Problem-Solving with Artificial Intelligence

𝗦𝘁𝗼𝗽 𝗮𝘀𝗸𝗶𝗻𝗴 𝗔𝗜 "𝗛𝗼𝘄 𝗰𝗮𝗻 𝘆𝗼𝘂 𝗵𝗲𝗹𝗽 𝗺𝗲?" 𝗦𝘁𝗮𝗿𝘁 𝗮𝘀𝗸𝗶𝗻𝗴 "𝗪𝗵𝗮𝘁 𝘀𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝗮𝗺 𝗜 𝘀𝗼𝗹𝘃𝗶𝗻𝗴?" Most people open ChatGPT and type vague requests like "help me with marketing" or "give me business ideas."  Then they wonder why the responses feel generic. The issue isn't the AI. It's your question. Problem definition beats prompt engineering every time. Instead of: "Help me grow my business" Try this: "My sales team is missing 30% of quarterly targets. Deals slowed from 60 to 90 days. Each missed quarter costs $2M in projected revenue." Now AI can actually help you. With a clear problem, you can ask targeted questions:  • Analyze patterns in top-performing deals  • Research what drives faster sales cycles in your industry • Generate hypotheses about pipeline bottlenecks 𝗧𝗵𝗲 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗶𝘀 𝘀𝗶𝗺𝗽𝗹𝗲: 1. Define the specific problem and its business impact 2. Quantify what success looks like 3. Use AI to research and validate solutions Six months of applying this approach will transform how you work.  Not because you become an AI expert, but because you master problem definition. The best AI users aren't prompt engineers. They're problem definers. 𝗥𝗲𝗮𝗱 𝘁𝗵𝗲 𝗳𝘂𝗹𝗹 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿 𝗵𝗲𝗿𝗲: https://lnkd.in/eHDpy-fn Found this helpful?  𝗟𝗶𝗸𝗲 𝗮𝗻𝗱 𝗿𝗲𝗽𝗼𝘀𝘁 to share with your network. 𝗙𝗼𝗹𝗹𝗼𝘄 𝗺𝗲 for more insights on using AI strategically in business. Got a specific problem you're trying to solve? 𝗗𝗠 𝗺𝗲 - I'd love to hear about it.

