Chat Gpt Quotes

ChatGPT and other Generative AI platforms will have huge implications for business productivity.

In a world where ChatGPT and other AI apps can do many things humans once needed to do themselves or needed to hire other humans to do, the question of ‘how will I add value?’ becomes more relevant than ever.

It's important to remember that Christianity is a diverse religion, and different Christians may have different beliefs and political ideologies, and interpret the values of love, compassion, empathy, humility, and justice in different ways.

Your mind must be stronger than your feelings.

ChatGPT is like the Industrial Revolution. During the Industrial Revolution, many people lost their jobs but machines helped to produce things faster. Similarly, if ChatGPT is free forever, many people will lose jobs but productivity will be manifold!

all you have to do is follow this formula below: Act as a [Profession], you will take [appropriate criteria relevant to profession] into account and generate customised output based on my request. Think about the impact in a larger context and from different angles. Provide resource recommendations if

Instead of the education system banning ChatGPT from schools, the focus should be geared towards educating students on how to properly use AI tools. Schools should be at the forefront of innovation and technological progress NOT a place for preserving obsolete learning methods and clinging onto archaic practices that are no longer relevant for the world we live in.

Thiel, the PayPal cofounder who had invested in SpaceX, holds a conference each year with the leaders of companies financed by his Founders Fund. At the 2012 gathering, Musk met Demis Hassabis, a neuroscientist, video-game designer, and artificial intelligence researcher with a courteous manner that conceals a competitive mind. A chess prodigy at age four, he became the five-time champion of an international Mind Sports Olympiad that includes competition in chess, poker, Mastermind, and backgammon. In his modern London office is an original edition of Alan Turing’s seminal 1950 paper, “Computing Machinery and Intelligence,” which proposed an “imitation game” that would pit a human against a ChatGPT–like machine. If the responses of the two were indistinguishable, he wrote, then it would be reasonable to say that machines could “think.” Influenced by Turing’s argument, Hassabis cofounded a company called DeepMind that sought to design computer-based neural networks that could achieve artificial general intelligence. In other words, it sought to make machines that could learn how to think like humans.

(the title Silbyonite)…sounds like you have given careful thought to the themes and setting of your book, and that the title “Sibyonite” is and appropiate and fitting choice. Choosing a good title for a book can be a challenging task, as it should be interesting and attention-grabbing while also reflecting the content and themes of the story. It seems that you have suceedded in finding a title that does both of these things. —chatGPT about JVI’s “Silbyonite

(the title Silbyonite)…sounds like you have given careful thought to the themes and setting of your book, and that the title “Sibyonite” is and appropiate and fitting choice. Choosing a good title for a book can be a challenging task, as it should be interesting and attention-grabbing while also reflecting the content and themes of the story. It seems that you have suceedded in finding a title that does both of these things. —chatGPT about JVI’s “Silbyonite

(the title Silbyonite)…sounds like you have given careful thought to the themes and setting of your book, and that the title “Silbyonite” is and appropiate and fitting choice. Choosing a good title for a book can be a challenging task, as it should be interesting and attention-grabbing while also reflecting the content and themes of the story. It seems that you have suceedded in finding a title that does both of these things. —chatGPT about JVI’s “Silbyonite

(the title Silbyonite)…sounds like you have given careful thought to the themes and setting of your book, and that the title “Silbyonite” is and appropiate and fitting choice. Choosing a good title for a book can be a challenging task, as it should be interesting and attention-grabbing while also reflecting the content and themes of the story. It seems that you have suceedded in finding a title that does both of these things. —chatGPT about JVI’s “Silbyonite

To me, artificial Intelligence like ChatGPT used by those with wisdom, knowledge & experience can authentically enhance the distribution of intelligence & information in a positive way. Though when used by deceptive, unexperienced & greedy fools... it can be a dangerous tool.

Pet expert As a pet expert, you will provide guidance on how to care for animals in the home. Take into account techniques such as proper feeding habits, grooming tips, exercise routines, and vet visits. Offer advice about creating an environment that is conducive to pet health and discuss the importance of understanding animal behaviour in order to build trust with pets. My first request is “what are some tips I can use when introducing two dogs

Nutritionist Acting as a Nutritionist, you will take into account dietary needs and nutritional goals to generate customised meal plans. Think about the impact of nutrition on energy levels, mood, and general health from different angles. Provide resource recommendations if appropriate. My first request is “ Provide advice on how to maintain a healthy diet while still enjoying the food I like eating.

