g-f(2)2649 Beyond Traditional AI: Unlocking the Power of Generative AI

 

genioux Fact post by Fernando Machuca and Gemini

Introduction:

The evolution of Artificial Intelligence (AI) has brought about a new paradigm shift, with Generative AI emerging as a transformative force. In the YouTube video, "The Evolution of AI: Traditional AI vs. Generative AI," the distinction between these two approaches is elucidated, revealing the immense potential of Generative AI to reshape industries and applications.

genioux GK Nugget:

"Generative AI leverages massive real-world data and large language models to create unique, tailored solutions, surpassing the limitations of traditional AI's reliance on internal data." — Fernando Machuca and Gemini, July 22, 2024

genioux Foundational Fact:

While traditional AI utilizes a company's internal data repository to build models for specific tasks, Generative AI harnesses the vast expanse of real-world data. This allows it to train large language models that, while powerful, require a prompting and tuning layer to align with specific business needs. The result is a feedback loop that constantly refines and improves the model's accuracy and relevance.

Conclusion:

Generative AI represents a significant leap forward in AI capabilities. Its ability to learn from massive amounts of diverse data and adapt to specific applications makes it a game-changer across industries. As businesses and individuals navigate the g-f New World, understanding and harnessing the potential of Generative AI will be crucial for staying ahead of the curve and driving innovation.

REFERENCES

The g-f GK Context

The Evolution of AI: Traditional AI vs. Generative AI, IBM Technology, YouTube channel, Jul 5, 2024.

• 28,289 views
• AI powered tools have been used for decades, but the recent breakthroughs in generative ai have pushed the topic front and center, but what's really different about new generative ai models like large language models (LLM) compared to the traditional AI. In this video Shad Griffin explains the fundamental difference in their architectures that have made Generative AI possible.
• AI news moves fast. Sign up for a monthly newsletter for AI updates from IBM → https://ibm.biz/BdmC5G

Classical summary of the YouTube video:

The video is about the difference between traditional AI and generative AI.

Traditional AI works in three parts: a repository where all the data is stored, an analytics platform to build models based on that data, and an application layer to use those models. For example, a Telco company might use this kind of AI to predict which customers are likely to churn. The company would build a model to identify those customers and then use an application to try to win them back with discounts or other incentives.

Generative AI is fundamentally different. It starts with a massive amount of data from the real world, not just from a single company’s repository. This data is used to train large language models, which are very powerful but lack the specifics of a particular business. To address this, generative AI uses a prompting and tuning layer. This layer tailors the large language models to a specific use case. So, going back to the Telco example, the company would use the prompting and tuning layer to make the large language model focus on the data that’s specific to their customers. Finally, generative AI also uses an application layer, just like traditional AI. There’s also a feedback loop, but it typically goes back to the prompting and tuning part to further improve the model.

In conclusion, the main difference between traditional AI and generative AI is the fundamental architecture. Traditional AI uses a company’s own data to build models, while generative AI uses a massive amount of data from the real world. This difference means that generative AI models can be much more powerful, but they need to be tuned to a specific use case before they can be useful.

Shad Griffin

Shad Griffin is an Open Group Certified Distinguished Data Scientist currently working at IBM⁴. He supports a large healthcare client in his role⁴. Shad has a rich background in data science, having held multiple roles across various industries including Media, Telecommunications, Chemicals, Petroleum, Retail, and Manufacturing⁴.

Before joining IBM, Shad led the Data Science activities at Verizon Business Unit headquartered in Dallas, Texas⁴. He holds an MS in Economic Research from the University of North Texas⁴.

Shad is also an active contributor to the academic community. He has given talks on topics such as "Building AI that is Fair, Transparent, and Trustworthy" for the UNT College of Information C.O.D.E. career series¹.

Shad Griffin resides in Denton, Texas, with his family⁴. His work in data science, particularly in the application of AI, continues to impact real people and transform the way we live and interact with each other⁴.

Source: Conversation with Copilot, 7/23/2024

(1) CODE Speaker Series: Shad Griffin - University of North Texas. https://calendar.unt.edu/event/code_speaker_series_shad_griffin.

(2) C.O.D.E. Series featuring Shad Griffin - YouTube. https://www.youtube.com/watch?v=dVoTdfIcyW8.

(3) The History of IBM: the Personal Computer to Watson. https://www.youtube.com/watch?v=UWQd-NZcb6Y.

(4) Building Machine Learning/Predictive Models in SPSS Modeler from IBM. https://www.youtube.com/watch?v=q1fyAvlSKCg.

(5) Chad Griffin - Wikipedia. https://en.wikipedia.org/wiki/Chad_Griffin.

(6) undefined. https://ci.unt.edu/code.

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Categorization

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Type: Nugget Knowledge, Free Speech

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REFERENCES

“genioux facts”: The online program on "MASTERING THE BIG PICTURE OF THE DIGITAL AGE”, g-f(2)2649, Fernando Machuca and Gemini, July 22, 2024, Genioux.com Corporation.

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