Stop spoon-feeding AI. Here's how to unleash its true potential. These advanced models like ChatGPT O1 require a different approach to prompting compared to their predecessors. Here's how to optimize your interactions with these cutting-edge AI systems: Here's how you can leverage advanced reasoning capabilities 𝗣𝗿𝗲𝘀𝗲𝗻𝘁 𝗖𝗼𝗺𝗽𝗹𝗲𝘅, 𝗢𝗽𝗲𝗻-𝗘𝗻𝗱𝗲𝗱 𝗣𝗿𝗼𝗯𝗹𝗲𝗺𝘀 - Instead of breaking down tasks, present the entire problem at once. - Allow the AI to devise its own problem-solving strategy. 𝗠𝗶𝗻𝗶𝗺𝗶𝘇𝗲 𝗣𝗿𝗲𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝘃𝗲 𝗜𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻𝘀 - Avoid providing step-by-step guidelines. - Give the AI freedom to develop novel approaches. 𝗘𝗻𝗰𝗼𝘂𝗿𝗮𝗴𝗲 𝗦𝗲𝗹𝗳-𝗗𝗶𝗿𝗲𝗰𝘁𝗲𝗱 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 - Ask the AI to break down the problem before solving it. - Let it identify key components and potential challenges. 𝗣𝗿𝗼𝗯𝗲 𝗳𝗼𝗿 𝗗𝗲𝗲𝗽𝗲𝗿 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 - Request explanations for the AI's thought process. - Ask "why" and "how" questions to explore its reasoning. 𝗙𝗮𝗰𝗶𝗹𝗶𝘁𝗮𝘁𝗲 𝗠𝘂𝗹𝘁𝗶-𝗦𝘁𝗲𝗽 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 - Present scenarios that require several logical leaps. - Allow the AI to make and explain intermediate conclusions. 𝗘𝘅𝗽𝗹𝗼𝗿𝗲 𝗛𝘆𝗽𝗼𝘁𝗵𝗲𝘁𝗶𝗰𝗮𝗹 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼𝘀 - Pose "what if" questions to test the AI's ability to extrapolate. - Encourage creative problem-solving in novel situations. 𝗟𝗲𝘃𝗲𝗿𝗮𝗴𝗲 𝗜𝗻𝘁𝗲𝗿𝗱𝗶𝘀𝗰𝗶𝗽𝗹𝗶𝗻𝗮𝗿𝘆 𝗖𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝗼𝗻𝘀 - Present problems that span multiple domains. - See how the AI synthesizes information across different fields. Here are some Best Practices I found: 𝗦𝘁𝗮𝗿𝘁 𝗕𝗿𝗼𝗮𝗱, 𝗧𝗵𝗲𝗻 𝗡𝗮𝗿𝗿𝗼𝘄 Begin with general queries and progressively focus based on the AI's responses. 𝗘𝗺𝗯𝗿𝗮𝗰𝗲 𝗔𝗺𝗯𝗶𝗴𝘂𝗶𝘁𝘆 - Don't shy away from unclear or incomplete information. - See how the AI handles uncertainty and makes reasonable assumptions. 𝗠𝗼𝗻𝗶𝘁𝗼𝗿 𝗳𝗼𝗿 𝗕𝗶𝗮𝘀𝗲𝘀 - Be aware that even advanced AIs can have biases. - Ask for multiple perspectives on sensitive topics. 𝗦𝘁𝗮𝘆 𝗔𝗱𝗮𝗽𝘁𝗮𝗯𝗹𝗲 - Be prepared to shift your approach based on the AI's responses. - Remain open to unexpected insights or solutions. 𝗩𝗲𝗿𝗶𝗳𝘆 𝗮𝗻𝗱 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗲 - While these AIs are highly capable, always cross-check critical information. - Use the AI's output as a starting point for further research or analysis.

🌟 Solving Complex Reasoning in Language Models: The “Tree of Problems” Framework by Inria Researchers We all have witnessed the power of large language models (LLMs) in executing advanced tasks like text generation, summarization, and translation. However LLMs still struggle with complex, multi-step reasoning. Traditional models often miss critical steps in complex tasks, leading to incomplete outcomes, especially when sequential decision-making is needed. Inria’s Tree of Problems (ToP) framework simplifies this problem-solving by breaking down tasks into smaller, manageable subproblems. Here’s how it works: 📍 Decomposition: Divides a main task into related subtasks, creating a hierarchical tree. 📍 Independent Solving: Each subproblem is tackled individually by a task-specific LLM. 📍 Merging Solutions: Solutions are combined bottom-up, creating an accurate final answer. 🧩 Results & Impact Empirical results show ToP’s impressive gains: ⬆ Sorting Tasks: 40% accuracy improvement over traditional methods. ⬆ Set Intersection: 19% boost in accuracy. ⬆ Keyword Counting: 5% accuracy gain. In sequential tasks like tracking and decision-making, ToP also outperformed other structured approaches with fewer computational calls. In the coming months, I think we will see more frameworks like ToP that will help LLMs handle complex reasoning efficiently, thereby enhancing their application in real-world settings. #AI #MachineLearning #ArtificialIntelligence #LanguageModels #TreeOfProblems #LLM #ComplexReasoning #NaturalLanguageProcessing #InriaResearch #AIFramework #TechInnovation #FutureOfAI #DeepLearning #TechForGood #AIApplications #Innovation

