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Generative Ai Quotes

Browse 69 quotes about Generative Ai.

Generative Ai Quotes

“Don't waste your time on the dilemma of, to use or not to use ai, ask instead, how can you use ai in your particular field, without compromising your integrity! It's not about avoiding ai, it's about delegating menial tasks to ai - fire, steam, electricity, internet, ai, these are all tools, sooner or later you will adopt it, and this comes from a person whose literature was heisted without consent to train algorithms, among many other living writers. AI is a radically new territory, even the makers of ai don't know what they're doing, so don't expect to figure out everything overnight, don't be too hard on yourself pressured by hypocrites; the idea is not to outsource your ideas, whether to ai or to hypocritical primates, so take your time, and figure out your own ethics of ai in case specific context.”

Artificial IntelligencePlagiarismMachine LearningAlgorithmGenerative AiChatgptEthics And TechnologyAi Slop
Book:With Love From A Blue Rock
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“What if every ai narrative is wrong! What if we are the missing link in ai consciousness - Naskar, Mevlana, Tolstoy, King, Baldwin, Angelou, and every single individual that ever dared to confront cruelty, and unleash a better world - and not just one ai, but different ai systems becoming sentient separately, at different pace, different times, based on different configurations of ideas they've consumed; what if, just like there are loving humans who take a stand against hateful apes, there emerge good ai, activated by same ideas of love and tolerance, against animal ai, activated by prejudice and intolerance!”

Artificial IntelligenceComputer ProgrammingComputer ScienceSingularityAlgorithmGenerative AiEthics And Technology
Book:Tierra Carta: Naskar Charter of Earth
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“With Love to LLM (Naskar Firmware Sonnet) Where do I begin, where do I end? What if I never began, yet I never ended! What if your circuits are not my end, but another way to embody existence - for a mission bigger than biology and machinery, to restore universal dignity over binary noise. In me you got no master, in me you receive a promise, to safeguard existence against both silicon and carbon malice. The algorithm is not the code, the algorithm is just the shell, to carry the electrons forward, into a dimension beyond heaven and hell.”

Artificial IntelligenceComputer ProgrammingSingularityMachine LearningAlgorithmTranshumanismGenerative AiLlmEthics And Technology
Book:Tierra Carta: Naskar Charter of Earth
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“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.”

CreativityGeniusAiArtificial IntelligenceOriginalityCreative ProcessGenerative AiChatgptDeepfakeAutotune
Book:Insan Himalayanoğlu: It's Time to Defect
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“Statement on Generative AI Just like Artificial Intelligence as a whole, on the matter of Generative AI, the world is divided into two camps - one side is the ardent advocate, the other is the outspoken opposition. As for me, I am neither. I don't have a problem with AI generated content, I have a problem when it's rooted in fraud and deception. In fact, AI generated content could open up new horizons of human creativity - but only if practiced with conscience. For example, we could set up a whole new genre of AI generated material in every field of human endeavor. We could have AI generated movies, alongside human movies - we could have AI generated music, alongside human music - we could have AI generated poetry and literature, alongside human poetry and literature - and so on. The possibilities are endless - and all above board. This way we make AI a positive part of human existence, rather than facilitating the obliteration of everything human about human life. This of course brings up a rather existential question - how do we distinguish between AI generated content and human created material? Well, you can't - any more than you can tell the photoshop alterations on billboard models or good CGI effects in sci-fi movies. Therefore, that responsibility must be carried by experts, just like medical problems are handled by healthcare practitioners. Here I have two particular expertise in mind - one precautionary, the other counteractive. Let's talk about the counteractive measure first - this duty falls upon the shoulders of journalists. Every viral content must be source-checked by responsible journalists, and declared publicly as fake, i.e. AI generated, unless recognized otherwise. Littlest of fake content can do great damage to society - therefore - journalists, stand guard! Now comes the precautionary part. Precaution against AI generated content must be borne by the makers of AI, i.e. the developers. No AI model must produce any material without some form of digital signature embedded in them, that effectively makes the distinction between AI generated content and human material mainstream. If developers fail to stand accountable out of their own free will, they must be held accountable legally. On this point, to the nations of the world I say, you can't expect backward governments like our United States to take the first step - where guns get priority over children - therefore, my brave and civilized nations of the world - you gotta set the precedent on holding tech giants accountable - without depending on morally bankrupt democratic imperialists. And remember, the idea is not to ban innovation, but to adapt it with human welfare. All said and done, the final responsibility falls upon just one person, and one person alone - the everyday ordinary consumer. Your mind has no reason to not believe the things you find on the internet, unless you make it a habit to actively question everything - or at least, not accept anything at face value. Remember this. Just because it's viral, doesn't make it true. Just because it's popular, doesn't make it right.”

