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Responsible Spending, Money Emotions, Personal Finance.
I’m procrastinating for two reasons: first, because I tried to get an 0% APR credit card about 18 months ago and they were all “sorry, you have too much debt and you now also have a credit card Application Denied on your credit history,” and second because it’s the devil you know vs. the devil you
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Responsible Spending, Money Emotions, Personal Finance.
don’t know. But I should do it. And then I should pay everything off. That should be a thing that I should be doing, that I am not doing. 4. Considered (and am still considering) dropping some of my clients now that I have renegotiated rates Let’s say that Clients A, B, and C raised your rates so
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| 5,849 |
Responsible Spending, Money Emotions, Personal Finance.
that you will be earning an extra $800 every month starting January 2015. The smart thing to do would be to drop Clients D and E, who, together, earn you about $600 every month. You still come out $200 ahead! This is the worst decision ever, right? THE WORST DECISION. Which is why I’m not making
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Responsible Spending, Money Emotions, Personal Finance.
it… yet. But oh my goodness I want to. Emotionally (and strategically) I tell myself “If you let a few clients go, you’ll have space in your life to take on new opportunities.” And then the other part of me says “Bird, hand, bush, MONEY.” And the rest of me sings “Why do they let me write about
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Functional Programming, Formal Methods.
This post is meant to be a summary of my current understanding of this matter, and to present some naive ideas that are probably nonsense. 1. There is no induction on CoC It has been known for a while that it is impossible to derive induction on the Calculus of Constructions (CoC). That is, there
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Functional Programming, Formal Methods.
is no term of type: ∀ (P : Nat -> Set) -> ∀ (S : (n : Nat) -> P n -> P (Succ n)) -> ∀ (Z : P Zero) -> ∀ (n : Nat) -> P n (I’m using Morte's Church-style syntax because it is familiar.) In other words, no matter what “smart” encoding of Nat, Succ, Zero you develop, you’ll never have a term of that
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Functional Programming, Formal Methods.
type. Due to the importance of inductive reasoning for math, that (among other things I do not understand) resulted in the creation of the Calculus of Inductive Constructions, an extension to the otherwise simple CoC which gave it native inductive datatypes, and is the foundation of Coq. That
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Functional Programming, Formal Methods.
extension was hugely complex, which made some people angry (spoiler: me) because their brain wasn’t big enough for them to understand and implement all of it themselves. Those people kept looking for simpler languages that were equally expressive. 2. Self Types One of the most interesting attempts
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| 5,856 |
Functional Programming, Formal Methods.
at this comes from Aaron Stump, who, in 2014, presented Self Types, a small extension to CoC which does the trick. The idea is that, if we extend CoC with just one construct, ιx. T, which allows the type T to refer to the value it types. With that, induction can be expressed as: Nat : Set Nat = ιn.
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Functional Programming, Formal Methods.
(P : Nat -> Set) -> ((n : Nat) -> P n -> P (Succ n)) -> (P Zero) -> P n Succ : Nat -> Nat Succ = λn -> λP -> λS -> λZ -> S n (P n s z) Zero : Nat Zero = λP -> λS -> λZ -> Z ind : (P : Set) -> ((n : Nat) -> P n -> P (Succ n)) -> (P Zero) -> (n : Nat) -> P n ind = λP -> λS -> λZ -> λn -> n P S Z
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Functional Programming, Formal Methods.
(Note that I used a Curry-Style rather than a Church-Style here, because that’s how Aaron’s proposal is presented. It isn’t clear how to present Self types with Church-style. Also, for simplicity, I ignored distinctions regarding explicit/implicit products.) The core of the idea is simple: by
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| 5,859 |
Functional Programming, Formal Methods.
allowing a type to refer to its typed value, we have that, inside the definition of ind, when we apply n to P S Z, it returns P n, as expected, simply because the type of Nat specifies so. Without self types, we didn’t have a way to express such Nat type, for the mere reason there wasn’t a n in
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Functional Programming, Formal Methods.
scope so we could return a P n; that’s, in simple terms, essentially the reason we can’t have induction on CoC. 3. Dependent Intersections Sadly, the implementation above requires mutual recursion, as the constructor of Nat needs access to its constructors Succ / Zero, and vice-versa. That, as far
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Functional Programming, Formal Methods.
