Podcasts about heuristics

In this episode of The Inspire Podcast, Bart sits down with Alex Draper, founder and CEO of DX Learning, to tackle a critical issue in the workplace: toxic leadership. Alex starts with a simple truth—bad leadership is toxic and has negative impacts. So why does it persist when no one sets out to be a bad leader? Alex explains that most toxic leadership isn't intentional—it's unintentional, and it stems from a lack of self-awareness and training. He discusses how leaders need to provide Clarity, Autonomy, Relationships, and Equity (his CARE model) and shares how to begin fostering CARE-driven leadership through data gathering, practical steps, and clear ways to measure progress. Alex's insights are essential at a time when employees are demanding more from their leaders—and when the opportunity exists to lead in a way that truly inspires. If you're a leader looking to create a healthier workplace culture and drive better results, this episode is for you. Learn more about Alex at https://www.dx-learning.com/ and https://alex-draper.com Connect with Alex on LinkedIn here: https://www.linkedin.com/in/alex-draper/ Show notes: 1:00 Introducing Alex Draper 1:32 What does DX Learning do? 1:56 Purpose is to wipe out toxicity in the workplace 2:55 How to get leaders to behave in positive ways 4:05 How to go into a company knowing that your specialty is toxic workplaces 4:21 Some people are truly toxic and try to cause stress 5:00 There are narcissists out there 6:00 There's no flawless team or human 6:19 Toxicity exists in every team 7:32 What are the signs of a toxic leader or culture 8:23 Levels of silence indicate a problem 8:56 Ideas and challenging the status quo - is that happening? 9:36 Lagging indicators: retention issues 10:39 What is a typical engagement like? 10:56 Engineering mindset - data! 11:59 What data should we gather? 12:28 70% of variance in employee engagement is down to the managers 12:41 If the team has an issue, it's more than likely the leadership does 12:51 Bottom up approach is not the right approach! 13:15 How to gather the data? 13:28 4 dimensions of high performing leadership/teams 14:54 How "Equity" fits in 16:48 Cognitive dissonance - gaps 17:07 Bart asks for an example of a gap dissonance between leaders and employees 19:21 Advice on collecting "listening data" 21:02 How can leaders provide more clarity? 21:49 Example of a leader working on clarity 24:18 The projection bias 24:48 Heuristics - brain shortcuts -cause issues 25:44 Example of 'autonomy' problem 26:01 Psychological safety = Autonomy 31:46 The "Equity" piece 33:05 Equity is the lagging indicator 33:34 How do you measure equity? 33:43 Fairness orientation 34:23 Equity is the output of the three controllables 34:53 Re-measure! 35:07 What should the measurement frequency be? 35:31 90 days for teams 36:32 Teams are always adapting 37:19 Change is the one consistent 38:22 How do you feel about the state of leadership in 2025 and onwards? 39:56 AI and how it will fit in the mix — emotional intelligence as the distinguishing factor 40:21 Where can people go to find out more? 40:48 Thank Yous 41:35 Show outro

This week, Keith and Mike attempt to rank the most important superficial traits that influence male sexual desire, beginning with a list that includes weight, ethnicity, facial attractiveness, figure, and everything else. What begins as an exercise in prioritization soon becomes a deep dive into personal biases, aesthetic heuristics, and a surprising amount of geometry. Keith provides a mechanical breakdown of sexual logistics with overweight partners, which leads to a somewhat academic discussion of bodily angles and the limitations of certain sexual positions. Mike contributes moral support, skepticism, and vivid analogies involving sport-fucking and Eastern European machinery. From there, the conversation leads to the relationship between apparent enthusiasm during sex and perceived long-term viability as a partner. The hosts consider whether women might accidentally disqualify themselves from relationship consideration by enjoying themselves too much during a first hookup. They explore the intersection of perceived chastity, authenticity of arousal, and the complicated social signaling involved in early sexual encounters. At no point does anyone suggest that human mating psychology is simple, pleasant, or fair. Later, the two evaluate a listener question involving pegging, face-sitting, and the limits of vulnerability in sexual dynamics. Mike posits that being a "vulnerable and whimpering mess" may carry a cost in perceived masculinity, especially outside the bedroom. Keith agrees, citing personal experiences and a fatherly punch in the film My Girl as evidence that competence and dominance remain socially desirable traits. The show wraps up with a discussion of another listener whose girlfriend experiences post-sex disgust and sadness. Various hypotheses are considered, including religious shame, misaligned intimacy expectations, and the absence of orgasms. Mike, citing their podcasting experience as a credential, suggests that breaking up may be the simplest solution. Twitter: @ymmvpod Facebook: ymmvpod Email: ymmvpod@gmail.com

We are living in the Disinformation Age — a time unlike any other in history. Never before have we been bombarded with so much information, yet so little clarity. Our feeds are flooded with misleading headlines, personal opinions disguised as facts, and viral narratives designed to manipulate us. Even the sharpest minds can fall for misinformation—so how do we learn to see through the noise?In this episode of The Scenic Route, I sit down with Dr. Brie Kara, a psychologist specializing in disinformation and media literacy, to break down:The real difference between misinformation and disinformation (and why it matters)Why our brains are wired to fall for cognitive biases and mental shortcutsHow disinformation campaigns hijack our instincts—and how to fight backPractical strategies to sharpen critical thinking and media literacy skillsWhy fact-checking isn't enough—and what to do insteadWe're constantly being pulled in different directions by algorithms, outrage-driven media, and our own subconscious biases. This episode will give you the tools to think more clearly, question more effectively, and reclaim your mental autonomy.Listen now and upgrade your brain's operating system!Mentioned in this episodeBrie Kara's websiteOn InstagramOn ThreadsThinking Fast & Slow by Daniel KahnemanNew episodes drop every Tuesday. See you on the Scenic Route._____________________________________________________________________READY FOR YOUR SCENIC ROUTE?Visit jenniferwalter.me — your cozy corner of the internet where recovering perfectionists come to breathe, dream, and embrace a softer way of living – while creating real change in their communities. Keep the conversation going: Instagram TikTok Threads DAILY DOSE OF CHILLTap into your inner wisdom and let it guide you.Need a gentle nudge in the right direction? The Scenic Route Affirmation Card Deck Deck is your online permission slip to trust your inner compass. Grab yours and let's see what wisdom awaits you today:

In this episode we dive into the concept of skilled intentionality with regards to the pitcher-batter interaction. We explore how intention shapes a player's behavior and performance, the importance of perceiving relevant information within the game (becoming attuned) and being calibrated is the basis for successful performance outcomes. We also discuss practical training strategies and the nuances of guiding player intentions.00:00 Introduction and Recap00:12 Understanding Skilled Intentionality02:13 Pitcher-Hitter Dynamics04:47 Intent and Self-Organization07:01 Heuristics and Intent in Sports14:15 Affordances and Calibration25:31 Practical Applications in Coaching37:29 Pitching Strategies and Hitter Disruption37:44 Boxing Analogy for Pitch Sequencing39:15 The Concept of Attunement40:02 Higher Order Variables in Sports44:54 What is the specifying information and high order variables in hitting?57:21 Pitching and Hitting Strategies58:26 Training Environments and Adaptability01:08:09 Guiding Athletes Through Questions01:10:15 Conclusion and Final ThoughtsIntro music by: Muellzyhttps://soundcloud.com/muellzymusicSupport Us & learn more about Ecological Dynamics (links below)⁠Donate to Finding the Edge:⁠⁠buymeacoffee.com/ftepod⁠⁠Ecological Dynamics ResourcesResources from Emergence a movement skill education company dedicated to helping coaches learn how to apply an ecological approach to understanding and developing movement skill.Get 7% off most courses by using code: Edge7Educational Products: ⁠⁠https://emergentmvmt.com/shop-2/⁠⁠Social MediaTwitter: ⁠⁠@Emergentmvmt⁠⁠Instagram: ⁠⁠@Emergentmvmt⁠⁠Patreon:⁠⁠ https://www.patreon.com/Emergentmvmt⁠⁠Follow Us!Join our Discord: ⁠⁠bit.ly/3a07z1B⁠⁠Find us on Twitter:@FTEpod@gboyum01@RobertFrey40@kyledupic@CoachgbakerSubscribe on Youtube: ⁠⁠bit.ly/34dZ7

On this episode, Nika Kabiri joins the show to the real drivers of consumer choice. Nika has spent 25+ studying decision-making, with 15 of those years in consumer insights. She has taught Decision Science at the University of Washington, and her expertise in decision-making has been featured in the Wall Street Journal, the Washington Post, […] The post 389: Uncovering the Real Drivers of Choice—biases, heuristics, and social influences first appeared on Persuasion by the Pint.

Mitzvah #71 -  Following The Many - The Heuristics of Experience, Law and Faith 

Let's continue our discussion on how our human wiring does its decision-making in order to avoid the dangers of mental shortcuts. Listen, answer the exercises and share your quick answers

InsightsNo. 1"Pride freezes us in our own image, beliefs, and positions." -Arthur C. BrooksNo. 2“If you can approach the world's complexities, both its glories and its horrors, with an attitude of humble curiosity, acknowledging that however deeply you have seen, you have only scratched the surface, you will find worlds within worlds, beauties you could imagine, and your own mundane preoccupations will shrink to proper size, not all that important in the greater scheme of things…” -NietzscheNo. 3Beliefs often come into our lives and get stuckA ThoughtMost of Life is Problem-SolvingWe solve problems with as little energy as possible. Energy conservation is a survival mechanism. To help, we create mental maps of how the world works so we don't have to process each situation from scratch. After touching a hot stove once, you don't waste time analyzing whether it will burn you again. That shortcut is called a heuristic.Another common heuristic, at least in some cultures: “Trustworthy people maintain eye contact.” The problem? Some trustworthy people struggle with eye contact—like those on the autism spectrum. And liars? They know the heuristic and can fake it.Or consider hiring. You see a résumé from someone who reminds you of a great former colleague—same school, similar background. Maybe they worked at companies you admire. Your brain says, shortcut time! You skip reference checks and deep questioning. That just cost you a year of headaches. The opposite happens too: you dismiss someone because they don't have the “right” pedigree.As situations grow more complex and our energy levels drop, shortcuts become more tempting and dangerous. I know I'm more emotional—and more prone to bad decisions—when I'm exhausted. Whether consciously or not, I'm in energy conservation mode and looking for shortcuts.Shortcuts evolved for survival. If a saber-toothed tiger blocked your path, you didn't stop to analyze—you ran. But most modern decisions don't demand that kind of speed. Today, the real risk isn't hesitation—it's acting without thinking.Heuristics still serve us well in many situations, but we must recognize when deeper thinking is required. The key is knowing when your primitive brain is in control and when emotion might lead you down the wrong path. The irony is that pausing to engage our slower, more rational mind often saves energy in the long run.Shortcuts aren't the problem—it's blindly trusting them or failing to recognize when you're relying on them.Take care and be good. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit kellyvohs.substack.com

Your brain makes thousands of decisions every day. Why not make the process easier?   In this episode, I share how heuristics, or “life rules” can lighten your cognitive load, simplify daily choices, and free up energy for what actually matters. I'll walk you through practical shortcuts like the one-touch rule, theme days, and energy matching to help you work with your brain instead of against it. I'll also share how a simple mindset shift, “everything is art,” transformed my approach to home design and decision-making.    What life rules could help you bring more ease, creativity, and flow into your day?   Quotes “Heuristics are systems of mental shortcuts. They're simple, they're efficient rules, and sometimes you learn them, or you might just create them, and it becomes something in hindsight that you realize you've had these systems your whole life.” (04:57 | Dr. Lara Salyer) “Flow neuroscience works a little differently than what you were taught. So the energy matching is basically assigning the most challenging or most creative tasks that will require a lot of brain power to your energy times.” (10:10 | Dr. Lara Salyer) “That's the interesting juxtaposition of a heuristic. It does give you control so that you can let go… Because you're controlling a preset decision matrix, shall we say, a shortcut of your thinking, so that you can let go and let flow.” (15:19 | Dr. Lara Salyer) “Your work shouldn't be all you do. It shouldn't be your life. You have a work-life masterpiece that blends colors and textures and overlaps, and you should be proud of what you do, but it shouldn't be your only identity.” (17:17 | Dr. Lara Salyer)   Links Book a Brainstorm Session: https://drlarasalyer.as.me/discovery Magic Membership workshop mentioned in this episode:  https://rightbrainrescue.com/p/membership-magic-design-your-3-tier-functional-medicine-membership-to-grow-and-scale Right Brain Rescue Book: https://www.amazon.com//dp/B08JCKBWK5/   Connect with Lara:  Website: https://drlarasalyer.com The Catalyst Way: https://drlarasalyer.com/catalyst Instagram: @drlarasalyer Facebook: https://www.facebook.com/drlarasalyer Linked-In: https://www.linkedin.com/in/drlarasalyer/ YouTube:  https://www.youtube.com/c/DrLaraSalyer TikTok: @Creativity.Doctor     Podcast production and show notes provided by HiveCast.fm

This post should not be taken as a polished recommendation to AI companies and instead should be treated as an informal summary of a worldview. The content is inspired by conversations with a large number of people, so I cannot take credit for any of these ideas.For a summary of this post, see the threat on X. Many people write opinions about how to handle advanced AI, which can be considered “plans.” There's the “stop AI now plan.” On the other side of the aisle, there's the “build AI faster plan.” Some plans try to strike a balance with an idyllic governance regime. And others have a “race sometimes, pause sometimes, it will be a dumpster-fire” vibe. ---Outline:(02:33) The tl;dr(05:16) 1. Assumptions(07:40) 2. Outcomes(08:35) 2.1. Outcome #1: Human researcher obsolescence(11:44) 2.2. Outcome #2: A long coordinated pause(12:49) 2.3. Outcome #3: Self-destruction(13:52) 3. Goals(17:16) 4. Prioritization heuristics(19:53) 5. Heuristic #1: Scale aggressively until meaningful AI software RandD acceleration(23:21) 6. Heuristic #2: Before achieving meaningful AI software RandD acceleration, spend most safety resources on preparation(25:08) 7. Heuristic #3: During preparation, devote most safety resources to (1) raising awareness of risks, (2) getting ready to elicit safety research from AI, and (3) preparing extreme security.(27:37) Category #1: Nonproliferation(32:00) Category #2: Safety distribution(34:47) Category #3: Governance and communication.(36:13) Category #4: AI defense(37:05) 8. Conclusion(38:38) Appendix(38:41) Appendix A: What should Magma do after meaningful AI software RandD speedupsThe original text contained 11 images which were described by AI. --- First published: January 29th, 2025 Source: https://www.lesswrong.com/posts/8vgi3fBWPFDLBBcAx/planning-for-extreme-ai-risks --- Narrated by TYPE III AUDIO. ---Images from the article:

How good are you at making decisions?How confident are you in your answer? The more aware you are of the way your mind works, the less sure you will be of your answer. Our decisions are fraught with biases and distortions.Thinking Fast and Slow is one of the most respected books on decision making. Daniel Kahneman's work won him a Nobel Prize for Economics. Some call it the bible for the developing field of Behavioural Economics.In it he shows a number of surprising ways we fool ourselves.Eduardo dos Santos Silva, Michael Ward, Romana Prochazkova and I met to discuss our insights from the book.Links:Eduardo Dos Santos SilvaMichael WardRomana ProchazkovaRob McPhillipsChapters:00:00 Introduction: Understanding Decision-Making Systems00:17 Key Insights from the Book01:10 Exploring Biases and Decision-Making01:40 The Importance of Diverse Teams02:55 Personal Reflections and Comparisons04:51 Frustrations with System One and System Two05:16 Regression to the Mean: A Key Concept06:13 Psychological Soundness and Boredom06:58 Head, Heart, and Gut: Different Systems?09:27 Decision-Making Processes and Logical Thinking13:04 The Book's Audience and Writing Style21:17 The Legacy of Kahneman and Tversky23:00 Visual Learning in Mathematics24:08 The Pyramid Pattern and Pattern Recognition26:57 Heuristics, Algorithms, and AI28:10 Cultural Differences and Fairness28:39 Book Readability and Summaries

We hear the wisdom from experienced players: Always play your two drops, Spend all your mana, and many more. But how do the numbers back those old truths? Do they stand scrutiny of testing them through data? Are they universal? Or are there corner cases? I test several Limited heuristics and hopefully you get to level up your game play through it! Ping me for coaching. Join the ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Discord⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, sign up for ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Patreon⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, and use this ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Linktree⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ for everything else! Watch this episode and see the slides: ⁠⁠⁠Episode #144 vid⁠⁠⁠ This podcast is sponsored by ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠mtgazone.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ - get your reading fix from the best and brightest Magic writers in the business. You can get the BulkBox if you are in the UK. Remember to use SIERKO10 code for a 10% discount! If you are outside of UK, you can find your local distributor on the ⁠⁠⁠⁠⁠BulkBox website⁠⁠⁠⁠⁠.

