SAN MURUGESAN’s Post

Advancements in AI Technology Lead to More Compact and Efficient Models In my recent post, I delve into the transformative evolution of AI in 2023. Post-ChatGPT era, we're witnessing a paradigm shift in AI, focusing on model compactness, optimized data usage, and broader applications. Model Compactness: Challenging the "bigger is better" AI trend, new research emphasizes smaller, more efficient models. Google DeepMind's Chinchilla is a standout, offering superior performance to GPT-3 at just a quarter of its size. Data Optimization: The shift from data quantity to quality is reshaping AI, with a focus on diverse data types like natural language, code, images, and videos. Practical Applications: We explore beyond AI’s potential to its real-world uses, including prompt engineering, model fine-tuning, and integrating LLMs into sophisticated systems. Special Mention: Retrieval augmented generation, enhancing LLM accuracy and user confidence. The journey to artificial general intelligence (AGI) continues, with current technologies being stepping stones to this exciting future. https://lnkd.in/deFSeDFD #AI #MachineLearning #Technology #Innovation #ReadyAI #ChatGPT #ArtificialIntelligence

AI Product Managers are staying ahead in the fast-paced world of AI with valuable insights into the most popular Large Language Models (LLMs). Discover how knowledge of leading LLMs can supercharge your strategies: 1. Enhance Product Development: Understand which LLMs lead the pack and leverage their capabilities to drive innovation in your AI products. 2 . Optimize Performance: Stay informed about the latest advancements in LLM technology to boost the performance and efficiency of your AI solutions. 3. Inform Decision-Making: Use insights from popular LLMs to make informed decisions about feature prioritization, model selection, and more. Unlock the full potential of LLMs and elevate your AI product management skills! Check out the article below: https://lnkd.in/gacuUg5h #ProductManagement #AI #ArtificialIntelligence #ProductDevelopment #TechInnovation #MachineLearning #DataScience #TechLeadership #DigitalTransformation #ProductStrategy

🚀 OpenAI's 2023 Breakthroughs and Summary 🌐 OpenAI has achieved groundbreaking milestones in 2023, marking a pivotal year for the advancement of artificial intelligence. Breakthrough Models: GPT-4: The successor to ChatGPT, GPT-4 boasts enhanced reasoning, factual language understanding, and code generation capabilities. Its beta release sparked discussions on AI's potential to revolutionize diverse fields. DALL-E 2: OpenAI's text-to-image model, DALL-E 2, pushes boundaries in image generation, inspiring creative exploration across various industries with its realistic and imaginative outputs. CLIP: A vision-language model, CLIP excels in understanding text-image relationships, offering unmatched accuracy for tasks like image search and captioning. Bridging modalities, CLIP has profound implications for image retrieval and multimodal search engines. 2023 Summary: 🛡️ Safety and Alignment: OpenAI prioritized safety and alignment research, focusing on interpretability, bias mitigation, and responsible AI development practices, ensuring AI systems align with human values. 🤝 Collaboration and Openness: OpenAI actively collaborated with research institutions, fostering open dialogue for responsible AI development, aiming to ensure AI advancements benefit society at large. 🌐 Impact on Fields: OpenAI's models are already influencing healthcare, education, and creative industries. Language models aid in drug discovery and personalized learning, while DALL-E 2 finds applications in product design and advertising. Looking Ahead: 🌍 Scaling for Impact: OpenAI aims to develop practical AI applications addressing real-world challenges across sectors. 🤖 Ethical Concerns: Continuing research on safety, fairness, and transparency to align AI development with ethical considerations. 🤝 Understanding and Collaboration: Fostering open dialogue and collaboration with the public and policymakers to build trust and understanding of AI. As OpenAI propels AI advancements, the future holds exciting developments that promise to shape our world profoundly. Stay tuned for more transformative strides! #OpenAI #AIInnovation #FutureTech #ArtificialIntelligence #TechAdvancements

Enjoy a wonderful ride through the world of having an understandable interaction with AI through: INTRODUCTION TO PROMPT ENGINEERING FOR GENERATIVE AI The Rise of AI and the Need for Effective Communication Artificial intelligence (AI) has permeated every facet of our lives, from streamlining daily routines to revolutionizing industries. Large language models (LLMs) are at the forefront of this AI revolution, capable of generating human-quality text, translating languages, writing different kinds of creative content, and answering your questions in an informative way. However, to harness the full potential of these powerful tools, we must master the art of communication with them – a skill known as prompt engineering.   Understanding Prompt Engineering Prompt engineering is the process of crafting effective prompts or inputs to guide AI models in generating desired outputs. It's a kin to providing clear instructions to a highly intelligent assistant. By understanding how these models function, we can significantly enhance their capabilities and the quality of their responses. How Large Language Models Work LLMs are trained on massive amounts of text data, learning patterns and relationships between words. When presented with a prompt, they generate text by predicting the most likely word or phrase to follow based on their training. This predictive nature is the foundation of their ability to create human-like text. Prominent examples of LLMs include OpenAI's GPT series (GPT-1, GPT-2, GPT-3, GPT-4), Meta's Jurassic-1 Jumbo, and image generation models like Dall-E, Midjourney, and Stable Diffusion. The Role of Tokens At the core of AI communication is the concept of tokens. These are the smallest units of text that LLMs can process. Words like "everyday" consist of two tokens: "every" and "day." Tokenization is the process of breaking down text into these units, enabling the model to understand and process information efficiently. Conclusion Prompt engineering is an emerging field with immense potential. As AI continues to evolve, the ability to effectively communicate with these models will become increasingly valuable. By understanding the fundamentals of how LLMs work and mastering the art of crafting prompts, we can unlock the full power of AI and drive innovation across various domains. #My3MTT #3MTTWeeklyReflection

