Training a generative AI model is carbon-intensive. But what about using one? Generative AI tools, like ChatGPT and DALL-E, are not so innocent. In a 2024 study, it was found that while training AI models is more energy-intensive than producing an outcome, it could take just a few weeks or months for usage emissions to exceed training emissions - for popular models like ChatGPT. And that’s due to the generative AI boom and the ubiquitous way AI tools are used. But how exactly? Well, generative AI can’t be powered without thirsty data centers, which need alarming amounts of water to cool them down and scary amounts of electricity to keep spouting out the answers to our questions. Read more about environmental AI’s impacts through these 20 stats & facts we’ve gathered, backed by recent reputable sources: https://lnkd.in/ePdNveBg Some of the stats may leave you wondering: should we boycott AI and revert to manual content creation? It’s a bit more complicated than that. #GenerativeAI
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Have you heard of AI hallucination? It’s when a chatbot, like ChatGPT, just makes stuff up. Completely fabricated responses—and it’s a major challenge in making AI truly reliable. Think of it like a Magic 8 Ball or rolling dice—large language models generate responses based on probabilities, not on verified facts. That’s why even the most advanced AI can come up with fake addresses or bogus legal references that look real but aren’t. My latest video dives into why AI hallucinates and why it’s so difficult to stop. Here are some practical ways to reduce AI hallucinations: ➡️ Retrieval-Augmented Generation (RAG): Integrate a retrieval system to provide verified information that grounds the AI’s responses in fact. ➡️ Training on High-Quality Data: Ensure the training data is clean, accurate, and diverse to minimize the risk of incorrect responses. ➡️ Reinforcement Learning from Human Feedback (RLHF): Use human evaluators to guide AI models on correct outputs, improving their factual accuracy. ➡️ Knowledge Grounding: Connect the AI to trusted external knowledge bases to verify the content before it generates a response. ➡️ Model Fine-Tuning: Continuously fine-tune the model with domain-specific datasets to increase accuracy and reduce misinformation. Want to learn more about how AI can transform your business while minimising risks like hallucinations? Book a free discovery call, and let’s explore how AI consultancy can drive real results for your business. 🚀 https://lnkd.in/e4vBCphp #AI #TechnologyExplained #AIDiscussion #MachineLearning #AIHallucination
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I don't know why AI is being trained on AI generated content. There's a reason anyone building a model of any kind separates training data and testing data. Overfitting is the best umbrella term for it. You want a model that is useful for every situation you throw it at, not perfect for one situation and bad for the rest. Training AI on its own work does exactly that. It overfits the AI to the original inputs at the cost of getting new and unique outputs. If your goal is to create a generative model, you're instead going to end up with a degenerative model. AI is a new space with a mind blowing amount of capital going into it. That's not a good reason to sacrifice good practices in the name of being the first to make some claim. The U.S. managed to be the first to land a person on the moon. This was done without throwing people at the moon until someone survived. The stakes with AI, for now, might be putting someone's life at risk. But there's no reason to build bad models to just to try to get a product out as soon as possible. (The linked article does have a steep paywall unfortunately.) https://lnkd.in/gegkFpJM
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Artificial Intelligence, shortly AI Do you have any idea about that? Oops!! Of course, you will; most of you know about AI tools like ChatGPT, Gemini, Pop AI etc.. etc… because it helps you for in many ways, right? Ok, by the way, now that AI is competing with human brains, mm... we can say that, IT IS SMART. If you want to be a smart person, you need to acquire AI knowledge. So, we TalentTurbo Training Academy provides AI courses for you by professional trainers. Get trained in an AI course and become a smarter person than AI technology.
