Boundlessly Curious by Qwoll

How can we live longer and healthier lives? What brings benefits to our brains? Qwoll asks a leading neurologist about her research. In this podcast we explore the following questions: What makes a SuperAger? How do you become one? What lifestyle factors improve the odds? How does learning play a role? How can we be supportive of those facing cognitive decline? Dr. Emily Rogalski of the University of Chicago Healthy Aging and Alzheimer Research Center is a leading neurologist who studies both Superaging and dementias. He research has been published in established journals like 'Brain' and the 'Journal of Neurology' Qwoll is a Berlin based start-up focused on helping adults be engaged as learners for life. The host is founder Kyle Blackwell, a curious lifelong learner, and alum of William & Mary and IE Business School.

Questions we explore Could a direct confrontation with between the US and Japan been possibly avoided in the Pacific? What conditions were needed for surrender? Why didn’t it happen sooner? Summary of the podcast Would a direct confrontation with the US in the Pacific been avoidable had it not been for Pearl Harbor? (Min 2-9) It was a power struggle for spheres of influence, so probably not avoidable It was politically difficult for the US to enter the war, but from a policy perspective, they almost needed to enter, and Roosevelt was looking for the opportunity to do so What was needed for surrender and why did it take so long? What was the calculus? (Min 10-19) Japan was defending really strong, and willing to fight at all costs. The US wanted the war to be over, while mounting losses Japan had quite a lot of victories and had gained new territories that it wanted to keep Sunk cost fallacy - they were winning territories while also losing many fathers and sons over the last 15 years Timeless lessons, Conclusion (Min 20-29) We should try to understand history from different perspectives; often without choice we hear a singular narrative History is complex, and it needs to be considered from broad brush strokes, all the way down to the specific factors of the individuals who have contributed to it

Questions we explore: Oppenheimer: how will the film be perceived in Japan What did the bomb mean for Japan and for Asia Should we look at history in a continuous flow? Summary of the Podcast: Intro and we discuss Oppenheimer, the movie (Min 2-13) “We shall see” how a Japanese audience responds to this; Japan sees itself as the last victim of the war The movie is biographical; it takes a neutral stance - let the audience decide for themselves The period that ensued in Asia after the bomb wasn’t a Cold War - there were many hot wars Learning about Takuma and what made him interested in the topic- (Min 13-22) He has Japanese - German roots and was amazed that the Pacific War wasn’t taught in his German textbooks growing up, this inspired a natural curiosity He wanted to explore his roots having grandparents as a veteran of the war  We discuss war then in now - that war was essentially a characteristic of civilization, and that now, until very recently we forgot about the threat of it Let’s start with Pearl Harbor - (Min 23-32) The linguistic perspective is part of his academia, and he wants to introduce the Japanese perspective Studying the lead up to Pearl Harbor, he sees that Tokyo intentionally mislead their diplomats in Washington We should look at history as both a by-product of the individual humans who influence outcomes and decisions, as well as considering it as a continuous flow of social values, norms, and ideas To understand the Pacific War, we should take a broader look at Asia (Min 33-45) It might not have been just the bombs, but rather the USSR invading Japan that really accelerated the surrender And we have to take the War in the Pacific had been going on much longer in 1941 - Japan had been fighting China going back to 1931. Japan’s military history was one of victories, and they consider themselves a great power; the dreams and vision weren’t static, they grew over time This is the first part of our conversation, stay tuned for the second part of it - which we will release next week. For further learnings: And check out Qwoll’s course on the Manhattan Project: iOS or Android Check out Melber’s book on Pearl Harbor: Link to Amazon in German or English

