#chat gpt application

[techugoappcompany]

9 Things You Need to Know About Chat Gpt: Chatbots that Provide Answers

Chat GPT is a term millions of readers might have seen mentioned on the Internet. It's a revolutionary technology that some even compare to Google. So, what is Chat GPT and how does it work? These are just a few of the many questions you might have. This guide will help you understand the technology and how it works.

[goodluckdetective]

To summarize my feelings on Ai: they’re essentially mimics on a massive scale. Which is impressive and useful in some areas (marking patterns, filling templates, ect) but very different from what they’re being sold as (things that understand the content they are creating).

My shorthand to explain it is as follows: you can teach a Parrot to say “objection” but that doesn’t mean you let that parrot go practice law.

[prismetric-technologies]

Using ChatGPT for mobile application development offers myriad advantages. It streamlines the development process by providing insightful suggestions, enhancing user experience, and optimizing functionality. Its versatile nature allows for personalized interactions, efficient problem-solving, and seamless integration of features. Additionally, ChatGPT accelerates development cycles, reduces costs, and ensures high-quality outcomes, making it an indispensable tool for modern mobile app development endeavors.

[dreamschat-dreamguystech]

Experience the next generation of conversational chat with Dreamschat and CHAT GPT. Trust in the privacy and security features of Dreamschat, including Data Encryption and data privacy controls. For more information, visit: https://dreamschat.dreamguystech.com or Reach us: business@dreamguystech.com / +91 9942576886

[jobbieresume]

https://jobbie.io

Visit now, and get yours sorted

[jcmarchi]

Unlocking New Possibilities in Healthcare with AI

New Post has been published on https://thedigitalinsider.com/unlocking-new-possibilities-in-healthcare-with-ai/

Unlocking New Possibilities in Healthcare with AI

Healthcare in the United States is in the early stages of a significant potential disruption due to the use of Machine Learning and Artificial Intelligence. This shift has been underway for over a decade, but with recent advances, seems poised for more rapid changes. Much work remains to be done to understand the safest and most effective applications of AI in healthcare, to build trust among clinicians in the use of AI, and to adjust our clinical education system to drive better use of AI-based systems.

Applications of AI in Healthcare

AI has been in evolution for decades in healthcare, both in patient-facing and back-office functions. Some of the earliest and most extensive work has occurred in the use of deep learning and computer vision models.

First, some terminology. Traditional statistical approaches in research–e.g. observational studies and clinical trials–have used population-focused modeling approaches that rely on regression models, in which independent variables are used to predict outcomes. In these approaches, while more data is better, there is a plateau effect in which above a certain data set size, no better inferences can be obtained from the data.

Artificial intelligence brings a newer approach to prediction. A structure called a perceptron processes data that is passed forward a row at a time, and is created as a network of layers of differential equations to modify the input data, to produce an output. During training, each row of data as it passes through the network–called a neural network–modifies the equations at each layer of the network so that the predicted output matches the actual output. As the data in a training set is processed, the neural network learns how to predict the outcome.

Several types of networks exist. Convolutional neural networks, or CNNs, were among the first models to find success in healthcare applications. CNNs are very good at learning from images in a process called computer vision and have found applications where image data is prominent: radiology, retinal exams, and skin images.

A newer neural network type called the transformer architecture has become a dominant approach due to its incredible success for text, and combinations of text and images (also called multimodal data). Transformer neural networks are exceptional when given a set of text, at predicting subsequent text. One application of the transformer architecture is the Large Language Model or LLM. Multiple commercial examples of LLMs include Chat GPT, Anthropics Claude, and Metas Llama 3.

What has been observed with neural networks, in general, is that a plateau for improvement in learning has been hard to find. In other words, given more and more data, neural networks continue to learn and improve. The main limits on their capability are larger and larger data sets and the computing power to train the models. In healthcare, the creation of privacy-protecting data sets that faithfully represent true clinical care is a key priority to advance model development.

LLMs may represent a paradigm shift in the application of AI for healthcare. Because of their facility with language and text, they are a good match to electronic records in which almost all data are text. They also do not require highly annotated data for training but can use existing data sets. The two main flaws with these models are that 1) they do not have a world model or an understanding of the data that is being analyzed (they have been called fancy autocomplete), and 2) they can hallucinate or confabulate, making up text or images that appear accurate but create information presented as fact.

Use cases being explored for AI include automation and augmentation for reading of radiology images, retinal images, and other image data; reducing the effort and improving the accuracy of clinical documentation, a major source of clinician burnout; better, more empathic, patient communication; and improving the efficiency of back-office functions like revenue cycle, operations, and billing.

Real-world Examples

AI has been incrementally introduced into clinical care overall. Typically, successful use of AI has followed peer-reviewed trials of performance that have demonstrated success and, in some cases, FDA approval for use.

