David Michaud’s Post

Flexing between deep thinking and fast thinking is a superpower! (Benefits below) ➡️ Versatility: Having the ability to switch between deep thinking and fast thinking allows us to adapt to different tasks, challenges, and environments. We can delve deeply into complex problems when necessary, and then swiftly pivot to make quick decisions or respond to rapidly changing situations. ➡️ Comprehensive understanding: Deep thinking enables us to thoroughly analyze issues, explore underlying causes, and develop nuanced perspectives. This depth of understanding can then inform fast thinking, ensuring that your quick decisions are grounded in solid reasoning and insights. ➡️ Efficiency: Fast thinking allows us to process information rapidly, which can be invaluable in time-sensitive scenarios or when faced with a high volume of tasks. Combining fast thinking with deep thinking ensures we can efficiently prioritize and address critical issues without sacrificing quality. ➡️ Innovation: Deep thinkers are often adept at generating creative ideas and exploring unconventional solutions, while fast thinkers excel at quickly evaluating and implementing these ideas. By integrating both styles of thinking, we can drive innovation by generating novel concepts and rapidly bringing them to fruition. ➡️ Problem-solving: Deep thinking enables us to thoroughly analyze complex problems and identify underlying patterns, while fast thinking helps generate and evaluate potential solutions rapidly. This dual approach enhances problem-solving abilities, allowing us to tackle challenges from multiple angles and arrive at effective resolutions more efficiently. Our team has both types of thinkers, and I’ve seen firsthand how beneficial it can be. It is truly a superpower of our organization!

Julie Sweetland, a senior adviser at the Frameworks Institute, points out in a recent report on public communication around adversity, trauma and resilience that, “If we can predict a communications problem, we can prepare for it.” The trouble is, as physicist Neils Bohr is reputed to have said, prediction is hard. Especially of the future. The history of human efforts to engage with the future is a fascinating and valuable field of study. There is much to learn from the diverse types of future-oriented thinking we have developed, from prophecy and strategy to foresight and speculation, even though these approaches diverge considerably in their methods. Reading tea leaves or examining goats’ entrails appear quite unlike programming computer simulations or writing science fiction stories. Yet these, and all forms of future-oriented thinking, share one fundamental prerequisite. Imagination. The human ability to imagine, describe and, for better or worse, believe in things that don’t - or don’t yet - exist, is what enables every vision, plan, prediction, daydream, forecast or fantasy. It’s one reason for keeping a tight rein on machinery like generative AI. AI can help you get more quickly to a particular comms output, but does not, and cannot, imagine. Similarly, while a steam train can travel from A to B faster than someone walking, making it part of a viable transport system needs humans who can imagine ticketing, seating, stations, timetables, and so on. If we want to follow up on Julie Sweetland’s useful observation, we need to imagine communications problems and their possible consequences and solutions. One way to build this capability in your team or organisation is by strengthening your imaginative ‘muscles’ with structured activities. As a vegan, I wouldn’t endorse the use of goats’ entrails, but why not try reading tea leaves? Or casting the I Ching? Or creating an ‘oracle’ with expert panels? There is a wealth of practices, from reading and writing SF to running heuristic modelling sessions, with long track records of creating value when applied to all kinds of situations. It just takes a little imagination… Below: Detail from a board game for developing future scenarios, created for Kaiser Aluminum in 1966.

Aspirational Living Series….. Problems can be solved through conventional approaches or out-of-the-box thinking. However, the current scenarios have changed drastically, making conventional methods insufficient at times. New-age thinking advocates lateral thinking, also known as critical thinking, as a skill that should be developed by everyone. Unfortunately, this area often lacks the attention needed for improvement. While the human mind is more powerful than current AI tools, it is often limited by conventional thinking patterns. Therefore, the ability to think proactively, considering all angles and aspects, is crucial for enhancing one's thinking skills. Every problem has multiple approaches for resolution, but the most efficient solution with a quick turnaround time is often the most appreciated in today's fast-paced world. Thus, prioritizing time-saving solutions over high-cost ones aligns with the ethos of new-age thinking. Embracing a flexible and innovative mindset, while leveraging both analytical and creative problem-solving techniques, can lead to more effective and impactful solutions in the face of complex and rapidly evolving challenges.

