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Articles by Steven Astorino
AI Theory vs. Reality - The First 5 of 10 Lessons from the Field
There’s a huge difference between the purely academic exercise of training machine learning (ML) models versus building end-to-end data-science solutions to help solve real enterprise problems.
iCON - WMCPA Spring Conference 2023
Algorithmic bias occurs when a computer system reflects the implicit values of its human designers in its behavior.
iCON - WMCPA Spring Conference 2023
Bias can be introduced into the algorithms of hardware and software in a variety of ways. By Steven Astorino, Mark Simmonds, and Jean-François Puget Editor's Note: This article is excerpted from chapter 3 of Artificial Intelligence: Evolution and Revolution Pre-existing Bias Pre-existing bias in an algorithm is a consequence of underlying social and institutional ideologies. Such ideas may influence or create personal biases within individual designers or programmers.
iCON - WMCPA Spring Conference 2023
Moving to the cloud is supposed to save money. That strategy is often successful, but sometimes related expenses can start climbing. Some areas to keep a weather eye on can help mitigate losses. By John Ghrist Many enterprises continue to move to the cloud as a way of managing their computing expenses. Added security, reduction in IT-related expenses of managing systems in-house, and user convenience often top the list of corporate motivations.
iCON - WMCPA Spring Conference 2023
“Explainability” is the process of explaining why an ML model behaved in a certain way, such as approving or declining a bank loan. By Steven Astorino, Mark Simmonds, and Jean-François Puget Editor's Note: This article is excerpted from chapter 3 of Artificial Intelligence: Evolution and Revolution In previous articles, a general look at Algorithmic Bias and Explainability was presented, next Types of Algorithmic Bias were examined.
Steven Astorino: To return to work, businesses must bridge the ‘readiness’ gap
Opinion: Return-to-work considerations are much more complicated in scope and potential consequence than the initial jump to remote work Author of the article: Steven Astorino Publishing date: Aug 17, 2020 • • 3 minute read An office environment in normal (i.e., pre-COVID) times. Statistics Canada has reported that once the pandemic has subsided, almost one-quarter (22.5 per cent) of businesses expect employees working remotely could account for a tenth or more of their workforce.
Helping Provide Insight in an Unstructured World
In my previous article, I talked about the coming together of SPSS with the IBM Data Science Experience, a valuable add-on to the Data Science Experience platform. In this article, I look at another addition to the platform — the pairing of Watson Explorer with Data Science Experience. If we strip away everything but the analysis portion, I believe data scientists generally fall into two main camps: the traditionalists and the mavericks.
Bringing Light to Dark Data
"Dark data" is data acquired through various computer operations that is generally excluded from decision-making or analysis because it can be difficult for computers to categorize or understand. In fact, much of this data is unstructured and is sometimes retained only for legal purposes. This IBM Big Data Analytics Hub article estimates that only 20% of data is visible to computer systems, leaving 80% categorized as dark.
Fast Data Ingestion, ML Equates to Smarter Decisions Faster
Human beings tend to filter out events they deem unimportant to avoid sensory overload. They can only process so much at any given time. Computer systems, however, must be able to handle a massive number of digital “events” – everything from changes in your car’s engine to millions of retail transactions – in real time or near-real time to help support a wide and growing range of applications and customized services.
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