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mariacallous · 5 months ago

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ChatGPT developer OpenAI’s approach to building artificial intelligence came under fire this week from former employees who accuse the company of taking unnecessary risks with technology that could become harmful.

Today, OpenAI released a new research paper apparently aimed at showing it is serious about tackling AI risk by making its models more explainable. In the paper, researchers from the company lay out a way to peer inside the AI model that powers ChatGPT. They devise a method of identifying how the model stores certain concepts—including those that might cause an AI system to misbehave.

Although the research makes OpenAI’s work on keeping AI in check more visible, it also highlights recent turmoil at the company. The new research was performed by the recently disbanded “superalignment” team at OpenAI that was dedicated to studying the technology’s long-term risks.

The former group’s coleads, Ilya Sutskever and Jan Leike—both of whom have left OpenAI—are named as coauthors. Sutskever, a cofounder of OpenAI and formerly chief scientist, was among the board members who voted to fire CEO Sam Altman last November, triggering a chaotic few days that culminated in Altman’s return as leader.

ChatGPT is powered by a family of so-called large language models called GPT, based on an approach to machine learning known as artificial neural networks. These mathematical networks have shown great power to learn useful tasks by analyzing example data, but their workings cannot be easily scrutinized as conventional computer programs can. The complex interplay between the layers of “neurons” within an artificial neural network makes reverse engineering why a system like ChatGPT came up with a particular response hugely challenging.

“Unlike with most human creations, we don’t really understand the inner workings of neural networks,” the researchers behind the work wrote in an accompanying blog post. Some prominent AI researchers believe that the most powerful AI models, including ChatGPT, could perhaps be used to design chemical or biological weapons and coordinate cyberattacks. A longer-term concern is that AI models may choose to hide information or act in harmful ways in order to achieve their goals.

OpenAI’s new paper outlines a technique that lessens the mystery a little, by identifying patterns that represent specific concepts inside a machine learning system with help from an additional machine learning model. The key innovation is in refining the network used to peer inside the system of interest by identifying concepts, to make it more efficient.

OpenAI proved out the approach by identifying patterns that represent concepts inside GPT-4, one of its largest AI models. The company released code related to the interpretability work, as well as a visualization tool that can be used to see how words in different sentences activate concepts, including profanity and erotic content, in GPT-4 and another model. Knowing how a model represents certain concepts could be a step toward being able to dial down those associated with unwanted behavior, to keep an AI system on the rails. It could also make it possible to tune an AI system to favor certain topics or ideas.

Even though LLMs defy easy interrogation, a growing body of research suggests they can be poked and prodded in ways that reveal useful information. Anthropic, an OpenAI competitor backed by Amazon and Google, published similar work on AI interpretability last month. To demonstrate how the behavior of AI systems might be tuned, the company's researchers created a chatbot obsessed with San Francisco's Golden Gate Bridge. And simply asking an LLM to explain its reasoning can sometimes yield insights.

“It’s exciting progress,” says David Bau, a professor at Northeastern University who works on AI explainability, of the new OpenAI research. “As a field, we need to be learning how to understand and scrutinize these large models much better.”

Bau says the OpenAI team’s main innovation is in showing a more efficient way to configure a small neural network that can be used to understand the components of a larger one. But he also notes that the technique needs to be refined to make it more reliable. “There’s still a lot of work ahead in using these methods to create fully understandable explanations,” Bau says.

Bau is part of a US government-funded effort called the National Deep Inference Fabric, which will make cloud computing resources available to academic researchers so that they too can probe especially powerful AI models. “We need to figure out how we can enable scientists to do this work even if they are not working at these large companies,” he says.

OpenAI’s researchers acknowledge in their paper that further work needs to be done to improve their method, but also say they hope it will lead to practical ways to control AI models. “We hope that one day, interpretability can provide us with new ways to reason about model safety and robustness, and significantly increase our trust in powerful AI models by giving strong assurances about their behavior,” they write.

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snickerdoodlles · 1 year ago

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Re AO3- Politely inquiring as to why you aren't worried, oh Cinnamon One? Thanks! Xoxo

1 part "that's not how it works" and 2 parts "LLM-generated writing has nothing to do with fanfic writers"

some quick context for anyone who's confused-- generative-AI is trained on large datasets, and a lot of training datasets for these LLMs* include data scraped from AO3. i know that the generative-AI Sudowrites has been specifically referenced in socmed posts encouraging AO3 authors to lock their fics, but i believe the AO3 announcement on AI & data-scraping was prompted by current events and debates on the presence of generative-AI in day-to-day society vs any specific situation/event/etc

*LLM stands for 'large language model' which is the type of AI we're talking about here

my first note here is that if locking fics makes you feel more comfortable posting fanfic, do it. it's a fantastic security feature and no one needs a reason to lock their fics beyond "i want to." if you're doing it specifically to stop data scraping for AI, beware it doesn't actually stop that from occurring, it just acts as a deterent (AO3 has said they've done some backend work to prevent data scraping from occurring again in the future, but there's no way they'll ever be able to stop it completely)

but there's also been a lot of...well. i'm not sure misinformation is quite the right phrase here, but a lot of misunderstanding on how LLMs work that's resulted in a lot of outraged or indignant posts on LLM-generated writing in conjunction with AO3, and that's resulted in some fearmongering in regards to the issue that doesn't help anyone :( so, why i'm personally not worried about this issue;

1 part "that's not how it works"

first things first: i don't think people appreciate the sheer scale of LLMs. to refer back to a name that's been mentioned several times in these posts, Sudowrites is a generative AI based on GPT-3, which is a LLM based on 175+ billion parameters. GPT-3 requires 800GB just to store it. GPT-4 is based on 500 billion parameters. these are two of the big LLMs, but even the small LLMs are working off of 3-7 billion parameters. LLMs are fucking huge.

