Atoa

Design Systems: A Front-End Engineer's Guide to Efficient Atomic Design

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

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

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

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

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

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

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

GenAIbots がモル概念、アボガドロ数、原子、分子を解明する方法

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

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

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

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

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

GenAIbots 如何揭开摩尔概念、阿伏伽德罗常数、原子和分子的神秘面纱

Authors: (1) Renato P. dos Santos, CIAGE – Centre for Generative Artificial Intelligence in Cognition and Education. Table of Links Abstract and Introduction Materials And Methods Results and Analyses Prompts and generated texts Conceptualizing chemical reactions Deepening on understanding of chemical reactions Question about combustion Question about a graph of gases turning into water over time Question about a graph of gases turning into water over time Question about changing of state Question about an animated representation of water molecules undergoing phase changes Question about plasma, a state of matter Question about chemical bondings Question about illustration of chemical bonds Question about the essence of the type of chemical bonding Further analysis Conclusions Limitations of the study and possible future studies Author Contributions, Conflicts of interest, Acknowledgements, and References Question about the difference between atoms, molecules, and moles : P1 ah. this thing also confuses me. what is the difference between atom, molecule and mole? ChatGPT: Certainly, it's a common area of confusion for many students. Let's break it down one by one, using simple analogies: 1. Atom: • Imagine an atom as a single LEGO brick. It's the tiniest piece of an element that still retains the properties of that element. Just like a single red LEGO brick represents the color red, a single hydrogen atom represents the element hydrogen. 2. Molecule: • When you connect several LEGO bricks together, you form a structure. In chemistry, when you connect several atoms together, you form a molecule. For instance, two hydrogen atoms (H) can connect to form a hydrogen molecule (H2). Similarly, two hydrogen atoms and one oxygen atom can connect to form a water molecule (H2O). ***3. Mole: *** • This one's a bit more abstract. Let's say you have a big box filled with identical LEGO structures (say, LEGO houses). If you count them and find there are exactly 6.022 x 1023 LEGO houses in that box, you have a "mole" of LEGO houses. • In chemistry, a mole is a specific number of things (atoms, molecules, electrons, etc.). Specifically, one mole of any substance contains exactly 6.022 x 10[23] of those entities. This number is called Avogadro's number. • The concept of a mole is a standardized way to talk about quantities in chemistry, much like a "dozen" is a standardized way to talk about 12 of something. So, to recap: • : Smallest unit of an element. Atom • : Made up of multiple atoms bonded together. Molecule • : A specific quantity (6.022 x 10[23]) of atoms, molecules, or other entities. Mole Do these analogies help clarify the differences for you? BingChat: I see that you are confused about the difference between atom, molecule and mole. These are three important terms in chemistry that describe different aspects of matter. Let me try to explain them to you in a simple way. An atom is the smallest unit of an element that retains its chemical identity. An element is a pure substance that consists of only one type of atom. For example, hydrogen, oxygen, carbon, and gold are elements. Each element has a unique symbol and atomic number that indicates how many protons it has in its nucleus. For example, hydrogen has the symbol H and atomic number 1, meaning it has one proton in its nucleus. Atoms are composed of subatomic particles such as protons, neutrons, and electrons. Protons and neutrons are found in the nucleus, and electrons orbit around the nucleus in shells or orbitals. The number of electrons in an atom determines its chemical behavior and reactivity [ ] 12 A molecule is a group of two or more atoms that are held together by chemical bonds. A chemical bond is a force of attraction between atoms that share or transfer electrons. There are different types of chemical bonds, such as covalent, ionic, metallic, and hydrogen bonds. A molecule can be composed of atoms of the same element or different elements. For example, oxygen gas (O2) is a molecule composed of two oxygen atoms bonded by a covalent bond. Water (H2O) is a molecule composed of two hydrogen atoms and one oxygen atom bonded by covalent bonds. A molecule has a specific shape and structure that determines its physical and chemical properties[ ]. 