It seems like AI is every where you turn these days, literally. Certainly at every conference I’ve been to this year it’s been the hot topic. The thing that’s coming through for me though is there’s a lot of confusion about what AI means for the arts, culture, heritage, attractions sectors. For some it equates to ChatGPT and the use of Chatbots, which is Generative AI where machine learning can generate new content for us such as text, images, video etc in response to our prompts. What I find really interesting is that AI has been around in some form or other since the 1960s, Yes you read that right it’s not a typo – the 1960s! At one of the most recent conferences I attended The Annual National Conference of Visitor Attractions - VAC Satpal Chana of Visit Britain told us about 4 AI trends we should be thinking about next: ➢ natural language becomes the language of data by 2030 – that means consumers for the very first time will become the creators as a majority ➢ make you/your workforce AI ready – all of them – levels of data and AI literacy are going to be a no.1 factor for success ➢ trust becomes a new currency of information – that links in to the need for an AI Policy that sits alongside a wider Data Management Policy ➢ open source is the new Superpower – that means collaborating to create culture specific models Finally top tip - there’s an incredibly useful example AI Policy on the Arts Marketing Association Culture Hive website that you can download for free. #AI #artsandculture #innovation
Helen Dunnett FRSA’s Post
More Relevant Posts
-
AI agents are not just technological tools - they are enablers of operational efficiency and innovation. For board-level conversations, this isn’t just about adopting the latest trend - it’s about positioning the organization to thrive in an AI-driven world. Why are AI agents important? ✅ Specialized AI agents outperform general ones, reducing costs and increasing reliability. ✅ They enable smarter workflows, from natural language database interactions to task automation. ✅ With the right design and evaluation, AI agents offer a scalable, future-proof solution for growth. Learn more about how AI agents are shaping the future of enterprise success in the carousel. 👉 #ArtificialIntelligence #AIagents #TechStrategy #BoardLeadership
We’ve seen the hype - GenAI, chatbots, copilots - and now, AI agents. But how do you separate the signal from the noise? Are these just new labels for old ideas? Below, we break down the "why" and "how" of building AI agents. We’ll show you where they fit in the matrix of chatbots, copilots, and agentic systems, and share key insights from real-world applications and R&D, including: 1. How AI agents outperform standalone LLMs in tasks like natural language database interactions. 2. Why specialization beats generalization in agentic systems. 3. Key design trade-offs to balance performance with cost and complexity. 4. The critical role of evaluation pipelines to avoid AI development pitfalls. 👉 Swipe through to discover how to build smarter, scalable AI agents for your LLM applications! Interested more? Read the full article here: 🚀 https://lnkd.in/dHrwUPhJ #AIagents #GenerativeAI #LLM #ArtificialIntelligence #AI
To view or add a comment, sign in
-
In an era where artificial intelligence, particularly Large Language Models (LLMs), plays a pivotal role in how we interact with technology, understanding how to communicate effectively with these systems is crucial. This is where prompt engineering steps in as a critical skill for the future. Crafting the right prompts can be the difference between getting a useful answer or a misleading one. Here’s why you should start mastering this skill and how it can transform your interactions with AI: 1️⃣ Enhances Accuracy: Proper prompts yield more precise and applicable responses from LLMs. 2️⃣ Drives Efficiency: Effective prompts can save time and resources by reducing the need for follow-ups. 3️⃣ Fosters Creativity: Skillful prompting unlocks more innovative and creative outputs from AI. 4️⃣ Improves Decision Making: Accurate data extracted via good prompts leads to better business decisions. 5️⃣ Boosts User Experience: Well-designed prompts enhance user engagement and satisfaction. 6️⃣ Facilitates Better Research: Researchers can obtain more relevant and deep insights when queries are well-formulated. 7️⃣ Encourages Ethical AI Use: Thoughtful prompts help in guiding AI to generate responsible and unbiased content. 8️⃣ Supports Personalized Interactions: Customized prompts can lead to responses that are more tailored to individual needs. 9️⃣ Optimizes Automation: Enhance the capability of AI in automating tasks by asking the right questions. 🔟 Prepares for Future Technologies: As AI evolves, so does the complexity of interactions, making prompt engineering increasingly essential. Whether you're a developer, a marketer, a data scientist, or just an AI enthusiast, understanding how to craft effective prompts is a skill that will give you a significant advantage in this tech-driven world. Start learning this crucial skill today, and ensure you're prepared to lead and succeed in the future of AI-driven innovation. 📘 Interested in diving deeper? Grab a copy of my book "Prompt Engineering: Unlocking Generative AI: Ethical Creative AI for All" here: https://amzn.to/3Uk2jz2 #PromptEngineering #AICommunication #FutureSkills #TechInnovation #LLMs #AI #generativeai #genai #ethicalai #topvoices
