Machine Learning Times
EXCLUSIVE HIGHLIGHTS
Hybrid AI Emerges To Tame LLMs – And Not A Moment Too Soon
 Originally published in Forbes The great potential of LLMs is...
AGI Is Infeasible. Instead, Pursue Superhuman Adaptable Intelligence
  Originally published in Forbes On a recent episode of the...
Artifact-Driven Development: Making It Possible to Query Large Analytics and AI Projects
 A practical introduction to making complex project structure explicit...
Incoherent AGI Hype Spurs An Industrywide Pivot To Hybrid AI
  Originally published in Forbes Recently on The Dr. Data Show,...

Original Content

Why You Must Twist Your Data Scientist’s Arm To Estimate AI’s Value

 Originally published in Forbes, June 11, 2024. If you’ve ever had a data scientist make a machine learning model for you, you probably first experienced excitement, followed by bewilderment. The potential power is awesome. In its enterprise application, predictive AI is the antidote to information overload. Too many prospective customers? Use a predictive model to prioritize them

3 Ways Predictive AI Delivers More Value Than Generative AI

 Originally published in Forbes, March 4, 2024. Which kind of AI should companies focus on—generative AI, which produces writing, computer code, images, video and other content, or predictive AI, which targets ads, marketing, fraud detection, risk management,...

AI Success Depends On How You Choose This One Number

 Originally published in Forbes, March 25, 2024. To do its job, AI needs your help. It has the potential to drive millions of operational decisions—such as whom to contact, approve, investigate, incarcerate, set up on a date...

Elon Musk Predicts Artificial General Intelligence In 2 Years. Here’s Why That’s Hype

 Originally published in Forbes, April 10, 2024 When OpenAI’s board momentarily ousted Sam Altman from his post as CEO last November, the media obsession was… intense. Why so much fuss about a corporate drama? The public mania—and...

Survey: Machine Learning Projects Still Routinely Fail to Deploy

 Originally published in KDnuggets. 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,...

Three Best Practices for Unilever’s Global Analytics Initiatives

    This article from Morgan Vawter, Chief Digital Officer at Unilever, serves as the foreword to The AI Playbook: Mastering the Rare Art of Machine Learning Deployment, by Eric Siegel. There’s almost no business outcome that...

Getting Machine Learning Projects from Idea to Execution

 Originally published in Harvard Business Review Machine learning might be the world’s most important general-purpose technology, but it’s notoriously difficult to launch. Outside of Big Tech and a handful of other leading companies, machine learning initiatives routinely...

Eric Siegel on Bloomberg Businessweek

  Listen to Eric Siegel, former Columbia University Professor, discuss his book The AI Playbook: Mastering the Rare Art of Machine Learning Deployment. Radio show: Bloomberg Businessweek Hosts: Carol Massar and Tim Stenovec. Producer: Paul Brennan. Click...

Effective Machine Learning Needs Leadership — Not AI Hype

 Originally published in BigThink, Feb 12, 2024.  Excerpted from The AI Playbook: Mastering the Rare Art of Machine Learning Deployment by Eric Siegel (February 6, 2024), published by The MIT Press. The ML (Machine Learning) industry has bitten forbidden...

Today’s AI Won’t Radically Transform Society, But It’s Already Reshaping Business

 Originally published in Fast Company, Jan 5, 2024. Eric Siegel had already been working in the machine learning world for more than 30 years by the time the rest of the world caught up with him. Siegel’s been...

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