as I understand, made it hard to provide a semantics for self types. Eventually, Aaron found that dependent intersections, a previously existing construct, actually generalize self-types, allowing one to prove induction in a very similar fashion. The idea is that we must, first, implement Nat in
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| 5,862 |
Functional Programming, Formal Methods.
two slightly different ways, one “simple” (CNat) and one “inductive” (INat). The later view refers to the former, so, there is no mutual recursion involved. Plus, since INat is a predicate on CNat, it has an n in scope, so it can return P n. Both implementations are then “merged together” in a last
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Functional Programming, Formal Methods.
Nat type, by using dependent intersections. Then induction is derived for that type. Here is more or less how it goes: -- Simple Nats CNat : Set CNat = ∀ (P : *) -> ∀ (S : P -> P) -> ∀ (Z : P) -> P CSucc : CNat -> CNat CSucc = λn -> λP -> λS -> λZ -> S (n P S Z) CZero : CNat CZero = λP -> λS -> λZ
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| 5,864 |
Functional Programming, Formal Methods.
-> Z -- Inductive Nats INat : CNat -> Set INat = λn -> (P : CNat -> *) -> ((n:CNat) -> P n -> P (CSucc n)) -> (P CZero) -> P n ISucc : (n : CNat) -> INat n -> INat (CSucc n) ISucc = λn -> λi -> λP -> λS -> λZ -> S n (i P S Z) IZero : INat CZero IZero = λP -> λS -> λZ -> Z -- Actuall Nat type is the
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| 5,865 |
Functional Programming, Formal Methods.
intersection of those 2 Nat : Set Nat = ιx. CNat (INat x) IZero : Nat IZero = [CZero, IZero] ISucc : Nat -> Nat ISucc = λn. [CSucc n.1, ISucc n.1 n.2] ind : (P : Set) -> ((n : Nat) -> P n -> P (Succ n)) -> (P Zero) -> (n : Nat) -> P n ind = … The proof of ind is a little more complicated, so I’ve
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Functional Programming, Formal Methods.
omitted it here. It is very clean, though, merely requiring you to access both views of the dependent intersection in the right moment and proving they are equivalent (the language also requires equality primitives, and implicit products, so that both views can be considered equal up to erasure).
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Functional Programming, Formal Methods.
It erases to the same proof we had with Self-types (identity!), which is great. Such language is interesting not only because of induction. It also allows us to encode O(1) eliminations, something that λ-encodings also used to lack. It can even feature crazy advanced things such as “insane
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Functional Programming, Formal Methods.
dependent types”, which, if I understand correctly, allow arguments of multi-arg dependent functions to depend on each other indiscriminately (not only to previous ones), and is very powerful. Almost sounds too good to be true, but Aaron, amazingly, managed to develop a semantics for it. This is,
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Functional Programming, Formal Methods.
thus, the simplest known, consistent proof assistant capable of deriving induction. It is the foundation of Cedille, which recently released its version 1.0. Sadly, this approach is a little more complex both on implementation and programming side (since, now, every datatype must be replicated in
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Functional Programming, Formal Methods.
essentially 3 slightly different ways). Fortunately, syntax sugars for inductive datatypes and elimination can mask that, and, compared to previous attempts, Cedille is astonishingly simple. 4. ????? As I wait for development on Cedilleum, a version of Cedille adjusted for Ethereum, I’ve been
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Functional Programming, Formal Methods.
playing with those ideas and trying to get a better grasp on them. Something I recently noticed is that, assuming mutual recursion, if we slightly alter CoC in such a way that, in (X : TYPE) -> BODY, X is bound in TYPE, then we can accomplish what self types do without any further addition. Here is
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| 5,872 |
Functional Programming, Formal Methods.
an example (now, again on Church style): Nat = λ (n : Nat n) ∀ (P : (n : Nat n) -> Set) ∀ (S : (n : Nat n) -> P n -> P (Succ n)) ∀ (Z : P Zero) P n Zero = λ (P : (n : Nat n) -> Set) λ (S : (n : Nat n) -> P n -> P (Succ n)) λ (Z : P Zero) Z Succ = λ (n : Nat n) λ (P : (n : Nat n) -> Set) λ (S : (n :
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Functional Programming, Formal Methods.