AI hallucinations present significant challenges as AI systems like GPT-3 and GPT-4 advance. These hallucinations occur when AI generates false or misleading content that appears coherent but is factually incorrect. The causes include limitations in training data, overgeneralization, and lack of real-world grounding. To address this, strategies such as improved training techniques, enhanced model architectures, and human-AI collaboration are essential. Ongoing research, ethical guidelines, and user education are critical to harness AI's potential while minimizing risks. Stay informed and critical of AI-generated content. #ResponsibleAi https://lnkd.in/gxKtKwrw

OpenAI has developed a new method to make AI-generated text more accurate and easier for humans to understand by using prover-verifier games. - 🧠 The "prover" AI generates solutions while the "verifier" AI checks for accuracy. - 🔍 The method balances accuracy with legibility, enhancing trust in AI applications. - 🏥 This approach is crucial for fields like medicine, finance, and law. #AI #MachineLearning #Innovation - 📈 Prover-verifier games involve alternating roles to ensure solutions are clear and verifiable. - 🔬 The method reduces human evaluator errors, making AI outputs more reliable. - 💼 Enhancing AI trust is essential for its broader use in critical domains. https://lnkd.in/gKvhuJG3

"Frontier" AI has become a new global agenda. With growing concerns over potential risks that generative AI such as ChatGPT, global leaders have launched initiatives on AI: U.S. President Joe Biden's executive order on AI in October 2023; the EU's AI Act, which was endorsed by the European Parliament in March 2024; November 2023 Joint Declaration on the way of dealing with the risks of AI, which was signed by U.S., EU and, notably, China. However, while we often hear global leaders saying "AI is potentially harmful", some people might be unclear about how generative AI would change the status-quo. Thus, it is of great importance to firstly crystallize what generative AI is and its potential benefits/risks. We've provided insights in the following article on the current place of AI and implications for future regulations on generative AI. Don't miss out on keeping up with the trend of advanced technology. Read the full article here https://lnkd.in/gJhUrVwV #AI #Technology #ChatGPT #US #China #EU #AIACt #GenerativeAI #International Affairs #MonolithLaw

OpenAI's Masterstroke: OpenAI introduced a new 5-tier system to track its progress toward Artificial General Intelligence (AGI). OpenAI has unveiled an incredible new 5-tier system that is set to transform the landscape of artificial intelligence as we know it. ↳ Level 1: Chatbots & Conversational AI At this foundational level, we have AI systems designed to communicate with humans in natural language. ↳ Level 2: Reasoners This level will implement AI with human-level problem-solving capabilities. ↳ Level 3: Agents At this stage, AI systems become agents that can take actions based on their decisions. ↳ Level 4: Innovators AI, that aids in invention. Innovators are AI systems that can aid in the creation of new technologies and solutions. ↳ Level 5: Organization AI, that works for an entire organization. These advanced AI systems can manage complex operations, strategize, and execute tasks that typically require a whole team of humans. OpenAI currently classifies itself at Level 1 and is nearing Level 2, which means they are closer to developing problem-solving AI, a significant step towards AGI. Although these advancements will unfold gradually over time, the future of AI promises an exciting journey of innovation and progress. #AritificialIntelligence #OpenAI #chatGPT #innovation #futureAI

Fascinating mid-week read! We find ourselves currently consumed by Generative AI, trying to get better and more creative with our prompts to get the 'goldilocks answer' to our query. As our dependency on AI increases, so does AI's on us, resulting in an unquenchable thirst for more specialized knowledge and data. Fueled by this massive data and internal algorithms, Gen AI is unlocking patterns and proposing theories beyond human capacity and comprehension. In this scenario, it might be tempting to consider the prospect of 'machines teach machines'. However, this sort of self-learning could lead to an 'unintelligible intelligence'. There's the danger of hallucinations and biases getting further entrenched into the supposedly more intelligent models and also the very real danger of anti-bias learning resulting in the creation of new biases altogether. "Perhaps we will ignore the outputs of some AI models, even if they are later found to be true, simply because they are incommensurate with what we currently understand—math proofs we can’t yet follow, brain models we can’t explain, knowledge we don’t recognize as knowledge. The ceiling provided by the internet may simply be higher than we can see. Whether self-training AI leads to catastrophic disaster, subtle imperfections and biases, or unintelligible breakthroughs, the response cannot be to entirely trust or scorn the technology—it must be to take these models seriously as agents that today can learn, and tomorrow might be able to teach us, or even one another." All this and more in this great read from The Atlantic on Unintelligible Future! https://lnkd.in/dg4JiQEX #GenAI #AI #ArtificialIntelligence https://lnkd.in/dg4JiQEX