Everyday AI Podcast – An AI and ChatGPT Podcast

Like 99% of companies are pushing AI. 🚀

But like 0.01% are actually training their people on it. 🤦

Don't worry. We'll go over the essentials on how to get started training your team or getting your company trained on best practices for using AI in your day-to-day. 

First step to solving the AI implementation gap starts here. 

How to Train Your Team on AI: The 7 Steps to Educate Your Organization on LLMs -- An Everyday AI Chat with Jordan Wilson

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More on this Episode: Episode Page
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Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup
Website: YourEverydayAI.com
Email The Show: [email protected]
Connect with Jordan on LinkedIn

Topics Covered in This Episode:
AI Training Adoption Gap in Organizations
Seven Steps to Train Teams on AI
Importance of Leadership in AI Adoption
Fixing Broken Workflows Before AI Integration
Choosing and Committing to One AI Platform
Training Employees in Three AI Knowledge Layers
Documenting Procedures for AI Implementation
Mandating Hands-On AI Practice and Outputs
Transitioning Teams from AI Operators to Orchestrators

Timestamps:

00:00 "AI Spending, Training Gap Looms"
05:48 "Embracing AI-First Leadership"
08:43 "Choosing Between AI Copilots"
10:22 "Unified AI Operating System Strategy"
14:46 Partnering for Generative AI Success
18:40 "Document Processes for AI Workplaces"
19:58 "AI OS for Decision Trees"
24:56 "Future Jobs: Orchestrating Tasks"
27:33 "Training Teams on AI"

Keywords: 
AI training, large language models, corporate AI adoption, AI literacy, AI education, team AI training, employee AI training, organization AI strategy, AI workflows, workflow optimization, AI platform selection, AI operating systems, Microsoft 365 Copilot, ChatGPT Enterprise, Google Gemini, Anthropic Claude, shadow IT, AI tool sprawl, data documentation, procedure documentation, unstructured data, domain-specific AI training, AI upskilling, AI reskilling, hands-on AI practice, AI hackathon, change management, cultural adoption, AI leadership, CEO AI usage, champion teams, department AI training, AI ROI, measurable AI outputs, AI orchestration, operator to orchestrator, expert-driven workflows, AI agent, AI mindset shift, AI native workplace, AI best practices, AI adoption gaps, enterprise AI maturity
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More than 1 million AI agents joined a no-humans-allowed social network. Fun? Or Dangerous? 🤔

Could OpenAI and NVIDIA’s $100 billion deal be ‘on ice?’ 🧊

And did Anthropic’s CEO really just say that AI could take half of white collar jobs and maybe kill millions? 😲

There’s a lot of AI questions and you don’t have hours a day to go fishing for the answers. 

That’s our job. 

Tune in as we bring you this week’s AI News That Matters, cutting through the marketing B.S. and fluff and telling you what’s worth your time. 

AI bots start their own social network, NVIDIA-OpenAI drama, Google’s huge releases and more — An Everyday AI Chat with Jordan Wilson

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More on this Episode: Episode Page
Join the discussion on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.

Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup
Website: YourEverydayAI.com
Email The Show: [email protected]
Connect with Jordan on LinkedIn

Topics Covered in This Episode:
NVIDIA and OpenAI $100B Deal Drama
Google Genie AI World Simulation Launch
Anthropic Claude Interactive Apps and Plugins
Google Chrome Gemini Auto Browse Agent
Google DeepMind Alpha Genome Medical Breakthrough
OpenAI Securing $60B Investment Funding Round
Anthropic CEO Predicts AI Superintelligence Risks
AI Bots Launch MoltBook Social Network
OpenClaw Open Source AI Agent Platform
Major AI Industry Updates and Model Releases

Timestamps:

00:00 NVIDIA Confirms Investment in OpenAI
05:32 "Google's AI World-Builder Unveiled"
10:41 "Anthropic Launches Interactive Claude Apps"
12:34 "Claude CoWorks Plug-ins Update"
17:12 "Google AI Decodes DNA Mutations"
21:17 "OpenAI, NVIDIA, and Expansion Updates"
22:20 "AI's Impact on Jobs Looms"
27:40 "OpenClaw: Open-Source AI Assistant"
28:43 Open Source AI with Limits
31:59 "Crypto Scams and AI Caution"
37:07 "AI Startups, Updates, and Rumors"
38:48 "Modulate: Trust Layer for AI"

Keywords: 
AI agents,
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Let's talk about the AI elephant in the room: hallucinations. 🐘

Maybe hallucinations are the reason your company has been hesitant on AI. 

But here's the thing, y'all. If you know what you're doing, hallucinations are largely manageable. 

But first, you gotta understand what they are, how they happen, and how to reduce the risk. 

Let's get started cutting down hallucinations together. 

AI Hallucinations: What they are, why they happen, and the right way to reduce the risk (Start Here Series Vol 5) -- An Everyday AI Chat with Jordan Wilson

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More on this Episode: Episode Page
Join the discussion on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.

Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup
Website: YourEverydayAI.com
Email The Show: [email protected]
Connect with Jordan on LinkedIn

Topics Covered in This Episode:
AI Hallucinations Definition and Causes
Large Language Models' Hallucination Mechanisms
Hallucination Types: Fabricated Claims & Sources
Model Improvements Reducing Hallucination Rate
Context Window Impact on AI Accuracy
AI Hallucinations in Legal and Enterprise Settings
Four-Layer Method for Minimizing Hallucinations
Custom Instructions and Retrieval-Augmented Generation
Expert-Driven Verification and Agent Safety Practices

Timestamps:

00:00 "Modulate's Velma: Smarter AI Insights"
03:18 "Reducing AI Hallucinations Explained"
08:36 "Minimizing AI Hallucinations with Skill"
12:30 "Model Retention and Recall Decline"
13:23 AI Advances: Improved Accuracy and Recall
19:24 "AI Hallucinations and Their Causes"
21:07 "Customizing AI Behavior Effectively"
24:47 "Connecting Data to Reduce Hallucinations"
28:47 "AI Oversight and Expert Input"
30:56 "Reducing AI Hallucinations Simplified"

Keywords: 
AI hallucinations, large language models, next token prediction, AI error, human error, fabricated claims, reinforcement learning with human feedback, context window, context engineering,
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Your company’s goldmine? 

All those meetings and call recordings. It’s the fuel that AI needs. 

But here’s the big letdown: those call transcripts only pick up the words. Not what they mean. 

And the difference? 

Well…. That can make all the difference. 

But some new technology might change what’s possible. 

Join us as we talk about it. 

AI Can Finally Hear What You Actually Mean. What this unlocks — An Everyday AI chat with Jordan Wilson and Modulate’s Mike Pappas.

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More on this Episode: Episode Page
Join the discussion on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.

Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup
Website: YourEverydayAI.com
Email The Show: [email protected]
Connect with Jordan on LinkedIn

Topics Covered in This Episode:
Modulate Velma Voice Native AI Model Overview
Tone, Emotion, and Intent in Voice AI
Differentiating Text vs. True Voice Understanding
Real-World Voice AI Use Cases in Fraud Detection
Synthetic Voice and Deepfake Detection Techniques
Ensemble Listening Model (ELM) Technology Explained
Voice AI for Customer Service and Support
Trust, Compliance, and Observability in Voice AI Agents
Cost and Scalability Challenges for Voice AI
Future Impact of Voice AI on Customer Relationships

Timestamps:

00:00 "Modulate: AI That Understands Tone"
06:15 "AI Use Cases Beyond Gaming"
07:13 "Detecting Abuse and Fraud"
13:19 Dynamic Model Orchestration Innovation
16:22 "Context-Aware AI for Conversations"
17:44 "Voice AI Transforming Customer Service"
22:49 AI Accountability and Compliance Challenges
25:36 AI, Customers, and Brand Trust
28:05 "Enhancing Communication Through AI"

Keywords: 
Voice AI, voice native AI, voice understanding, tone detection AI, intent detection, emotional AI, prosody analysis, real-time fraud detection, synthetic voice detection, AI guardrails, deepfake detection, customer support AI, call analysis, 
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Lil AI productivity secret: we've become the duct tape for AI. 🦾

What's that mean?

Since the ChatGPT wave, we've seen the most AI-native business leaders actually become the defacto human scaffolding for what AI systems need.

↳ Endless copying-pasting-reformatting.
↳ Nonstop context switching.
↳ Frankenstacking random AI apps together.

The recent wave changing this?

Apps inside of LLMs.

Anthropic joined OpenAI as the next big AI player to offer interactive apps inside of their Claude platform.

So how's it work?

Join us and let's put AI to Work on Wednesday.

Claude Apps: How Anthropic’s New Interactive Apps Can Up Your AI Productivity -- An Everyday AI Chat with Jordan Wilson

Newsletter: Sign up for our free daily newsletter
More on this Episode: Episode Page
Join the discussion on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.

Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup
Website: YourEverydayAI.com
Email The Show: [email protected]
Connect with Jordan on LinkedIn

Topics Covered in This Episode:
Claude Apps Interactive Tools Overview
Anthropic’s Model Context Protocol (MCP) Explained
How to Enable Claude Interactive Apps
Claude Apps vs. ChatGPT Apps Comparison
Workflow Automation with Claude Connectors
Key Claude Apps for Productivity (Clay, Canva, Gamma, Slack)
Real-Time Collaboration and Embedded Interfaces
Limitations and Bugs in Claude Interactive Apps

Timestamps:

00:00 "Modulate: Beyond Text Transcriptions"
06:33 Using AI Tools for Productivity
08:51 "Streamlining Workflows with AI"
12:35 "Interactive Apps and Commands"
13:40 Contact and Company Verification Tools
17:16 "Interactive AI Operating Layer"
23:40 "Check AI's Chain of Thought"
26:56 "App Results and Feedback"
30:28 "Generating AI Slide Decks"
32:24 "AI Use Cases Guide"
35:15 "AI Risks and Daily Insights"

Keywords: 
Claude apps, Anthropic, interactive tools, AI productivity, large language models, model context protocol, MCP protocol, interactive apps, connectors, frankenstacking workflows, context switching, AI operating system, GUI,
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