"It is crazy to think this. LLMs can string together convincing sequences of words based on analysis of previous statistical patterns, but they do not know the meaning of any of the words they input and output, or how these words relate to the real world. They are consequently incapable of the critical-thinking abilities required to offer reliable advice or 'intelligent contributions' — the kind of critical-thinking skills that should be our business, as educators, to promote. "Critical thinking — which almost everyone agrees is crucial to the mission of higher education — is a notoriously difficult concept to define. We prefer the philosopher Robert H. Ennis’s pragmatic definition: 'reasonable, reflective thinking that is focused on deciding what to believe or do.' Critical thinking, according to Ennis, involves the following skills: "Being open-minded and mindful of alternatives. Trying to be well-informed. Judging well the credibility of sources. Identifying conclusions, reasons, and assumptions. Judging well the quality of an argument, including the acceptability of its reasons, assumptions, and evidence. Developing and defending a reasonable position. Asking appropriate clarifying questions. Formulating plausible hypotheses; planning experiments well. Defining terms in a way that’s appropriate for the context. Drawing conclusions when warranted, but with caution. Integrating all items in this list when deciding what to believe or do." https://lnkd.in/eZQZHU2f

Weight, wait don't tell me Trust takes a lot to build and almost nothing to lose In opaque systems small errors may be used as rationale to stop usage even if on balance the automation or model is still better than alternatives (even humans). I love voice to text transcript of phone messages, meetings and especially long talks like the annual presidential address. Increasingly these are becoming available in real time and even as translations. The quality of transcription is imperfect, but usable for many circumstances. It's risky and inadequate without validation methods or a human in the loop for some legal, medical and other contexts. I personally spell a huge percentage of words wrong initially, some so badly that spell check can't reasonably predict what word I was aiming for. Unlike voice transcription, I use malaprops and homonyms less frequently than simple misspellings. I'm still listening to Kahneman's book "Noise" about how we can minimize errors in human judgement. A big theme is not over estimate experts for consistency and reproducibility and that simple models, consistently applied often out perform the humans on which the models were built. Distrust of models, either through a lack of mechanistic understanding of what drives them or how to reproduce consistent results can cause us to throw the baby out with the bathwater. A huge area of research in ML is transparency and observability. This is in part pragmatic to drive and justify model adoption in the real world and increasingly due to legal compliance requirements. Reading Kahneman or Annie Duke or any of the other great behavioral economists makes me want to go back to graduate school, but every day we have ways we can incorporate and apply their research to improve our own decision making and that or those around us.

I had the pleasure of attending the Corporate Research Forum (CRF) event, a Systems Thinking Approach to Organisational Change earlier this week, led by Beth Gunderson. Two days of insights, provocations, and a helpful reminder that sometimes slowing down is the fastest way forward. My key points-to-ponder: ⏱ vs. 🌦 Recognising if the problem we are trying to solve is complicated (like a watch) or complex (like the weather). Especially relevant today as we try to understand the implications of generative AI, a technology which embodies complexity through its emergent properties. Applying what we know about solving complicated problems in a world of complexity is a recipe for frustration and fatigue. ⬅ ➡ The phrase 'polarity thinking' is often thrown around and risks creating frustration through abstraction. Reframe challenges, like the global vs. local organisational tension, as 'dilemmas to manage' rather than 'problems to solve'. 🗺 Understand the big picture and challenge ourselves to anticipate second order consequences. Engage widely, consider an issue from multiple perspectives, and be wary of short term symptomatic fixes. Would love to hear thoughts from others. Have you encountered a situation where applying systems thinking principles has helped you be more effective? #change #systemsthinking #culturetransformation

Good prompting is like practicing Applied Intelligence:  • Critical Thinking • Abstract Thinking • Concrete Thinking • Divergent Thinking • Convergent Thinking • Holistic Thinking • Sequential Thinking • Scenario Thinking • Intuitive Thinking • Reflective Thinking • Metacognitive Thinking • Systems Thinking • Pattern Thinking • Analytical Thinking • Creative Thinking You're basically "installing" a cognitive architecture if you do this right. Doing this with a system prompt is best, but at the very least, do this with a GPT/Bot/Gem or Agent.

