Generative AI: Disruption, Transformation, and Ethical Challenges

The rise of Generative AI has sparked a revolution, promising groundbreaking changes across various sectors. However, this technological leap brings transformative changes, societal implications and ethical concerns to the forefront. Let's delve into Generative AI, exploring its influence on society, businesses, and the future. Understanding this phenomenon is crucial for navigating the challenges and harnessing the benefits of this rapidly evolving technology.

Disruptive vs. Incremental Technologies

To understand the potential of Generative AI, it is essential to distinguish between disruptive and Incremental technologies.

Incremental technologies refer to gradual changes that improve existing products or services without fundamentally altering the market. In contrast, disruptive technologies bring about revolutionary changes, creating new markets and displacing established ones. Disruptive technologies will cause changes in society and our business environment.

An example of disruptive technology is the transition from scissor kicks to the Fosbury Flop in high jumping. In the first one, the performance increased gradually, while the adoption of the Second caused immediate, large performance increases.

Generative AI: Hype or Transformation?

Over the past decade, numerous technologies have promised great changes, but their impact often falls short of initial expectations. Blockchain, 5G, virtual reality, nanotechnology, 3D printing, quantum computing, smart cities, NFTs, the metaverse, the Internet of Things, and self-driving cars have all been touted as revolutionary

but have not yet fully delivered on their promises.

Generative AI is different. It possesses the potential to significantly alter various aspects of our lives, from business and creative endeavors to our understanding of society and the world. The question remains: is Generative AI merely another overhyped technology, or is it a true Game-changer?

The Story of Mosaic and The Web

There is another advantage to being old.

One of the advantages of being old is that you have a lot in your past. The worldwide web and the first browsers were seen as revolutionary. The questions remain how will the Generative AI revolution change society and our business environment, but it is safe to say that its impact will be massive.

Bias Amplification

The issue of biases in training data presents a significant challenge. Generative AI models learn from vast datasets, often reflecting societal biases related to race, gender, and other attributes

. These biases can lead to discriminatory outcomes if left unchecked. For example, image generation models may produce stereotypes when asked to depict professionals, reinforcing existing prejudices. The generated content reflects stereotypes about race and gender.

Therefore it is important to consider the ethics of GenAI.

The EU AI Act and Regulation

The EU AI Act is designed to manage potential risks and minimize social harms while presenting a risk-based approach.

However, one must question if the EU AI Act is actually protecting the citizens without also being hindered by the US of Chinese legislature, where sovereignty may be seen as being more important than regulation.

Job Displacement: A Looming Concern

As AI technology advances, its impact on the job market becomes a growing worry

. Financial publications highlight this issue as a challenge that should be taken seriously. While new job categories may emerge, the transformation could lead to significant job displacement. The rapid growth of the AI industry will require a workforce with new skills, rendering certain existing jobs obsolete.

As jobs in banking change, staff need to be trained in new jobs.

AI in Education

Here are some tips from the UNESCO world heritage website. Instead of having kids write essays, you should help them to ingest the knowledge. For that same reason, it's important to have a Good AI read list as described by Mikey Wrigley. You should also help teachers prepare students for a future shaped by AI.

Counter Language Biases

A data bias has been described, highlighting certain phrases, such as using the WORD Delve as a sign of abstract generation. A deliberate attempt must be made to counter these language biases. Otherwise, these biases may get embedded in our world views. A balanced analysis must be attempted, and all efforts must be made to generate balanced images.

AI Market Leadership: Key Players in Generative AI

OpenAI, Google AI, Anthropic, Grok, Apple Intelligence, Amazon Bedrock, Meta AI, Cohere, and Databricks are major companies participating in GenAI.

These providers are competing to innovate in the Generative AI field, and each one is investing massive amounts. While it's difficult to pick a winner, some things should be considered, as below.

The Architecture of Generative AI

Modern Generative AI is based on Large Language Models, or LLMs for short.

Large language models are based on a type of artificial intelligence trained by learning from Patterns of exising data. This allows generating a wide variety of data, such as 3d models, text, images, audio, and videos.

It's powered by multiple different open and closed source models:

• OpenAI.
• Google AI.
• Anthropic.

The model ingests the different types of data. This is transformed and used to create parameters of a neural network, which allows a model to learn. AI's advantage of learning and creating data is the ability to create foundation models.

Image Generation

One of the first widespread use cases for GenAI, and also for multimodal AI, has been image generation. Many models such as Imagen 3, Midjourney 6.5, and Flux 1.1 Pro are available. Models like these have become very realistic.

Music Generation

The ability to generate Music from just a text Prompt has also become viable, which has been made available by models such as SUNO.