Could an AI mimic your voice or face well enough to trick your bank or your family?

In This Episode, You'll Learn:

• The Reality of Biometric Hacking: How bad actors are using AI voice cloning to bypass phone banking security and authorize fraudulent transactions.

• The "Vector Database" Defense: Why you need to think like an AI to protect yourself, reducing the "connectable dots" of your digital footprint (metadata, location history, and public posts).

• 7 Steps to Digital Hygiene: A comprehensive checklist for safety, including multi-factor authentication strategies, "trust but verify" communication protocols, and spotting unnatural artifacts in video calls.

Don't forget to vote in this weeks poll: Clones

What is Ohio's AI niche, and how is it fueling a multi-billion dollar tech boom right now? We're breaking down the key insights from the second annual OhioX AI Summit, revealing why major tech companies are pouring billions into data centers in the state.

In This Episode, You'll Learn:

• Ohio is a major hub for data centers, with over $27 billion already invested
• The cooling requirements for data centers are often misunderstood
• The most effective AI literacy strategy is hands-on, applied training
• The core change management strategy is to focus on workflows, not job replacement

• OhioX (Nonprofit organization promoting Ohio's tech growth)
• Robert Sprague (Ohio Treasurer)
• Merle Madrid (from AWS)
• Will Burris (from Expeed Software)

This Weeks Poll: Environmental Impact

AI planned our Thanksgiving! The surprising part? What it forgot.

Host Becka tried to offload Thanksgiving meal planning to three top AI models.

• The Power of Detailed AI Planning: The AI successfully generated a comprehensive two-day cooking timeline, a grocery list sorted by store section, and a safe, creative leftovers plan (including a surprising suggestion).
• The Critical Omission (and Prompting Lesson): Despite the high level of detail, all AI models failed to provide something important. This underscores the necessity of a solid prompt.
• AI as a Teammate, Not a Replacement: AI is an invaluable organizational assistant for complex tasks like holiday dinner planning, but human review and strong prompting are essential for a successful, complete outcome.

Resources Mentioned:

• Becka's Thanksgiving Prompt
• Claude's Response

Do you have a delicious family tradition for Thanksgiving (like pie for breakfast!) or a favorite side dish? Tell us your favorite part of the meal in the comments!

And don't forget to take this weeks poll: Pie for Breakfast?

How did one of the very first machine learning programs actually work? This episode dives into the mechanics of Arthur Samuel's famous 1950s checkers-playing AI.

• The checkers program used a combination of a scorecard (to rate board positions) and a look-ahead function (to explore future moves) to choose the best action.

• The system learned through reinforcement learning; moves that led to wins were positively reinforced, causing the scorecard's feature weights to be adjusted automatically over hundreds of games.

• The program utilized the minimax strategy—choosing the move that maximizes its own score while minimizing the maximum damage the opponent could inflict.

• A technique called pruning was used to dramatically increase the search depth by intelligently skipping unproductive branches, a concept vital for efficiency in modern computing.

This weeks poll: Checkers or Chess

How does an AI go from a blank slate to a powerful tool? It's not magic… it's a detailed, multi-stage training process.

In This Episode, You'll Learn:

• The five essential stages of AI training: Data Collection, Tokenization, Pretraining, Post-training, and Continuous Improvement.
• What Supervised Learning is and how labeled "flashcards" or "gold standard" examples help fine-tune a model's accuracy.
• The power of Unsupervised Learning in the pre-training phase, where models find hidden patterns in massive, unlabeled datasets (like Spotify recommendations).
• How Reinforcement Learning from Human Feedback (RLHF) uses a reward system (like ranking bowls of ramen) to make models more helpful and aligned.

This weeks poll: Human Feedback

🗓️ Is your Large Language Model stuck in the past? Every AI has a knowledge cutoff date, but Retrieval Augmented Generation (RAG) is the key to feeding it the current facts, proprietary documents, and domain expertise it needs right now.

• The Problem RAG Solves

• How RAG Works

• Semantic Search vs. Keyword Search

Be sure to vote in this weeks poll: R.A.G. Knowledge

How does Anthropic's Claude Projects feature stack up against OpenAI's version? If you're struggling to organize your chats and maintain context, "Projects" are the answer, but the two platforms have major differences you need to know about.

In This Episode, You'll Learn:

• Which interface is cleaner, more intuitive?
• A key differences between Claude’s Projects and ChatGPT’s version.
• How Claude solves the “context window” problem.

👇 YOUR ACTION STEP 💬 Which platform wins for you? After hearing the comparison, are you on Team Claude for its clean UI or Team ChatGPT for its free access and citations? Let us know your favorite!

And don’t forget to vote in this weeks poll: Current "Projects" Usage

Struggling to keep your ChatGPT conversations organized and find yourself repeating instructions over and over? The ChatGPT Projects feature is here to help.

In This Episode, You'll Learn:

• What Projects Are (And Why They're Not Just Folders)
• The Power of Project-Level Instructions
• How Projects Enable Context Engineering
• Practical Use Cases

👇 YOUR ACTION STEP Have you tried the new ChatGPT Projects feature? Share your most creative or useful project idea in the comments below! And vote in our poll: AI Frustrations