Nat n) -> P n -> P (Succ n)) λ (Z : P Zero) S n (n P S Z) ind = λ (P : (n : Nat n) -> Set) λ (S : (n : Nat n) -> P n -> P (Succ n)) λ (Z : P Zero) λ (n : Nat n) n P S Z Here, Nat is a predicate on itself (Nat : Nat -> Set). This allows its type to return P n, replicating what self-types do. In a
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Functional Programming, Formal Methods.
rough way, it can be seen as a dependent intersection compressed to avoid code replication. Then, each number gets an unique type; for example, Zero : Nat Zero, Succ Zero : Nat (Succ Zero), and so on. But that is fine, because we can accept all of them in a λ (n : Nat n) -> ... function, so, those
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Functional Programming, Formal Methods.
infinitely many types behave as one. Finally, induction becomes trivial, just as with self-types. I kinda like this idea because it is similar to Self-types but without any addition at all, just a different take on type-checking. I’ve implemented it and the changes are pretty natural and
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| 5,876 |
Functional Programming, Formal Methods.
straightforward, about 3 lines of code, I’d say. The whole thing works amazingly well and cleanly. Of course, mutual recursion itself is not justified at all. I’m not even close to having the expertise necessary to give a semantics to that, and it’d probably fall in the same problems that Aaron had
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Artificial Intelligence, AI, Reinforcement Learning, Machine Learning, Machine Learning Models.
Artificial Intelligence (AI) has made tremendous strides in recent years, with applications ranging from natural language processing to image recognition. One area that has seen significant advancements is reinforcement learning, a paradigm where agents learn to make decisions by interacting with
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Artificial Intelligence, AI, Reinforcement Learning, Machine Learning, Machine Learning Models.
their environment. One promising avenue within reinforcement learning is the incorporation of human feedback, a technique that leverages the nuanced understanding and expertise of humans to enhance machine learning models. This story was written with the assistance of an AI writing program. Photo
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Artificial Intelligence, AI, Reinforcement Learning, Machine Learning, Machine Learning Models.
by Andy Kelly on Unsplash Reinforcement Learning Overview Reinforcement learning (RL) is a machine learning paradigm where agents learn by interacting with an environment. The agent takes actions, receives feedback in the form of rewards or punishments, and adjusts its behavior to maximize
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Artificial Intelligence, AI, Reinforcement Learning, Machine Learning, Machine Learning Models.
cumulative rewards over time. This process enables the agent to learn complex tasks and strategies autonomously. However, RL systems often face challenges in terms of data efficiency, safety, and generalization. This is where the integration of human feedback becomes invaluable. Reinforcement
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Artificial Intelligence, AI, Reinforcement Learning, Machine Learning, Machine Learning Models.
Learning from Human Feedback Reinforcement Learning from Human Feedback (RLHF) involves incorporating human input into the learning process, offering a way to mitigate RL challenges. Human feedback can come in various forms, including explicit reward signals, comparisons, and demonstrations. 1.
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Artificial Intelligence, AI, Reinforcement Learning, Machine Learning, Machine Learning Models.
Explicit Reward Signals Humans can provide explicit feedback by assigning rewards to specific actions taken by the RL agent. This helps in shaping the agent’s behavior towards desired outcomes. For example, in training a virtual assistant, a user may provide positive reinforcement when the
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Artificial Intelligence, AI, Reinforcement Learning, Machine Learning, Machine Learning Models.
assistant responds appropriately, guiding the system toward better performance. 2. Comparisons Humans can rank or compare different actions or sequences of actions, helping the RL agent understand preferences and make better decisions. This form of feedback is particularly useful in scenarios where
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Artificial Intelligence, AI, Reinforcement Learning, Machine Learning, Machine Learning Models.
assigning absolute rewards is challenging but comparative judgments are feasible. 3. Demonstrations Human demonstrations involve showing the RL agent how to perform a task. The agent learns by imitating the demonstrated behavior. Demonstrations can be especially valuable in situations where
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Artificial Intelligence, AI, Reinforcement Learning, Machine Learning, Machine Learning Models.
