Apps Consultants

?? AI Takes Scientific Discovery to New Heights! ?? MIT's latest breakthrough, SciAgents, leverages multi-agent AI systems and intelligent knowledge graphs to autonomously generate and refine research hypotheses with unmatched creativity and speed, surpassing traditional human-driven methods. ???? From bio-inspired materials to cross-disciplinary innovations, AI is poised to lead the charge in scientific discovery by uncovering hidden connections in scientific data that human researchers might miss. ???? How can AI revolutionize R&D in various domains? How can AI help organizations innovate? Post your comments below. The full paper link is in the comments below. ?? #AI #Science #Innovation #MaterialsScience #ResearchRevolution #ArtificialIntelligence #ScientificDiscovery

?? Multi-Agent Systems for Complex Problem Solving! ?? For solving real world business challenges, we may have to explore multiple perspectives . The latest research on Agent-Oriented Planning in Multi-Agent Systems introduces a game-changing approach to harness AI potential for real-world problems. ?? What’s the breakthrough? The study unveils a novel framework with a "meta-agent" orchestrating specialized agents, breaking down complex queries into actionable sub-tasks. This ensures solvability, completeness, and non-redundancy, leading to 10-15% higher accuracy in solving complex tasks compared to traditional systems. ?? Why should Business and IT leaders care? Multi-agent systems can revolutionize decision-making and automation in supply chain management, finance, and cross-functional operations. By streamlining task management, automating problem-solving, and reducing costs, organizations can drive efficiency and effectiveness. ?? Ready to explore how multi-agent systems can work for your business? This is about leveraging cutting-edge AI to drive real business outcomes. Let’s chat about how your organization can benefit from this innovative approach! ???? Link to paper in the comments below. #ArtificialIntelligence #MachineLearning #BusinessTransformation #AI #MultiAgentSystems #TechInnovation #BusinessIntelligence

?? Could Complexity Be the Catalyst for True AI Intelligence? ?? What if developing intelligent AI wasn’t about massive datasets or sophisticated architectures, but also about exposure to complexity? A recent study from Yale, Columbia, and Northwestern Universities challenges conventional AI wisdom by suggesting that complexity might be a necessity to spark intelligence. By training models on simple rule-based systems that generate complex behaviors, the researchers found that models exposed to the "edge of chaos"—a zone between order and randomness—developed enhanced reasoning abilities. This discovery raises intriguing possibilities: ?? Data as a Tool for Intelligence: Instead of focusing solely on data volume or model size, future research can explore how varying the complexity of training data shapes a model’s adaptability and problem-solving skills. Could we optimize LLM performance by curating data that strikes the perfect balance between predictability and chaos? ?? Insights into Human Cognition: The “edge of chaos” isn’t just an AI concept—it’s thought to reflect how the human brain operates, balancing structure and randomness. Could understanding AI behavior at this edge help us decode the underlying mechanisms of human intelligence? These findings could reshape the way we think about training AI. Imagine AI models that are more robust, adaptable, and capable of true intelligence—simply by learning to navigate complexity. It’s a thought worth exploring, whether you’re building the next-gen AI systems or pondering the intricacies of our own cognitive processes. https://lnkd.in/gGB-XCkU #AI #MachineLearning #Complexity #EmergentIntelligence #TechResearch #DataScience #Innovation

https://lnkd.in/gU2X_iMC

I have been testing the 01-Preview for a few days and I believe that chaining of prompts and looping constructs is still important especially in complex domains. In a simple test where I have asked o1-Preview to solve a New York times puzzle, it needed guard rails and direction. So building AI agents with these models requires more than prompting an understanding of the structure of the problem space, the properties of the solution space such as an understanding of the implicit cognitive judgements that humans tend to make over the results. Even with the simple problem, the o1-preview model made mistakes and all of the mistakes had to be fed back as a prompt. To implement something So fine-tuning of the o1-preview models with guard rails, parameters on which to reflect on, mental model archetypes to think about the problem like an expert and a knowledge base will see a jump in the tasks that we assign AI rather than relegate it to a low skilled laborer working on mundane and boring tasks. So I agree about the thin wrappers over LLM only if they are not creative and do not embed any domain expertise. As far as fine-tuning and the use of multiple prompts I believe that to build a fully observable data pipeline, it still better to break up the problem solving into chunks of sub-problems and create a prompt for each sub-problem so that there is better visibility in how the solution is working and when it fails where it is failing. AI will fail as do humans and we need to be forgiving and provide appropriate feedback to correct and improve itself over time.

Impressive Performance. Just requires patience and specific feedback that is actionable for these models to improve and surpass human level performance.

?? AI is breaking barriers in creativity! A recent Stanford study reveals that AI-generated research ideas are deemed more novel and creative than those from human experts! ???? Could this signify a new era where AI leads the way in innovation? ??? Exciting times ahead as AI evolves from a tool to a true partner in groundbreaking discoveries. Are we witnessing the rise of AI as the ultimate creative force? ???? #AI #Creativity #Innovation #ResearchRevolution #ArtificialIntelligence #FutureOfWork. Find out more in the Stanford paper: https://lnkd.in/guNPnmbU

A significant part of my current work involves constructing AI applications using different frameworks. Beyond the frameworks, it's crucial to distill domain knowledge and reassess problems within workflows through a business lens. I recently wrote an article focusing on a measure theoretic approach to assess large language models across diverse business domains. #AI #BusinessPerspective #realworldAI

Dear Professionals and Enthusiasts, We are excited to share our detailed case study on the innovative use of Generative AI in skill building and evaluation. This document explores how cutting-edge AI technologies can enhance the evaluation process for skilled professionals. Why Download? Innovative Insights: Understand the impact of Generative AI on professional development. Real-World Applications: See how AI is used across various professions. Success Stories: Learn about the challenges and solutions in AI-driven skill evaluation. Get Your Copy Simply provide your email to download the full case study: Download Case Study @ https://lnkd.in/gq-NA7an Unlock the Future of Skill Evaluation! For more information, contact us at [email protected]. Best regards, Apps Consultants

Art of Questioning from Apps