When we hear about Artificial Intelligence (AI), the very first thought comes into our mind as it's being our personal home, office, or driving assistant. Because of the existing media representation on AI and the current progress of AI in the technology market, it is undoubtedly obvious to have such expectations from it. The involvement of AI in the technology space has been driven to a certain extent since the proliferation, especially in data analytics, and in which market data analytics to be precise. Many market researchers and data analysts believe that AI is an essential factor driving better performance efficiency and customer satisfaction, which eventually helps companies get better sales and revenues. According to one market survey, around 93% of market researchers consider AI as an industry opportunity, and 80% agree on AI driving a positive impact on the market.
In this week's real-time analytics news: HPE launched HPE Swarm Learning, a privacy-preserving, decentralized machine learning framework for the edge. Keeping pace with news and developments in the real-time analytics market can be a daunting task. We want to help by providing a summary of some of the important news items our staff came across this week. Hewlett Packard Enterprise (HPE) announced the launch of HPE Swarm Learning, an AI solution to accelerate insights at the edge, from diagnosing diseases to detecting credit card fraud, by sharing and unifying AI model learnings without compromising data privacy. HPE Swarm Learning is a privacy-preserving, decentralized machine learning framework for the edge or distributed sites.
The global data and intelligence solutions provider, Provenir, is leading the marketplace through its data insights innovation and technologies. The US-based software technology company which supports the international fintech industry, ensures the marketplace is a global data and intelligence ecosystem that makes accessing data fast and easy. Now, Provenir has invited industry professionals to join them in their latest webinar that outline how can AI-powered risk decisioning can play a part in transforming the entire credit risk decisioning process. The session, which is presented by key industry leaders, explores how technology continues to evolve and advances in big data, digital transformation, and AI/ML are creating new opportunities for financial services and fintechs to improve their credit decisioning processes. The webinar panel discussion is being moderated by FinTech Magazine and will provide a spectrum of topics for discussion that outline the importance of using AI/ML to transform credit risk decisioning.
Artificial intelligence (AI) isn't new to the world of stock picking, but it hasn't really been an option for retail investors. Traditionally, powerful artificial intelligence systems – and the high-octane brainpower needed to develop and operate them – have been available only to institutional investors. Financial technology company Danelfin, formerly known as Danel Capital, is trying to change all that. Danelfin has developed an analytics platform that harnesses the power of big data technology and machine learning. The goal is to level the playing field by giving regular investors access to institutional-level technology that helps them make smarter decisions with their tactical stock picks.
Petropoulos, Fotios, Apiletti, Daniele, Assimakopoulos, Vassilios, Babai, Mohamed Zied, Barrow, Devon K., Taieb, Souhaib Ben, Bergmeir, Christoph, Bessa, Ricardo J., Bijak, Jakub, Boylan, John E., Browell, Jethro, Carnevale, Claudio, Castle, Jennifer L., Cirillo, Pasquale, Clements, Michael P., Cordeiro, Clara, Oliveira, Fernando Luiz Cyrino, De Baets, Shari, Dokumentov, Alexander, Ellison, Joanne, Fiszeder, Piotr, Franses, Philip Hans, Frazier, David T., Gilliland, Michael, Gönül, M. Sinan, Goodwin, Paul, Grossi, Luigi, Grushka-Cockayne, Yael, Guidolin, Mariangela, Guidolin, Massimo, Gunter, Ulrich, Guo, Xiaojia, Guseo, Renato, Harvey, Nigel, Hendry, David F., Hollyman, Ross, Januschowski, Tim, Jeon, Jooyoung, Jose, Victor Richmond R., Kang, Yanfei, Koehler, Anne B., Kolassa, Stephan, Kourentzes, Nikolaos, Leva, Sonia, Li, Feng, Litsiou, Konstantia, Makridakis, Spyros, Martin, Gael M., Martinez, Andrew B., Meeran, Sheik, Modis, Theodore, Nikolopoulos, Konstantinos, Önkal, Dilek, Paccagnini, Alessia, Panagiotelis, Anastasios, Panapakidis, Ioannis, Pavía, Jose M., Pedio, Manuela, Pedregal, Diego J., Pinson, Pierre, Ramos, Patrícia, Rapach, David E., Reade, J. James, Rostami-Tabar, Bahman, Rubaszek, Michał, Sermpinis, Georgios, Shang, Han Lin, Spiliotis, Evangelos, Syntetos, Aris A., Talagala, Priyanga Dilini, Talagala, Thiyanga S., Tashman, Len, Thomakos, Dimitrios, Thorarinsdottir, Thordis, Todini, Ezio, Arenas, Juan Ramón Trapero, Wang, Xiaoqian, Winkler, Robert L., Yusupova, Alisa, Ziel, Florian
Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.
