Coin.AI: A Proof-of-Useful-Work Scheme for Blockchain-based Distributed Deep Learning

One decade ago, Bitcoin was introduced, becoming the first cryptocurrency and establishing the concept of "blockchain" as a distributed ledger. As of today, there are many different implementations of cryptocurrencies working over a blockchain, with different approaches and philosophies. However, many of them share one common feature: they require proof-of-work to support the generation of blocks (mining) and, eventually, the generation of money. This proof-of-work scheme often consists in the resolution of a cryptography problem, most commonly breaking a hash value, which can only be achieved through brute-force. The main drawback of proof-of-work is that it requires ridiculously large amounts of energy which do not have any useful outcome beyond supporting the currency. In this paper, we present a theoretical proposal that introduces a proof-of-useful-work scheme to support a cryptocurrency running over a blockchain, which we named Coin.AI. In this system, the mining scheme requires training deep learning models, and a block is only mined when the performance of such model exceeds a threshold. The distributed system allows for nodes to verify the models delivered by miners in an easy way (certainly much more efficiently than the mining process itself), determining when a block is to be generated. Additionally, this paper presents a proof-of-storage scheme for rewarding users that provide storage for the deep learning models, as well as a theoretical dissertation on how the mechanics of the system could be articulated with the ultimate goal of democratizing access to artificial intelligence.Comment: 17 pages, 5 figure

Artificial Intelligence in the Medical System: Four Roles for Potential Transformation

Artificial intelligence (AI) looks to transform the practice of medicine. As academics and policymakers alike turn to legal questions, including how to ensure high-quality performance by medical AI, a threshold issue involves what role AI will play in the larger medical system. This Article argues that AI can play at least four distinct roles in the medical system, each potentially transformative: pushing the frontiers of medical knowledge to increase the limits of medical performance, democratizing medical expertise by making specialist skills more available to non-specialists, automating drudgery within the medical system, and allocating scarce medical resources. Each role raises its own challenges, and an understanding of the four roles is necessary to identify and address major hurdles to the responsible development and deployment of medical AI

Artificial Intelligence in the Medical System: Four Roles for Potential Transformation

Artificial intelligence (AI) looks to transform the practice of medicine. As academics and policymakers alike turn to legal questions, a threshold issue involves what role AI will play in the larger medical system. This Article argues that AI can play at least four distinct roles in the medical system, each potentially transformative: pushing the frontiers of medical knowledge to increase the limits of medical performance, democratizing medical expertise by making specialist skills more available to non-specialists, automating drudgery within the medical system, and allocating scarce medical resources. Each role raises its own challenges, and an understanding of the four roles is necessary to identify and address major hurdles to the responsible development and deployment of medical AI

A Visual Programming Paradigm for Abstract Deep Learning Model Development

Deep learning is one of the fastest growing technologies in computer science with a plethora of applications. But this unprecedented growth has so far been limited to the consumption of deep learning experts. The primary challenge being a steep learning curve for learning the programming libraries and the lack of intuitive systems enabling non-experts to consume deep learning. Towards this goal, we study the effectiveness of a no-code paradigm for designing deep learning models. Particularly, a visual drag-and-drop interface is found more efficient when compared with the traditional programming and alternative visual programming paradigms. We conduct user studies of different expertise levels to measure the entry level barrier and the developer load across different programming paradigms. We obtain a System Usability Scale (SUS) of 90 and a NASA Task Load index (TLX) score of 21 for the proposed visual programming compared to 68 and 52, respectively, for the traditional programming methods