32 A mole is a unit of measurement that is used to count the number of atoms or molecules in a given amount of substance. A mole is defined as the amount of substance that contains as many particles (atoms or molecules) as there are atoms in 12 grams of carbon-12. This number is called Avogadro’s number and it is approximately 6.022 x 10[23]. That means one mole of any substance contains 6.022 x 10[23] particles (atoms or molecules). For example, one mole of oxygen gas contains 6.022 x 10[23] molecules of O2, and one mole of water contains 6.022 x 1023 molecules of H2O. The mass of one mole of a substance is called its molar mass and it is equal to its molecular weight in grams. For example, the molecular weight of oxygen gas is 32 g/mol and the molecular weight of water is 18 g/mol[ ]. 42 So, to summarize, an atom is the smallest unit of an element, a molecule is a group of atoms bonded together, and a mole is a unit of measurement that counts the number of particles in a substance. I hope this helps you understand the difference between atom, molecule and mole. :) *: An atom is the smallest unit of matter that still has all of the properties of its element. An element is a substance that cannot be broken down into simpler substances by chemical means.* Bard A molecule is a group of two or more atoms held together by chemical bonds. A chemical bond is a force that holds atoms together. A mole is a unit of measurement that is used to count atoms and molecules. One mole of any substance contains 6.02214076×10^23 particles of that substance. This number is known as Avogadro's constant. Here is a table that summarizes the key differences between atoms, molecules, and moles: Here are some examples of atoms, molecules, and moles: • Atoms: Hydrogen (H), helium (He), oxygen (O), carbon (C) • Molecules: Water (H2O), carbon dioxide (CO2), methane (CH4) • Moles: One mole of hydrogen atoms, one mole of water molecules, one mole of carbon dioxide molecules I hope this helps to explain the difference between atoms, molecules, and moles. Please let me know if you have any other questions. *: Excellent question! Those three terms can definitely be confusing. Let's go through them one-by-one:* Claude Atom - The basic building block of matter. Atoms are made up of protons, neutrons, and electrons. An atom is the smallest unit of an element that maintains the properties of that element. For example, a hydrogen atom has 1 proton; a carbon atom has 6 protons. Molecule - A group of two or more atoms bonded together. A molecule is the smallest unit of a chemical compound that maintains the properties of that compound. For example, a water molecule contains 2 hydrogen atoms and 1 oxygen atom bonded together (H2O). Mole - This is a number representing 6.02 x 10^23 things. It's usually used to represent a certain quantity of atoms or molecules. For example, 1 mole of carbon contains 6.02 x 10^23 carbon atoms. So in summary: • Atoms are the basic units of elements • Molecules are groups of bonded atoms that make up compounds • Moles are a way to represent 6.02 x 10^23 of something (useful for relating atomic/molecular masses to macroscopic masses) Let me know if this helps explain the distinction between these three important chemistry concepts! I'm happy to provide more examples as well. , by saying “Certainly, it's a common area of confusion for many students”, recognises a element by acknowledging the difficulty many students face. By saying, "Let's break it down one by one," it demonstrates Instructional strategy. The phrase “using simple analogies" clearly indicates the use of , while the question "Do these analogies help clarify?" shows an component by asking for feedback. saying "I see that you are confused" provides , while the phrase "Let me try to explain" indicates an Instructional Strategy and "in a simple way" attempts at by simplifying the information. Finally, "I hope this helps" and the inclusion of the smiley emoticon ":)" are gestures for Personalization & Pe , by explaining "An atom is..." and, later, offering "Here is a table," appeals to an , demonstrated by definition-based learning and visual data organisation, concluding with "I hope this helps" and “Please let me know if you have any other questions” demonstrates and . , by starting with “Excellent question" and acknowledging that the subject “can definitely be confusing,” recognises gives Reinforcement & feedback. It then invites, "Let's go through them one-by-one," pointing to an Instructional Strategy through systematic explanation. It also uses by providing two examples. It concludes with "Let me know if this helps," an element, and encouraging feedback. ChatGPT Reinforcement & Feedback Analogy & Comparative Illustrations Engagement & interactivity BingChat Reinforcement & Feedback Detail Depth & Content Recommendation rsonable Touch. Bard Instructional Strategy Engagement & Interactivity Reinforcement & Feedback Claude Analogy & Comparative Illustrations Engagement & Interactivity This paper is under CC BY-SA 4.0 DEED license. available on arxiv

