In this episode of the Crazy Wisdom Podcast, host Stuart interviews Maranda Dziekonski, a CS executive and CCO. They talk about business, touching on topics like the importance of human aspects in business, the power of having a strong mission, and addressing company crises. Dikonsky also explains how she defines business and shares intriguing insights about the role of philosophy in business. Further, they discuss the state of manufacturing in the U.S. and how AI might affect the world of work. She also provides tips for startups on maturing while maintaining their core ethos. The conversation delves into contrasting the perceptions around the end of San Francisco's reign as a tech hub. 00:02 Introduction and Guest Welcome 00:23 Understanding Business: A Deep Dive 01:03 The Importance of People in Business 03:04 Bridging the Gap between Ideals and Reality in Business 07:08 The Role of Philosophy in Business 07:22 The Human Component in Business 08:24 The Challenge of Measuring ROI 09:20 The Value Cycle and Problem Statements 20:26 The Future of Manufacturing 23:29 The Impact of AI on the Tech Industry 26:00 Chat GPT and its Impact 26:30 Critical Thinking in the Age of AI 28:26 Challenges of Dealing with Automated Systems 29:50 The Pendulum Swing in Customer Service 31:39 The Competitive Landscape of Startups 32:39 Life's Challenges and Managing Stress 37:42 The Hippie-Business Connection in Startups 44:48 The Evolution of Tech Hubs: From Michigan to San Francisco 46:54 Is San Francisco Dead? The Future of Tech Hubs   Definition of Business Maranda defines business as a transaction between two entities, which could involve exchanging money for a service or a service for another service. Learnings in Business Emphasizes the importance of people in business. Shares a personal experience from the SVB crisis, highlighting the value of transparency and honesty in leadership. Team members offered to delay their paychecks, illustrating the strength of a committed and inspired workforce. Bridging the Gap Between Ideals and Reality Maranda stresses the need for flexibility and contingency planning in business. Highlights the human component in business planning, accounting for personal desires, and motivations. Importance of Diverse Perspectives Advocates for including multiple perspectives in business processes and decisions. Discusses the culture of debate and openness in her current company, where challenging ideas, even those of higher executives, is encouraged. Philosophy and Business Believes that business and philosophy are interlinked, especially when dealing with human behaviors and decisions. Points out the human-driven nature of ROI (Return on Investment) and its subjective assessment. Handling Intangibles in Business Discusses the challenges and methods of quantifying intangibles, like time savings in processes. Shares experiences from her past roles to illustrate how intangibles can be made tangible through efficiency gains. Manufacturing and AI's Impact on Jobs Reflects on her manufacturing background and the ongoing relevance of manufacturing jobs. Discusses the potential disruptive impact of AI on tech jobs, emphasizing the need for critical thinking skills. Balancing Tangible and Intangible Aspects in Business Highlights the unpredictability in business and life, advocating for adaptability and understanding that not everything can be measured or foreseen. Manufacturing in the U.S. and Global Supply Chain Shares insights on the fragility of the supply chain and the complexities of onshoring manufacturing. Future of Work and Human Skills Ponders over the future role of humans in an increasingly automated world, stressing the importance of skills that are uniquely human, like critical thinking. Conclusion Maranda’s insights reflect a deep understanding of the human element in business and the need for adaptability, diversity, and ethical leadership. She stresses the importance of preserving company ethos during growth and balancing the drive for innovation with operational maturity. Notable Quotes "People matter more than most leaders credit in their business plan." "Flexibility is key in business; expect plans to fail and have backups." "Multiple perspectives are crucial for a well-rounded business approach." "Human behavior plays a significant role in ROI and business decisions." "Efficiency and innovation can make intangible benefits tangible." "Critical thinking will be a safeguard against automation in jobs." "The unpredictability of life and business requires adaptability." "Manufacturing's complexity cannot be overlooked in the push for onshoring." "The future of work may pivot more towards uniquely human skills." "Maintaining company ethos is crucial during growth and scaling."