From Scientific Breakthroughs to Life-Saving Solutions with Google AI Google’s cutting-edge AI tools are reshaping science and delivering real-world impact: #AlphaFold 3 (Google DeepMind): Decoded 200M+ protein structures, driving breakthroughs in treatments for diseases like malaria and antibiotic resistance. #Connectomics AI (Brain Mapping): Partnered with Harvard to map the human brain at microscopic detail, advancing cures for Alzheimer’s and epilepsy. #GraphCast: Provides 10-day weather forecasts faster and more accurately, predicting cyclones and flooding with unmatched precision. #FloodHub: Expanded flood predictions to 100+ countries, protecting 700M+ people with 7-day lead times in regions like Bangladesh. #FireSat: Detects classroom-sized wildfires in 20 minutes, empowering firefighters to save lives and natural resources. #Quantum AI (Chemistry Simulations): Worked with UC Berkeley to simulate complex chemical reactions, paving the way for advanced batteries, solar cells, and carbon capture. #AIforFusion: Stabilized plasma inside nuclear reactors, bringing us closer to clean, limitless fusion energy. #GNoME (Graph Networks for Materials Exploration): Discovered 380,000 new stable materials for sustainable solar cells, batteries, and superconductors. #AlphaGeometry 2 & #AlphaProof: Solved 83% of International Math Olympiad geometry problems, enabling AI to assist with advanced mathematical discoveries. These tools are transforming challenges into solutions, redefining what’s possible with AI. What would you like AI doing next? #AI #GoogleDeepMind #FloodHub #QuantumAI #FireSat #Innovation #accessAlchemy read more : https://lnkd.in/g9YF9Z89

Problem Solving: The Art of Navigating Complexity in the AI Era I've learned that in enterprise settings, problems rarely come with neat definitions or clear boundaries. They're messy, interconnected, and often evolving as we work on them, and solutions dont appear magically; you have to work on them from multiple perspectives. While AI excels at solving well-defined problems, the uniquely human skill lies in unpacking complexity by breaking down ambiguous challenges into workable components. This means becoming comfortable with uncertainty, asking better questions, and resisting the urge to jump to solutions. It's like compound interest for problem-solving; the more you invest in understanding the problem space, the greater your returns in solution effectiveness. The most effective problem solvers I work with have mastered four capabilities:   1. Deconstructing multi-layered problems into manageable pieces   2. Studying the problem from different perspectives.   3. Iterating rapidly between hypothesis and testing, and   4. Synthesizing insights across domains and stakeholders. However, I've discovered that AI can serve as an exceptional thought partner in this iterative process. When facing complex challenges, I utilize AI to stress-test my hypotheses, explore potential blind spots I might miss, and rapidly prototype various solutions to the problem. It's like having an always-on collaborator, and a whole slew of subject matter experts in different domains who can help you think through multiple scenarios simultaneously. The future belongs to leaders who can dance with ambiguity while maintaining human agency in defining problems and making decisions. With AI as our thought partner, every one of us can now possess superpowers, accessing knowledge in any domain and accelerating thinking cycles that once took weeks and months to complete, now into minutes and hours. Foundry for AI by Rackspace (FAIR™) D Scott Sanders Ben Blanquera #ProblemSolving #AI #Leadership #CriticalThinking #EnterpriseSolutions #FutureOfWork #ComplexSystems

Neuro-Quantum Breakthrough: A Revolutionary Path to Discovering Optimal Solutions Introduction In a remarkable convergence of neuroscience, quantum mechanics, and artificial intelligence, Professor Shantanu Chakrabartty of Washington University in St. Louis has unveiled NeuroSA, an innovative computational framework poised to redefine complex problem-solving. By blending insights from human neurobiology with quantum principles, NeuroSA opens new possibilities for tackling some of the toughest optimization problems across industries—from logistics to drug discovery. Key Details The Challenge of the “Discovery Problem” • Traditional AI and machine learning algorithms often excel at solving problems using known pathways or memorized sequences. • However, they struggle with the “discovery problem”—finding new, optimal solutions without pre-established rules or steps. • Chakrabartty likens this distinction to solving a Rubik’s Cube: • Solving by memorization = algorithmic efficiency. • Discovering a new method to solve = creative optimization. • NeuroSA is explicitly designed to address this higher-level cognitive challenge. How NeuroSA Works • NeuroSA integrates neuromorphic computing (mimicking brain-like structures and processes) with quantum mechanical principles. • This hybrid approach enables: • Nonlinear, probabilistic exploration of solution spaces. • Dynamic adaptation to evolving problem constraints. • Parallel evaluation of multiple potential solutions simultaneously. • By leveraging both biological and quantum mechanisms, NeuroSA can navigate complex, high-dimensional optimization landscapes more efficiently than conventional algorithms. Key Achievements and Applications • Demonstrated superior performance in logistics optimization, drug molecule design, and multi-objective problem solving. • Reduced computation time while achieving more novel and diverse solutions compared to classical methods. • Offers a scalable framework for industries requiring adaptive, real-time optimization under uncertain conditions. Why This Discovery Matters A Paradigm Shift in AI Problem-Solving • NeuroSA represents a departure from rigid, stepwise AI algorithms, moving toward systems capable of autonomous discovery akin to human creative thinking. • Bridges the gap between symbolic AI and biologically inspired computing, expanding AI’s problem-solving toolkit. Why This Matters The introduction of NeuroSA is more than a computational improvement—it signifies a transformational leap in how machines can “think” and discover. By emulating the human brain’s adaptability and combining it with quantum computing’s exploratory power, NeuroSA brings us closer to AI systems that don’t just solve problems faster but imagine and create new solutions previously beyond computational reach. Keith King https://lnkd.in/gHPvUttw