How GenAIbots Explain the Difference Between Atoms, Molecules, and Moles

কিভাবে GenAIbots পরমাণু, অণু এবং মোলের মধ্যে পার্থক্য ব্যাখ্যা করে

Wie GenAIbots den Unterschied zwischen Atomen, Molekülen und Molen erklären

Cómo los GenAIbots explican la diferencia entre átomos, moléculas y moles

Comment les GenAIbots expliquent la différence entre les atomes, les molécules et les taupes

GenAIbots परमाणुओं, अणुओं और मोल्स के बीच अंतर कैसे समझाते हैं

GenAIbots が原子、分子、モルの違いを説明する方法

GenAIbot이 원자, 분자, 두더지의 차이점을 설명하는 방법

Como GenAIbots explicam a diferença entre átomos, moléculas e toupeiras

Как GenAIbots объясняют разницу между атомами, молекулами и кротами

GenAIbotlar Atomlar, Moleküller ve Benler Arasındaki Farkı Nasıl Açıklıyor?

Cách GenAIbots giải thích sự khác biệt giữa nguyên tử, phân tử và nốt ruồi

GenAIbots 如何解释原子、分子和摩尔之间的区别

Last week, two remarkable events occurred: Meta announced its Llama 3, the most capable and highly anticipated open large language model (LLM) to date, and Boston Dynamics introduced its new fully electric Atlas robot platform — a stark departure from their hydraulic robots! At first glance, these developments may seem unrelated, but they are actually deeply interconnected, with the potential to synergistically drive each other forward and reshape how we work with and implement AI. At the heart of this connection is the transformative power of advanced AI. Breakthroughs in natural language processing and machine learning, exemplified by Llama 3, extend beyond language alone. These techniques, from deep neural networks to reinforcement learning, are also propelling significant advancements in computer vision, motion planning, and robot control. As language models like Llama 3 continue to expand the boundaries of AI’s ability to understand and interact with the world, they also lay the groundwork for more intelligent and capable robots. The implications, of course, extend beyond communication. The same techniques used to train LLMs on vast amounts of text data are also applicable to learning from massive datasets of sensor readings, images, and simulations for robots. Moreover, the open-source nature of models like Llama 3 democratizes access to state-of-the-art AI, enabling a broader spectrum of researchers and companies to integrate these capabilities into their robotic systems. At least start playing on that field! Every successful (and unsuccessful) attempt by Atlas to manipulate an object, navigate a cluttered factory floor, or assist a human worker becomes a valuable data point for the model, just as it does for Llama 3. Using this innovative model, Meta developed a standalone app, meta.ai, and embedded it into WhatsApp, Instagram, and Facebook, collecting vast and highly personalized data. This could lead us toward synthetic social networks or — which is more likely — an extremely personalized, embodied AI experience. As Llama 3 and the electric Atlas converge, they may accelerate each other’s development, blurring the lines between software and hardware, bits and atoms. As language models become more proficient at understanding and interacting with the world, robots will become more capable of applying that knowledge. This doesn’t necessarily mean evil. More capable and advanced robots, which can be fine-tuned and improved with your personal data and specifics, could take over mundane tasks at home. Imagine, instead of hundreds of text editors, finally having a robot that can load and unload the dishwasher and do the laundry. Currently, launches like Llama 3 are seen as enhancing AI’s understanding and processing capabilities, but in the long term, they will be one of the milestones in building and deploying machines that are finely attuned and aligned with us to assist in our daily lives. https://youtu.be/29ECwExc-_M?si=UyexFH1jOfd5TGY9&embedable=true Other Impressive Models: (I didn’t send you the FOD digest last Monday because I was at a conference dedicated to citizen diplomacy. Therefore, today, we have an extensive list of recently launched models that are worth checking out, along with other relevant research papers.) Mixtral-8x22B: Mistral AI introduced a scalable sparse mixture of expert models optimizing cost and latency by selectively using parameters during inference, offering a high capacity and efficiency model for further training and applications →read the paper
Rerank 3: Launched by Cohere, this model enhances enterprise search and Retrieval Augmented Generation systems, improving accuracy in document retrieval across multiple languages and data formats →read the paper
Idefics2: Hugging Face's model that excels in integrating text and image data, significantly improving on OCR and visual question-answering tasks →read the paper
Reka Core, Flash, and Edge: A series of multimodal language models from Reka that process text, images, video, and audio, demonstrating high performance across diverse tasks →read the paper
Ferret-UI: Developed by Apple, this model specializes in mobile UI interaction, enhancing user experience by accurately performing tasks tailored to the unique properties of UI screens →ead the paper
Zamba: Zyphra's compact and efficient SSM Hybrid model is designed for performance with reduced training data needs, optimized for consumer hardware →read the paper
YaART: Yandex's advanced text-to-image diffusion model that optimizes training efficiency and quality with smaller, high-quality datasets →read the paper
RHO-1: A novel approach by Xiamen University focusing on Selective Language Modeling to enhance efficiency by prioritizing useful tokens during training →read the paper
RecurrentGemma: Google DeepMind's model that moves past traditional transformers by incorporating recurrences for more efficient long-sequence processing →read the paper