This Crazy Wisdom Podcast episode features Paul Lambert, CEO of Quilt, a start-up working to revolutionize home heating and cooling systems. Quilt's solution is an all-electric system that replaces traditional fossil fuels, significantly reducing households' contributions to global warming. Lambert shares Quilt's approach to HVAC, emphasizing overall comfort, efficiency, and incorporating modern technologies like machine learning and AI. Quilt has a waitlist in anticipation of their 2024 launch. Time Stamps 00:00 Introduction to the Crazy Wisdom Podcast 00:39 Guest Introduction: Paul Lambert, Founder and CEO of Quilt 00:49 Understanding Quilt: A Revolutionary Heating and Cooling System 02:42 The Efficiency and Benefits of Heat Pumps 07:58 The Quilt Experience: Installation and User Interface 08:35 The Business Aspects and Challenges of Quilt 24:20 The Future of Quilt and Home Energy Systems 30:35 Understanding AI, Machine Learning, and Their Role in Quilt 37:16 Closing Remarks Key Points Quilt aims to introduce a state-of-the-art HVAC system that is not only highly efficient but also contributes to reducing the environmental impact traditionally associated with home temperature regulation. Lambert explains that a heat pump, a core component of Quilt's system, functions much like a refrigerator, transferring heat instead of generating it, leading to remarkable efficiency gains. You can put 100 watts into the machine and, effectively, get 400 watts of heating power into the home. Lambert brings an impressive resume to the table, with experience leading a 120-person team and an investment fund at Google. His transition from Google to Quilt was fueled by a high level of conviction to address climate change.. Quilt's strategy revolves around combining mature core technologies into a seamless system that offers advantages over centralized heating and cooling methods. The company aims to address the inherent inefficiencies in traditional duct systems and provide a more tailored, room-by-room control approach to homeowners. Lambert believes that smart software and AI integration will further drive efficiency and user comfort in Quilt's system, making a substantial difference in energy consumption and sustainability. Conclusion: Lambert envisions Quilt as a platform that not only offers economic benefits to consumers but also contributes positively to the environment. With plans to roll out real home implementations by 2024, Quilt is poised to become a significant player in the home energy market, offering a cleaner, more efficient alternative to traditional heating and cooling methods. Quotes: "We're not just creating energy; we're moving it from one place to another. It's like rotating the energy." "By simply moving the heat that already exists, we can achieve insane levels of efficiency." "Choosing to work on the biggest problem means a commitment to provide a better world for the next generation. In my case, it’s addressing climate change head-on." "In the home, our jobs are to keep you warm and safe. We’re extending this by making sure we also keep you comfortable and do it sustainably."

Intro Allison Durham Focus: Exploring AI, Software Development, and the Human Mind What is the Human Mind? Allison doesn't make a distinction between the brain and the mind. She sees the mind as a dynamic range of cognitive experiences that include thoughts, perception, and self-awareness. The mind exists alongside the human experience and is fully integrated with bodily sensations. On Consciousness Allison discusses the topic of consciousness, noting that awareness can vary in its intensity. She mentions an intriguing question: Can awareness exist without the brain? She recalls an interesting conversation with a friend who asked her about consciousness and awareness. The Experience of Dreams Allison describes a dream she had that was "rooted in Earth," contrasting it with another dream featuring a monstrous, otherworldly creature. She emphasizes her ability to fully visualize experiences in her dreams, even though she struggles with visualization in her waking life. Aphantasia and Visualization Allison brings up the concept of Aphantasia, where people have difficulty visualizing images. She explores the idea that visualization might be trainable, mentioning techniques such as the "candle technique" to improve skill. She notes that while most people can recall memories with images, these people also often have underdeveloped other sensory recall like smell and hearing. Software Development and AI Allison talks about Rust, a systems-level programming language she enjoys using. She delves into the concept of Site Reliability Engineering (SRE), explaining it stems from Google's earlier operations methods. She praises GitLab for packaging all the tools needed for DevOps, making it more accessible. She explores the concept of MLOps, which focuses on getting machine learning models into production. She finds the speed of open-source AI development both exciting and challenging, noting that problems can't be fully solved before new ones appear. Personal Psychology Framework Allison discusses her psychological framework, leaning heavily on mindfulness-based tactics. She believes in being fully aware of one's thoughts and emotional state, and she finds this awareness essential for taking proper action in life. Final Thoughts She mentions her website, AdjectiveAllison.com, and her social media handle, AdjectiveAllison on X. Time Stamps: 2:30 - Discussing the nature of the mind and its relationship to the brain and awareness 5:00 - Allison explains her experience with aphantasia 7:30 - Stuart talks about training himself to visualize through meditation 9:00 - Whether imagination and visualization can be trained as skills 11:00 - Allison's perspective on not training her own visualization abilities right now 12:00 - Allison's interest in learning Rust programming language 14:00 - Using ChatGPT to assist with engineering problems as a "rubber duck debugger" 16:00 - Explanation of DevOps, APIs, serverless solutions like Repl.it 19:00 - How AI may or may not change API and engineering architectures 21:00 - Automation as connecting APIs; engineers building instead of using no-code 23:00 - AI unlikely to change API interface itself, complexity happens behind it 24:00 - Allison's favorite psychological framework is mindfulness 25:30 - Aligning with specific frameworks depending on the problem  

