Artificial Intelligence & Blockchain Synergy


BICA Labs Laboratories for Biologically Inspired Cognitive Architectures 2. Blockchain: Distributed Ledger Technology (DLT) TRUSTED PERSISTENT DATA RECORDS INSIDE TRUSTLESS ENVIRONMENTS WITHOUT CENTRAL GOVERNANCE SECURED BY ECONOMIC INCENTIVES 4. Distributed database (ledger) Distributed computations (state changes), Turing-complete OR incomplete Peer-to-peer mesh network Cryptographically secured TECHNOLOGY ECONOMICS BLOCKCHAIN Multiagent economy Game theory Free open market Non-state decentralized economies linked to particular types of resources or businesses 6. NO CENTRALIZATION 7. NO DATA OLIGOPOLY 9. Blockchain tech intro 10. Important Blockchains Bitcoin Ripple Ethereum ZCashDashNEM Most popular cryptocurrency & source code base Value transfer network Smart contracts Proof of Importance Governance model Zero-knowledge 11. Most important ready-to-go DLTs cores Bitcoin Core Graphene Scorex Tendermint Ripple / Stellar Language C/C C Scala Go C Consensus PoW dPoS PoW, 2x PoS PoS Blockchains Bitcoin, Dash, Litecoin, ... BitShares, Steem, Golos – (Waves experiments) Cosmos (under dev) Ripple, Stellar, Infra (e-Auction) " " Most proved Blazingly fast Modular PoS Fast & proved "–" Hard to understand Complex model Limited functionality, no real-world impl Immature Complex model 16. Languages for working with DLTs cores C/C -- Bitcoin, Graphene, Ripple: performance Go -- Ethereum, Tehndermint Python -- Ethereum experiments with blockchain Rust -- Ethereum, Bitcoin: performance efficient code Scala -- Scorex (fast blockchain prototyping) Java -- NEM JavaScript (Angular, React): UI & APIs 17. Meta-languages for smart contracts Solidity: JavaScript-like Serpent: Python-like Viper: Python-like (Serpent 2.0) 18. Blockchain & AI Technical Synergy 19. Civilization 4.0 key factors Quantum Computing Generic Artificial Intelligence Transhumanism Life extension Cyborgization Cosmic Expansion SINGULARITY 31.

The Tally Protocol: scaling Ethereum with untapped GPU power


But it means the network gets choked by games like CryptoKitties. But if we want blockchain to live up to its biggest promises, we need a solution that can coordinate large ecosystems with diverse governance models, managing huge numbers of actions all happening at once. The good news is--a lot of that is just number crunching. And GPUs are very good at that. That's why Cardstack is creating Tally: a Layer 2 protocol that lets dApps harness GPU power to perform heavy-duty calculations.

Why Cryptocurrencies Use So Much Energy--and What to Do About It

Communications of the ACM

That market cap has grown more than 20 times since last year, when the cryptocurrency boom began. With bitcoin's booming popularity comes problems. Speculation in bitcoin and other cryptocurrencies is rampant. Scams abound, and plenty of initial coin offerings (ICOs) have overpromised or underdelivered spectacularly. Through it all, the world has focused on how bitcoin and other cryptocurrencies could implode and go to zero--or make you rich--depending on who you ask.

Nebula-AI (NBAI): The convergence of AI and Blockchain


The blockchain space is full of projects without a clear use case and clear value, but, as we'll see, there are also projects which are quite the opposite. Nebula-AI (NBAI) is creating a decentralized AI computing platform which will make AI DAPPS (which they call DAI Apps) a reality. The first product and demo that they released is Quant-AI: A cutting-edge trading price prediction tool. In this article, we'll dive into the specifics of this project and take a look at Quant AI and the broader concept of DAI Apps. Thanks to the use of GPUs and parallel computing for algorithms like machine learning (ML) AI has finally become widely viable.

On The Subject of Thinking Machines – Towards Data Science


Alan Turing would have been proud of our achievements in computer vision, speech, natural language processing and autonomous systems. However, there are still many challenges and we are still some distance from building machines that can pass the Turing test. In this paper, we discuss some of the biggest questions concerning intelligent machines and we attempt to answer them, as much as can be explained by modern AI. Turing choose to avoid answering this question directly, however, it is important to have a clear and concise meaning of thinking that incorporates lessons from neuroscience and Artificial Intelligence. We define thinking as "The process by which we evaluate features learned from past experiences in order to make decisions about new problems" In the context of human thinking, when you see a person and you are faced with the task of determining who the person is (The New Problem), a activity (The Process) begins in your brain that goes through the search space of all the people whose face you can remember (The Experience), you then begin to consider the nose, eyes, skin color, dressing, height, speech and any other observable treats (The Features), the process then attempts to match these features to a particular person based on people we have seen before, if no satisfactory match is found, the brain concludes that this is a stranger (The Decision).