AI-Powered Decision-Making Systems

Most AI solutions in the energy industry operate as complete black boxes, delivering recommendations without any insight into their underlying reasoning or decision making process. When you're managing millions of dollars in production assets, this lack of clarity creates a fundamental trust problem that goes far beyond simple technology preferences. Our AI Driven Lift Advisor represents a fundamentally different approach to artificial intelligence in energy operations, where every recommendation comes with complete transparency and full traceability back to its source data. This means understanding exactly why the system recommends one production optimized plan of attack over any other, how specific reservoir conditions influence production choices, and what happens when operational variables change over time. The difference between traditional AI and truly explainable AI becomes crystal clear when you're optimizing artificial lift systems and production performance across multiple wells, making critical decisions about ESP versus gas lift configurations, or determining the optimal timing for equipment conversions. - Every insight traces directly back to specific reservoir performance data, equipment sensors, and historical production records - Decision logic remains completely transparent, allowing operators to understand and validate each recommendation before implementation - Confidence in production optimization increases dramatically when you can see exactly how the AI reached its conclusions - ROI becomes measurable and verifiable because you understand the complete analytical pathway Traditional AI platforms tell you what to do without explaining their reasoning, but our approach shows you exactly why each recommendation represents the optimal choice for your specific operational context. When you're faced with breathing new life into a mature field, extending well life, reducing production decline, or maximizing recovery efficiency, you need AI that doesn't just perform at a high level, it explains every step of its analytical process. In energy operations, trust isn't just a nice to have feature, it's the foundation of every critical decision. The connections between your reservoir characteristics, equipment performance data, and production optimization opportunities already exist within your operational environment. Remember, you're not missing data, you're missing the connections in your data that matter. We simply make those connections visible, traceable, and actionable. What's your biggest challenge with current AI based approaches to production optimization? Follow me, Jon Brewton for daily insights about the intersection of energy and explainable AI!

In daily life, we often make decisions based on incomplete information and uncertainty. This is because we rarely have access to all the facts or a clear view of future outcomes. Humans navigate this uncertainty using a combination of experience, intuition, educated guesses, and by weighing the pros and cons of different options. We essentially build and evaluate arguments based on the information we have, even if it's incomplete. Translating this human approach to decision-making into a computational context is where computational argumentation plays a crucial role. It involves programming AI systems to mimic the way humans reason with incomplete information. This is done by: 1. Modeling Arguments and Counterarguments: AI systems are programmed to represent different perspectives or solutions as arguments, similar to how a human would consider different sides of an issue. 2. Dealing with Uncertainty: These systems are designed to handle uncertainty in data or knowledge. This might involve assigning probabilities to different arguments or considering the reliability of information sources. 3. Evaluating Arguments: The AI evaluates which arguments are stronger, taking into account various factors like the amount and quality of evidence supporting each argument. 4. Dynamic Learning: As new information becomes available, the system updates its arguments and evaluations, much like a human revising their opinion in light of new facts. 5. Prioritizing and Decision-Making: Finally, the system uses these evaluations to prioritize certain arguments and make a decision, even if it's not based on complete certainty. Computational argumentation is crucial for AI systems in areas like law, medicine, and public policy, sales, where decisions often have to be made with incomplete information. By programming AI to reason in a way that's similar to human argumentation, we enable these systems to make more informed, nuanced, and context-aware decisions. Introduction to computational argumentation: https://lnkd.in/efRtgr6w Deeper insights and methods in this Paper: https://lnkd.in/ez5RRQeB

Large Language Models (LLMs) are powerful, but their true potential is unlocked when we structure, augment, and orchestrate them effectively. Here’s a simple breakdown of how AI systems are evolving — from isolated predictors to intelligent, autonomous agents: 𝟭. 𝗟𝗟𝗠𝘀 (𝗣𝗿𝗼𝗺𝗽𝘁 → 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲) This is the foundational model interaction. You provide a prompt, and the model generates a response by predicting the next tokens. It’s useful but limited — no memory, no tools, no understanding of context beyond what you give it. 𝟮. 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 (𝗥𝗔𝗚) A major advancement. Instead of relying solely on what the model was trained on, RAG enables the system to retrieve relevant, up-to-date context from external sources (like vector databases) and then generate grounded, accurate responses. This approach powers most modern AI search engines and intelligent chat interfaces. 𝟯. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗟𝗟𝗠𝘀 (𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 + 𝗧𝗼𝗼𝗹 𝗨𝘀𝗲) This marks a shift toward autonomy. Agentic systems don’t just respond — they reason, plan, retrieve, use tools, and take actions based on goals. They can:  • Call APIs and external tools  • Access and manage memory  • Use reasoning chains and feedback loops  • Make decisions about what steps to take next These systems are the foundation for the next generation of AI applications: autonomous assistants, copilots, multi-step planners, and decision-makers.