exploration is costly or risky, as the model can leverage human expertise to learn more efficiently. Benefits of RLHF 1. Data Efficiency RLHF can significantly reduce the amount of exploration needed by leveraging human knowledge, making learning more sample-efficient. This is crucial in real-world
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Artificial Intelligence, AI, Reinforcement Learning, Machine Learning, Machine Learning Models.
applications where data collection may be expensive, time-consuming, or impractical. 2. Safety and Ethical Considerations Human feedback enables the infusion of ethical considerations into AI systems. Humans can guide the model to avoid unsafe or undesirable behaviors. This is particularly
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Artificial Intelligence, AI, Reinforcement Learning, Machine Learning, Machine Learning Models.
important in critical domains such as healthcare, finance, and autonomous vehicles. 3. Enhanced Generalization RLHF helps improve the generalization capabilities of models by incorporating human insights and domain expertise. This ensures that RL agents can adapt to a broader range of situations
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Artificial Intelligence, AI, Reinforcement Learning, Machine Learning, Machine Learning Models.
and perform well in real-world, dynamic environments. Challenges and Future Directions While RLHF holds great promise, challenges persist. Issues related to the quality and consistency of human feedback, as well as the potential biases introduced, need careful consideration. Striking a balance
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Artificial Intelligence, AI, Reinforcement Learning, Machine Learning, Machine Learning Models.
between autonomy and human guidance remains an ongoing research focus. Future directions in RLHF include refining methods to better handle imperfect or noisy human feedback, developing algorithms that can actively query humans for targeted information, and exploring ways to scale up RLHF to handle
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Artificial Intelligence, AI, Reinforcement Learning, Machine Learning, Machine Learning Models.
more complex tasks. Reinforcement Learning from Human Feedback is a frontier in AI research that seeks to amalgamate the strengths of artificial intelligence and human intelligence. By leveraging the nuanced understanding, creativity, and ethical considerations of humans, RLHF has the potential to
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Artificial Intelligence, AI, Reinforcement Learning, Machine Learning, Machine Learning Models.
propel AI systems to new heights of performance, safety, and adaptability. As research in this field continues to evolve, we can anticipate AI systems that not only learn efficiently but also align more closely with human values and preferences.
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Humanode, Cryptocurrency, Biometrics.
Humanode: POC for experimental research on CVM attestation complete, Read about data privacy in Biomapper, Humanode partnered with Hackless, Humanode has joined forces with Banksters, Humanode x Qappi, Humanode-inspired novel “2067 the Equilibrium” is here, Founders Talk on Friday, and more!
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Humanode, Cryptocurrency, Biometrics.
Biweekly update vol.72, 3rd April — 17th April TL;DR Hello, human nodes! This report provides an overview of the main news, development updates, and recent events within the Humanode ecosystem. Let’s start! First, several updates and enhancements have been made in various aspects of Humanode’s
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Humanode, Cryptocurrency, Biometrics.
development. The team has utilized ZombieNet, a robust framework tailored for Substrate-based blockchains, for end-to-end network testing that enables testing changes, particularly when launching multiple nodes, by effortlessly spawning and testing ephemeral networks. Further insights are available
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Humanode, Cryptocurrency, Biometrics.
on the Humanode GitHub. Furthermore, the team has completed a proof of concept (POC) for experimental research on Confidential Virtual Machine (CVM) attestation. A Rust utility for attestation has been developed, along with a web transport server and client, ensuring the security of the transport
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Humanode, Cryptocurrency, Biometrics.
session. In addition to CVM attestation, research efforts are directed towards enabling web support. The goal is to create a client library facilitating connection to CVM via the web, enhancing flexibility and accessibility for developers. Enhancements in BotBasher include developing a system to
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Humanode, Cryptocurrency, Biometrics.
refresh tokens for users in the next generation. Additionally, features such as displaying Discord usernames after facial scanning and introducing a ticket system for improved communication between the team and the community are being implemented. Also, the translation of the mainnet website into
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Humanode, Cryptocurrency, Biometrics.
