Jeremy Sinon’s Post

Here it comes: Generative AI Engine Optimization https://lnkd.in/e2pVMMfQ

How do you recognize AI generated content? #openai has recently taken down their ai-generated classifier. It was not accurate enough and somewhat biased. Where do we go from here? I remember more than half a year ago the questions from university professors - “How do we tell the generated submissions apart from hand written ones?”. Here is an interesting article making progress on models generating content that is subtly watermarked and thus recognizable. This seems to be a good direction to go. https://lnkd.in/eY9BvXCn

What happens when AI (large language models) becomes a part of Google's search algorithm? I wrote an article called "Out with the Old SEO" where I explore this topic in-depth — it's not looking good for the status quo. The article was given Medium's "Boost", which is given to articles of high quality. It's on the long side, but contains some very important ideas. Check it out! https://lnkd.in/eKqzRjew As always, I shoot from the hip, so apologize for any friendly fire. #SEO #AI #LLM #ChatGPT #Bard #Google

You can now generate AI images directly in the Google Search bar: Google has announced that users who have opted-in for its Search Generative Experience (SGE) will be able to create AI images directly from the standard Search bar. This expands on Google's vision for the future of web searching, where the system generates coherent responses to natural language prompts. Users can type in a description, and the engine will create multiple image options for them to choose from. These generated images can be exported or shared. There are limitations to these features, including eligibility restrictions and blocking the creation of photorealistic images of human faces or notable people. Google is also implementing a metadata watermark to indicate generated images. - Artificial Intelligence topics! #ai #artificialintelligence #intelligenzaartificiale

LLM and Search (move fast) The FreshLLMs paper mentioned that the authors evaluated their approach vs Perplexity AI on April 26 2023 https://lnkd.in/gnE6d73Y In November 2023, Perplexity AI announced that they implemented their new search engine using FreshLLMs inspired approach https://lnkd.in/gAaHUe4u

FreshLLMs: Enhancing Large Language Models with Search Engine Aumentation. In this study, authors introduce FreshQA, a dynamic QA benchmark that challenges LLMs to adapt to fast changing world knowledge. #AI #LLM https://lnkd.in/dxyCGrwK

Ever wondered how search engines are becoming more intelligent in providing precise, fact-based results? The answer lies in a groundbreaking paradigm called "Retrieval-Augmented Generation" or RAG. 🔍 What is Retrieval-Augmented Generation? RAG is a game-changing concept where relevant documents or data are collected based on a query and added as a prompt to fine-tune a language model's response. Think of it as a research assistant for generating content. By retrieving and integrating pertinent information, RAG empowers language models to create more relevant, factually accurate, and context-rich content. 🌐 The Pioneers Behind RAG While Microsoft introduced this innovation with the new Bing, kudos go to the Facebook AI Research team. They presented the concept back in May 2020 in a paper titled "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks." Neeva, the privacy-focused search engine, was the first to implement this in a major public search engine, powering highly specific featured snippets. 📚 Why RAG Matters Language models (LLMs) are powerful, but they have limitations based on their training data. They're information-locked, with their knowledge typically capped at a certain point in time. RAG changes the game. It's all about staying updated. When you use Bing or ChatGPT plugins like AIPRM, you're leveraging this approach to get the most current and precise information available. 📝 How RAG Works Imagine you're writing a research paper, and you need specific, up-to-date facts to strengthen your arguments. RAG serves as your trusty research assistant. You give it a prompt, and it retrieves the most relevant, factually accurate information from its extensive knowledge base. This boosts the precision and credibility of your content. While language models offer broad responses based on probabilities, RAG elevates precision and citation of sources. 🌟 The Future of Search Generative Experiences RAG is a leap forward in providing users with not just information but accurate and context-rich knowledge. It's transforming how we search, learn, and create content. As this approach becomes more common, expect to see more innovations that make content generation and search results even smarter and more efficient. Are you ready to explore the power of retrieval-augmented generation in your search experiences? The future is unfolding, and it's full of possibilities! 🌐🔮 #SearchInnovation #AI #RAG #FutureTech #ContentGeneration

GPT-4, short for "Generative Pre-trained Transformer 4," is the latest iteration of OpenAI's revolutionary language model. It builds upon the capabilities of its predecessors, offering enhanced natural language understanding and generation. Read the full article here: https://lnkd.in/eb9cRAqZ

ZeroGPT and copyleaks are scamming people. Washington post, Futurism, Huffington Post and more all agree with this. No AI detector is accurate, and here’s a brief explanation why (so you can convince the SEO guy to get off your back) 1. This is because AI detection tools use writing patterns and language structures that are typical of AI-generated content, which can incorrectly flag human-written articles that have a similar style. 2. AI detectors use machine learning and natural language processing to analyze text features like: How fluently it reads, How often certain words appear, Repetitive language, Word choice probabilities, and Tone. 3. AI detectors are trained on datasets that contain text from various sources, including human and AI-generated content. However, AI tools are trained on materials created by humans with human biases, and they cannot reliably distinguish between biased and unbiased material. 4. AI detectors can estimate a rough probability that content is AI-generated, but they cannot directly validate the true origin of text. For example, some AI detection companies claim their false positive rate is only 4%, but if a university checks 3000 academic papers, this means that 120 papers will be labeled as AI-generated even though they are not. So, what to do? Well nothing, because this doesn’t change anything. SEO? The simple rule of thumb these days is quality, whether AI writes it or you - quality ranks easier, quality is unique thought backed with good references. Go and have a conversation with the SEO guy and your boss today. Because if they are making you rewrite your original content in the same of copyleaks - they are at fault, and your time is being wasted.