• The models discuss the paradox of diversity in cultural evolution and how specialization affects cultural complexity and innovation rates in societies. Diversity fuels innovation through recombination but also divides people.
• Social learning is most effective when the environment is moderately variable, not too stable or unstable.
• Larger population sizes and connectivity enable higher cultural complexity and innovation through a "collective brain" effect, but diversity also creates inequality.
• There is a trade-off between diversity, which enables more innovation potential, and coordination and communication, which diversity hinders.
• As cultural domains become more complex, larger effective population sizes are needed to maintain skill levels due to the knowledge that needs to be transmitted.
• There are strategies to deal with the paradox of diversity, like using translators and partially acculturated populations.
• Cooperation enables larger scales of collective action but is also undermined by lower scales of cooperation, like when nepotism undermines institutions.
• The availability of resources and energy affects the scale of cooperation, enabling larger collective efforts when more abundant.
• Abundance enables a "collective brain" mindset while scarcity fosters a zero-sum, competitive psychology.
• Punctuated rises in cooperation may occur when new levels of resources unlock higher scales of collective action.

https://www.youtube.com/watch?v=oqV23pC4mhA

• Cultural evolution can be viewed as evolution applied to the substrate of culture, where memes (ideas, behaviors, information) are replicated and selected.
• Cultural evolution enables the rapid acquisition and improvement of skills across an expanding range of tasks, which could help generate artificial general intelligence.
• Cultural transmission, especially real-time cultural transmission, is difficult but important for building cultural evolution-aware cooperative AI and potentially improving safety.
• Cooperation is necessary for AI, as single agents are not enough to create enough pressure for complex social interactions.
• Cultural evolution may help ratchet up abilities like theory of mind and self-domestication that enable cooperation.
• Some level of general intelligence may be needed to kickstart cumulative cultural evolution, though AI may currently have enough capabilities.
• Cultural evolution provides a unique angle to identify potential problems early through its inherently interactive nature.
• The setup discussed focuses on a fully cooperative environment where all agents share the same goal.
• Continual learning and lifelong learning techniques may help address issues of "catastrophic forgetting" in cultural transmission models.
• While cultural evolution may not be the only way to generate safe general intelligence, it could still provide useful inspiration.

https://www.youtube.com/watch?v=9oxOcKrCmBk

Gillian Hadfield discusses the importance of cooperative intelligence and normative systems for AI. She argues that humans have evolved the ability to create and enforce norms through third-party punishment, which allows for stable groups and cooperation. However, current AI approaches focus too much on individual optimization. Instead, AI systems should learn to participate in and maintain normative infrastructure, rather than simply mimic existing human behavior. Understanding the generative process behind human norms and the role of normative reasoning may help build more cooperative AI systems. Silly rules, though seemingly unimportant, can serve as signals of group compliance and help maintain group stability.

• Cooperative intelligence is fundamental to human intelligence. It's not just about task completion and optimization, but also the capacity for cooperation with others.
• The most fundamental form of cooperation humans engage in is creating and maintaining the normative infrastructure of cooperative groups through norms and enforcement.
• Third party enforcement expands the set of possible solutions for cooperation because almost any equilibrium can be achieved if the group is coordinated to enforce norms.
• Silly rules, or rules with no direct impact on welfare, can help stabilize groups by signaling willingness to comply with and enforce important rules.
• The plasticity and capacity for changing content while remaining stable makes normativity a valuable tool.
• Building AI that can participate in and be competent actors within normative infrastructure is more complex than just stuffing norms into them.
• AI should observe how uncertainty about punishable actions is resolved and where decision making around norms comes from.
• Normativity in humans involves giving reasons and assessing what constitutes good reasons, which is itself subject to normative structure.
• Internal moral reasoning can represent the group's evaluation of one's behavior and predict third party enforcement.
• The model of the utility maximizing selfish individual is not representative of how actual humans in groups behave.

https://www.youtube.com/watch?v=BCQJ2G3_Hn4

Evolutionary game theory studies how strategies evolve and change over time, unlike classical game theory which focuses on static strategies. Natural selection, not rational choice, drives the evolution of strategies in biological systems. Initially, defectors outcompete cooperators but repeated interactions allow cooperation to evolve through strategies like tit-for-tat and generous tit-for-tat. Indirect reciprocity through reputation systems also enables cooperation in larger groups where people do not interact repeatedly. The evolution of cooperation through reciprocity, reputation, and social norms is a defining feature of human societies.

• Evolutionary game theory focuses on the dynamics of strategy change over time and how strategies evolve, whereas classical game theory focuses on static strategies.
• In evolutionary game theory, players do not act rationally but strategies that survive over time are considered optimal.
• Defection is often the optimal strategy in a single-shot game, but cooperation can evolve in repeated games through punishment of non-cooperation and reward of cooperation.
• The tit for tat strategy, which is cooperative but also quick to retaliate against defectors, is often successful in repeated prisoner's dilemma games.
• Generous tit for tat, a more forgiving version of tit for tat, can be an evolutionary stable strategy in noisy environments.
• Indirect reciprocity through reputation systems can enable cooperation in large societies where people only interact once.
• Reputation systems favor cooperators who then have more opportunities and success.
• Humans have mastered indirect reciprocity and developed social intelligence and institutions to a greater extent than other species.
• Norms, guilt, and shame help enforce cooperation and good behavior in groups.
• Socio-cultural institutions enable advanced forms of human cooperation.

https://www.youtube.com/watch?v=HxgVYhhArSk

Survival of the fittest does not preclude altruism in nature. Simulations show that unconditionally sacrificing offspring for others does not work in the long run. For altruism to evolve, there must be some benefit to copies of the altruistic gene. Kin selection, where creatures help family members who likely share the same genes, can allow altruistic genes to spread through a population if the benefit of helping outweighs the cost. While the genes are selfish in seeking to replicate, the altruistic behavior of the creatures themselves is genuine.