i think it might surprise some people to realize just how long AI has been around. the first recognized AI was made in 1943. neural networks (the "brains" of AI) were first developed in the 1980s. people have been working on generative-AI specifically for almost 20 years now. but it took 3 big factors before generative-AI was even possible:

1- neural networks that could do unsupervised learning,

2- hardware that could handle the computing requirements and neural networks needs,

and 3- the development of the internet into what it's been for the past 10 years, and the sheer scale of information now stored within it

so here's my point: LLMs weren't "trained on data from AO3"--AO3 is a database who's stored material was pulled alongside data from online journals, literary magazines, library databases, newspapers, video transcripts, blogs, Wikipedia and so much more than i can ever list to make these training datasets. individual AO3 writers are drops in a pool and AO3 is a bucket in an ocean of information. AO3 as an own individual entity has negligible impact on how LLMs were trained or what they do, nevermind individual stories.

honestly, this alone should be a huge relief for some people--i saw posts going around where people were appalled at the idea of their fanfic being used to train a generative-AI that could hurt professional writers. so great news! your fics have no meaningful impact on any of this in any way that conceivably matters! you can post your fics for anyone to see and read and even download with absolutely zero guilt for how generative-AI is affecting jobs.

2 parts "LLM-generated writing has nothing to do with fanfic writers"

if you want to learn how LLMs work, do it outside of tumblr, it's too complex to explain here (this dive into how ChatGPT works is a good starting point for anyone interested, personally i learned a lot looking up lectures on 'deep learning'). but for a simplified overview of it for anyone who doesn't care, LLMs are just figuring out what word comes next in a sequence. basically, you give a LLM a prompt. from that prompt, it determines what your topic is, then it spits out the first token (tokens are the 'language' of LLMs, in this case it's spitting out a word or short phrase). then the LLM spits out the second token based on the first token. then spits out the third token based on the first token, second token, and combination of the tokens. and so forth, until it's reached the end of the prompt.

LLMs are just writing sentences word-by-word. i remember doing something very similar when i first started analyzing what i loved about my favorite writers--i had a notebook where i wrote out sentences that i especially loved, usually looking at description or a funny piece of dialogue, with the goal of figuring out how to write like them. this lasted for maybe a month before i moved on to analyzing story structure, narrative pacing, etc because sentences are just lines of words. anyone can put words into a nice sounding sentence. they can even put several words into nice sounding sentences that sound nice when read together. but writing, and everything about it that makes it special, is so more than writing nice sounding sentences. giving an a concept a narrative, or creating distinctive characters with their own voices, or building a setting/world, or connecting ideas to themes--generative-AI can't do any of that. it's just determining which token comes next after the previously generated ones. it can do that with a lot of variety--baby writer me was working off a bookshelf, LLMs are working off things like the entire internet--but that's still all it can do: write nice sounding sentences.

there's another aspect to generative-AI at play here too--in every example you've seen of LLM-generated writing, did you notice that they're all limited to less than 500 words? prompts shown in newscast articles/segments are usually 300-500 words, Sudowrites only offers written passages of up to 300 words, and even ChatGPT recommends keeping responses limited to under ~800 tokens (even tho it offers responses of up to...4000 tokens i think?)

this is because each generated token comes with an error value. i don't want to bog down this already long response with how that exactly works, but let's say the first token comes with an error value of 0.0002 (*im picking random numbers for this). that error value carries over to the second token (which can have its own error value of let's say 0.0007). then that combined error value carries over to the third generated token, which also has its own separate error value, and so forth. and while each individual error value is negligible, they add up with each additional token and eventually the overall gained error is too high and the LLM cannot properly/accurately produce the next token (this is called error propagation, and it's non-linear in the case of LLMs)

i will stop torturing people with math nd statistics concepts, but the long and short of this means that after a certain number of words are generated, the LLM's response starts breaking down. maybe at first it starts sounding a little stale or the wording gets awkward, but if it keeps going, the LLM starts spitting out gibberish, and you have to end the prompt and start a new one. this is why those generative-AI writing examples have a word limit to them, the LLMs can't write more than that small section of writing on their own.

so, add up all of that, LLMs already aren't going to replace story writers any time soon. they just can't do it. furthermore, the response you get from an LLM is only as good as the prompt you give it and it's working off such a huge dataset, that responses are going to be really broad. if you want a more tailored response, you have to feed it extra context alongside the prompt. and in the case of fanfic specifically, fic is entirely based on previously known context. it's written with a very specific context in mind, it expects readers to enter with at least some level of knowledge on that specific context, and works within that level of context even in the cases of AUs. fic writers play in someone else's sandbox, which is not something that LLMs are naturally capable of doing

but frankly, even if they did, they still have zero relevance to fic writers

the people currently affected by LLM-generated writing are journalists, who jobs have been under fire for years. the editors in published magazines getting slammed with LLM-generated writing because it was sold as a shortcut. writers rooms for shows, which act as an important stepping stone but execs have been trying to reduce and cut out for years. and even more that i'm not listing.

these are people's livelihoods that are being impacted by generative-AI. situations where managers and executives don't care about the fact that LLMs can't write like people do because they only see a money-saver instead of art.

like, 100%--if locking your fics feels more reassuring to you personally, absolutely lock them. that's the point of the feature. but the attitude of acting like AO3 has any relevance how LLMs are trained or that generative-AI has any meaningful impact on fic writers is just such a self-centered view of the actual issue at hand. and, if you will excuse me getting a little snarky here, anyone up in arms over AO3 being one of the many databases getting scraped is about 20 years too late to worrying about internet privacy.