To view or add a comment, sign in
-
-
AI has remained one of the leading global trends for the last few years. Its power is spreading across industries to simplify workers' routine tasks and create cutting-edge user experiences. Our latest project, Gieni, is the world's first chat-based AI model that answers questions related to the CNC industry. It receives deep market insights from the specialized database and can understand natural language queries. Our contribution: ✅ Assembled and verified a comprehensive and reliable global database ✅ AI is trained to answer questions and comprehend CNC-related terminology (including industry-specific jargon) ✅ Resolved ambiguities in user queries to provide precise responses ✅ A monetization model with balanced free and paid access ✅ Solution to encourage user feedback ✅ Ensured responsible and ethical usage of AI ✅ Technical infrastructure and integrations We have successfully released an MVP version for beta users. Now, we are working on improving and scaling the chatbot. Discover more by the following: https://lnkd.in/dUZD_rb7 #Rolique, #AI, #SoftwareDevelopment, #CaseStudy
To view or add a comment, sign in
-
-
Shedding Light on the Black Box: Why Explainable AI Matters? Artificial intelligence (AI) is revolutionizing industries, but complex algorithms can sometimes act as a black box, leaving us wondering "how" they reach decisions. This is where Explainable AI (XAI) comes in! What is XAI? XAI is a field focused on making AI models more transparent and understandable. It helps us interpret the reasoning behind AI decisions, fostering trust and responsible development. Why is XAI important? Trust and Transparency: By understanding how AI arrives at its conclusions, we can build trust in its recommendations and avoid biases. Improved Decision-Making: XAI allows us to identify areas for improvement within AI models, leading to more effective and reliable outcomes. Regulatory Compliance: In many sectors, regulations require the explainability of AI-driven decisions. XAI helps ensure compliance. Tools for Explainable AI: SHAP (Shapley Additive exPlanations): This open-source tool offers a variety of explainability techniques, making it a versatile choice for many machine learning models LIME (Local Interpretable Model-agnostic Explanations): Another open-source favourite, LIME excels at explaining individual predictions for any kind of mode ELI5 (Explain Like I'm 5): This library focuses on generating explanations in natural language, making it easier for non-technical audiences to understand AI decisions The future of AI is bright, but only if we can trust its decision-making. Share your thoughts on how to bridge the gap between AI experts and the everyday user. #XAI #artificialintelligence #machinelearning #responsibleAI #technology
To view or add a comment, sign in
-
You’ve likely heard the buzz around AI agents. But the real question is how can they create value? AI agents aren’t just an evolution of Generative AI. They’re transforming how companies automate workflows, leverage data, and accelerate innovation. AI agents unlock capabilities pure LLMs simply can’t. Explore the carousel to learn more: #AIagents #GenerativeAI
We’ve seen the hype - GenAI, chatbots, copilots - and now, AI agents. But how do you separate the signal from the noise? Are these just new labels for old ideas? Below, we break down the "why" and "how" of building AI agents. We’ll show you where they fit in the matrix of chatbots, copilots, and agentic systems, and share key insights from real-world applications and R&D, including: 1. How AI agents outperform standalone LLMs in tasks like natural language database interactions. 2. Why specialization beats generalization in agentic systems. 3. Key design trade-offs to balance performance with cost and complexity. 4. The critical role of evaluation pipelines to avoid AI development pitfalls. 👉 Swipe through to discover how to build smarter, scalable AI agents for your LLM applications! Interested more? Read the full article here: 🚀 https://lnkd.in/dHrwUPhJ #AIagents #GenerativeAI #LLM #ArtificialIntelligence #AI
To view or add a comment, sign in
-
Ever feel like your AI chatbot is giving you the “right” answer—but it’s just not the useful one? That’s a common problem with standard generative AI. While large language models (LLMs) have come a long way, they often fall short for businesses because they’re built on public datasets—not your unique, in-house knowledge. That’s where Retrieval-Augmented Generation (RAG) changes the game. RAG allows LLMs to access and pull in specific, external information—like your company’s knowledge base or real-time data—before generating a response. So instead of generic answers, you get tailored, contextually relevant responses backed by real citations. Imagine a customer service chatbot that can reference exact account details or policy information in real time. With RAG, you’re equipping AI with the insights it needs to be a true business asset—not just another tech tool. RAG is making AI more actionable and effective, bringing us closer to realizing the real promise of generative AI in the workplace. #AI #RAG #Innovation #BusinessAI #CustomerExperience
To view or add a comment, sign in
-
-