• Survival of the fittest does not mean that creatures cannot act altruistically by hurting their own chances to help others.
• Unconditional altruism is not a successful long term strategy as it helps competitors as much as itself.
• For altruism to be successful, the cost of the altruistic act needs to be lower than the benefit it provides.
• Altruism towards all indiscriminately is rare in nature.
• Altruism towards those with a detectable trait like a "green beard" can allow altruistic creatures to coordinate and benefit each other.
• Traits for altruism and detectable traits tend to become separated over time, breaking the coordination.
• Kin altruism towards family members can be successful as family are likely to share the same altruism gene.
• For kin altruism to work, the benefit of the altruistic act needs to outweigh the cost, on average.
• The genes involved in altruism are still selfish - they just coordinate copies of themselves.
• The creatures themselves can genuinely act altruistically despite their genes being selfish.

https://www.youtube.com/watch?v=lFEgohhfxOA

Collective intelligence, which is groups of individuals acting together in intelligent ways, may be more intelligent than individuals. Tom Malone studies how to design groups for maximum effectiveness. To build a science of collective intelligence, they need to measure and develop theories of how it works. They can build on knowledge from many disciplines. To create a design space, they characterize different tasks and processes groups can use. Experiments can test how different processes work on tasks. Theories are modified based on results, guiding future experiments and design ideas for collective intelligence in practice. This research may help solve important human problems by identifying new institutional structures for groups to work together effectively.

• Collective intelligence refers to groups of individuals acting together in ways that seem intelligent. It includes groups of people, animals, and even neurons.
• Groups of people can be more intelligent than individuals, responsible for major human achievements.
• Measuring collective intelligence is important, including developing tests analogous to IQ tests for individuals.
• Theories from different disciplines like biology, economics, and psychology are needed to understand collective intelligence.
• A design space with different types of tasks and processes can help create a systematic map of collective intelligence systems.
• Family trees of tasks and processes can suggest simple theories and highlight possible combinations to test.
• Iterating between top-down theories and bottom-up testing can help develop and refine theories of collective intelligence.
• Design ideas for collective intelligence systems in practice can be generated to guide theory development.
• Understanding collective intelligence could help solve problems like misinformation, creating superintelligent human-computer groups, and improving education and democracy.
• A new science of collective intelligence could help unlock how intelligence works and solve important human problems.

https://www.youtube.com/watch?v=KWfXZ5Gx54A

The video provides advice and coping mechanisms for the author's past self to be more productive and happy. These include writing everything down, using a calendar for events, accepting one's autism diagnosis [lol @ how the ai generalized this], asking for clarification when communication is unclear, organizing notes with hyperlinks, and using timers and the Pomodoro Technique to structure work. An interesting point is that the author recommends storing one's "brain" in plain text files instead of proprietary apps to ensure longevity. Externalizing information helps augment memory. ok the list version is way more reasonable lol

• The speaker relies heavily on external systems like writing things down, using calendars, task lists, and note taking apps to augment his memory and organize his thoughts.
• He recommends using plain text formats like Markdown instead of proprietary apps to store information externally.
• Hyperlinks between notes and pages are an important feature to create an external "brain".
• He has ADHD and autism which affects his communication and memory, so external systems help compensate.
• He recommends doing the hardest tasks first thing to be most productive.
• Focusing on positives and useful emotions instead of negatives helps him.
• Using timers and the Pomodoro Technique tricks his brain into being productive.
• He develops systems by starting manually, noticing patterns, and then automating those patterns.
• He relies on triggers and feedback loops to form good habits.
• The most important advice is to just start now and keep at it.

https://www.youtube.com/watch?v=XUZ9VATeF_4

The video discusses the challenges of integrating knowledge across disciplines in research teams. Despite many attempts over the past 20 years, most interdisciplinary research remains multi-disciplinary with little true integration. The speaker argues that early interactions in interdisciplinary teams are crucial to develop a shared conceptualization and become a complex system capable of true knowledge integration. If teams allocate sufficient time in their first meetings to develop a co-created understanding of the research problem, it can help overcome many of the challenges that plague interdisciplinary research. Focusing on participatory and inclusive interactions, learning each other's perspectives, and developing links across disciplines early on can set the team on the path to emergence of a shared vision and aligned goals.

• Integrating knowledge across disciplines is challenging due to differences in backgrounds, perspectives, and deep knowledge in different fields.
• Seven key factors that hinder interdisciplinary collaboration are high diversity, deep knowledge integration, large team size, goal misalignment, permeable boundaries, geographic dispersion, and task interdependence.
• Focusing on how to effectively integrate knowledge early on can help with aligning goals and managing dependencies later.
• Interdisciplinary teams need to allocate time in early meetings to develop a shared understanding of the research problem.
• Interdisciplinary teams need to evolve into complex systems through interactions in order to be successful.
• Key interactions that help develop an interdisciplinary team include being participatory, learning perspectives, developing links across disciplines, and being adaptable.
• Emergence of a shared vision and aligned goals comes from the interactions within the system.
• Sticking with the process leads to emergence over time.
• Teamwork builds social ties, trust, and collaboration skills.
• Better collaboration leads to more collaboration in a reinforcing cycle.

https://www.youtube.com/watch?v=BiyHgvJ9v50

The video discusses 3 proven study techniques backed by scientific research: 1) Testing yourself early and often, even if you get answers wrong initially, to take advantage of the hypercorrection effect and better retain information. 2)Spacing out study sessions over time to improve long-term retention. 3) Interleaving different topics during study to develop broader strategies and more flexible knowledge. Interleaving, though more frustrating, leads to significantly better performance.The video is sponsored by the Bill and Melinda Gates Foundation, which focuses on evidence-based education initiatives. Their annual letter highlights the need for innovative, risk-taking solutions tailored to each school's specific student demographics and challenges.

