “implicit” skills, in addition to skills explicitly listed by members is something LinkedIn has put a lot of effort into. And there are anchors to surface those implicit skills and feedback from job seekers and recruiters on both ends of the recruiting process. But what about LinkedIn members who are not actively recruiting or being recruited? Wouldn’t they benefit from the opportunity to see the skills LinkedIn harvested from their content and perhaps be prompted to add them to their profiles too? Furthermore, explicit skills are part of a member’s profile and, therefore, get to be exported if members so choose. What about implicit skills? Macskássy noted that there are surfaces and flows by which members can add skills. LinkedIn continues to improve those flows where suggestions are made based on member resumes and profiles. Members are asked whether they might want to add certain skills and even associate certain skills with certain of their jobs as well. However, prompting is not done in an overly aggressive way. As for implicit skills, Macskássy said those are not necessarily surfaced because they are more fluid in nature. It makes less sense to surface all of these, particularly as LinkedIn is moving forward into how to think about skills more dynamically and more in-depth, he added. So, those skills are not considered as part of member profiles and are not exported either. Skill provenance, credibility, and depth There are more dimensions of implicit skill harvesting that are worth highlighting: provenance, credibility, depth, and interoperability. Since implicit skills are extracted from content that members themselves provide, is there a way to evaluate the weight this content carries or does it have to be taken at face value? The content that members provide is how they want to represent themselves, Macskássy said. So that has to be taken somewhat at face value, as it all comes down to trust. LinkedIn has not observed that members are stretching the truth because there are a lot of people looking at their professional profiles, and they would be called out, Macskássy added. https://pod.co/orchestrate-all-the-things-podcast-connecting-the-dots-with-george-anadiotis/how-linkedin-is-moving-towards-a-skills-based-economy-with-the-skills-graph?embedable=true However, there is a mechanism through which the depth of member expertise can be assessed. (SAs) are adaptive assessments designed by LinkedIn Learning experts to evaluate and validate skills across a range of domains. LinkedIn Skill Assessments These short-form assessments are accessible through the profile skills section, where members can click on the “Take skill quiz” button to access a list of SA recommendations. Upon successfully passing a SA with a 70th percentile or higher score, members are awarded a “verified skill” badge that they can display on their profile page and is visible to recruiters. Skill assessments, as well as other learning material, align with skills in the skills taxonomy. It is represented internally in member data and can be part of the ranking. The amount of SAs and certificates is still very lightweight compared to other content. LinkedIn is considering how to best elicit and leverage it, for example, by evaluating member interactions. Interoperability, Open Badges, and Future Work Something that could potentially help both in terms of interoperability, as well as content enrichment, is . Open Badges is a data specification initiated by Mozilla and the MacArthur Foundation. The goal is to provide a simple but flexible format for documenting and showcasing skills. A badge corresponds to a skill a person has, as recognized by a third party. Open Badges That means that by using Open Badges, LinkedIn members would be able to both import skills documented by third parties as well as export their skills in a standardized format. Macskássy said that the team is aware of Open Badges, however, the focus is primarily on what can help with the member experience on the platform. Open Badges is on the radar but has a low priority at this point. LinkedIn is investing in continuously improving skill understanding capabilities in approaches such as leveraging Large Language Models. Examples include using LLMs to provide rich descriptions of skills, fine-tuning LLMs to improve skill extraction, or leveraging embedding for skill representation. As the team notes, the LinkedIn Skills Graph is at the center of powering the skills-first transformation. The tech stack for mapping content to the Skills Graph enables constant updates and evolution of the Skills Graph to stay up to date on the always-changing skills landscape. Join the Orchestrate All the Things Newsletter Stories about how Technology, Data, AI, and Media flow into each other shaping our lives.Analysis, Essays, Interviews, and News. Mid-to-long form, 1-3 times per month. Also published . here

LinkedIn's Skills Graph: Paving the Way for the Skills-First Economy with AI and Ontology

