It’s easy to take continuous integration (CI) and continuous delivery/deployment (CD) for granted these days, but these have been transformational concepts that have drastically changed the face of software development over the past thirty years.
While CI/CD is synonymous with modern software development best practices, today’s machine learning (ML) practitioners still lack similar tools and workflows for operating the ML development lifecycle on a level on par with software engineers. For background, follow a brief history of transformational CI/CD concepts and how they’re missing from today’s ML development lifecycle.
Business analytics (BA) is the process of evaluating data in order to gauge business performance and to extract insights that may facilitate strategic planning. It aims to identify the factors that directly impact business performance, such as ie. revenue, user engagement, and technical availability. BA takes data from all business levels, from product and marketing, to operations and finance.
Organizations in the financial services sector face a unique set of challenges as they consider how to wrangle and process the vast amount of data they collect. During our Financial Services Summit, I was lucky enough to speak to Brian Anthony, chief data officer for the Municipal Securities Rulemaking Board (MSRB), to learn how the MSRB is integrating technologies such as artificial intelligence (AI) and machine learning to modernize its data.
For the next interview in our series speaking to technology and IT leaders around the world, we’ve welcomed experienced CIO and lead AI expert at BagsID, Erik Van Breusegem to share his thoughts on the current state of AI.
In part 1 of this blog post, we discussed the need to be mindful of data bias and the resulting consequences when certain parameters are skewed. Surely there are ways to comb through the data to minimise the risks from spiralling out of control. We need to get to the root of the problem. In 2019, the Gradient institute published a white paper outlining the practical challenges for Ethical AI.
Advances in the performance and capability of Artificial Intelligence (AI) algorithms has led to a significant increase in adoption in recent years. In a February 2021 report by IDC, they estimate that world-wide revenues from AI will grow by 16.4% in 2021 to USD $327 billion. Furthermore, AI adoption is becoming increasingly widespread and not just concentrated within a small number of organisations.
In my main position, as a data scientist at SIL International, I work on expanding language possibilities with AI. Practically this includes applying recent advances in Natural Language Processing (NLP) to low resource and multilingual contexts. We work on things like spoken language identification, multilingual dialogue systems, machine translation, and translation quality estimation.
For the newest instalment in our series of interviews asking leading technology specialists about their achievements in their field, we’ve welcomed Matthew Renze. Matthew is a specialist in artificial intelligence who has given over 100 talks on this very subject and is the author of numerous AI courses on Pluralsight.