Enjoyed this episode and want to send a gift? Click here  In this episode, we explore the profound impact of calm and peace in a world teetering on the edge of fear and chaos. Discover how our self-awareness and intentional choices can ripple out to create a more compassionate and connected world. Learn how emotional contagion works, the difference between fear and love, and the small but significant ways we can embody calm to inspire change.  Key Points:       1. Humanity's Great Leaps Forward     From air travel to modern communication, history shows us how radical ideas often become everyday staples. Today, we are invited to make a different kind of leap, one that shifts us from fear-based systems to a consciousness rooted in love and compassion.      2. Emotional Contagion: The Power of Influence     Our emotions are contagious, shaping the environments we enter. Fear spreads quickly and visibly, but love and calm, though quieter, have the power to influence and change our reality. When we embody calm, we ripple that effect outward, creating a positive impact on those around us.       3. The Two Camps: Fear vs. Love     Fear manifests as greed, control, and division, while love camps with hope, kindness, and connection. Choosing love requires courage and self-awareness, but it allows us to dismantle fear-based patterns and build a foundation for peace and belonging.       4. Practices to Anchor Calm in Chaos     -   Self-Awareness:   Recognize fear in your body and stay present instead of reacting.   -   Consumption Audit:   Evaluate what you consume and ensure it nourishes your peace.   -   Gratitude for the Unseen:  Honor those who quietly serve humanity with love and kindness.   -   Metta Meditation:  Practice loving-kindness meditation to spread compassion within and beyond yourself.        Quotes for Reflection:     - “Fear barks for attention, but love quietly transforms the world.”   - “The leap we're being invited to take isn't outward, it's into our own inner selves.”   - “World peace begins with inner peace.”    Call to Action: If this episode resonated with you, take a moment to reflect on what seeds you're planting in your daily life. Share this episode with someone who might need a reminder of the quiet power of calm. Don't forget to subscribe, rate, and review The Radiant Presence Podcast to help spread the message of peace and connection.  Check out the Softening into Stillness program here

EPISODE 126 | Cognitive Biases and the Brain: Thanks, Evolution! (Because Reasons 11) The first of two episodes looking at cognitive biases - this one at how memory works and how we prevent input overload by filtering out information. Hopefully, this will give us some insight into why people think they way they do.  The primary source material for this is the Cognitive Biases Codex, created by Buster Benson and John Manoogian III, as used by the EU's Marie Curie CogNovo program for Conspiracy-Theories.EU. Like what we do? Then buy us a beer or three via our page on Buy Me a Coffee. You can also SUBSCRIBE to this podcast. Review us here or on IMDb!  SECTIONS Memories Can't Wait - Misinformation effect, testing effect, processing effect, spacing effect, Google effect, two types of absentmindedness, next-in-line effect, list length effect, serial position effect, suffix effect, part-list cueing effect, peak-end rule, duration neglect Fading affect bias, negativity bias, leveling and sharpening, Maude sees a Black man, suggestibility; false memory (UFO abductions, Satanic Panic), misattribution of memory, cryptoamnesia, source confusion (eyewitness testimony) Too Much Information - The availability heuristic, repetition makes it true - the illusory truth effect and the mere exposure effect, attentional bias, context effect, mood-congruent memory bias, cue-dependent forgetting, the frequency illusion and Baader-Meinhof Phenomenon, the base rate fallacy, the empathy gap (cold-to-hot and hot-to-cold), omission bias The bizarreness effect, humor effect, isolation effect (Von Resteroff effect), and picture superiority effect; bias blind spot, the introspection illusion, naïve cynicism, confirmation bias, congruence bias, choice-supportive bias (post-purchase bias), selective perception and the ostrich effect, observer-expectancy effect (also experimenter effect), subjective validation (the personal validation effect) helps conspiracy theory formation, the Semmelweis reflex, the continued influence effect (people don't unlearn mis- or disinformation easily once it's been integrated) Anchoring, conservatism, distinction bias, contrast bias, the focusing effect, the framing effect, the money illusion or price illusion and the price of milk; perceiving change - Weber and Fechner, the discrimination threshold, Numerical Cognition Music by Fanette Ronjat More Info Cognitive biases codex Cognitive Biases on Conspiracy-Theories.EU Conspiracy-Theories.EU website About CogNovo Marie Skłodowska-Curie Actions website What Is Cognitive Bias? 7 Examples & Resources (Incl. Codex) on Positive Psychology List of Cognitive Biases and Heuristics on The Decision Lab How Our Brains Make Memories in Smithsonian Psychology study uncovers new details about the cognitive underpinnings of belief in conspiracy theories on PsyPost Conspiracy theories in New Scientist 24 cognitive biases that are warping your perception of reality on the World Economic Forum Conspiracy theory and cognitive style: a worldview Beliefs in conspiracy theories and the need for cognitive closure Social Media, Cognitive Reflection, and Conspiracy Beliefs Cognitive Bias articles on ScienceDirect Cognitive Biases and Brain Biology Help Explain Why Facts Don't Change Minds at the University of Connecticut Cognitive Bias 101: What It Is and How To Overcome It at the Cleveland Clinic 4 widespread cognitive biases and how doctors can overcome them at the American medical Association How Cognitive Biases Influence the Way You Think and Act on VeryWellMind 24 cognitive biases stuffing up your thinking plus cards at yourbias.is Identify Cognitive Biases in Business Decision‑Making at Mailchimp   Follow us on social: Facebook Twitter Other Podcasts by Derek DeWitt DIGITAL SIGNAGE DONE RIGHT - Winner of a 2022 Gold Quill Award, 2022 Gold MarCom Award, 2021 AVA Digital Award Gold, 2021 Silver Davey Award, 2020 Communicator Award of Excellence, and on numerous top 10 podcast lists.  PRAGUE TIMES - A city is more than just a location - it's a kaleidoscope of history, places, people and trends. This podcast looks at Prague, in the center of Europe, from a number of perspectives, including what it is now, what is has been and where it's going. It's Prague THEN, Prague NOW, Prague LATER 

Send us a textFree Ultimate SEO Guide https://fireusmarketing.com/the-ultimate-seo-guide/Could understanding how people think and feel give you an edge in SEO?In this episode of The Digital Revolution Podcast, Eli is joined by Giulia Panozzo, a neuroscientist turned marketer. She discusses the intersection of neuroscience and marketing, emphasizing the growing role of user experience in SEO. Giulia shares her personal journey from Italy to becoming a director of customer acquisition and a neuromarketing consultant. She also delves into practical applications of neuromarketing principles in SEO, including tools for eye tracking, consumer behavior analysis, and the importance of trust and emotional connection in marketing.00:00 Intro01:13 Meet the SEO Expert02:21 Giulia's Background and Journey07:09 Phobias and the Brain10:16 Exercises That Can Help The Brain Overcome Phobias13:19 What Currently Excites Giulia21:14 Understanding Neuromarketing22:28 How Neuromarketing Principles Can Better The Overall SEO Experience23:34 Improving User Experience with Search Console25:20 The Importance of Familiarity in Branding26:17 Using Microsoft Clarity for Better UX27:36 Understanding Consumer Behavior and Cognitive Biases29:00 Eye Tracking and Other Neural Measures30:38 Tools or Services For Eye Tracking32:21 Psychological Triggers in SEO Copy35:04 Numerical Biases and Marketing Tactics37:39 Heuristics and Decision Making in Marketing41:07 The Role of Emotion and Trust in Marketing44:32 Future of Neuromarketing and SEO47:16 Conclusion and Final ThoughtsDon't forget to help us grow by subscribing and liking us on YouTube!Go to TheDigitalRevolutionPodcast.com to learn more!Leave Some Feedback: What should we talk about next? Please let us know in the comments below. Did you enjoy this episode? If so, please leave a short review. Connect With Us:Fire Us Marketing Instagram LinkedIn YouTube The Digital Revolution Podcast Instagram LinkedIn YouTube Eli Adams Personal LinkedIn TikTok

Retirement Lifestyle Show, hosts Roshan Loungani and Adrian Nicholson journey into the realm of financial psychology, exploring how various psychological concepts influence retirement planning and investing. They discuss the status quo bias, heuristics, analysis paralysis, mental accounting, the endowment affect, herd behavior, and narrative fallacy, providing insights and examples to help listeners understand the impact of these biases on their financial decisions. The conversation emphasizes the importance of self-awareness in managing finances and making informed investment choices. Hashtags: financial psychology, retirement planning, investing, behavioral finance, decision making, status quo bias, heuristics, analysis paralysis, mental accounting, endowment affect, herd behavior, narrative fallacy Chapters 00:00 Introduction to Financial Psychology 03:13 Understanding Status Quo Bias 05:58 Heuristics in Financial Decision Making 10:57 Analysis Paralysis: The Dangers of Overthinking 15:00 Mental Accounting and Its Implications 21:14 The Endowment EAect: Emotional Attachments in Finance 27:57 Herd Behavior in Investing 31:58 Narrative Fallacy: The Power of Compelling Stories Follow Us At: Website: https://retirementlifestyleshow.com/ https://www.retirewithroshan.com https://youtu.be/hKVzI87v0tA https://twitter.com/RoshanLoungani https://www.linkedin.com/in/roshanloungani/ https://www.facebook.com/retirewithroshan/ https://www.linkedin.com/in/financialerik/ https://www.linkedin.com/in/adrian-nicholson-74b82b13b All opinions expressed by podcast hosts and guests are solely their own. While based on information they believe is reliable, neither Arete Wealth nor its affiliates warrant its completeness or accuracy, nor do their opinions reflect the opinion of Arete Wealth. This podcast is for general informational purposes only and should not be regarded as specific advice or recommendations for any individual. Before making any decisions, consult a professional

We continue talking with Tim about Overfitting and Heuristics in Philosophy (2024), considering Tim's overall project and view of what philosophy should be doing and with what tools. We get into modeling, ethics, public philosophy, and more. Get more at partiallyexaminedlife.com. Visit partiallyexaminedlife.com/support to get ad-free episodes and tons of bonus discussion, including a supporter-exclusive PEL Nightcap further reflecting on this episode. Sponsor: Apply for convenient term life insurance from Fabric by Gerber Life at meetfabric.com/PEL.

Oxford philosophy professor Timothy Williamson talks to us about his new book, Overfitting and Heuristics in Philosophy. How can we best apply the insights of philosophy of science to philosophy itself? Maybe some alleged philosophical counter-examples are just the result of psychological heuristics gone wrong. Get more at partiallyexaminedlife.com. Visit partiallyexaminedlife.com/support to get ad-free episodes and tons of bonus discussion. Sponsor: Get a $1/month e-commerce trial at shopify.com/pel.

In the wake of CubeCon 2024, Andy and Anthony talk about how they approach drafting new Cube environments. They talk about their general draft strategies and heuristics they start with to get a sense of what's going to be important in the environment just by looking at the packs being passed. View all cards mentioned in this episode Discussed in this episode: Anthony's Big Bottle Opener Episode 48: Playing to Win: Evaluating New Cubes Episode 215: Using Little Pieces of Paper as a Supermassive Random Number Generator Anthony's Fire Swamp and C/Ube Decks Game Objects Cube The Fire Swamp Keldan's Simple Cube Streisand Effect Timestamps 0:00 - Introduction 6:09 - Are we good at Magic? 8:56 - How do we approach drafting a completely unfamiliar list 19:29 - Should you look at the available mana fixing in a cube? 26:08 - The categories of cards to pay attention to in the draft 30:25 - How often should you end up playing your pack 1, pick 1 in Cube draft? 31:59 - The importance of recontexualizaion 40:26 - The Value of Having a Plan 48:04 - How much do you trust the Cube designer? 57:24 - Heuristics for having a good time Check us out on Twitch and YouTube for paper Cube gameplay. You can find the hosts' Cubes on Cube Cobra: Andy's “Bun Magic” Cube Anthony's “Regular” Cube If want us to do a pack 1, pick 1 from your cube submit it on our website. You can find both your hosts in the MTG Cube Talk Discord. Send in questions to the show at mail@luckypaper.co or our p.o. box: Lucky Paper PO Box 4855 Baltimore, MD 21211 If you'd like to show your support for the show, please leave us a review on iTunes or wherever you listen. Musical production by DJ James Nasty.

Conflicted, Confused, and Ready to Vote - Uncovering the discombobulated thoughts of the great undecided who will sway the election. We dive into the hidden forces in our minds that sway our decisions when we don't know how to decide. From Free Will to Confirmation Bias, Appearances and a Turn of Phrase. What doesn't matter and what could make all the difference in a voters mind. We cover: Where do our preferences come from How we shortcut confusing decisions into simple flawed decisions The impact of last-minute scandals What each side doesn't understand? The irrational nature of sensible people Who has been overlooked by politics and what are they thinking The 60th US election coming on November 5th between Kamala Harris and Donald Trump has been on the news non stop for a year. Finally, it will all be over and we can move on with our lives. 13% of people remain undecided but with their vote being crucially important in an otherwise 50:50 race, the nuanced factors that sway their thinking have never been more important. Tune in to uncover the psychology of last-minute voters and the hidden mind games controlling the election. Upgrade to Premium:

This week we're talking about the factors that are going to tip the election one way or the other in the final few days, as we're down to a very, very few voters who ultimately hold our fate in their hands. To try to get some insight I wanted to ask my friend, Sam Harris, who hosts one of the fastest growing and most popular psychology podcasts on earth. It's called Growth Mindset and it's a really interesting dive in each episode into a different idea from psychology and how you can apply it to what you do. So what is REALLY going to push the final undecided voters in the final days? 03:45 Heuristics and Decision Making 07:14 Impact of Recent Events on Voter Decisions 12:41 Trump's Unique Appeal 19:27 The Role of Podcasts in Campaigns 32:32 Collective Dissatisfaction and Political Change 35:36 Conclusion: Reflecting on Progress