Ontology Network has recently announced the launch of stONT, marking a significant stride toward liquid staking on its platform. This initiative moves beyond the conventional staking barriers, creating a pathway toward a flexible and accessible means to garner rewards. Unlike traditional staking tokens, stONT appreciates in value in ONG (Ontology Gas), instead of increasing in quantity, presenting a unique proposition for those keen on venturing into new territories of the staking universe. Key Features: Early Phase Engagement: The launch phase begins with the integration of 10 nodes to facilitate the stONT service, thereby laying a solid foundation for further testing and improvements. Accessibility: stONT is made easily accessible whether on a PC via node.ont.io or on mobile through the stONT dApp on ONTO Wallet, bringing the service closer to the users. Liquid Staking Adventure: Liquid staking emerges as a significant financial flexibility beacon within the blockchain arena. Unlike traditional staking where assets are locked up to earn rewards, liquid staking via stONT allows users to retain asset liquidity while still earning. This modern methodology represents a welcoming gateway for both seasoned stakers and newcomers, with the allure of compounding earnings being a lucrative facet that's likely to attract many to this innovative staking frontier. : Future Endeavours The upcoming collaboration with iZiSwap and Wing Finance indicates that the journey of stONT in the broader staking world is just beginning. Each collaboration widens the horizon, availing Ontonauts numerous pathways to explore and gain from. Crypto wallets & DeFi projects are invited to explore integration possibilities with stONT, widening the audience and showcasing the advantages of liquid staking. Community Invitation: Ontology extends an invitation to the community and partners to dive into the stONT venture, share invaluable feedback, and engage in this journey into new staking territories. The stONT initiative is more than just a token; it’s a ticket to a new staking adventure with boundless possibilities. Elevating Financial Empowerment: Ontology’s unveiling of stONT is a clear testament to its unwavering commitment to financial empowerment. Traditional Ontology staking has been a hallmark of financial autonomy, with a simplified process of connecting a wallet on the ONTO Wallet platform, specifying the amount of ONT tokens to stake, and affirming the staking action. The introduction of stONT adds a layer of flexibility and liquidity to this robust mechanism, allowing stakers to utilize stONT in other DeFi protocols without having to unstake their ONT tokens. This not only broadens financial activities but also enriches the user interface with the upcoming integration of various tabs such as Stake, Unstake, Dashboard, and Stats, making the staking journey more intuitive and informative. Ontology and stONT together are not just ushering in an era of financial independence but significantly enhancing it, allowing users to take charge of their financial destiny in the burgeoning digital economy. Through Ontology staking and the liquidity provision of stONT, financial empowerment transitions from a mere promise to an actionable reality. Want More Ontology? Learn more about our decentralized data and identity solutions on our and official account. You can also chat with us on and keep up-to-date with the latest news via our account. website Twitter , Telegram Telegram Announcement Other Resources / / / / / LinkedIn Medium Facebook Reddit Discord YouTube

