Originally published in Forbes The great potential of LLMs is significantly compromised by their Achilles heel: a deadly reliability problem. Predictive AI can address this problem – and that represents the next killer app for predictive AI. Enterprises such as Instacart, HP, Salesforce and Twilio are now adopting this inevitable, crucial pivot. Here’s the late breaking […]

  Originally published in Forbes On a recent episode of the Dr. Data Show, my co-host Luba Glouhova and I tackled a new paper authored by AI luminary Yann LeCun alongside other researchers. We had been tipped off by another co-author of the paper, AI researcher Philippe Wyder, who reached out on social media to say the paper related […]

 A practical introduction to making complex project structure explicit for humans and AI, with examples from predictive analytics and enterprise ML. Large analytics and AI projects contain more than source code. Predictive analytics and enterprise ML projects make this especially visible: they contain intermediate datasets, derived tables, feature definitions, model inputs, evaluation results, decisions, workflow […]

  Originally published in Forbes Recently on The Dr. Data Show, my co-host Luba Gluhova and I dug into the evolving discourse surrounding artificial general intelligence – and its stubborn incoherence. A recent publication by the venture capital firm Sequoia Capital projected the arrival of AGI by 2026, defining the concept simply as “the ability to figure things […]

  Originally published in Forbes The AI executives are at it again, promising human-level machines in the near future. In Davos, the CEOs of Google DeepMind and Anthropic each doubled down on the near-term arrival of artificial general intelligence – the hypothetical capacity for a machine to do most anything a human can – giving it 50% […]

  Originally published in Forbes When Henry Castellanos first presented his machine learning model to his company’s executives, he found himself fighting off a certain self-doubt that is so common among data professionals, it’s almost universal. On one hand, his model looked great. It did a sturdy job predicting which dental patients would fail to show […]

 Originally published in Forbes Most predictive AI projects fail to launch into production. The number crunching is sound and the data scientist delivers a viable machine learning model – but stakeholder objections sadly preclude deployment. To better meet stakeholders where they are, ML professionals are spearheading a movement to focus on predictive AI’s business value. Rather than sticking with the traditional […]