Introduction: John Lee is an early-stage investor focusing on deep tech, enterprise software, cybersecurity, AI/ML, and automation. What is Deep Tech? Deep tech companies are similar to biotech companies in terms of capital structure. Unlike traditional tech startups, they have a longer incubation period to accrue value. Capital Allocation and Business Models Scarcity of capital drives better business models. Interesting hybrid models exist that are designed to develop revenue in the near term. Examples from the Portfolio Rare Base: Focuses on finding cures for rare diseases by characterizing cells and testing drugs on them. Sells IP assets as a novel business idea. Count: Aims to make accounting firms more efficient by acquiring existing firms and implementing AI techniques. Innovation in Deep Tech Despite a renaissance in funding, deep tech has not yet penetrated the lives of ordinary consumers. The scarcity of capital is forcing companies to be more creative in their go-to-market strategies. Notable Geographical Clusters San Francisco and Boston are significant clusters for deep tech companies. Artificial Intelligence and Large Language Models (LLMs) LLMs are seen as collections of human intelligence trained on internet content. Emergent properties arise from the crosstalk across different nodes in LLMs. Future of LLMs Doubts exist about the ability of current models to scale. OpenAI aims to develop models with 100 trillion parameters, although the feasibility of this is debated. The Intersection of Science and Deep Tech Science is transitioning from an "artisanal" practice to an engineering discipline. Application Areas Reproductive Innovations: Research is being conducted on how to effectively assist couples in having children later in life. Energy: A debate exists over the effectiveness of fusion vs fission in energy production. Business Model Concerns Long regulatory approval times are a challenge. Jazz Venture Partners focuses on the convergence of deep tech and science, aiming for creative translation.

What is Cerebrium? Michael Louis, the episode's guest, introduces Cerebrium as a platform that deals with abstractions on two levels, specifically focusing on GPUs and scaling for machine learning applications. The Importance of GPUs in LLMs Why are GPUs essential for large language models (LLMs)? GPUs have the capability to handle tens of gigabytes of data, which makes them superior to CPUs for LLM tasks. The cost challenge: Running GPUs 24/7 is not feasible due to their high costs. Serverless computing is becoming crucial for making GPU usage affordable. Financial Considerations Generating a million API requests could cost tens of thousands of dollars, highlighting the importance of cost-efficient solutions. Enterprise-level solutions could cost a couple of million dollars annually. Use Cases and Limitations Instacart uses specific and fine-tuned models for its operations. When customer support bots answer queries, each question processed by a 100-billion parameter model can cost approximately ten cents. One of the challenges is latency; the time delay is often too high for practical applications. The Role of Specialized Chips Companies like AWS are developing specialized chips for specific use cases to combat latency and other issues. GPT-4 and similar models are opening doors for generative AI and traditional machine learning applications. The Future: AGI vs. Autonomous Agents Autonomous agents and Artificial General Intelligence (AGI) differ in their approximations and semantic understanding. AGI is considered the terminal reference for fully self-aware AI, while autonomous agents operate based on defined workflows. Impact of AI and Ethical Considerations Michael Louis aims to make machine learning more accessible for medium-sized enterprises. There is a significant challenge in regulating technology that we do not yet fully understand, like cryptocurrency. Global Perspectives and Social Impact Louis discusses the high unemployment rate in South Africa and the importance of understanding the privilege of a job. He emphasizes the role of power in making a social impact. Building B2B Relationships In-person relationships are essential for B2B success. Louis stresses the need for penetrating network circles for business advantage. Personal Journey and the Importance of Adaptability From his early days in South Africa studying computer science to becoming an entrepreneur, Louis talks about his life journey. He shares the experience of selling a startup to Walmart and what led him to his current venture. Robotics and AI Louis believes that context awareness for robotics is the future, citing examples from Boston Dynamics and others. He discusses the ethical implications of robots in everyday life and their potential in various fields like space travel and mining.