Interest in AI Agents has surged, and it’s no wonder - they offer powerful ways to tackle tasks, learn dynamically, and collaborate with other AI systems. Here’s an at-a-glance overview of what makes these agents tick, their capabilities, and the reasoning frameworks that drive them. Core Components of an AI Agent - Agent Core: Think of this as the agent’s “central processor,” where data flows in and decisions flow out. - Memory Module: Maintains context over time - like a long-term memory that helps the agent learn from past tasks. - Perception Module: Interprets incoming data from the environment (could be sensor inputs, text prompts, or other signals). - Planning Module: Devises strategies and outlines steps to achieve goals or solve complex problems. - Action Module: Executes the chosen plan - taking the agent’s internal decisions and making them happen in the real (or virtual) world. - Tools Integration: Allows the agent to tap into external services, APIs, or tools, expanding its capabilities beyond built-in functions. What Can They Do? - Advanced Problem Solving: These agents can juggle multiple tasks: creating project outlines, writing and debugging code, or summarizing lengthy documents. - Self Reflection and Improvement: They can critique their own outputs and refine them, learning from mistakes or inefficiencies to consistently raise their performance. - Tool Utilization: By leveraging specific tools (like running unit tests or web searches), agents can check and validate their outputs on the fly, then adjust as needed. - Collaborative AI: Picture a multi-agent setup where one agent proposes ideas, and another offers critical feedback. This iterative give-and-take elevates quality and depth of results. Reasoning Approaches - Chain of Thought (CoT): Breaks problems into smaller steps, improving clarity and accuracy. - ReAct (Reasoning and Acting): Weaves together the thought process and the agent’s actions, adjusting to new information in real time. - Tree-of-Thoughts (ToT): Similar to CoT but takes it a step further, branching out ideas so the agent can explore multiple paths and backtrack if needed. Ready for 2025? AI Agents could cause a major shift in how we work, innovate, and collaborate. From planning tasks to critiquing themselves for continuous improvement, they’re set to become core players in businesses and research labs alike. Which aspect of AI agents do you find most compelling? #innovation #technology #future #management #startups

A recent paper (https://lnkd.in/egxPKsNe) has highlighted a game-changing leap in AI—using reinforcement learning to move from Conversational AI to Reasoning AI. This shift is monumental for building AI systems. While traditional conversational AI generates responses based on patterns, reasoning AI takes it further by: - Taking actions and making decisions: Agents can now proactively solve problems, not just respond. - Adapting dynamically: With reinforcement learning, AI gains flexibility, adjusting to new tasks and environments. - Thinking beyond prompts: Reasoning capabilities bridge the gap between static dialogue and dynamic problem-solving. This innovation unlocks goal-oriented workflows where AI acts like a collaborator, not just a tool. It’s a huge step forward for creating adaptable, autonomous systems that can truly transform how we work and interact with technology. Thoughts on this new frontier? Let’s discuss! #AI #ReinforcementLearning #ReasoningAI #LLMs