CEOs of publicly traded companies are often in the news talking about their new AI initiatives, but few of them have built anything with it. Drew Houston from Dropbox is different; he has spent over 400 hours coding with LLMs in the last year and is now refocusing his 2,500+ employees around this new way of working, 17 years after founding the company.Timestamps00:00 Introductions00:43 Drew's AI journey04:14 Revalidating expectations of AI08:23 Simulation in self-driving vs. knowledge work12:14 Drew's AI Engineering setup15:24 RAG vs. long context in AI models18:06 From "FileGPT" to Dropbox AI23:20 Is storage solved?26:30 Products vs Features30:48 Building trust for data access33:42 Dropbox Dash and universal search38:05 The evolution of Dropbox42:39 Building a "silicon brain" for knowledge work48:45 Open source AI and its impact51:30 "Rent, Don't Buy" for AI54:50 Staying relevant58:57 Founder Mode01:03:10 Advice for founders navigating AI01:07:36 Building and managing teams in a growing companyTranscriptAlessio [00:00:00]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, partner and CTO at Decibel Partners, and there's no Swyx today, but I'm joined by Drew Houston of Dropbox. Welcome, Drew.Drew [00:00:14]: Thanks for having me.Alessio [00:00:15]: So we're not going to talk about the Dropbox story. We're not going to talk about the Chinatown bus and the flash drive and all that. I think you've talked enough about it. Where I want to start is you as an AI engineer. So as you know, most of our audience is engineering folks, kind of like technology leaders. You obviously run Dropbox, which is a huge company, but you also do a lot of coding. I think that's how you spend almost 400 hours, just like coding. So let's start there. What was the first interaction you had with an LLM API and when did the journey start for you?Drew [00:00:43]: Yeah. Well, I think probably all AI engineers or whatever you call an AI engineer, those people started out as engineers before that. So engineering is my first love. I mean, I grew up as a little kid. I was that kid. My first line of code was at five years old. I just really loved, I wanted to make computer games, like this whole path. That also led me into startups and eventually starting Dropbox. And then with AI specifically, I studied computer science, I got my, I did my undergrad, but I didn't do like grad level computer science. I didn't, I sort of got distracted by all the startup things, so I didn't do grad level work. But about several years ago, I made a couple of things. So one is I sort of, I knew I wanted to go from being an engineer to a founder. And then, but sort of the becoming a CEO part was sort of backed into the job. And so a couple of realizations. One is that, I mean, there's a lot of like repetitive and like manual work you have to do as an executive that is actually lends itself pretty well to automation, both for like my own convenience. And then out of interest in learning, I guess what we call like classical machine learning these days, I started really trying to wrap my head around understanding machine learning and informational retrieval more, more formally. So I'd say maybe 2016, 2017 started me writing these more successively, more elaborate scripts to like understand basic like classifiers and regression and, and again, like basic information retrieval and NLP back in those days. And there's sort of like two things that came out of that. One is techniques are super powerful. And even just like studying like old school machine learning was a pretty big inversion of the way I had learned engineering, right? You know, I started programming when everyone starts programming and you're, you're sort of the human, you're giving an algorithm to the, and spelling out to the computer how it should run it. And then machine learning, here's machine learning where it's like actually flip that, like give it sort of the answer you want and it'll figure out the algorithm, which was pretty mind bending. And it was both like pretty powerful when I would write tools, like figure out like time audits or like, where's my time going? Is this meeting a one-on-one or is it a recruiting thing or is it a product strategy thing? I started out doing that manually with my assistant, but then found that this was like a very like automatable task. And so, which also had the side effect of teaching me a lot about machine learning. But then there was this big problem, like anytime you, it was very good at like tabular structured data, but like anytime it hit, you know, the usual malformed English that humans speak, it would just like fall over. I had to kind of abandon a lot of the things that I wanted to build because like there's no way to like parse text. Like maybe it would sort of identify the part of speech in a sentence or something. But then fast forward to the LLM, I mean actually I started trying some of like this, what we would call like very small LLMs before kind of the GPT class models. And it was like super hard to get those things working. So like these 500 parameter models would just be like hallucinating and repeating and you know. So actually I'd kind of like written it off a little bit. But then the chat GPT launch and GPT-3 for sure. And then once people figured out like prompting and instruction tuning, this was sort of like November-ish 2022 like everybody else sort of that the chat GPT launch being the starting gun for the whole AI era of computing and then having API access to three and then early access to GPT-4. I was like, oh man, it's happening. And so I was literally on my honeymoon and we're like on a beach in Thailand and I'm like coding these like AI tools to automate like writing or to assist with writing and all these different use cases.Alessio [00:04:14]: You're like, I'm never going back to work. I'm going to automate all of it before I get back.Drew [00:04:17]: And I was just, you know, ever since then, I mean, I've always been like coding like prototypes and just stuff to make my life more convenient, but like escalated a lot after 22. And yeah, I spent, I checked, I think it was probably like over 400 hours this year so far coding because I had my paternity leave where I was able to work on some special projects. But yeah, it's a super important part of like my whole learning journey is like being really hands-on with these things. And I mean, it's probably not a typical recipe, but I really love to get down to the metal as far as how this stuff works.Alessio [00:04:47]: Yeah. So Swyx and I were with Sam Altman in October 22. We were like at a hack day at OpenAI and that's why we started this podcast eventually. But you did an interview with Sam like seven years ago and he asked you what's the biggest opportunity in startups and you were like machine learning and AI and you were almost like too early, right? It's like maybe seven years ago, the models weren't quite there. How should people think about revalidating like expectations of this technology? You know, I think even today people will tell you, oh, models are not really good at X because they were not good 12 months ago, but they're good today.Drew [00:05:19]: What's your project? Heuristics for thinking about that or how is, yeah, I think the way I look at it now is pretty, has evolved a lot since when I started. I mean, I think everybody intuitively starts with like, all right, let's try to predict the future or imagine like what's this great end state we're going to get to. And the tricky thing is like often those prognostications are right, but they're right in terms of direction, but not when. For example, you know, even in the early days of the internet, 90s when things were even like tech space and you know, even before like the browser or things like that, people were like, oh man, you're going to have, you know, you're going to be able to order food, get like a Snickers delivered to your house, you're going to be able to watch any movie ever created. And they were right. But they were like, you know, it took 20 years for that to actually happen. And before you got to DoorDash, you had to get, you started with like Webvan and Cosmo and before you get to Spotify, you had to do like Napster and Kazaa and LimeWire and like a bunch of like broken Britney Spears MP3s and malware. So I think the big lesson is being early is the same as being wrong. Being late is the same as being wrong. So really how do you calibrate timing? And then I think with AI, it's the same thing that people are like, oh, it's going to completely upend society and all these positive and negative ways. I think that's like most of those things are going to come true. The question is like, when is that going to happen? And then with AI specifically, I think there's also, in addition to sort of the general tech category or like jumping too fast to the future, I think that AI is particularly susceptible to that. And you look at self-driving, right? This idea of like, oh my God, you can have a self-driving car captured everybody's imaginations 10, 12 years ago. And you know, people are like, oh man, in two years, there's not going to be another year. There's not going to be a human driver on the road to be seen. It didn't work out that way, right? We're still 10, 12 years later where we're in a world where you can sort of sometimes get a Waymo in like one city on earth. Exciting, but just took a lot longer than people think. And the reason is there's a lot of engineering challenges, but then there's a lot of other like societal time constants that are hard to compress. So one thing I think you can learn from things like self-driving is they have these levels of autonomy that's a useful kind of framework in driving or these like maturity levels. People sort of skip to like level five, full autonomy, or we're going to have like an autonomous knowledge worker that's just going to take, that's going to, and then we won't need humans anymore kind of projection that that's going to take a long time. But then when you think about level one or level two, like these little assistive experiences, you know, we're seeing a lot of traction with those. So what you see really working is the level one autonomy in the AI world would be like the tab auto-complete and co-pilot, right? And then, you know, maybe a little higher is like the chatbot type interface. Obviously you want to get to the highest level you can to build a good product, but the reliability just isn't, and the capability just isn't there in the early innings. And so, and then you think of other level one, level two type things, like Google Maps probably did more for self-driving than in literal self-driving, like a billion people have like the ability to have like maps and navigation just like taken care of for you autonomously. So I think the timing and maturity are really important factors to include.Alessio [00:08:23]: The thing with self-driving, maybe one of the big breakthroughs was like simulation. So it's like, okay, instead of driving, we can simulate these environments. It's really hard to do when knowledge work, you know, how do you simulate like a product review? How do you simulate these things? I'm curious if you've done any experiments. I know some companies have started to build kind of like a virtual personas that you can like bounce ideas off of.Drew [00:08:42]: I mean, fortunately in a company you generate lots of, you know, actual human training data all the time. And then I also just like start with myself, like, all right, I can, you know, it's pretty tricky even within your company to be like, all right, let's open all this up as quote training data. But, you know, I can start with my own emails or my own calendar or own stuff without running into the same kind of like privacy or other concerns. So I often like start with my own stuff. And so that is like a one level of bootstrapping, but actually four or five years ago during COVID, we decided, you know, a lot of companies were thinking about how do we go back to work? And so we decided to really lean into remote and distributed work because I thought, you know, this is going to be the biggest change to the way we work in our lifetimes. And COVID kind of ripped up a bunch of things, but I think everybody was sort of pleasantly surprised how with a lot of knowledge work, you could just keep going. And actually you were sort of fine. Work was decoupled from your physical environment, from being in a physical place, which meant that things people had dreamed about since the fifties or sixties, like telework, like you actually could work from anywhere. And that was now possible. So we decided to really lean into that because we debated, should we sort of hit the fast forward button or should we hit the rewind button and go back to 2019? And obviously that's been playing out over the last few years. And we decided to basically turn, we went like 90% remote. We still, the in-person part's really important. We can kind of come back to our working model, but we're like, yeah, this is, everybody is going to be in some kind of like distributed or hybrid state. So like instead of like running away from this, like let's do a full send, let's really go into it. Let's live in the future. A few years before our customers, let's like turn Dropbox into a lab for distributed work. And we do that like quite literally, both of the working model and then increasingly with our products. And then absolutely, like we have products like Dropbox Dash, which is our universal search product. That was like very elevated in priority for me after COVID because like now you have, we're putting a lot more stress on the system and on our screens, it's a lot more chaotic and overwhelming. And so even just like getting the right information, the right person at the right time is a big fundamental challenge in knowledge work and these, in the distributed world, like big problem today is still getting, you know, has been getting bigger. And then for a lot of these other workflows, yeah, there's, we can both get a lot of natural like training data from just our own like strategy docs and processes. There's obviously a lot you can do with synthetic data and you know, actually like LMs are pretty good at being like imitating generic knowledge workers. So it's, it's kind of funny that way, but yeah, the way I look at it is like really turn Dropbox into a lab for distributed work. You think about things like what are the big problems we're going to have? It's just the complexity on our screens just keeps growing and the whole environment gets kind of more out of sync with what makes us like cognitively productive and engaged. And then even something like Dash was initially seeded, I made a little personal search engine because I was just like personally frustrated with not being able to find my stuff. And along that whole learning journey with AI, like the vector search or semantic search, things like that had just been the tooling for that. The open source stuff had finally gotten to a place where it was a pretty good developer experience. And so, you know, in a few days I had sort of a hello world type search engine and I'm like, oh my God, like this completely works. You don't even have to get the keywords right. The relevance and ranking is super good. We even like untuned. So I guess that's to say like I've been surprised by if you choose like the right algorithm and the right approach, you can actually get like super good results without having like a ton of data. And even with LLMs, you can apply all these other techniques to give them, kind of bootstrap kind of like task maturity pretty quickly.Alessio [00:12:14]: Before we jump into Dash, let's talk about the Drew Haas and AI engineering stuff. So IDE, let's break that down. What IDE do you use? Do you use Cursor, VS Code, do you use any coding assistant, like WeChat, is it just autocomplete?Drew [00:12:28]: Yeah, yeah. Both. So I use VS Code as like my daily driver, although I'm like super excited about things like Cursor or the AI agents. I have my own like stack underneath that. I mean, some off the shelf parts, some pretty custom. So I use the continue.dev just like AI chat UI basically as just the UI layer, but I also proxy the request. I proxy the request to my own backend, which is sort of like a router. You can use any backend. I mean, Sonnet 3.5 is probably the best all around. But then these things are like pretty limited if you don't give them the right context. And so part of what the proxy does is like there's a separate thing where I can say like include all these files by default with the request. And then it becomes a lot easier and like without like cutting and pasting. And I'm building mostly like prototype toy apps, so it's like a front end React thing and a Python backend thing. And so it can do these like end to end diffs basically. And then I also like love being able to host everything locally or do it offline. So I have my own, when I'm on a plane or something or where like you don't have access or the internet's not reliable, I actually bring a gaming laptop on the plane with me. It's like a little like blue briefcase looking thing. And then I like literally hook up a GPU like into one of the outlets. And then I have, I can do like transcription, I can do like autocomplete, like I have an 8 billion, like Llama will run fine.Alessio [00:13:44]: And you're using like a Llama to run the model?Drew [00:13:47]: No, I use, I have my own like LLM inference stack. I mean, it uses the backend somewhat interchangeable. So everything from like XLlama to VLLM or SGLang, there's a bunch of these different backends you can use. And then I started like working on stuff before all this tooling was like really available. So you know, over the last several years, I've built like my own like whole crazy environment and like in stack here. So I'm a little nuts about it.Alessio [00:14:12]: Yeah. What's the state of the art for, I guess not state of the art, but like when it comes to like frameworks and things like that, do you like using them? I think maybe a lot of people say, hey, things change so quickly, they're like trying to abstract things. Yeah.Drew [00:14:24]: It's maybe too early today. As much as I do a lot of coding, I have to be pretty surgical with my time. I don't have that much time, which means I have to sort of like scope my innovation to like very specific places or like my time. So for the front end, it'll be like a pretty vanilla stack, like a Next.js, React based thing. And then these are toy apps. So it's like Python, Flask, SQLite, and then all the different, there's a whole other thing on like the backend. Like how do you get, sort of run all these models locally or with a local GPU? The scaffolding on the front end is pretty straightforward, the scaffolding on the backend is pretty straightforward. Then a lot of it is just like the LLM inference and control over like fine grained aspects of how you do generation, caching, things like that. And then there's a lot, like a lot of the work is how do you take, sort of go to an IMAP, like take an email, get a new, or a document or a spreadsheet or any of these kinds of primitives that you work with and then translate them, render them in a format that an LLM can understand. So there's like a lot of work that goes into that too. Yeah.Alessio [00:15:24]: So I built a kind of like email triage system and like I would say 80% of the code is like Google and like pulling emails and then the actual AI part is pretty easy.Drew [00:15:34]: Yeah. And even, same experience. And then I tried to do all these like NLP things and then to my dismay, like a bunch of reg Xs were like, got you like 95% of the way there. So I still leave it running, I just haven't really built like the LLM powered version of it yet. Yeah.Alessio [00:15:51]: So do you have any thoughts on rag versus long context, especially, I mean with Dropbox, you know? Sure. Do you just want to shove things in? Like have you seen that be a lot better?Drew [00:15:59]: Well, they kind of have different strengths and weaknesses, so you need both for different use cases. I mean, it's been awesome in the last 12 months, like now you have these like long context models that can actually do a lot. You can put a book in, you know, Sonnet's context and then now with the later versions of LLAMA, you can have 128k context. So that's sort of the new normal, which is awesome and that, that wasn't even the case a year ago. That said, models don't always use, and certainly like local models don't use the full context well fully yet, and actually if you provide too much irrelevant context, the quality degrades a lot. And so I say in the open source world, like we're still just getting to the cusp of like the full context is usable. And then of course, like when you're something like Dropbox Dash, like it's basically building this whole like brain that's like read everything your company's ever written. And so that's not going to fit into your context window, so you need rag just as a practical reality. And even for a lot of similar reasons, you need like RAM and hard disk in conventional computer architecture. And I think these things will keep like horse trading, like maybe if, you know, a million or 10 million is the new, tokens is the new context length, maybe that shifts. Maybe the bigger picture is like, it's super exciting to talk about the LLM and like that piece of the puzzle, but there's this whole other scaffolding of more conventional like retrieval or conventional machine learning, especially because you have to scale up products to like millions of people you do in your toy app is not going to scale to that from a cost or latency or performance standpoint. So I think you really need these like hybrid architectures that where you have very like purpose fit tools, or you're probably not using Sonnet 3.5 for all of your normal product use cases. You're going to use like a fine tuned 8 billion model or sort of the minimum model that gets you the right output. And then a smaller model also is like a lot more cost and latency versus like much better characteristics on that front.Alessio [00:17:48]: Yeah. Let's jump into the Dropbox AI story. So sure. Your initial prototype was Files GPT. How did it start? And then how did you communicate that internally? You know, I know you have a pretty strong like mammal culture. One where you're like, okay, Hey, we got to really take this seriously.Drew [00:18:06]: Yeah. Well, on the latter, it was, so how do we say like how we took Dropbox, how AI seriously as a company started kind of around that time, that honeymoon time, unfortunately. In January, I wrote this like memo to the company, like around basically like how we need to play offense in 23. And that most of the time the kind of concrete is set and like the winners are the winners and things are kind of frozen. But then with these new eras of computing, like the PC or the internet or the phone or the concrete on freezes and you can sort of build, do things differently and have a new set of winners. It's sort of like a new season starts as a result of a lot of that sort of personal hacking and just like thinking about this. I'm like, yeah, this is an inflection point in the industry. Like we really need to change how we think about our strategy. And then becoming an AI first company was probably the headline thing that we did. And then, and then that got, and then calling on everybody in the company to really think about in your world, how is AI going to reshape your workflows or what sort of the AI native way of thinking about your job. File GPT, which is sort of this Dropbox AI kind of initial concept that actually came from our engineering team as, you know, as we like called on everybody, like really think about what we should be doing that's new or different. So it was kind of organic and bottoms up like a bunch of engineers just kind of hacked that together. And then that materialized as basically when you preview a file on Dropbox, you can have kind of the most straightforward possible integration of AI, which is a good thing. Like basically you have a long PDF, you want to be able to ask questions of it. So like a pretty basic implementation of RAG and being able to do that when you preview a file on Dropbox. So that was the origin of that, that was like back in 2023 when we released just like the starting engines had just, you know, gotten going.Alessio [00:19:53]: It's funny where you're basically like these files that people have, they really don't want them in a way, you know, like you're storing all these files and like you actually don't want to interact with them. You want a layer on top of it. And that's kind of what also takes you to Dash eventually, which is like, Hey, you actually don't really care where the file is. You just want to be the place that aggregates it. How do you think about what people will know about files? You know, are files the actual file? Are files like the metadata and they're just kind of like a pointer that goes somewhere and you don't really care where it is?Drew [00:20:21]: Yeah.Alessio [00:20:22]: Any thoughts about?Drew [00:20:23]: Totally. Yeah. I mean, there's a lot of potential complexity in that question, right? Is it a, you know, what's the difference between a file and a URL? And you can go into the technicals, it's like pass by value, pass by reference. Okay. What's the format like? All right. So it starts with a primitive. It's not really a flat file. It's like a structured data. You're sort of collaborative. Yeah. That's keeping in sync. Blah, blah, blah. I actually don't start there at all. I just start with like, what do people, like, what do humans, let's work back from like how humans think about this stuff or how they should think about this stuff. Meaning like, I don't think about, Oh, here are my files and here are my links or cloud docs. I'm just sort of like, Oh, here's my stuff. This, this, here's sort of my documents. Here's my media. Here's my projects. Here are the people I'm working with. So it starts from primitives more like those, like how do people, how do humans think about these things? And then, then start from like a more ideal experience. Because if you think about it, we kind of have this situation that will look like particularly medieval in hindsight where, all right, how do you manage your work stuff? Well, on all, you know, on one side of your screen, you have this file browser that literally hasn't changed since the early eighties, right? You could take someone from the original Mac and sit them in front of like a computer and they'd be like, this is it. And that's, it's been 40 years, right? Then on the other side of your screen, you have like Chrome or a browser that has so many tabs open, you can no longer see text or titles. This is the state of the art for how we manage stuff at work. Interestingly, neither of those experiences was purpose-built to be like the home for your work stuff or even anything related to it. And so it's important to remember, we get like stuck in these local maxima pretty often in tech where we're obviously aware that files are not going away, especially in certain domains. So that format really matters and where files are still going to be the tool you use for like if there's something big, right? If you're a big video file, that kind of format in a file makes sense. There's a bunch of industries where it's like construction or architecture or sort of these domain specific areas, you know, media generally, if you're making music or photos or video, that all kind of fits in the big file zone where Dropbox is really strong and that's like what customers love us for. It's also pretty obvious that a lot of stuff that used to be in, you know, Word docs or Excel files, like all that has tilted towards the browser and that tilt is going to continue. So with Dash, we wanted to make something that was really like cloud-native, AI-native and deliberately like not be tied down to the abstractions of the file system. Now on the other hand, it would be like ironic and bad if we then like fractured the experience that you're like, well, if it touches a file, it's a syncing metaphor to this app. And if it's a URL, it's like this completely different interface. So there's a convergence that I think makes sense over time. But you know, but I think you have to start from like, not so much the technology, start from like, what do the humans want? And then like, what's the idealized product experience? And then like, what are the technical underpinnings of that, that can make that good experience?Alessio [00:23:20]: I think it's kind of intuitive that in Dash, you can connect Google Drive, right? Because you think about Dropbox, it's like, well, it's file storage, you really don't want people to store files somewhere, but the reality is that they do. How do you think about the importance of storage and like, do you kind of feel storage is like almost solved, where it's like, hey, you can kind of store these files anywhere, what matters is like access.Drew [00:23:38]: It's a little bit nuanced in that if you're dealing with like large quantities of data, it actually does matter. The implementation matters a lot or like you're dealing with like, you know, 10 gig video files like that, then you sort of inherit all the problems of sync and have to go into a lot of the challenges that we've solved. Switching on a pretty important question, like what is the value we provide? What does Dropbox do? And probably like most people, I would have said like, well, Dropbox syncs your files. And we didn't even really have a mission of the company in the beginning. I'm just like, yeah, I just don't want to carry a thumb driving around and life would be a lot better if our stuff just like lived in the cloud and I just didn't have to think about like, what device is the thing on or what operating, why are these operating systems fighting with each other and incompatible? You know, I just want to abstract all of that away. But then so we thought, even we were like, all right, Dropbox provides storage. But when we talked to our customers, they're like, that's not how we see this at all. Like actually, Dropbox is not just like a hard drive in the cloud. It's like the place where I go to work or it's a place like I started a small business is a place where my dreams come true. Or it's like, yeah, it's not keeping files in sync. It's keeping people in sync. It's keeping my team in sync. And so they're using this kind of language where we're like, wait, okay, yeah, because I don't know, storage probably is a commodity or what we do is a commodity. But then we talked to our customers like, no, we're not buying the storage, we're buying like the ability to access all of our stuff in one place. We're buying the ability to share everything and sort of, in a lot of ways, people are buying the ability to work from anywhere. And Dropbox was kind of, the fact that it was like file syncing was an implementation detail of this higher order need that they had. So I think that's where we start too, which is like, what is the sort of higher order thing, the job the customer is hiring Dropbox to do? Storage in the new world is kind of incidental to that. I mean, it still matters for things like video or those kinds of workflows. The value of Dropbox had never been, we provide you like the cheapest bits in the cloud. But it is a big pivot from Dropbox is the company that syncs your files to now where we're going is Dropbox is the company that kind of helps you organize all your cloud content. I started the company because I kept forgetting my thumb drive. But the question I was really asking was like, why is it so hard to like find my stuff, organize my stuff, share my stuff, keep my stuff safe? You know, I'm always like one washing machine and I would leave like my little thumb drive with all my prior company stuff on in the pocket of my shorts and then almost wash it and destroy it. And so I was like, why do we have to, this is like medieval that we have to think about this. So that same mindset is how I approach where we're going. But I think, and then unfortunately the, we're sort of back to the same problems. Like it's really hard to find my stuff. It's really hard to organize myself. It's hard to share my stuff. It's hard to secure my content at work. Now the problem is the same, the shape of