Questions explored Why does systems thinking matter? What does the future AI - human relationship look like? Will AI replace or augment human skillsets? Where should I be investing my time and efforts as a 21st century knowledge worker? Summary Introducing Mohammad.. the importance of systems thinking (Min 1-6) In the early 2000s, Mohammad was a software developer but realized he needed a "more holistic understanding of the context” Systems don’t always work or get adopted and it all comes down to how technology interacts with its context of user, organizations and work context - this is called socio-technical behavior. Algorithms are “not neutral” - they redistribute power, so there is a political element to consider Understanding the Human-AI Relationship (Min 7-11) While systems will become “smarter and smarter,” there are specific parts of work which are human centric There is also an augmentation narrative, AI can augment us and we can augment it, in a sense we learn and develop together. We can get better together. Are we experiencing an economic transformation similar to the Industrial Revolution? (Min 12-20) Past experiences can be insightful and helpful for understanding the present.. it’s clear some jobs will be gone and in other cases new ones will be created The term AI has been around since 1956. It’s not a very new concept, but the form has changed. What is interesting is that now algorithms are learning from us, and gaining some tacit knowledge - knowledge which is usually gained though experience We need to change the way we work and we’re going to develop a symbiotic relationship with technology We can’t be fully reliant on algorithms, there’s going to be a ‘human in the loop’ for the foreseeable future (Min 21-35) One issue is the black box problem. As algorithms learn, they often can’t explain why they made a decision a certain way. People need to be held accountable. It’s ok if your Netflix recommendation makes an error, but if something actually fails, are our legal and governmental systems simply going to be OK with “sorry, the algorithm predicted that”? There’s also situations where holistic knowledge is needed: Emergency medicine: there’s lots of edge cases in the ER, you can’t just feed an algorithm Customer service: We’re human, we need and want a human touch. We want to be listened to and empathized with Crisis management: COVID hit, we hadn’t had a pandemic in 100 years, there was no data initially to tell policy makers what to do Executive decision making: we’re given lots of competing and not always quantifiable information. How do we decide which to pursue? How should we be adapting to develop a symbiotic relationship with technology? (Min 36-50) Give up things which are not in our competitive abilities, spelling bees and multiplication tables aren’t so important anymore; focus on where we have a competitive advantage - creativity, critical thinking, ethics, and soft skills. Improve AI literacy - understand the limits of the machine, and the capabilities; systems can ‘hallucinate’... understand that Use technology… you learn from experience, play around with and run tests with Chat GPT or other LLMs Developing socio-technical organizations - organizations where people are working effectively alongside AI systems are going to have the greatest advantages. Learning, development, and the future, how does it look? (Min 51-61) Emotional intelligence is critical If history provides us any lessons it’s that some of the highest payed jobs weren’t necessarily resilient to automation. We should be cautiously optimistic about the future: we’re bound to be liberated from mundane tasks, but at the same time we are just seeing the tip of the iceberg, there’s bound to be workplace change and emergent needs to re-skill