Among the earliest use cases in which AI performs well have been AI detecting disease in retinal exam images and radiology. For retinal exams, published literature on the performance of these models has been followed by the deployment of automated fundoscopy to detect retinal disease in ambulatory settings. Studies of image segmentation, with many published successes, have resulted in multiple software solutions that provide decision support for radiologists, reducing errors and detecting abnormalities to make radiologist workflows more efficient.

Newer large language models are being explored for assistance with clinical workflows. Ambient voice is being used to enhance the usage of Electronic Health Records (EHRs). Currently, AI scribes are being implemented to aid in medical documentation. This allows physicians to focus on patients while AI takes care of the documentation process, improving efficiency and accuracy.

In addition, hospitals and health systems can use AI’s predictive modeling capabilities to risk-stratify patients, identifying patients who are at high or increasing risk and determining the best course of action. In fact, AI’s cluster detection capabilities are being increasingly used in research and clinical care to identify patients with similar characteristics and determine the typical course of clinical action for them. This can also enable virtual or simulated clinical trials to determine the most effective treatment courses and measure their efficacy.

A future use case may be the use of AI-powered language models in doctor-patient communication. These models have been found to have valid responses for patients that simulate empathetic conversations, making it easier to manage difficult interactions. This application of AI can greatly improve patient care by providing quicker and more efficient triage of patient messages based on the severity of their condition and message.

Challenges and Ethical Considerations

One challenge with AI implementation in healthcare is ensuring regulatory compliance, patient safety, and clinical efficacy when using AI tools. While clinical trials are the standard for new treatments, there is a debate on whether AI tools should follow the same approach. Another concern is the risk of data breaches and compromised patient privacy. Large language models trained on protected data can potentially leak source data, which poses a significant threat to patient privacy. Healthcare organizations must find ways to protect patient data and prevent breaches to maintain trust and confidentiality. Bias in training data is also a critical challenge that needs to be addressed. To avoid biased models, better methods to avoid bias in training data must be introduced. It is crucial to develop training and academic approaches that enable better model training and incorporate equity in all aspects of healthcare to avoid bias.

The use of AI has opened a number of new concerns and frontiers for innovation. Further study of where true clinical benefit may be found in AI use is needed. To address these challenges and ethical concerns, healthcare provider organizations and software companies must focus on developing data sets that accurately model healthcare data while ensuring anonymity and protecting privacy. Additionally, partnerships between healthcare providers, systems, and technology/software companies must be established to bring AI tools into practice in a safe and thoughtful manner. By addressing these challenges, healthcare organizations can harness the potential of AI while upholding patient safety, privacy, and fairness.

[newspatron]

Chat GPT-4o: The AI Revolution Unveiled

What do you think of Chat GPT-4o? Share your thoughts and experiences in the comments!

Close all those open tabs in your browser (and mobile apps!), because things are about to get seriously interesting in the world of AI. OpenAI has just unveiled GPT-4o, and it’s not just an upgrade – it’s a game-changer. Picture this: an AI that understands not only your words but also your voice, your photos, and even your videos. It’s like stepping into the future, and it’s all happening right…

[e-m-dallas]

Amid this ever-changing world,

The noblest quest to be found,

Far from being the most profound,

Is to seek and search, submerged

In knowledge.

I want to know and understand:

To learn the truth from the lie

Of the many lives of the spy,

All the stories told and banned,

They try hide.

Books, O guardians of the past,

Give me the secrets you keep —

Of long dead men and bleating sheep,

Of the here and the long past,

— in your gut

[nikparihar]

The possibilities with ChatGPT are limitless, and it's a model to keep an eye on in the future. Transform Your Customer Experience with ChatGPT Integration, and Learn How to Integrate the World's Most Advanced Language Model Into Your Existing Systems Today!

Submit your requirement at: https://digittrix.com/submit-your-requirement

[gurmeetweb]

Understanding the Benefits of Chat GPT for Business Applications As businesses look for ways to increase productivity and efficiency, chat GPT (General Purpose Technology) has become increasingly popular. Chat GPT is a type of artificial intelligence that uses natural language processing to generate conversations with customers. It is designed to provide automated customer service and help customers find answers quickly. The benefits of chat GPT for businesses are numerous. Companies can save time and money by automating their customer service operations, as well as cut down on customer wait times. Chat GPT can also help businesses gain insights into customer behavior, allowing them to make better decisions about their services. Additionally, businesses can use chat GPT to provide personalized customer service and improve customer satisfaction. One of the key benefits of chat GPT is its ability to generate https://digitaltutorialsapp.com/understanding-the-benefits-of-chat-gpt-for-business-applications/?utm_source=tumblr&utm_medium=socialtumbdigitutorials&utm_campaign=camptumbdigitutorials

[art-ro-vert]

Please be honest, it’s anonymous, so one one will hate on you!