Prasad Kawthekar is the CEO of Dashworks.ai The Evolution of Enterprise Software: Three waves of enterprise software: On-premise enterprise software. Software deployed top-down on the internet. The third wave we currently live in: SaaS consumers. Challenges are becoming increasingly pronounced. Comparing Web Search with Internal Searches: Web search benefits from standardized languages and platforms, such as HTML. Internal company applications deal with varied data sources and lack user data for training. The Role of AI in Knowledge Management: AIs excel at parsing unstructured data. Legacy products relied on custom parsers and integrations, making maintenance challenging. Modern AIs, such as LLMs, offer real-time, generalizable solutions. LLMs negate the need for data indexing and simplify integrations. Challenges in Data Analytics: Many teams aspire to be data-driven. Navigating data warehouses can be intricate. Ensuring accurate, relevant data is paramount. AI's Potential and Limitations: AIs can pinpoint unstructured data, like Zoom transcripts and Slack messages, but human interpretation is crucial. Current AI technology has boundaries in terms of memorization, accuracy, and relevance. The future vision: intuitive AI tools that everyone can use, even without technical expertise. The Evolution of Software Development with AI: Coding is moving towards more abstraction, making it as simple as conversing with a person. Programmers are becoming more productive. A potential surge in the number of people who can create custom tools using AI. Economic and Technical Implications of AI: Speculating on the future economy and the role of AI. Infrastructure challenges, including storage, database, and compute layers. The promising direction of infrastructure provisioning using AI. Future Directions for Dashworks: Emphasizing data warehousing and analytics. The significance of manipulating APIs and building reliable systems. Ambitions for the upcoming six months, such as enhancing API call functionalities.

Show Notes for the Camilo Acosta Episode on Crazy Wisdom Introduction Camilo Acosta joins us to discuss a broad range of topics surrounding AI, from its application in healthcare to its growing influence in Latin America. He dives deep into how AI technologies are evolving and what that means for businesses and consumers alike. Follow Camilo here Key Takeaways The Astounding Capability of AI in Healthcare AI technologies can predict potential maladies with high accuracy. Notable advancements in healthcare AI are taking place at Mayo Clinic. The Current State of AI Prediction AI technologies have moved beyond generative images and text and now predict what we want to see or read. Future of AI: Action Over Prediction The next step in AI evolution is not just predicting events but acting on those predictions. The Three-Step Process for Decision Making Collect data, apply judgment, and make a decision. Noteworthy AI Companies Up-and-coming companies are making strides in areas like AI robotic surgery. The Role of AI in Latin America Latin America presents a blue ocean opportunity for new AI businesses. Financial Services and AI Banks like BBVA are starting to integrate AI technologies. Communication Through WhatsApp 70% of businesses in Latin America are using WhatsApp for various activities. AI and Automation AI enables more complex workflows than rules-based automation. Emerging Trends and Technologies LLMs, open source contributions, and more are shaping the future of AI. Detailed Discussion Most Interesting AI Developments Medical Field: The ability for AI to predict potential health issues is not just promising but is already showing results. State of AI Predictive Technology Prediction Machines: Current AI technology is capable of generating text and images based on prompts and predicting user behavior. Future of AI Taking Action: The ultimate goal for AI is to not just predict but also to act on those predictions. Healthcare Innovations Mayo Clinic: Mayo Clinic is at the forefront of implementing AI in healthcare, with particular emphasis on diagnostics. Decision-Making in AI Data, Judgment, Decision: A typical decision-making process in AI involves collecting data, applying judgment based on that data, and then making decisions. Latin America's AI Landscape Blue Ocean Opportunity: There's a lack of strong incumbents in the AI space, providing new entrants with less competition. Automation vs. AI Differences: Rules-based automation is not AI; AI involves more complex workflows and decision-making processes. Quotable Moments "The next step in AI evolution is not just predicting events but acting on those predictions." "Latin America presents a blue ocean opportunity for new AI businesses."

