Proof of Training (PoT): Harnessing Crypto Mining Power for Distributed AI Training

In the midst of the emerging trend of integrating artificial intelligence (AI) with crypto mining, we identify three major challenges that create a gap between these two fields. To bridge this gap, we introduce the proof-of-training (PoT) protocol, an approach that combines the strengths of both AI and blockchain technology. The PoT protocol utilizes the practical Byzantine fault tolerance (PBFT) consensus mechanism to synchronize global states. To evaluate the performance of the protocol design, we present an implementation of a decentralized training network (DTN) that adopts the PoT protocol. Our results indicate that the protocol exhibits considerable potential in terms of task throughput, system robustness, and network security

Web3 Meets AI Marketplace: Exploring Opportunities, Analyzing Challenges, and Suggesting Solutions

Web3 and AI have been among the most discussed fields over the recent years, with substantial hype surrounding each field's potential to transform the world as we know it. However, as the hype settles, it's evident that neither AI nor Web3 can address all challenges independently. Consequently, the intersection of AI and Web3 is gaining increased attention, emerging as a new field with the potential to address the limitations of each. In this article, we will focus on the integration of web3 and the AI marketplace, where AI services and products can be provided in a decentralized manner (DeAI). A comprehensive review is provided by summarizing the opportunities and challenges on this topic. Additionally, we offer analyses and solutions to address these challenges. We've developed a framework that lets users pay with any kind of cryptocurrency to get AI services. Additionally, they can also enjoy AI services for free on our platform by simply locking up their assets temporarily in the protocol. This unique approach is a first in the industry. Before this, offering free AI services in the web3 community wasn't possible. Our solution opens up exciting opportunities for the AI marketplace in the web3 space to grow and be widely adopted

Stress blow-up analysis when suspending rigid particles approach boundary in 3D Stokes flow

The stress concentration is a common phenomenon in the study of fluid-solid model. In this paper, we investigate the boundary gradient estimates and the second order derivatives estimates for the Stokes flow when the rigid particles approach the boundary of the matrix in dimension three. We classify the effect on the blow-up rates of the stress from the prescribed various boundary data: locally constant case and locally polynomial case. Our results hold for general convex inclusions, including two important cases in practice, spherical inclusions and ellipsoidal inclusions. The blow-up rates of the Cauchy stress in the narrow region are also obtained. We establish the corresponding estimates in higher dimensions greater than three.Comment: 33 page

Stress blow-up analysis when a suspending rigid particle approaches the boundary in Stokes flow: 2D case

It is an interesting and important topic to study the motion of small particles in a viscous liquid in current applied research. In this paper we assume the particles are convex with arbitrary shapes and mainly investigate the interaction between the rigid particles and the domain boundary when the distance tends to zero. In fact, even though the domain and the prescribed boundary data are both smooth, it is possible to cause a definite increase of the blow-up rate of the stress. This problem has the free boundary value feature due to the rigidity assumption on the particle. We find that the prescribed local boundary data directly affects on the free boundary value on the particle. Two kinds of boundary data are considered: locally constant boundary data and locally polynomial boundary data. For the former we prove the free boundary value is close to the prescribed constant, while for the latter we show the influence on the blow-up rate from the order of growth of the prescribed polynomial. Based on pointwise upper bounds in the neck region and lower bounds at the midpoint of the shortest line between the particle and the domain boundary, we show that these blow-up rates obtained in this paper are optimal. These precise estimates will help us understand the underlying mechanism of the hydrodynamic interactions in fluid particle model.Comment: 43 pages, to appear in SIAM J. Math. Ana