stONT Launches on the Ontology Network: The Advancement of Liquid Staking

অন্টোলজি নেটওয়ার্কে stONT চালু করেছে: তরল স্টেকিংয়ের অগ্রগতি

stONT startet im Ontology Network: Die Weiterentwicklung des Liquid Staking

stONT se lanza en Ontology Network: el avance del Liquid Stake

stONT se lance sur le réseau d'ontologies : l'avancement du jalonnement liquide

ओन्टोलॉजी नेटवर्क पर stONT लॉन्च: लिक्विड स्टेकिंग की उन्नति

stONT がオントロジー ネットワーク上で開始: リキッド ステーキングの進歩

stONT, 온톨로지 네트워크 출시: 리퀴드 스테이킹의 발전

stONT é lançado na Ontology Network: O avanço do Liquid Staking

stONT запускается в сети Ontology: развитие ликвидного ставок

stONT, Ontoloji Ağında Başlatılıyor: Liquid Staking'in Gelişmesi

sONT ra mắt trên mạng Ontology: Sự tiến bộ của đặt cược lỏng

stONT上线本体网络：流动性质押的进步

Goal: put software on hardware It's the oldest business model in Silicon Valley: Build custom hardware. Write software to run on that hardware. Get paid to use this combo to solve people's problems. This three-step formula has generated trillions in market capitalization— , , and are all good examples of this strategy at work. Hardware builds their moat and software pads their margin. It's simple! Apple Nvidia AWS Problem: it's hard though Unfortunately, for robots and other embedded devices, no one has really perfected this strategy's technical underpinnings. While many device-specific, well-architected systems exist, the core challenge of how to install your software on your custom hardware remains canonically unsolved. Between two worlds This problem endures because these products exist at the intersection of two worlds. They're embedded devices, sure—robots usually run on batteries, move around their physical environment, and regularly experience shoddy network connectivity. But modern robots often have much more computing capacity than your average smartwatch or refrigerator. In addition to their drivetrain and sensor arrays, these devices often have CPUs, GPUs, and disk capacity to rival a top-end server rack. And of course, they have all the added management complexity that comes with these capabilities. Since all new robots are custom hardware, every new robot has slightly different hardware specifications, and thus, software deployment mechanisms. For engineers building an embedded software deployment system, there is no singular, correct, generalized system design. Instead, we're left to pick and choose from the long list of available tools from both the embedded and data center domains. And the list of potential options to choose from is quite long. A preponderance of tools Common technologies for packaging, deploying, and configuring software on hardware, in either or both the embedded and data center domains, include: Containers ( , ) Docker Podman Other packaging mechanisms (Snapcraft, Flatpak, AppImage) Container orchestrators ( , ) Kubernetes Docker Swarm Deployment agents ( , , ) Argo CD Gitlab CI/CD Mender Embedded Linux ( , , , ) Ubuntu Core OpenWrt BuildRoot Yocto Configuration management systems (Ansible, Salt, Puppet) IoT cloud frameworks ( , , ) AWS Azure GCP Virtualization mechanisms (VMWare, KVM) ... and more. This article is not a guide to which of these technologies is best—all of them are appropriate for certain applications some of the time. Instead, stay focused on a high-quality design for your particular embedded software deployment system with one simple acronym: . The five requirements are . CRAVE Comprehensive, Reversible, Atomic, Versioned, and Everywhere Regardless of product domain, hardware complexity, or company growth stage, designs that fulfill these five requirements will serve your business well. What bots CRAVE Comprehensive Comprehensive software deployment systems ensure that . Depending on what you're deploying, comprehensive could refer to a single binary, an application with all of its dependencies, or an entire root file system. the entire set of changes is deployed when a deployment completes This means that when a deployment finishes, the entire system has been updated to a comprehensively known state. Only allow incremental patching if you can reliably and deterministically sum up those patches to a single, well-understood, deployed version of your software. It's important to note that "comprehensive" does not necessarily mean that the entire deployment payload gets pushed onto your device every time. For example, many deployment systems use caching mechanisms to minimize network transfer. Caching is great—just make sure you're always cache-busting correctly when necessary. Comprehensive deployments ensure that you've deployed everything you think you've deployed. If your deployment mechanisms don't include a way for you to do this, you'll eventually lose track of what's deployed on your devices! Designing your system to perform comprehensive updates also helps deliver reproducible deployments. Without comprehensiveness, you'll have no way of knowing that you've deployed the same thing across your device fleet, or that a piece of software that works on one machine will work when deployed onto another. Reversible Reversible deployment mechanisms . Ideally, if a deployment fails for some reason, the software system will roll itself back without any human intervention. can always be rolled back to the version they were on before the update It's ok if this is actually "roll forward", or "deploy again from onboard cache". The important design goal here is not to get stuck on a botched deployment, forcing