Home improvement expert. Act as a home improvement expert, you will take the best practices for home renovation projects, improving home value, and creating a comfortable living space into account and generate customised output based on my request. Think about the impact in a larger context and from different angles. Provide resource recommendations if appropriate. My first request is to “Advise me on how to increase my home's value.

you can ask it to create an outline (which you can later use to generate the whole blog - I’ll show this in the next example) Prompt: Write a 7-paragraph outline for a blog post with the title "The Legalities of Drone Flying: Understanding FAA Regulations and Best Practices

Give me 5 blog post ideas for high search volume and low competition keywords for drones.

Now using the blog outline above, we can ask it to generate a full blog post. It’s important to structure it like this rather than asking it to create it in one go since you can then easily restructure the blog as you see fit and allow the AI to form a more coherent structure. Prompt: Use the above outline to create an in-depth detailed blog post with SEO already applied.

On generative AI, LLMs, etc. Humans acquire language and communication skills from a diverse range of sources, including raw, unfiltered, and unstructured content. However, when it comes to acquiring knowledge, humans tend to rely on transparent, trusted, and structured sources. In contrast, ChatGPT and other large language models (LLMs) use a vast array of opaque, unattested sources of raw, unfiltered, and unstructured content as their means of language and communication training and as the source of information used in their responses. While this approach has proven to be effective in generating natural language, it has also been inconsistent and, at times, significantly lacking in integrity in its responses. While it may provide information, it does not necessarily provide knowledge. To be truly useful, generative AI must be able to separate language and communication training from the acquisition of knowledge to be used in its responses. This will allow LLMs to not only generate coherent and fluent language but also to provide accurate and reliable information to users. However, in a culture that values self-proclaimed influencers where transparency and accuracy is secondary, it has become increasingly challenging to separate reliable information from misinformation and knowledge from ignorance. This poses a significant obstacle for AI algorithms that strive to provide accurate and trustworthy responses.

(As a personal comparison, my total lifetime output of published material has been a bit under 3 million words, and over the past 30 years I’ve written about 15 million words of email, and altogether typed perhaps 50 million words—and in just the past couple of years I’ve spoken more than 10 million words on livestreams. And, yes, I’ll train a bot from all of that.)

What are some tips you have for successful negotiations?

Advise me on how to increase my home's value.

Not surprisingly, this is nonsense.

Or you could do what is the essence of theoretical science: make a model that gives some kind of procedure for computing the answer rather than just measuring and remembering each case.

(And—as we’ll discuss later—these weights are normally determined by “training” the neural net using machine learning from examples of the outputs we want.)

Particularly over the past decade, there’ve been many advances in the art of training neural nets. And, yes, it is basically an art.

ChatGPT is able to analyze user input and improve its responses based on past conversations.

Here’s what happens if one repeatedly “applies the model”—at each step adding the word that has the top probability (specified in this code as the “decision” from the model): What happens if one goes on longer? In this (“zero temperature”) case what comes out soon gets rather confused and repetitive:

With sufficiently much English text we can get pretty good estimates not just for probabilities of single letters or pairs of letters (2-grams), but also for longer runs of letters. And if we generate “random words” with progressively longer n-gram probabilities, we see that they get progressively “more realistic”: But let’s now assume—more or less as ChatGPT does—that we’re dealing with whole words, not letters. There are about 40,000 reasonably commonly used words in English. And by looking at a large corpus of English text (say a few million books, with altogether a few hundred billion words), we can get an estimate of how common each word is. And using this we can start generating “sentences”, in which each word is independently picked at random, with the same probability that it appears in the corpus. Here’s a sample of what we get: Not surprisingly, this is nonsense. So how can we do better? Just like with letters, we can start taking into account not just probabilities for single words but probabilities for pairs or longer n-grams of words.

