What Makes the "Best" AI Model?

Introduction

Only 10 years ago, most people saw artificial intelligence as the work of science fiction movies. Now, 56% of Americans use AI at least several times a week. 

How did AI become so prevalent so quickly?

While early AI abilities included things like spell check, artificial intelligence is much bigger than that. It includes a lot of planning, optimization, logic, and more sub-disciplines, some with an emphasis on helping computers make decisions with imperfect information.

A multitude of everyday activities are enhanced with AI. Virtual maps, music playlists, fitness trackers, home security systems, and even your spam box all rely on artificial intelligence to behave like a human, summarizing massive amounts of data and creating custom recommendations. Businesses have been able to leverage this modern technology to perform increasingly complex tasks. Whether businesses expose that to their customers, to the public, or use it for internal support, the ability to instantly find answers to questions  can be useful.

AI models are as varied as the domains they serve. Large language models (LLMs) are especially complex; they comprise billions of parameters that work together to determine the best output. As developers and tech companies create new models, all are geared to matching customer needs to specific data sets. 

In reality, there is not one model that is “the best.” The best AI model for a smart fridge is not going to be the best model for an architecture company with a new skyscraper project, nor will it be the best model for a healthcare system to allow patients to schedule their Telehealth appointments. To determine what makes the best AI model for a specific use, further considerations are needed. 

Understanding AI Model Quality

An artificial intelligence model is essentially a ginormous set of data. Models process vast and complex data to extract information, but the set of information used is different from one model to another – and the number of models is growing exponentially, particularly in the language category. In 2020, almost no LLMs existed, but by December 2024, there were more than 200. This growth rate significantly outpaces other categories including audio, video, biology, and even gaming – none of which have more than 50 models in existence.

With such a rapid growth of LLMs, it’s safe to say they are not created equal.

AI models don’t create themselves; developers must input data into the model. During the building process, developers must first identify the data and its potential sources, and begin the collection process – often synthetic data produced by AI. Data sources can include social media, surveys, data warehouses, or internal databases. External sources can be unreliable, expensive, or both. Once data has been collected, it must be processed and cleaned, and algorithms must be selected to build predictive models.

With so much variability in the building process, it’s clear that each model will offer results different from the next. The best AI model for your specific uses must be evaluated on the specific metrics in order to provide the best outcomes for your company.

Key Attributes of Superior AI Models

Accurate and precise responses are critical. When a model consistently provides the best outputs, users begin to trust the technology, leveraging the model for an increased number of tasks. Positive experiences lead to positive outcomes, and ultimately to the success of the model itself.

A good model will also be efficient. The ability to process increasing amounts of data while maintaining speed and accuracy characterize top-ranking models, saving time, effort, and energy for their users. In turn, users begin to integrate additional tasks into their AI models, and the ability of the model to scale appropriately offers big benefits for growing companies.

Generalization capability is a challenge for many AI models and LLMs, but developers continue to strive to push boundaries. While AI models can only produce responses based on their specific datasets, providing accurate data summaries and overviews across multiple applications has yet to be consistently seen.

Individuals and businesses must have some sort of understanding of how selected AI models interpret data to produce responses. Rather than saving time and money, responses requiring fact-checking may end up causing the user more work than is saved, so the ability to understand and trust responses means the AI model is a helpful tool rather than a hindrance.

Technical Innovations 

Certain AI models contain algorithms that continuously learn, shift, and adapt. Rather than adopting one set of rules, state-of-the-art algorithms in superior AI models will learn what data sets best reflect the query at hand, even as the data evolves over time. While one AI model can provide the best results for one specific task, they are often unable to recall data from fundamentally divergent areas of expertise. 

The ability to learn and adapt has created a paradigm shift in AI as a whole. Rather than ‘robot-like’ repetitive responses, machine learning allows successful AI models to respond appropriately to novel situations across a variety of domains; however, one model cannot excel at every task, every time. 

Practical Considerations

When selecting an AI model to address any challenge, a variety of practical considerations must be pondered. While some models provide excellent responses, some are inconsistent or noisy. Some models may be easily integrated into existing platforms more easily than others. Some provide excellent reliability, but may be cost-prohibitive for regular use. 

Security is and will continue to be a priority when interfacing with AI models. As data continues to be incorporated into the model used, care must be taken to maintain security and compliance, especially in healthcare, government, finance, or manufacturing sectors where proprietary information could potentially make its way to public platforms.

Streamlining your existing workflows while offering consistency, privacy, and affordability is the ultimate goal. While several AI models may offer similar features and benefits, the challenge becomes the research and decision-making process that could undermine the value of the AI model in the first place. 

Conclusion

Harnessing the power of AI models and LLMs can bring incredible benefits to your business. Generating consistently accurate responses not only streamlines internal workflows, it also keeps you well-positioned in the marketplace.  

Emerging trends and advancements are pushing the boundaries of creativity, adaptability, and problem-solving. Innovative architectures, larger data sets, and expanded frameworks continue to support the development of increasingly superior models.

As the case with all technologies, the pursuit of the best and most innovative AI models is a journey that changes rapidly. Efficiency, context-awareness, and alignment with human values will continue to improve at rapid rates, and the best model today will likely be different from the best model in the future. 

Infuzu brings brand new Intelligent Model Selection algorithms that can mitigate the challenges of navigating the intricacies of determining which model is the best for your organization. Easy integration of Infuzu into your existing workflow allows you to leverage the best model for each individual query, ensuring the best responses, cost effectiveness, and alignment with your values.

Learn more about how Infuzu can support your business by clicking here.