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The Potential and its Moral Repercussions of Generative AI

Generative AI completely transforms how people interact with the internet and the outside world.  

By

CIO Applications Europe | Tuesday, December 20, 2022

Generative AI is revolutionising how organisations experience the internet and the world around them.

FREMONT, CA: Generative AI completely transforms how people interact with the internet and the outside world. The market is expected to reach USD 422.37 billion by 2028, with global AI investment increasing from USD 12.75 million in 2015 to USD 93.5 billion in 2021.

Although this perspective can give the impression that generative AI is the magic bullet for advancing our global civilization, it comes with a crucial caveat: The ethical ramifications are not yet clear. This is a serious issue that might prevent further development and expansion.

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Most generative AI use cases offer more beneficial, less expensive solutions. For instance, generative adversarial networks (GANs) are ideally suited for advancing medical research and accelerating the development of innovative drugs.

The future of text, image, and code generation is becoming increasingly obvious due to generative AI. AI text and image synthesis already extensively use tools like GPT-3 and DALLE-2. They have gotten so good at these jobs that it is practically hard to tell the difference between human content and AI content.

Technology related to generative AI is developing so quickly that it is already outperforming our capacity to foresee potential problems. If they want to stay ahead of the curve and experience long-term, sustainable market growth, we need to find global solutions to important ethical concerns.

It is crucial to first briefly go through the operation of foundation models like GPT-3, DALLE-2, and associated technologies. These technologies use deep learning to produce more lifelike images, text, and speech to outdo competing models. Then, to produce stronger, more advanced results, laboratories like OpenAI and Mid journey train their AI using vast datasets from billions of users.

Although there isn't a set rule for this, it has already come up in legal contexts. The DABUS AI developers, who are in charge of the Artificial Inventor Project, submitted patent applications, but both the U.S. Patent and Trademark Office and the European Patent Office rejected them because they listed the AI as the inventor. Non-human inventors are not qualified for legal recognition. Australia and South Africa, however, have decided that AI can be listed as an inventor on patent applications. Generative AI is essentially a tool that should be used by a human creator, like using Photoshop to create or modify an image. The opposing argument asserts that AI and maybe its creators should own the rights. Developers who produce the most effective AI models would want to own the copyright to their original content. However, it is extremely unlikely that this will be a long-term success.

The fact that these AI models are reactive should also be noted. That implies that the models can only respond or generate outputs in response to the information provided. That once more places the power in human hands. AI cannot be an original creator because even the models left to improve themselves are ultimately driven by the data that humans provide them.

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