Welcome to our second episode of the Qwoll podcast “Boundlessly Curious” in which we interview behavioral economist, Dr. Sherry Banuri. Questions explored What is behavioral economics all about? Are markets perfect, are people rational? What can behavioral sciences teach us about goal achievement? How can we apply lessons from the book “Thinking Fast and Slow”? Overview of the podcast Why behavioral economics? What is it all about? (Minutes 2-6) One of the things that hooked Dr. Banuri in is in understanding systems and wanting to predict where the future is going to go… “The economy is a supremely complex thing” and distilling it into something simpler and easier to understand is “brilliant” We tend to assume people are rational and calculating, but then we see a divergence with our math and the real world. As he got into deeper into economics, he realized this interest in mapping human behavior Are people rational, are markets perfect? (Minutes 7-15) We’ve had experiments with central planning and price fixing and it doesn’t really hit the efficiency of a perfectly competitive market; on the other hand we don’t really have great perfectly competitive markets either Behavioral economics was kind of looked down on, but what’s ‘turned the tide’ is that human behavior is not random; we make predictable errors which are possible to model On topics such as labor supply, or negotiating deals, people often do not rationally optimize; they deviate from the predicted outcome due to factors like fairness, happiness, and social pressures Tell us about your new book, The Decisive Mind? What are you after? (Minutes 16-26) Dr. Banuri wants to help people change their behavior in order to reach goals more effectively. There’s something called the intention-action gap. For example, I have an intention to lose weight, but my actions are not in line with my intentions; how does one close that? With the book, he wants to help people think through these things ahead of time. “I’ve personally had a lot of challenges that I’ve had to overcome, and I think science has something for us to learn from here,” he says. He believes we need to put in the work ahead of time, it’s a framework which can be applied flexibly When you stop following your intention / plan, does it lead you into a worse rabbit hole? (Minutes 27-32) More or less, yes, we start to relate sadness, guilt and other negative emotions to that experience, and we try to avoid that We generally try to avoid bad information about ourselves which is why 80% of people, when asked, believe they are ‘above-average’; so we have this and other biases Psychology plays a big role in understanding human behavior, although economists tend to draw a line between them What’s “Thinking Fast and Slow” all about and how it can be applied? (Minutes 32-39) The author, behavioral economist Daniel Kahneman, simplifies logic into two thinking systems - System 1 and System 2 In System 2, “thinking slow”, we are rational and calculating, but this requires quite a lot of brain power; System 1 is “thinking fast”, we use gut feelings and have a default behavior Generally we have a preference for System 1, because System 2 is energetically costly, so we need to make System 1 work for us Applications for learning and other things (Minutes 39-45) Learning is tiring and requires cognitive capacity, timing is important; learn when you have energy! About Dr. Sheheryar (Sherry) Banuri is an associate professor in behavioral economics at the University of East Anglia in Norwich, UK. He’s studied topics like motivation and incentives and how these things play a role in labor, public service and economic development. He’s the author of soon to be two books. Qwoll is a lifelong learning startup based in Berlin, Germany with a mission to 'perpetuate curious minds'. Founder & host Kyle Blackwell holds an MBA degree from IE Business School and an undergraduate degree from William & Mary.

Welcome to our first Qwoll podcast “Boundlessly Curious”…. Questions explored * Has social media in fact compounded tribalism and polarization? * Can we reliably measure the positive and negative consequences? * How will generative AI play a role in how we interact with social media? * What does the future of social media look like? Summary of the Conversation Intro to Oliver’s background as a researcher (minutes 1-8) and a discussion around social media, emotions and polarization (8-15) * Oliver describes how people are mix emotions and rationality and how emotional responses have been exacerbated in the online sphere * We talk about the origins of the connection power of like minded communities  Social media and the political sphere: the Arab Spring, Brexit and Trump (minutes 16-21) * You can’t take the genie out of the bottle, analyzing life without social media is like analyzing life without electricity * Context matters greatly; you can’t very easily say social media caused these things even though it is tempting, but we can say we have more visibility into different perspectives than we’ve had before - certain conversations we’re always happening but many of them we’re offline Measuring the net effect of social media (minutes 22-30) * Not so easy, but we can say that there’s been a lot of bad and not a lot of good. Thinking like a scholar or researcher / you have to narrow the scope to answer these questions * “Social media has been pretty horrible” for teenage girls in Western countries; but maybe, it’s been less negative for elderly people to interact with family and friends; and there’s millions of couples who have met on online dating platforms Generative AI and disinformation (minutes 31-40) * So now we might be able to exponentially misinform without a coordinated human effort, that might not be so much worse than it is currently.. but maybe it just becomes more fun? * You might also have more powerful ideas for identifying misinformation now too though at our disposal Social platforms like Twitter are making it more difficult to research (minutes 41-45) * Twitter had previously been allowing researchers free access to data, now under Elon Musk leadership, $42,000 per month is the asking price for .3% of the companies data * This has relevant implications for our ability to study digital life and that’s not good Future of social media (minutes 46-50) * Oliver thinks the trend is for the internet to become ever more personalized and effective at serving our needs, and therefore more addictive… Follow us on Substack Get updated when we release new episodes and publish new articles : qwoll.substack.com/subscribe Support our cause! Lastly, we’re a fledgling start-up on a mission to help busy people stay engaged as lifelong learners. Please consider supporting our mission, donate to Qwoll today: USD EUR