Why it’s called “generative” AI !

Generative AI has become more mainstream than ever, thanks to the popularity of ChatGPT, the proliferation of image-to-text tools and the appearance of catchy avatars on our social media feeds.

Generative AI presents a compelling opportunity to augment employee efforts and make the enterprise more productive.?But we still need to uncover more questions in the coming articles: Which use cases will deliver the most value for my business? Which AI technology is best suited for your needs? Is it secure? Is it sustainable? How is it governed? And how do I ensure my AI projects succeed??

It's all started 2014 !

A major leap in the development of generative AI came in 2014, with the introduction of Generative Adversarial Networks (GANs) by Ian Goodfellow, a researcher at Google. GANs are a type of neural network architecture that uses two networks, a generator, and a discriminator.

In 2017, another significant breakthrough came when a group at Google released the famous Transformers paper, “Attention Is All You Need.”

What’s the Difference Between AI, Machine Learning, and Generative AI?

AI is the broadest term among the three. It refers to the concept of creating machines or software that can mimic human intelligence, perform tasks traditionally requiring human intellect, and improve their performance based on experience.

Machine Learning (ML) is a subset of AI. It involves creating and using algorithms that allow computers to learn from data and make predictions or decisions, rather than being explicitly programmed to carry out a specific task. Machine learning models improve their performance as they are exposed to more data over time.

Generative AI is a subset of machine learning. It refers to models that can generate new content (or data) similar to the data they trained on. In other words, these models don’t just learn from data to make predictions or decisions – they create new outputs that can produce new data, images, text, or music.

The important point to understand is that Generative AI is not just copying what it has seen before but creating something new based on the patterns it has learned. That’s why it’s called “generative” AI.

What’s the Controversy Surrounding Generative AI?

Here are also some serious concerns around generative AI, especially as it grows rapidly with little to no regulation or oversight.

Ethical Concerns

Ethically, there are concerns about the misuse of generative AI for creating misinformation or generating content that promotes harmful ideologies.

Bias in Training Data

Since AI models learn from the data they are trained on, they may reproduce and amplify existing biases in that data. This can lead to unfair or discriminatory outputs.

Copyright and Intellectual Property

Additionally, if an AI model generates content based on copyrighted material included in its training data, it could potentially infringe on the original creators’ rights.

What are the key components to adopt Generative AI

At the core of generative AI stack are Foundation Models (FMs), which function as the ‘brain’ and enable human-like reasoning. These models can be proprietary, developed by organizations such as Open AI, Anthropic, or Cohere, or they can be open-source. Developers also have the option to train their own models.

But the dilemma is that, to get more accurate outputs from a generative AI model, organizations need to give third-party AI tools access to enterprise-specific knowledge and proprietary data. And companies that don’t take the proper precautions could expose their confidential data to the world.

To date, many available AI models lack information about data provenance, testing and safety or performance parameters.?For many businesses and organizations, this can introduce uncertainties that slow adoption of generative AI, particularly in highly regulated industries.

But end user will interact with the model thru application. The applications layer?includes end-to-end apps or third-party APIs that integrate generative AI models into user-facing products.

Some applications will have embedded proprietary models, and some other applications will integrate with any Generative AI Model thru API. This is why it’s criterial to know how you will operationalize the model, and this is where we can think about the platform underneath to host, train, tune, and Govern the model

The infrastructure and Platform layer of a generative AI tech stack is a critical component that consists of hardware and software components necessary for creating and training AI models. Hardware components in this layer may involve specialized processors like GPUs or TPUs that can handle the complex computations required for AI training and inference.

Given the cost to train and maintain foundation models, enterprises will have to make choices on how they incorporate and deploy them for their use cases.

There are considerations specific to use cases and decision points around cost, effort, data privacy, intellectual property and?security. It is possible to use one or more deployment options within an enterprise trading off against these decision points.?