We’ve seen the hype - GenAI, chatbots, copilots - and now, AI agents. But how do you separate the signal from the noise? Are these just new labels for old ideas? Below, we break down the "why" and "how" of building AI agents. We’ll show you where they fit in the matrix of chatbots, copilots, and agentic systems, and share key insights from real-world applications and R&D, including: 1. How AI agents outperform standalone LLMs in tasks like natural language database interactions. 2. Why specialization beats generalization in agentic systems. 3. Key design trade-offs to balance performance with cost and complexity. 4. The critical role of evaluation pipelines to avoid AI development pitfalls. 👉 Swipe through to discover how to build smarter, scalable AI agents for your LLM applications! Interested more? Read the full article here: 🚀 https://lnkd.in/dHrwUPhJ #AIagents #GenerativeAI #LLM #ArtificialIntelligence #AI
To view or add a comment, sign in
-
Hello AI Nerds!! 🚀 Unlocking the Future of AI with Speculative Decoding! 🔓 Did you know that over 80% of enterprises are expected to adopt generative AI tools by 2026, a massive jump from less than 5% just this year? 📈 One of the game-changers in this space is Speculative Decoding. This innovative technique optimizes the performance of large language models (LLMs) by allowing for low-latency inference. Imagine drafting tokens with a compact model and quickly verifying them with a target LLM – it’s like having your cake and eating it too! 🍰 Here's what makes this breakthrough so essential: Speed Matters: With speed improvements of up to 1.5x, businesses can enhance user experiences in applications requiring real-time processing, such as interactive chatbots or document analysis. 🌐 Sustainability Focus: As the use of generative AI surges, so does the responsibility to minimize energy consumption. Speculative decoding helps streamline processes, aligning with the industry's push for more sustainable AI practices. 🌱 As AI professionals, we need to embrace these advancements to stay ahead in this rapidly evolving landscape. 👉 What strategies are you implementing to harness these breakthroughs in your organization? Let's spark a conversation! 💬 #AI #MachineLearning #GenerativeAI #SpeculativeDecoding #Sustainability #Innovation #LLMs
To view or add a comment, sign in
-
-
𝐂𝐡𝐚𝐭𝐛𝐨𝐭 𝐀𝐫𝐞𝐧𝐚 is redefining 𝐡𝐨𝐰 𝐰𝐞 𝐞𝐯𝐚𝐥𝐮𝐚𝐭𝐞 𝐀𝐈 𝐦𝐨𝐝𝐞𝐥𝐬 by focusing on what really matters—human preferences. 🤖 Innovative evaluation: A platform where users directly compare chatbot answers, gathering over 240,000 votes to rank AI models by real-world performance. 🌍 Broad participation: With 90,000+ users contributing prompts in 100+ languages, the arena ensures unmatched diversity in AI testing. 📊 Reliable metrics: Uses cutting-edge statistical techniques to generate accurate rankings, ensuring both efficiency and credibility. 🤝 Collaboration at scale: Trusted by AI leaders like OpenAI and Google, it features over 50 top-tier models, from GPT-4 to LLaMA. 🚀 Shaping the future: Combats the limitations of static benchmarks by embracing live, open-ended scenarios that reflect real-world AI usage. 👉 Paper: https://lnkd.in/gHmfsEiW #AI #HumanCenteredAI #GenerativeAI 🔍 Why it’s impactful: Chatbot Arena democratizes AI evaluation, making it accessible and transparent for researchers, developers, and enthusiasts. 🧠 Smarter data collection: Its crowdsourced feedback aligns closely with expert ratings, proving that collective insights can rival professional assessments. ♻️ Repost if you enjoyed this post and follow me, César Beltrán Miralles, for more curated content about generative AI!
To view or add a comment, sign in
-
-
🚀🚀 4 EFFECTIVE WAYS TO PROMPT GENERATIVE AI TOOLS. It is not an overstatement to say that, the most effective skilled people of any industry will be the most effective users of AI tools in their industry - the most effective prompt engineers. Whether it is a chef trying to quickly publish a cooking manual; a painter trying to gain new perspectives from AI generated images or a programmer quickly trying to write codes - those who know how to get the best completions from effective prompts will be the biggest winners. So, these are a few ways to get the best results from GenAI tools: ✅. Be Specific and Clear: Provide detailed instructions and specific information about what you want. Ambiguous prompts can lead to vague or irrelevant responses. For example, instead of asking, "Tell me about AI," ask, "Explain the impact of AI on healthcare advancements in the past decade." ✅. Be descriptive and conversational: The more descriptive and conversational you are, the more likely you are to get the best completions or answers. Since GenAI tools are built on Large Language Models (LLMs), descriptive questions offer detailed information, while conversational questions mimic natural dialogue, both of which can improve the quality of the AI's answers. ✅. Use Context and Examples: Offer context or examples to guide the AI's response. This helps the AI understand the desired format or content style. For instance, "Describe the main features of a smartphone in a similar style to a product review." ✅. Set Constraints and Boundaries: Define the scope and limitations of the response. This can include word limits, tone, or focus areas. For example, "Summarize the key points of this article in 150 words or less." By employing these strategies, you can significantly enhance the quality and relevance of the outputs generated by GenAI tools. Drop your thoughts in the comment section 😁🚀🚀 #GenAI #AI #Artificialintelligence #cloudcomputing #IT #Techoperations
To view or add a comment, sign in
-