AI is demanding B-schools shift to finding the good questions as more important than valuation of the answers. IMD based authors provide one solid approach. https://lnkd.in/gMeEsCY7

Critical thinking vs Analytical thinking vs Creative Thinking vs... Human thinking patterns encompass a wide variety of cognitive processes, each serving different functions in how we solve problems, process information, and make decisions. And not only as isolated individuals, but also as an outcome of a societal system. In the context of this philosophical thinking, where we are being challenged by the computational powers of AIs these days, the recently relased research shared by Apple about "Understanding the Limitations of Mathematical Reasoning in Large Language Models" helps us realize the reach we can expect from LLMs as an architecture to support the development of proper automations and human job replacements. https://lnkd.in/dqPHKa-9 https://lnkd.in/dV_gMdFm https://lnkd.in/dDDmZUyN For a sunday morning, I found it to be a good excuse to reflect about how we - humans - do actually use our own thinking patterns and resources to take decisions, and where we should put our focus to exploit our differential value in the future to come. Enjoy

𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐎𝐯𝐞𝐫𝐜𝐨𝐧𝐟𝐢𝐝𝐞𝐧𝐜𝐞 𝐁𝐢𝐚𝐬 𝐚𝐧𝐝 𝐇𝐨𝐰 𝐭𝐨 𝐂𝐨𝐮𝐧𝐭𝐞𝐫𝐚𝐜𝐭 𝐈𝐭 In today's fast-paced world, making accurate judgments is more critical than ever. Yet, one cognitive bias that often trips us up is **overconfidence bias**. 𝑾𝒉𝒂𝒕 𝑰𝒔 𝑶𝒗𝒆𝒓𝒄𝒐𝒏𝒇𝒊𝒅𝒆𝒏𝒄𝒆 𝑩𝒊𝒂𝒔? Overconfidence bias occurs when our subjective confidence in our judgments exceeds the objective accuracy of those judgments. In simpler terms, we overestimate our knowledge, abilities, or the precision of our information. This can lead to miscalculations, overlooked risks, and flawed decisions. 𝑾𝒉𝒚 𝑫𝒐𝒆𝒔 𝑰𝒕 𝑴𝒂𝒕𝒕𝒆𝒓? - 𝘋𝘦𝘤𝘪𝘴𝘪𝘰𝘯-𝘔𝘢𝘬𝘪𝘯𝘨: Overconfidence can lead to underestimating challenges or overcommitting resources. - 𝘙𝘪𝘴𝘬 𝘈𝘴𝘴𝘦𝘴𝘴𝘮𝘦𝘯𝘵: It may cause us to ignore potential pitfalls, assuming things will go as planned. - 𝘊𝘰𝘭𝘭𝘢𝘣𝘰𝘳𝘢𝘵𝘪𝘰𝘯: Overconfident individuals might dismiss valuable input from others, stifling innovation and teamwork. 𝑯𝒐𝒘 𝒕𝒐 𝑪𝒐𝒖𝒏𝒕𝒆𝒓𝒂𝒄𝒕 𝑶𝒗𝒆𝒓𝒄𝒐𝒏𝒇𝒊𝒅𝒆𝒏𝒄𝒆 𝑩𝒊𝒂𝒔 1. 𝘚𝘦𝘦𝘬 𝘋𝘪𝘷𝘦𝘳𝘴𝘦 𝘗𝘦𝘳𝘴𝘱𝘦𝘤𝘵𝘪𝘷𝘦𝘴: Engage with colleagues who offer different viewpoints to challenge your assumptions. 2. 𝘌𝘮𝘣𝘳𝘢𝘤𝘦 𝘏𝘶𝘮𝘪𝘭𝘪𝘵𝘺: Recognize the limits of your knowledge and be open to learning. 3. 𝘜𝘴𝘦 𝘋𝘢𝘵𝘢 𝘢𝘯𝘥 𝘌𝘷𝘪𝘥𝘦𝘯𝘤𝘦: Base decisions on empirical evidence rather than gut feelings. 4. 𝘐𝘮𝘱𝘭𝘦𝘮𝘦𝘯𝘵 𝘊𝘩𝘦𝘤𝘬𝘭𝘪𝘴𝘵𝘴: Utilize checklists or decision frameworks to ensure all factors are considered. 5. 𝘙𝘦𝘧𝘭𝘦𝘤𝘵 𝘰𝘯 𝘗𝘢𝘴𝘵 𝘖𝘶𝘵𝘤𝘰𝘮𝘦𝘴: Regularly review previous decisions to identify patterns of overconfidence. By acknowledging and addressing overconfidence bias, we can make more balanced decisions, foster better collaboration, and ultimately achieve more successful outcomes. PS. This post was created with GPT o1-preview.