In the spirit of the last couple of years, we review developments in what we have identified as the key technology drivers for the 2020s in the world of databases, data management and AI. We are looking back at 2021, trying to identify patterns that will shape 2022. Today we pick up from where we started with part one of our review, to cover AI and knowledge graphs. In principle, we try to approach AI holistically. To take into account positives and negatives, from the shiny to the mundane, and from hardware to software.
As a result, all major cloud providers are either offering or promising to offer Kubernetes options that run on-premises and in multiple clouds. While Kubernetes is making the cloud more open, cloud providers are trying to become "stickier" with more vertical integration. From database-as-a-service (DBaaS) to AI/ML services, the cloud providers are offering options that make it easier and faster to code. Organizations should not take a "one size fits all" approach to the cloud. For applications and environments that can scale quickly, Kubernetes may be the right option. For stable applications, leveraging DBaaS and built-in AI/ML could be the perfect solution. For infrastructure services, SaaS offerings may be the optimal approach. The number of options will increase, so create basic business guidelines for your teams.
This is the accepted version of an article with the same name, published in the Special Issue "Federated Learning and Blockchain Supported Smart Networking in Beyond 5G (B5G) Wireless Communication" in Computer Networks. Abstract Federated machine learning (FL) allows to collectively train models on sensitive data as only the clients' models and not their training data need to be shared. However, despite the attention that research on FL has drawn, the concept still lacks broad adoption in practice. One of the key reasons is the great challenge to implement FL systems that simultaneously achieve fairness, integrity, and privacy preservation for all participating clients. To contribute to solving this issue, our paper suggests a FL system that incorporates blockchain technology, local differential privacy, and zero-knowledge proofs. Our implementation of a proof-of-concept with multiple linear regression illustrates that these state-of-the-art technologies can be combined to a FL system that aligns economic incentives, trust, and confidentiality requirements in a scalable and transparent system. A Blockchain blockchain eliminates the need for a centralized authority, provides transparency, enforces the federated learning protocol, and provides a decentralized infrastructure for the collection of fees and the distribution of rewards. The reward payment is calculated based on the client's clients' Federated learning enables multiple clients FIM Research Center 1. Introduction The application of machine learning (ML) promises far-reaching potentials across industries . ML has already proven successful in many areas, such as web search or recommender systems in e-commerce, in which a lot of high-quality data exists . While researchers address ML's growing demand for compute power and use of data with, e.g., distributed ML approaches where multiple computing nodes share their resources [3, 4, 5] and quality issues with data processing, access to data is not only a technical issue. Both traditional ML and distributed ML approaches assume that their training data is centralized by nature, preventing the applicability of ML approaches to domains in which data is sensitive and distributed at the same time. To avoid that ML approaches must rely on data to which only a centralized organization or individual has full access, federated machine learning (FL) can aggregate the less sensitive ML models that were independently and locally trained by individual clients [6, 7].
Now, it has raised $100 million to fuel its growth, a round of funding that values H2O.ai at $1.7 billion post-money ($1.6 billion pre-money). This is a Series E round, and it's being led by a strategic backer, the Commonwealth Bank of Australia (CBA), which has been a customer of the startup and will be using the backing to kick off a deeper partnership between the two to build new services. Others in the round include Goldman Sachs, Pivot Investment Partners, Crane Venture Partners and Celesta Capital. Further plans for the funding include building more products for H2O.ai as a whole, and hiring more talent to continue expanding the company's H2O AI Hybrid Cloud platform. This is not the first time that a customer has led a round as a strategic backer: in 2019, Goldman Sachs led the company's Series D of $72.5 million.
AI technology is associated with making machines and related processes intelligent through the use of advanced computer programming solutions. The AI technology market is poised to grow at a robust pace driven by its increasing adoption in an expanding range of applications in varied industries. The growing need to analyze and interpret burgeoning volumes of data and the escalating demand for advanced AI solutions to improve customer services are expected to fuel growth in the AI market. With significant improvements being seen in data storage capacity, computing power and parallel processing capabilities, the adoption of AI technology in various end-use sectors is on the rise. The rising adoption of cloud-based services and applications, rapid growth of big data, and the increasing need for intelligent virtual assistants are also contributing to the rapid growth of AI market.