Ownership preserving AI Market Places using Blockchain

We present a blockchain based system that allows data owners, cloud vendors, and AI developers to collaboratively train machine learning models in a trustless AI marketplace. Data is a highly valued digital asset and central to deriving business insights. Our system enables data owners to retain ownership and privacy of their data, while still allowing AI developers to leverage the data for training. Similarly, AI developers can utilize compute resources from cloud vendors without loosing ownership or privacy of their trained models. Our system protocols are set up to incentivize all three entities - data owners, cloud vendors, and AI developers to truthfully record their actions on the distributed ledger, so that the blockchain system provides verifiable evidence of wrongdoing and dispute resolution. Our system is implemented on the Hyperledger Fabric and can provide a viable alternative to centralized AI systems that do not guarantee data or model privacy. We present experimental performance results that demonstrate the latency and throughput of its transactions under different network configurations where peers on the blockchain may be spread across different datacenters and geographies. Our results indicate that the proposed solution scales well to large number of data and model owners and can train up to 70 models per second on a 12-peer non optimized blockchain network and roughly 30 models per second in a 24 peer network

Viz: A QLoRA-based Copyright Marketplace for Legally Compliant Generative AI

This paper aims to introduce and analyze the Viz system in a comprehensive way, a novel system architecture that integrates Quantized Low-Rank Adapters (QLoRA) to fine-tune large language models (LLM) within a legally compliant and resource efficient marketplace. Viz represents a significant contribution to the field of artificial intelligence, particularly in addressing the challenges of computational efficiency, legal compliance, and economic sustainability in the utilization and monetization of LLMs. The paper delineates the scholarly discourse and developments that have informed the creation of Viz, focusing primarily on the advancements in LLM models, copyright issues in AI training (NYT case, 2023), and the evolution of model fine-tuning techniques, particularly low-rank adapters and quantized low-rank adapters, to create a sustainable and economically compliant framework for LLM utilization. The economic model it proposes benefits content creators, AI developers, and end-users, delineating a harmonious integration of technology, economy, and law, offering a comprehensive solution to the complex challenges of today's AI landscape

IDMoB: IoT Data Marketplace on Blockchain

Today, Internet of Things (IoT) devices are the powerhouse of data generation with their ever-increasing numbers and widespread penetration. Similarly, artificial intelligence (AI) and machine learning (ML) solutions are getting integrated to all kinds of services, making products significantly more "smarter". The centerpiece of these technologies is "data". IoT device vendors should be able keep up with the increased throughput and come up with new business models. On the other hand, AI/ML solutions will produce better results if training data is diverse and plentiful. In this paper, we propose a blockchain-based, decentralized and trustless data marketplace where IoT device vendors and AI/ML solution providers may interact and collaborate. By facilitating a transparent data exchange platform, access to consented data will be democratized and the variety of services targeting end-users will increase. Proposed data marketplace is implemented as a smart contract on Ethereum blockchain and Swarm is used as the distributed storage platform.Comment: Presented at Crypto Valley Conference on Blockchain Technology (CVCBT 2018), 20-22 June 2018 - published version may diffe

XSRL: An XML web-services request language

One of the most serious challenges that web-service enabled e-marketplaces face is the lack of formal support for expressing service requests against UDDI-resident web-services in order to solve a complex business problem. In this paper we present a web-service request language (XSRL) developed on the basis of AI planning and the XML database query language XQuery. This framework is designed to handle and execute XSRL requests and is capable of performing planning actions under uncertainty on the basis of refinement and revision as new service-related information is accumulated (via interaction with the user or UDDI) and as execution circumstances necessitate change

Estimating commitment in a digital market place environment

The future generation of mobile communication shall be a convergence of mobile telephony and information systems which promises to change people's lives by enabling them to access information when, where and how they want. It presents opportunities to offer multimedia applications and services that meet end-toend service requirements. The Digital Marketplace framework will enable users to have separate contracts for different services on a per call basis. In order for such a framework to function appropriately, there has to be some means for the network operator to know in advance if its network will be able to support the user requirements. This paper discusses the methods by which the network operator will be able to determine if the system will be able to support another user of a certain service class and therefore negotiate parameters like commitment, QoS and the associated cost for providing the service, thus making the Digital Marketplace wor

Usage of Network Simulators in Machine-Learning-Assisted 5G/6G Networks

Without any doubt, Machine Learning (ML) will be an important driver of future communications due to its foreseen performance when applied to complex problems. However, the application of ML to networking systems raises concerns among network operators and other stakeholders, especially regarding trustworthiness and reliability. In this paper, we devise the role of network simulators for bridging the gap between ML and communications systems. In particular, we present an architectural integration of simulators in ML-aware networks for training, testing, and validating ML models before being applied to the operative network. Moreover, we provide insights on the main challenges resulting from this integration, and then give hints discussing how they can be overcome. Finally, we illustrate the integration of network simulators into ML-assisted communications through a proof-of-concept testbed implementation of a residential Wi-Fi network