the problem and the shape of the solution is pretty different. You know, instead of a hundred files on your desktop, it's now a hundred tabs in your browser, et cetera. But I think that's the starting point.Alessio [00:26:30]: How has the idea of a product evolved for you? So, you know, famously Steve Jobs started by Dropbox and he's like, you know, this is just a feature. It's not a product. And then you build like a $10 billion feature. How in the age of AI, how do you think about, you know, maybe things that used to be a product are now features because the AI on top of it, it's like the product, like what's your mental model? Do you think about it?Drew [00:26:50]: Yeah. So I don't think there's really like a bright line. I don't know if like I use the word features and products and my mental model that much of how I break it down because it's kind of a, it's a good question. I mean, I don't not think about features, I don't think about products, but it does start from that place of like, all right, we have all these new colors we can paint with and all right, what are these higher order needs that are sort of evergreen, right? So people will always have stuff at work. They're always need to be able to find it or, you know, all the verbs I just mentioned. It's like, okay, how can we make like a better painting and how can we, and then how can we use some of these new colors? And then, yeah, it's like pretty clear that after the large models, the way you find stuff share stuff, it's going to be completely different after COVID, it's going to be completely different. So that's the starting point. But I think it is also important to, you know, you have to do more than just work back from the customer and like what they're trying to do. Like you have to think about, and you know, we've, we've learned a lot of this the hard way sometimes. Okay. You might start with a customer. You might start with a job to be on there. You're like, all right, what's the solution to their problem? Or like, can we build the best product that solves that problem? Right. Like what's the best way to find your stuff in the modern world? Like, well, yeah, right now the status quo for the vast majority of the billion, billion knowledge workers is they have like 10 search boxes at work that each search 10% of your stuff. Like that's clearly broken. Obviously you should just have like one search box. All right. So we can do that. And that also has to be like, I'll come back to defensibility in a second, but like, can we build the right solution that is like meaningfully better from the status quo? Like, yes, clearly. Okay. Then can we like get distribution and growth? Like that's sort of the next thing you learned is as a founder, you start with like, what's the product? What's the product? What's the product? Then you're like, wait, wait, we need distribution and we need a business model. So those are the next kind of two dominoes you have to knock down or sort of needles you have to thread at the same time. So all right, how do we grow? I mean, if Dropbox 1.0 is really this like self-serve viral model that there's a lot of, we sort of took a borrowed from a lot of the consumer internet playbook and like what Facebook and social media were doing and then translated that to sort of the business world. How do you get distribution, especially as a startup? And then a business model, like, all right, storage happened to be something in the beginning happened to be something people were willing to pay for. They recognize that, you know, okay, if I don't buy something like Dropbox, I'm going to have to buy an external hard drive. I'm going to have to buy a thumb drive and I have to pay for something one way or another. People are already paying for things like backup. So we felt good about that. But then the last domino is like defensibility. Okay. So you build this product or you get the business model, but then, you know, what do you do when the incumbents, the next chess move for them is I just like copy, bundle, kill. So they're going to copy your product. They'll bundle it with their platforms and they'll like give it away for free or no added cost. And, you know, we had a lot of, you know, scar tissue from being on the wrong side of that. Now you don't need to solve all four for all four or five variables or whatever at once or you can sort of have, you know, some flexibility. But the more of those gates that you get through, you sort of add a 10 X to your valuation. And so with AI, I think, you know, there's been a lot of focus on the large language model, but it's like large language models are a pretty bad business from a, you know, you sort of take off your tech lens and just sort of business lens. Like there's sort of this weirdly self-commoditizing thing where, you know, models only have value if they're kind of on this like Pareto frontier of size and quality and cost. Being number two, you know, if you're not on that frontier, the second the frontier moves out, which it moves out every week, like your model literally has zero economic value because it's dominated by the new thing. LLMs generate output that can be used to train or improve. So there's weird, peculiar things that are specific to the large language model. And then you have to like be like, all right, where's the value going to accrue in the stack or the value chain? And, you know, certainly at the bottom with Nvidia and the semiconductor companies, and then it's going to be at the top, like the people who have the customer relationship who have the application layer. Those are a few of the like lenses that I look at a question like that through.Alessio [00:30:48]: Do you think AI is making people more careful about sharing the data at all? People are like, oh, data is important, but it's like, whatever, I'm just throwing it out there. Now everybody's like, but are you going to train on my data? And like your data is actually not that good to train on anyway. But like how have you seen, especially customers, like think about what to put in, what to not?Drew [00:31:06]: I mean, everybody should be. Well, everybody is concerned about this and nobody should be concerned about this, right? Because nobody wants their personal companies information to be kind of ground up into little pellets to like sell you ads or train the next foundation model. I think it's like massively top of mind for every one of our customers, like, and me personally, and with my Dropbox hat on, it's like so fundamental. And, you know, we had experience with this too at Dropbox 1.0, the same kind of resistance, like, wait, I'm going to take my stuff on my hard drive and put it on your server somewhere. Are you serious? What could possibly go wrong? And you know, before that, I was like, wait, are you going to sell me, I'm going to put my credit card number into this website? And before that, I was like, hey, I'm going to take all my cash and put it in a bank instead of under my mattress. You know, so there's a long history of like tech and comfort. So in some sense, AI is kind of another round of the same thing, but the issues are real. And then when I think about like defensibility for Dropbox, like that's actually a big advantage that we have is one, our incentives are very aligned with our customers, right? We only get, we only make money if you pay us and you only pay us if we do a good job. So we don't have any like side hustle, you know, we're not training the next foundation model. You know, we're not trying to sell you ads. Actually we're not even trying to lock you into an ecosystem, like the whole point of Dropbox is it works, you know, everywhere. Because I think one of the big questions we've circling around is sort of like, in the world of AI, where should our lane be? Like every startup has to ask, or in every big company has to ask, like, where can we really win? But to me, it was like a lot of the like trust advantages, platform agnostic, having like a very clean business model, not having these other incentives. And then we also are like super transparent. We were transparent early on. We're like, all right, we're going to establish these AI principles, very table stakes stuff of like, here's transparency. We want to give people control. We want to cover privacy, safety, bias, like fairness, all these things. And we put that out up front to put some sort of explicit guardrails out where like, hey, we're, you know, because everybody wants like a trusted partner as they sort of go into the wild world of AI. And then, you know, you also see people cutting corners and, you know, or just there's a lot of uncertainty or, you know, moving the pieces around after the fact, which no one feels good about.Alessio [00:33:14]: I mean, I would say the last 10, 15 years, the race was kind of being the system of record, being the storage provider. I think today it's almost like, hey, if I can use Dash to like access my Google Drive file, why would I pay Google for like their AI feature? So like vice versa, you know, if I can connect my Dropbook storage to this other AI assistant, how do you kind of think about that, about, you know, not being able to capture all the value and how open people will stay? I think today things are still pretty open, but I'm curious if you think things will get more closed or like more open later.Drew [00:33:42]: Yeah. Well, I think you have to get the value exchange right. And I think you have to be like a trustworthy partner or like no one's going to partner with you if they think you're going to eat their lunch, right? Or if you're going to disintermediate them and like all the companies are quite sophisticated with how they think about that. So we try to, like, we know that's going to be the reality. So we're actually not trying to eat anyone's like Google Drive's lunch or anything. Actually we'll like integrate with Google Drive, we'll integrate with OneDrive, really any of the content platforms, even if they compete with file syncing. So that's actually a big strategic shift. We're not really reliant on being like the store of record and there are pros and cons to this decision. But if you think about it, we're basically like providing all these apps more engagement. We're like helping users do what they're really trying to do, which is to get, you know, that Google Doc or whatever. And we're not trying to be like, oh, by the way, use this other thing. This is all part of our like brand reputation. It's like, no, we give people freedom to use whatever tools or operating system they want. We're not taking anything away from our partners. We're actually like making it, making their thing more useful or routing people to those things. I mean, on the margin, then we have something like, well, okay, to the extent you do rag and summarize things, maybe that doesn't generate a click. Okay. You know, we also know there's like infinity investment going into like the work agents. So we're not really building like a co-pilot or Gemini competitor. Not because we don't like those. We don't find that thing like captivating. Yeah, of course. But just like, you know, you learn after some time in this business that like, yeah, there's some places that are just going to be such kind of red oceans or just like super big battlefields. Everybody's kind of trying to solve the same problem and they just start duplicating all each other effort. And then meanwhile, you know, I think the concern would be is like, well, there's all these other problems that aren't being properly addressed by AI. And I was concerned that like, yeah, and everybody's like fixated on the agent or the chatbot interface, but forgetting that like, hey guys, like we have the opportunity to like really fix search or build a self-organizing Dropbox or environment or there's all these other things that can be a compliment. Because we don't really want our customers to be thinking like, well, do I use Dash or do I use co-pilot? And frankly, none of them do. In a lot of ways, actually, some of the things that we do on the security front with Dash for Business are a good compliment to co-pilot. Because as part of Dash for Business, we actually give admins, IT, like universal visibility and control over all the different, what's being shared in your company across all these different platforms. And as a precondition to installing something like co-pilot or Dash or Glean or any of these other things, right? You know, IT wants to know like, hey, before we like turn all the lights in here, like let's do a little cleaning first before we let everybody in. And there just haven't been good tools to do that. And post AI, you would do it completely differently. And so that's like a big, that's a cornerstone of what we do and what sets us apart from these tools. And actually, in a lot of cases, we will help those tools be adopted because we actually help them do it safely. Yeah.Alessio [00:36:27]: How do you think about building for AI versus people? It's like when you mentioned cleaning up is because maybe before you were like, well, humans can have some common sense when they look at data on what to pick versus models are just kind of like ingesting. Do you think about building products differently, knowing that a lot of the data will actually be consumed by LLMs and like agents and whatnot versus like just people?Drew [00:36:46]: I think it'll always be, I aim a little bit more for like, you know, level three, level four kind of automation, because even if the LLM is like capable of completely autonomously organizing your environment, it probably would do a reasonable job. But like, I think you build bad UI when the sort of user has to fit itself to the computer versus something that you're, you know, it's like an instrument you're playing or something where you have some kind of good partnership. And you know, and on the other side, you don't have to do all this like manual effort. And so like the command line was sort of subsumed by like, you know, graphical UI. We'll keep toggling back and forth. Maybe chat will be, chat will be an increasing, especially when you bring in voice, like will be an increasing part of the puzzle. But I don't think we're going to go back to like a million command lines either. And then as far as like the sort of plumbing of like, well, is this going to be consumed by an LLM or a human? Like fortunately, like you don't really have to design it that differently. I mean, you have to make sure everything's legible to the LLM, but it's like quite tolerant of, you know, malformed everything. And actually the more, the easier it makes something to read for a human, the easier it is for an LLM to read to some extent as well. But we really think about what's that kind of right, how do we build that right, like human machine interface where you're still in control and driving, but then it's super easy to translate your intent into like the, you know, however you want your folder, setting your environment set up or like your preferences.Alessio [00:38:05]: What's the most underrated thing about Dropbox that maybe people don't appreciate?Drew [00:38:09]: Well, I think this is just such a natural evolution for us. It's pretty true. Like when people think about the world of AI, file syncing is not like the next thing you would auto complete mentally. And I think we also did like our first thing so well that there were a lot of benefits to that. But I think there also are like, we hit it so hard with our first product that it was like pretty tough to come up with a sequel. And we had a bit of a sophomore slump and you know, I think actually a lot of kids do use Dropbox through in high school or things like that, but you know, they're not, they're using, they're a lot more in the browser and then their file system, right. And we know all this, but still like we're super well positioned to like help a new generation of people with these fundamental problems and these like that affect, you know, a billion knowledge workers around just finding, organizing, sharing your stuff and keeping it safe. And there's, there's a ton of unsolved problems in those four verbs. We've talked about search a little bit, but just even think about like a whole new generation of people like growing up without the ability to like organize their things and yeah, search is great. And if you just have like a giant infinite pile of stuff, then search does make that more manageable. But you know, you do lose some things that were pretty helpful in prior decades, right? So even just the idea of persistence, stuff still being there when you come back, like when I go to sleep and wake up, my physical papers are still on my desk. When I reboot my computer, the files are still on my hard drive. But then when in my browser, like if my operating system updates the wrong way and closes the browser or if I just more commonly just declared tab bankruptcy, it's like your whole workspace just clears itself out and starts from zero. And you're like, on what planet is this a good idea? There's no like concept of like, oh, here's the stuff I was working on. Yeah, let me get back to it. And so that's like a big motivation for things like Dash. Huge problems with sharing, right? If I'm remodeling my house or if I'm getting ready for a board meeting, you know, what do I do if I have a Google doc and an air table and a 10 gig 4k video? There's no collection that holds mixed format things. And so it's another kind of hidden problem, hidden in plain sight, like he's missing primitives. Files have folders, songs have playlists, links have, you know, there's no, somehow we miss that. And so we're building that with stacks in Dash where it's like a mixed format, smart collection that you can then, you know, just share whatever you need internally, externally and have it be like a really well designed experience and platform agnostic and not tying you to any one ecosystem. We're super excited about that. You know, we talked a little bit about security in the modern world, like IT signs all these compliance documents, but in reality has no way of knowing where anything is or what's being shared. It's actually better for them to not know about it than to know about it and not be able to do anything about it. And when we talked to customers, we found that there were like literally people in IT whose jobs it is to like manually go through, log into each, like log into office, log into workspace, log into each tool and like go comb through one by one the links that people have shared and like unshares. There's like an unshare guy in all these companies and that that job is probably about as fun as it sounds like, my God. So there's, you know, fortunately, I guess what makes technology a good business is for every problem it solves, it like creates a new one, so there's always like a sequel that you need. And so, you know, I think the happy version of our Act 2 is kind of similar to Netflix. I look at a lot of these companies that really had multiple acts and Netflix had the vision to be streaming from the beginning, but broadband and everything wasn't ready for it. So they started by mailing you DVDs, but then went to streaming and then, but the value probably the whole time was just like, let me press play on something I want to see. And they did a really good job about bringing people along from the DVD mailing off. You would think like, oh, the DVD mailing piece is like this burning platform or it's like legacy, you know, ankle weight. And they did have some false starts in that transition. But when you really think about it, they were able to take that DVD mailing audience, move, like migrate them to streaming and actually bootstrap a, you know, take their season one people and bootstrap a victory in season two, because they already had, you know, they weren't starting from scratch. And like both of those worlds were like super easy to sort of forget and be like, oh, it's all kind of destiny. But like, no, that was like an incredibly competitive environment. And Netflix did a great job of like activating their Act 1 advantages and winning in Act 2 because of it. So I don't think people see Dropbox that way. I think people are sort of thinking about us just in terms of our Act 1 and they're like, yeah, Dropbox is fine. I used to use it 10 years ago. But like, what have they done for me lately? And I don't blame them. So fortunately, we have like better and better answers to that question every year.Alessio [00:42:39]: And you call it like the silicon brain. So you see like Dash and Stacks being like the silicon brain interface, basically forDrew [00:42:46]: people. I mean, that's part of it. Yeah. And writ large, I mean, I think what's so exciting about AI and everybody's got their own kind of take on it, but if you like really zoom out civilizationally and like what allows humans to make progress and, you know, what sort of is above the fold in terms of what's really mattered. I certainly want to, I mean, there are a lot of points, but some that come to mind like you think about things like the industrial revolution, like before that, like mechanical energy, like the only way you could get it was like by your own hands, maybe an animal, maybe some like clever sort of machines or machines made of like wood or something. But you were quite like energy limited. And then suddenly, you know, the industrial revolution, things like electricity, it suddenly is like, all right, mechanical energy is now available on demand as a very fungible kind of, and then suddenly we consume a lot more of it. And then the standard of living goes way, way, way, way up. That's been pretty limited to the physical realm. And then I believe that the large models, that's really the first time we can kind of bottle up cognitive energy and offloaded, you know, if we started by offloading a lot of our mechanical or physical busy work to machines that freed us up to make a lot of progress in other areas. But then with AI and computing, we're like, now we can offload a lot more of our cognitive busy work to machines. And then we can create a lot more of it. Price of it goes way down. Importantly, like, it's not like humans never did anything physical again. It's sort of like, no, but we're more leveraged. We can move a lot more earth with a bulldozer than a shovel. And so that's like what is at the most fundamental level, what's so exciting to me about AI. And so what's the silicon brain? It's like, well, we have our human brains and then we're going to have this other like half of our brain that's sort of coming online, like our silicon brain. And it's not like one or the other. They complement each other. They have very complimentary strengths and weaknesses. And that's, that's a good thing. There's also this weird tangent we've gone on as a species to like where knowledge work, knowledge workers have this like epidemic of, of burnout, great resignation, quiet quitting. And there's a lot going on there. But I think that's one of the biggest problems we have is that be like, people deserve like meaningful work and, you know, can't solve all of it. But like, and at least in knowledge work, there's a lot of own goals, you know, enforced errors that we're doing where it's like, you know, on one side with brain science, like we know what makes us like productive and fortunately it's also what makes us engaged. It's like when we can focus or when we're some kind of flow state, but then we go to work and then increasingly going to work is like going to a screen and you're like, if you wanted to design an environment that made it impossible to ever get into a flow state or ever be able to focus, like what we have is that. And that was the thing that just like seven, eight years ago just blew my mind. I'm just like, I cannot understand why like knowledge work is so jacked up on this adventure. It's like, we, we put ourselves in like the most cognitively polluted environment possible and we put so much more stress on the system when we're working remotely and things like that. And you know, all of these problems are just like going in the wrong direction. And I just, I just couldn't understand why this was like a problem that wasn't fixing itself. And I'm like, maybe there's something Dropbox can do with this and you know, things like Dash are the first step. But then, well, so like what, well, I mean, now like, well, why are humans in this like polluted state? It's like, well, we're just, all of the tools we have today, like this generation of tools just passes on all of the weight, the burden to the human, right? So it's like, here's a bajillion, you know, 80,000 unread emails, cool. Here's 25 unread Slack channels. Here's, we all get started like, it's like jittery like thinking about it. And then you look at that, you're like, wait, I'm looking at my phone, it says like 80,000 unread things. There's like no question, product question for which this is the right answer. Fortunately, that's why things like our silicon brain are pretty helpful because like they can serve as like an attention filter where it's like, actually, computers have no problem reading a million things. Humans can't do that, but computers can. And to some extent, this was already happening with computer, you know, Excel is an aversion of your silicon brain or, you know, you could draw the line arbitrarily. But with larger models, like now so many of these little subtasks and tasks we do at work can be like fully automated. And I think, you know, I think it's like an important metaphor to me because it mirrors a lot of what we saw with computing, computer architecture generally. It's like we started out with the CPU, very general purpose, then GPU came along much better at these like parallel computations. We talk a lot about like human versus machine being like substituting, it's like CPU, GPU, it's not like one is categorically better than the other, they're complements. Like if you have something really parallel, use a GPU, if not, use a CPU. The whole relationship, that symbiosis between CPU and GPU has obviously evolved a lot since, you know, playing Quake 2 or something. But right now we have like the human CPU doing a lot of, you know, silicon CPU tasks. And so you really have to like redesign the work thoughtfully such that, you know, probably not that different from how it's evolved in computer architecture, where the CPU is sort of an orchestrator of these really like heavy lifting GPU tasks. That dividing line does shift a little bit, you know, with every generation. And so I think we need to think about knowledge work in that context, like what are human brains good at? What's our silicon brain good at? Let's resegment the work. Let's offload all the stuff that can be automated. Let's go on a hunt for like anything that could save a human CPU cycle. Let's give it to the silicon one. And so I think we're at the early earnings of actually being able to do something about it.Alessio [00:48:00]: It's funny, I gave a talk to a few government people earlier this year with a similar point where we used to make machines to release human labor. And then the kilowatt hour was kind of like the unit for a lot of countries. And now you're doing the same thing with the brain and the data centers are kind of computational power plants, you know, they're kind of on demand tokens. You're on the board of Meta, which is the number one donor of Flops for the open source world. The thing about open source AI is like the model can be open source, but you need to carry a briefcase to actually maybe run a model that is not even that good compared to some of the big ones. How do you think about some of the differences in the open source ethos with like traditional software where it's like really easy to run and act on it versus like models where it's like it might be open source, but like I'm kind of limited, sort of can do with it?Drew [00:48:45]: Yeah, well, I think with every new era of computing, there's sort of a tug of war between is this going to be like an open one or a closed one? And, you know, there's pros and cons to both. It's not like open is always better or open always wins. But, you know, I think you look at how the mobile, like the PC era and the Internet era started out being more on the open side, like it's very modular. Everybody sort of party that everybody could, you know, come to some downsides of that security. But I think, you know, the advent of AI, I think there's a real question, like given the capital intensity of what it takes to train these foundation models, like are we going to live in a world where oligopoly or cartel or all, you know, there's a few companies that have the keys and we're all just like paying them rent. You know, that's one future. Or is it going to be more open and accessible? And I'm like super happy with how that's just I find it exciting on many levels with all the different hats I wear about it. You know, fortunately, you've seen in real life, yeah, even if people aren't bringing GPUs on a plane or something, you've seen like the price performance of these models improve 10 or 100x year over year, which is sort of like many Moore's laws compounded together for a bunch of reasons like that wouldn't have happened without open source. Right. You know, for a lot of same reasons, it's probably better that we can anyone can sort of spin up a website without having to buy an internet information server license like there was some alternative future. So like things are Linux and really good. And there was a good balance of trade to where like people contribute their code and then also benefit from the community returning the favor. I mean, you're seeing that with open source. So you wouldn't see all this like, you know, this flourishing of research and of just sort of the democratization of access to compute without open source. And so I think it's been like phenomenally successful in terms of just moving the ball forward and pretty much anything you care about, I believe, even like safety. You can have a lot more eyes on it and transparency instead of just something is happening. And there was three places with nuclear power plants attached to them. Right. So I think it's it's been awesome to see. And then and again, for like wearing my Dropbox hat, like anybody who's like scaling a service to millions of people, again, I'm probably not using like frontier models for every request. It's, you know, there are a lot of different configurations, mostly with smaller models. And even before you even talk about getting on the device, like, you know, you need this whole kind of constellation of different options. So open source has been great for that.Alessio [00:51:06]: And you were one of the first companies in the cloud repatriation. You kind of brought back all the storage into your own data centers. Where are we in the AI wave for that? I don't think people really care today to bring the models in-house. Like, do you think people will care in the future? Like, especially as you have more small models that you want to control more of the economics? Or are the tokens so subsidized that like it just doesn't matter? It's more like a principle. Yeah. Yeah.Drew [00:51:30]: I mean, I think there's another one where like thinking about the future is a lot easier if you start with the past. So, I mean, there's definitely this like big surge in demand as like there's sort of this FOMO driven bubble of like all of big tech taking their headings and shipping them to Jensen for a couple of years. And then you're like, all right, well, first of all, we've seen this kind of thing before. And in the late 90s with like Fiber, you know, this huge race to like own the internet, own the information superhighway, literally, and then way overbuilt. And then there was this like crash. I don't know to what extent, like maybe it is really different this time. Or, you know, maybe if we create AGI that will sort of solve the rest of the, or we'll just have a different set of things to worry about. But, you know, the simplest way I think about it is like this is sort of a rent not buy phase because, you know, I wouldn't want to be, we're still so early in the maturity, you know, I wouldn't want to be buying like pallets of over like of 286s at a 5x markup when like the 386 and 486 and Pentium and everything are like clearly coming there around the corner. And again, because of open source, there's just been a lot more com

