Confused by AI jargon and unsure which tools actually move the needle for your business?
We break down the real differences between traditional algorithms, large language models (LLMs), and agents — including agentic AI — and give practical guidance leaders can use now.
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Topics Covered in This Episode:
- Choosing AI: Algorithms vs. Agents
- Understanding AI Models and Agents
- Using Conditional Statements in AI
- Importance of Data in AI Training
- Risk Factors in Agentic AI Projects
- Innovation through AI Experimentation
- Evaluating AI for Business Solutions
Timestamps:
00:00 AWS AI Leader Departs Amid Talent War
03:43 Meta Wins Copyright Lawsuit
07:47 Choosing AI: Short or Long Term?
12:58 Agentic AI: Dynamic Decision Models
16:12 "Demanding Data-Driven Precision in Business"
20:08 "Agentic AI: Adoption and Risks"
22:05 Startup Challenges Amidst Tech Giants
24:36 Balancing Innovation and Routine
27:25 AGI: Future of Work and Survival
Keywords:
AI algorithms, Large Language Models, LLMs, Agents, Agentic AI, Multi agentic AI, Amazon Web Services, AWS, Vazhi Philemon, Gen AI efforts, Amazon Bedrock, talent wars in tech, OpenAI, Google, Meta, Copyright lawsuit, AI training, Sarah Silverman, Llama, Fair use in AI, Anthropic, AI deep research model, API, Webhooks, MCP, Code interpreter, Keymaker, Data labeling, Training datasets, Computer vision models, Block out time to experiment, Decision-making, If else conditional statements, Data-driven approach, AGI, Teleporting, Innovation in AI, Experiment with AI, Business leaders, Performance improvements, Sustainable business models, Corporate blade.
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