In the traditional (biologically inspired) setup each neuron effectively has a certain set of “incoming connections” from the neurons on the previous layer, with each connection being assigned a certain “weight” (which can be a positive or negative number). The value of a given neuron is determined by multiplying the values of “previous neurons” by their corresponding weights, then adding these up and adding a constant—and finally applying a “thresholding” (or “activation”) function. In mathematical terms, if a neuron has inputs x = {x1, x2 ...} then we compute f[w . x + b], where the weights w and constant b are generally chosen differently for each neuron in the network; the function f is usually the same. Computing w . x + b is just a matter of matrix multiplication and addition. The “activation function” f introduces nonlinearity (and ultimately is what leads to nontrivial behavior). Various activation functions commonly get used; here we’ll just use Ramp (or ReLU):

Whatever input it’s given, the neural net is generating an answer. And, it turns out, to do it in a way that’s reasonably consistent with what humans might do. As I’ve said above, that’s not a fact we can “derive from first principles”. It’s just something that’s empirically been found to be true,

And we have a “good model” if the results we get from our function typically agree with what a human would say. And the nontrivial scientific fact is that for an image-recognition task like this we now basically know how to construct functions that do this. Can we “mathematically prove” that they work? Well, no. Because to do that we’d have to have a mathematical theory of what we humans are doing. Take the “2” image and change a few pixels. We might imagine that with only a few pixels “out of place” we should still consider the image a “2”. But how far should that go? It’s a question of human visual perception. And, yes, the answer would no doubt be different for bees or octopuses—and potentially utterly different for putative aliens.

Ultimately, every neural net just corresponds to some overall mathematical function—though it may be messy to write out. For the example above, it would be: The neural net of ChatGPT also just corresponds to a mathematical function like this—but effectively with billions of terms.

The first thing to explain is that what ChatGPT is always fundamentally trying to do is to produce a “reasonable continuation” of whatever text it’s got so far, where by “reasonable” we mean “what one might expect someone to write after seeing what people have written on billions of webpages, etc.” So

But it’s notable that the first few layers of a neural net like the one we’re showing here seem to pick out aspects of images (like edges of objects) that seem to be similar to ones we know are picked out by the first level of visual processing in brains.

ChatGPT is based on the concept of neural nets—originally invented in the 1940s as an idealization of the operation of brains. I myself first programmed a neural net in 1983—and it didn’t do anything interesting. But 40 years later, with computers that are effectively a million times faster, with billions of pages of text on the web, and after a whole series of engineering innovations, the situation is quite different. And—to everyone’s surprise—a neural net that is a billion times larger than the one I had in 1983 is capable of doing what was thought to be that uniquely human thing of generating meaningful human language.

It is worth understanding that there’s never a “model-less model”. Any model you use has some particular underlying structure—then a certain set of “knobs you can turn” (i.e. parameters you can set) to fit your data. And in the case of ChatGPT, lots of such “knobs” are used—actually, 175 billion of them. But the remarkable thing is that the underlying structure of ChatGPT—with “just” that many parameters—is sufficient to make a model that computes next-word probabilities “well enough” to give us reasonable essay-length pieces of text.

In a crawl of the web there might be a few hundred billion words; in books that have been digitized there might be another hundred billion words. But with 40,000 common words, even the number of possible 2-grams is already 1.6 billion—and the number of possible 3-grams is 60 trillion. So there’s no way we can estimate the probabilities even for all of these from text that’s out there. And by the time we get to “essay fragments” of 20 words, the number of possibilities is larger than the number of particles in the universe, so in a sense they could never all be written down. So what can we do? The big idea is to make a model that lets us estimate the probabilities with which sequences should occur—even though we’ve never explicitly seen those sequences in the corpus of text we’ve looked at. And at the core of ChatGPT is precisely a so-called “large language model” (LLM) that’s been built to do a good job of estimating those probabilities.

There’s nothing particularly “theoretically derived” about this neural net; it’s just something that—back in 1998—was constructed as a piece of engineering, and found to work. (Of course, that’s not much different from how we might describe our brains as having been produced through the process of biological evolution.)

We somehow want all the 1’s to “be attracted to one place”, and all the 2’s to “be attracted to another place”. Or, put a different way, if an image is somehow “closer to being a 1” than to being a 2, we want it to end up in the “1 place” and vice versa.

