Generative AI: Applications, Limitations, Risks and Future Outlook

Generative artificial intelligence (generative AI, GAI, or GenAI) is a worldwide game-changer in the early 2020s. Its endless possibility of generating open-ended content across industries has caught the special attention of investors, organization users, and individual users right from its first years of debut.

So, what lay behind the hype? Let’s discover the applications, limitations, risks, and future outlook of generative AI with LTS Group right below.

What is Generative AI? 

Generative AI is artificial intelligence capable of generating text, images, videos, and other media, using generative models.

As of early 2024, popular generative AI examples and their core functionality are as below:

• ChatGPT, Bard: text generation
• Midjourney, Stable Diffusion: image generation
• GitHub Copilot, AlphaCode: code-generation
• Podcast.ai: voice synthesis
• Synopsys, Cadence: chip design.

While the hype of generative AI started in the early 2020s after the debut and buzz of groundbreaking generative AI tools like ChatGPT and Bing Chat, the early seeds of generative AI evolution date back nearly a century.

How Does Generative AI Work? 

Simply put, generative AI models like GPT-4, BERT, and OpenAI’s DALL-E learn the patterns and structure of their training data and then generate new data with similar vibes.

Generative AI works like a creative chef. It learns from a bunch of input examples, figuring out the recipes and flavors. When it’s in action, you toss in a prompt, be it in the form of a text, an image, a video, a design, musical notes, or any input that the AI system can understand, and it cooks up something new by mixing and matching what it learned. It’s like a chef making a dish based on their cooking experiences.

Common Generative AI Applications in Business 

Generative AI, as it is so-called, is AI for digital item generation. At a high level, fundamental applications of generative AI that have been found are: text generation, image generation, video generation, speech generation, design creation, music composition, and even data augmentation.

From those fundamental applications, we have the most feasible applications of gen AI:

• Content creation: Generative AI can assist in producing high-quality and relevant content types such as AI art gen, AI image gen, generative image AI, AI text gen, AI story gen, and AI voice gen.

• Personalized customer interactions: By understanding customer preferences, generative AI can help businesses create personalized interactions, such as chatbots that tailor responses based on individual needs.

• Fraud detection: Generative AI can enhance security measures by identifying patterns and anomalies in data, contributing to more effective fraud detection in financial transactions.

• Product design optimization: Businesses can leverage generative AI to explore various design possibilities, optimizing product designs for functionality, aesthetics, and user experience.

• Demand forecasting: Generative AI can analyze historical data and market trends to provide accurate predictions for demand, helping businesses optimize inventory and production planning.

Furthermore, a wide range of gen AI use cases originates to solve industry-specific problems across various industries, including art, retail and eCommerce, healthcare, finance, gaming, marketing, education, manufacturing, and more to come.

Here are some ways generative AI applications impact specific industry verticals:

• Healthcare: Generative AI can be employed to analyze medical images, assisting in the early detection of diseases and providing more accurate diagnostics.

• Retail and eCommerce: Businesses can utilize generative AI for personalized shopping experiences, recommending products based on customer preferences and enhancing virtual try-on features.

• Education: Generative AI can aid in the development of personalized learning materials, adapting content based on individual student progress and providing targeted educational support.

• Finance: Generative AI plays a crucial role in risk assessment and fraud prevention, analyzing vast datasets to identify unusual patterns and potential security threats.

• Gaming: In the gaming industry, generative AI can enhance the creation of realistic graphics, generate dynamic storylines, and even adapt gameplay based on individual player behavior.

• Manufacturing: Manufacturers can employ generative AI to optimize production processes by predicting equipment maintenance needs, improving supply chain management, and utilizing computer vision for quality control.

Also, GenAI application development can be in the form of a web app, mobile app, desktop software, or integrated into existing platforms.

Outstanding Gen AI Tools: ChatGPT, Bard, and DALL-E 2 

Let’s explore the most outstanding generative AI interfaces: ChatGPT, Bard, and Bing.

ChatGPT 

What is ChatGPT?

ChatGPT is an AI-powered chatbot that generated buzz right from its first months of debut. The first version, utilizing OpenAI’s GPT-3.5 architecture, was released on November 30, 2022, followed by the GPT-4 version on March 14, 2023. Users can access any of the ChatGPT models based on subscription options.

ChatGPT has cognitive abilities like understanding the context and making sense of information to provide relevant and coherent responses. It uses natural language processing (NLP) to generate human-like cognitive answers in text. Its answer to the same question can vary for different prompts.

ChatGPT was considered the starter of the gen AI hype in late 2022 and early 2023, acquiring 1 million users just 5 days after launching and 100 million monthly active users after 2.5 months.