Also, reply the most major thing you did, for example if you used it for both minor and major writing help, reply “the major”

Reblog for sample!

[hesperocyon-lesbian]

since when are you pro-chat-gpt

I’m not lol, I’m ambivalent on it. I think it’s a tool that doesn’t have many practical applications because all it’s really good at doing is sketching out a likely response based on a prompt, which obv doesn’t take accuracy into account. So while it’s terrible as, say, a search engine, it’s actually fairly useful for something hollow and formulaic like a cover letter, which there are decent odds a human won’t read anyway

The thing about “AI”, both LLMs and AI art, is that both the people hyping them up and the people fervently against them are annoying and wrong. It’s not a plagiarism machine because that’s not what plagiarism is, half the time when someone says that they’re saying it copied someone’s style which isn’t remotely plagiarism.

Basically, the backlash against these pieces of tech centers around rhetoric of “laziness” which I feel like I shouldn’t need to say is ableist and a straightforwardly capitalistic talking point but I’ll say it anyway, or arguments around some kind of inherent “soul” in art created by humans, which, idk maybe that’s convincing if you’re religious but I’m not so I really couldn’t care less.

That and the fact that most of the stars about power usage are nonsense. People will gesture at the amount of power servers that host AI consume without acknowledging that those AI programs are among many other kinds of traffic hosted on those servers, and it isn’t really possible to pick apart which one is consuming however much power, so they’ll just use the stats related to the entire power consumption of the server.

Ultimately, like I said in my previous post, I think most of the output of LLMs and AI art tools is slop, and is generally unappealing to me. And that’s something you can just say! You’re allowed to subjectively dislike it without needing to moralize your reasoning! But the backlash is so extremely ableist and so obsessed with protecting copyright that it’s almost as bad as the AI hype train, if not just as

[centrally-unplanned]

I am not a full AI skeptic but when it comes to AI-as-writer types I find its endorsers to be all the counterexample you need. Look at this:

Which is a fine enough basic idea, this has applications ofc. Then you zoom in:

And its like what on earth are you asking about. That is not an ambiguous sentence - particularly if you have any inkling at all of the plot of the Screwtape Letters, which you should if you are reading it. There is nothing in need of explanation here!

Even more silly, GPT's response isnt wrong, but because the sentence is a not-subtle, direct statement its 'explanation' is just a long-winded rephrasing of the sentence, it adds no value. But that didnt stop this person from copying the entire text into his notes apparently! His notes are an anti-synthesis of the text, *reducing* its meaning-per-word.

As an aid to a highschool freshman reading it, sure, this has value, its a google search tutor generating novel links on the spot. But these images were selected by the tweet author to highlight its value as a research aid for serious analysis, this should be the best it has to offer. What it shows instead is this use is an extremely poor fit for the tool.

I fully believe future developments will progress the tool in this direction; my point instead is how much of the hype is just froth right now. This tweet was not born of the impressive results of Chat-GPT; it was born of the impressive reach one can get shoehorning Chat-GPT into your content.

[templeofshame]

it's wild to me how often people are just like use chat gpt for things it can't possibly know. like if you're writing a grant application about your little org's inclusion efforts, chat gpt can make up vapid bs, but you the human actually know what your org is doing... why would a human suggest another human ask chat gpt to answer a question that they personally know about and that isn't available to chat gpt?

[txttletale]

I'm interested in your thoughts about Large Language Models. I'm much more opposed to them than text to image generators for similar reasons I'm opposed to crypto. The use cases seem so vastly over exaggerated, and I'm particularly concerned by unmonitored uses. Like I have good reason to believe it's a very dangerous technology and my efforts to oppose dangerous uses of it in the charity I work for have consisted of opposing all uses because they've been dangerous for obvious reasons. Some developers put together a chat bot to give out cancer advice.

i think you're basically right about this but i think that as with AI art the problem isnt the technology itself but the societal and material conditions around it -- in this case disastrously irresponsible and deceptive marketing fueled by uncritical stenographic reporting. like, by far imo the biggest danger of something like chatGPT is people trusting a machine that is basically good at authoritativbely lying to give them advice and help make decisions. like the lawyers who used chatGPT to help submit their court filing and it just fucking made up a bunch of citations -- the idea of this beocming a pervasive problem across multiple fields is like, terrifying. but again that's not because like, the idea of a program that can dynamically produce text is ontologically evil or without legimate application -- it's because of the multimillion dollar marketing push of GPT & similar models as 'AI assistants' that you can 'talk to' rather than 'a way to generate a bunch of text with no particular relation to reality'.