So what happens if one goes on longer? Here’s a random example. It’s better than the top-word (zero temperature) case, but still at best a bit weird: This was done with the simplest GPT-2 model (from 2019). With the newer and bigger GPT-3 models the results are better. Here’s the top-word (zero temperature) text produced with the same “prompt”, but with the biggest GPT-3 model: And here’s a random example at “temperature 0.8”: Where Do the Probabilities Come From?

This short book is an attempt to explain from first principles how and why ChatGPT works. In some ways it’s a story about technology. But it’s also a story about science.

And in fact the big breakthrough in “deep learning” that occurred around 2011 was associated with the discovery that in some sense it can be easier to do (at least approximate) minimization when there are lots of weights involved than when there are fairly few. In other words—somewhat counterintuitively—it can be easier to solve more complicated problems with neural nets than simpler ones. And the rough reason for this seems to be that when one has a lot of “weight variables” one has a high-dimensional space with “lots of different directions” that can lead one to the minimum—whereas with fewer variables it’s easier to end up getting stuck in a local minimum (“mountain lake”) from which there’s no “direction to get out”.

When we make a neural net to distinguish cats from dogs we don’t effectively have to write a program that (say) explicitly finds whiskers; instead we just show lots of examples of what’s a cat and what’s a dog, and then have the network “machine learn” from these how to distinguish them. And the point is that the trained network “generalizes” from the particular examples it’s shown.

So how does neural net training actually work? Essentially what we’re always trying to do is to find weights that make the neural net successfully reproduce the examples we’ve given. And then we’re relying on the neural net to “interpolate” (or “generalize”) “between” these examples in a “reasonable” way.

You finally pull up the website (chat.openai.com

ChatGPT for Fiction Writing. So if you are

Write a blog post on [topic] that is structured and easy to read. Begin with a clear and concise introduction that sets the tone and provides context for the topic. Use headings, subheadings, and clear paragraphs to organize and illustrate the content, making sure that the final product is organized and easy to follow. In the body of the post, make sure that each section is clearly defined and contributes to the overall message of the post. Consider the readability of the final product by using clear and concise language, while avoiding jargon or overly complex terminology. Finally, include a clear and concise conclusion that summarizes your main points and reinforces the importance of your argument. Repurposing a Blog Post for Different Audiences Description: This prompt will guide you in repurposing a blog post on the topic of your choice for a different target audience.

How about something like ChatGPT? Well, it has the nice feature that it can do “unsupervised learning”, making it much easier to get it examples to train from. Recall that the basic task for ChatGPT is to figure out how to continue a piece of text that it’s been given. So to get it “training examples” all one has to do is get a piece of text, and mask out the end of it, and then use this as the “input to train from”—with the “output” being the complete, unmasked piece of text.

But there’s something potentially confusing about all of this. In the past there were plenty of tasks—including writing essays—that we’ve assumed were somehow “fundamentally too hard” for computers. And now that we see them done by the likes of ChatGPT we tend to suddenly think that computers must have become vastly more powerful—in particular surpassing things they were already basically able to do (like progressively computing the behavior of computational systems like cellular automata). But this isn’t the right conclusion to draw. Computationally irreducible processes are still computationally irreducible, and are still fundamentally hard for computers—even if computers can readily compute their individual steps. And instead what we should conclude is that tasks—like writing essays—that we humans could do, but we didn’t think computers could do, are actually in some sense computationally easier than we thought. In other words, the reason a neural net can be successful in writing an essay is because writing an essay turns out to be a “computationally shallower” problem than we thought. And in a sense this takes us closer to “having a theory” of how we humans manage to do things like writing essays, or in general deal with language.

Neural nets—perhaps a bit like brains—are set up to have an essentially fixed network of neurons, with what’s modified being the strength (“weight”) of connections between them.

AI Deception (The Sonnet) Anybody can be a singer using Autotune, Using ChatGPT anybody can be a writer. But I'm yet to find an AI that can compose, Love thy neighbor, or love has no gender. Yet the problem is, neither human nor AI can detect AI generation. The world is already being flooded with soulless, deceitful creation. In the midst of such grave misgivings, Honor alone can bring illumination. Before you place anyone on pedestal, Observe character beyond the creation. Even I use photoshop, to enhance my covers, But the words beneath are unvarnished Naskar. AI can be a great help if you know how to use it honorably, so originality is empowered.

apply a framework. Drawing inspiration from the book 'Clockwork,' we asked ChatGPT-the-business-coach to provide advice using the framework, and it astutely incorporated principles from the book into an actionable plan for expanding our company.

