-
Survey: Machine Learning Projects Still Routinely Fail to Deploy
Eric Siegel highlights the chronic under-deployment of ML projects, with only 22% of data scientists saying their revolutionary initiatives usually deploy, and a lack of stakeholder visibility and detailed planning as key issues, in his industry survey and book "The AI Playbook."
-
Models Are Rarely Deployed: An Industry-wide Failure in Machine Learning Leadership
In this article, Eric Siegel summarizes the recent KDnuggets poll results and argues that the pervasive failure of ML projects comes from a lack of prudent leadership. He also argues that MLops is not the fundamental missing ingredient – instead, an effective ML leadership practice must be the dog that wags the model-integration tail.
-
New Poll: What Percentage of Your Machine Learning Models Have Been Deployed?
Take a moment to participate in the latest KDnuggets poll and let the community know what percentage of your machine learning models have been deployed.
-
How Machine Learning Works for Social Good
We often discuss applying data science and machine learning techniques in term so of how they help your organization or business goals. But, these algorithms aren't limited to only increasing the bottom line. Developing new applications that leverage the predictive power of AI to benefit society and those communities in need is an equally valuable endeavor for Data Scientists that will further expand the positive impact of machine learning to the world.
-
Six Ethical Quandaries of Predictive Policing
When predictive machine learning models are applied to real-life scenarios, especially those that directly impact humans, such as cancer detection and other medical-related applications, the risks involved with incorrect predictions carry very high stakes. These risks are also prominent in how machine learning is applied in law enforcement, and serious ethical questions must be considered.
-
Machine Learning’s Greatest Omission: Business Leadership
Eric Siegel's business-oriented, vendor-neutral machine learning course is designed to fulfill vital unmet learner needs, delivering material critical for both techies and business leaders.
-
Predicting the President: Two Ways Election Forecasts Are Misunderstood
With election cycles always seeming to be in season, predictions on outcomes remain intriguing content for the voting citizens. Misinterpretation of election forecasts also runs rampant, and can impact perceptions of candidates and those who post these predictions. A better fundamental understanding of probability can help improve our collective notion of futurism, and how we monitor elections.
-
Accuracy Fallacy: The Media’s Coverage of AI Is Bogus
Such as the gross exaggerations Stanford researchers broadcasted about their infamous "AI gaydar" project, there exists a prevalent "accuracy fallacy" in relation to AI from the media. Find out more about how the press constantly misleads the public into believing that machine learning can reliably predict psychosis, heart attacks, sexuality, and much more.
-
Five Ways Your Safety Depends on Machine Learning
Eric Siegel tells you about five ways your safety depends on machine learning, which actively protects you from all sorts of dangers, including fires, explosions, collapses, crashes, workplace accidents, restaurant E. coli, and crime.
-
AI is a Big Fat Lie
Is AI legit? This treatise by Eric Siegel, which ridicules the widespread myth of artificial intelligence, is enlightening and actually pretty funny. It's time for the term AI to be “terminated.”
|
