Nokia has announced the launch of its Data Marketplace, a blockchain-based service providing real-time access to massive trusted datasets. "Our customers need secure and trusted access to data for effective business decision making. With Nokia Data Marketplace, enterprises and CSPs can now benefit from richer insights and predictive models to drive digital ways of working and tap into new revenue streams." Nokia Data Marketplace accelerates AI initiatives through federated learning. This approach, combined with orchestration capabilities, facilitates collaborative development of highly accurate machine learning models for analytics use cases.
Machine learning and artificial intelligence might be the future of everything in the Fintech sector. Generally, integration of AI improves results since the technology applies methods derived from common aspects of human intelligence but is beyond human scale. In this context, AI empowers business processes by providing a deeper understanding of customer needs. The adoption of technology in this sector has substantially made banking easier. People can now carry out major bank-related tasks online, mainly from any device that has an internet connection.
Breakthroughs in AI and innovations in applying blockchain for personal data control and monetization enable new ways to make money off of personal information that most people currently give away for free. Here we highlight three data science and business model innovations, starting with breakthrough ML technology that learns on the fly. There's an emergent machine learning technology out there that offers a clever new way of finding and classifying unstructured content. In geek-speak, the technology is a vertical, personalized search engine that doesn't require expensive knowledge graphs. In human speak, it's a context-sensitive, human-in-the-loop search engine that uses search criteria and implicit user feedback to recommend high-quality results.
Cryptocurrency is gaining more ground each year, which means the space demands an even higher level of understanding for anyone who wants to actually come out ahead. What was once a niche interest for very specific groups of investors will soon be accepted by MasterCard and Tesla, while PayPal started integrating the currency late last year. On top of that, crypto trading is surging in popularity as well, emerging as a niche stock market for people who want to experiment with investing from the comfort of their laptop. If you're new to the world of investing, or if you're a seasoned investor worried about losing your edge, The Quantitative Crypto Trading Strategies Bundle is definitely worth a look at $145. It offers intermediate to advanced training on every aspect of cryptocurrency training, from programming and sorting out risks to the implementation of long-term strategies.
Takeaway: Before AI and the rise of FinTech, very few industry giants had the bandwidth to deal with the inherently quantitative nature of our now tech-savvy world. These AI use cases detail how AI has been a game-changer for FinTech. We've scoped out these real-world AI use cases so we could detail how artificial intelligence has been a game-changer for FinTech. Few verticals are such a perfect match for the improved capabilities brought by the AI revolution like the financial sector. Traditional financial services have always struggled with massive volumes of records that need to be handled with maximum accuracy.
The rapid increase of the data scale in Internet of Vehicles (IoV) system paradigm, hews out new possibilities in boosting the service quality for the emerging applications through data sharing. Nevertheless, privacy concerns are major bottlenecks for data providers to share private data in traditional IoV networks. To this end, federated learning (FL) as an emerging learning paradigm, where data providers only send local model updates trained on their local raw data rather than upload any raw data, has been recently proposed to build a privacy-preserving data sharing models. Unfortunately, by analyzing on the differences of uploaded local model updates from data providers, private information can still be divulged, and performance of the system cannot be guaranteed when partial federated nodes executes malicious behavior. Additionally, traditional cloud-based FL poses challenges to the communication overhead with the rapid increase of terminal equipment in IoV system. All these issues inspire us to propose an autonomous blockchain empowered privacy-preserving FL framework in this paper, where the mobile edge computing (MEC) technology was naturally integrated in IoV system.
A COMBINATION of recent events has seen a rapid acceleration in the adoption and incorporation of technologies by a wide range of firms and institutions in the global financial sector. Whether this adoption has been spurred on by the global financial crisis of 2008; the need to adhere to regulation; or the immediate need to pivot and handle the consequences of Covid-19 and its impact on customers and staff, firms in the finance industry are embracing financial technologies (fintech) into their daily processes. Designed to drive enhancement in services and improve efficiencies in back-office operations, it has seen a thriving sector developed beyond traditional'Wall Street' financing. The prospect of the part that machine learning (ML) could play is generating a lot of momentum. The financial sector is well-placed to benefit from machine learning, with large volumes of historical structured and unstructured data to learn from.