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Inside the Generative AI Revolution

Martin Musiol talks about the state of generative AI today, privacy and intellectual property concerns, the strongest use cases for generative AI, and what the future holds.

Nov 2022

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Guest
Martin Musiol

Martin is a Data Science Manager at IBM, as well as Co-Founder and an instructor at Generative AI, teaching people to develop their own AI that generates images, videos, music, text, and other data.


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Host
Adel Nehme

Adel is a Data Science educator, speaker, and Evangelist at DataCamp where he has released various courses and live training on data analysis, machine learning, and data engineering. He is passionate about spreading data skills and data literacy throughout organizations and the intersection of technology and society. He has an MSc in Data Science and Business Analytics. In his free time, you can find him hanging out with his cat Louis.

Key Quotes

One use case that has a lot of impact is applications with GitHub Co-Pilot. When you start coding, you start with a certain command, and then you start writing the command that you are going to code, but as you're writing the command, AI suggests the right piece of code or some standard functions that you may want to include. This application is getting quite good, which is bringing administrative coding time close to zero. As it generates  the code, you just then accept what it is providing to you, or you continue with a different command and it provides you with something different that you can implement. This reduces development time significantly.

Generally speaking, I don't think companies have integrated generative AI much into their existing services or have created many new services with is. Frankly, many companies are not even aware of generative ai or looking at all of the potential applications in law, healthcare, banking, marketing, and education. There are countless possible applications, such as simplifying contracts, image generation for maybe some customized product packaging, confirming medical diagnoses, etc.

Key Takeaways

1

We are still in the early stages of adopting generative AI, which means there are still many unexplored possibilities for implementing generative AI to drive value for companies.

2

There are many potential legal gray areas, especially in copyright and intellectual property, in regard to what datasets companies use, as well as how they access and use those datasets to develop generative AI tools.

3

Generative AI has the ability to significantly reduce coding time, which will empower data scientists and ML engineers to develop new, more advanced tools and AI models faster and more efficiently.

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