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cetaceanhandiwork · 1 year ago

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Have you seen the AutoGPT framework? That adds a scaffolding to LLMs so that they can run indefinitely with a memory store, would that be Turing complete?

so there's three caveats here:

it's been 20 years since I actually studied this topic, and have forgotten like 95% of what I've learned

a wrapper program that runs an FSM in a loop with extra input from an oracle (the internet) is a lot harder to reason about than an FSM on its own

I'm found the GitHub for this but I'm not gonna read that many lines of code for free

all that said, my initial skim of the AutoGPT codebase is that the way it's implemented is making some pretty extraordinary assumptions about GPT's ability to generate sensible results for the kind of prompts it uses.

it's essentially trying to break down work into bite sized pieces by telling the text generator to:

deepdream a bureaucracy of specialized task runners

handle a user request by writing delegated tasks for members of the bureaucracy

execute those tasks as though you're the recipient member of the bureaucracy

repeat until "done"

there's a couple different ways that this can go wrong

first off, it's not clear to me whether the above hierarchical breakdown of work is being done in a way that's allowed to loop "until done". if not, we're back at FSMs.

second off, it's not clear to me whether the text generator can generate subtasks competently, the way AutoGPT is requesting them, without already being turing complete. I see a lot of hay being made in the prompts about "explain your reasoning", as context to be passed along to future invocations in order to produce more meaningful results, but answering that prompt requires an amount of introspection where I'd be surprised if an FSM was capable of generating a real answer, instead of some "sounds normal" mimic handwave. and if these output fields are garbage, then the proof by induction falls apart that the preserved context is making outputs better and not worse; you'd get something that maybe has all the physical organs to emulate a turing machine, but miswired such that it'll never actually succeed at computing anything beyond the sum of its parts.

#ai Topic #long post

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celiastjamesoscar · 11 months ago

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GRACE OMG I AM DYING SOMETHING EMBARRASSING JUST HAPPENED. I'm sitting here at work, barbecue sauce on my titties (Orange is the new Black reference, which you sadly don't get 😉) minding my own business until I received an email from a band merch store that my order has been shipped. The thing is, I didn't order anything from them. So I freaked out a bit because who the hell has all my information and what about payment, until I decided to send the customer support an email to clarify the situation. I let GPT formulate it for me, obviously, but here's the thing. Instead of copying and pasting what gpt wrote, I copied a Nat fic I was reading simultaneously and sent that instead 🤦🏻‍♀️ So now they got 2 emails frome me. One with a Natasha fic which may or may not have included smut, and the correct one where I apologized and explained the situation. I am dreading their response...

You know what, I actually DO get that reference!!! Only from vine though, not Orange is the New Black. OH MY GOD GIRL 😭😭😭 I WOULD HAVE ACTUALLY DIED FROM EMBARRASSMENT!!! I hope we have learned our lesson about using Chat GPT now 🤨

#bender chats #clexa is forever ♾️

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rajubhadra · 2 years ago

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govindhtech · 23 days ago

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Mastering Language Models: AI Conversation Building Block

What are language Models?

One kind of machine learning model that has been taught to perform a probability distribution over words is called a language models. In short, a model uses the context of the provided text to forecast the next best word to fill in a blank space in a sentence or phrase.

Since language models enable computers to comprehend, produce, and analyze human language, they are an essential part of natural language processing (NLP). A large text dataset, such a library of books or articles, is mostly used to train them. The next word in a phrase or the creation of fresh, grammatically and semantically coherent material are then predicted by models using the patterns they have learned from this training data.

The Capabilities of language models

Have you ever noticed how the Microsoft SwiftKey and Google Gboard keyboards have clever capabilities that automatically suggest whole phrases while you’re composing text messages? Among the many applications of language models is this one.

Many NLP activities, including text summarization, machine translation, and voice recognition, require it.

Creation of content: Content creation is one of the domains where language models excel. This involves using the information and terms supplied by people to generate whole texts or portions of them. Press releases, blog entries, product descriptions for online stores, poetry, and guitar tabs are just a few examples of the kind of content that may be found there.

POS (part-of-speech) labeling: World-class POS tagging performance is achieved by extensive use of this model. POS tagging assigns a noun, verb, adjective, or other part of speech to each document word. The models can estimate a word’s POS based on its context and the words that surround it in a phrase since they have been trained on vast volumes of annotated text data.

Addressing questions: It is possible to train language models to comprehend and respond to queries both with and without the provided context. They may respond in a variety of ways, such by selecting from a list of possibilities, paraphrasing the response, or extracting certain words.

Summary of a text: Documents, articles, podcasts, movies, and more may all be automatically condensed into their most essential chunks using language models. Models may be used to either summarize the material without using the original language or to extract the most significant information from the original text.

Examination of sentiment: Because it can capture the tone of voice and semantic orientation of texts, the language modeling technique is a strong choice for sentiment analysis applications.

AI that can converse: Voice-enabled apps that need to translate voice to text and voice versa inevitably include language models. This may respond to inputs with relevant text as part of conversational AI systems.

Translation by machine: Machine translation has been improved by ML-powered language models’ capacity to generalize well to lengthy contexts. It may learn the representations of input and output sequences and provide reliable results rather than translating text word for word.

Finishing the code: The capacity of recent large-scale language models to produce, modify, and explain code has been outstanding. They can only, however, translate instructions into code and verify it for mistakes to finish basic programming jobs.

Key aspects of language models

1. Natural Language Processing (NLP)

Language models use NLP approaches to analyze human language and extract meaningful components from words, phrases, and paragraphs.

2. Training

The models are trained on large datasets of text sources such as books, webpages, papers, and more. Training helps them anticipate the next word in a phrase and write like humans by teaching grammar, context, and word connections.

3. Deep Learning

Most recent language models, such as GPT and BERT, rely on deep learning, particularly transformer topologies, to effectively interpret language patterns. Transformers are effective at addressing long-term text dependencies.

4. Applications

It may be used for different activities, such as:

Text creation: Writing tales, poetry, and essays.

Translation: Language translation.