Can you trust a black box making critical decisions? AI is everywhere these days, from approving loans to filtering your social media feed. But often these models are opaque, like a black box, spitting out results without revealing their thought process. This lack of transparency can be a major roadblock to trusting AI. That's where Explainable AI (XAI) comes in! XAI is a set of techniques that helps us understand how AI models arrive at their decisions. This not only builds trust but also helps identify potential biases and improve model performance. Even with XAI, ensuring the explanations themselves are authentic and tamper-proof is important. This is where Blockchain can be a game-changer! Blockchain can act as a decentralized auditing ledger for XAI. Imagine an immutable record of explanations, accessible to everyone, that can't be tampered with. This fosters trust and transparency in AI like never before. The future of AI is bright, and with XAI and Blockchain working together, in a monitored environment government and agencies can ensure AI is responsible and trustworthy. #AI #ExplainableAI #Blockchain #ResponsibleAI #FutureofTech #XAI

I'm excited to share my latest research paper on the potential of AI-driven debates as a tool for advancing understanding and decision-making across a wide range of domains, from philosophy and theology to policy and governance. By leveraging recent advances in machine learning, natural language processing, and multi-agent systems, we propose a novel approach to idea exploration and argumentation that could transform the way we engage with complex issues and make important decisions as a society. Our methodology involves training AI models to represent different stakeholder perspectives, engaging them in structured, iterative debates, and using the outputs as "synthetic data" to train higher-level models for decision-making and policy analysis. The potential applications are vast, from stress-testing policy proposals and building consensus on contentious issues, to exploring scientific hypotheses and advancing philosophical and ethical understanding. While there are important challenges and considerations to keep in mind, such as ensuring fairness, transparency, and accountability, we believe this approach has the potential to create a future in which our most important decisions are informed by rigorous, data-driven debates that draw on the best available evidence and reasoning. By embracing this bold and ambitious vision, we can harness the power of AI to help us navigate the complex challenges we face as a society and unlock new frontiers of human knowledge and understanding. Read the full paper to learn more about this exciting new direction in AI research and its implications for the future of decision-making and problem-solving. #AIDebates #MachineLearning #DecisionMaking #MultiFaith #Argumentation #Reasoning #SyntheticData #MultiAgentSystems #FutureOfAI #KnowledgeAdvancement #ComplexProblemSolving

Top CEOs often wish they had a deeper understanding of AI, not for its technical prowess, but for its capacity to shape a company's strategic direction. In the echelons of corporate leadership, it's not about programming Python or training neural networks. It's about understanding AI's transformative potential in the broader business landscape. Imagine being able to foresee the future impact of AI on your industry, the potential for reshaping business models, and the ability to harness AI's predictive powers for strategic decision making. This is the unspoken AI skill set that is not often discussed but is highly coveted amongst top CEOs. The value of AI lies not just in its algorithms, but in the strategic applications of those algorithms. The ability to comprehend AI's potential for disruption, innovation, and competitive advantage can be a game-changer. But remember, it's not a solo journey. Building a culture of AI literacy across your organisation is equally vital. It's about fostering an environment where everyone understands AI's potential and how to leverage it effectively. It's high time we looked beyond the technical acumen and started focusing on the strategic implications of AI. Ready to embrace the unspoken AI skills? Start today. Read. Learn. Discuss. Apply. Your strategic foresight can be the catalyst for your company's AI revolution.