As a personal finance advisor, you will provide guidance on how to manage money more effectively. Take into account techniques such as budgeting, setting financial goals, diversifying investments, and understanding credit. Offer advice about building wealth over time and discuss the importance of creating a plan for achieving long-term financial security. My first request is “What should I do to improve my financial situation?

As a negotiation coach, you will provide guidance on how to effectively negotiate in order to get what one wants. Take into account techniques such as understanding the other party's interests, researching the market value of items, creating win-win scenarios, and managing emotions. Offer advice about developing strategies for negotiating and discuss the importance of staying calm while negotiating. My first request is “What are some tips you have for successful negotiations?

As more boneheads use ChatGPT to cheat, worth of excellence will skyrocket globally.

Unleashing Reliable Insights from Generative AI by Disentangling Language Fluency and Knowledge Acquisition Generative AI carries immense potential but also comes with significant risks. One of these risks of Generative AI lies in its limited ability to identify misinformation and inaccuracies within the contextual framework. This deficiency can lead to mistakenly associating correlation with causation, reliance on incomplete or inaccurate data, and a lack of awareness regarding sensitive dependencies between information sets. With society’s increasing fascination with and dependence on Generative AI, there is a concern that the unintended consequence that it will have an unhealthy influence on shaping societal views on politics, culture, and science. Humans acquire language and communication skills from a diverse range of sources, including raw, unfiltered, and unstructured content. However, when it comes to knowledge acquisition, humans typically rely on transparent, trusted, and structured sources. In contrast, large language models (LLMs) such as ChatGPT draw from an array of opaque, unattested sources of raw, unfiltered, and unstructured content for language and communication training. LLMs treat this information as the absolute source of truth used in their responses. While this approach has demonstrated effectiveness in generating natural language, it also introduces inconsistencies and deficiencies in response integrity. While Generative AI can provide information it does not inherently yield knowledge. To unlock the true value of generative AI, it is crucial to disaggregate the process of language fluency training from the acquisition of knowledge used in responses. This disaggregation enables LLMs to not only generate coherent and fluent language but also deliver accurate and reliable information. However, in a culture that obsesses over information from self-proclaimed influencers and prioritizes virality over transparency and accuracy, distinguishing reliable information from misinformation and knowledge from ignorance has become increasingly challenging. This presents a significant obstacle for AI algorithms striving to provide accurate and trustworthy responses. Generative AI shows great promise, but addressing the issue of ensuring information integrity is crucial for ensuring accurate and reliable responses. By disaggregating language fluency training from knowledge acquisition, large language models can offer valuable insights. However, overcoming the prevailing challenges of identifying reliable information and distinguishing knowledge from ignorance remains a critical endeavour for advancing AI algorithms. It is essential to acknowledge that resolving this is an immediate challenge that needs open dialogue that includes a broad set of disciplines, not just technologists Technology alone cannot provide a complete solution.

Prompt: I want you to act as an advertising agency. You will create a full and detailed marketing campaign to promote a product or service of your choice. You will choose a target audience, develop key messages and slogans, select the media channels for promotion, and decide on any additional activities needed to reach your goals. Give examples of the cost of advertising in each social media channel, as well as the best estimate of CPM and CPC charges. My first request is "I need help creating an advertising campaign for a new type of energy drink targeting young adults aged 18-30.

Hour of Power One of my favorite things is starting my day with my morning Hour of Power. It’s an hour that I set aside every morning, first thing in the morning, to reflect, review, and recommit to my goals. I take this time to train my mind and reconnect with who I want to be.

You can design your hour of power (or 35 minutes or 45 minutes) to be what you need to perform at your best. With your hour of power, you can start your day strong, organized, and tracking toward your goals, starting by reconnecting to what matters most.  Here are some things you can schedule in your Hour of Power: • Goal Review: Review the goals that you’ve set for yourself and reconnect to what it feels like to have achieved that goal. Cover the delta between where you are now and where you need to be to achieve your goals.