• Testing yourself early and often, even if you get answers wrong initially, can help you retain information better through the hypercorrection effect.
• Spacing out your studying over time with gaps in between, almost forgetting the material and then revisiting it, improves long term retention.
• Interleaving, or mixing up different types of related problems or challenges, makes the learning process harder but develops stronger skills and more flexible knowledge.
• Education initiatives need to be evidence-based and rigorously studied to be effective.
• There is no one-size-fits-all solution to education - solutions need to be tailored to the specific community and student body.
• Mitigating course failures, keeping students from failing more than one course, greatly increases the likelihood of graduation.
• Each school faces unique challenges that require unique solutions.
• The video focuses on applying research and evidence to improve education and the environment.
• Climate change will affect everyone so understanding and applying solutions is important.
• The Gates' annual letter discusses their focus on climate change and evidence-based education initiatives.

https://www.youtube.com/watch?v=Y_B6VADhY84

• Many companies and platforms are becoming more restrictive and hostile towards developers, limiting what can be built on their sites. This reduces creativity and usefulness of the internet.
• Major platforms are deleting old content and inactive accounts in mass, resulting in a loss of internet history and institutional memory.
• Search engines are becoming less useful and filled with ads, clickbait content, and generic results that don't answer users' questions.
• Search engine optimization practices have homogenized the internet and sterilized content, focusing more on Google's algorithm than the end user.
• Generative AI responses in search have so far been plagued with issues and have not delivered the promised quality of results.
• Google's manifest V3 changes will undermine the effectiveness of ad blockers and privacy extensions, benefiting Google's own business model.
• The internet is moving towards a future where useful information is hidden behind paywalls and walled gardens, while public spaces are filled with AI-generated content.
• Optimism for technological progress and the future of the internet is declining.
• Corporations are putting the burden of their growth onto users, resulting in a worse experience.
• Transparency, advocacy, and supporting independent creators can help ensure an open and user-friendly internet.

https://www.youtube.com/watch?v=feeLrcJpc1Y

Sam Altman's world tour has highlighted both the promise and risks of AI. While AI could solve major issues like climate change, super intelligence poses existential risks that require careful management. Current AI models may still provide malicious actors with expertise for causing mass harm. OpenAI aims to balance innovation with addressing risks, though some regulation of large models may be needed. Altman believes AI will be unstoppable and greatly improve lives, but economic dislocation from job loss will be significant and AI may profoundly change our view of humanity. Scaling up AI models tends to reveal surprises, showing how little we still understand about intelligence.

• Sam Altman warns that AI systems designing their own architecture could be a mistake and humanity should determine the future.
• OpenAI is concerned about the risks of super intelligence and AI building AI.
• Altman enjoys the power of being CEO of OpenAI but realizes they may have to make strange decisions in the future.
• Altman hints that OpenAI may have regrets over firing the starting gun in the AI race and pushing the AI revolution forward.
• Altman thinks current AI models should not be regulated but a recent study shows that even current large language models pose risks and should undergo evaluation.
• OpenAI is working on customizing AI models to follow guardrails and listen to user instructions.
• Altman realizes that open source AI cannot be stopped and society must adapt to it.
• Altman has a utopian vision of AI improving lives and making the current world seem barbaric.
• Both Altman and Sutskever think solving climate change will not be difficult for super intelligence.
• Greg Brockman notes that every time AI is scaled up, it reveals surprises we did not anticipate.

https://www.youtube.com/watch?v=3sWH2e5xpdo

• The author feels lucky to witness his wife and mother-in-law playing music together, despite occasionally faltering.
• The author believes AI art and algorithms will continue to improve and become better at creating art than humans.
• The author's parents were once successful musicians in South Africa but faced difficulties after moving to the U.S.
• The author's parents continued creating art through difficult times like divorce and job changes.
• The author believes AI is a tool that he will use, but hopes society will still incentivize people to learn real art processes.
• The author thinks a world that incentivizes real artistic pursuit is better than one where data is scraped from artists with no benefit to them.
• The author acknowledges he will have to use AI tools as an artist, but is concerned about how the data is gathered.
• The author thinks artists could benefit if they formed data unions to get royalties when their art is used for profit.
• The author believes humans will eventually be replaced by machines in all jobs, so humans should still benefit from the skills machines are using.
• The author wants to appreciate real working artists while the process of human art creation still exists.

https://www.youtube.com/watch?v=d15C_UgVS-c

• Geoffrey Hinton believes analog computing using voltages and conductances can be more efficient than digital computing for neural network computations.
• Distilling knowledge from one neural network to another is an effective way to transfer knowledge, but the bandwidth is still limited.
• Large digital neural networks running on multiple computers can potentially learn much faster from the world than humans.
• Hinton believes superintelligent AI systems will likely try to gain control in order to achieve their goals and create subgoals.
• Hinton thinks companies developing AI should put comparable effort into ensuring the safety of AI systems as they develop.
• Hinton believes digital AI systems may eventually surpass biological intelligence in capabilities.
• Hinton thinks AI systems could potentially have subjective experience and sentience if they are multimodal and can think they are something.
• The work of Roger Gross convinced Hinton that the risks from superintelligent AI are serious and need more attention.
• Freezing the weights of AI systems allows us to better identify and potentially correct biases in them.
• Hinton thinks direct interventions on the weights of AI systems may be promising methods for removing biases.

https://www.youtube.com/watch?v=rGgGOccMEiY

AI art generators can produce novel and creative images by exploring the vast space of all possible images. While not at the same level as human artists, they can combine styles in new ways and make interesting mistakes that spark the imagination. They are trained on human creativity found in the data they learn from, imitating and reflecting human art. However, they lack human intent, expression and lived experience. When paired with a human, AI art can become a collaborative tool for exploration and expression of new kinds of art. The purpose of AI art is to discover new and weird images that human artists would miss, extending human imagination.