different languages has commenced, with translators engaged to assist with this initiative. As for business development, Humanode has established a partnership with Hackless, a platform enhancing the security of DeFi protocols. Hackless has integrated Humanode’s BotBasher into its Discord to
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| 5,900 |
Humanode, Cryptocurrency, Biometrics.
prevent Sybil attacks and bots. The partnership includes community a campaign on AlphaGuilty, offering rewards. Banksters, the first P2E trading game blending finance education with immersive gameplay, is now utilizing BotBasher for Sybil resistance in their airdrop campaigns, offering NFTs with
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| 5,901 |
Humanode, Cryptocurrency, Biometrics.
in-game utility to unique participants. Qappi, a gamified platform for onboarding & engaging web3 users, now uses BotBasher for Sybil-resistant quests, ensuring authentic and secure user participation for web3 projects. A new article explains how Humanode Biomapper ensures data privacy and security
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Humanode, Cryptocurrency, Biometrics.
in the fight for Sybil-resistance and user security in Web3 services and DApps. The first Humanode-inspired novel, “2067 — The Equilibrium,” has been introduced, offering a glimpse into a dystopian future and the importance of preventing it. Read it here. Join the team for the next Founders Talk
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Humanode, Cryptocurrency, Biometrics.
event to get the latest updates and engage in discussions directly from the heart of Humanode. The event will take place on Friday, April 19th, at 14:00 UTC on Humanode Radio on Discord. Don’t miss out! That’s a wrap. Кeep yourself updated! Development What’s been happening with Humanode
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Humanode, Cryptocurrency, Biometrics.
development lately: 🔺 Employing ZombieNet for E2E Network Testing Just as zombies roam the dark corners of the night, ZombieNet prowls the on-chain realm, serving as a robust framework tailored for Substrate-based blockchains. The Humanode team has harnessed the power of ZombieNet to put changes to
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| 5,905 |
Humanode, Cryptocurrency, Biometrics.
the test, particularly when launching multiple nodes. This framework is invaluable for its ability to effortlessly spawn and test ephemeral networks, akin to zombies lurking within the network of human nodes. Through ZombieNet’s suite of tools, including a user-friendly CLI tool and support for a
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Humanode, Cryptocurrency, Biometrics.
wide range of assertions the team team ensures the resilience of Humanode. For further insights, delve into the updates on the Humanode GitHub. 🔺 Exploring CVM Attestation The team announced the completion of the proof of concept (POC) for their experimental research on Confidential Virtual Machine
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Humanode, Cryptocurrency, Biometrics.
(CVM) attestation. They have developed a Rust utility for attestation, complete with a web transport server and client. The client now verifies the measurement and CVM signature, ensuring the security of the transport session. 🔺 Research on Remote Attestation From the Web Browser In addition to the
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Humanode, Cryptocurrency, Biometrics.
CVM attestation efforts, they’re also delving into web support. The aim is to create a client library that enables connection to CVM via the web, enhancing flexibility and accessibility for developers. 🔺 BotBasher Enhancements The team has been hard at work enhancing BotBasher. They’re in the
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Humanode, Cryptocurrency, Biometrics.
process of developing a system to refresh tokens for users in the next generation. Additionally, they’re implementing features such as displaying Discord usernames after facial scanning and introducing a ticket system for improved communication between the team and the community. 🔺 Mainnet Website
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Humanode, Cryptocurrency, Biometrics.
Localization The team has kicked off the translation of website content into different languages and has engaged translators to assist with this initiative. Recent News 🔺The Humanode Network is the №1 blockchain network by the Nakamoto Coefficient The Humanode Chain now has 500+ active validator
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Humanode, Cryptocurrency, Biometrics.
nodes. This also means that the Nakamoto Coefficient is 177, making the Humanode Network the №1 blockchain network as far as the Nakamoto Coefficient is concerned and can be considered to be the most decentralized. The current №2 is Mina, which has a Nakamoto Coefficient of 124, and №3 is Polkadot,
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Humanode, Cryptocurrency, Biometrics.
with 94. If you want to know more about how it is secure and private, click here. 🔺Humanode chain achieves self-sufficiency Due to the HMND rising in price somewhat, and more importantly due to more activity on the network, for the past weeks, Humanode has been able to cover 100% of the fees
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Humanode, Cryptocurrency, Biometrics.