Noticing the incredible popularity of ChatGPT, Microsoft extended its investment in OpenAI from $1 billion in 2019 to $13 billion in 2023 and integrated a version of GPT into its Bing search engine.

Bard 

Bard, or Google Bard, is a conversational generative AI chatbot developed by Google. Bard was initially built on the LaMDA family of large language models (LLMs), later upgraded to PaLM and then to Gemini. Basically, Bard and ChatGPT have similar functionality – generating cognitive text-based responses.

The popularity of ChatGPT coupled with the implementation of GPT into Bing rushed Google to introduce its Bard public-facing chatbot on March 21, 2023. Later that year, in October, Google released Assistant with Bard, a personal assistant powered by generative AI. Assistant with Bard has been expected to replace and augment Google Assistant’s existing capabilities.

ChatGPT and Bard have their strengths and weaknesses compared to each other. For example, Bard’s information is considered more up-to-date thanks to its real-time access to the internet. At the same time, many recognize ChatGPT as a better writer, especially in writing poems.

DALL-E 

DALL-E is a text-to-image generation model that can create realistic images and art from a description in natural language. OpenAI introduced the first version, DALL-E in January 2021, while DALL·E 2 came out a year later, and DALL-E 3 debuted in October 2023.

DALL-E was trained on a large data set of images and their corresponding text descriptions. During actions, it uses this learned knowledge to generate new images based on provided text prompts. As a result, DALL-E models are the magic behind visually appealing images created by Bing Image Creator, Canva, and more.

You may want to know How to make an AI.  

Limitations and Risks of Generative AI 

So, what are the concerns around generative AI?

Limitations of generative AI 

Bias and unintended outputs

Simply put, generative AI models learn from data during initial training, retraining, and active learning. If data is incorrect or carries biases, gen AI models can produce biased or inappropriate outputs. 

Have you ever tried to manipulate ChatGPT’s responses by inputting leading questions or telling ChatGPT that its answers were incorrect? In such cases, it may return responses to your desired answer, especially when the responses are inferential and not fact-based.

AI hallucination 

AI hallucination is when a LLM generates false or misleading information that is plucked from thin air, and presented as factual or accurate. 

For example, when Google Bard was prompted “What new discoveries from the James Webb Space Telescope can I tell my 9-year-old about?”, Bard’s response included “JWST took the very first pictures of a planet outside of our own solar system”, which was wrong.

According to IBM, common reasons for AI hallucination are “sometimes AI algorithms produce outputs that are not based on training data, are incorrectly decoded by the transformer, or do not follow any identifiable pattern.”

Dependent on data  

Generative AI models don’t possess independent thinking abilities. Although LLM models can cognitively create open-ended content, they don’t invent new knowledge but synthesize and reproduce the information they have been trained on.

Moreover, many LLMs are trained on data with cutoff dates, such as ChatGPT, and rely on people to feed new data, resulting in outdated information.

As a result, if the trained data is limited, the model’s outputs may also be limited.

Lack of maturity 

As generative AI is in its early stages, many of its applications need more time to be fully tested for feasibility, impact, compliance, and risks. The hype of gen AI may subside as the reality of implementation sets in.

Also, according to Pitchbook, there seems to be a realization among founders and investors that some early companies were interesting experiments, but not great businesses.

So, what’s ahead is less a question of what generative AI can do and more a question of how each business will discover tailored applications and integrate them into its business model effectively and responsibly.

Risks of generative AI

Inaccurate AI-generated responses

This type of risk often occurs on the AI user side, associated with using incorrect AI-generated information or following incorrect AI-generated advice.

At an organizational level, it can hurt the ability of a business to function at different levels. For instance, including inaccurate AI-generated content in blog posts can harm brand reputation, while wrong demand predictions from AI-backed forecast models can result in business losses.

Model drift 

Model drift, or model decay, happens when the accuracy of a model declines over time when production data differs from trained data. This can negatively affect the business KPIs.

An example of AI model drift could occur in an NLP model used for sentiment analysis in social media. Suppose the model is trained on data from a specific period and captures the sentiment patterns prevalent during that period. Over time, societal changes, language trends, or evolving sentiments may cause the model’s performance to decline as it encounters new data that differs significantly from its training set.

Legal risks 

This type of risk often arises on the AI provider side, mostly associated with data privacy and copyright constraints.

AI models intensively consume data, so it’s crucial to handle data ethics, data privacy, and copyrights carefully—both for the data they were trained with and the data they generate.

But as the era of generative AI has just started, its regulations are still evolving. AI businesses either stay current with regulation changes, adhere to evolving regulations, or risk facing legal issues.

