According to Deltec Bank, Bahamas – "Artificial intelligence and big data can be combined to create powerful predictive machine learning models that can be used for predicting risks associated with loan default, market crash, customer churn, fraudulent transactions, money laundering to name the few." Big Data is referred to as the huge amount of abundant data that is getting generated due to the digitalization of the economy. Whereas, artificial intelligence in the field of making computers make decisions without explicitly programmed, usually with the help of machine learning techniques. Big Data and AI actually complement each other because machine learning models require data, in some cases a huge amount of data to create accurate modes. In this post, we will see how the finance and banking industry is leveraging both Big Data and AI to their advantage.
Micro Focus (NYSE: MFGP) today announced the Vertica 10 Analytics Platform, which includes major updates for operationalizing machine learning at scale and expanding deployment options for Vertica in Eon Mode, enabling the most intensive variable workloads across major cloud and on-premises data centers. With Vertica 10, organizations are better equipped to unify their data siloes and take advantage of the deployment models that make sense now and in the future in order to monetize exponential data growth and capture real-time business opportunities. "Over the years, many organizations have successfully captured massive amounts of data, but are now challenged with getting the business insights they need to become data-driven. The market demand to leverage cloud architectures separating compute from storage needs to be balanced with the higher costs and increased risk of cloud-only data warehouses, while machine learning projects with tremendous potential have struggled to make their way into production," said Colin Mahony, Senior Vice President and General Manager, Vertica, Micro Focus. "Vertica 10 expands the options for a unified analytics strategy to address growing data siloes, a mix of cloud, on-premises, and hybrid environments, and the pressing need to operationalize machine learning at scale."
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Artificial intelligence's emergence into the mainstream of enterprise computing raises significant issues -- strategic, cultural, and operational -- for businesses everywhere. What's clear is that enterprises have crossed a tipping point in their adoption of AI. A recent O'Reilly survey shows that AI is well on the road to ubiquity in businesses throughout the world. The key finding from the study was that there are now more AI-using enterprises -- in other words, those that have AI in production, revenue-generating apps -- than organizations that are simply evaluating AI. Taken together, organizations that have AI in production or in evaluation constitute 85% of companies surveyed.
Job seekers interact more with advancing tech than they realize as more companies turn to automated tools in talent acquisition. The hiring process has come a long way from the days of paper resumés and cold calls via landline. Online job sites are now staples in talent acquisition, but artificial intelligence (AI) and machine learning are elevating the recruiting and hiring landscape. When asked about the current status of AI and machine learning in hiring, Mark Brandau, principal analyst on Forrester's CIO team said, "All vendors are moving in that direction without question. The power of AI lies in its ability to process high volumes of data at fast speeds, improving efficiency and productivity for organizations. Those same features and benefits can also be applied to the hiring process. "As organizations look to AI and machine learning to enhance their practices, there are two goals in mind," said Lauren Smith, vice president of Gartner's HR practice. "The first is how do we drive more efficiency in the process?
Today, the world is all about industry 4.0 and the technologies brought in by it. From Artificial Intelligence (AI) to Big Data Analytics, all technologies are transforming one or the other industries in some ways. AI-powered Cognitive Computing is one such technology that provides high scale automation with ubiquitous connectivity. More so, it is redefining how IoT technology operates. The need for Cognitive computing in the IoT emerges from the significance of information in present-day business.
Question: You lead the "Scientific Data Management" research group at TIB – Leibniz Information Centre for Science and Technology. You focus your research on how big data technologies can be used in the health sector to improve health care. What exactly are you researching? The amount of available big data has grown drastically in the last decade, and it is expected a faster growth rate in the coming years. Specifically, in the biomedical domain, there are a wide variety of methods, e.g.
ThetaRay, a provider of Big Data and artificial intelligence (AI)-enhanced analytics tools, has joined Microsoft's (NASDAQ:MSFT) partner program, One Commercial Partner, which provides various cloud-powered solutions. ThetaRay's anti-money laundering (AML) solution for correspondent banking can be accessed through Microsoft's Azure Marketplace. A large US bank has reportedly signed an agreement to use the solution. "We are proud to join the One Commercial Partner program and offer Microsoft Azure customers access to our industry-leading AML for Correspondent Banking solution." "Global banks are increasingly de-risking or abandoning their correspondent banking relationships due to a lack of transparency and fears of money laundering and regulatory fines. Our solution provides banks with the … ability to reverse the trend and grow their business by allowing full visibility into all links of the cross-border payment chain, from originator to beneficiary."
A challenge on the data science community site Kaggle is asking great minds to apply machine learning to battle the COVID-19 coronavirus pandemic. As COVID-19 continues to spread uncontrolled around the world, shops and restaurants have closed their doors, information workers have moved home, other businesses have shut down entirely, and people are social distancing and self-isolating to "flatten the curve." It's only been a few weeks, but it feels like forever. If you listen to the scientists, we have a way to go still before we can consider reopening and reconnecting. The worst is yet to come for many areas.
The explosion of breakthroughs, investments, and entrepreneurial activity around artificial intelligence over the last decade has been driven exclusively by deep learning, a sophisticated statistical analysis technique for finding hidden patterns in large quantities of data. A term coined in 1955--artificial intelligence--was applied (or mis-applied) to deep learning, a more advanced version of an approach to training computers to perform certain tasks--machine learning--a term coined in 1959. The recent success of deep learning is the result of the increased availability of lots of data (big data) and the advent of Graphics Processing Units (GPUs), significantly increasing the breadth and depth of the data used for training computers and reducing the time required for training deep learning algorithms. The term "big data" first appeared in computer science literature in an October 1997 article by Michael Cox and David Ellsworth, "Application-controlled demand paging for out-of-core visualization," published in the Proceedings of the IEEE 8th conference on Visualization. They wrote that "Visualization provides an interesting challenge for computer systems: data sets are generally quite large, taxing the capacities of main memory, local disk, and even remote disk. We call this the problem of big data. When data sets do not fit in main memory (in core), or when they do not fit even on local disk, the most common solution is to acquire more resources."