Cognitive biases are systematic errors in thinking that can influence clinical judgment and potentially lead to adverse outcomes for patients. By understanding these biases, anesthesia providers can improve their decision-making process and enhance patient safety. Today we'll take a deep dive into the world of cognitive biases and how they affect decision-making in anesthesia practice. Here's some of what we discuss in this episode: Heuristics or decision-making shortcuts can lead to cognitive biases. How the different biases impact the performance of CRNAs. The framing effect and the gambler's fallacy influence decision-making. Effective team communication reduces the likelihood of confirmation bias or anchoring bias.   Visit us online: https://beyondthemaskpodcast.com/ The 1099 CRNA Institute: https://aana.com/1099 ***Use coupon code BEYOND1999 to get 20% off through November 2024 Get the CE Certificate here: https://beyondthemaskpodcast.com/wp-content/uploads/2020/04/Beyond-the-Mask-CE-Cert-FILLABLE.pdf Help us grow by leaving a review: https://podcasts.apple.com/us/podcast/beyond-the-mask-innovation-opportunities-for-crnas/id1440309246 Donate to Our Heart Your Hands here: https://www.ourheartsyourhands.org/donate  Support Team Emma Kate: https://grouprev.com/haloswalk2024-shannon-shannon-brekken

Highlights from this week's conversation include:Reporting and Analytics Discussion (1:09)Automation in Reporting (3:16)AI's Impact on Analytics (5:00)Data Quality Challenges (6:56)Reinventing Reporting (9:23)Automated Reporting Services (14:35)Growth Trajectory of Reporting Tools (16:01)Market Size Comparison (18:04)Static vs. Time Series Data (21:27)Differentiating Reporting and Analytics (26:26)Ad Hoc Analysis vs. Reporting (29:52)The Role of Data Scientists (34:03)Planning the Reset (38:36)Focus on Business Problems (41:30)Identifying Business Needs (44:01)Heuristics and Intuition (48:03)Importance of Monitoring (52:20)Observability in Analytics (55:53)Observability in Metrics (58:08)Consistency in Definitions (1:02:39)Impact of Changing Definitions and Final Takeaways (1:04:15)The Data Stack Show is a weekly podcast powered by RudderStack, the CDP for developers. Each week we'll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.

In this episode, Sarah walks through 10 of the best (and little-known) psychological heuristics (or mental shortcuts) you can use to “hack” the human mind so you can resonate deeper with your customers and grow your brand.   Learn more at: https://www.sarahlevinger.co Twitter: https://x.com/SarahLevinger Linkedin: https://www.linkedin.com/in/sarahlevinger/ Instagram: https://www.instagram.com/sarah.levinger/ Watch me on YouTube: https://www.youtube.com/channel/UCKwfjt_7PU5N_2fTfHemXXg   Thanks to Cytrus for the theme song, “Sky High” You can follow and find them on Spotify:https://open.spotify.com/track/1oKGDsxjRdQlf2xHLZsiSJ?si=8fbd275dbbb54cbf  

Kruw runs the most successful Wasabi wallet 2.0 coordinator and is the kind of privacy advocate who actually builds. In this episode, he talks about continuing Wasabi, the shortcomings of Samourai & how blockchain analysis works. Time stamps: Introduction (00:00:39)
 Recap of Wasabi Wallet Discussions (00:01:33)
 Samourai Wallet's Decentralization Announcement (00:02:28)
 Challenges Faced by Privacy Projects (00:03:56)
 Shift in Bitcoin Culture (00:04:40)
 Impact of Regulatory Pressures (00:05:32)
 Wasabi's Resilience (00:06:40)
 Exploring Bitcoin's Privacy Features (00:07:53)
 Demonstrating CoinJoin Transactions (00:08:36)
 Differences Between CoinJoin and Monero (00:10:24)
 Challenges with Bitcoin Transaction Privacy (00:11:23)
 Concerns Over High Transaction Fees (00:13:11)
 Wasabi's New Features for Privacy (00:17:40)
 User-Friendly Features in Wasabi 2.0 (00:20:14) Transaction Speedup Options (00:21:18)
 Critique of UTXO Management in Wasabi 2.0 (00:23:05)
 Wasabi Wallet User Control (00:23:38)
 Switching Coordinators Feature (00:24:03)
 Decentralized Coordinator Community (00:24:50)
 User Preferences in Coordinator Selection (00:26:18)
 Coordinator Fees Discussion (00:30:56)
 Drivechains and Mining Concerns (00:31:04)
 Market Dynamics of Coordinator Fees (00:34:25)
 Collecting Dust from Transactions (00:37:43)
 Trezor's Coinjoin (00:38:09)
 Wasabi Wallet's Development Funding (00:41:14)
 Samourai Wallet's Blockchain Analysis Tool (00:42:36)
 Address Reuse and Privacy Challenges (00:46:49)
 Mistakes in Privacy Usage (00:49:40)
 Discrediting Wasabi Wallet (00:50:21)
 Cash Fusion (00:51:42)
 Reputation and Arrests (00:53:23)
 Bitcoin Privacy Camps (00:54:33)
 Join Market's Unique Position (00:55:56)
 Legal Risks in Bitcoin Coordination (00:56:21)
 The Fight for Bitcoin's Future (01:00:01)
 Personal Ethics and Legal Protections (01:01:42)
 Concerns Over Martyrdom (01:03:14)
 Physical Tokens and Authenticity (01:04:12)
 Critique of Samourai Wallet (01:05:10)
 Transparency Issues in Samurai Wallet (01:08:09)
 Closed Source vs. Open Source Debate (01:09:55)
 Default Privacy Features in Samurai Wallet (01:11:26)
 Ricochet Transactions and Fees (01:13:13)
 Government Awareness of Transactions (01:14:41)
 Implications of Fee Collection (01:15:01)
 Personal Standards and Hypocrisy (01:15:53)
 New Pepe Avatar Presentation (01:16:21)
 Nostr vs. Twitter (01:18:21)
 Support for CTV & Covenants (01:20:11) Governance and Community Dynamics (01:22:38)
 Concerns about ETF Privacy (01:22:53)
 Complexity of Consensus Changes (01:24:08)
 Community Understanding of Proposals (01:28:20)
 Censorship Resistance in Bitcoin (01:31:01)
 Market Dynamics and Filters (01:32:46)
 Historical Context of Data on Bitcoin (01:34:08)
 Criticism of Jimmy Song (01:35:31)
 Litecoin's Role in Segwit Activation (01:36:30)
 Bitcoin's Cultural Stagnation (01:37:47)
 Ranking Privacy Coins (01:39:45)
 Liquidity vs. Privacy (01:40:26)
 Technological Advancements in Privacy (01:41:39)
 User Resistance to New Technology (01:42:54)
 10101 Shutdown (01:43:09)
 Drivechains and Altcoins (01:44:16)
 Monero Liquidity Concerns (01:45:58)
 Future of Bitcoin vs. Altcoins (01:46:29)
 Cultural Conflicts in Bitcoin Development (01:47:20)
 Blockstream's Influence (01:48:26)
 Whale Bots in Bitcoin (01:51:49)
 Michael Saylor's Influence (01:52:52)
 Bitcoin's Future and Development (01:55:07)
 Censorship Resistance vs. Privacy (01:59:08)
 Satoshi's Coins and Community Reaction (02:00:05)
 Blockchain Analysis Case (02:01:08) Challenges of Privacy Services (02:04:15) Tracking Methods in Blockchain (02:05:20) Understanding Transaction Outputs (02:06:39) Heuristics for Blockchain Analysis (02:09:03) Personal Experience with Blockchain Analysis (02:10:06) Samurai Wallet's Controversies (02:10:58) Victims vs. Heroes in Privacy Projects (02:13:37) Bitcoin Soft Forks for Privacy (02:16:46) Statechains and Privacy (02:18:18) Interactive Transactions Challenges (02:20:08) Monero and Chainalysis (02:22:25) Network Level Privacy Issues (02:23:01) Future of Privacy Tools (02:24:01) Zano's Hybrid Consensus Model (02:24:49) Tail Emission Interest (02:25:13) Consensus Mechanisms Explained (02:26:11) Future of Bitcoin on Layer 2 (02:27:05) Lightning Network, Finally (02:28:28) Privacy Concerns in Lightning (02:29:39) Confidentiality vs. Privacy (02:29:24) Splicing and Lightning Efficiency (02:30:12) Public vs. Unannounced Lightning Channels (02:32:40) Custodial Systems and Privacy (02:34:12) Investment in Privacy Technologies (02:35:31) Nym Security Token Overview (02:36:57) Challenges of Decentralization (02:40:03) Tor and Bitcoin Privacy (02:41:22) Running a Tor Relay (02:42:10) Discussion on Transaction Value (02:48:55) Counterarguments on Spam Incentives (02:49:34) Block Size Debate (02:51:20) Challenges of Block Size Increases (02:52:41) Ethereum vs. Bitcoin Cash (02:57:48) Dynamic Block Size Mechanisms (02:58:57) Running Full Nodes on Mobile (03:00:10) Wrap Up and Future Prospects (03:03:49)

In this episode of PsychChat, I discuss the pervasive behaviour of defensive decision-making in the workplace. Listen to this episode, where I share tips to mitigate such behaviour in the workplace.ReferencesArtinger, F., Petersen, M., Gigerenzer, G., & Weibler, J. (2015). Heuristics as adaptive decision strategies in management. Journal of Organizational Behavior, 36(S1), S33-S52.Brockner, J., & Higgins, E. T. (2001). Regulatory focus theory: Implications for the study of emotions at work. Organizational Behavior and Human Decision Processes, 86(1), 35-66.Edmondson, A. C. (1999). Psychological safety and learning behavior in work teams. Administrative Science Quarterly, 44(2), 350-383.Gigerenzer, G. (2014). Risk savvy: How to make good decisions. Penguin.Greenhalgh, L., & Rosenblatt, Z. (1984). Job insecurity: Toward conceptual clarity. Academy of Management Review, 9(3), 438-448.Higgins, E. T. (1998). Promotion and prevention: Regulatory focus as a motivational principle. Advances in Experimental Social Psychology, 30, 1-46.Hobfoll, S. E. (1989). Conservation of resources: A new attempt at conceptualizing stress. American Psychologist, 44(3), 513-524.Hobfoll, S. E., Halbesleben, J., Neveu, J. P., & Westman, M. (2018). Conservation of resources in the organizational context: The reality of resources and their consequences. Annual Review of Organizational Psychology and Organizational Behavior, 5, 103-128.Marx-Fleck, S., Junker, N. M., Artinger, F., & van Dick, R. (2021). Defensive decision making: Operationalization and the relevance of psychological safety and job insecurity from a conservation of resources perspective. Journal of Occupational and Organizational Psychology,  Vol 94 (3), 485-788.Mello, M. M., Chandra, A., Gawande, A. A., & Studdert, D. M. (2010). National costs of the medical liability system. Health Affairs, 29(9), 1569-1577.