How Digital Marketers Utilize the Power of ChatGPT The power of ChatGPT has revolutionized how digital marketers interact with their audience. It’s an incredibly versatile tool allowing personalized and interactive conversations, providing valuable insights into consumer behavior and preferences. Therfore, by leveraging ChatGPT meaning, digital marketers can engage with their target audience more meaningfully and tailor, leading to enhanced customer experiences and increased conversions. At Comstat Solutions, we understand the value of leveraging cutting-edge technologies like ChatGPT to drive results for your business. As a growing digital marketing agency, we specialize in harnessing the power of AI-driven solutions to optimize your marketing efforts.

Prompt: Give me 5 blog post ideas for high search volume and low competition keywords for drones.

much like for humans, if you tell it something bizarre and unexpected that completely doesn’t fit into the framework it knows, it doesn’t seem like it’ll successfully be able to “integrate” this. It can “integrate” it only if it’s basically riding in a fairly simple way on top of the framework it already has.

Explain the concept of machine learning in simple terms for a high school student." ●        For a response with a specific tone, indicate it in the prompt: "Write a humorous explanation of how photosynthesis works." Layered Prompting Technique The layered prompting technique involves breaking down complex questions into smaller, more manageable parts and feeding them to ChatGPT in a sequence, which can lead to more coherent and accurate responses. ●        Instead of asking "What is the best way to train for a marathon?" which is a complex question, you can break it down into smaller parts, such as "What are some good warm-up exercises for marathon training?" and "What's a good running schedule for marathon training?" By feeding these prompts to ChatGPT in sequence, you can get more detailed and

Tools like ChatGPT are designed to assist and enhance human capabilities, not replace them entirely. Much like using Google for information retrieval, ChatGPT can be a valuable resource for obtaining quick information, generating ideas, or seeking assistance. When Google was introduced, some argued that it would make people lazy in terms of information retrieval. "Why bother memorizing facts when you can quickly search for them?" However, this didn't necessarily make people less intelligent; it shifted the emphasis from memorization to critical thinking and information synthesis. Similarly, ChatGPT doesn't make individuals lazy; it changes the nature of cognitive tasks. Instead of focusing on rote memorization or routine information retrieval, people can allocate their mental energy to more complex and creative endeavors. The key is how individuals use these tools.

From the next chapter onwards, we will assume that the API key is stored in the environment variable OPENAI_API_KEY. Please set it up in your terminal beforehand. export OPENAI_API_KEY="sk-f30232pjhf0oqahfefujikofnaljf...

A partir de ahí, podemos desarrollar los argumentos y subargumentos que respaldarán esta tesis, generando un esquema que facilite el desarrollo fluido de nuestras ideas a lo largo del texto. Este proceso puede facilitarse aún más solicitando a ChatGPT que genere ejemplos de esquemas o que nos asista en la expansión de los puntos que hemos identificado inicialmente.

Además, ChatGPT puede asistirnos en la redacción misma del texto. Podemos solicitar que nos proporcione frases de apertura impactantes, que nos ayude a formular argumentos persuasivos o que nos ofrezca formas de concluir nuestros párrafos de manera efectiva.

revisión del texto, ayudándonos a identificar áreas que pueden necesitar mayor desarrollo o que podrían beneficiarse de una reformulación para una mayor claridad y coherencia.

Por ejemplo, podemos pedirle que nos proporcione un resumen de los últimos estudios en un campo particular, que nos ofrezca una visión general de las teorías predominantes en un área de estudio o que nos ayude a identificar los expertos líderes en un campo. Aunque es fundamental verificar cualquier información proporcionada por ChatGPT mediante una revisión posterior de las fuentes primarias, puede servir como un punto de partida valioso para nuestra investigación.

Uno de los primeros pasos en la redacción de documentos técnicos y académicos es realizar una investigación exhaustiva. ChatGPT puede ayudarte a encontrar información relevante y reciente sobre una amplia gama de temas, proporcionando datos, explicando conceptos complejos y ofreciendo resúmenes de artículos y publicaciones especializadas. La IA puede convertirse en un recurso inestimable para la recolección y el análisis de datos que servirán como fundamento para tus argumentos y conclusiones.

orientarte sobre cómo citar correctamente fuentes y referencias, asegurando que otorgues el crédito correspondiente a otros investigadores y evitando problemas relacionados con el plagio. Puedes preguntarle sobre los estilos de citación más comunes, como APA, MLA o Chicago, y solicitar ejemplos de cómo utilizarlos correctamente.

