Generic Generative AI vs. Specialized AI: What Are the Differences?

This is why there is a movement towards niche navigators in the generative-AI landscape: Specialized gen-AI built for one singular use case or domain.

We believe specialized AI - or narrow AI how it’s sometimes referred to - is the main use case businesses will bet on in 2024. So let’s take a closer look at both generic and specialized gen-AI to understand the differences and the main use cases in today’s workplace.

Generic generative AI: Strengths and use cases

Let’s start with the definition, to be on the same page.

‍Generative AI is a branch of artificial intelligence that focuses on creating new data, such as text, images, or music. It can be used to generate creative content, personalize experiences, and augment data sets.

Although AI research is not new, AI developments have accelerated with the public launch of OpenAI’s Chat GPT. Since then, generic generative AI has flexed its muscles in various applications. By now it is a tool we have all come to know - not to mention to love - and many of us incorporated LLMs as fixed tools in our daily work-routines.

Some practical use cases that are already seeing widespread adoption include:

• Content generation: Generating text, code, scripts, musical pieces, email, letters, etc.
• Data augmentation: Expanding datasets for machine learning models.
• Problem-solving: Approaches challenges with a broad perspective, aiding in brainstorming sessions.
• Personalization: Creating personalized experiences for users.
• Marketing: Creating engaging content for social media and email campaigns.
• Customer service: Personalizing interactions with customers.
• Product development: Generating new product ideas.

Unlike rule-based AI models, generative AI employs deep learning to understand and generate data, mimicking human intelligence and the cognitive abilities of the human brain. It's a leap forward, allowing machines to exhibit creativity and produce content in various forms.

For example, artificial general intelligence can autonomously generate articles, poems, and even fictional stories. This form of AI can “talk” like human beings when engaged in a chat, and can bridge language gaps. General AI “understands” any language, as the chatbots have speech recognition and natural language processing abilities.

Generic AI is also able to distill vast amounts of information into concise and coherent summaries. And based on the context of a conversation, AI applications can engage in problem-solving and decision-making. Gen-AI tools are flexible, can adapt to various tasks with minimal task-specific training, and can run 24/7 through automation workflows, making them a cost-effective solution for a wide range of applications. This frees up resources like you and me to focus on other endeavors - more fulfilling endeavors, maybe, that aren’t suitable for machine intelligence, but are perfect for humans.

To sum it up, generic gen-AI has the following strengths:

• Versatility: Can be applied to a wide range of tasks and domains.
• Creativity: Can generate new and original content.
• Adaptability: Learns from diverse datasets, making it flexible in various scenarios.
• Scalability: Can handle large amounts of data.
• Interpretability: Models can be understood and explained by humans.
• Innovation: Enables creative generation of content, designs, and solutions.

Limitations of generic generative AI

As can no longer be denied, the possibilities of Gen-AI are promising. But, as is usually the case with new technology, it is not entirely without risks. The systems are fuelled by large datasets, and the training data can be anything from books to web pages and articles. This creates large language models (LLMs) like ChatGPT for example, that are capable of remarkable things, but, on the other hand, know their limitations and weaknesses.

Where they excel at giving answers to logical prompts and mimicking human behavior, they seem to have difficulties with understanding symbolic reasoning. They can’t comprehend what is "true" and therefore, the model output it delivers may deviate from the desired result, experiencing misalignment with input context or even generating false information, a phenomenon known as hallucination.

So, in big lines, general generic AI has the following limitations:

• General accuracy: May not be as accurate as specialized AI in specific tasks.
• Lack of depth: Might not excel in specialized tasks due to a broad focus.
• Quality control: May require human intervention to ensure high-quality outputs.
• Resource intensive: Training a generic model can be computationally demanding.
• Explainability: Models may be difficult to explain to non-experts.
• Interpretability: Outputs may lack specificity, making it challenging to understand reasoning.

These challenges can pose issues in critical applications where precision is crucial. To address the limitations of generic generative AI, there's a growing realization of the need for more contextually enriched AI models for specific tasks.

What's the difference between general AI and specialized AI?

Before we move on, a quick note on the difference between general AI and specialized AI.

While both general and specialized AI models leverage advanced machine learning, they are fundamentally different in scope, application, and performance. General AI, often known as generic or foundational AI, is designed to handle a broad range of tasks across various domains, whereas specialized AI, sometimes referred to as narrow AI, is tailored for specific functions within a well-defined context.

• General AI is built to be flexible. It draws from a vast range of data and knowledge, giving it the ability to tackle multiple types of requests, from text generation to image creation, regardless of the industry or context. This versatility makes it ideal for broad applications like customer service automation, creative content generation, or generalized language translation.
• Specialized AI, on the other hand, is crafted for one primary use case or domain. It’s fine-tuned to meet the needs of a specific industry, such as healthcare, finance, or recruitment, and is deeply embedded with domain-specific knowledge. This focus allows specialized AI to deliver higher accuracy and relevancy within its defined scope but limits its ability to perform outside its trained area.

Thus, while generic generative AI excels in versatility, domain-specific tasks often demand a more nuanced understanding of context and industry jargon, like thematic analysis, structured data extraction, entity recognition, and strict rule enforcement. Generic generative AI often lacks the specificity required for that. And so, in the AI generative jungle, the need for specialized generative AI seems to have arisen.