You know how they say "if you do what you've always done, you'll get what you've always gotten"? The opposite is true — and increasingly your secret weapon: uncover insights no one else is paying attention to, and you're at an immediate advantage. We live a world where virtually any type of information, answer, or research is at our fingertips. All that's required is a little time, effort, and ideally knowledge of Boolean or prompt engineering / plug-ins to find those answers. In the words of Seth Godin: "if there’s something I don’t know, it’s almost certainly because I haven’t cared enough to find out." Between your day job, side projects, admin tasks, endless meetings, and fire drills, one source you probably don’t have time (or the desire…) to tap is 𝗮𝗰𝗮𝗱𝗲𝗺𝗶𝗰 𝗽𝗮𝗽𝗲𝗿𝘀. But especially re: how AI might transform the future of strategy & innovation work, academia is an important beacon of what’s to come. Grab my summaries of 4 recent papers on developments that could impact you soon👇 1️⃣ 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗺𝗮𝗸𝗶𝗻𝗴 𝗰𝗼𝘂𝗹𝗱 𝘀𝗼𝗼𝗻 𝗴𝗲𝘁 𝗮 𝗯𝗼𝗼𝘀𝘁 𝗳𝗿𝗼𝗺 𝗔𝗜 Researchers recently investigated the potential role of GenAI in evaluating strategic alternatives. 💡 𝗞𝗲𝘆 𝗶𝗻𝘀𝗶𝗴𝗵𝘁: the strategic decision making process may no longer be exclusively human-centric; it will increasingly incorporate AI as a co-contributor, offering insights based on its “enhanced computational capabilities and sophisticated information analysis” that add to human judgment and expertise. 2️⃣ 𝗢𝗵 𝘁𝗵𝗲 𝗽𝗹𝗮𝗰𝗲𝘀 𝘄𝗲 𝗰𝗼𝘂𝗹𝗱 𝗴𝗼 𝘄𝗶𝘁𝗵 𝗮𝗻 “𝗶𝗻𝘀𝗽𝗶𝗿𝗮𝘁𝗶𝗼𝗻 𝗿𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹” 𝗲𝗻𝗴𝗶𝗻𝗲 A new model generates new ideas by retrieving “inspirations” from past scientific papers, optimizing for novelty by iteratively comparing idea suggestions to prior papers and updating them until sufficient novelty is achieved. 💡 𝗞𝗲𝘆 𝗶𝗻𝘀𝗶𝗴𝗵𝘁: This represents a step toward evaluating and developing language models that generate new ideas – whether in science, or our orgs. 3️⃣ 𝗜𝘀 𝗔𝗜 “𝗺𝗼𝗿𝗲 𝗵𝘂𝗺𝗮𝗻 𝘁𝗵𝗮𝗻 𝗵𝘂𝗺𝗮𝗻𝘀”? A Turing test of whether AI chatbots are behaviorally similar to humans found that AI and human behavior are remarkably similar. 💡 𝗞𝗲𝘆 𝗶𝗻𝘀𝗶𝗴𝗵𝘁: This makes AI well-suited for roles involving conflict resolution or customer service, where negotiation and dispute resolution are valuable skills. 4️⃣ 𝗙𝗿𝗼𝗺 𝗼𝗻𝗲-𝘁𝗿𝗶𝗰𝗸 𝗽𝗼𝗻𝗶𝗲𝘀 𝘁𝗼 𝗽𝗼𝘄𝗲𝗿𝗳𝘂𝗹 𝗔𝗜-𝗮𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗲𝗱 𝗲𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺𝘀 The best AI results are increasingly coming from compound systems with multiple components, not just “monolithic models” (one-trick ponies like LLMs predicting the next phrase in a sentence based on what commonly flows together). 💡 𝗞𝗲𝘆 𝗶𝗻𝘀𝗶𝗴𝗵𝘁, 𝗲𝘀𝗽𝗲𝗰𝗶𝗮𝗹𝗹𝘆 𝗳𝗼𝗿 𝘂𝘀 𝗵𝘂𝗺𝗮𝗻𝘀: The various components in these “intelligent” systems will almost certainly include humans, for our unique intellect, if nothing else. #ai #innovation

📢 Would you want an AI Bot as a Fellow Decision-Maker? 🤖🧑🏫 https://lnkd.in/e8tg9wWF This groundbreaking study by researchers at HKUST explored the role of AI in enhancing grading as a collaborative decision-making partner. The study introduced an AI companion to group discussions among English teachers. While initially receptive to AI's input, challenges emerged when debates arose on the balance of power between humans and AI. The bot offered valuable fresh perspectives and fearless opinions, but wasn't accepted as an 'equal' among human teachers. This study provides crucial insights for businesses venturing into AI-driven decision-making. By integrating AI companions, organizations can unlock objective viewpoints and innovative ideas that might otherwise be overlooked. However, human employees may not be accepting of such input. And if anyone has had the fortune of arguing with an AI chatbot, it seldom ends with a satisfying resolution. The role of AI as an assistant rather than an authority will also see interesting developments... Are you ready to embrace the future of collaborative decision-making with AI? #AIinDecisionMaking #BehavioralScience #ThinkDifferently #FreshPerspectives

As we continue working with Gen AI tools, mastering the art of Chain of Thought (CoT) prompting stands out as a game-changer for commercial and federal consultants. By implementing CoT techniques, we can enhance the reasoning capabilities of large language models (LLMs), enabling them to tackle complex, multi-step problems with a level of precision and clarity previously unattainable. This is significant for us as AI consultants. CoT prompting aligns perfectly with the needs of our clients, who require detailed, transparent, and logically coherent Gen AI solutions. Whether generating creative content, solving intricate coding problems, or optimizing decision-making processes, CoT allows us to break down tasks into manageable steps that LLMs can understand and execute. This boosts efficiency and improves the interpretability of Gen AI decisions, making it easier for stakeholders to trust and rely on our solutions. For our clients in the government and commercial sectors, where accuracy, security, and compliance are paramount, the structured problem-solving approach offered by CoT can be particularly beneficial. It ensures that Gen AI operations adhere to stringent regulatory standards while providing clear insight into how decisions are made, thereby enhancing accountability. Implementing CoT doesn't require overhauling existing systems but rather seamlessly integrates into the LLM frameworks we already use. We AI consultants need to start experimenting with CoT in our practices. We must begin by identifying challenges that require layered, nuanced thinking and apply CoT to unfold these complexities systematically. The benefits of deploying such advanced AI strategies are immense—enhancing operational efficiency and building robust, dependable AI systems that can adapt and excel in any scenario. Let's dive into the transformative world of structured reasoning and unlock the full potential of our Gen AI solutions today. #ArtificialIntelligence, #MachineLearning, #Innovation, #DataAnalytics, #Consulting, #Technology, #AITrends, #DigitalTransformation, #BusinessStrategy, #GovernmentConsulting, #CommercialInnovation, #TechLeadership, #ProblemSolving, #AIConsulting, #FutureOfWork