“Started earlier this year, the remote-first Pagos is building a data ‘platform’ and API-driven micro-services that it says can integrate with any payment stack. The end goal is to drive better performance and ‘optimization’ of a business’ existing payments infrastructure.
In the short term, Pagos is offering services such as ‘immediate’ payment data visualizations, automatic notifications on payment trends or problems and up-to-date bank identification number (BIN) details to manage customers and track costs. Looking ahead, the company is planning to offer network tokenization and account updater services.
‘Pagos is led by two of the most accomplished payments product experts in the business, and their relationships, domain expertise and firsthand experience with these pain points is incredibly valuable,’ he said in a written statement.”
“Respondents to an informal social media survey that I’ve been running for the past couple of years report that 25% of organizations use 10 or more BI platforms, 61% of organizations use four or more, and 86% of organizations use two or more.
We currently see BI fabric manifesting in one or more of the following architectures and technologies:
“Hackland explains to VentureBeat how Williams F1 is looking to exploit data to make further advances up the grid and how emerging technologies, such as artificial intelligence (AI) and quantum computing, might help in that process.
But what we’ve realized is trying to create data lakes just hasn’t worked. It hasn’t given us the actual intelligence that we wanted, so we often refer to data puddles. It’s much better to have many of these puddles that are well-structured and the data is well understood. And then, through a middleware layer, we can get to the graphical user interfaces.”
“Nearly 90 percent of ML models built globally are never brought to light, primarily because they cannot adjust to the variety of information available in real-world applications.
The solution Andrew Ng has proposed is to put aside the architecture of an AI model and focus on what it is working with, i.e. the data. By paying close attention to what a model learns and improving the quality of data, and subsequently retraining the ML model, engineers can build higher quality systems in a much shorter time.
Andrew Ng believes that the right people can put this idea to use constructively to counter many issues, such as manufacturing, treating diseases, energy consumption and food production, all with the help of AI-backed with the appropriate data.”