New Innovation Models in Medical AI

In recent years, scientists and researchers have devoted considerable resources to developing medical artificial intelligence (AI) technologies. Many of these technologies—particularly those that resemble traditional medical devices in their functions—have received substantial attention in the legal and policy literature. But other types of novel AI technologies, such as those related to quality improvement and optimizing use of scarce facilities, have been largely absent from the discussion thus far. These AI innovations have the potential to shed light on important aspects of health innovation policy. First, these AI innovations interact less with the legal regimes that scholars traditionally conceive of as shaping medical innovation: patent law, FDA regulation, and health insurance reimbursement. Second, and perhaps related, a different set of innovation stakeholders, including health systems and insurers, are conducting their own research and development in these areas for their own use without waiting for commercial product developers to innovate for them. The activities of these innovators have implications for health innovation policy and scholarship. Perhaps most notably, data possession and control play a larger role in determining capacity to innovate in this space, while the ability to satisfy the quality standards of regulators and payers plays a smaller role relative to more familiar biomedical innovations such as new drugs and devices

New Innovation Models in Medical AI

In recent years, scientists and researchers have devoted considerable resources to developing medical artificial intelligence (AI) technologies. Many of these technologies—particularly those that resemble traditional medical devices in their functions—have received substantial attention in the legal and policy literature. But other types of novel AI technologies, such as those related to quality improvement and optimizing use of scarce facilities, have been largely absent from the discussion thus far. These AI innovations have the potential to shed light on important aspects of health innovation policy. First, these AI innovations interact less with the legal regimes that scholars traditionally conceive of as shaping medical innovation: patent law, FDA regulation, and health insurance reimbursement. Second, and perhaps related, a different set of innovation stakeholders, including health systems and insurers, are conducting their own research and development in these areas for their own use without waiting for commercial product developers to innovate for them. The activities of these innovators have implications for health innovation policy and scholarship. Perhaps most notably, data possession and control play a larger role in determining capacity to innovate in this space, while the ability to satisfy the quality standards of regulators and payers plays a smaller role relative to more familiar biomedical innovations such as new drugs and devices

Naujos technologijos kaip galimas katalizatorius demokratizuojant miestų paveldo išsaugojimo praktiką: 3D skenavimo ir dirbtinio intelekto atvejis

 The conflict between heritage protection and urban infrastructure development rationales creates a context for inclusion, participation and dialogue of different heritage-related communities. However, developed in the pre-computer age of administrative practice, are often incapable, partially or completely, to accommodate the ‘new-era’ community oriented participatory practices. In this article, authors discuss the mutual effects of IT in the process of democratization of urban heritage preservation. The authors create and argue the conceptual model of distributed ledger technologies (DLT) in participatory UHP. The model demonstrates how technologies can become catalysts for democratization in situations when the regulatory and administrative change (on its own) is too inert. The article hypothesizes that novel technological developments which aim at or have the potential for increasing community involvement and democratization of administrative practice, exert their effects directly through technology-based participatory practices.Prieštaravimas tarp paveldosaugos ir miestų infrastruktūros plėtros sukuria ne tik įtampas, bet ir sąlygas įvairių su paveldu susijusių bendruomenių įtraukčiai, dalyvavimui ir dialogui. Tačiau dauguma paveldosaugos administravimo praktikų ir jas taikančių institucijų, atsiradę laikais, kai dar nebuvo kompiuterio, sunkiai prisitaiko prie skaitmeninių technologijų paskatintų pokyčių bei galimybių, orientuotų į bendruomenių dalyvavimą sprendimų priėmime. Šiame straipsnyje autoriai aptaria abipusį IT poveikį miestų paveldo išsaugojimo demokratizavimo procese. Straipsnyje daroma prielaida, kad nauji technologiniai sprendimai, kuriais gali būti didinamas bendruomenės įsitraukimas yra svarbus įrankis demokratizuojant paveldosaugos administracines praktikas. Autoriai sukūrė ir pagrindžia koncepcinį paskirstytų duomenų technologijų modelį ir jo taikymą dalyvaujamajame miestų paveldo išsaugojime. Modelis parodo, kaip technologijos gali tapti demokratizacijos katalizatoriais tais atvejais, kai reguliavimo ir administraciniai pokyčiai (savaime) yra pernelyg inertiški