Ben Dubow studied the Middle East during his undergrad and took a job tracking terrorist groups.  After a brief stint at a large tech company, he launched Omelas, a company that combines AI and subject matter expertise to deliver intelligence to national security professionals.In today's episode, our Senior Content Advisor Q McCallum caught up with Ben to learn more about what Omelas is up to and how the company applies AI and data analysis to its mission.Along the way they explore the value of data in context; why it's important to ask the right questions of the right data, and not just the whole pool; the power of involving humans in the data pipeline; and what it takes to do NLP and NER at scale.  The two also talk about the impact of generative AI on democracy and authoritarianism.  A topic which, interestingly enough, holds lessons for corporations that plan to release AI chatbots.Links mentioned in this episode:

• Ben's LinkedIn profile
• Omelas website
• Ben's writing on the Center for European Policy Analysis (CEPA) website
• Article in Les Echos describing the project "Le Monde in English": "« Le Monde » parie sur l'étranger pour stimuler sa croissance"
• Q's write-up on "Risk Management for Generative AI Bots" is available on both his O'Reilly Radar page and his blog.

If you've been in the data game long enough, you've probably seen this before: a stakeholder or product owner approaches you with a project that's 95% done, and they'd like you to … "sprinkle some AI on it."  They've heard that this "AI" thing can be useful so they want some of it in their latest effort.Data scientist-turned-product person Noelle Saldana has experienced the "sprinkle some AI on it" request more times than she'd care to remember.  Our Senior Content Advisor Q McCallum met up with Noelle to explore this phenomenon.  How does this happen? (Hint: "corporate FOMO.")  What should you do when stakeholders insist on implementing AI that isn't actually going to help?  What about when your data scientist peers seem like they're doing this for the sake of "résumé-driven development?"Ultimately, the pair work through the bigger issue: how do you make peace with companies throwing money at AI like this? And how can these companies use this approach to their advantage?As a bonus, Noelle shares how she made the move from a data scientist role into product management.  If this path sounds interesting to you, take a listen.

• Noelle's Data Council talk, "Hot Takes and Tragic Mistakes: How (not) to Integrate Data People in Your App Dev Team Workflows"
• Find Noelle on LinkedIn: https://www.linkedin.com/in/noellesio/
• Q's blog post (which came out much better thanks to Noelle's help): "AI isn't something you just add to a company"