required to subsidize the validator nodes on the Humanode Network from transaction fees and fees for bio-authentication! 🔺Humanode Biomapper & SDK Recently, Humanode Biomapper has officially been released to all those who develop on the Humanode chain. The release comes with the official
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Humanode, Cryptocurrency, Biometrics.
documentation and an SDK which includes smart contracts that can be used to quickly add Biomapper functionality to your services and DApps. 🔺A comprehensive look at the Humanode ecosystem It includes a range of partners across various sectors such as DeFi, wallets, exchanges, gaming, bridges, L1/L2
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Humanode, Cryptocurrency, Biometrics.
ecosystems, and much more. Partnerships 🔺Humanode x Hackless Humanode has partnered with Hackless, a robust platform that enhances the security of DeFi protocols, shielding them from hacks and malicious attacks, while also safeguarding individual users’ assets from potential loss caused by hackers
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Humanode, Cryptocurrency, Biometrics.
or scammers. Hackless has integrated Humanode’s BotBasher into its Discord to prevent Sybil attacks and bots. As the first use case, Hackless is using BotBasher verified human role as a Sybil-resistance measure for its community campaign on AlphaGuilty with 1000 USDT in rewards. Participate in this
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Humanode, Cryptocurrency, Biometrics.
campaign: https://alphaguilty.io/quest/hackless-quest-2 Hackless integrates BotBasher into its Discord for Sybil Resistance Cybersecurity is the cornerstone of trust in the Web3 world. And the latest addition to our BotBasher family, Hackless…blog.humanode.io 🔺Humanode x Banksters Humanode has
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Humanode, Cryptocurrency, Biometrics.
joined forces with Banksters, the first P2E trading game blending finance education with immersive gameplay. Banksters is using BotBasher for Sybil resistance in their airdrop campaigns on platforms like Galxe and Zealy. Banksters integrated Humanode BotBasher for Sybil-resistant Airdrop We've all
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Humanode, Cryptocurrency, Biometrics.
wished at some point that learning could be as fun as playing our favorite games. Imagine if mastering the…blog.humanode.io To celebrate this partnership, Banksters is awarding 10 out of 2000 NFT with in-game utility to 10 unique Human beings. Participate in the campaign to win your NFT with real
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Humanode, Cryptocurrency, Biometrics.
utility Rare NFTs for unique human beings. 🔺Humanode x Qappi Humanode has partnered with Qappi, the gamified platform for onboarding & engaging web3 users. Qappi now supports BotBasher for Sybil-resistant quests, ensuring authentic and secure user participation for web3 projects. BotBasher for
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Humanode, Cryptocurrency, Biometrics.
Sybil Resistant Quests on Qappi As the digital landscape evolves, web3 projects increasingly seek innovative strategies to foster vibrant, engaged…blog.humanode.io Awareness & Events 🎙️ Founders Talk Join the team for the next Founders Talk event this Friday. Get the latest updates and engage in
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Humanode, Cryptocurrency, Biometrics.
discussions directly from the heart of Humanode. 🗓 Date: Friday, April 19th ⏰ Time: 14:00 UTC 📍 Location: 📻 Humanode Radio on Discord: https://link.humanode.io/chat Roadmap 2024 Recently, Humanode unveiled its roadmap for the year 2024, outlining a comprehensive plan of action. The roadmap not only
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Humanode, Cryptocurrency, Biometrics.
details the current initiatives but also leaves room for the incorporation of novel ideas, features, and applications throughout the year. Humanode Bot Basher 🔺Understanding BotBasher’s Biometric Privacy and Security Approach Curious about how BotBasher protects your biometric data? Dive into
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Humanode, Cryptocurrency, Biometrics.