JetMoE: An economical LLM from the MIT-IBM Watson AI Lab using a mixture-of-experts architecture to achieve high performance at reduced costs →read the paper News from The Usual Suspects © Google open-sourced Gemini Cookbook
and is merging its Research, DeepMind, and Responsible AI teams to accelerate its “capacity to deliver capable AI”
DeepMind published an article about the ethics of advanced AI assistants, stressing the significance of their integration into daily life Hugging Face https://x.com/QGallouedec/status/1782430246957994422?embedable=true Stanford published the 2024 AI Index report, reflecting the escalating impact of AI on society. It contains data with new estimates on AI training costs and detailed insights into the responsible AI landscape. It also introduces a new chapter on AI's influence on science and medicine. Highlights include the substantial cost of training state-of-the-art models, such as GPT-4 and Gemini Ultra; the dominance of the U.S. in producing top AI models; and significant investment growth in generative AI despite overall funding declines. Additionally, the report notes a major increase in AI-related regulations in the U.S. and heightened public awareness and nervousness about AI's future impact. Enjoyed This Story? I write a weekly analysis of the AI world in the Turing Post newsletter. We aim to equip you with comprehensive knowledge and historical insights so you can make informed decisions about AI and ML. Last week, two remarkable events occurred: Meta announced its Llama 3 , the most capable and highly anticipated open large language model (LLM) to date, and Boston Dynamics introduced its new fully electric Atlas robot platform — a stark departure from their hydraulic robots! At first glance, these developments may seem unrelated, but they are actually deeply interconnected, with the potential to synergistically drive each other forward and reshape how we work with and implement AI. Llama 3 Llama 3 electric Atlas robot electric Atlas robot At the heart of this connection is the transformative power of advanced AI. Breakthroughs in natural language processing and machine learning, exemplified by Llama 3, extend beyond language alone. These techniques, from deep neural networks to reinforcement learning, are also propelling significant advancements in computer vision, motion planning, and robot control. As language models like Llama 3 continue to expand the boundaries of AI’s ability to understand and interact with the world, they also lay the groundwork for more intelligent and capable robots. The implications, of course, extend beyond communication. The same techniques used to train LLMs on vast amounts of text data are also applicable to learning from massive datasets of sensor readings, images, and simulations for robots. Moreover, the open-source nature of models like Llama 3 democratizes access to state-of-the-art AI, enabling a broader spectrum of researchers and companies to integrate these capabilities into their robotic systems. At least start playing on that field! Every successful (and unsuccessful) attempt by Atlas to manipulate an object, navigate a cluttered factory floor, or assist a human worker becomes a valuable data point for the model, just as it does for Llama 3. Using this innovative model, Meta developed a standalone app, meta.ai , and embedded it into WhatsApp, Instagram, and Facebook, collecting vast and highly personalized data. This could lead us toward synthetic social networks or — which is more likely — an extremely personalized, embodied AI experience. As Llama 3 and the electric Atlas converge, they may accelerate each other’s development, blurring the lines between software and hardware, bits and atoms. As language models become more proficient at understanding and interacting with the world, robots will become more capable of applying that knowledge. meta.ai meta.ai synthetic social networks synthetic social networks This doesn’t necessarily mean evil. More capable and advanced robots, which can be fine-tuned and improved with your personal data and specifics, could take over mundane tasks at home. Imagine, instead of hundreds of text editors, finally having a robot that can load and unload the dishwasher and do the laundry. Currently, launches like Llama 3 are seen as enhancing AI’s understanding and processing capabilities, but in the long term, they will be one of the milestones in building and deploying machines that are finely attuned and aligned with us to assist in our daily lives. https://youtu.be/29ECwExc-_M?si=UyexFH1jOfd5TGY9&embedable=true https://youtu.be/29ECwExc-_M?si=UyexFH1jOfd5TGY9&embedable=true Other Impressive Models: (I didn’t send you the FOD digest last Monday because I was at a conference dedicated to citizen diplomacy. Therefore, today, we have an extensive list of recently launched models that are worth checking out, along with other relevant research papers.) (I didn’t send you the FOD digest last Monday because I was at a conference dedicated to citizen diplomacy. Therefore, today, we have an extensive list of recently launched models that are worth checking out, along with other relevant research papers.) Mixtral-8x22B: Mistral AI introduced a scalable sparse mixture of expert models optimizing cost and latency by selectively using parameters during inference, offering a high capacity and efficiency model for further training and applications →read the paper Rerank 3: Launched by Cohere, this model enhances enterprise search and Retrieval Augmented Generation systems, improving accuracy in document retrieval across multiple languages and data formats →read the paper Idefics2: Hugging Face's model that excels in integrating text and image data, significantly improving on OCR and visual question-answering tasks →read the paper Reka Core, Flash, and Edge: A series of multimodal language models from Reka that process text, images, video, and audio, demonstrating high performance across diverse tasks →read the paper Ferret-UI: Developed by Apple, this model specializes in mobile UI interaction, enhancing user experience by accurately performing tasks tailored to the unique properties of UI screens →ead the paper Zamba: Zyphra's compact and efficient SSM Hybrid model is designed for performance with reduced training data needs, optimized for consumer hardware →read the paper YaART: Yandex's advanced text-to-image diffusion model that optimizes training efficiency and quality with smaller, high-quality datasets →read the paper RHO-1: A novel approach by Xiamen University focusing on Selective Language Modeling to enhance efficiency by prioritizing useful tokens during training →read the paper RecurrentGemma: Google DeepMind's model that moves past traditional transformers by incorporating recurrences for more efficient long-sequence processing →read the paper JetMoE: An economical LLM from the MIT-IBM Watson AI Lab using a mixture-of-experts architecture to achieve high performance at reduced costs →read the paper Mixtral-8x22B: Mistral AI introduced a scalable sparse mixture of expert models optimizing cost and latency by selectively using parameters during inference, offering a high capacity and efficiency model for further training and applications →read the paper Mixtral-8x22B: →read the paper Rerank 3: Launched by Cohere, this model enhances enterprise search and Retrieval Augmented Generation systems, improving accuracy in document retrieval across multiple languages and data formats →read the paper Rerank 3: →read the paper Idefics2: Hugging Face's model that excels in integrating text and image data, significantly improving on OCR and visual question-answering tasks →read the paper Idefics2: →read the paper Reka Core, Flash, and Edge: A series of multimodal language models from Reka that process text, images, video, and audio, demonstrating high performance across diverse tasks →read the paper Reka Core, Flash, and Edge: →read the paper Ferret-UI: Developed by Apple, this model specializes in mobile UI interaction, enhancing user experience by accurately performing tasks tailored to the unique properties of UI screens →ead the paper Ferret-UI: →ead the paper Zamba: Zyphra's compact and efficient SSM Hybrid model is designed for performance with reduced training data needs, optimized for consumer hardware →read the paper Zamba: →read the paper YaART: Yandex's advanced text-to-image diffusion model that optimizes training efficiency and quality with smaller, high-quality datasets →read the paper YaART: →read the paper RHO-1: A novel approach by Xiamen University focusing on Selective Language Modeling to enhance efficiency by prioritizing useful tokens during training →read the paper RHO-1: →read the paper RecurrentGemma: Google DeepMind's model that moves past traditional transformers by incorporating recurrences for more efficient long-sequence processing →read the paper RecurrentGemma: →read the paper JetMoE: An economical LLM from the MIT-IBM Watson AI Lab using a mixture-of-experts architecture to achieve high performance at reduced costs →read the paper JetMoE: →read the paper News from The Usual Suspects © Google Google open-sourced Gemini Cookbook and is merging its Research, DeepMind, and Responsible AI teams to accelerate its “capacity to deliver capable AI” DeepMind published an article about the ethics of advanced AI assistants, stressing the significance of their integration into daily life open-sourced Gemini Cookbook Gemini Cookbook and is merging its Research, DeepMind, and Responsible AI teams to accelerate its “ capacity to deliver capable AI ” merging capacity to deliver capable AI DeepMind published an article about the ethics of advanced AI assistants, stressing the significance of their integration into daily life published Hugging Face Hugging Face https://x.com/QGallouedec/status/1782430246957994422?embedable=true https://x.com/QGallouedec/status/1782430246957994422?embedable=true Stanford Stanford published the 2024 AI Index report, reflecting the escalating impact of AI on society. It contains data with new estimates on AI training costs and detailed insights into the responsible AI landscape. It also introduces a new chapter on AI's influence on science and medicine. Highlights include the substantial cost of training state-of-the-art models, such as GPT-4 and Gemini Ultra; the dominance of the U.S. in producing top AI models; and significant investment growth in generative AI despite overall funding declines. Additionally, the report notes a major increase in AI-related regulations in the U.S. and heightened public awareness and nervousness about AI's future impact. published the 2024 AI Index report , reflecting the escalating impact of AI on society. It contains data with new estimates on AI training costs and detailed insights into the responsible AI landscape. It also introduces a new chapter on AI's influence on science and medicine. Highlights include the substantial cost of training state-of-the-art models, such as GPT-4 and Gemini Ultra; the dominance of the U.S. in producing top AI models; and significant investment growth in generative AI despite overall funding declines. Additionally, the report notes a major increase in AI-related regulations in the U.S. and heightened public awareness and nervousness about AI's future impact. the 2024 AI Index report Enjoyed This Story? I write a weekly analysis of the AI world in the Turing Post newsletter. We aim to equip you with comprehensive knowledge and historical insights so you can make informed decisions about AI and ML. Turing Post

