Flexibility and Cost vs Performance and Features | Open Source vs Closed Source LLMs

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AI Unlocked

EVO AI

16 episodes

5 days ago

Join the experts at EVO AI as we unlock the power of Artificial Intelligence. We cover everything from Machine Learning & Artificial Neural Networks to GANs, MLOps, AIOps and Deep Learning. We look at latest developments, available platforms & technologies and how we can use these new emerging tools to get ahead. All in an easy-to-understand format that includes both technical details Q&As and "how-to" episodes allowing everyone to join in: from AI-curious folk all the way to entrepreneurs and executives. Tune in every Saturday evening if you're eager to stay at the forefront of AI innovation.

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Flexibility and Cost vs Performance and Features | Open Source vs Closed Source LLMs

AI Unlocked

30 minutes 29 seconds

1 year ago

Flexibility and Cost vs Performance and Features | Open Source vs Closed Source LLMs

In this episode about Open-Source vs Closed-Source LLMs, we will cover the following:

Introduction

Brief introduction to the topic.

Overview of what will be covered in the episode, including historical perspectives and future trends.

Chapter 1: Historical Context of Open-Source AI

The origins and evolution of open-source AI.
Milestones in open-source AI development.
How historical developments have shaped current open-source AI ecosystems.

Chapter 2: Historical Context of Closed Source AI

The beginnings and progression of closed-source AI.
Key historical players and pivotal moments in closed-source AI.
Influence of historical trends on today's closed-source AI landscape.

Chapter 3: Understanding Open-Source AI

Definition and characteristics of open-source AI.
Key players and examples in the open-source AI landscape.
Advantages: community collaboration, transparency, innovation.
Challenges: maintenance, security, quality control.

Chapter 4: Exploring Closed Source AI

Definition and characteristics of closed-source AI.

Major companies and products in the closed-source AI arena.
Benefits: proprietary technology, dedicated support, controlled development.
Limitations: cost, lack of customization, dependency on vendors.

Chapter 5: Comparative Analysis

Direct comparison of open-source and closed-source AI ecosystems.

Market share, adoption rates, development speed, innovation cycles.
Community engagement and support structures.

Case studies: Successes and failures in both ecosystems.

Chapter 6: Building Applications: Practical Considerations

How developers can leverage open-source AI for application development.

Utilizing closed-source AI platforms for building applications.
Trade-offs: Cost, scalability, flexibility, intellectual property concerns.
Real-world examples of applications built on both types of ecosystems.

Chapter 7: Future Trends and Predictions

Emerging trends in both open-source and closed-source AI.

Predictions about the evolution of these ecosystems.
Potential impact on the AI development community and industries.

Conclusion and Wrap-Up

Recap of key points discussed.

Final thoughts and takeaways for the audience.
Call to action: encouraging listener engagement and feedback.