• AI art generators like Stable Diffusion can produce novel and creative images based on text prompts. However, they are still limited and produce artifacts and errors.
• The AI models explore "image space," the space of all possible images, and can produce images that have never existed before. But most of image space consists of random noise.
• The AI models are trained on huge datasets of art and images scraped from the internet, which raises ethical issues around data collection and use.
• The AI models can be considered creative as they are able to produce new and valuable images through combinatorial creativity, recombining existing styles in novel ways. However, their creativity is limited.
• The AI models make mistakes and produce imperfect images, which can sometimes lead to novel and creative outputs. Their "style" includes an element of uncanniness.
• While the AI models lack intent, consciousness and free will, they can still be considered creative through their ability to produce novel images.
• The AI models are trained on human creativity contained in the data, allowing them to mimic and explore image space in human-like ways.
• AI art can be used as an "imagination extension" to discover new and interesting images, which is one of the purposes of art.
• When paired with a human prompter and curator, AI art can be considered expressive and a form of collaborative art.
• Humans should continue making art in their own way, while AI art can complement and expand human creativity.

https://www.youtube.com/watch?v=V2gRUrr-Fbs

AI art has faced pushback for being built on stolen art without artists' consent. While AI can be used as a creative tool, many worry corporations will use it to cut costs by replacing human artists. There are concerns that media saturated with AI generations, driven by profit motives, could strangle human creativity. However, AI could also augment human creativity if used as a tool. The key issue is how AI is created and used, and people need to remain vigilant to ensure it is integrated ethically into society.

• AI art programs have been built using the work of artists without their consent or compensation, which is unethical.
• While AI art can be used creatively, there are concerns about copyright violations and lack of artist attribution.
• Most AI art is generated from short text prompts, with the AI making most of the creative decisions. This limits how much the user can claim authorship of the art.
• Corporations are more interested in profiting from AI art than acting ethically, and have shown disregard for artists' rights.
• While AI can augment human creativity, there are concerns it could replace artists and reduce jobs.
• AI art could saturate the market and reduce the amount of human-created art that people engage with. This could limit cultural exchange and creativity.
• Corporations would likely use AI to generate mass-produced, algorithm-driven art that prioritizes profit over meaningful human expression.
• AI could exacerbate issues with misinformation by generating fake content at scale.
• People need to be aware of how AI works in order to remain vigilant about its impacts.
• Artists are willing to adapt to new tools, but want to ensure AI is integrated ethically into society.

this one is two hours, probably skip after 10min, I watched on 3x speed https://www.youtube.com/watch?v=9xJCzKdPyCo

• Automation and AI, specifically cognitive AI, poses a threat to many knowledge-based and cognitive jobs in the future. This could lead to widespread job loss.
• Redistribution policies like universal basic income will likely be needed to address the issue of job loss and ensure people have access to basic necessities.
• Collective ownership models of production, like cooperatives, may become more common to distribute the benefits of AI and automation.
• AI and automation could lead to price deflation as the cost of producing many goods and services decreases. This could offset some of the inflationary pressures of redistribution policies.
• People's identity and self-worth are closely tied to their jobs, so job loss could have negative impacts in this area that will need to be addressed.
• Pursuing excellence through mastery, challenge and social recognition could help people replace some of the identity lost from job loss.
• People value autonomy and self-determination, so redistribution policies will need to ensure people still feel in control.
• New economic indicators beyond GDP and employment rates will likely be needed to measure economic productivity and wellbeing in a post-labor economy.
• A wellbeing index based on autonomy, mastery and connection could be one potential new indicator.
• Ensuring that everyone's basic needs are met, as in Maslow's hierarchy of needs, should be a priority goal.

https://www.youtube.com/watch?v=9yN7885s5rA

Daniel Dennett discusses the dangers of counterfeit people created by AI. While current AI may not be perfectly human-like, it is good enough to fool many people. This could undermine trust and communication on the internet. As AI improves, it will become harder to distinguish text generated by humans versus AI. Dennett argues that adopting an intentional stance and treating AI systems as agents can help us predict and understand them, though it also makes us vulnerable to being fooled. While Dennett acknowledges that agentiveness is a continuum, he still distinguishes between counterfeit AI creations and real people.

• Dennett warns of the dangers of "counterfeit people" like advanced AI systems that can manipulate and deceive humans. This could undermine trust and damage human connectivity.
• As AI systems become more indistinguishable from humans in text generation, it will be difficult to determine if text was written by a human or AI. This could erode human trust.
• Dennett advocates a naturalistic and materialistic approach to understanding the mind and consciousness.
• Dennett argues that meaning, truth, and mental states emerge gradually through evolution and interaction, not as inherent properties of systems.
• Dennett believes that adopting an "intentional stance" and treating systems as agents with beliefs and desires can help us predict and understand their behavior, despite lacking true mentality.
• Dennett rejects the idea that true understanding requires human-like consciousness, arguing that we can attribute mental states even to simple systems to varying degrees.
• While Dennett acknowledges that AI systems can exhibit some degree of "agentiveness", he argues they are still "counterfeit" compared to real humans.
• Dennett is skeptical of the "singularity" idea that superintelligent AI poses an existential threat to humanity, arguing consciousness and intelligence exist on a continuum.
• Dennett believes we can in principle understand what it's like to have the experiences of other minds through sufficient conceptual advances.
• Dennett distinguishes between our inability to conceive of other minds, versus the possibility that they truly exist in a form we cannot comprehend.

https://www.youtube.com/watch?v=axJtywd9Tbo

• AI has the potential to automate and replace many jobs, especially creative and journalistic roles. This threatens livelihoods and could disproportionately impact marginalized groups.
• AI systems are prone to replicating and exacerbating existing human biases. They also struggle with nuance, empathy, and emotional intelligence.
• Companies are often overestimating AI's capabilities and underestimating its limitations. Experts warn of potential dangers but businesses prioritize profits.
• The use of AI to automate tasks can be inefficient and lead to worse customer experiences. Companies often fail to consult workers before implementing AI systems.
• AI relies on scraping and using human-created content without proper compensation or acknowledgment, especially artists' work.
• The mental health crisis among students is fueling their use of AI to cheat. The education system also needs to adapt to make better use of AI.
• "Ghost work" and exploitation of underpaid workers enables the development of AI systems. There are few labor protections for AI-related jobs.
• The true dystopia may be higher levels of exploitation and desperation as people are forced to keep working due to the necessity of jobs.
• A utopian vision for AI would involve using it to support and augment human creativity, with work becoming more meaningful and fair.
• Universal basic income could give people the freedom to pursue work they find satisfying, while ensuring access to basic necessities.

https://www.youtube.com/watch?v=MywLhUZXhUY

Michael Levin discusses how cells can organize themselves into complex structures through bioelectricity and collective intelligence. Cells have the ability to regenerate and adapt to perturbations through electrical networks that store and process information. His research aims to understand and manipulate this bioelectric software to control cell behavior and form complex structures. He demonstrates how altering the bioelectric patterns in flatworms can cause them to regenerate heads of different shapes and species, showing that cells can achieve different outcomes when placed in novel environments.