AI Industries Converge: Llama 3 and Electric Atlas Have More In Common Than You Think

Authors:
(1) Maria Rigaki, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic and maria.rigaki@fel.cvut.cz;
(2) Sebastian Garcia, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic and sebastian.garcia@agents.fel.cvut.cz. Table of Links Abstract & Introduction Threat Model Background and Related Work Methodology Experiments Setup Results Discussion Conclusion, Acknowledgments, and References Appendix 2 Threat Model The threat model for this work assumes an attacker that only has black-box access to a target (classifier or AV) during the inference phase and can submit binary files for static scanning. The target provides binary labels (0 if benign, 1 if malicious). The attacker has no or limited information about the target architecture and training process, and they aim to evade it by modifying the malware in a functionality-preserving manner. In the case of classifiers, the attacker may have some knowledge of the extracted features, but this is not the case for antivirus systems. Some knowledge of the training data distribution is assumed. However, it may only be partially necessary. Finally, the attacker aims to minimize the interaction with the target by submitting as few queries as possible. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Maria Rigaki, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic and maria.rigaki@fel.cvut.cz; (2) Sebastian Garcia, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic and sebastian.garcia@agents.fel.cvut.cz. Authors: Authors: (1) Maria Rigaki, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic and maria.rigaki@fel.cvut.cz; (2) Sebastian Garcia, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic and sebastian.garcia@agents.fel.cvut.cz. Table of Links Abstract & Introduction Abstract & Introduction Threat Model Threat Model Background and Related Work Background and Related Work Methodology Methodology Experiments Setup Experiments Setup Results Results Discussion Discussion Conclusion, Acknowledgments, and References Conclusion, Acknowledgments, and References Appendix Appendix 2 Threat Model The threat model for this work assumes an attacker that only has black-box access to a target (classifier or AV) during the inference phase and can submit binary files for static scanning. The target provides binary labels (0 if benign, 1 if malicious). The attacker has no or limited information about the target architecture and training process, and they aim to evade it by modifying the malware in a functionality-preserving manner. In the case of classifiers, the attacker may have some knowledge of the extracted features, but this is not the case for antivirus systems. Some knowledge of the training data distribution is assumed. However, it may only be partially necessary. Finally, the attacker aims to minimize the interaction with the target by submitting as few queries as possible. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv

Understanding the Threat Model: Black-Box Attacks on Malware Detection Systems

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

GenAI - Soon to Be Great for Automating Dumb Attacks

The Noonification: 100 Days of AI, Day 17: The Different Ways Security Attacks are Created Using LLMs (4/1/2024)