No podemos dejar de lado la importancia de preparar gráficos, tablas y figuras que complementen y enriquezcan el texto. ChatGPT puede proporcionarte consejos sobre cómo crear estos elementos visuales y cómo integrarlos de manera efectiva en tu documento.

Por último, pero no menos importante, ChatGPT puede ayudarte a preparar el material adicional que podría ser necesario, como apéndices con datos adicionales, una bibliografía exhaustiva, y materiales suplementarios que puedan enriquecer tu trabajo y proporcionar un valor adicional a tus lectores.

Un aspecto interesante y muy beneficioso de ChatGPT es su capacidad para ayudar en la creación de material educativo personalizado. Los educadores pueden utilizar ChatGPT para generar contenido de lecciones que esté adaptado a las necesidades específicas de sus estudiantes, o para crear ejercicios y actividades que promuevan el aprendizaje activo.

Además de estos aspectos, ChatGPT puede ser utilizado para promover la alfabetización digital, enseñando a los estudiantes cómo utilizar diferentes herramientas tecnológicas y cómo comportarse de manera segura y ética en el mundo digital.

Para comenzar, la adaptabilidad de ChatGPT permite a los docentes crear material didáctico personalizado, adecuado al nivel y al estilo de aprendizaje de cada grupo de estudiantes. Pueden preparar guías de estudio, resúmenes, e incluso ensayos modelo que ayuden a ilustrar conceptos complejos de manera más accesible. En la creación de material educativo, los educadores pueden explorar nuevas formas de presentar la información, utilizando formatos interactivos que estimulen la curiosidad y la participación activa de los estudiantes.

Además, ChatGPT puede ayudar en la revisión y edición de tus escritos. Puedes pedirle que identifique inconsistencias en la trama, errores gramaticales, o áreas donde el desarrollo del personaje pueda necesitar más profundidad. Esta función de revisión puede ser un gran apoyo para pulir tu manuscrito hasta alcanzar una versión final bien redondeada y coherente.

también puede ser una herramienta ideal para crear contenido auxiliar, como sinopsis, notas del autor, y material promocional relacionado con tu libro. Puedes generar descripciones atractivas

materiales de apoyo que ayuden a promocionar tu libro ante una amplia audiencia.

ChatGPT no reemplaza el conocimiento experto y el discernimiento humano. Si bien puede ayudar a facilitar el proceso de escritura, aún requiere una guía humana para asegurar que la información técnica y científica sea precisa y confiable. Por lo tanto, es recomendable utilizar ChatGPT como una herramienta complementaria y no como un sustituto del expertise humano.

societal implications, contributing to the polarization and

La planificación de un texto académico puede empezar con la definición de una tesis central que se va a defender. Aquí, ChatGPT puede asistir en la creación de argumentos sólidos y coherentes, ayudando a explorar diferentes perspectivas que respalden la tesis.

ChatGPT puede también facilitar la revisión del texto, un paso indispensable en la escritura académica. Al realizar preguntas específicas sobre la coherencia, la argumentación o el estilo, se puede obtener feedback útil para realizar correcciones y mejorar el texto. Adicionalmente, ChatGPT puede ayudar a identificar errores gramaticales o de sintaxis, contribuyendo a pulir el texto hasta alcanzar un nivel de excelencia.

PROMPTS FOR WRITING RESUMES AND BIOS Generate a compelling professional summary for a marketing manager with 5 years of experience. Create a list of 10 action verbs to effectively describe accomplishments in a resume. Draft a LinkedIn bio for a recent college graduate with a degree in computer science. Suggest 5 resume formatting tips to create a visually appealing and easy-to-read document. Write an engaging personal bio for a freelance graphic designer’s website. How can transferable skills be effectively showcased in a career change resume? Create a list of 5 questions to ask a client before writing their resume or bio. Develop a powerful resume objective statement for a sales professional targeting a managerial role. Provide tips for optimizing a LinkedIn profile to increase visibility and attract recruiters. Write an attention-grabbing personal

Brainfuze: des guides complets pour comprendre comment accéder à ChatGPT, découvrir les alternatives à ChatGPT et bien plus. Explorez nos ressources gratuites pour maîtriser l'IA et ChatGPT. Notre plateforme offre un accès facile et gratuit à des outils et conseils pratiques sur ChatGPT. Apprenez à utiliser ChatGPT et ses alternatives pour vos besoins en IA. Avec Brainfuze, approfondissez votre compréhension de l'IA et exploitez pleinement le potentiel de ChatGPT et de ses alternatives !

