Machine Learning Times
EXCLUSIVE HIGHLIGHTS
2 More Ways To Hybridize Predictive AI And Generative AI
  Originally published in Forbes Predictive AI and generative AI...
How To Overcome Predictive AI’s Everyday Failure
  Originally published in Forbes Executives know the importance of predictive...
Our Last Hope Before The AI Bubble Detonates: Taming LLMs
  Originally published in Forbes To know that we’re in...
The Agentic AI Hype Cycle Is Out Of Control — Yet Widely Normalized
  Originally published in Forbes I recently wrote about how...

Left-hand

In Coronavirus Response, AI is Becoming a Useful Tool in a Global Outbreak

 Originally published in StatNews, January 29, 2020 Artificial intelligence is not going to stop the new coronavirus or replace the role of expert epidemiologists. But for the first time in a global outbreak, it is becoming a useful tool in efforts to monitor and respond to the crisis, according to health data specialists. In prior

Deepfakes Security Risks

  Originally published in KDNuggets, January, 2020. Deepfakes have instilled panic in experts since they first emerged in 2017. Microsoft and Facebook have recently announced a contest to identify deepfakes more efficiently. Deepfakes, videos where a person’s...

Predictive Maintenance Drives Big Gains in Real World

 Originally published in Datanamani, January 8, 2020. For today’s leading deep learning methods and technology, attend the conference and training workshops at Deep Learning World Las Vegas, May 31-June 4, 2020.   Nobody likes doing work that isn’t needed,...

Applying Occam’s Razor to Deep Learning

 Originally published in Memo’s Island, December 15, 2019 Preamble: Changing concepts in machine learning due to deep learning Occam’s razor or principle of parsimony has been the guiding principle in statistical model selection. In comparing two models,...

Data Project Checklist

 Originally published in Fast.ai, January 7, 2020. As we discussed in Designing Great Data Products, there’s a lot more to creating useful data projects than just training an accurate model! When I used to do consulting, I’d...

How Our Primary Model Works

 Originally published in FiveThirtyEight, January 9, 2020 Here at FiveThirtyEight, we’ve never built a complete back-to-front model of the presidential primaries before. Instead, in 2008, 2012 and 2016, we issued forecasts of individual primaries and caucuses on...

The 4 Hottest Trends in Data Science for 2020

 Originally published in Towards Data Science, January 8, 2020 2019 was a big year for all of Data Science. Companies all over the world across a wide variety of industries have been going through what people are...

The Problem with Hiring Algorithms

  Originally published in EthicalSystems.org, December 1, 2019 In 2004, when a “webcam” was relatively unheard-of tech, Mark Newman knew that it would be the future of hiring. One of the first things the 20-year old did,...

5 Statistical Traps Data Scientists Should Avoid

 Originally published in KDnuggets, October 2019. Fallacies are what we call the results of faulty reasoning. Statistical fallacies, a form of misuse of statistics, is poor statistical reasoning; you may have started off with sound data, but...

Why Machine Learning at the Edge?

 Originally published in SAP Blogs, October 16, 2019. For today’s leading deep learning methods and technology, attend the conference and training workshops at Deep Learning World Las Vegas, May 31-June 4, 2020.   Machine learning algorithms, especially deep learning...

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