Chapter 1:Summary of Nudge"Nudge: Improving Decisions About Health, Wealth, and Happiness" is a book by behavioral economist Richard H. Thaler and legal scholar Cass R. Sunstein, first published in 2008. The book explores the concept of "libertarian paternalism" and suggests that private and public institutions can help people make better choices in their lives without eliminating freedom of choice. The central idea of the book is that by properly designing the context in which individuals make decisions—what Thaler and Sunstein call the "choice architecture"—it is possible to influence the choices people make in order to benefit them. A "nudge," as defined by the authors, is any aspect of this choice architecture that alters people's behavior in a predictable way without forbidding any options or significantly changing their economic incentives.Thaler and Sunstein argue that nudges are essential because of human cognitive limitations and biases. For instance, people tend to stick with default options, tend to be influenced by how choices are framed, and often act against their own long-term interests due to present bias and a range of other decision-making flaws.The book covers a variety of areas in which nudges can be applied, including retirement savings plans, healthcare choices, and environmental conservation. For example, automatically enrolling employees into retirement savings plans but giving them the option to opt-out increases savings participation rates dramatically."Nudge" addresses ethical concerns and emphasizes the importance of ensuring that nudges are transparent and never deceitful. It also argues that nudges should be designed to simplify decision-making and improve people's welfare by steering them towards decisions that reflect their own true preferences.Overall, "Nudge" is a significant contribution to the field of behavioral economics, providing insights into how subtle changes in the way choices are presented can have a profound impact on human behavior.Chapter 2:The Theme of Nudge"Nudge: Improving Decisions About Health, Wealth, and Happiness" is a book authored by Richard H. Thaler and Cass R. Sunstein, first published in 2008. It does not contain a traditional narrative or characters as it is a non-fiction work grounded in the fields of behavioral economics and decision theory. The central thesis of the book revolves around the concept of "libertarian paternalism" and how small design changes in the environment (termed "nudges") can significantly affect individual choices in a way that improves their own welfare. Below are the key plot points, character development, and thematic ideas of the book: Key Plot Points1. Introduction of Libertarian Paternalism:- The book introduces the idea of libertarian paternalism, which endeavors to steer people towards making decisions that would improve their lives while preserving their freedom to choose.2. Choice Architecture:- Thaler and Sunstein discuss "choice architecture," the way choices can be presented to people that influences their decision-making without restricting options.3. Heuristics and Biases:- An exploration of the various cognitive biases and heuristics that typically impede rational decision-making and how these can be redirected through nudges to spawn better choices.4. Aspects of Nudging:- Various methods of nudging are discussed, such as default settings, feedback mechanisms, and the structuring of complex choices in simpler, more digestible forms.5. Applications of Nudging:- The book discusses applications in diverse fields, including finance (e.g., encouraging savings), health (e.g., influencing food choices), education, and environment, illustrating how nudges can lead to substantial improvements in societal well-being. Character...

In this engaging episode of the Performance Initiative Podcast, hosts Dr. Grant Cooper and Dr. Zinovy Meyler sit down with renowned Harvard psychologist Professor Daniel Gilbert to explore the complexities of happiness and how our choices affect our well-being. Delving into the psychology behind happiness, they discuss the common misconceptions about activities like volunteering, raising children, and owning pets. The conversation uncovers the significant role of social connections, the impact of sleep, and the importance of taking decisive actions in life. Through compelling examples and studies, Professor Gilbert sheds light on the counterintuitive nature of happiness, guiding listeners to understand what truly brings lasting satisfaction and joy.(00:00) Introduction(03:06) The Paradox of Choice(07:42) Medical Decision-Making and Heuristics(16:07) The Art and Science of Medicine(21:19) The Role of Psychology in Medicine(24:31) The Fallibility of Human Imagination(45:21) Gratitude and Perspective in Life(50:06) The Big Sort: Communities of Like-Minded Individuals(50:47) Political Ideologies and Media Siloing(52:12) Shared Experiences and the Melting Pot of America(53:03) The Challenge of Modern Isolation(54:42) The Paradox of Wealth and Happiness(56:50) Social Connections vs. Wealth: What Truly Brings Happiness?(59:21) Purpose and Happiness: Are They Interconnected?(01:01:34) The Role of Social Interaction in Fulfillment(01:09:41) Children and Happiness: A Complex Relationship(01:18:33) The Importance of Sleep and Health in Happiness(01:26:34) Action vs. Inaction: Regrets and Life Choices(01:30:36) Concluding Thoughts and TakeawaysProfessor Daniel Gilbert is a renowned social psychologist and a faculty member at Harvard University, best known for his research on affective forecasting, happiness, and decision-making. He is the author of the New York Times bestseller Stumbling on Happiness, which explores the quirks of human cognition and how we often mispredict our own emotional responses to future events. Gilbert's work has significantly influenced the field of psychology, offering profound insights into how people perceive happiness and the choices that lead to a fulfilling life. In this episode, he shares his expertise on the psychology of happiness, challenging common beliefs and providing a deeper understanding of what truly contributes to well-being.Thanks For ListeningSocials:YouTube: https://www.youtube.com/channel/UCKPNCI1-HBSZmiHNAlAjiIwWebsite: https://www.performanceinitiativepodcast.com/Instagram: https://www.instagram.com/performanceinitiative

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Turning 22 in the Pre-Apocalypse, published by testingthewaters on August 23, 2024 on LessWrong. Meta comment for LessWrong readers[1] Something Different This Way Comes - Part 1 In which I attempt to renegotiate rationalism as a personal philosophy, and offer my alternative - Game theory is not a substitute for real life - Heuristics over theories Introduction This essay focuses on outlining an alternative to the ideology of rationalism. As part of this, I offer my definition of the rationalist project, my account of its problems, and my concept of a counter-paradigm for living one's life. The second part of this essay will examine the political implications of rationalism and try to offer an alternative on a larger scale. Defining Rationalism To analyse rationalism, I must first define what I am analysing. Rationalism (as observed in vivo on forums like LessWrong) is a loose constellation of ideas radiating out of various intellectual traditions, amongst them Bayesian statistics, psychological decision theories, and game theory. These are then combined with concepts in sub-fields of computer science (AI and simulation modelling), economics (rational actor theory or homo economicus), politics (libertarianism), psychology (evolutionary psychology) and ethics (the utilitarianism of Peter Singer). The broad project of rationalism aims to generalise the insights of these traditions into application at both the "wake up and make a sandwich" and the "save the world" level. Like any good tradition, it has a bunch of contradictions embedded: Some of these include intuitionism (e.g. when superforecasters talk about going with their gut) vs deterministic analysis (e.g. concepts of perfect game-players and k-level rationality). Another one is between Bayesianism (which is about updating priors about the world based on evidence received, generally without making any causal assumptions) vs systemisation (which is about creating causal models/higher level representations of real life situations to understand them better). In discussing this general state of rhetorical confusion I am preceded by Philip Agre's Towards a Critical Technical Practice, which is AI specific but still quite instructive. The broader rationalist community (especially online) includes all sorts of subcultures but generally there are in group norms that promote certain technical argot ("priors", "updating"), certain attitudes towards classes of entities ("blank faces"/bureaucrats/NPCs/the woke mob etc), and certain general ideas about how to solve "wicked problems" like governance or education. There is some overlap with online conservatives, libertarians, and the far-right. There is a similar overlap with general liberal technocratic belief systems, generally through a belief in meritocracy and policy solutions founded on scientific or technological principles. At the root of this complex constellation there seems to be a bucket of common values which are vaguely expressed as follows: 1. The world can be understood and modelled by high level systems that are constructed based on rational, clearly defined principles and refined by evidence/observation. 2. Understanding and use of these systems enables us to solve high level problems (social coordination, communication, AI alignment) as well as achieving our personal goals. 3. Those who are more able to comprehend and use these models are therefore of a higher agency/utility and higher moral priority than those who cannot. There is also a fourth law which can be constructed from the second and third: By thinking about this at all, by starting to consciously play the game of thought-optimisation and higher order world-modelling, you (the future rationalist) have elevated yourself above the "0-level" player who does not think about such problems and naively pur...

Send us a Text Message.We all use mental shortcuts to make decisions. Those mental shortcuts are the reason that first sentence in this episode description is so short. Savvy marketers tap into these cognitive biases to influence out choices and drive success. In this episode, Nathan Yeung shares why understanding these shortcuts is crucial for creating effective marketing strategies and how trust and avoiding overconfidence play pivotal roles in winning over consumers.  Listen For4:21 Utilizing Availability and Accessibility Heuristics9:08 Channel Strategy and Consumer Habits11:35 Ethical Use of Cognitive Biases14:15 Trust and Overconfidence in MarketingGuest: Nathan Yeung, Find Your AudienceWebsite | Email | LinkedIn Rate this podcast with just one click Leave us a voice message we can share on the podcast  https://www.speakpipe.com/StoriesandStrategiesStories and Strategies WebsiteDo you want to podcast? Book a meeting with Doug Downs to talk about it.Apply to be a guest on the podcastConnect with usLinkedIn | X | Instagram | You Tube | Facebook | ThreadsRequest a transcript of this episodeSupport the Show.

Heuristics are cognitive shortcuts or rules of thumb that people employ to make decisions and solve problems efficiently. Essentially, they are mental strategies based on past experiences and knowledge that help us bypass complex thinking processes. While heuristics often lead to satisfactory solutions, they can also introduce biases and errors in judgment. These mental shortcuts are essential for navigating the complexities of everyday life. They allow us to make quick decisions without overanalyzing every option. For instance, choosing a restaurant based on recommendations from friends or estimating the cost of groceries based on previous shopping trips are common examples of heuristics in action. Understanding these cognitive shortcuts is valuable for fields like psychology, economics, and artificial intelligence, as it helps us to identify potential biases and develop strategies to mitigate their impact.

Every day we make a million little decisions without ever giving it a second thought. This is because our brains are wired to take shortcuts and make quick decisions. If you understand and follow the shortcuts your users are taking, you can shift the focus to enhancing their experience and help them achieve their goals on your site quickly and efficiently.This week, Drive and Convert takes you on a journey through the six heuristics for digital experience optimization and how they come together to create a better digital experience for users.Check out the full episode to learn:How to build trust, confidence, and credibilityHow to create a smooth path to purchase for usersWhat heuristics are and the potential outcomes of utilizing themIf you have questions, ideas, or feedback to share, connect with us on LinkedIn. We're Jon Macdonald and Ryan Garrow.

The last few weeks we've had some really explosive theories on the show, which have caused a stir in the comments and beyond. We sit down to talk about the philosophy behind why we believe exploring far out theories is so important, and use Paul Feyerabend's Against Method as the backbone for our discussion. We introduce the idea of scientific anarchy, which encourages us to lean into the uncomfortable reality that ideas cannot be prized simply because of their age, or their apparent agreement with existing data. The history of science is littered with theories that were wrong but useful, detectors that are engineered to give us the results we are expecting, and the weighty knowledge that the absence of evidence tells us nothing about what we'll find when we take a closer look at the inner workings of nature. Sign up for our Patreon and get episodes early + join our weekly Patron Chat https://bit.ly/3lcAasB AND rock some Demystify Gear to spread the word! https://demystifysci.myspreadshop.com/ Pick up some Feyerabend and support the pod when you do it here: https://amzn.to/3Y7bcQ8 (00:00) Go! (00:09:56) Podcast insider conspiracy theories (00:16:10) Surrendering to being a useful idiot (00:20:55) becoming a valuable mouthpiece (00:23:48) Patronage of the billionaire king (00:28:24) Two types of conspiracies (00:35:48) Can a free internet rise again? (00:44:35) Monoliths vs chaos, enter Paul Feyerabend (00:48:56) Heuristics and other cognitive offloads (00:55:08) Calcifying opinion into fact (00:58:02) Science is a historical process (01:01:37) Subservience of science to technology (01:04:31) Why Michael Hudson doesn't take libertarians seriously (01:06:49) Handling non-human entities (01:09:40) Triggering the psychological immune system (01:14:06) Counter-rule science (01:17:07) The approach of the Platonic Redditor (01:21:49) An argument for scientific anarchy (01:26:25) All theories have a little bit of gold (01:29:18) Does the buck stop anywhere? (01:35:45) All models suck, but I want to believe (01:41:39) Searching for the needle in the hay (01:47:26) Closing thoughts #sciencepodcast #longformpodcast #ScientificAnarchy, #PaulFeyerabend, #AgainstMethod, #PhilosophyOfScience, #ExploringTheories, #UncomfortableTruths, #ScientificRevolution, #ScientificDebate, #FarOutTheories, #ChallengingNorms, #SciencePhilosophy, #InnovativeIdeas, #BreakthroughScience, #ScientificMethod, #CriticalThinking, #AlternativeScience, #RevolutionaryThoughts, #NewPerspectives, #ScienceAndPhilosophy, #HistoryOfScience Check our short-films channel, @DemystifySci: https://www.youtube.com/c/DemystifyingScience AND our material science investigations of atomics, @MaterialAtomics https://www.youtube.com/@MaterialAtomics Join our mailing list https://bit.ly/3v3kz2S PODCAST INFO: Anastasia completed her PhD studying bioelectricity at Columbia University. When not talking to brilliant people or making movies, she spends her time painting, reading, and guiding backcountry excursions. Shilo also did his PhD at Columbia studying the elastic properties of molecular water. When he's not in the film studio, he's exploring sound in music. They are both freelance professors at various universities. - Blog: http://DemystifySci.com/blog - RSS: https://anchor.fm/s/2be66934/podcast/rss - Donate: https://bit.ly/3wkPqaD - Swag: https://bit.ly/2PXdC2y SOCIAL: - Discord: https://discord.gg/MJzKT8CQub - Facebook: https://www.facebook.com/groups/DemystifySci - Instagram: https://www.instagram.com/DemystifySci/ - Twitter: https://twitter.com/DemystifySci MUSIC: -Shilo Delay: https://g.co/kgs/oty671

The first AI Engineer World's Fair talks from OpenAI and Cognition are up!In our Benchmarks 101 episode back in April 2023 we covered the history of AI benchmarks, their shortcomings, and our hopes for better ones. Fast forward 1.5 years, the pace of model development has far exceeded the speed at which benchmarks are updated. Frontier labs are still using MMLU and HumanEval for model marketing, even though most models are reaching their natural plateau at a ~90% success rate (any higher and they're probably just memorizing/overfitting).From Benchmarks to LeaderboardsOutside of being stale, lab-reported benchmarks also suffer from non-reproducibility. The models served through the API also change over time, so at different points in time it might return different scores.Today's guest, Clémentine Fourrier, is the lead maintainer of HuggingFace's OpenLLM Leaderboard. Their goal is standardizing how models are evaluated by curating a set of high quality benchmarks, and then publishing the results in a reproducible way with tools like EleutherAI's Harness.The leaderboard was first launched summer 2023 and quickly became the de facto standard for open source LLM performance. To give you a sense for the scale:* Over 2 million unique visitors* 300,000 active community members* Over 7,500 models evaluatedLast week they announced the second version of the leaderboard. Why? Because models were getting too good!The new version of the leaderboard is based on 6 benchmarks:*

This episode covers the prickly topic of judgment. Puja delves into its pervasive nature, exploring how it affects our relationships and personal well-being. She shares personal anecdotes, psychological insights, and mindset shifts to help listeners reduce judgment and cultivate healthier, deeper, more soulful connections. Through mindfulness and self-awareness, Puja guides us towards a more empathetic and understanding approach to interacting with others. Key Points The Impact of Judgment on Well-being: Judgment creates a sense of constriction and tension in both the heart and body.Holding judgment harms us and distances us from love and other positive relational qualities. Understanding Heuristics: Heuristics are mental shortcuts that simplify our decision-making process but can lead to biases and stereotypes. While heuristics are helpful in many daily situations, they can be detrimental when applied to human interactions Developing Present Moment Awareness: Practicing mindfulness and meditation helps slow down the brain's automatic judgment processes. Increased self-awareness allows us to challenge and expand our limited perspectives. Binocular Vision in RelationshipsSeeing the full picture, rather than just our limited perspective, reduces conflict and enhances understanding. Binocular vision fosters stability and a deeper connection in relationships. Replacing Judgment with CuriosityApproaching others with genuine curiosity rather than judgment opens the door to empathy and deeper understanding. Assuming good intentions helps us respond with grace and compassion, improving our mental well-being and interactions.   Quotes   "When judgment enters the room, love leaves. It is impossible for love and judgment to co-exist in the same space." "Content is linear and context is non-linear. What we perceive and base our judgment on is just a very small piece of the big picture."  "We feel better when we extend grace and kindness towards somebody. When we choose peace, grace, and compassion, we are not choosing stress, agitation, and frustration."   Reflection Prompts: 1. Reflect on a recent time when you felt judgment towards someone. How did it affect your feelings and actions? 2. Consider a person you often judge. How might you approach them with curiosity instead? What questions could you ask to understand their perspective better? 3. Write about a time when you were judged unfairly. How did it make you feel, and what would you have preferred instead?   Call to Action Share the Episode: Think of one person who could benefit from today's insights on judgment and love. Share this episode with them. Rate and Review If you've been enjoying the Radiant Presence Podcast, please take a moment to rate and review it on your listening platform. Your feedback helps us reach more listeners.   Recommended Episodes for Reference: Introduction: What is My Presence Worth? The Myth of Work-Life Balance (And a Sustainable Solution to Burnout)