In this thought-provoking episode, we delve into a myriad of topics ranging from the complexities of curatorial practice in art to the secular study of religion. Nico Sarian guides us through an intellectual journey that stretches from the art scene in Moscow to the academic corridors of the University of Toronto, touching on astrology, Zen Buddhism, and the nature of suffering along the way. Prepare for a multi-layered discussion that challenges conventional wisdom and explores the intersections of art, religion, and culture. Join the awesome whatsapp community that Nico is helping to build Early Life and Academic Background Nico Sarian began his career journey after completing high school and entering the world of fashion. Intrigued by the art world, he transitioned into academia due to a spiritual crisis. Nico studied art history and philosophy at King's College in London, and this led to work as an art curator in Moscow. Defining a Curator According to Nico, the role of a curator involves managing a triangle between works of art, the public, and the space or architecture. Failing to manage all three elements would reduce a curator to merely a critic. The role of a curator is likened to that of a priest in that they serve as a mediator between these elements. Evolution into Religious Studies Nico later began teaching at a British art college in Moscow, which fueled his interest in theory, aesthetics, and teaching. After discovering the field of religious studies, he started studying it in a secular context at the University of Toronto. Influence of Russian Culture Nico spent considerable time in Russia and was in a relationship with a Russian woman for four to five years. He studied the Russian language to immerse himself fully in the culture, stating that Russia felt like another planet to him. Esoteric Studies Nico discussed various aspects of astrology, Zen Buddhism, and Shintoism. He mentioned Richard Tarnas as a researcher who places astrology in a historical context and contrasts Vedic and Western astrology. The demotion of Pluto to an asteroid sparked a debate about its relevance in modern astrology and contemporary life. The Philosophical Angle Nico is a critic of modernity and holds a deep academic viewpoint. His questioning of our modern belief systems was influenced by philosophers like Bruno Latour, who claimed, “We have never been modern.” Questioning Religion and Spirituality Nico wrestles with the concept of secularism in religious studies. His inability to give a straight answer to whether he believes in God stems from his view that the term 'secular' is an illusion. Life Philosophy Nico argues that suffering is not a problem; rather, it's the inability to deal with suffering that becomes the problem. He spent three years studying Sanskrit and used the term “Duhka” to discuss the concept of suffering. Future Directions Nico mentioned the idea of creating an infinite discussion board that incorporates various perspectives, aiming to foster and create symbolic capital. The episode delved into Nico's multifaceted life journey, from art and fashion to philosophy and religious studies. He provided a comprehensive view of how different disciplines intersect and inform each other, offering a critique of modernity along the way.

Introduction: Guest: Julie Fredrickson, Managing Partner at Chaotic Capital. Chaotic Capital is a seed-stage investment vehicle that focuses on ideas adapting humanity to complexity. Follow her on Twitter Adapting to Complexity: Julie raises the essential question: "What is the primary factor linking humanity to complexity?" As things change, it's vital to learn how to adapt and evolve. It's about more than just the traditional liberal-conservative divide. Understanding Change: Discussion on "Chesterton’s fence" – a principle highlighting the importance of understanding the reasons behind a tradition before discarding it. We often don't have all the information about why certain systems or structures exist. The concept of a "conservative accelerationist" is introduced. What traditions or values do people want to preserve in the face of rapid change? Historical Perspective & Our Understanding: Examples from history highlight our ever-evolving understanding. A millennium ago, the earth was thought to be centered; 500 years ago, it was believed flat. The movie "Men in Black" underscores the fluid nature of human understanding, challenging our conceptions and paradigms. Julie emphasizes the power of human inference – our unique ability to draw conclusions from information. Given our current knowledge, how does this skill evolve, and what questions do we need to ask to better understand our surroundings? Role and Limitations of AI: The conversation delves into whether we've provided enough context to AI systems. A significant concern is AI alignment – to what or whose standards is AI being aligned? Is it the median human, societal norms, or something else? The prospect of AI achieving human-like inference and skepticism is explored. Can AI be trained to question and fact-check itself? The Internet, Open Source, and Corporations: A