EP 347: Google's Gemini Gems - What they are and how they compare to custom GPTs

08/29/24 • 46 min

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Everyday AI Podcast – An AI and ChatGPT Podcast

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Google silently released its answer to custom GPTs with its Gemini Gems. In short, Gems allows users to create a smaller or more customized version of their big model, Google Gemini.
↳ But how does it work?
↳ Is it any good?
↳ And how does it compare to ChatGPT's custom GPTs?
We bring you the answers.
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More on this Episode: Episode Page
Join the discussion: Ask Jordan questions on Google Gemini
Related Episode: Ep 253: Custom GPTs in ChatGPT – A Beginner’s Guide
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:
1. Comparison between GPTs and Gems
2. Features of Google Gems
3. Demo of Google Gems
Timestamps:
02:15 Daily AI news
03:47 High-profile organizations opt out of Apple's AI data collection.
7:00 Google Gemini Gems explained
12:21 Enable Google Gemini for all users and platforms.
14:30 GPT-4 chatbot leaderboard shows significant improvements.
18:55 Mixed feelings about inconsistent unique feature. Limited functionality.
21:50 Improve productivity with free PPP course. Visit podpp.com.
24:31 Use Google Workspace gems to search efficiently.
28:53 Google Gems failed to follow instructions effectively.
30:04 Evaluating text search for Google Docs efficiency.
34:11 Google Gems' ability to guide accurately needs improvement.
37:11 Google Gems may pull unwanted information, problematic.
41:57 Break down process into 5 steps.
43:57 Referencing Google Docs and sharing prompt.
46:05 Google gems are not as good as GPTs.
Keywords:
Google Gems, GPTs, OpenAI, Google Workspace, Google Gemini, Gmail, AI newsletter, ChatGPT, Google IO developer conference, custom gems, Google gem manager, Google account, Gemini 1.5 Pro, tech issues, Customer testimonial, Screen sharing, Live documents, Custom versions, Jordan Wilson, Nvidia earnings, Apple Bot Extended, AI training data collection, everyday AI, AI for daily practical use, AI experts, Instruction following, Admin console settings, Document access, Dynamic docs, GPT 4 Model.

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Previous Episode

EP 346: AI Hype vs. Reality - Insights from EY Consulting Leader

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95% of companies are investing in AI. Yet, adoption is still low. So is AI more hype, or is it foundational in the future of work? We’ll be tackling those questions with one of the world’s largest consulting firms, as EY’s Traci Gusher joins us.
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More on this Episode: Episode Page
Join the discussion: Ask Jordan and Traci questions on AI
Related Episodes: Ep 331: Why Fear of AI is Stifling Adoption
Ep 206: There is No AI Hype – This is how the world works now
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:
1. Importance of Data in AI Implementation
2. AI Education and Responsible AI Implementation
3. AI for Business Growth
4. Rethinking Business Processes Using AI
Timestamps:
01:35 Daily AI news
05:20 About Traci and EY
08:14 AI becoming integral in personal and professional lives.
13:16 Companies adopting generative AI for product innovation.
14:18 Organizations hesitant about using AI due to concerns.
18:21 Centralized data processes crucial for business success.
20:46 Organizations focus on innovation using generative AI.
25:28 Data governance crucial for quality and analysis.
27:14 AI master class educates boards on responsibility.
Keywords:
Tracey Gusher, AI, data lineage, data quality, data traversal, AI education, AI master class, responsible AI implementation, reimagining processes, revenue streams, data investment, Jordan Wilson, Everyday AI Show, generative AI, organizational sectors, value-driven AI, top growth, bottom growth, ChatGPT, experimentation, transformation, technology maturity, data maturity, data management, decentralization, data governance, automation, applied AI, analytics, good data.

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Next Episode

EP 348: Large Language Model Best Practices - 7 mistakes to fix

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In today's episode, we're diving into the 7 most common mistakes people make while using large language models like ChatGPT.
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More on this Episode: Episode Page
Join the discussion: Ask Jordan questions on AI
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:
1. Understanding the Evolution of Large Language Models
2. Connectivity: A Major Player in Model Accuracy
3. The Generative Nature of Large Language Models
4. Perfecting the Art of Prompt Engineering
5. The Seven Roadblocks in the Effective Use of Large Language Models
6. Authenticity Assurance in Large Language Model Usage
7. The Future of Large Language Models
Timestamps:
02:30 LLM knowledge cut-off
09:07 Models trained with fresh, quality data crucial.
10:30 Daily use of large language models poses risks.
14:59 Free chat GPT has outdated knowledge cutoff.
18:20 Microsoft is the largest by market cap.
21:52 Ensure thorough investigation; models have context limitations.
26:01 Spread, repeat, and earn with simple actions.
29:21 Tokenization, models use context, generative large language models.
33:07 More input means better output, mathematically proven.
36:13 Large language models are essential for business survival.
Keywords:
Large language models, training data, outdated information, knowledge cutoffs, OpenAI's GPT 4, Anthropics Claude Opus, Google's Gemini, free version of Chat GPT, Internet connectivity, generative AI, varying responses, Jordan Wilson, prompt engineering, copy and paste prompts, zero shot prompting, few shot prompting, Microsoft Copilot, Apple's AI chips, OpenAI's search engine, GPT-2 chatbot model, Microsoft's MAI 1, common mistakes with large language models, offline vs online GPT, Google Gemini's outdated information, memory management, context window, unreliable screenshots, public URL verification, New York Times, AI infrastructure.