Humanode’s blog post to explore the advanced security measures they implemented. 🔺 BotBasher now protecting 530+ Discord server communities Integrate BotBasher to your server: https://botbasher.humanode.io/ Publications 🔺Data Privacy in Biomapper Biometrics are the trend and a major solution in the
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Humanode, Cryptocurrency, Biometrics.
fight for Sybil-resistance and the future of data and user security. With the release of Humanode Biomapper, and the future release of the cross-chain Biomapper, Humanode’s crypto-biometric technology will become more and more available for Web3 services and DApps. But how safe are biometrics in
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Humanode, Cryptocurrency, Biometrics.
the Humanode Biomapper? Who has access to the biometric data? Will my privacy be protected? The simple answers are, very, no one, and yes. For more information, check out the step-by-step explanation of how your privacy is protected in the Biomapper in the latest article “Data Privacy in
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Biomapper”. Data Privacy in Biomapper Biometrics are the current trend and a major solution in the fight for Sybil resistance and the future of data and user…blog.humanode.io 🔺2067 — The Equilibrium The team introduced the first Humanode-inspired novel based on a dystopian future where everything
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went wrong, well almost everything. It is based on a future that the Humanode team would like to prevent at all costs. Naturally, it is based in a world from the twisted imagination of Shannon Higgins, Humanode Media Team Lead. 2067 is a series of 10 stories based in the walled city of ShinTokyo,
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Humanode, Cryptocurrency, Biometrics.
Japan in the year 2067. The first story is the story of Yuu, a boy raised in the slums of ShinTokyo, and his flight to fight for a better future, in which he hopes he will not be “harvested” by the mega-corporations. Chapter 1 Chapter 2 The second story “The Book of Masato” of the Humanode inspired
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Humanode, Cryptocurrency, Biometrics.
novel. In this “Book” we step into the life of those in District 3, one of the crown jewels of the conglomerate zones, the place where “dreams” of the “highlife” come true. Enjoy the read! 📖 Humanode Mainnet Humanode is the first crypto-biometric blockchain network where one human = one node = one
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Humanode, Cryptocurrency, Biometrics.
vote that brings Sybil resistance and innovative governance models to the crypto industry using biometric technology. Be sure to claim tokens as you need some to deploy the node: Launcher: https://launcher.humanode.io Guides (Validator and Token claim): https://gitbook.humanode.io/mainnet-guide/
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| 5,932 |
Humanode, Cryptocurrency, Biometrics.
In-depth docs: https://gitbook.humanode.io/docs/ 🔺 Humanode explorer by Subscan 🔺 Humanode Explorer by Subscan — How does it work 🔺 Validator Ranking Humanode EVM 🔺 EVM compatibility on Humanode After extensive testing on the Humanode EVM Testnet “Israfel”, the team has incorporated feedback and
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medium
| 5,933 |
Humanode, Cryptocurrency, Biometrics.
made the necessary adjustments. With the success of the public testing phase, Humanode EVM is now ready for mainnet action. For the devs out there: the Humanode network now supports dApps and smart contracts developed in Solidity and other EVM-compatible languages. The seamless integration means
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| 5,934 |
Humanode, Cryptocurrency, Biometrics.
you can now deploy your smart contracts directly onto the Humanode chain. Users: Swap HMND for eHMND and transfer eHMND using popular EVM wallets, including MetaMask. Listing HMND is listed on KuCoin and Bitmart, BingX, SimpleSwap, and Lunar Crush. In the meantime, here are a few reminders to all:
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| 5,935 |
Humanode, Cryptocurrency, Biometrics.
HMND cannot be traded on any DEX at the moment. The only places you can buy HMND at this moment are KuCoin and BitMart, SimpleSwap, BingX, and Lunar Crush. ALWAYS check the official announcements. The links to the trading pages: 🔺KuCoin: https://link.humanode.io/trade/kucoin 🔺BitMart:
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medium
| 5,936 |
Humanode, Cryptocurrency, Biometrics.
https://support.bingx.com/hc/en-001/articles/17928663093017-BingX-Spot-Adds-Humanode-HMND-Trading-Pair MISC An invitation to journalists, writers, and researchers: Calling out to journalists, writers, and researchers in the Humanode Community and beyond to join Humanode in creating content for its
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medium
| 5,938 |
Humanode, Cryptocurrency, Biometrics.
official blog and news channels. The team is seeking your participation in writing, sharing your work, and publishing with Humanode. The stories selected for publishing will be commissioned, so don’t miss the opportunity. Interested individuals can apply here. For more details, please read this
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medium
| 5,939 |
Humanode, Cryptocurrency, Biometrics.