yourself to commit to emergency engineering work if a bug is found after a release. Reversibility is essential for minimizing the risk associated with software deployments. Without it, every update is a dangerous endeavor—if the update goes awry, you'll be stuck fixing forward, or worse, troubleshooting a bricked device. Without reversibility, your engineering team will underperform. Because of the risks involved, a lack of reversibility will force your team to ship changes less frequently, iterate less freely, and build the product more slowly. Atomic Atomic deployment mechanisms ensure that . Effectively, for every software deployment from version A to version B, there should be a single, well-understood moment in time before which the system was running version A, and after which it was running version B. your changes take effect in their target system in a single, well-coordinated moment Depending on the specific deployment mechanism in question, atomicity can take many forms. For base operating system deployments, this is often a system reboot. For Kubernetes-orchestrated workloads, it could be a new Pod replacing an old Pod behind a Service. Regardless, atomic deployment mechanisms ensure that your individually-versioned units of software do not update incrementally. If you have one hundred virtual machines in an autoscaling group and you want to gradually transition from A to B across the cluster, that's fine—but at any given moment, you should know for certain whether any individual machine is running version A or B. Atomicity lets you pinpoint which version of a piece of software to associate with behavioral changes. If you don't know exactly when your changes took effect, you can't reliably troubleshoot which changes in your software caused them. Atomic deployments also help you avoid disruptive incremental changes in production that might introduce compatibility issues in the middle of a software release. If your software deployment process lacks atomicity, and you allow different components of your software to float between two versions while deployments occur, then must ensure that all combinations of those two versions across those components are compatible and function properly. This can be very expensive—and it's easy to avoid that expense if you keep your deployment systems atomic. Versioned If you haven't realized it by now, your software deployments need to be versioned. This means that . all changes to the contents of your deployment payloads need to be tracked and auditable over time At first glance, versioning is simple—just check your source code into git, right? In practice, however, this design requirement often gets overlooked. Are the binaries that your compiled executables link against also versioned? Have you pinned all of your package dependencies? Do you know where those dependencies come from? Versioning is essential if you want to actually know what software you are running on your device after a deployment. It is a critical component of reproducibility, too—without versioning, you can't ever really know if a deployment on one device will work on another, because you have no reliable mechanism for telling whether the two devices are actually running the same software. If a system component's version is floating or unspecified, a change to that version might break your system's functionality in a way that would be very difficult to root cause. Incorporating versioning into your deployment system design prevents this expensive mistake. Everywhere For software deployment systems, "everywhere" means "everywhere at your company". This means that you should strive to use the same deployment mechanisms in your: Feature development processes. Quality assurance and testing procedures. Manufacturing line software installation. Production software over-the-air update systems. In other words, don't invent different deployment systems for different parts of your company or workflow. Abandoning this design requirement can be tempting, but you will regret it eventually. Over time, changes and fixes will make it into some deployment systems, but not others. And as the implementation details diverge, software that works properly in one part of your company will stop working in another. This will cause chaos and mistrust, and slow down your feature delivery. Once this deployment system divergence takes hold, it will naturally worsen over time. As separate individuals and teams run into deployment issues, they'll develop local, ad hoc solutions to solve the problems they run into. This leads to your engineering organization duplicating lots of automation work while delivering product updates more slowly and less reliably. Build what bots CRAVE The five requirements of the CRAVE framework are an ideal—it's probably impossible to build a system that fulfills them all completely. Software engineering is a game of tradeoffs—for every product, at every company, engineers should prioritize these criteria as time allows, weighing them against the needs of their employers. Even if you can't build exactly the deployment systems your robots CRAVE, it's still worth it to get as close as possible to this ideal. The five design criteria outlined in this article can help steer your company away from the classic "works on my machine" quagmire, where intermittent, environment-specific bugs are never root-caused or fixed and software quality rots away over time. Tell your friends! 👇 Also published here.

