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.

Limited access to computing resources and training data poses significant challenges for individuals and groups aiming to train and utilize predictive machine learning models. Although numerous publicly available machine learning models exist, they are often unhosted, necessitating end-users to establish their computational infrastructure. Alternatively, these models may only be accessible through paid cloud-based mechanisms, which can prove costly for general public utilization. Moreover, model and data providers require a more streamlined approach to track resource usage and capitalize on subsequent usage by others, both financially and otherwise. An effective mechanism is also lacking to contribute high-quality data for improving model performance. We propose a blockchain-based marketplace called "PredictChain" for predictive machine-learning models to address these issues. This marketplace enables users to upload datasets for training predictive machine learning models, request model training on previously uploaded datasets, or submit queries to trained models. Nodes within the blockchain network, equipped with available computing resources, will operate these models, offering a range of archetype machine learning models with varying characteristics, such as cost, speed, simplicity, power, and cost-effectiveness. This decentralized approach empowers users to develop improved models accessible to the public, promotes data sharing, and reduces reliance on centralized cloud providers.

Archil Cheishvili is CEO and co-founder at GenesisAI, a global network of artificial intelligence products and services. With the latest advancements in artificial intelligence (AI), achieving human-like intelligence is gradually transitioning to the realm of possibility. And with disruptors like the Covid-19 pandemic ravaging the global economy, the race to achieve artificial general intelligence may have sped up significantly. I would like to share some insights on the challenges and opportunities intertwined with the path to achieving AGI and the future ahead. What is artificial general intelligence?

Futurists sometimes claim that artificial intelligence (AI) will make radiologists obsolete. Their argument has been that compared to humans, algorithms are better and faster at analyzing medical images such as X-rays. So why has this hype failed to become reality? In this opinion piece, Ulysses Isidro and Saurabh Jha write, "For radiology AI to be widely adopted, it needs to overcome several barriers. Most of all, it needs to make the radiologist's work simpler."

Artificial intelligence shows promise for solving many practical societal problems in areas such as healthcare and transportation. However, the current mechanisms for AI model diffusion such as Github code repositories, academic project webpages, and commercial AI marketplaces have some limitations; for example, a lack of monetization methods, model traceability, and model auditabilty. In this work, we sketch guidelines for a new AI diffusion method based on a decentralized online marketplace. We consider the technical, economic, and regulatory aspects of such a marketplace including a discussion of solutions for problems in these areas. Finally, we include a comparative analysis of several current AI marketplaces that are already available or in development. We find that most of these marketplaces are centralized commercial marketplaces with relatively few models.

Every time a company changes its CEO, many changes always follow. Some of those changes are positive. I expect this to happen at IBM as Ginni Rometty steps down and Arvind Krishna steps up as CEO. Let's take a look at the challenges he faces and what we can expect as investors, customers and workers. I have been following IBM for quite a long time.

At HIMSS20 next month, Nuance Communications, an AI-based clinician information systems vendor, will introduce new systems to the Nuance AI Marketplace for Diagnostic Imaging designed to provide a one-stop shop for radiologists to review and purchase AI models to improve their workflow. The AI Marketplace is similar to the idea of an app store – a digital marketplace that enables subscribers to purchase applications, or, in the case of Nuance, AI models. The goal is to connect developers to radiologists, creating a direct line of communication to help build and improve AI models to meet their evolving needs and improve functionality of algorithms from development to practice. The AI Marketplace provides radiologists with tools for reading and reporting efficiencies, giving radiologists a unique opportunity to integrate AI models from various developers, collaborate on AI development and improvement, and ease administrative burdens of physicians overall, said Karen Holzberger, senior vice president and general manager, diagnostics, at Nuance Communications. "The solutions available in our AI Marketplace automate routine reporting and image analysis, aid in diagnosis, and help uncover incidental findings," Holzberger explained.

Today at RSNA, IBM Watson Health is announcing two new products, and showcasing a variety of partnerships and clients that are using our advanced technologies to improve the way radiologists deliver care. We are delighted to announce these collaborations at RSNA highlighting our advancements in medical imaging globally," said Anne Le Grand, General Manager, Imaging, Life Sciences, and Oncology, IBM Watson Health. "From helping clinicians to identify potential missed findings to seeing a summary view of patient records quickly, our innovative technologies are at the forefront of Watson Health's mission to help enable clinicians to more effectively respond to the world's most pressing health challenges." Clinical Review 3.0, a tool recently launched in the UK that analyzes medical imaging studies and their associated reports to identify potentially missed findings, facilitating higher quality and more comprehensive care for the patient. IBM Watson Health Imaging has recently engaged with Fortrus Ltd, to grow upon the reseller's strong relationship with the UK public sector, which includes a single supplier outcome-based Managed Services framework. The Imaging AI Marketplace is a single-source solution designed to help simplify the complex process of locating, purchasing, deploying and managing the vast array of AI imaging applications. The Imaging AI Marketplace is carefully curated and contains only FDA-cleared solutions alongside Watson Health developed AI solutions. Guerbet, a global specialist in contrast agents and solutions for diagnostic and interventional imaging, also recently signed an exclusive joint development agreement to develop an artificial intelligence software solution to support prostate cancer diagnostics and monitoring, utilizing MR imaging. This deal extends their earlier collaboration regarding liver cancer signed in January 2018. In addition, 4ways, a fast-growing private teleradiology network in the UK that enables UK-based radiologists to work remotely over a leading technology platform, has committed to underpin its ambitious growth strategy with IBM Watson Health's Merge PACS 8.0 platform, upgrading its current platform to support their business growth. Merge PACS is a workflow platform that is designed to help simplify physicians' reading activities and can empower IT leaders with advanced control of the flow of studies throughout the enterprise. "We're committed to constantly investing in and upgrading our IT provision to be able to offer our clients and partners the very best service.

App marketplaces are bridging the gap between AI creators and users. Words such as "democratize" and "ecosystem" are making their way from government and biology textbooks into the radiology lexicon, as a community of developers, engineers, and clinicians are interacting to establish AI marketplaces. These electronic storefronts are gathering places where vendors and consumers can share ideas and technology to create and use a variety of AI imaging tools. Within an AI marketplace, radiologists have equal opportunity to pick and choose from any of the available apps. They can also share feedback with developers as to how an app worked for them or even assist in the development of new apps customized for their clinical practices. And, they can be active participants in the development process without any knowledge of writing code. "With AI, there's a whole ecosystem of participants--industry leaders, health care startups, and research institutions--getting involved in creating apps and marketplaces where they can be made available to everyone," says Abdul Hamid Halabi, director of healthcare with NVIDIA.

The Eluvio Content Fabric is an open, high performance software network enabling the management and distribution of video in ways never before possible. In this talk, Eluvio founder and CEO Michelle Munson will introduce the design principles and key technology of the platform, and demonstrate its first use cases for over the top video -- on demand, live, and linear -- and new opportunities for content monetization enabled by this novel environment. Why DNA is a useful molecule for storing digital information. What is the emerging set of technologies in biotech and synthetic biology that allow for the storage of digital information in synthetic DNA molecules. And how can data in DNA form can be computed on directly using biomolecular computing techniques, and what advantages this approach will offer over traditional forms of computation.