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: OthelloGPT learned a bag of heuristics, published by jylin04 on July 2, 2024 on The AI Alignment Forum. Work performed as a part of Neel Nanda's MATS 6.0 (Summer 2024) training program. TLDR This is an interim report on reverse-engineering Othello-GPT, an 8-layer transformer trained to take sequences of Othello moves and predict legal moves. We find evidence that Othello-GPT learns to compute the board state using many independent decision rules that are localized to small parts of the board. Though we cannot rule out that it also learns a single succinct algorithm in addition to these rules, our best guess is that Othello-GPT's learned algorithm is just a bag of independent heuristics. Board state reconstruction 1. Direct attribution to linear probes indicate that the internal board representation is frequently up- and down-weighted during a forward pass. 2. Case study of a decision rule: 1. MLP Neuron L1N421 represents the decision rule: If the move A4 was just played AND B4 is occupied AND C4 is occupied update B4+C4+D4 to "theirs". This rule does not generalize to translations across the board. 2. Another neuron L0377 participates in the implementation of this rule by checking if B4 is occupied, and inhibiting the activation of L1N421 if no. Legal move prediction 1. A subset of neurons in mid to late MLP layers classify board configurations that are sufficient to make a certain move legal with an F1-score above 0.99. These neurons have high direct attribution to the logit for that move, and are causally relevant for legal move prediction. 2. Logit lens suggests that legal move predictions gradually solidify during a forward pass. 3. Some MLP neurons systematically activate at certain times in the game, regardless of the moves played so far. We hypothesize that these neurons encode heuristics about moves that are more probable in specific phases (early/mid/late) of the game. Review of Othello-GPT Othello-GPT is a transformer with 25M parameters trained on sequences of random legal moves in the board game Othello as inputs[1] to predict legal moves[2]. How it does this is a black box that we don't understand. Its claim to fame is that it supposedly 1. Learns an internal representation of the board state; 2. Uses it to predict legal moves which if true, resolves the black box in two[3]. The evidence for the first claim is that linear probes work. Namely, for each square of the ground-truth game board, if we train a linear classifier to take the model's activations at layer 6 as input and predict logits for whether that square is blank, "mine" (i.e. belonging to the player whose move it currently is) or "yours", the probes work with high accuracy on games not seen in training. The evidence for the second claim is that if we edit the residual stream until the probe's outputs change, the model's own output at the end of layer 7 becomes consistent with legal moves that are accessible from the new board state. However, we don't yet understand what's going on in the remaining black boxes. In particular, although it would be interesting if Othello-GPT emergently learned to implement them via algorithms with relatively short description lengths, the evidence so far doesn't rule out the possibility that they could be implemented via a bag of heuristics instead. Project goal Our goal in this project was simply to figure out what's going on in the remaining black boxes. 1. What's going on in box #1 - how does the model compute the board representation? 1. How does the model decide if a cell is blank or not blank? 2. How does the model decide if a cell is "mine" or "yours"? 2. What's going on in box #2 - how does the model use the board representation to pick legal moves? Results on box #1: Board reconstruction A circuit for how the model computes if a cell is blank or not blan...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Formal verification, heuristic explanations and surprise accounting, published by Jacob Hilton on June 25, 2024 on The AI Alignment Forum. ARC's current research focus can be thought of as trying to combine mechanistic interpretability and formal verification. If we had a deep understanding of what was going on inside a neural network, we would hope to be able to use that understanding to verify that the network was not going to behave dangerously in unforeseen situations. ARC is attempting to perform this kind of verification, but using a mathematical kind of "explanation" instead of one written in natural language. To help elucidate this connection, ARC has been supporting work on Compact Proofs of Model Performance via Mechanistic Interpretability by Jason Gross, Rajashree Agrawal, Lawrence Chan and others, which we were excited to see released along with this post. While we ultimately think that provable guarantees for large neural networks are unworkable as a long-term goal, we think that this work serves as a useful springboard towards alternatives. In this post, we will: Summarize ARC's takeaways from this work and the problems we see with provable guarantees Explain ARC's notion of a heuristic explanation and how it is intended to overcome these problems Describe with the help of a worked example how the quality of a heuristic explanation can be quantified, using a process we have been calling surprise accounting We are also sharing a draft by Gabriel Wu (currently visiting ARC) describing a heuristic explanation for the same model that appears in the above paper: max_of_k Heuristic Estimator Thanks to Stephanie He for help with the diagrams in this post. Thanks to Eric Neyman, Erik Jenner, Gabriel Wu, Holly Mandel, Jason Gross, Mark Xu, and Mike Winer for comments. Formal verification for neural networks In Compact Proofs of Model Performance via Mechanistic Interpretability, the authors train a small transformer on an algorithmic task to high accuracy, and then construct several different formal proofs of lower bounds on the network's accuracy. Without foraying into the details, the most interesting takeaway from ARC's perspective is the following picture: In the top right of the plot is the brute-force proof, which simply checks every possible input to the network. This gives the tightest possible bound, but is very long. Meanwhile, in the bottom left is the trivial proof, which simply states that the network is at least 0% accurate. This is very short, but gives the loosest possible bound. In between these two extremes, along the orange Pareto frontier, there are proofs that exploit more structure in the network, leading to tighter bounds for a given proof length, or put another way, shorter proofs for a given bound tightness. It is exciting to see a clear demonstration that shorter proofs better explain why the neural network has high accuracy, paralleling a common mathematical intuition that shorter proofs offer more insight. One might therefore hope that if we understood the internals of a neural network well enough, then we would be able to provide provable guarantees for very complex behaviors, even when brute-force approaches are infeasible. However, we think that such a hope is not realistic for large neural networks, because the notion of proof is too strict. The basic problem with provable guarantees is that they must account for every possible way in which different parts of the network interact with one another, even when those interactions are incidental to the network's behavior. These interactions manifest as error terms, which the proof must provide a worst-case bound for, leading to a looser bound overall. The above picture provides a good demonstration of this: moving towards the left of the plot, the best bound gets looser and looser. Mo...

Lean practitioners often discuss the importance of delivering value to customers and understanding their needs. However, Danny Nathan takes this to a new level with his unique approach to lean customer development. His business is a testament to this. Intrigued by his methods, I invited him to join me on a journey to the Edges of Lean, where he shared his insights and practices.    Danny Nathan:   Danny is a versatile entrepreneur who has made a name for himself by helping companies create and launch new products and ventures. With a background in various fields like product design, marketing, and strategy, Danny is known for his ability to solve complex business challenges. As the founder of Apollo 21, a company that combines business consultancy, product design, and venture studio, Danny helps companies foster innovation, drive growth, and overcome obstacles to scaling by developing cutting-edge technology. KEY TOPICS IN THIS PODCAST: 00:02:59 - Transition from Acting to Technology 00:04:14 - Types of Products Developed by Apollo 21 00:10:42 - Challenges with Misunderstanding MVP 00:12:02 - Lean Customer Development Explained 00:13:14 - Importance of Validating Customer Needs 00:18:13 - Learning from Customer Development 00:20:04 - Identifying Ideal Customer Groups 00:21:34 - Process of Customer Interviews 00:26:24 - Creating Conviction Through Learning 00:27:00 - Differentiating Apollo 21's Approach 00:29:13 - Ensuring Against Post-Launch Failure 00:30:37 - Customer Observation Techniques 00:39:06 - Handling Multiple Stakeholders 00:40:17 - Heuristic vs. Data-Driven Processes KEY TAKEAWAYS:    Lean customer development is about discovering the purest pain point and validating the need, potential solution, and willingness of customers to pay for it before building anything. The focus should be on delivering value to the customer and understanding their needs.  Lean customer development helps streamline the process of bridging the gap between discovery and minimum viable product (MVP). The concept of an MVP has evolved and can be interpreted differently by different organizations. The goal of an MVP is to learn as quickly as possible, mitigate risk, and minimize cost and time expenditure. Innovation is great, but it is crucial to innovate something that is valuable to the customer and that they are willing to buy. Lean Startup provides a clear-cut series of actions to move towards creating something new, but it is vital to adapt and customize the methodology to fit specific needs. Lean customer development involves talking to customers, asking the right questions, and gaining a clear understanding of their pain points and needs.  The process starts with defining the problem space and then talking to a wide range of people to discover pain points and potential solutions.      Memorable Quotes From Danny Nathan::   “Fall in love with the problem and not the solution.”     CONNECT WITH Danny Nathan::        LinkedIn: https://www.linkedin.com/in/amongmany/  Website: https://apollo21.io    

Behind every emerging technology is a great idea propelling it forward. In the new Microsoft Research Podcast series, Ideas, members of the research community at Microsoft discuss the beliefs that animate their research, the experiences and thinkers that inform it, and the positive human impact it targets. In this episode, host Gretchen Huizinga talks with Principal Researcher Behnaz Arzani. Arzani has always been attracted to hard problems, and there's no shortage of them in her field of choice—network management—where her contributions to heuristic analysis and incident diagnostics are helping the networks people use today run more smoothly. But the criteria she uses to determine whether a challenge deserves her time has evolved. These days, a problem must appeal across several dimensions: Does it answer a hard technical question? Would the solution be useful to people? And … would she enjoy solving it?Learn more:Solving Max-Min Fair Resource Allocations Quickly on Large Graphs | Publication, February 2024Finding Adversarial Inputs for Heuristics using Multi-level Optimization | Publication, February 2024MetaOpt: Examining, explaining, and improving heuristic performance | Microsoft Research blog, January 2024A Holistic View of AI-driven Network Incident Management | Publication, October 2023Behnaz Arzani: Painting, storytelling, and other hobbies | Microsoft Research bio page

Episode 127I spoke with Christopher Thi Nguyen about:* How we lose control of our values* The tradeoffs of legibility, aggregation, and simplification* Gamification and its risksEnjoy—and let me know what you think!C. Thi Nguyen as of July 2020 is Associate Professor of Philosophy at the University of Utah. His research focuses on how social structures and technology can shape our rationality and our agency. He has published on trust, expertise, group agency, community art, cultural appropriation, aesthetic value, echo chambers, moral outrage porn, and games. He received his PhD from UCLA. Once, he was a food writer for the Los Angeles Times.I spend a lot of time on this podcast—if you like my work, you can support me on Patreon :)Reach me at editor@thegradient.pub for feedback, ideas, guest suggestions. Subscribe to The Gradient Podcast:  Apple Podcasts  | Spotify | Pocket Casts | RSSFollow The Gradient on TwitterOutline:* (00:00) Intro* (01:10) The ubiquity of James C. Scott* (06:03) Legibility and measurement* (12:50) Value capture, classes and measurement* (17:30) Political value choice in ML* (23:30) Why value collapse happens* (33:00) Blackburn, “Hume and Thick Connexions” — projectivism and legibility* (36:20) Heuristics and decision-making* (40:08) Institutional classification systems* (46:55) Back to Hume* (48:27) Epistemic arms races, stepping outside our conceptual architectures* (56:40) The “what to do” question* (1:04:00) Gamification, aesthetic engagement* (1:14:51) Echo chambers and defining utility* (1:22:10) Progress, AGI millenarianism* (disclaimer: I don't know what's going to happen with the world, either.)* (1:26:04) Parting visions* (1:30:02) OutroLinks:* Chrisopher's Twitter and homepage* Games: Agency as Art* Papers referenced* Transparency is Surveillance* Games and the art of agency* Autonomy and Aesthetic Engagement* Art as a Shelter from Science* Value Capture* Hostile Epistemology* Hume and Thick Connexions (Simon Blackburn) Get full access to The Gradient at thegradientpub.substack.com/subscribe

Episode Summary: In this episode of "Financial Advisors Say The Darndest Things," host AB Ridgeway explores the psychology behind consumer behavior, particularly focusing on the allure of coupons and discounts. Using the example of Burger King's promotional offers, Ridgeway delves into how these seemingly beneficial deals can actually lead to unnecessary spending and financial pitfalls.Key Takeaways:Coupon Fallacy: While coupons may appear to save money, they often lead to increased spending rather than genuine savings. Ridgeway emphasizes that using a coupon doesn't negate the fact that money is still being spent, highlighting the importance of discerning between saving and spending less.Heuristics in Decision Making: Ridgeway discusses the concept of heuristics, mental shortcuts that influence quick decision-making. He explains how marketers capitalize on these cognitive biases to drive consumer behavior, leading individuals to make emotional rather than logical purchasing choices.Anchoring Effect: The episode explores the anchoring bias, where individuals rely heavily on initial information when making decisions. Ridgeway illustrates how consumers perceive discounts based on the initial price, overlooking the actual expenditure and falling into the trap of unnecessary purchases.Awareness vs. Intentional Spending: Ridgeway highlights the role of advertisements and promotions in raising consumer awareness and stimulating unplanned spending. He warns against succumbing to marketing tactics that encourage spending on items not originally intended for purchase.Financial Awareness and Responsibility: Ultimately, Ridgeway encourages listeners to cultivate financial mindfulness and resist impulsive spending habits. By understanding the psychological tricks employed by advertisers, individuals can make more informed and intentional financial decisions.Quotes:"When you have a coupon, you are not saving more money, you're technically spending less money.""Unfortunately, when we're making quick decisions, especially about purchases with coupons, we tend to make them emotionally and try to justify them logically.""Your money is still gone. But where do we get this concept of coupons actually saving us money? Well, it comes from a mental heuristic."

Episode Summary: In this episode of "Financial Advisors Say The Darndest Things," host AB Ridgeway explores the psychology behind consumer behavior, particularly focusing on the allure of coupons and discounts. Using the example of Burger King's promotional offers, Ridgeway delves into how these seemingly beneficial deals can actually lead to unnecessary spending and financial pitfalls.Key Takeaways:Coupon Fallacy: While coupons may appear to save money, they often lead to increased spending rather than genuine savings. Ridgeway emphasizes that using a coupon doesn't negate the fact that money is still being spent, highlighting the importance of discerning between saving and spending less.Heuristics in Decision Making: Ridgeway discusses the concept of heuristics, mental shortcuts that influence quick decision-making. He explains how marketers capitalize on these cognitive biases to drive consumer behavior, leading individuals to make emotional rather than logical purchasing choices.Anchoring Effect: The episode explores the anchoring bias, where individuals rely heavily on initial information when making decisions. Ridgeway illustrates how consumers perceive discounts based on the initial price, overlooking the actual expenditure and falling into the trap of unnecessary purchases.Awareness vs. Intentional Spending: Ridgeway highlights the role of advertisements and promotions in raising consumer awareness and stimulating unplanned spending. He warns against succumbing to marketing tactics that encourage spending on items not originally intended for purchase.Financial Awareness and Responsibility: Ultimately, Ridgeway encourages listeners to cultivate financial mindfulness and resist impulsive spending habits. By understanding the psychological tricks employed by advertisers, individuals can make more informed and intentional financial decisions.Quotes:"When you have a coupon, you are not saving more money, you're technically spending less money.""Unfortunately, when we're making quick decisions, especially about purchases with coupons, we tend to make them emotionally and try to justify them logically.""Your money is still gone. But where do we get this concept of coupons actually saving us money? Well, it comes from a mental heuristic."

A Master Class: Unlocking The Psychology of  Customer Experience With this episode, we begin an eight-part series exploring customer behavior and the psychology that drives it. Each part will delve into the various psychological aspects of Customer Experiences, offering practical advice on understanding and influencing them. Our focus today is on why customers make quick decisions and how you can sway those decisions in your favor. Understanding customer behavior is complex and influenced by multiple factors, a concept known in academic circles as high causal density. There's no one-size-fits-all solution for a perfect Customer Experience transformation; it requires a combination of approaches. Human nature often seeks shortcuts for cognitive tasks, leading to heuristic processes. Heuristics, or mental shortcuts, simplify decision-making. We discuss four common heuristics: Anchoring and Adjustment: People use familiar examples to estimate values quickly, which can lead to biases, especially in pricing strategies. Focusing Effect: When faced with information overload, individuals focus on crucial details to aid decision-making. Recognizing what customers prioritize can influence their choices. Availability Heuristic: Our brains estimate likelihood based on easily accessible examples. Understanding what information customers readily recall helps shape perceptions. Representativeness: People often categorize based on stereotypes or representative traits. Recognizing customer expectations can guide your design approach. Considering these heuristics, we emphasize the importance of first impressions, understanding customer values, managing expectations, and aligning your design with customer decision-making shortcuts. Recognizing the diversity in customer decision-making preferences is crucial, and segmentation plays a vital role in tailoring experiences. This episode explores various influences and group psychological concepts tied to customer decisions. Our goal is to provide a holistic understanding of experience psychology, making you more agile in grasping customer behavior and creating plans for compelling experiences that encourage return visits.   In this episode, you will also learn: The significance of first impressions and their impact on customer anchors. How to identify valuable areas for customers and use them for effective comparisons. The role of customer expectations and how to align your design with their anticipated experiences. The importance of providing information shortcuts to simplify decision-making. Strategies for segmentation to cater to diverse customer decision-making preferences. The overarching theme of simplification as a design approach offers customers shortcuts to decision-making and potential sales.

