Generative AI: Opportunities, Ethical Concerns, and the Power of Mega Companies

Generative AI is full of possibilities, offering innovative solutions and creative opportunities across various fields. However, its vast potential is largely controlled by a few mega companies, leading to significant ethical and practical concerns. This concentration of power raises questions about data privacy, accessibility, and the future of creative industries.

Sasha Yanshin offers a passionate critique of Adobe’s approach to terms and conditions (T&Cs) and the use of Generative AI (Sasha, “Adobe Is Pathetic” – YouTube). Yanshin highlights the threat of data harvesting and the overly complex nature of software licensing that accompanies Adobe’s AI tools. It’s interesting to note that Meta has made similar announcements about having the right to all users’ data to train AI models (Jones, “Meta Lets Users Choose What Data Is Used for AI Training.”). The difference here is stark: with Meta, most users are the product, but Adobe’s users are customers who pay for services.

Generative AI has enormous potential, but it is data-addicted. It thrives on vast amounts of information to learn and generate outputs, making it accessible primarily to those who can harvest enough data and afford the substantial computational power required. This scenario leads to a critical question: is this concentration of power and access to Generative AI fair? Should we, as a society, find a way to push mega corporations to act more fairly?

The creative industries face significant risks from the rise of AI. Generative AI can produce artworks, music, and even written content, potentially displacing human creators. The threat of AI replacing jobs in these sectors is a genuine concern, as the technology can mimic human creativity at a fraction of the cost and time. Artists, writers, musicians, and other creatives might find themselves competing with machines that can generate vast amounts of content quickly and cheaply.

However, it’s essential to balance this view by acknowledging the positive impacts of Generative AI. For instance, AI can lead to new types of creativity by offering tools that were previously unimaginable. It can assist artists in creating more complex and intricate designs, provide writers with innovative narrative structures, and help musicians compose unique sounds (“MuseNet”). Moreover, Generative AI opens up creative opportunities for individuals with disabilities, who might otherwise struggle to create using traditional methods (“Project Euphonia.”). These tools can democratize creativity, allowing more people to participate and express themselves.

The current model, where AI capabilities are centralised within a few large corporations, limits the broader application of this technology. Decentralisation could be a solution, making AI tools more accessible to a wider audience. By reducing dependency on mega companies, we could foster a more equitable landscape for AI development and application. Open-source AI projects and community-driven initiatives are examples of how this decentralisation could be achieved, ensuring that the benefits of Generative AI are more evenly distributed.

Yanshin’s critique of Adobe and Meta underscores the need for transparency and fairness in AI’s development and deployment. As these technologies become more integrated into our daily lives, it is crucial to consider the ethical implications of data harvesting and the power dynamics at play. Consumers and creators alike must advocate for fairer practices and greater access to AI tools.

In conclusion, Generative AI holds immense promise but is currently constrained by the control of a few powerful corporations. While the risks to creative industries are significant, the potential for new forms of creativity and inclusivity cannot be ignored. Striving for a more decentralised and fair AI landscape will help ensure that the benefits of this technology are enjoyed by all, not just the privileged few.


AI usage declaration

Generative AI (ChatGPT) was used to help in the editing of this piece and for research. Dall-e was used to generate the featured image






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