In the presentation below, Clément Lesaege, co-founder of , presented the attack costs of different oracle networks and how to calculate the potential profit from corrupting them. Kleros https://youtu.be/bzqZnUC5VkI?si=T2azT5qDPdzunW4Y&embedable=true Below is a glossary of key concepts mentioned during Clement’s talk, intended as a supplement to his video presentation. About Kleros is a decentralized protocol built on the Ethereum blockchain that leverages crowdsourcing and game theory to arbitrate disputes quickly, and economically. The protocol’s core product is the Kleros court, where disputes are settled through a majority vote by randomly selected jurors incentivized to participate using , the native token of the platform. Kleros $PNK 51% Attacks A 51% attack or a majority attack occurs when more than half of the power to submit information is controlled by a single person or group of people. Essentially, this group has the ability to submit whatever information that is beneficial to them. Due to the decentralized nature of blockchains and Oracle protocols, 51% attacks will always be a risk. Because of this, protocols must utilize methods to make attacks economically unfeasible. Check out for a deep dive into all things related to oracle manipulation. this article Arbitration Protocols Arbitration protocols are decentralized systems that operate on predefined rules and smart contracts and are designed to address on-chain disputes. When disputes arise, both parties submit their case details and evidence to a panel of randomly selected jurors who evaluate the information and vote on the verdict. These jurors are rewarded, using the native token, when they arbitrate honestly or may have their staked tokens slashed in case of misconduct. Examples of arbitration protocols include and . are some popular examples of on-chain arbitration. Kleros Aragon Court Here Forking Forking refers to the process of creating a new version of a blockchain’s protocol, resulting in the splitting of the original blockchain into two separate chains. Forks occur when there are fundamental disagreements or updates within the community about the direction or rules of the blockchain. There are two main types of forks, namely . hard forks and soft forks Re-Staking In a network, participants lock up a certain amount of their cryptocurrency as collateral to become validators or participate in the consensus process. Re-staking involves taking the rewards earned from staking, such as transaction fees or newly minted tokens, and adding them back to the initial stake, effectively compounding the amount of staked tokens. PoS This allows participants to benefit from both the initial staked amount and the additional rewards generated. MEV Maximal Extractable Value (MEV) refers to the profits that miners or validators can potentially make by reordering and including transactions in a specific order within a block. On the blockchain, transaction ordering can affect the final outcome and pricing of transactions. Profits from MEV are gained by miners who engage in practices such as front-running, back-running, and sandwich attacks. This involves prioritizing transactions that are likely to profit, such as executing trades just ahead of large transactions or exploiting arbitrage opportunities between different trading platforms. A prominent example of MEV is the on the decentralized finance (DeFi) protocol . flash loan attack bZx Learn more about Kleros here: Kleros Website Kleros Documentation Kleros Twitter Clément Lesaege Twitter The Blockchain Oracle Summit is the world’s only technical summit that dives deep into the use cases, limitations, and impacts of oracles on the wider blockchain ecosystem. Leading speakers worldwide gathered in Paris to share their work and experience building and using Oracle solutions. Article by . Michael Abiodun

How to Calculate The Attack Costs Of Different Oracle Networks: The Key Concepts

বিভিন্ন ওরাকল নেটওয়ার্কের আক্রমণ খরচ কিভাবে গণনা করা যায়: মূল ধারণা

So berechnen Sie die Angriffskosten verschiedener Oracle-Netzwerke: Die Schlüsselkonzepte

Cómo calcular los costos de ataque de diferentes redes Oracle: los conceptos clave

Comment calculer les coûts d'attaque de différents réseaux Oracle : les concepts clés

विभिन्न Oracle नेटवर्कों की हमले लागत की गणना कैसे करें: मुख्य अवधारणाएँ

さまざまなOracleネットワークの攻撃コストを計算する方法: 重要な概念

다양한 Oracle 네트워크의 공격 비용을 계산하는 방법: 주요 개념

Como calcular os custos de ataque de diferentes redes Oracle: os principais conceitos

Как рассчитать стоимость атаки на различные сети Oracle: ключевые понятия

Farklı Oracle Ağlarının Saldırı Maliyetleri Nasıl Hesaplanır: Temel Kavramlar

Cách tính toán chi phí tấn công của các mạng Oracle khác nhau: Các khái niệm chính

如何计算不同预言机网络的攻击成本：关键概念

ATOA

paywithatoa.co.uk

-- employees

Since n.d.

COMPANY RANKING

ATOA

EVERGREEN INDEX #1526

Representing this company? Click here to claim and customize this page!

Atoa WIKI

Apply to get your Tech Company News Page live on HackerNoon.com today!

ATOA

paywithatoa.co.uk

-- employees

Since n.d.

COMPANY RANKING

ATOA

EVERGREEN INDEX #1526

RELATED COMPANIESRANK

STORIES ABOUT ATOA

Representing this company? Click here to claim and customize this page!

Atoa WIKI

Apply to get your Tech Company News Page live on HackerNoon.com today!