Natural discussion with chatbots.

Summarization: Shortening lengthy articles.

Answering questions with knowledge.

5. Examples

Famous language models include:

GPT-3 by OpenAI generates human-like writing and powers AI apps.

BERT by Google: Useful for search and sentiment analysis, optimized for linguistic context.

The Text-to-Text Transfer Transformer (T5) treats all NLP issues as text production problems and is used for many purposes.

It can allow robots to read, write, and speak like humans.

The Future of language models

Historically, AI business applications concentrated on predictive activities including forecasting, fraud detection, click-through rates, conversions, and low-skill job automation. These restricted uses took great effort to execute and interpret, and were only practical at large scale. However, massive language models altered this.

Large language models like GPT-3 and generative models like Midjouney and DALL-E are transforming the sector, and AI will likely touch practically every part of business in the next years.

Top language model trends are listed below.

Scale and intricacy: The quantity of data and parameters learned on language models will certainly scale.

Multimodality: Integration of language models with visuals, video, and music is intended to enhance their worldview and allow new applications.

Explaining and showing: With more AI in decision-making, ML models must be explainable and transparent. Researchers are trying to make language models more understandable and explain their predictions.

Conversation: It will be utilized increasingly in chatbots, virtual assistants, and customer service to interpret and react to user inputs more naturally.

Language models are projected to improve and be utilized in more applications across fields.

Read more on govindhtech.com

#MasteringLanguageModels #AIConversation #BuildingBlock #machinelearning #voicetext #DALE #naturallanguageprocessing #NLP #OpenAI #ai #Keyaspectslanguagemodels #deeplearning #languagemodels #Applications #technology #technews #news #govindhtech

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marketingprofitmedia · 2 months ago

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tamanna-sarker · 8 months ago

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patient-education · 9 months ago

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Revolutionizing Patient Education with “X-Plain Health AI”

Imagine a world where every patient's educational journey is uniquely tailored to their health needs, language, and learning style. This is not a distant dream but a present reality, thanks to the groundbreaking advancements in AI with “X-Plain Health AI,” a custom GPT app redefining the landscape of patient education. How is this innovative technology transforming how we understand and interact with healthcare information? Let's explore the future, which is already here.

What are Custom GPTs? Custom GPTs are a specialized iteration of the Generative Pre-trained Transformer models designed specifically for targeted applications. Unlike general-purpose models like ChatGPT, custom GPTs are fine-tuned with specific datasets, tailored instructions, and integrated functionalities to cater to particular needs. This specialization enables them to produce outputs that are highly adaptive and aligned with specific objectives, making them invaluable in fields like patient education.

Traditional vs. AI-based Patient Education

In traditional settings, patient education relies on printed materials, videos, and digital resources, which often fall short in addressing the diverse needs of patients. These methods, while informative, lack the personalization necessary for effective healthcare communication. Custom GPTs revolutionize this domain by offering tailored, dynamic, and interactive educational content. This AI-driven approach aligns with individual health conditions, literacy levels, and cultural backgrounds, enhancing patient understanding, engagement, and adherence to medical guidance.

Consider the case of Laila, a 52-year-old woman recently diagnosed with type 2 diabetes. She's overwhelmed with the flood of generic information available online. That's where 'X-Plain Health AI' comes in. Unlike traditional GPT models like ChatGPT, our custom GPT is fine-tuned with vast medical data. When Laila uses X-Plain Health AI, she receives personalized information. The system adjusts the complexity of the language to match her understanding and even considers her cultural background. This means Laila gets tailored advice on managing her diabetes, dietary recommendations suited to her lifestyle, and explanations of medical terms in a way she can easily comprehend. It's like having a virtual health educator, always ready to provide information that’s specifically relevant to her, rather than a one-size-fits-all approach.

X-Plain Health AI

Custom GPTs for patient education can generate detailed, specific information about a patient's condition and treatment options, adjust the complexity of the language to suit the patient's literacy level, facilitate interactive learning sessions for heightened engagement, and offer materials in multiple languages, considering cultural nuances.

Released on February 5, 2024, X-Plain Health AI is a pioneering custom GPT model developed by the Patient Education Institute. Accessible through the ChatGPT Store, it promises unprecedented personalization in patient education. What does “X-Plain Health AI” offer beyond what ChatGPT can do? Some key features include:

Prompting users to set their preferences for language, content depth, difficulty level, and other parameters.

Ask users to select a health topic, then offer targeted questions to refine information to specific interests like causes, symptoms, alternative treatments, risks, coping strategies, support options, and more.

Adjusting the complexity of the text according to the user’s preferences and previous interactions.

Engage with users to explain complex terms, present statistics, or compare treatment options tailored to their specific requests.

Asking questions to verify understanding.

Using our extensive database of peer-reviewed, up-to-date content, which includes over 25 million words, a video library with over 2,500 videos, and 2,500 interactive quizzes for comprehension testing, ensuring that users receive up-to-date information in their preferred educational format.

Upon the user’s request, display one of our +10,000 illustrations and +100,000 slides to show diagrams and anatomical and procedural illustrations (in progress).

Here is a scenario illustrating how X-Plain Health AI interacts with a user: John, a 40-year-old man, logs into X-Plain Health AI.

— X-Plain Health AI: "Hello! How may I assist you today? Do you have any specific health topics or questions in mind? Also, would you like me to address you by a name?”

— John: “You can call me John. I want to learn about managing high blood pressure.”

— X-Plain Health AI: “Welcome, John! To provide you with personalized information about managing high blood pressure, I have a few questions for you. First, what is your preferred language for communication?"

— John: "English, please."

— X-Plain Health AI: "Great, English it is. How would you rate your understanding of high blood pressure management on a scale from 1 to 5?"

— John: "Probably around a 2."