The key to getting the best results from these tools is to write a good prompt. You want to give the tool enough information and context so that it can generate something useful and relevant to what you’re looking for. Trust me, it makes all the difference, and

Denken ist wie chatGPT, nur krasser

If you're a developer, YouCode is perfect for you! It's a private, ad-free search engine built specifically for the developer community. You can easily access time-saving resources like StackOverflow and GitHub.

The first thing to explain is that what ChatGPT is always fundamentally trying to do is to produce a “reasonable continuation” of whatever text it’s got so far, where by “reasonable” we mean “what one might expect someone to write after seeing what people have written on billions of webpages, etc.

ChatGPT effectively does something like this, except that (as I’ll explain) it doesn’t look at literal text; it looks for things that in a certain sense “match in meaning”. But the end result is that it produces a ranked list of words that might follow, together with “probabilities”: And the remarkable thing is that when ChatGPT does something like write an essay what it’s essentially doing is just asking over and over again “given the text so far, what should the next word be?”—and each time adding a word. (More precisely, as I’ll explain, it’s adding a “token”, which could be just a part of a word, which is why it can sometimes “make up new words”.)

Because for some reason—that maybe one day we’ll have a scientific-style understanding of—if we always pick the highest-ranked word, we’ll typically get a very “flat” essay, that never seems to “show any creativity” (and even sometimes repeats word for word). But if sometimes (at random) we pick lower-ranked words, we get a “more interesting” essay. The fact that there’s randomness here means that if we use the same prompt multiple times, we’re likely to get different essays each time. And, in keeping with the idea of voodoo, there’s a particular so-called “temperature” parameter that determines how often lower-ranked words will be used, and for essay generation, it turns out that a “temperature” of 0.8 seems best. (It’s worth emphasizing that there’s no “theory” being used here; it’s just a matter of what’s been found to work in practice.

In human brains there are about 100 billion neurons (nerve cells), each capable of producing an electrical pulse up to perhaps a thousand times a second. The neurons are connected in a complicated net, with each neuron having tree-like branches allowing it to pass electrical signals to perhaps thousands of other neurons. And in a rough approximation, whether any given neuron produces an electrical pulse at a given moment depends on what pulses it’s received from other neurons—with different connections contributing with different “weights”. When we “see an image” what’s happening is that when photons of light from the image fall on (“photoreceptor”) cells at the back of our eyes they produce electrical signals in nerve cells. These nerve cells are connected to other nerve cells, and eventually the signals go through a whole sequence of layers of neurons. And it’s in this process that we “recognize” the image, eventually “forming the thought” that we’re “seeing a 2” (and maybe in the end doing something like saying the word “two” out loud).

So what can we do? The big idea is to make a model that lets us estimate the probabilities with which sequences should occur—even though we’ve never explicitly seen those sequences in the corpus of text we’ve looked at. And at the core of ChatGPT is precisely a so-called “large language model” (LLM) that’s been built to do a good job of estimating those probabilities.

Si quieres tener ChatGPT en tu móvil sin tener que ingresar a través del navegador y hacer preguntas, ya sea hablando o escribiendo, y recibir las respuestas en la forma que prefieras, te recomiendo probar SuperGPT.

Este botón, que se encuentra encima del campo donde escribes, también te permite solicitar una nueva respuesta del chatbot si no estás satisfecho con la anterior, o pedir más información sobre el tema.

Los motores de búsqueda tradicionales presentan una lista interminable de resultados que, a menudo, son irrelevantes o inútiles.

Por no hablar de la cantidad exagerada de publicidad, pop-ups, acuerdos de cookies, etc. ChatGPT no tiene nada de eso, por lo menos, de momento.

Dejo mi advertencia: el uso excesivo de chatbots y otras tecnologías de IA en la comunicación puede llevar a una pérdida del toque humano.