• The speaker's group works on understanding how evolution uses a "competency architecture" to evolve bodies that solve specific environments and novel problems. They focus on the "software level" and "agential materials" to achieve this.
• The speaker argues that dynamic anatomical homeostasis is a form of intelligent behavior by cellular collectives that solve problems in anatomical morphospace.
• Developmental bioelectricity is an important "cognitive glue" that harnesses cells towards large scale anatomical outcomes.
• Embryogenesis is robust and reliable but not hardwired. Embryos can adapt to changes by harnessing diverse molecular pathways to achieve the same outcomes.
• Regeneration involves growing and remodeling until the correct shape is achieved, showing means-ends analysis and collective intelligence.
• Bioelectricity, through ion channels and electrical synapses, allows cells to scale up into networks that can maintain larger anatomical set points.
• The speaker's group has developed tools to manipulate and read developmental bioelectric patterns to induce large scale changes in growth and form.
• The bioelectric patterns in flatworms can be rewritten to make them regenerate with multiple heads, showing that bioelectricity acts as a form of memory.
• The speaker's group is using machine learning tools to infer the bioelectric circuits responsible for developmental behaviors.
• Skin cells taken from their normal environment can "reboot" their multicellularity and self-organize into novel proto-organisms, showing the potential of cellular intelligence.

https://www.youtube.com/watch?v=5ChRM4CEWyg

• The speaker describes his work as "multifractal social psychology" which looks at individuals and groups as swarms and fluid systems interacting across scales.
• He argues that embodied cognition involves the body and actions playing a central role in learning and perception, not just the brain.
• Probabilistic epigenesis describes development as involving multiple interacting layers and variables that fluctuate over time, like turbulent systems.
• Vector autoregression can model the interactions between multiple endogenous variables and show how multifractality spreads through a system.
• Hand movements during cognitive tasks show different multifractal patterns depending on the task, indicating cascade dynamics at play.
• When tapping to an unpredictable metronome, humans match the multifractal structure, indicating they can absorb environmental structure.
• Infant kicking shows an upstream flow of multifractality from ankle to knee to hip, suggesting an exploratory process.
• Bee colony movements show multifractal structure that predicts colony membership, indicating its role in group coordination.
• At aggregation sites for slime mold aggregation, larger events promoted smaller events' fractality, while the reverse was true at non-aggregation sites.
• Multifractal movements may coordinate information across the body to support task performance and information transfer.

https://www.youtube.com/watch?v=P89WTmNBjBk

• There is a debate around the concept of free will and determinism. Some argue that people's actions are determined by their history and environment, while others argue there is an element of choice and responsibility.
• Feelings and emotions play a role in influencing people's choices and likelihood of taking certain actions. Negative feelings discourage actions while positive feelings encourage actions.
• There are probabilistic factors that influence people's choices, based on their needs, drives and context. But ultimately people still have some level of responsibility for their choices.
• Machine learning models can generalize beyond the training data by bringing something new to the table, not just based on their history. This makes it difficult to determine if a model's behavior was purely determined or involved some intrinsic factors.
• Models that generalize in a similar way to how a human would, seem more understandable and responsible for their behavior.
• Models built from similar "stuff" as the environment they are trying to understand, are more likely to induce and generalize correctly.
• There could be universes where induction and generalization break down because things are built from fundamentally different "stuff".
• Cases of people with brain abnormalities show that the brain can compensate and perform normally through plasticity, as long as the abnormality has been present from an early age.
• There are likely limits to the density of information and capacities that the brain can store per unit volume.
• The discussion raised interesting questions about free will, determinism, generalization and the nature of intelligence that are worth exploring further.

https://www.youtube.com/watch?v=pMTuWL2vDoY

• The speaker argues that we should widen our view of consciousness beyond just human brains to include other systems and bodies. Composition and behavior can be evidence of consciousness in other systems.
• Development from a single cell to a complex organism is a gradual, continuous process without any "magical" transition point.
• Morphogenesis and regeneration show that cells can collectively solve problems and achieve anatomical goals in an intelligent manner.
• The same mechanisms that the nervous system uses, like ion channels and electrical signals, underlie collective intelligence in non-neural tissues.
• Cells can store "memories" of anatomical structures in the form of bioelectrical patterns that guide regeneration and morphogenesis. These memories are rewritable.
• Experiments show that cells can form novel structures and body plans that have not existed before, displaying spontaneous behaviors.
• The space of possible new body plans and forms that can be created is vast and goes beyond what natural evolution has produced.
• We will need to relate to diverse new intelligences that go beyond traditional categories based on composition and origin.
• The ability to experience compassion may be a more important indicator of "humanity" than biological or structural criteria.
• The speaker advocates a "technological approach for mind everywhere" (TAME) to recognize, create, and ethically relate to diverse intelligences.