— X-Plain Health AI: "Understood, we'll keep things straightforward. Are you seeking information on causes, symptoms, complications, diet, exercise, medication, or all of these topics?"

— John: "Mostly diet and exercise."

— X-Plain Health AI: "Okay. I can provide dietary guidelines and exercise tips that effectively manage high blood pressure. Before we proceed, do you have any dietary restrictions or physical limitations I should be aware of?"

— John: "I'm a vegetarian and have a knee injury, so nothing too strenuous."

— X-Plain Health AI: "Thanks for sharing that, John. I'll tailor the information accordingly. [Provides tailored advice on a vegetarian diet and low-impact exercises suitable for someone with a knee injury]. Would you like me to ask a few questions to ensure you've understood the information clearly?"

— John: "Yes, that would be helpful."

— X-Plain Health AI: "Alright, let's start with a simple one: Can you name two vegetables that are particularly beneficial for managing high blood pressure?"

— John: [Responds with his answers]

— X-Plain Health AI: "That's correct! [Provides feedback on his answers]. Let’s try another question to see how you’re doing with the exercise recommendations."

Challenges and Considerations

The deployment of custom GPTs in patient education presents challenges, particularly in privacy and information accuracy. Ensuring compliance with healthcare privacy laws, such as HIPAA, and maintaining the accuracy of the information provided by these AI models are of utmost importance. It's crucial to continually remind users that AI-generated content should be seen as supplementary to professional medical advice, not a replacement.

In OpenAI's custom GPT environment, users have control over their data. If they permit ChatGPT to learn from their interactions, the chats with custom GPTs remain private and are not shared with the creators. A key challenge is ensuring GPT models adhere to healthcare privacy laws like HIPAA. ChatGPT and custom GPT models consistently remind users not to share any personally identifiable information during conversations. Users also have the option to adjust their settings to prevent ChatGPT from retaining their chat history. However, these settings may not be straightforward and could be inadvertently altered during updates or when opting into new services. In addition, the field is rapidly developing, and so are OpenAI’s privacy and data training policies.

Two years following the launch of X-Plain in 1995, we established a clear distinction between Clinical X-Plain and Public X-Plain. Clinical X-Plain serves a crucial role in clinical environments, offering informed consent, discharge instructions, and specific guidance on medications and home care directly to patients. In contrast, Public X-Plain is a patient education resource available on healthcare institutions' websites, assisting patients in learning about and preparing for clinical visits. In this context, X-Plain Health AI is envisioned as a public-facing tool accessible online to aid patients in understanding and preparing for healthcare encounters. It is distinct from the discharge documents that healthcare providers typically hand to patients following outpatient, inpatient, or emergency room visits.

Monetization

The introduction of the GPT Store by OpenAI marks a significant shift in the landscape of AI application development. Like an app store for AI technologies, this platform allows creators to publish and potentially monetize their GPT models. Although still in its infancy, the revenue-sharing model indicates a promising avenue for creators to benefit financially from their innovative AI solutions in healthcare.

What do we know so far? OpenAI covers the cost of tokens for GPTs in the OpenAI GPT Store. This means neither the user nor the creator of a custom GPT bears the token cost. However, access to the GPT Store and its features is limited to ChatGPT Plus Subscribers and OpenAI Enterprise customers.

The monetization model for OpenAI's GPT Store is still unspecified and vague. OpenAI states that it will involve revenue sharing with creators, where creators are paid based on the usage and utility of their custom GPTs. Initially, the revenue model may start with a straightforward revenue share, the specifics of which are yet to be detailed. Later, there might be options for subscription-based access to individual GPTs, depending on demand.

These monetization policies are dynamic and tentative; for the most current monetization models, check OpenAI's official website.

Our patient education solutions are exclusively licensed to healthcare institutions and stakeholders, not directly to patients. However, with OpenAI's proposed model for monetization, we are poised to indirectly offer X-Plain Health AI to patients and individual users for the first time, a departure from our traditional approach of licensing only to healthcare service providers. Given the importance of ensuring accuracy and privacy, we will maintain X-Plain Health AI as a public patient education tool rather than a clinical one until these critical factors are fully guaranteed.

The Future

Looking ahead, the potential of AI in patient education is immense. Future developments in custom GPTs are expected to include a deeper understanding of language nuances, more sophisticated bias mitigation techniques, and enhanced integration with other applications.

Regarding its integration with other applications, future custom GPT models for patient education will offer capabilities such as:

Seamlessly combining several aspects of patient care, including treatment planning, patient education, and follow-up care.

Dynamically adapting educational content in real-time based on the user's preferences, interaction style, and updates in their health records.

Utilize predictive analytics to identify potential health risks and deliver tailored educational content to patients accordingly.

Enhancing patient engagement and understanding using augmented reality in educational materials.

Final Notes

As custom AI models like X-Plain Health AI evolve, it’s crucial to steer their integration and governance in patient engagement proactively. To ensure these advancements serve the best interests of all involved, consider the following:

Healthcare thought leaders should commit to ongoing research and develop oversight processes. Regular evaluations of custom GPTs for accuracy, privacy protection, and clinical effectiveness are essential. This continuous monitoring facilitates responsible innovation that truly benefits patients.

Policymakers must act promptly to establish data governance laws specifically for healthcare AI. These regulations should balance patient rights protection with the encouragement of empowering innovations.

Patients should be encouraged to learn about and use AI-powered educational tools. They need to recognize that, while valuable, these tools are not replacements for professional medical advice.

By fostering frameworks that promote accountability in tandem with technological advancement, we can revolutionize how individuals learn about and manage their health.

As we stand on the brink of a new healthcare age, X-Plain Health AI represents a pivotal shift from one-size-fits-all to a world where patient education is as adapted as a tailored suit.

