Demystifying Generative AI: Understanding the Technology Behind the Magic

Ask anyone about “generative AI,” and they will probably give their version. From the much-talked-about ChatGPT to Photoshop AI’s generative fill tool to Lyrebird, generative AI has been the talk of the town for months despite being around since 2020. Thanks to the launch of OpenAI’s ChatGPT in 2022, generative AI has captured the attention of users globally since then. But how much of this new technology is being understood?

Speculations are causing many to fear that generative AI might soon take over roles and jobs. Before dismissing this tool of the future, keep an open mind and learn how generative AI will be the key to transforming how things work and people work.

Generative AI vs AI

Generative AI emerges as a transformative force, birthing fresh content, crafting conversational replies, shaping designs, fabricating synthetic data, and even giving rise to the intriguing realm of deep fakes. In contrast, traditional AI has predominantly concentrated on unravelling patterns, rendering decisions, refining analytical insights, grouping information, and unearthing instances of fraud. While traditional AI algorithms process new data to generate a simple result, generative AI commonly begins with a prompt where users often submit a starting query or data set to guide the content generation.

How it Works

Generally, generative AI commences with a prompt - this can take the shape of text, images, videos, designs, musical notations or any other input within the AI system capabilities. A range of AI algorithms then generates fresh content as a reaction to the provided prompt, with the content spanning a spectrum, from essays and problem solutions to lifelike fabrications derived from images or audio recordings of individuals.

Today, generative AI pioneers are developing better user experiences that allow users to describe requests in plain, simple language - tonality, style, or any other elements - users can adjust and decide what results they want to reflect.

Use Cases for Generative AI

Generative AI holds immense potential in content creation, offering its prowess across a spectrum of applications. The technology’s accessibility is expanding significantly, catering to users from diverse backgrounds and boasting the capabilities to calibrate for unique applications. Generative AI use cases include chatbots for customer service and technical support, writing email responses, and resumes.

Limitations of Generative AI

Like everything else, nothing is perfect, not even advanced technology. Some limitations to consider when using generative AI: sources are sometimes not identified, making it challenging to access the biases of sources. Realistic-sounding content also makes it harder to identify inaccurate information. All of which can lead to misleading information, so users need to fact-check the results and not take it at face.

Let’s Talk Benefits

For AI architectural advancements, foundation models like generative pre-trained transformers - the driving force behind ChatGPT - stand out. These innovations hold the potential to streamline, enhance, and independently carry out a spectrum of business and IT processes, whether it’s automation, human or machine augmentation. The benefits of generative AI include accelerated product development, elevated customer experiences, heightened employee productivity, and more.

In a webinar poll by Garter of over 2,500 executives, 38% indicated customer experience and retention as the primary purpose of their generative AI investments, followed by revenue growth as the secondary purpose (26%), cost optimisation (17%) and business continuity (7%).

Let’s Talk Risks

The risks linked with generative AI are substantial and continuously changing. Malicious entities have already harnessed this technology to produce “deep fakes,” replicate products and fabricate elements for intricate fraudulent activities.

ChatGPT and similar tools undergo training using extensive volumes of publicly accessible data. Since these systems do not adhere to regulations like the General Data Protection Regulation (GDPR) and copyright laws, it is crucial to be cautious when using these platforms.

Despite the general usefulness of these tools, there is admittedly a lack of transparency in how generative AI works in the first place. Additionally, when it comes to Intellectual property (IP) and copyright issues, there are no existing, substantiated guarantees for data governance and protection concerning corporate information.

In this case, users should always be mindful that data entered into generative AI tools, such as ChatGPT, will become public information. With that said, organisations should take proactive steps in anticipation of potential misuse of generative AI systems by malicious entities in cyber and fraud activities, including guarding against tactics like “deep fakes” used for manipulating personnel through social engineering. Thus, implementing appropriate controls to mitigate these risks is paramount.

Generative AI Tools to Explore

There is a spectrum of generative AI tools. Writers can explore the use of ChatGPT, Google Bard, Jasper, AI-Writer and Lex, while creators have AI tools for image generation, such as Dall-E 2 and Midjourney, code generation tools like CodeStarter and Codex, and voice synthesis tools like Descript and Listnr.

The Future of Generative AI

The capabilities and user-friendly nature exhibited by ChatGPT hold considerable potential in paving the way for the extensive integration of generative AI. Advancements within AI development platforms anticipate an expedition on the progression of enhanced generative AI functionalities across various domains, text, images, video, 3D content, pharmaceuticals, supply chains, logistics, and business workflows. While the efficiency of novel specialised tools is undeniable, the transformative potential of generative AI resides in its seamless integration into the very fabric of the familiar tools we use. As such, these tools are not something to fear but rather something to get excited about.