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What Is The Best Way To Control Today’s AI? Controlling today's AI is a big challenge. We want AI to do what we want, but making sure it behaves right is tough. It’s like teaching a kid to follow the rules. In a famous line over 60 years ago, early AI pioneer Norbert Wiener summed up one of the core challenges that humanity faces in building artificial intelligence: “If we use, to achieve our purposes, a mechanical agency with whose operation we cannot interfere effectively…we had better be quite sure that the purpose put into the machine is the purpose which we really desire.” Today, we are using a method called RLHF to guide AI behavior. RLHF stands for reinforcement learning from human feedback. It's like giving AI a sense of right and wrong. This method started around 2017 and has become super important, especially for language models like ChatGPT. ChatGPT became super popular because it was easy to talk to and helpful, thanks to RLHF. Here's how RLHF works: First, researchers build a second model called a reward model. This model learns from humans what responses are good or bad. Then, they fine-tune the main AI model to produce responses that the reward model likes. This process is complex and hard to get right. But it's important for making sure AI behaves well. And while RLHF works great, newer methods might change how we make sure AI acts in line with what humans want. Controlling AI isn't easy. But with methods like RLHF, we're getting better at it. And as technology evolves, so will our ways of keeping AI in check. #digitaltransformation #mobileappdevelopment #webdevelopment #Appmetry
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I think in the upcoming years when everyone is using #AI, what will really set the industry leaders apart from others is that industry leaders will have their own ML solutions built and trained inhouse whereas the others will simply have some #LLM wrappers. The biggest problem is that for AI-naive organizations, all they think of when they think of AI is #ChatGPT. We’re in a time when people want to classify something, instead of using a classification algorithm, their first instinct is to ask ChatGPT to classify it even when they know it’s going to be a hit or miss! This also means that they’re focusing less on data quality since LLM (ChatGPT and not traditional LLMs like BERT) doesn’t require any #data training. This makes it an uphill battle to get them to provide resources to even implement the traditional #ML resources which require completely different set of resources than an LLM wrapper! Building trust and increasing AI literacy is such a bigger component and an even bigger need now that many people and companies can’t think beyond LLMs. What do you think? ♻️ Repost if you also believe that we need AI Literacy now more than ever!💜 Thanks, Shreya 🦋
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🌟Meet CriticGPT: Making AI Even Smarter!** 🌟 IntroducingCriticGPT, a cool new tool built on GPT-4. 🚀 CriticGPT helps improve ChatGPT by spotting mistakes and giving detailed feedback. This makes it easier for human trainers to fix errors and make AI responses even better. Here's why CriticGPT is awesome: 🔍Finds Mistakes:** Identifies errors in AI responses. 🧠Better Training:** Provides trainers with clear feedback, speeding up the learning process. 🌐Smarter AI:** Ensures AI responses are more accurate and helpful. With CriticGPT, AI becomes smarter and more reliable. It’s a big step towards better communication between humans and AI. Exciting times ahead in the journey to improve AI! 🌟 https://lnkd.in/ehDdaBAX #AI #GPT4 #TechInnovation #AITraining #SmartAI #FutureTech #CriticGPT
Finding GPT-4’s mistakes with GPT-4
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So you say you want generative AI in your construction technology. Curious, do you know what it took to teach ChatGPT what it knows today? The internet. It took the creation, contextualization and build out of the world wide web to power the world's first chat bots. The question is, what are you doing today with your data to build that same context and library for a generative AI system to learn from? This is a crucial step that cannot be overlooked. Data needs context. Data needs experience. Data needs good and bad results. Data needs to be connected. If you want to start thinking about ways that generative AI can help your teams with project management, you need to start by asking this question Do our systems and processes support powering and teaching AI? #contech #capitalprojects #enablinginnovation #construction #projectmanagementsoftware #ai
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🤖 Generative AI: The Ultimate Copycat? We often hear concerns about generative AI systems like ChatGPT potentially violating intellectual property or plagiarizing content. But let's take a step back - in many ways, these AI models are simply mimicking how humans learn and build knowledge. Think about it: 💡 Athletes study great players and mimic their techniques. 🎤 Musicians are inspired by different styles before creating their own sound. 🎨 Artists are influenced by masterpieces throughout history. And us regular folks? We read, watch, and absorb information from countless sources daily, synthesizing it into our own understanding. Developers have been "copying" code snippets from books, Stack Overflow, and now AI assistants for decades. The sources change, but the process remains 🔄 Of course, there are valid IP concerns when training AI on proprietary data. But perhaps we're still measuring this new reality through an old lens? 🔍 Generative AI is the ultimate "copycat" - but that's how we all learn and grow. The sooner we embrace this paradigm shift, the better we can harness its potential. 💡 #ai #genai #llm
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Looking to dip your toes into the generative #AI waters, whether personally or professionally? Here are 3 tips to save you a lot of time (and money) as you embark on your journey: *** 1. Use ChatGPT. Unless you have a very specific use case, #ChatGPT is currently the best place to start learning how to interact with generative AI models. Ask questions how you normally would, or ask questions in the best way you can think of how to phrase them. ChatGPT is quite good at understanding what you're getting at. *** 2. Avoid AI avatar and video creation. As of today, spending time trying to create an AI avatar or AI video from just text is not a wise use of time. It can be fun to play around with, but that technology is just not ready for use in any meaningful way quite yet. *** 3. Learn by doing. Generative AI is a technology where the best way to learn it is to use it. The return on investment between taking a course or watching YouTube videos about how to use generative AI versus the return on investment from just using it yourself are night-and-day different. Bonus Tip: If you are really short on time and you have a highly specific use case, check out Perplexity AI and ask what the best generative AI tool is for your use case. 📸: Generated with Starry AI
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Master AI implementation to enhance your business operations with #AI. 🤝 Follow us on Discord 🔜: https://lnkd.in/gt823Zd3 🤝 Follow us on Whatsapp 🔜 https://wapia.in/wabeta _ ❇️ Summary: The 2024 Complete ChatGPT & Gemini AI Advanced E-Degree is a course that teaches how consumer-facing AI tools can benefit businesses. 35% of companies worldwide are already using AI, with 50% planning to use it in 2024. The course covers various AI tools, including ChatGPT, Gemini AI, GPT 3.5, and more, and teaches how to leverage them for creativity, productivity, and content generation. The course is on sale for $29.99, down from $790. Hashtags: #chatGPT #AIforBusiness #AItraining
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