#AI-in-Patient-Education #Custom-GPT-for-Healthcare #X-Plain-Health-AI #Interactive-Patient-Education #Patient-Engagement #Patient-Empowerment #Patient-Education-Chatbots #Adaptive-Learning

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foresight-opinion-trading · 10 months ago

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OpenAI Gears Up to Launch the GPT Store: A New Frontier for AI Creations

Introduction After a period of anticipation and delays, OpenAI is on the brink of launching its much-awaited GPT Store. This innovative platform is set to revolutionize the way AI agents, based on OpenAI's robust large language models, are shared and monetized. https://aieventx.com/openais-devday-reveal-custom-chatgpts-and-the-new-frontier-in-ai-interactivity/ What is the GPT Store? The GPT Store, a unique marketplace for AI creations, will enable users to sell and share their customized AI agents, crafted using OpenAI's GPT-4 large language model. This development marks a significant step in democratizing AI technology, allowing more individuals to contribute to and benefit from AI advancements. Official Launch and Builder Engagement OpenAI has officially announced the launch date for next week, exciting those registered as GPT Builders. In preparation, an email was sent to these builders, urging them to align their AI creations with brand guidelines and to set their GPTs to public mode.

The Journey to Launch The concept of the GPT Store was first introduced at OpenAI's November developers conference, promising a platform for ChatGPT Plus and enterprise subscribers to develop and share ChatGPT-style chatbots. These custom bots could range from entertaining, like explaining Gen Z memes, to practical uses like negotiation guidance. The store aims to extend beyond OpenAI's current offering, where custom GPTs are accessible via the explore tab of ChatGPT Plus, by enabling user-generated content and potential revenue streams for creators. Monetization and Creator Compensation One of the most intriguing aspects of the GPT Store is the proposed monetization model. OpenAI plans to implement a system where creators can earn based on the usage of their AI agents in the store. Details of this compensation model are yet to be disclosed, but it signifies a groundbreaking approach to rewarding AI innovation. A Rocky Road to Launch The path to the store's launch hasn't been smooth. Initially slated for a November release, the launch faced delays due to unforeseen circumstances, including the brief firing and rehiring of CEO Sam Altman. The postponement stretched into December, with further delays leading to the current launch date. Conclusion The upcoming GPT Store is set to open new horizons for AI enthusiasts and creators. It represents a leap towards a more inclusive and participative AI ecosystem, where creativity meets technology. As we await its official launch, the potential of the GPT Store in shaping the future of AI engagement and commerce remains a highly anticipated development. Read the full article

#ChatGPT #GPT4 #GPTStore #OpenAI

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thewirely · 11 months ago

Link

OpenAI has announced a delay in the launch of its AI app store, the GPT Store, which was originally scheduled for release this year. The new expected launch date is set for early 2024, with the delay attributed to the leadership changes that transpired in November, shortly after the initial announcement. This development was reported by Axios, which obtained a memo addressed to users and developers. The memo explained that the GPT Store's launch has been rescheduled for early next year due to unexpected factors that have kept the company occupied. Additionally, it outlined forthcoming enhancements to the feature, including an improved configuration interface and debug messages. Efforts have been made to seek further information from OpenAI and related parties, and any responses received will be provided in an update. The original announcement of the store's launch in the current month had raised questions during OpenAI's Dev Day conference in November. While a functional mockup of the store and several fine-tuned models called GPTs were available for examination, numerous details remained unclarified. During a Q&A session with CEO Sam Altman and CTO Mira Murati, who had briefly stepped down from their roles, inquiries were made regarding OpenAI's plans for customer charges and developer compensation within the store. Their response indicated a degree of uncertainty, suggesting that decisions would be made as the project progressed. Given the flexibility of the initial launch plan and the disruption caused by the internal leadership changes, it is not surprising that the delay has occurred. With the onset of the winter holidays and the need to navigate a new board and other priorities, the idea of launching a major product under pressure was likely considered impractical. For existing OpenAI customers, the creation and sharing of GPTs among themselves remains possible. However, these models will not be publicly listed or eligible for participation in any revenue-sharing initiatives (if OpenAI decides to implement one) until the official store launch takes place.

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toptipsai · 1 year ago

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How to build my own custom ChatGPT with OpenAI's GPT 4