What Robots CRAVE: How to Deploy Software Onto Hardware Platforms

Gamification and the future of culture. Memecoins are cryptocurrency tokens inspired by internet memes. By now, you’ve probably heard of $DOGE coin, the first-ever memecoin launched in 2013 as a joke about Bitcoin. Billy Markus, the co-creator of Doge, sold his holdings for a modest $10,000. Today, the market cap of DOGE is $52 billion. If Billy had held onto his DOGE coins, they’d be worth almost $20 million today. Dare I say, memecoins are here to stay. There are probably hundreds of thousands of unique memecoins in existence today — some have gone to zero, others are dormant, some are still in flux, and a few are seeing steady growth. I have done freelance work in the crypto industry since 2018, but I never fully ventured into memecoins. I thought Bitcoin, a more established cryptocurrency, was volatile enough. When you’re just getting used to the concept of volatile markets and digital, invisible money, diving into something even less established, more volatile, and based on a meme seemed reckless. Why invest in a joke? At the time, it all seemed shady. It took me a few years to truly understand the appeal of memecoins. The surge in Bitcoin that followed Donald Trump’s win in the 2024 presidential election gave me perspective. BTC hit $67,000 on November 5th, 2024. After Trump, a self-proclaimed pro-Bitcoin president, was declared the winner, the price shot up over 39%, currently sitting at $94,000. This is an insane amount of growth — a new all-time high for Bitcoin. As the crypto surge unfolded, I noticed that memecoins were surging alongside Bitcoin. Google trends for crypto-related searches were going up, and people were claiming massive 300x returns on memecoin investments — truly life-changing money. The prices of these alternative coins are incredibly volatile. For example, take $PNUT, which is based on an internet-famous squirrel that was confiscated and euthanized by the New York government. Its price has shot up 1692% since November 4th, after Elon Musk tweeted about the late squirrel, saying, “America was saved by a squirrel and a meme coin!” Of course, the tweet boosted the price even further. I decided to buy some Solana since many memecoins use the Solana blockchain due to its fast transaction speeds. Hundreds of coins are created every minute on the Solana ecosystem. You can easily and quickly send and receive Solana to buy many popular and rising memecoins. I traded, made some money, and lost some too. But I learned something that, perhaps, many already in the trenches of memecoin trading have long known: there is an insane amount of money to be made very quickly with memecoins — along with a ton of risk. Trading these tokens so quickly, tokens that are so widely volatile, is essentially a game. It’s a competition between those with insider knowledge, those without, the holders with the most assets, chart analysts who can buy or sell at the right time, those with proper exit strategies, those using bots and AI to immediately purchase new tokens upon launch, and those who can tell which memes are here to stay and which are fleeting. Memecoin trading is, at its core, about trading memetic culture. To me, this is the future of gaming and finance — an intersection that will be the bedrock of Web3, the future of the internet. Blockchain technology allows for instant transactions and trading of digital assets. This opens up the possibility for the auctioning and trading of in-game assets. I believe that Artificial intelligence will serve as the automation layer to facilitate the development of assets and process transactions. AI will make scouting for tradable items intelligent and efficient — essentially gamifying economics. Gaming will be the activity, AI will be the processing mechanism, and together they will bridge the gap to massive cryptocurrency adoption. In the future, assets and money will be tokenized, and blockchain will facilitate the movement of massive amounts of data, making NFTs more accessible. Gaming is key because people already collect digital assets in video games. If these in-game assets could be traded for currency or used to bid on other tangible and digital items, we’d see a more rapid flow of money throughout the industry and some incredible “gamer to investor” stories. What we’re seeing now are the foundational elements of a world similar to Ready Player One (2018), where the line between the digital and real world is blurred. The world is increasingly gamified. What will this mean for the future? How will people understand culture when trading culture is the future? This is not financial advice, but I urge you to research memecoins and cryptocurrency. The world is being digitized as we speak. I believe the players need to invest and the investors need to play. Is it crazy that we may be witnessing a collision of the serious and the unserious, the comedy meme and the business world? Could this explain the relaxed attire of business moguls due to the influence of tech industry CEOs? Will high fashion and sartorial works be that of memes? Will everything of timelessness become a meme? Is becoming meme the mummification of great ideas? Editor’s note: This article is for informational purposes only and does not constitute investment advice. Cryptocurrencies are speculative, complex, and involve high risks. This can mean high prices volatility and potential loss of your initial investment. You should consider your financial situation, investment purposes, and consult with a financial advisor before making any investment decisions. The HackerNoon editorial team has only verified the story for grammatical accuracy and does not endorse or guarantee the accuracy, reliability, or completeness of the information stated in this article. #DYOR Gamification and the future of culture. Memecoins are cryptocurrency tokens inspired by internet memes. By now, you’ve probably heard of $DOGE coin, the first-ever memecoin launched in 2013 as a joke about Bitcoin. Billy Markus, the co-creator of Doge, sold his holdings for a modest $10,000. Today, the market cap of DOGE is $52 billion. If Billy had held onto his DOGE coins, they’d be worth almost $20 million today. $DOGE Dare I say, memecoins are here to stay. There are probably hundreds of thousands of unique memecoins in existence today — some have gone to zero, others are dormant, some are still in flux, and