Find Greater Resilience By Avoiding These 3 Leadership Blind Spots
When scientists with the Human Genome Project first discovered our full genetic code in 2003, they were surprised by its lack of complexity. Humans have only 20-25,000 protein coded genes. Compare that number to a water flea, which has 30,000. Were humans pulled out of the oven before we were baked?
Creatures like fleas, lizards, sharks and giraffes are hard-wired from their DNA. These creatures rely solely on instinct as a means of processing the world. As a result, their genetic coding is more complex and more fixed. 'Our [human]
Humans are designed to adapt, as we are built to learn from our surroundings and circumstances. Our 'incomplete' genetic code is built so that our experiences will expand on the framework, allowing for us to continuously expand our capabilities (if we choose to do so). We are more than just instincts and protein code - our ever-expanding nature has put us at the top of the food chain. The human operating system was designed around resilience: our experience, and our ability to adapt and learn, completes the picture and creates human development. And that development doesn't stop with childhood - our brains are constantly responding to new stimuli and new information, learning and growing, coming back stronger after defeat. When we step away from misunderstandings and blind spots, we see evidence all around us of our resilience and capacity for change.
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Psychology Aims For A Unified Theory Of Cognition And AI Will Be A Big Help To Get There
In today's column, I examine the ongoing pursuit by psychology to devise a unified theory of cognition. The deal is this. There have been numerous attempts that have been floated regarding proposed unified theories or models of cognition. Subsequently, by and large, those theories or models have been sharply criticized as being at times incomplete, illogical, unfounded, and otherwise not yet fully developed. The desire and need for a true and comprehensive unified theory of cognition persists and remains exasperatingly elusive. Into this pursuit comes the use of AI, especially modern-era AI such as generative AI and large language models (LLMs). Can we make a substantive forward leap on devising a unified theory of cognition via leaning into contemporary AI and LLMs? Some say abundantly yes, others wonder if doing so will be a distraction and lead us down a primrose path. Let's talk about it. 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I frequently speak up about these pressing matters, including in an appearance last year on an episode of CBS's 60 Minutes, see the link here. You might find of keen interest that AI and psychology have had a longstanding relationship with each other. There is a duality at play. AI can be applied to the field of psychology, as exemplified by the advent of AI-powered mental health apps. Meanwhile, psychology can be applied to AI, such as aiding us in exploring better ways to devise AI that more closely approaches the human mind and how we think. See my in-depth analysis of this duality encompassing AI-psychology and psychology-AI at the link here. The Enigma Of Human Cognition The American Psychological Association (APA) defines cognition this way: One nagging mystery underlies how it is that we can think and embody cognition. All sorts of biochemical elements in our brain seem to work in a manner that gives rise to our minds and our ability to think. But we still haven't cracked the case on how those neurons and other elements in our noggin allow us to do so. Sure, you can trace aspects at a base level, yet explaining how that produces everyday cognition is a puzzle that won't seem to readily be solved. This certainly hasn't stopped researchers from trying dearly to figure things out. Hope springs eternal that the mysteries of cognition will be unraveled and we will one day know precisely the means by which cognition happens. Nobel prizes are bound to be awarded. Fame and fortune are in the cards. And imagine what else we might do to help and overcome cognitive disorders, along with potentially enhancing cognition to nearly unimaginably heightened levels. This is undoubtedly one of the most baffling mysteries of all time, and there is a purist sense of absolute joy and satisfaction in solving it. Various Types Of Models When seeking to come up with a unified theory of cognition, the route taken usually entails these four major paths: You can use only one of those approaches, or you can use two or more. If you opt to use two or more, your best bet is to make sure each model aligns with the other models being utilized. Any misalignment will indubitably bring criticism and skepticism raining down upon you. For example, if you propose a conceptual model and a mathematical model, but those two don't sync up, it becomes an easy line of attack to suggest that your theory is hogwash. AI And Computational Models A tempting avenue for cognition modeling these days