Moreover, generated content may unintentionally infringe on copyright or intellectual property rights, presenting additional legal challenges.

Responsible AI in the generative era 

As a quick refresher, a gen AI model is trained on data in the form of text, voice, image, code, video, etc., and reproduces the information it has been trained on.

From there, it introduces concerns around fairness, toxicity, and intellectual property, among other things. Does its training data adhere to data privacy principles and is inclusive enough? Do its outputs respect intellectual property rights? Will people use its outputs for healthy purposes?

One example of a fairness concern is a recruitment screener trained on male-dominant performance data prefers male candidates over female candidates.

One instance of toxicity is deep fakes, created using AI algorithms, which have been misused for deceptive and toxic purposes.

In another scenario, gen AI tools might also infringe copyright by generating outputs that resemble existing works but don’t cite the sources.

To address such concerns, both the gen AI tool makers, lawmakers, and users take responsibility. Below are common possible actions on each side.

On the tool maker side:

• Ensure data fairness and data ethics by eliminating data bias and respecting data privacy principles. To avoid bias, be mindful of sampling, observer, interpretation, and confirmation biases. Additionally, safeguards data privacy through techniques such as anonymization, encryption, backup, and access control.

• Develop qualified detection technologies to combat AI-generated deep fakes and other toxic uses.

• Advocate for transparency and disclosure when utilizing AI-generated content, especially in scenarios where the source or authorship might be ambiguous.

On the lawmaker side:

• Introduce and improve laws that regulate the ethical use of generative AI, addressing issues such as fairness, privacy, and intellectual property.

• Work towards establishing industry standards and guidelines for the development, deployment, and usage of generative AI technologies.

• Facilitate educational programs for the public, policymakers, and businesses to raise awareness about the ethical considerations surrounding generative AI.

On the user side:

• Keep in mind that AI-generated content originates from existing knowledge bases, thus necessitating critical evaluation and cross-referencing to verify information.

• Ensure responsible and ethical utilization of AI-generated content for either informational or commercial intents.
• Provide constructive feedback to contribute to the development of responsible AI practices.

Gen AI and the Future of Work 

Generative AI is knocking on the door of many industries and is poised to change many business functions. What will it mean for your workers?

According to the International Monetary Fund (IMF), “Advanced economies will experience the benefits and pitfalls of AI sooner than emerging market and developing economies, largely because their employment structure is focused on cognitive-intensive roles.”

But in general, gen AI can affect workers both positively and negatively.

First, gen AI may fade out many existing positions while adding job opportunities regarding its inputs and outputs.

• Jobs with high risks of replacement: routine data entry, basic customer support roles, etc.

• Jobs rise regarding gen AI inputs: jobs in the field of data science, such as data scientists, data annotators, data engineers, and data analysts as well as in the field of AI model development, like AI model developers, machine learning engineers, and algorithm specialists.

• Jobs rise regarding gen AI outputs: new positions like AI prompt engineers, content quality assessors, and ethical AI specialists.

Concurrently, Gen AI also complements human labor and enhances efficiency across industries. The gains in productivity could result in higher growth for businesses and higher incomes for most workers. The thing is how each business will discover tailored applications and integrate them into its business model effectively and responsibly.

Frequently Asked Questions About Gen AI 

1. What is the difference between GenAI and AI? 

In short, GenAI is a subset of AI.

AI refers to the use of technologies to give machines the ability to perform tasks that require human intelligence.

So, there are two major parts in the definition of AI: technologies and tasks.

AI technologies comprise many subsets, such as GenAI, computer vision, robotics, and machine learning (ML). Meanwhile, types of tasks that AI can perform include content generation, object recognition, task automation, etc.

Mapping the AI subsets and AI-enabled tasks, we have GenAI responsible for content generation, including the generation of text, images, or other media, using generative models.

2. What does GenAI do? 

GenAI generates content in the form of text, images, videos, and other media, using generative models.

3. Why is GenAI exciting?

GenAI is exciting because it opens up possibilities for creating open-ended content with fewer resources in various domains. Its ability to generate new, human-like content has practical applications in fields such as art, design, content creation, and even problem-solving.

Also, as generative AI applications are in their early stages of exploration, more and more applications may come and surprise us, making the field more mysterious and exciting.

Final Thoughts 

Generative AI is definitely among the most game-changing concepts in the early 2020s. When generative AI meets responsible AI, or in other words, GenAI’s generative capability is used responsibly, a new world of responsible applications will open up for positive impacts.

If you have any questions about generative AI, feel free to contact us at:

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• Mail: contact@ltsgroup.tech
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