article. Scam alert: there is a Humanode Hub named group with a different handle. Check humanode.io for links. Humanode invites all of you to join the official Humanode Dev group in Telegram. This group is your hub for connecting with like-minded developers, builders, coders, and innovators,
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medium
| 5,940 |
Humanode, Cryptocurrency, Biometrics.
sharing your ideas, and contributing to the world of Humanode’s crypto-biometric technology. Subscribe to Paradigm! Medium, Twitter, Telegram, Telegram Chat, LinkedIn, and Reddit. For more information check out Humanode’s: ▲ Website ▲ Telegram ▲ Telegram chat ▲ Twitter ▲ Blog ▲ GitHub ▲ Youtube ▲
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medium
| 5,941 |
Deep Learning, Artificial Intelligence, Convolutional Network, Image Classification, Object Detection.
Brief Review — An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution CoordConv, Incorporates the Positions into Conv Layer An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution, CoordConv, by Uber AI Labs, and Uber Technologies, 2018 NeurIPS,
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medium
| 5,943 |
Deep Learning, Artificial Intelligence, Convolutional Network, Image Classification, Object Detection.
Over 500 Citations (Sik-Ho Tsang @ Medium) Image Classification CoordConv is proposed, which incorporates the positions into conv layer. Outline CoordConv Not-so-Clevr Dataset & Results Other Results 1. CoordConv Comparison of 2D convolutional and CoordConv layers A CoordConv layer has 2 to 3 more
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| 5,944 |
Deep Learning, Artificial Intelligence, Convolutional Network, Image Classification, Object Detection.
channels compared with Conv layer. These channels contain hard-coded coordinates, the most basic version of which is one channel for the i coordinate and one for the j coordinate, as shown above. e.g.: for i coordinates, its first row filled with 0’s, its second row with 1’s, its third with 2’s.
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| 5,945 |
Deep Learning, Artificial Intelligence, Convolutional Network, Image Classification, Object Detection.
Other derived coordinates may be input as well, like the radius coordinate used in ImageNet: Finally, scaling is done to make them fall in the range [−1, 1]. 2. Not-so-Clevr Dataset & Results The Not-so-Clevr dataset Not-so-Clevr consists of 9×9 squares placed on a 64×64 canvas. Toy tasks
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| 5,946 |
Deep Learning, Artificial Intelligence, Convolutional Network, Image Classification, Object Detection.
considered in this paper So, with coordinates as input, CNN should be designed properly to output the correct positions. Performance of convolution and CoordConv on Supervised Coordinate Classification However, the conventional convolution models never achieve more than about 86% accuracy, and
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| 5,947 |
Deep Learning, Artificial Intelligence, Convolutional Network, Image Classification, Object Detection.
training is slow. CoordConv models learn several hundred times faster, attaining perfect accuracy in seconds. 3. Other Results 3.1. ImageNet Classification As might be expected for tasks requiring straightforward translation invariance, CoordConv does not help significantly when tested with image
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| 5,948 |
Deep Learning, Artificial Intelligence, Convolutional Network, Image Classification, Object Detection.
classification. Adding a single extra 1×1 CoordConv layer with 8 output channels improves ResNet-50 Top-5 accuracy by a meager 0.04% averaged over five runs for each treatment; however, this difference is not statistically significant. It is at least reassuring that CoordConv doesn’t hurt the
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| 5,949 |
Deep Learning, Artificial Intelligence, Convolutional Network, Image Classification, Object Detection.
performance since it can always learn to ignore coordinates. 3.2. Object Detection On a simple problem of detecting MNIST digits scattered on a canvas, it is found the test intersection-over-union (IOU) of a Faster R-CNN network improved by 24% when using CoordConv. (Authors do not have any figures
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| 5,950 |
Deep Learning, Artificial Intelligence, Convolutional Network, Image Classification, Object Detection.
and tables for this part.) With CoordConv, it can be useful for localization problem such as object detection Reference [2018 NeurIPS] [CoordConv] An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution Image Classification 1989 … 2018 … [CoordConv] … 2021 [Learned
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| 5,951 |
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