The Difference Between General Artificial Intelligence and Generative Artificial Intelligence: What You Need to Know (II)

• What is Generative Artificial Intelligence?

When we talk about Generative Artificial Intelligence (GenAI), as the name of the technology suggests, this type of Artificial Intelligence specializes in generating content. Whether it's text, images, videos, or music, Generative Artificial Intelligence is increasingly used by users through applications like ChatGPT, Midjourney, DALL-E, and many others, according to Gartner experts. Generative Artificial Intelligence (GenAI) is a subfield of artificial intelligence specializing in creating new, original content such as text, images, sounds, or even 3D models based on existing data. Unlike other types of artificial intelligence that only analyze or classify data, GenAI focuses on generating creative outputs that did not previously exist, using sophisticated machine learning algorithms.

How Does Generative Artificial Intelligence Work?

GenAI uses neural networks, particularly generative models like Generative Adversarial Networks (GANs) or machine learning models based on transformers, such as GPT (Generative Pre-trained Transformer), to generate original content. These models are trained on large datasets, learn the patterns from them, and use these patterns to create new instances that resemble the training data but are not direct copies. Generative AI also uses several specific algorithms for this technology, such as:

• Generative Adversarial Networks (GANs):

GANs operate through an adversarial process between two neural networks. One is a generator that creates new examples, and the other is a discriminator that evaluates whether those examples are authentic or generated. This iterative process helps the generator create increasingly realistic examples.

• Transformer Models:

Transformer models, like GPT, are designed to understand and generate sequences of data. They are trained on massive amounts of data and can generate coherent results, mimicking human style.

Advantages of Generative Artificial Intelligence

As one of the most common AI technologies among users, Generative Artificial Intelligence has already become part of the daily lives of those who use it. Whether we're talking about assistance with writing tasks, advanced and fast searches on desired topics, or even cooking recipes, this technology, used through programs like ChatGPT, is increasingly integrating into society and becoming a "partner" to users. Therefore, the main advantages of Generative Artificial Intelligence are:

1.Assisted Creativity:

GenAI can help artists, writers, musicians, and designers develop their ideas or quickly create new content, offering suggestions or prototypes based on their initial input.

2.Efficiency in Content Generation:

It can speed up the creative production process by automating tasks that would normally require a lot of time and effort.

3.Simulation and Prototyping:

In fields like engineering or scientific research, generative AI can simulate scenarios or product prototypes before they are physically created, saving significant resources.

4.Adaptability and Personalization:

GenAI can generate personalized content for each user, whether it's advertisements, gaming experiences, or consumer products, tailored to individual preferences and needs.

Because of this, foundational models, including generative pre-trained transformers (which power ChatGPT), represent one of the architectural innovations in artificial intelligence that can be used to automate, assist people or machines, and autonomously execute workflows in business or IT. According to experts, the benefits of generative artificial intelligence include faster product development, improved customer experience, and increased employee productivity, but the details depend on the specific use case. However, end users should have realistic expectations regarding the value they seek to obtain, especially when using such a service, which has major limitations. It is important to note that Generative Artificial Intelligence creates content that can be inaccurate or biased, making human validation essential and possibly limiting the time saved by employees. Experts recommend linking use cases to key performance indicators (KPIs) to ensure that any project either improves operational efficiency or brings profit or better experiences. In a recent survey organized by Gartner during a webinar with over 2,500 executives, 38% of participants said that customer experience and retention represent the main goals of their investments in generative artificial intelligence. These were followed by revenue growth (26%), cost optimization (17%), and business continuity (7%).

What are the Risks of Generative Artificial Intelligence?

According to Gartner, the risks associated with generative artificial intelligence are significant and evolving rapidly. A wide range of malicious actors has already used this technology to create "deep fakes" or product replicas and generate artifacts designed to support increasingly complex scams. ChatGPT and similar tools are trained on large amounts of publicly available data. They are not designed to comply with the General Data Protection Regulation (GDPR) and other copyright laws, so it is imperative to pay attention to how your company uses these platforms. The surveillance risks that must be monitored include:

1.Lack of Transparency:

Generative AI models and ChatGPT are unpredictable, and even the companies that develop them do not always fully understand how they work.

2.Accuracy:

Generative AI systems sometimes produce inaccurate or fabricated responses. Evaluate all results for accuracy, appropriateness, and usefulness before relying on or publicly sharing them.

3.Bias:

It is necessary to have policies or controls in place to detect and manage biased results in accordance with company policy and any relevant legal requirements.

4.Intellectual Property (IP) and Copyright:

Currently, there are no verifiable guarantees regarding the governance and protection of confidential company data. Users should assume that any data or queries entered into ChatGPT and its competitors will become public information, and companies are advised to implement controls to avoid unintentional exposure of IP.

5.Cybersecurity and Fraud:

Companies must be prepared for malicious actors using generative AI systems for cyberattacks and fraud, such as using "deep fakes" in the social engineering of personnel, and ensure that controls are in place to mitigate risks. Consult with your cyber insurance provider to verify how much your existing policy covers AI-related breaches.

6.Sustainability:

Generative AI consumes significant amounts of electricity. Choose providers that reduce energy consumption and use high-quality renewable energy sources to mitigate the impact on sustainability goals.

• In Conclusion

Although both General Artificial Intelligence (AGI) and Generative Artificial Intelligence (GenAI) represent significant advances in the field of AI, they serve different purposes and address distinct problems. AGI aspires to reach a human-like level of intelligence, capable of understanding and solving a wide range of tasks and complex problems in a generalized manner. In contrast, GenAI specializes in generating new content based on existing data, offering creative and efficient solutions to specific tasks such as generating text, images, or sounds. The essential difference lies in purpose and applicability: AGI aims to develop versatile and adaptable intelligence, while GenAI is a powerful tool for automating and augmenting creative processes. Both technologies will have a significant impact on the future, but the development of AGI remains a long-term goal, while GenAI is already transforming industries and workflows today, according to experts in the field.

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