What's coming next in AI? 🤔

While no one knows, spending time in Silicon Valley and San Francisco recently helped give me a better idea of what's around the corner.

So what should you be paying attention to?

Tune in LIVE as we discuss.

Ep 878: What’s Coming Next: 5 AI Trends, Problems and Opportunities around the Corner (Replay)

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Website: YourEverydayAI.com
Email The Show: info@youreverydayai.com
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Topics Covered in This Episode:

1. Autonomous Cars vs Human Drivers Analysis
2. Decline of AI Generalists in Workforce
3. Rise of AI Specialists and Translators
4. AI Homework Trend Among Office Workers
5. Challenges of Enterprise AI Training Gaps
6. FOMAT: Fear of Missing Agent Time
7. AI Agent Acceleration Problem and Opportunity
8. Impact of Rapid AI Development on Enterprises

Timestamps:

00:00 Recent trip and AI insights

05:08 Podcast takeaways from the past week

09:44 Testing autonomous vehicles on roads

13:22 Introduction to Start Here series

16:12 Importance of AI Integration Skills

20:09 Experimenting with AI at Home

21:58 Discussing AI workloads at home

26:16 Navigating AI tools and FOMO

29:05 AI labs opening up about models

30:49 AI capabilities are rapidly advancing

34:48 Join the newsletter feedback loop

Keywords:

AI trends, AI problems, AI opportunities, autonomous cars, autonomous vehicles, Waymo, driverless cars, human drivers, AI generalists, AI specialists, agentic AI, acceleration of AI, AI learning curve, AI homework, enterprise AI, AI skills gap, workplace AI adoption, knowledge gap in AI, AI education, AI training, digital transformation, AI modalities, text to text AI, agentic orchestration, trust and observability teams, AI job roles, job description changes, local AI models, open source AI, PDF parsing AI, MVP demos, FOMAT,

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People are calling the new ChatGPT Voice their “AGI moment.” 🤖

After testing it over the past week, I understand why.

It seems like the missing link between the ever-capable desktop agent and the human worker not having to type in front of said desktop all day.

It’s less like “voice mode” and more like having your own JARVIS.

After testing thousands of AI tools, this has instantly become one of the most useful features I’ve ever used.

Ep 877: ChatGPT Voice is Like Jarvis: How to use the New Feature and the 7 biggest unlocks (Replay)

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Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup
Website: YourEverydayAI.com
Email The Show: info@youreverydayai.com
Connect with Jordan on LinkedIn

Topics Covered in This Episode:

1. ChatGPT Voice as Jarvis-Style AI Agent
2. ChatGPT Voice vs. Old Voice Modes
3. Full Duplex GPT Live Model Explained
4. Desktop ChatGPT Voice: Codex Integration
5. Advanced ChatGPT Voice Workflow Use Cases
6. Utilizing Connectors and MCP Integrations
7. Stacking and Scheduling Tasks with Voice
8. Hands-Free Computer Control via iPhone
9. Durable Written Artifacts and Accountability
10. Omnipresent Desktop Orb Agent Features
11. Live Demo: Real-World Voice Command Tasks
12. Productivity Gains from Voice-Driven Automation

Timestamps:

00:00 Experiencing the new ChatGPT voice

04:30 AI demonstration details

08:44 Accessing GPT data sources

11:00 Differentiating Codex from ChatGPT Voice

16:08 Limitations of GPT Live integrations

19:00 Automating Tasks with Voice Commands

21:47 Using ChatGPT voice on iPhone

25:09 Using AirPods with your Mac

27:09 Starting a Chat GPT Demo

31:21 Launching the Parrot Program

35:47 Demoing JGPT's capabilities

39:18 Demonstrating multitasking techniques

40:58 Working with ChatGPT Voice

Keywords:

ChatGPT Voice, Jarvis mode, AGI moments, natural language interface, hands-free AI, desktop agent, Codex, GPT Live model, full duplex voice mode, AI-powered workflow, agent management interface, voice assistant, productivity AI, computer control AI, voice commands, dynamic data access, connectors, MCP servers, project management AI, automated document creation, email triage, AI scheduling, repeatable tasks, AI skill creation, workflow automation, mobile to desktop integration, voice-based task stacking, durable artifacts, audit trail, AI voice chat, omnipresent agent, drag-and-drop orb, ChatGPT app, browser automation, programmatic control, cloud storage organization, image creation, transcribing YouTube reviews, AI learning, AI scheduling, business productivity AI, workflow augmentation, CRM integration, LinkedIn enrichment, poll result analysis, open rate comparison, Slack integration, Google Drive automation, ERP integration, finance workflows, interactive dashboards, AI trend updates, change log analysis.

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You've probably never heard of Inkling.

It's the newest (and first) model from Thinking Machines Labs, and it could very well be a small snowball that picks up major momentum in today's enterprise AI landscape.

If you haven’t heard of Thinking Machines, they’re led by Mira Murati, the former CTO at OpenAI.

The big bet with Inkling?

The future of AI could be using smaller models fine-tuned and optimized for smaller tasks.

Will it work?

Tune in live as we dive in.

The Most Important AI Model You’ll Probably Never Use That Just Dropped -- An Everyday AI Chat With Jordan Wilson (Replay)

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More on this Episode: Episode Page
Today's Episode 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: info@youreverydayai.com
Connect with Jordan on LinkedIn

Topics Covered in This Episode:

1. Inkling AI Model Launch Overview
2. Thinking Machines Lab Leadership Highlight
3. Inkling's Multimodal and Agentic Capabilities
4. Open Source vs. Proprietary AI Models
5. Enterprise Procurement with American AI Models
6. AI Fine Tuning as a Service (Tinker)
7. Benchmark Scores: Inkling vs. Frontier Models
8. Customization and Model Shopping for Enterprises
9. AI Token Costs Driving Model Efficiency
10. Bridgewater Case Study: AI Model Customization
11. Frontier Models Enabling Efficient Fine-Tuning
12. Future Trends: Specialized Small Language Models

Timestamps:

00:00 Inkling: A new AI model release

05:43 Inkling AI model details

09:08 China's dominance in open source AI

11:48 Launch and model updates discussed

15:21 Concerns over using Chinese open-source models

19:06 Training smaller AI models

20:22 Using GPT for AI Model Training

23:54 Predicting Rise of Small Language Models

28:38 Choosing the right AI model

Keywords:

Inkling, Thinking Machines Lab, Meera Muradi, former OpenAI CTO, open source AI model, American AI model, fine tuning as a service, enterprise AI, multimodal AI, agentic models, customizable AI, Tinker, enterprise distribution, model procurement, Chinese open source models, strategic reset, model overhang, capabilities gap, AI model shopping, model routing, cost-conscious enterprises, artificial intelligence index, 975 billion parameter model, text-image-audio AI, open weights, proprietary AI models, customization accessibility, small language models, AI workflows, context window, Bridgewater use case, model distillation, GPU infrastructure, API costs, token efficiency, fine-tuned models, post training, AI competitive leverage, recurring financial judgment, AI benchmarks, middle tier models, automated model evaluation, privacy and workflow mapping, economical AI models, model rental, model routing automation.

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