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<\/p>\nJJ Rosen
\n\u00a0|\u00a0 Special to The Tennessean<\/p>\n
<\/p>\n
Artificial Intelligence Shapes Insurance Decisions in Florida<\/p>\n
Discover how artificial intelligence impacts insurance decisions in Florida, and how it’s in a lawsuit involving Medicare care denial.<\/p>\n
Artificial intelligence is a tool that excels at recognizing patterns in vast amounts of data to make predictions.Common AI terms include Machine Learning (ML), Large Language Models (LLMs), and neural networks.AI systems are “trained” using massive datasets, and the quality of this data determines the model’s performance.AI can amplify human capabilities by automating repetitive work and analyzing data, but it still requires human direction and judgment.<\/p>\n
There\u2019s a moment in every new technology wave when many people begin to nod confidently while quietly thinking, I have no idea what\u2019s going on. Artificial Intelligence has brought us squarely into one of those moments.<\/p>\n
So, let\u2019s fix that without turning this into a graduate-level seminar or making anyone feel like they need to dust off their calculus textbook.<\/p>\n
Think of AI less like a mysterious super-brain and more like an eager intern. It doesn\u2019t know things the same way you and I do. It doesn\u2019t wake up with opinions about coffee or politics. What it does have, is an uncanny ability to spot patterns faster than any human and then use those patterns to predict what comes next.<\/p>\n
At its core, AI is just math wrapped in data. Lots of data.<\/p>\n
If you remember nothing else, remember this: modern AI is largely about prediction, not magic.<\/p>\n
Given a prompt like \u201cThe capital of France is\u2026\u201d, AI predicts the most likely next word is \u201cParis.\u201d Not because it knows geography in the human sense, but because it has seen billions of examples where those words appear together.<\/p>\n
It\u2019s essentially auto-complete on steroids.<\/p>\n
With pervasive new technologies, there\u2019s always a ton of new and confusing acronyms and terms that tend to crop up.<\/p>\n
Let\u2019s walk through some of the AI jargon you may hear at your next dinner party.<\/p>\n
Machine Learning (ML)<\/p>\n
This is the umbrella term. Instead of programming rules (\u201cif X, then Y\u201d), we feed lots of data to systems, and then we let them figure out which conclusions to draw from it.<\/p>\n
It\u2019s the difference between giving someone a recipe versus letting them taste 10,000 dishes and reverse-engineer cooking.<\/p>\n
Model<\/p>\n
A model is the trained system \u2014 the \u201cbrain\u201d that has received and learned from data. Think of it like a seasoned chef who has seen enough ingredients to improvise.<\/p>\n
When people say \u201cwe trained a model,\u201d they just mean they exposed it to massive amounts of data so it could learn patterns.<\/p>\n
Training Data<\/p>\n
This is what the model learns from. The quality of an AI is heavily dependent on the amount and quality of its data.<\/p>\n
Garbage in, garbage out still applies \u2014 just at scale.<\/p>\n
Neural Networks<\/p>\n
This is where things start to sound intimidating, but the concept is simple. Neural networks are layered systems inspired (loosely) by the human brain. They process information in stages, refining predictions as they go.<\/p>\n
Picture a panel of judges, each adding their opinion before a final decision is made.<\/p>\n
Large Language Models (LLMs)<\/p>\n
This is what powers tools like ChatGPT. These models are trained on enormous amounts of text to understand and generate language.<\/p>\n
They don\u2019t \u201cthink,” they predict sequences of words in a way that feels like thinking.<\/p>\n
Prompting<\/p>\n
\u00a0This is just asking an AI system to do something. The better your prompt, the better your result.<\/p>\n
So, how does all this actually work?<\/p>\n
Imagine you\u2019re teaching a child to recognize dogs.<\/p>\n
You show thousands of pictures: big dogs, small dogs, cartoon dogs, maybe even a Chihuahua in a sweater. Over time, the child begins to identify patterns \u2014 fur, shape, ears \u2014 and can recognize a dog they\u2019ve never seen before.<\/p>\n
AI does the same thing, just with vastly more data and zero emotional attachment to the Chihuahua.<\/p>\n
During training, the model makes guesses, checks how wrong it was, and adjusts. This process repeats billions of times until it believes it\u2019s pretty good at predicting outcomes.<\/p>\n
If you have a basic understanding of how AI works, it is a little easier to navigate the thousands of new AI-based products that show up every month. \u00a0\u00a0<\/p>\n
Here\u2019s the practical takeaway: AI is a tool for amplifying human capability, but it cannot replace it outright.<\/p>\n
It can draft emails, analyze data, generate code, and summarize reports. But it still needs direction, judgment, and context to function properly \u2014 all things that only humans can provide.<\/p>\n
The companies winning with AI aren\u2019t the ones chasing buzzwords. They\u2019re the ones asking simple questions:<\/p>\n
Where are we repeating work? Can the work pattern be automated?Where are we drowning in data? Could that data inform any AI system and create further efficiency?Where could faster decisions help?<\/p>\n
AI thrives in those gaps.<\/p>\n
If you think of AI as a prediction engine trained on massive data, most of the mystery disappears. It is quietly reshaping how work gets done, favoring those who understand it just enough to use it wisely.<\/p>\n
And if you\u2019re still unsure, don\u2019t worry. Even the experts are often guessing.<\/p>\n
JJ Rosen is the founder of Atiba<\/a>, a custom software development firm<\/a>, and Nashville IT support company<\/a>,. Visit Atiba.com for more info.<\/p>\n","protected":false},"excerpt":{"rendered":"JJ Rosen \u00a0|\u00a0 Special to The Tennessean Artificial Intelligence Shapes Insurance Decisions in Florida Discover how artificial intelligence…\n","protected":false},"author":2,"featured_media":2295,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[24,601,25,594,593,206,599,598,624,772,66,607,777,592,622,134,163,600],"class_list":{"0":"post-2294","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-ai","8":"tag-ai","9":"tag-artificial","10":"tag-artificial-intelligence","11":"tag-enabled","12":"tag-highlights","13":"tag-intelligence","14":"tag-learning","15":"tag-machine","16":"tag-machine-learning-u0026-artificial-intelligence","17":"tag-neutral","18":"tag-news","19":"tag-overall","20":"tag-overall-neutral","21":"tag-story","22":"tag-story-highlights-ai-enabled","23":"tag-technology","24":"tag-technology-news","25":"tag-u0026"},"_links":{"self":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/2294","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/comments?post=2294"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/2294\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media\/2295"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media?parent=2294"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/categories?post=2294"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/tags?post=2294"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}