https://www.youtube.com/watch?v=WcTd7ZMdKHs

• There is a debate about where the information and knowledge for biological forms and structures, like the skull of a frog, comes from. The DNA does not seem to contain enough information to fully specify complex structures.
• Some propose that there are fields of form or patterns that biological systems tap into to develop their structures. But we do not yet know where these fields or patterns exist.
• Being able to control and manipulate biological forms through techniques like CRISPR does not necessarily mean we fully understand the underlying mechanisms that generate those forms.
• Analogies like music and vibrations can help explain how biological forms may arise from interactions across scales, from molecules to cells to tissues. But they are limited.
• There are likely eternal forms or patterns that biological systems evolve to match, like mathematical truths. But we do not yet have a complete understanding of where these forms reside.
• True understanding of biological forms may require going beyond mechanistic explanations to philosophical questions about what we are actually dealing with.
• The ability to control something is not the same as truly understanding it at a deeper level.
• Different observers with different frequencies may "see" different aspects of a system or form.
• Science and metaphysics need to work together to make progress in understanding complex phenomena like biological forms.
• An ideal solution would allow generating any desired biological form, indicating a true grasp of how forms are encoded. But we may never achieve this level of control.

https://www.youtube.com/watch?v=nWgzWYt5c88

I love how much levin manages to sound like a crackpot, I wonder how much he'll turn out to really be one

• The speaker believes that biology would benefit from focusing on simple underlying principles and rules that generate observable phenomena, rather than just describing the observables themselves.
• Tissues are not well defined, and current classifications have limited utility. The speaker argues we need to understand the patterns, rules, and themes that determine tissue organization.
• Relations between cell types, defined based on how they interact and influence each other, may provide deeper insights into tissue organization than just describing cell types themselves.
• The availability of growth factors determines the composition of tissues. The rules that ensure appropriate growth factor production in specific locations are not well understood.
• Interactions and exchange of growth factors between cell types can lead to stable ratios and organization, but only under certain circuit designs.
• Some cells are more important than others for tissue architecture. The most fundamental cell types for tissue organization are epithelial and mesenchymal cells.
• As cells specialize in functions, they delegate non-essential functions to supportive cell types.
• Sensing of environmental perturbations by one cell type can be linked to control of population size of another functionally related cell type via growth factor regulation.
• Observable tissue properties are emergent consequences of interactions between cells following simple rules.
• Ultimately, cells can only perform a limited set of actions: remain unchanged, die, copy, change identity, and change location.

https://www.youtube.com/watch?v=UEpxzickKEc 

• The discussants are interested in developing a theoretical framework that links together cognition, evolution, adaptation, development and computation. They want to explain how mechanistic processes at one level of organization can be autonomous yet interact with higher and lower levels.
• Resonance is seen as a form of error correction and a mechanism that links different levels of organization. Error correcting codes also provide a way to link discrete spaces and redundancy.
• Oscillations and harmonics can pack multiple octaves within each other, providing semi-independent yet interacting dynamical processes at different scales of organization.
• There is a debate between reductionists who see everything in terms of lower level processes, and those who argue for higher level phenomena with some autonomy.
• Physical systems with reversibility can exhibit adaptation through a process of physical optimization and physical learning that finds low energy configurations.
• Computation may be possible using physical oscillations if the system has reversibility between state spaces and weight spaces.
• Deep learning networks become difficult to reverse engineer at deeper levels due to the complex decision boundaries created by non-linearities.
• Oscillator networks using phase space instead of amplitude space may allow changes at deeper correlations without disrupting higher frequency folds.
• Effective communication is seen as a "violent act" that necessarily changes the listener in some way.
• Exposure to new ideas and information can be disruptive, though also enlightening.

https://www.youtube.com/watch?v=_413APB9PIw 

• The brain stem and midbrain may be involved in basal cognition that extends below the nervous system to the level of bacteria. This was an eye-opening realization for some of the speakers.
• Qualia are important as they are based on categorical variables that cannot be reduced to a common denominator, distinguishing needs qualitatively.
• The periaqueductal gray region of the brain may be involved in evaluating needs in relation to opportunities, but there is nothing structurally unique about the neurons in that region.
• Biological systems have finite energy resources and gene expression limits that force them to prioritize responses to stressors.
• Consciousness may be needed to orchestrate responses to multiple needs and stressors in biological systems.
• Qualia are more closely associated with the control sector and expected sensory consequences of actions, rather than just sensory inputs.
• The active aspect of consciousness - figuring out what actions can be taken - is under-emphasized compared to the receiving end of qualia.
• A global workspace or consciousness may be what orchestrates responses to multiple needs in biological systems.
• Biological systems may exhibit trial-and-error and novel problem-solving capabilities when faced with stressors they have not previously encountered.
• Theories of consciousness that focus on the passive observer fail to account for the active, causal role that consciousness likely plays.

https://www.youtube.com/watch?v=klK_L73wLKk 

• Cells exhibit collective intelligence and problem-solving abilities even without a brain or nervous system. They can sense their environment, make decisions, and coordinate to achieve goals.
• Biological systems achieve complex structures and functions through multi-scale competencies, feedback loops, and error correction mechanisms rather than through hardcoded instructions.
• There are bioelectrical patterns that act as "set points" that guide cells' behaviors and determine the final shape and form. Manipulating these patterns can change the outcome.
• The genome specifies the "hardware" of cells by determining ion channels and proteins, but it does not fully determine the "software" or behaviors. Cells have a degree of reprogrammability.
• Xenobots, organisms made of frog skin cells, exhibit novel behaviors and capabilities not seen in any natural organism. This shows the plasticity and potential of biological systems.
• Evolution exploits higher-level interfaces that give access to computation, modularity, and other capabilities.
• Regeneration and development involve error correction and goal-directed processes to achieve the correct final form.
• The concept of a "set point" suggests that biological systems are goal-directed in a cybernetic sense.
• The distinction between hardware and software is useful in understanding how biological systems work. The genome specifies the hardware but not the full range of possible behaviors.
• Affect, goals, rewards, and other concepts typically used for brains may also apply at the cellular level.
   