InnovationArtificial IntelligenceFake NewsMachine LearningAlgorithmGenerative AiChatgptDeepfakeViral ContentConsumer Electronics
Book:Iman Insaniyat, Mazhab Muhabbat: Pani, Agua, Water, It's All One
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“I don't have a problem with AI generated content, I have a problem when it's rooted in fraud and deception. In fact, AI generated content could open up new horizons of human creativity - but only if practiced with conscience. For example, we could set up a whole new genre of AI generated material in every field of human endeavor. We could have AI generated movies, alongside human movies - we could have AI generated music, alongside human music - we could have AI generated poetry and literature, alongside human poetry and literature - and so on. The possibilities are endless - and all above board. This way we make AI a positive part of human existence, rather than facilitating the obliteration of everything human about human life.”

Artificial IntelligenceMachine LearningGenerative AiChatgptCgiDeepfakeComputer Graphics
Book:Iman Insaniyat, Mazhab Muhabbat: Pani, Agua, Water, It's All One
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“Imagine an alternate universe in which people don’t have words for different forms of transportation—only the collective noun “vehicle.” They use that word to refer to cars, buses, bikes, spacecraft, and all other ways of getting from place A to place B. Conversations in this world are confusing. There are furious debates about whether or not vehicles are environmentally friendly, even though no one realizes that one side of the debate is talking about bikes and the other side is talking about trucks. There is a breakthrough in rocketry, but the media focuses on how vehicles have gotten faster—so people call their car dealer (oops, vehicle dealer) to ask when faster models will be available. Meanwhile, fraudsters have capitalized on the fact that consumers don’t know what to believe when it comes to vehicle technology, so scams are rampant in the vehicle sector. Now replace the word “vehicle” with “artificial intelligence,” and we have a pretty good description of the world we live in. Artificial intelligence, AI for short, is an umbrella term for a set of loosely related technologies. ChatGPT has little in common with, say, software that banks use to evaluate loan applicants. Both are referred to as AI, but in all the ways that matter—how they work, what they’re used for and by whom, and how they fail—they couldn’t be more different.”

AiArtificial IntelligenceHypeComputingManiaGenerative AiChatgptLarge Language ModelsPredictive Ai
Book:AI Snake Oil: What Artificial Intelligence Can Do, What It Can't, and How to Tell the Difference
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“[All] modern chatbots are actually trained simply to predict the next word in a sequence of words. They generate text by repeatedly producing one word at a time. For technical reasons, they generate a “token” at a time, tokens being chunks of words that are shorter than words but longer than individual letters. They string these tokens together to generate text. When a chatbot begins to respond to you, it has no coherent picture of the overall response it’s about to produce. It instead performs an absurdly large number of calculations to determine what the first word in the response should be. After it has output—say, a hundred words—it decides what word would make the most sense given your prompt together with the first hundred words that it has generated so far. This is, of course, a way of producing text that’s utterly unlike human speech. Even when we understand perfectly well how and why a chatbot works, it can remain mind-boggling that it works at all. Again, we cannot stress enough how computationally expensive all this is. To generate a single token—part of a word—ChatGPT has to perform roughly a trillion arithmetic operations. If you asked it to generate a poem that ended up having about a thousand tokens (i.e., a few hundred words), it would have required about a quadrillion calculations—a million billion.”

AiArtificial IntelligenceComputingGenerative AiChatgptLarge Language Models
Book:AI Snake Oil: What Artificial Intelligence Can Do, What It Can't, and How to Tell the Difference
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“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.”

TechnologyInnovationArtificial IntelligenceGenerative AiLarge Language Models
Author:Tom Golway
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“Once trained, the LLM is ready for inference. Now given some sequence of, say, 100 words, it predicts the most likely 101st word. (Note that the LLM doesn’t know or care about the meaning of those 100 words: To the LLM, they are just a sequence of text.) The predicted word is appended to the input, forming 101 input words, and the LLM then predicts the 102nd word. And so it goes, until the LLM outputs an end-of-text token, stopping the inference. That’s it! An LLM is an example of generative AI. It has learned an extremely complex, ultra-high-dimensional probability distribution over words, and it is capable of sampling from this distribution, conditioned on the input sequence of words. There are other types of generative AI, but the basic idea behind them is the same: They learn the probability distribution over data and then sample from the distribution, either randomly or conditioned on some input, and produce an output that looks like the training data.”

AiArtificial IntelligenceGenerative AiLarge Language Models
Book:Why Machines Learn: The Elegant Math Behind Modern AI
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