What is GEN AI?

By Mark Kelly


(Read time 5 minutes)

In this article we discuss Generative AI and the most requested FAQ sent to AI Ireland.

Generative AI represents a subset of artificial intelligence that specializes in generating a wide range of content forms, such as text, visuals, audio, and synthetic data. While the concept isn’t new, with its origins traced back to the 1960s with the development of chatbots, the current surge of interest in generative AI is largely due to the creation of intuitive user interfaces that allow users to generate high-quality text, graphics, and videos swiftly.

Significant advancements in generative AI occurred in 2014 with the advent of Generative Adversarial Networks (GANs). These sophisticated machine learning algorithms revolutionized the field by creating convincingly authentic images, videos, and audio of real people.

This revolutionary technology has presented both opportunities and challenges. On the positive side, it has potential applications in enhancing movie dubbing and creating rich educational content. However, it also raises serious concerns about the creation of deepfakes – digitally manipulated images or videos that appear realistic – and potential cybersecurity threats to businesses, including deceptive requests that convincingly impersonate an employee’s superior. Below we go into more detail.

1.   What is generative AI?
2.   Why is there a sudden increase in attention towards generative AI?
3.   What are the advantages and practical implementations of generative AI?
4.   What potential risks could be associated with generative AI?
5.   Can you provide some real-world examples of how generative AI is being used today?
6.   How can generative AI enhance business value?
7.   Which sectors are likely to be most affected by the advent of generative AI?
8.   What guidelines should one follow while employing generative AI?
9.   Is it necessary to establish a usage policy for generative AI?
10. What implications will generative AI have on the future landscape of work?
11. What should be the initial steps when starting with generative AI?
12. What investments are required to facilitate generative AI?
13. What does Gartner foresee for the future usage of generative AI?
14. Who are the primary technology providers in the generative AI sector?
15. Does the rise of generative AI signify the onset of artificial general intelligence (AGI)?

1. What is Generative AI?

Generative AI is a technology that learns from existing data to create new realistic artifacts, such as images, music, text, software code, or product designs. It uses foundational AI models that require complex mathematics and significant computing power, which are primarily prediction algorithms. Currently, generative AI is frequently used to generate content from natural language inputs, and it’s paving the way for innovations in areas like drug development, chip design, and material science.

2. Why is there growing interest in Generative AI?

GenAI has gained mainstream popularity with the 2022 launch of OpenAI’s ChatGPT, a chatbot known for its human-like interactions. Generative AI will become a universal technology, akin to electricity and the internet, as it finds more innovative applications in daily life and work.

3. What are the benefits and applications of Generative AI?

4. What are the potential risks of Generative AI?

Foundation models, such as generative pretrained transformers, can automate, enhance, or autonomously execute business and IT processes. Generative AI’s advantages encompass rapid product development, enriched customer experiences, and increased employee productivity. However, the results are use-case specific and can be hindered by artifacts’ inaccuracy or bias, making human validation crucial. A recent consulting poll by Gartner reveals 38% of executives invest in generative AI for customer experience and retention.

Generative AI comes with substantial risks, including creating ‘deep fakes’ and other fraudulent artifacts. As tools like ChatGPT are trained on public data, they may not comply with GDPR and other laws. Concerns include lack of transparency, inaccuracy, bias, and IP and copyright issues. Additionally, malicious actors may use generative AI for cyber and fraud attacks, and the technology’s high powerconsumption may affect sustainability goals. Therefore, it is essential to monitor regulatory developments in various regions concerning generative AI.

5. What are the ways generative AI can add business value?

Generative AI has the potential to present disruptive opportunities for businesses, including increased revenue, reduced expenses, enhanced productivity, and improved risk management. In the near future, it is predicted to become a significant competitive edge. The value-added by generative AI can be split into three main categories:

Revenue Opportunities: Generative AI can accelerate the creation of new products like new drugs, household cleaners, novel flavors and fragrances, new alloys, and improved diagnostic tools, potentially opening new channels of revenue.

Cost and Productivity Opportunities: By augmenting workers’ abilities, generative AI can facilitate tasks such as drafting and editing text, images and other media, or improving chatbot performance. It can optimize long-term talent by fostering a symbiotic relationship between employees and AI.

Risk Opportunities: Generative AI enhances risk mitigation by providing broader and deeper visibility into data, such as customer transactions and software code, improving pattern recognition, and identifying potential risks more quickly.

6. Which sectors are poised for major disruptions due to generative AI?

Generative AI is set to disrupt various sectors, including pharmaceuticals, manufacturing, media, architecture, interior design, engineering, automotive, aerospace, defense, medical, electronics, and energy. These disruptions will not only affect core processes but also impact marketing, design, corporate communications, training, and software engineering that span across many organizations.

7. What guidelines should be followed when using generative AI?

As technologies that provide AI trust and transparency become an essential part of generative AI solutions, there are certain ethical guidelines that should be adhered to when using large language models and other generative AI models.

8. Is it necessary to create a policy for the use of generative AI?

Yes, it is highly advisable to formulate a policy for generative AI use within your organization. This helps avoid unregulated use and provides a framework for compliance.

9. What is the future of work with generative AI?

Generative AI is expected to transform the way the workforce functions. With many being content creators in the business environment, their roles are set to change significantly with the rise of generative AI.

10. What is the starting point with generative AI?

Several enterprises have already initiated generative AI pilots for code generation, text generation, or visual design. To establish a pilot, you can consider one of three routes: Off-the-shelf, Prompt engineering, or Custom.

11. What are the costs associated with enabling generative AI?

The costs for generative AI can vary greatly, from being almost negligible to running into millions, depending on the use case, scale, and requirements of the company.

12. Who are the main players in the generative AI market?

The generative AI market is booming, with numerous specialty providers and significant investments from enterprise application providers and major organizations like Microsoft, Google, Amazon Web Services (AWS), and IBM.

13. Could we be witnessing the dawn of Artificial General Intelligence (AGI)?

This question is a matter of perspective and is subject to extensive debate. AGI refers to the concept of machines possessing the capability to match or even surpass human intelligence, thereby being capable of resolving issues they haven’t been explicitly trained for. This idea triggers a wide range of responses, including both excitement and apprehension regarding the future.