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When a paradigm shift occurs, it is not always obvious to those affected by it.

But there is no eye of the storm equivalent when it comes to generative artificial intelligence (AI).

The technology ishere. There are already variouscommercial productsavailable fordeployment, and organizations that can effectively leverage it in support of theirbusiness goalsare likely to outperform their peers that fail to adopt the innovation.

Still, as with many innovations, uncertainty and institutional inertia reign supreme which is why understanding how the large language models (LLMs) powering AI work is critical to not just piercing the black box of the technologys supposed inscrutability, but also to applying AI tools correctly within an enterprise setting.

The most important thing to understand about the foundational models powering todays AI interfaces and giving them their ability to generate responses is the simple fact that LLMs, like Googles Bard, Anthropics Claude, OpenAIs ChatGPT and others, are just adding one word at a time.

Underneath the layers of sophisticated algorithmic calculations, thats all there is to it.

Thats because at a fundamental level, generative AI models are built to generate reasonable continuations of text by drawing from a ranked list of words, each given different weighted probabilities based on the data set the model was trained on.

Read more:There Are a Lot of Generative AI Acronyms Heres What They All Mean

While news of AI that can surpass human intelligence are helping fuel the hype of the technology, the reality is far more driven by math than it is by myth.

It is important for everyone to understand that AIlearns from data at the end of the day [AI] is merely probabilistics and statistics, Akli Adjaoute, AI pioneer and founder and general partner at venture capital fund Exponion, told PYMNTS in November.

But where do the probabilities that determine an AI systems output originate from?

The answer lies within the AI models training data. Peeking into the inner workings of an AI model reveals that it is not only the next reasonable word that is being identified, weighted, then generated, but that this process occurs on a letter by letter basis, as AI models break apart words into more manageable tokens.

That is a big part of whyprompt engineering for AI models is an emerging skillset. After all, different prompts produce different outputs based on the probabilities inherent to each reasonable continuation, meaning that to get the best output, you need to have a clear idea of where to point the provided input or query.

It also means that the data informing the weight given to each probabilistic outcome must berelevantto the query. The more relevant, the better.

See also:Tailoring AI Solutions by Industry Key to Scalability

While PYMNTS Intelligence has found that more than eight in 10 business leaders (84%) believe generative AI will positively impactthe workforce, generative AI systems are only as good as the data theyre trained on. Thats why the largest AI players are in an arms race toacquire the best training data sets.

Theres a long way to go before theres afuturistic version of AIwhere machines think and make decisions. Humans will be around for quite a while,Tony Wimmer, head of data and analytics atJ.P. Morgan Payments, told PYMNTS in March. And the more that we can write software that has payments data at the heart of it to help humans, the better payments will get.

Thats why, to train an AI model to perform to the necessary standard, many enterprises are relying ontheir own internal datato avoid compromising model outputs. By creating vertically specialized LLMs trained for industry use cases, organizations can deploy AI systems that are able to find the signal within the noise, as well as to be further fine-tuned to business-specific goals with real-time data.

AsAkli Adjaoutetold PYMNTS back in November, if you go into a field where the data is real, particularly in thepayments industry, whether its credit risk, whether its delinquency, whether its AML [anti-money laundering], whether its fraud prevention, anything that touches payments AI can bring a lot of benefit.

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The rest is here:
Demystifying AI: The Probability Theory Behind LLMs Like OpenAI's ChatGPT - PYMNTS.com