a few are seeing steady growth. I have done freelance work in the crypto industry since 2018, but I never fully ventured into memecoins. I thought Bitcoin, a more established cryptocurrency, was volatile enough. When you’re just getting used to the concept of volatile markets and digital, invisible money, diving into something even less established, more volatile, and based on a meme seemed reckless. Why invest in a joke? At the time, it all seemed shady. It took me a few years to truly understand the appeal of memecoins. The surge in Bitcoin that followed Donald Trump’s win in the 2024 presidential election gave me perspective. BTC hit $67,000 on November 5th, 2024. After Trump, a self-proclaimed pro-Bitcoin president, was declared the winner, the price shot up over 39%, currently sitting at $94,000. This is an insane amount of growth — a new all-time high for Bitcoin. As the crypto surge unfolded, I noticed that memecoins were surging alongside Bitcoin. Google trends for crypto-related searches were going up, and people were claiming massive 300x returns on memecoin investments — truly life-changing money. The prices of these alternative coins are incredibly volatile. For example, take $PNUT , which is based on an internet-famous squirrel that was confiscated and euthanized by the New York government. Its price has shot up 1692% since November 4th, after Elon Musk tweeted about the late squirrel, saying, “America was saved by a squirrel and a meme coin!” Of course, the tweet boosted the price even further. $PNUT I decided to buy some Solana since many memecoins use the Solana blockchain due to its fast transaction speeds. Hundreds of coins are created every minute on the Solana ecosystem. You can easily and quickly send and receive Solana to buy many popular and rising memecoins. I traded, made some money, and lost some too. But I learned something that, perhaps, many already in the trenches of memecoin trading have long known: there is an insane amount of money to be made very quickly with memecoins — along with a ton of risk. Trading these tokens so quickly, tokens that are so widely volatile, is essentially a game. It’s a competition between those with insider knowledge, those without, the holders with the most assets, chart analysts who can buy or sell at the right time, those with proper exit strategies, those using bots and AI to immediately purchase new tokens upon launch, and those who can tell which memes are here to stay and which are fleeting. Memecoin trading is, at its core, about trading memetic culture. To me, this is the future of gaming and finance — an intersection that will be the bedrock of Web3, the future of the internet. Blockchain technology allows for instant transactions and trading of digital assets. This opens up the possibility for the auctioning and trading of in-game assets. I believe that Artificial intelligence will serve as the automation layer to facilitate the development of assets and process transactions. AI will make scouting for tradable items intelligent and efficient — essentially gamifying economics. Gaming will be the activity, AI will be the processing mechanism, and together they will bridge the gap to massive cryptocurrency adoption. In the future, assets and money will be tokenized, and blockchain will facilitate the movement of massive amounts of data, making NFTs more accessible. Gaming is key because people already collect digital assets in video games. If these in-game assets could be traded for currency or used to bid on other tangible and digital items, we’d see a more rapid flow of money throughout the industry and some incredible “gamer to investor” stories. What we’re seeing now are the foundational elements of a world similar to Ready Player One (2018), where the line between the digital and real world is blurred. The world is increasingly gamified. What will this mean for the future? How will people understand culture when trading culture is the future? Ready Player One This is not financial advice, but I urge you to research memecoins and cryptocurrency. The world is being digitized as we speak. I believe the players need to invest and the investors need to play. Is it crazy that we may be witnessing a collision of the serious and the unserious, the comedy meme and the business world? Could this explain the relaxed attire of business moguls due to the influence of tech industry CEOs? Will high fashion and sartorial works be that of memes? Will everything of timelessness become a meme? Is becoming meme the mummification of great ideas? Editor’s note: This article is for informational purposes only and does not constitute investment advice. Cryptocurrencies are speculative, complex, and involve high risks. This can mean high prices volatility and potential loss of your initial investment. You should consider your financial situation, investment purposes, and consult with a financial advisor before making any investment decisions. The HackerNoon editorial team has only verified the story for grammatical accuracy and does not endorse or guarantee the accuracy, reliability, or completeness of the information stated in this article. #DYOR Editor’s note: This article is for informational purposes only and does not constitute investment advice. Cryptocurrencies are speculative, complex, and involve high risks. This can mean high prices volatility and potential loss of your initial investment. You should consider your financial situation, investment purposes, and consult with a financial advisor before making any investment decisions. The HackerNoon editorial team has only verified the story for grammatical accuracy and does not endorse or guarantee the accuracy, reliability, or completeness of the information stated in this article. #DYOR Editor’s note: This article is for informational purposes only and does not constitute investment advice. Cryptocurrencies are speculative, complex, and involve high risks. This can mean high prices volatility and potential loss of your initial investment. You should consider your financial situation, investment purposes, and consult with a financial advisor before making any investment decisions. The HackerNoon editorial team has only verified the story for grammatical accuracy and does not endorse or guarantee the accuracy, reliability, or completeness of the information stated in this article. #DYOR

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