is to rely upon an AI-based computational model that leverages the latest generative AI and LLMs. You can essentially repurpose a popular LLM, i.e., OpenAI's ChatGPT, which garners 400 million weekly active users, or Anthropic Claude, Google Gemini, Meta Llama, and so on. Those off-the-shelf LLMs are ready-made for experimenting on psychology-based premises. I recently explained how contemporary generative AI is devised to react to psychological ploys and techniques, an intriguing facet that is both helpful and potentially hurtful, see my coverage at the link here. One monumental wrinkle is whether a conventional LLM is suitable for representing a semblance of human cognition. Allow me to elaborate on this vital point. The mainstay of LLMs makes use of an artificial neural network (ANN). This is a series of mathematical functions that are computationally rendered in a computer system. I refer to this as an artificial neural network to try and distinguish it from a true neural network (NN) or wetware that is inside your head. Please be aware that ANNs are an exceedingly loosely contrived variation of true NNs. They are not the same. An ANN is quite far from real NNs and, in contrast, is many magnitudes simpler. 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Feed it into an existing LLM, doing so via a method such as RAG (retrieval-augmented generation), see my RAG elicitation at the link here. Voila, perhaps you've tuned up conventional generative AI to better simulate human behavior. A recent research study took that innovative approach. In an article entitled 'A Foundation Model To Predict And Capture Human Cognition' by Marcel Binz et al, Nature, July 2, 2025, the paper made these key points (excerpts): Details Of The Approach The researchers chose to use Meta Llama as their base LLM. The data augmentation was done via the use of the increasingly popular technique known as QLoRA (quantized low-rank adaptation), a distant cousin of RAG. They transcribed 160 experiments into natural language data. It was publicly available data. The types of experiments included many of the classics in psychology, such as memory recall, supervised learning, decision-making, multi-armed bandits, Markov decision processes, and others. 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But the incremental benefits could be misleadingly tying us to a road that leads to a dead-end. Could the desire to attain a unified model of cognition be the kick in the pants to the AI field to look beyond the groupthink of today's AI and LLMs? I certainly hope so. As General George S. Patton once proclaimed: 'If everyone is thinking alike, then somebody isn't thinking.'
Associated Press
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SLB Sustainability Impact Awards
At SLB, making a difference starts with collaboration. Through partnerships in local communities, our employees work hand in hand with others to drive sustainability projects that leave a lasting impact. The annual SLB Sustainability Impact Awards program allocates capital to teams around the world to deliver innovative, high-impact projects that enhance sustainability outcomes — both in SLB's operations and in local communities. Each project starts with a simple question: How can we make the most impact? These initiatives are designed to be scalable, replicable and deliver sustainable results. Alongside climate action and nature initiatives, SLB's community investments in 2024 supported socioeconomic impact across three key areas: science, technology, engineering and mathematics (STEM) education, inclusion and living conditions. Here are a few highlights: Inspiring the next generation in STEM SLB partnered with the She Is Foundation and NASA Space Center Houston on the She Is an Astronaut program. This initiative gave 24 girls from underserved communities in Colombia, Ecuador and Peru the opportunity to participate in a four-month virtual program that built their confidence and skills in STEM. The program culminated in a week-long trip to NASA's Space Center in Houston, where the girls explored space science through hands-on learning. Supporting workplace accessibility The Including Talent, Reducing Inequality program provided job skills training to 90 people with disabilities in Brazil. SLB also hosted Brazil's first fully accessible job fair in Macaé, which welcomed more than 520 attendees. Customers and training organizations took part, and over 40 SLB employees volunteered to support the event. Strengthening healthcare access SLB supported healthcare infrastructure in underserved regions of West Africa. In Nigeria, solar inverter systems were installed in 10 health centers, expanding healthcare access and improving the reliability of medical equipment. In Ghana, SLB helped build a pediatric ward at a hospital to address bed shortages and enhance healthcare services. Learn more about these and other initiatives in SLB's Sustainability Report. View original content here. Visit 3BL Media to see more multimedia and stories from SLB