retrospective on the internet's evolution, from closed corporate internets to open source. The dilemma of whether we'll be using corporate Large Language Models (LLMs) or open-sourced LLMs in the next five years. Culture, Symbols, and Power: Julie's career in fashion, beauty, and luxury offers insights into symbols and semiotics. Language isn't the only medium of communication. Who truly owns culture, and who holds the power? Transhumanism and Medical Ethics: Julie discusses her college days as a medical ethicist researching cloning during 2002-2006. The topic of transhumanism is broached. Julie posits that in many ways, we are already transhuman, referencing technologies like fertility treatments. There's a debate about the risks of corporate interests dominating AI and transhumanist technologies. The future is uncertain, and it's impossible to predict the outcomes fully. Preparing for the Age of Acceleration: Quoting William Gibson, "The future is here, but it's unevenly distributed," Julie ponders the accelerating pace of change. She seeks solace and insights from groups focused on effective accelerationism and those termed "doomer optimists." Julie touches on resource scarcity, arguing that human wants will always exceed available resources. She briefly addresses the topic of prepping and the challenges associated with it. Conclusion: Julie underscores the importance of agency. It's essential to ask who's influencing our decisions and who's potentially programming our behaviors.

Introduction: David Hundley is a Machine Learning Engineer who has been deeply involved with experimenting with Large Language Models (LLMs). Follow along on his twitter Key Insights & Discussions: Discoveries with LLMs: David recently explored a unique function of LLMs that acted as a 'dummy agent'. This function would prompt the LLM to search the internet for a current movie, bypassing its training limitations. David attempted to utilize this function to generate trivia questions, envisaging a trivia game powered by the LLM. However, he faced challenges in getting the agent to converge on the desired output. Parsing the LLM's responses into a structured output proved especially difficult. Autonomous Agents & AGI: David believes that AGI (Artificial General Intelligence) essentially comprises autonomous agents. The prospect of these agents executing commands directly on one's computer can be unnerving. However, when LLMs run code, they operate within a contained environment, ensuring safety. Perceptions of AI: There's a constant cycle of learning and revisiting motivations and goals in the realm of AI. David warns against anthropomorphizing LLMs, as they don't possess human motivations. He stresses that the math underpinning AI doesn't align with human emotions or motivations. Emergent Behavior & Consciousness: David postulates that everything in the universe sums up to a collective result. There's debate over whether living organisms possess true consciousness, and what it means for AGI. The concept of AGI emulating human intelligence is complex. The human psyche is shaped by countless historical experiences and stimuli. So, if AGI were to truly replicate human thought, it would require vast amounts of multimodal input. A challenging question raised is how one tests for consciousness in AGI. David believes that as we continue to push technological boundaries, our definition of consciousness will keep shifting. Rights & Ethics of AI: With advancing AI capabilities, the debate around the rights of AI entities intensifies. David also touches upon the topic of transhumanism, discussing the trajectory of the universe and the evolution of humans. He contemplates the potential paths of evolution, like physically merging with technology or digitizing our consciousness. AI's Impact on Coding & Jobs: David reflects on the early days of AI in coding. He acknowledges the transformative potential of AI in the field but remains unworried about AI taking over his job. Instead, he focuses on how AI can aid in problem-solving. He describes LLMs as "naive geniuses" - incredibly capable, yet still requiring guidance. Open Source & OpenAI: David discusses the concept of open source, emphasizing the transparency it offers in understanding the data and architecture behind AI models. He acknowledges OpenAI's significant role in the AI landscape and predicts that plugins like ChatGPT will bridge the gap to further automation. Math's Role in AI: The conversation delves into the importance of math in AI, with David detailing concepts like gradient descent and its role in building neural networks. David also touches on the evolution of AI models, comparing the capabilities of models with 70 billion parameters to those with 7 billion. He predicts that models with even more parameters, perhaps in the trillions, will emerge, further emulating human intelligence. Future Prospects & Speculations: David muses on the future trajectory of LLMs, drawing parallels with the evolution of AlphaGo to AlphaZero. The episode concludes with philosophical musings on the nature of consciousness and its implications on world religions.