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.

Introduction: In this episode, the renowned Pablos Holman (Website) discusses how science fiction is transitioning into reality, particularly in the realm of technology. Drawing on his expertise, Pablos sheds light on revolutionary concepts that are pushing the boundaries of what was once believed impossible. Main Points: Space-based Solar Energy: Pablos recently witnessed the conception of a company aiming to place solar panels in space. The primary challenges on Earth, such as clouds and nighttime, are nonexistent in space, which experiences constant light. The innovative solution involves capturing this 24/7 solar energy in space and transmitting it to Earth through radio waves, a concept called "Vertis Solis." This can potentially offer scalable, carbon-free energy. The Dyson Sphere Concept: A theoretical idea that involves surrounding a star with solar panels to harness its energy. While it remains an extreme long-term science fiction aspiration, its possibilities are intriguing. Wireless Energy Transfer: Wireless energy transfer isn't new; it's how radio and wifi work. However, they aren't optimized for transferring substantial energy. With advances in beam steering technology, this might soon change. By focusing solar arrays directly at antennas on Earth, efficient energy transfer can be realized. Space Exploration and Ecosystem: Cost has always been a limiting factor. From the space shuttle days, where reusable rockets were only a dream, we've come a long way with companies like SpaceX bringing the cost down significantly. With the lowered cost, activities like manufacturing and asteroid mining in space become more feasible. These ventures, although promising, also come with huge technical risks. Manufacturing in Space: There are inherent advantages to space manufacturing. The vacuum of space can produce better-quality computer chips. The absence of gravity also has significant effects on the atomic scale, potentially enhancing semiconductor production and other goods. Investment Curve and Deep Tech: Holman emphasizes that while software is indeed revolutionizing industries, it's essential not to neglect deep tech. Unlike typical startups focused on short-term applications, deep tech aims at the fundamental challenges facing humanity. Challenges with 3D Printing: While the concept of 3D printing was groundbreaking, its real-world application faced obstacles. The solution lies in a different approach – building layer by layer, utilizing recyclable inputs like used coffee grounds, thereby revolutionizing manufacturing. Misconceptions and Course Corrections: Pablos asserts that humans often latch onto the wrong stories or misconceptions, hindering progress. He cites examples like the confusion between reactors and bombs, and the historical outlawing of psychedelics, which are now considered potential treatments for PTSD. Closing Remarks: Pablos stresses the importance of storytellers in shaping our future. The right narrative can direct our collective efforts towards genuinely transformative goals. He invites listeners to connect with him and shares that he's fortunate to meet innovative minds daily, always pushing the boundaries of possibility. Contact: For those intrigued by Pablos' insights, he can be reached at pablos@deepfuture.tech and also listen to the Deep Future podcast.

Show Notes for Crazy Wisdom Podcast Episode with Subutai Ahmad Introduction The episode features Subutai Ahmad, the CEO of Numenta and a pioneering figure in both neuroscience and artificial intelligence (AI). The discussion navigates the complex relationship between the human brain's architecture and contemporary AI models like deep learning systems. Topics range from the historical evolution of these disciplines to the cutting-edge research that could shape their future. Historical Perspective The initial inspiration for artificial neural networks came from our rudimentary understanding of how neurons and connections work, going back to the 1940s. Donald Hebb significantly influenced the back-propagation model developed in the 1980s. Hebb's work, combined with the discoveries of Hubel and Wiesel in the '50s, laid the groundwork for understanding how neurons learn features from the visual world, including edge detectors and higher-level shapes. State of Neural Networks Today Despite advancements, today’s neural networks still rely on a simplified model of what a neuron is, and they differ fundamentally from biological systems. One glaring difference is in power consumption; a human brain uses only about 20 watts, while running a deep learning network can require power equivalent to an entire city. Learning Modes and Algorithms Deep learning systems usually operate in two modes: inference and training. In contrast, the human brain doesn't distinguish between these states, learning continuously from environmental stimuli. Algorithms, particularly back propagation, are still part of the problem. They try to minimize error, unlike the brain, which adapts and learns contextually. The Numenta Angle Founded by Jeff Hawkins and Donna Dubinsky, Numenta has been researching to understand the principles underlying brain function. Recently, they have focused on applying this understanding to AI. Their approach comprises three main pillars: Efficiency: Using 'sparsity' to mimic the brain's efficient use of connections. Neuron Model: Incorporating the complex nature of neurons for continuous learning. Cortical Columns: Employing a standardized neural circuitry model to replicate intelligence. The Road Ahead For the future, Subutai discusses the need for AI systems to be autonomous and embodied, suggesting that agency and embodiment are crucial aspects of intelligent systems. He also touches on the importance of including elements like neuromodulators and even explores the potential role of quantum physics in neural processing. Conclusion We are in a transformative era where AI is far from being fully realized. Organizations are still trying to grasp how to incorporate these technologies effectively. However, the future is promising, especially with interdisciplinary approaches like Numenta's that blend neuroscience with AI, focusing on understanding the brain's core principles to improve AI's capabilities.