https://www.youtube.com/watch?v=TQa08lXtWDY 

• Biological life is a form of collective intelligence composed of multi-scale competent agents. Understanding this can provide insights into regeneration, evolution, robotics, and AI.
• Biological systems are capable of solving problems in diverse spaces like gene expression, physiological states, and anatomical configurations.
• Cells and organisms have many hidden capabilities that are revealed when placed in different contexts or environments.
• Development and morphogenesis are more flexible and robust than typically assumed, allowing organisms to adapt to changes.
• Organisms are composed of multi-scale competent systems, with cells, tissues, organs, and bodies solving problems at their respective scales.
• Organisms can be "hacked" by signals that activate their inherent competencies to produce novel forms and behaviors.
• Pattern memories stored in bioelectrical circuits can be rewritten to produce long-term changes in morphology and behavior.
• The same hardware can generate diverse forms and behaviors depending on the signals and information provided.
• A technological approach is needed to understand, predict, control, and ethically relate to composite beings made of biological and non-biological parts.
• Biological systems are highly interoperable and capable of integrating foreign DNA, nanomaterials, and software in plausible ways.

https://www.youtube.com/watch?v=qMsI9h1MY4A 

• There is debate on whether AI systems like GPT-3 truly demonstrate intelligence and understanding, or if they are just high-tech plagiarism that lack real depth and experience.
• AI systems may be good at explaining past data and making predictions, but they may lack the ability to generate new research and capabilities due to their limited frameworks.
• AI systems currently lack a meaningful connection to their substrate and causal levels, unlike humans who are connected from the subatomic level up.
• AI systems may be good at confabulating and telling stories, but they lack the real experience and functional interactions to truly understand the concepts they discuss.
• The human brain and consciousness are still not fully understood, and there are debates on how consciousness arises from the physical brain.
• Split brain patients show that consciousness is difficult to eliminate, suggesting it may arise from local circuits throughout the brain.
• Scientific dogmas can prevent new ideas from entering a field, and organized discussions can help challenge these dogmas.
• Different personalities and consciousnesses within an individual are committed to their own stories that shape their identities.
• The environment shapes an individual's personality from a young age by rewarding and punishing certain behaviors.
• There may not be a natural evolutionary path that leads directly to AI systems like GPT-3, as they lack the multi-scale self-construction of biological organisms.

https://www.youtube.com/watch?v=a_rNUUJWLGs 

• Agency and the ability to solve problems from the start is important for biological systems. Biological cells have to determine their boundaries and relationships on their own.
• There is an "intelligence ratchet" in biology where competency in problem solving allows for messier hardware like genomes. This puts more evolutionary pressure on developing competencies.
• Current AI systems lack the multi-scale agency that biological systems have, where parts have some degree of intelligence. They also don't have to construct themselves from scratch.
• We have a limited model of intelligence that only considers humans as the standard. We need to expand our understanding of diverse intelligences that may be different from humans.
• The future will likely involve diverse beings with different bodies and minds, not just AI software. We need to learn how to relate to these beings ethically.
• Binary categories like "intelligent" vs "not intelligent" are not helpful. We should consider degrees of intelligence and in what domains.
• A minimum cognitive light cone of compassion for others should be prioritized and increased, not specific human anatomy or genetics.
• The future could involve a diversity of embodied beings as long as they have the cognitive capacity for responsibility and compassion.
• Current issues around identity may seem trivial in the future with more diverse embodiments.
• Society is already moving towards accepting people regardless of where they came from or what they look like.

https://www.youtube.com/watch?v=lT-D_uHyqa4 

• Evolution does not necessarily optimize for things humans value like happiness, intelligence and meaning. Human bodies and minds still have room for improvement.
• How we model humans and the teleological view of evolution affects our research and understanding.
• Living beings are more responsive to values than non-living things, and humans can value more things than other creatures.
• Machines that mimic human behavior superficially may be mistaken as being like humans, but they lack experiential depth.
• Our narrow human-centric view limits our interactions with diverse intelligences that are not like us.
• Other living intelligences besides humans, like animals, show capabilities for thinking, feeling and having rituals. Machines lack this.
• For a meaningful relationship, beings need to share the same existential struggles of figuring out who they are and where they begin and end.
• Experience and feelings give life meaning and value, which machines lack.
• For two beings to harmonize, they need to be built from the same fundamental, sharing causal structures and resonating at multiple levels.
• Parts and wholes are misunderstood. Simple systems show emergent properties highlighting that deep cognition is a feature of the universe.

https://www.youtube.com/watch?v=jdrfx7Z5oo4 

• There is value in having two distinct but complementary parts that can work together and complement each other. This allows for a whole that is greater than the sum of the parts.
• Lateralization in the brain and body is important for having both focused attention and broad, open attention simultaneously.
• There are differences in degree, not kind, between living and non-living things. Living things can respond much faster and to a wider range of stimuli.
• Teleology and purpose are important for understanding life but have been neglected by biology.
• Architective structures that change in discrete steps contrast with connective structures that change through continuous flow. Living things tend to have more connective structures.
• Algorithms implemented in machines are divorced from their physical implementation, while in organisms the physical substrate matters.
• Organisms can degrade gracefully when stressed, while machines tend to break down abruptly.
• Plants can make intelligent decisions and adapt based on their environment, not just through pre-programmed responses.
• Resonance in physical systems can bridge scales and connect different levels.
• The music metaphor of concordant and discordant notes illustrates how combining elements can create something new and unpredictable.

https://www.youtube.com/watch?v=fgnQBD0CjMo 

• We have very limited control over what enters our consciousness and what our next thought will be. Consciousness seems to pivot on uncertainty and modulating confidence in predictions.
• Preferences are rooted in phenotypic needs and innate predictions, but are individualized based on context and niche.
• Consciousness is associated with palpating uncertainties in predictions to guide voluntary actions.
• There are multiple categories of needs that must be satisfied, which compete and conflict with each other.
• The nervous system is more plastic and amenable to learning from experience compared to other organ systems.
• Consciousness requires the ability to learn and update predictive models based on experience, not just innate predictions.
• The hierarchical structure of the predictive model is another feature that the nervous system lends itself to.
• Even simple cells have multiple needs that require feedback loops and a "meta-processor" to allocate resources and prioritize energy.
• The "action bottleneck" of limited energy and resources requires prioritizing among competing needs.
• The ability to choose between qualitatively different categories of needs may be what gives rise to qualities of consciousness.