The need for specialized Generative AI

Unlike generic artificial intelligence, specialized AI is designed to perform a specific task or task domain extremely well. It typically requires large amounts of training data and is often used in industries such as recruitment, healthcare, finance, and manufacturing.

Imagine a recruitment organization where AI is used for candidate screening. Now, if we bring in gen-AI, it will be able to extract information from resumes or interviews in real-time, understand details about experience, education and skills, and assist in screening candidates.

Convenient, right? However, fully understanding the industry specifics, as well as the subtleties of job roles, is something that generic gen-AI is not capable of. It lacks industry expertise and context understanding, which potentially leads to candidate recommendations that could be suboptimal.

Therefore, organizations might need to validate the outputs manually. The price being paid for that is time, and thus money, but also: trust. Trust in the output gen-AI generates. But the thing is: can we really blame the gen-AI models and automation algorithms? Not really, right? Since, despite its capabilities - remarkable as they are - generic gen-AI is simply limited. And that is where specialized gen-AI starts to work its magic.

In contrast to gen-AI, specialized gen-AI can be fine-tuned, and it’s able to understand the context more deeply because of it. Some common use cases include:

• Recruitment: Taking over the screening process, intake, or candidate profile creation, making zero-admin recruitment possible.
• Medical diagnosis: Analyzing medical images to diagnose diseases.
• Fraud detection: Identifying fraudulent transactions.
• Finance: Identifying investment opportunities and fraud.
• Manufacturing: Optimizing production processes and quality control.
• Retail: Recommending products to customers and managing inventory.
• Customer support: AI designed for handling particular types of customer queries.
• Data analysis: Specialized AI for analyzing specific datasets in research or finance.

Let’s expand one example, and take the case of recruitment.

Specialized gen-AI models understand industry jargon, job description, and desired qualifications, as well as qualifications and experience. They can offer better accuracy in assessing candidate qualifications. Moral here? Gen-AI can provide support in handling a wide range of tasks, generate diverse types of content, trained on broad datasets, and therefore capable to produce outputs across various domains.

They are flexible and broadly functional, for sure, but also reduced to those logical prompts. And that is not entirely without danger. Generic gen-AI models generate from the data it is fueled with. The datasets directly influence the quality of the outputs. The better the prompts, the better the output.

Now, when drawing from the internet-powered data, a question of accuracy arises. The internet is filled with misinformation, misstatements and conflicting reports - data that thus directly influences the outcome. Thinking about that then the possibility arises that we run into a wall of possible unusable answers or hallucination. Artificial intelligence, in the end, is not ‘artificial superintelligence’. It’s just a trained large language model.

Specialized gen-AI models bring opportunities to break through that proverbial wall. They are tailored for specific domains or tasks, and trained on datasets that focus on a particular subject. As a result, niche AI generates more efficient, precise, and accurate outputs, and excels in specific domains like recruitment.

In summary, specialized gen-AI has the strengths and limitations below.

‍Strengths:

• High accuracy: Tailored for specific tasks, leading to higher accuracy and efficiency.
• Resource optimization: Requires less computational power for task-specific training.
• Domain expertise: Can understand and operate within specific domains, and excels in domains where in-depth knowledge is crucial.
• Robustness: Can handle real-world data and variations.
• Autonomy: Can operate without human intervention.

Limitations:

• Narrow scope: Limited to the specific task it's designed for.
• Rigidity: May struggle when faced with tasks outside its predefined scope.
• Data requirements: May require large amounts of specialized data.
• Integration challenges: Might require additional effort to integrate with other systems.
• Cost: Development and deployment of specialized AI can be expensive.

Beyond the horizon: The future of generative AI

As we navigate the generative AI wonderland, the road ahead is both thrilling and challenging. The future holds the promise of a harmonious dance between generic and specialized generative AI.

Imagine a world where adaptable models seamlessly integrate with highly precise, domain-specific counterparts. This convergence is not just a technical achievement; it's a paradigm shift in how the integration of specialized AI agents into process-driven workflows can revolutionize how businesses operate.

By automating repetitive tasks, businesses can optimize resource allocation. Generic generative AI, on the one hand, excels in automating routine tasks, contributing to increased efficiency and productivity, and freeing up human powers for more complex and creative endeavors. Specialized agents, in turn, ensure that each step in a workflow is handled with precision, reducing the likelihood of errors, and enhancing the overall quality of outputs. Contributing to the overall efficiency and reliability of the process.

Over to you

As we bid adieu to this journey through the Generic landscape, the landscape turned out to be vibrant, teeming with possibilities and challenges alike.

Generic generative AI has demonstrated its versatility, enabling it to autonomously generate content across diverse domains. Its flexibility, exemplified by tools like ChatGPT, has made it an integral part of daily work routines for various tasks. However, the rise of specialized generative AI, often referred to as niche navigators, suggests a shift in focus towards models designed for specific tasks.

These models, acting as specialized agents in a 'conveyor belt' approach, offer enhanced performance and precision, contributing to the efficiency and accuracy of workflow processes, revolutionizing how businesses operate.

As we navigate the developing generative AI landscape, the future holds the promise of a harmonious dance between generic and specialized generative AI.