Building your own custom chatbot using OpenAI's GPT-4 involves several key steps: understanding the GPT-4 model, setting up a development environment, integrating the OpenAI API, designing conversation flows, and finally testing and deploying your chatbot. Below is a detailed guide on how to accomplish this: 1. Understanding GPT-4 - What is GPT-4: GPT-4 is an advanced language model developed by OpenAI, capable of understanding and generating human-like text. It can answer questions, write essays, summarize texts, translate languages, and even generate code. - API Access: To use GPT-4, you need access to the OpenAI API. This requires registering for an API key on the OpenAI website. 2. Setting Up Your Development Environment - Choose a Programming Language: Python is widely used for this purpose due to its simplicity and the powerful libraries available for AI and machine learning. - Install Required Libraries: You will need to install the openai library, which can be done using pip: pip install openai 3. Integrating OpenAI API - API Key Configuration: Store your API key in a secure manner, ideally as an environment variable or a configuration file. import openai openai.api_key = 'your-api-key' - Basic API Call: Start by making a basic call to the API to ensure everything is set up correctly. response = openai.Completion.create(engine="text-davinci-004", prompt="Hello, world!", max_tokens=5) print(response.choices.text.strip()) https://www.youtube.com/watch?v=5--JexprHuk&ab_channel=AppOfTheDay-SkillLeapAI 4. Designing Conversation Flows - Define the Bot’s Purpose: Clearly define what you want your chatbot to do. This could range from customer service to providing information about a specific topic. - Scripting Dialogues: Design a script of possible dialogues and how the chatbot should respond. While GPT-4 is good at generating responses, guiding its context helps in maintaining relevant and accurate conversations. - Overview: Introduction to the importance of conversation flows in chatbot design, emphasizing how they impact user experience. - Objective: Outline the goal to provide practical insights and examples for creating effective conversation flows. Understanding Conversation Flows - Definition and Importance: Explain what conversation flows are and why they are crucial for chatbot effectiveness. - Basic Principles: Discuss the principles of good conversation design, such as clarity, brevity, and user-centricity. Setting Up Your Environment - Tools and Languages: Suggest tools and programming languages (e.g., Python) suited for designing chatbots. - Installation of Key Libraries: Guide on installing necessary libraries for chatbot development, such as openai. Designing Basic Conversation Flows - Start Simple: Show how to design a basic conversation flow with greetings and common user queries. - Code Example: Provide a simple Python script using OpenAI’s GPT to handle greetings. import openai def basic_chatbot_response(user_input): openai.api_key = 'your-api-key' start_sequence = "nAI:" restart_sequence = "nHuman: " response = openai.Completion.create( engine="text-davinci-004", prompt=f"Human: {user_input}" + start_sequence, temperature=0.7, max_tokens=150, top_p=1, frequency_penalty=0, presence_penalty=0, stop= ) return response.choices.text.strip() print(basic_chatbot_response("Hello!")) https://www.youtube.com/watch?v=ZAD7k2kSRjU&ab_channel=TheAIAdvantage Advanced Conversation Flows - Handling Complex Queries: Discuss strategies for managing more complex user interactions, such as multi-turn conversations. - Context Management: Explore how to maintain context in a conversation for coherent responses. - Personalization: Tips on personalizing responses based on user data (with user consent). - Code Snippet: A more advanced example showing context management. chat_history = '' def advanced_chatbot_response(user_input, chat_history): prompt = f"{chat_history}nHuman: {user_input}nAI:" response = openai.Completion.create( engine="text-davinci-004", prompt=prompt, max_tokens=150 ) chat_history += f"nHuman: {user_input}nAI: {response.choices.text.strip()}" return response.choices.text.strip(), chat_history user_input = "What's the weather like today?" response, chat_history = advanced_chatbot_response(user_input, chat_history) print(response) 5. Advanced Features and Customization - Context Management: For a more coherent conversation, maintain a context or a chat history. This ensures the bot remembers previous parts of the conversation. - Personalization: You can customize responses based on user data (with user consent) to make interactions more personalized and engaging. Creating an advanced and customized user experience in chatbot design involves leveraging sophisticated features and tailoring the chatbot to specific user needs. - Overview: Discuss the significance of advanced features and customization in creating a more engaging and efficient chatbot. - Objective: Set the goal to offer practical guidance on implementing advanced functionalities in chatbot design. Understanding Advanced Features - Defining Advanced Features: Explain what constitutes advanced features in the context of chatbots, like context management, personalization, and natural language understanding. - Importance: Highlight how these features can significantly improve user experience and chatbot effectiveness. https://www.youtube.com/watch?v=1wPbZaQtSkA&ab_channel=AIFoundations Setting Up Your Development Environment - Tools and Languages: Suggest advanced tools and programming languages, focusing on Python due to its extensive support for AI and machine learning. - Library Installation: Guide on installing necessary libraries, such as TensorFlow or PyTorch for more complex AI functionalities. # Install TensorFlow pip install tensorflow Implementing Context Management - Maintaining Conversation Context: Discuss the importance of context in providing relevant and coherent responses. - Code Example: Demonstrate how to maintain context in a conversation using a Python script. import openai # Function to maintain context def respond_with_context(user_input, chat_history): openai.api_key = 'your-api-key' response = openai.Completion.create( engine="text-davinci-004", prompt=f"{chat_history}nUser: {user_input}nAI:", max_tokens=150 ) chat_history += f"nUser: {user_input}nAI: {response.choices.text.strip()}" return response.choices.text.strip(), chat_history # Example usage chat_history = "" user_input = "Tell me more about AI." response, chat_history = respond_with_context(user_input, chat_history) print(response) Personalization Techniques - User Data Utilization: Explain how to use user data to personalize conversations. Discuss obtaining consent for data use. - Adaptive Responses: Showcase techniques for adapting responses based on user preferences or past interactions. - Code Snippet: Example of generating personalized greetings or recommendations. def personalized_greeting(user_name, time_of_day): return f"Good {time_of_day}, {user_name}! How can I assist you today?" print(personalized_greeting("Alex", "morning")) Leveraging Natural Language Understanding (NLU) - Advanced NLU: Delve into advanced natural language understanding for more accurate intent recognition and response generation. - Integrating NLU Tools: Guide on integrating external NLU services or libraries. - Example: Demonstrate a simple intent recognition using TensorFlow or a similar library. Testing and Refining Custom Features - Iterative Testing: Emphasize the need for thorough testing of advanced features to ensure they function as intended. - User Feedback: Discuss incorporating user feedback to refine these features. - Performance Analysis: Guide on using analytics to evaluate the effectiveness of the customizations. Best Practices for Advanced Chatbot Design - Scalability: Tips on designing chatbots that can scale with increasing user interactions. - Security and Privacy: Reinforce best practices for maintaining user data security and privacy. - Continuous Improvement: Strategies for keeping the chatbot updated with the latest advancements in AI and user experience trends. This article serves as a roadmap for developers and chatbot enthusiasts to enhance their chatbot's capabilities through advanced features and customization. The provided code snippets and insights pave the way for creating more engaging, personalized, and effective chatbot interactions, crucial for a superior user experience. 6. Testing and Debugging - Thorough Testing: Test your chatbot extensively with different scenarios to ensure it responds as expected. - Debugging: Monitor the responses for any inaccuracies or nonsensical replies and adjust your setup accordingly. 7. Deployment - Choose a Platform: Decide where you want to deploy your chatbot (website, app, social media platforms). - Integration: Use relevant APIs or webhooks to integrate your chatbot with the chosen platform. 8. Monitoring and Maintenance - Performance Monitoring: Regularly check how the chatbot is performing and how users are interacting with it. - Updates and Improvements: Continuously update the conversation script and improve the bot based on user feedback. 9. Ethical Considerations - Data Privacy: Ensure that your chatbot adheres to data protection laws and user privacy standards. - Content Filtering: Implement filters to prevent the generation of inappropriate or harmful content. Conclusion Building a custom chatbot with OpenAI's GPT-4 can be a rewarding project, offering vast possibilities in terms of conversation quality and user engagement. While the technical setup is important, equally crucial is the continuous improvement based on user feedback and ethical considerations in deployment and data handling. Read the full article