rational vc Key Takeaways  People often underestimate the role of chance in their achievementsMild success can be explainable by skill, but wild success is attributable to variance In the long run, the “lucky” regress to the mean Understand Power Laws when investing; the wins of a few investments make up for the losses on many investments, and then some Survivorship Bias: the tendency to focus on successful individuals without considering those who failed due to random factors The probability of the loss must be judged in connection with the magnitude of the outcome; it is not the likelihood of an event that matters, but the magnitude of the outcome in connection with the likelihood of the event that does  Maximizing the probability of winning does not maximize the expected value from the game The confidence in which you make your decision is more important than the expected value that comes from that decision A mistake is not something to be determined after the fact, but in the light of the information until that point Read the full notes @ podcastnotes.orgEvery podcast episode we explore a Lindy book, and find ideas you can use in business and life. Join 3,000+ curious minds and avid readers @ rationalvc.com to get free access to essays and exclusive content. For the video version of episode click here.   Timestamps: (00:00) Intro / chit-chat (20:11) Randomness & Luck (24:46) Monte Carlo Simulation (31:09) Ergodicity (31:39) Hindsight Bias (38:00) Survivorship Bias (39:50) Asymmetric Bets / John & Nero (49:53) Skewness & Asymmetry (57:19) Pascal's Wager (1:00:53) Induction & Chaos Theory (1:03:22) Chapter 11 (1:08:45) System-1 vs System-2 Thinking (1:10:03) Satisficing (1:20:08) Normative vs Positive Thinking (1:25:52) Signal vs Noise (1:28:20) Heuristics (1:33:45) Final Part of Book (Part 3's Importance) (1:44:41) Favourite Quotes / Our Lives (2:06:11) Final Thoughts - Our website (all essays and podcasts): rationalvc.com  Our investment fund: rational.fund  Cyrus' Twitter: x.com/CyrusYari  Iman's Twitter: x.com/iman_olya -   Disclaimer: The materials provided are solely for informational or entertainment purposes and do not constitute investment or legal advice. All opinions expressed by hosts and guests are solely their own opinions and do not reflect the opinion of their employer(s). #Lindy #knowledge #books

Tom Chatfield is a British author and tech philosopher, interested in improving our experiences and understanding of technology. He is the author of several books on good thinking in today's tech-dominated world, including “Critical Thinking” and “How to Think”. He also teaches these skills to diverse audiences, ranging from schools to corporate boardrooms, and he has recently designed a successful online course on Critical Thinking for the Economist education. His most recent book is Wise Animals, an exploration of the co-evolution of humanity and technology—and the lessons our deep past may hold for the present. He's also an experienced Chair, Non-Executive Director, advisor and speaker across the private and public sectors.  -> Inscreva-se aqui no módulo 3 dos workshops de Pensamento Crítico: «Decidir Melhor». Registe-se aqui para ser avisado(a) de futuras edições dos workshops. _______________ Índice: (3:00) Introduction in English (5:06) How did you end up writing about critical thinking and technology? | Is critical thinking a soft or a hard skill? | Heuristics and biases (work of Daniel Kahnemen and Amos Trvsersky) | The art of knowing when to seek ‘cognitive reinforcements' | Why communicating nuances and uncertainties is so hard today. | Arguments when our basic assumptions differ | Why critical thinking is not about being always right. | The importance of challenging our assumptions. (32:46) Why asking questions is the best way to dispute arguments. | The importance of creating trust to have open discussions. | Useful tricks to improve collective decision-making: pre-mortems; obligation to dissent; Oxford-style debates | How much of corporate work today runs around sending and replying to emails | The Amazon memo | ask religious schools | The importance of thinking before talking: book Robert Poynton - Do Pause: You Are Not A To Do List (47:45) Difference between teaching critical thinking to 12 year olds and corporate audiences? | The ubiquity of business jargon | Richard Feynman and the power of questions | Why did SpaceX give up on “catching” falling fairings? | Thomas Kuhn on paradigm shifts | Richard Feynman On The Folly Of Crafting Precise Definitions (1:09:06) New book: Wise Animals: How Technology Has Made Us What We Are | Impact of mass interactive media on democracy. | impact of social media on social health. Book by Jonathan Haidt: The Anxious Generation _______________ Today we're diving into an enlightening conversation with Tom Chatfield, a British author and tech philosopher. Tom is the author of several books on good thinking in today's tech-dominated world, including “Critical Thinking” and “How to Think”. He also teaches these skills to diverse audiences, ranging from schools to corporate boardrooms, and he has recently designed a successful online course on Critical Thinking for the Economist education. In his most recent book, Wise Animals, Tom explores our relationship with technology, examining the lessons that our ancestral past may hold for our present challenges.  In this thought-provoking conversation with Tom, we discussed his advice for how to think more critically in today's complex world. We talked about strategies to combat the influence of cognitive biases in our mind, as popularized by thinkers like the late Daniel Kahneman and Amos Tversky, and the importance (and difficulty) of challenging our own assumptions. We also discussed the importance of creating trust in order to be able to have open conversations, and some techniques for deep discussions and good decision making in all contexts.  In the final part, we turned our focus to Tom's latest book, which explores our relationship with technology, and I asked his view on two big impacts technology is currently having in society: the destabilizing effect of mass interactive media on traditional democratic structures, exacerbating polarization and eroding public trust in institutions; and the troubling rise of what many experts refer to as an “Epidemic of Mental Illness” among children and teenagers, driven by pervasive social media use.  ______________ Obrigado aos mecenas do podcast: Francisco Hermenegildo, Ricardo Evangelista, Henrique Pais João Baltazar, Salvador Cunha, Abilio Silva, Tiago Leite, Carlos Martins, Galaró family, Corto Lemos, Miguel Marques, Nuno Costa, Nuno e Ana, João Ribeiro, Helder Miranda, Pedro Lima Ferreira, Cesar Carpinteiro, Luis Fernambuco, Fernando Nunes, Manuel Canelas, Tiago Gonçalves, Carlos Pires, João Domingues, Hélio Bragança da Silva, Sandra Ferreira , Paulo Encarnação , BFDC, António Mexia Santos, Luís Guido, Bruno Heleno Tomás Costa, João Saro, Daniel Correia, Rita Mateus, António Padilha, Tiago Queiroz, Carmen Camacho, João Nelas, Francisco Fonseca, Rafael Santos, Andreia Esteves, Ana Teresa Mota, ARUNE BHURALAL, Mário Lourenço, RB, Maria Pimentel, Luis, Geoffrey Marcelino, Alberto Alcalde, António Rocha Pinto, Ruben de Bragança, João Vieira dos Santos, David Teixeira Alves, Armindo Martins , Carlos Nobre, Bernardo Vidal Pimentel, António Oliveira, Paulo Barros, Nuno Brites, Lígia Violas, Tiago Sequeira, Zé da Radio, João Morais, André Gamito, Diogo Costa, Pedro Ribeiro, Bernardo Cortez Vasco Sá Pinto, David , Tiago Pires, Mafalda Pratas, Joana Margarida Alves Martins, Luis Marques, João Raimundo, Francisco Arantes, Mariana Barosa, Nuno Gonçalves, Pedro Rebelo, Miguel Palhas, Ricardo Duarte, Duarte , Tomás Félix, Vasco Lima, Francisco Vasconcelos, Telmo , José Oliveira Pratas, Jose Pedroso, João Diogo Silva, Joao Diogo, José Proença, João Crispim, João Pinho , Afonso Martins, Robertt Valente, João Barbosa, Renato Mendes, Maria Francisca Couto, Antonio Albuquerque, Ana Sousa Amorim, Francisco Santos, Lara Luís, Manuel Martins, Macaco Quitado, Paulo Ferreira, Diogo Rombo, Francisco Manuel Reis, Bruno Lamas, Daniel Almeida, Patrícia Esquível , Diogo Silva, Luis Gomes, Cesar Correia, Cristiano Tavares, Pedro Gaspar, Gil Batista Marinho, Maria Oliveira, João Pereira, Rui Vilao, João Ferreira, Wedge, José Losa, Hélder Moreira, André Abrantes, Henrique Vieira, João Farinha, Manuel Botelho da Silva, João Diamantino, Ana Rita Laureano, Pedro L, Nuno Malvar, Joel, Rui Antunes7, Tomás Saraiva, Cloé Leal de Magalhães, Joao Barbosa, paulo matos, Fábio Monteiro, Tiago Stock, Beatriz Bagulho, Pedro Bravo, Antonio Loureiro, Hugo Ramos, Inês Inocêncio, Telmo Gomes, Sérgio Nunes, Tiago Pedroso, Teresa Pimentel, Rita Noronha, miguel farracho, José Fangueiro, Zé, Margarida Correia-Neves, Bruno Pinto Vitorino, João Lopes, Joana Pereirinha, Gonçalo Baptista, Dario Rodrigues, tati lima, Pedro On The Road, Catarina Fonseca, JC Pacheco, Sofia Ferreira, Inês Ribeiro, Miguel Jacinto, Tiago Agostinho, Margarida Costa Almeida, Helena Pinheiro, Rui Martins, Fábio Videira Santos, Tomás Lucena, João Freitas, Ricardo Sousa, RJ, Francisco Seabra Guimarães, Carlos Branco, David Palhota, Carlos Castro, Alexandre Alves, Cláudia Gomes Batista, Ana Leal, Ricardo Trindade, Luís Machado, Andrzej Stuart-Thompson, Diego Goulart, Filipa Portela, Paulo Rafael, Paloma Nunes, Marta Mendonca, Teresa Painho, Duarte Cameirão, Rodrigo Silva, José Alberto Gomes, Joao Gama, Cristina Loureiro, Tiago Gama, Tiago Rodrigues, Miguel Duarte, Ana Cantanhede, Artur Castro Freire, Rui Passos Rocha, Pedro Costa Antunes, Sofia Almeida, Ricardo Andrade Guimarães, Daniel Pais, Miguel Bastos, Luís Santos _______________ Esta conversa foi editada por: Hugo Oliveira _______________ Bio: Tom Chatfield is a British author and tech philosopher, interested in improving our experiences and understanding of technology. His most recent book is Wise Animals, an exploration of the co-evolution of humanity and technology—and the lessons our deep past may hold for the present. His recent work around future skills and technology includes designing and presenting the Economist‘s new business course Critical Thinking: Problem-solving and decision-making in a complex world. Tom's non-fiction books exploring digital culture, including How To Thrive in the Digital Age (Pan Macmillan) and Live This Book! (Penguin), have appeared in over thirty languages. His bestselling critical thinking textbooks and online courses, developed in partnership with SAGE Publishing, are used in schools and universities across the world. He's also an experienced Chair, Non-Executive Director, advisor and speaker across the private and public sectors. Topics he's written about recently include the ethics of AI, what it means to think well, technology in deep time and the philosophy of fake news.

When someone asks, "What's your favorite restaurant?" odds are you're inclined to recommend a place you've eaten at recently—even if it's not really your favorite. It's just top of mind. Why do we weigh recent events so heavily? And how does this tendency impact important decisions, like whom to vote for or how to conduct medical procedures? In this episode of Choiceology with Katy Milkman, we look at a phenomenon that can cause us to overweight recent events compared to earlier events and make suboptimal decisions. The 1968 presidential election was one of the closest elections in American history. Following an eventful year of civil unrest, war, and high-profile assassinations, eleventh-hour political machinations from Lyndon B. Johnson and Richard Nixon majorly impacted results. "October surprises," or last-minute revelations in the days before a November election, can weigh heavily on voters' minds at the polling booths. John A. Farrell documents the surprising events leading up to 1968 Election Day and President Richard Nixon's narrow victory.John A. Farrell is a historian and celebrated political biographer. He is the best-selling author of Richard Nixon: The Life, and his latest book is Ted Kennedy: A Life.Next, Katy speaks with Manasvini Singh about her research on recency effect and its impacts on physician decision-making in the delivery room. You can learn more in the Science paper Manasvini authored, titled "Heuristics in The Delivery Room."Manasvini Singh is an assistant professor of social and decision sciences at Carnegie Mellon University. Her research focuses on topics at the intersection between decision theory and health policy.Choiceology is an original podcast from Charles Schwab. If you enjoy the show, please leave a rating or review on Apple Podcasts. Important DisclosuresThe comments, views, and opinions expressed in the presentation are those of the speakers and do not necessarily represent the views of Charles Schwab.Data contained herein from third party providers is obtained from what are considered reliable source. However, its accuracy, completeness or reliability cannot be guaranteed and Charles Schwab & Co. expressly disclaims any liability, including incidental or consequential damages, arising from errors or omissions in this publication. All corporate names and market data shown above are for illustrative purposes only and are not a recommendation, offer to sell, or a solicitation of an offer to buy any security. Supporting documentation for any claims or statistical information is available upon request. Investing involves risk including loss of principal.The policy analysis provided by the Charles Schwab & Co., Inc., does not constitute and should not be interpreted as an endorsement of any political party.The book How to Change: The Science of Getting from Where You Are to Where You Want to Be is not affiliated with, sponsored by, or endorsed by Charles Schwab & Co., Inc. (CS&Co.). Charles Schwab & Co., Inc. (CS&Co.) has not reviewed the book and makes no representations about its content.Apple, the Apple logo, iPad, iPhone, and Apple Podcasts are trademarks of Apple Inc., registered in the U.S. and other countries. App Store is a service mark of Apple Inc.Google Podcasts and the Google Podcasts logo are trademarks of Google LLC.Spotify and the Spotify logo are registered trademarks of Spotify AB.(0324-PY6W.)

Successful investors know that choosing what to sell is just as important as choosing what to buy. However, findings from a recent study suggest that even professional portfolio managers are subject to psychological forces while making buying and selling decisions. On this episode, Mark Riepe discusses these recent findings with Alex Imas, professor of behavioral science and economics at the University of Chicago Booth School of Business and co-author of the study "Selling Fast and Buying Slow: Heuristics and Trading Performance of Institutional Investors."Follow Financial Decoder for free on Apple Podcasts or wherever you listen.Financial Decoder is an original podcast from Charles Schwab. For more on the series, visit schwab.com/FinancialDecoder. If you enjoy the show, please leave us a rating or review on Apple Podcasts. Important DisclosuresInvestors should consider carefully information contained in the prospectus, or if available, the summary prospectus, including investment objectives, risks, charges, and expenses. You can request a prospectus by calling 800-435-4000. Please read the prospectus carefully before investing.The information provided here is for general informational purposes only and should not be considered an individualized recommendation or personalized investment advice. The investment strategies mentioned here may not be suitable for everyone. Each investor needs to review an investment strategy for his or her own particular situation before making any investment decision. All expressions of opinion are subject to change without notice in reaction to shifting market conditions. Data contained herein from third-party providers is obtained from what are considered reliable sources. However, its accuracy, completeness, or reliability cannot be guaranteed. Examples provided are for illustrative purposes only and not intended to be reflective of results you can expect to achieve.The comments, views, and opinions expressed in the presentation are those of the speakers and do not necessarily represent the views of Charles SchwabInvesting involves risk, including loss of principal.​Past performance is no guarantee of future results, and the opinions presented cannot be viewed as an indicator of future performance. All names and market data shown above are for illustrative purposes only and are not a recommendation, offer to sell, or a solicitation of an offer to buy any security. Supporting documentation for any claims or statistical information is available upon request.Performance may be affected by risks associated with non-diversification, including investments in specific countries or sectors. Additional risks may also include, but are not limited to, investments in foreign securities, especially emerging markets, real estate investment trusts (REITs), fixed income, small capitalization securities and commodities. Each individual investor should consider these risks carefully before investing in a particular security or strategy.The Schwab Center for Financial Research is a division of Charles Schwab & Co.Apple, the Apple logo, iPad, iPhone, and Apple Podcasts are trademarks of Apple Inc., registered in the U.S. and other countries. App Store is a service mark of Apple Inc.(0324-RY6R)

Do you feel good? Are you energetic? Are you sleeping well? These are all obvious clues to whether the marketing, hustling, and entrepreneurial lifestyle (and habits) are taking a toll on your brain.   In this flash-back episode, Molly Pittman talks with Lauren Alexander, the VP of Marketing at Neurohacker.com, who shares some insights that will actually make you a better marketer. Her expertise will give you helpful tips for leading your team, optimizing focus, ‘budgeting' your mental energy, and more. Don't worry; we're not trying to get you to ‘do more.' Instead, you'll learn how the brain works and what you can do as a marketer to feel better.  You Will Learn:  5 Things You Have To Know About The Brain (To Feel Better) One of the easiest (and free) neuro hacks to change your life Symptoms You Might Not Recognize Are Connected to ‘Mental Drain' “If you can really dial in and optimize your brain activity, you'll have a cascade of benefits throughout the body.” Lauren Alexander   LINKS Lauren's custom link for your 75% discount, neurohacker.com/smartmarketer More Episodes and Resources, https://smartmarketer.com/blog/?cat=podcasts Molly Pittman, https://mollypittman.com/ Smart Marketer Courses, https://smartmarketer.com/courses/ The Smart Marketer Agency, https://smartmarketeragency.com/ YOUR ENGAGEMENT MATTERS Thank you to our listeners for the 5-Star Reviews and meaningful messages! This podcast has surpassed our expectations, and we have you to thank for that! If you're enjoying the show, please be sure to follow us (and leave us a review) on Apple Podcasts (https://podcasts.apple.com/us/podcast/the-smart-marketer-podcast/id1522629407) And/Or Follow and Subscribe wherever you listen to podcasts! Please also share these episodes on social media and tag us on your next post #WeOutHere Instagram: @SmartMarketerIG, @mollypittmandigital, @johngrimmshawdigital, @pep_hufen.   TIME STAMPS 00:00 “Now, with the tools that are making our ability to execute ideas so much faster, we're also working even more, and so I think it's time for all of us to take a moment and ask, what's my strategy for dealing with all of this? - Because it's insane.” Lauren Alexander 01:38 What is Neurohacking? 03:52 The Expanding Scope Of A Marketer's Work 10:00 Five Things You Need To Know About Your Brain 11:36 Think of Mental Energy As Currency 13:05 Three Ways the Body is conserving mental energy 14:51 Consider The ‘Escalation Of Commitment' Heuristic 17:04 “The brain filters out unnecessary information so that important stuff gets through. But the problem is it doesn't always filter what the correct important stuff is.” Lauren Alexander 18:23 A Gratitude Practice 19:20 Symptoms You Might Not Recognize Are Connected to ‘Mental Drain' 22:50 How Process Can Help Your Systems and Team Focus 27:22 Cost-free ways to build your mental energy' bank account.' 29:40 You can stimulate Neuroplasticity (critical to building your mental energy). 34:25 Be Kind To Yourself, “It's Bigger Than Grit.”