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tombeane-blog · 1 year ago

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Same Planet, Different Worlds

I know too much and most of it is wrong. My grandsons know too little and much of it is questionable.

But by sheer volume of mental stuff - I win.

My doctor and my physical therapist are maybe two generations younger than me.

I keep saying to both, "Hey doc, my back still hurts after all this modern medical technology and exercise. Don't you know someone in Tijuana that knows somebody that knows some other guy who could smuggle a couple of magic pills across the border that would fix all this?". "Wink wink nudge nudge - not those Fentanyl thngies"

And all l keep getting from them is the same spiel - "You're back pain is all muscle weakness Tom. You're just gonna have to work harder and work harder on all three major muscle groups - especially the core."

"Doc, I'm way past all that. I only have two major muscle groups."

"What's that?"

"Those that hurt and those that don't hurt."

=========================================

I had an argument recently with one of my grandsons who believes that just because he is 60 years younger, and, better looking than me, his brain still works and mine doesn't.

It got heated and loud - even involving a few creative variations of the F word.

Afterwards, thinking maybe it's a generational thing and maybe, just maybe, my brain is mis-rememberating, I logged onto the World Wide Google and asked Chatty Kathy / GPT to explain it to me.

Before the research, I was convinced that I was 100% right and he was just wrong.

I hate to admit it, but it turns out that the exact opposite is true and that, according to the GPT, He was wrong and I was right.

=========================================

I recently saw an article on the Internet that pointed out that I have been washing my armpits wrong. 80 frigging years gone by and now they're telling me! Schmucks!

So first I'd like to apologize to all of my friends, family and co-workers who have had to put up with the odoriferous result of my lack of proper armpit hygeination.

Now I'm wondering if I should pass this wisdom along to my teen age grandsons so they don't spend the rest of their lives in an unhygienic cesspool wondering why nobody stands close to them.

But I hesitate to do so because for some reason, every time I try to tell them how to live their lives, they ignore me - or worse.

I can envision the short bitter conversation already. (I've paraphrased it for clarity.)

"Hey (names redacted), I saw on the WWW that you are washing your armpits wrong. How about I teach you how to do it properly?"

"How about you stay out of my life, you old fool."

"Besides, old-timer, don't you know you can't believe everything you read or see on the Internet?"

"So here Pops, I got some Spanish advice for you - largate estupido pendejo!"

"Anyway Pa, I'll believe it's true when I see it on the Instagram."

"That's MISTER pendejo to you! And, I got your German lesson right here - Teletubbyzurückwinker!"

=========================================

I recently found out that one of my 15 year old grandsons is exactly the same weight as me.

I can't help wondering, "So how come he looks like that and I look like this?"

=========================================

For the past 30 years or so I've been invisible to anyone under the age of 30.

So now every time I go into a store staffed by youngsters, I take souvenirs?.

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chatgtps-blog · 1 year ago

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What is ChatGPT?

ChatGPT is an easy-to-use AI tool that enables users to ask questions about anything from physics to computer code. The free program resembles a search engine and allows users to type in questions or commands in the prompt bar, which runs across the bottom of the app. ChatGPT answers instantly, and users can continue the conversation by typing into the prompt bar again.

In addition to its ability to explain words and code, the tool can also help students learn new topics by explaining concepts and answering questions. As this capability becomes more refined, it could change the way students study a subject. Educators could also use the app to create virtual tutors that would provide customized, interactive lessons for students.

The software is currently available in the iOS and Android app stores, but is also accessible on the web through a browser. Users can choose which device they prefer to use and sync their conversations and preferences, so that ChatGPT will be able to respond to them appropriately.

Its use is gaining popularity in the Chatgtp education sector, where it can be used as an academic assistant to answer student questions and offer feedback on assignments. Teachers can also use it to teach students how to research topics and conduct experiments in the classroom. The app is especially useful for students in STEM subjects, where it can help them understand complex ideas and formulas.

For example, if a student is struggling with physics, the bot can provide explanations and step-by-step instructions on how to solve problems. The app can also help students develop a deeper understanding of the subject by connecting it to real-world applications, including how to apply scientific theories to everyday situations.

While it's tempting to think that a program like ChatGPT can eventually replace Google's answer engine, it may take a long time before this will be the case. Many experts believe that machine learning will be used to complement human expertise, rather than replace it. Nonetheless, the development of this technology is making it possible to achieve this goal sooner than expected.

OpenAI's ChatGPT is a language model that is designed to mimic the structure of natural language. It is an unsupervised learning model, which means that it doesn't require a lot of data to train. Instead, it can start with a piece of text and then generate text that is similar to it. This model is called GPT and stands for Generative Pre-trained Transformer.

One of the main advantages of this model is that it can learn a lot from small amounts of input, which makes it more practical than other AI languages. In fact, the training process for this model was completed on a supercomputer with just a few thousand lines of text. In comparison, some other models need millions of lines of text to be trained. This makes it a promising candidate for use in chatbots and other software applications. It can be used to answer questions and give advice, or it can be programmed to perform certain tasks, such as writing emails or drafting essays.

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