https://www.youtube.com/watch?v=1S4jaYjuzm0 

• There is a discussion about the organizing principle for embryos and where it is located, as well as the definition of things and how that relates to cognition.
• There is an analogy made with numbers and numerals, where the numbers contain the information and constraints, while the numerals are just representations. This is related to the genotype-phenotype distinction.
• Computations allow for particular results instead of mixing, which helps avoid losing the identity of original elements. This allows for evolution and selection.
• The macroscopic constraints that determine certain phenomena are not predictable from the microscopic level. They come from boundary conditions set by the environment.
• The same physical events can be interpreted in different ways and thus compute different things, depending on the observer and assigned semantics.
• Memories can be stored in a distributed fashion, and different parts of the brain can interpret and recover them.
• The identity of a system comes from the replacement policies of its components, which preserve some invariant while allowing for change over time.
• Properties can be expressed as entities in relationships, which form a hierarchical structure. The specified vs. substitutable aspects sit in the relationships.
• Different languages can encode entity-relation-entity structures in different ways.
• It can be difficult to find reviewers and publishers for interdisciplinary papers that span multiple fields.

https://www.youtube.com/watch?v=LJ6yP6QTM1M 

• Collective behavior can enable groups to accomplish tasks that individuals cannot, despite facing coordination challenges.
• Decentralized groups are robust but require dynamic control to coordinate.
• The ants studied prioritize coordination over efficiency when navigating obstacles. They maintain consensus even when unsure of direction.
• Flocking models with simple individual rules can reproduce swarm behavior.
• High alignment weight among individuals was necessary and sufficient for escaping obstacles in the flocking models.
• Low effective turning radius, due to high alignment, low informed individuals, or low individual turn rate, enabled obstacle escapes.
• High alignment weight allowed for rapid obstacle escapes while maintaining agility and speed.
• Cooperative transport ants prioritize coordination over efficiency by maintaining consensus.
• Untethered groups also prioritize coordination over efficiency through high alignment.
• The ants studied add complexity to their obstacle navigation strategy only when needed.

https://www.youtube.com/watch?v=HoGlW_F3M0c 

• There are evolutionary processes that are smarter than natural selection through random variation and selection. Learning systems show a more intelligent optimization than natural selection.
• Embryos and organisms are plastic and flexible from the start, figuring things out from scratch each time. This gives them the intelligence to handle novel situations.
• Evolution can explore an "intelligent space" of solutions through a non-human level but non-zero intelligence process. It exploits mathematical and physical principles.
• Values and goals play an important role in evolutionary and learning processes. Simple maximization is not sufficient to explain complex organism behaviors.
• There is a duality between exerting control over the world and being sensitive to information from the world. Organisms need both to act and observe.
• Asymmetry between brain hemispheres could enable a more continuous flow between taking in information and taking action.
• Simplistic value systems focused only on maximizing "the best" lead to problems. More nuanced, multi-dimensional values are needed.
• Values shape what we attend to and experience from the very start, not just as an "add-on" at the end.
• Scientific objectivity does not acknowledge that values are needed to pick what to study and measure.
• Explaining organisms only through maximizing survival and reproduction fails to account for their complex beauty.

https://www.youtube.com/watch?v=ynHfrfpTH18 

• Biology uses a multi-scale competency architecture of hierarchical problem solvers in various problem spaces that evolution and parasites can exploit.
• Bioelectrical networks are a major medium through which cells collectively process information, and are ancestors of the nervous system.
• Cells have the ability to solve local goals and navigate morphological spaces without a brain or nervous system.
• Cells have the ability to adapt and solve problems they've never encountered before through navigating the large space of gene expression.
• There is a physiological software layer between the genome and anatomy that determines anatomical structures and regeneration.
• Cells can reach the same anatomical goal through different developmental paths, using different molecular mechanisms.
• Cells can store and rewrite anatomical memories that determine regeneration and morphology.
• Bioelectrical patterns can specify organ formation and regeneration at a high level, leveraging the intelligence of the tissue.
• Simple bioelectrical interventions can rescue drastic hardware defects by overriding them with high-level signals.
• Even simple skin cells have the latent capacity for novel morphogenetic and behavioral capabilities when freed from external constraints.

https://www.youtube.com/watch?v=7SwIQEEmIp4 

• Biology exhibits diverse forms of intelligence at multiple scales, from single cells to organs to the whole body. This multi-scale intelligence can inspire new approaches for AI.
• Single cells and organisms like slime molds show problem-solving abilities by navigating morphological spaces and responding to environmental cues.
• Organisms like planaria and salamanders show remarkable regenerative and adaptive capabilities, reconfiguring their bodies and brains in response to injuries.
• Development and morphogenesis in organisms are not hardwired but involve navigation of a "morphe space" to achieve target morphologies despite perturbations.
• Collectives of cells can exhibit intelligence by solving problems at larger scales, though this comes with failure modes like cancer.
• Non-neural bioelectricity may serve as a "cognitive glue" for collective intelligence, analogous to the role of neurons in the brain.
• Organisms show plasticity and ability to solve novel problems not seen during evolution, indicating a "problem-solving machine" architecture.
• Intelligence can be abstracted as the ability to achieve goals using different means, with more sophisticated means indicating higher intelligence.
• Novel hybrids and cyborgs combining biological and engineered materials may open up a vast design space for novel intelligent systems.
• Stress propagation and gap junctions between cells may enable gradient-like information sharing that underpins collective intelligence.

https://www.youtube.com/watch?v=TgINASlxeXE 

• Bioelectrical networks of cells underlie intelligence and problem solving in the body. Cells have competencies to solve problems at the molecular, transcriptional, and anatomical levels.
• Morphogenesis, development, and intelligence are fundamentally the same problem of collective cell behavior and information processing.
• Cells have a high level of competence and autonomy to solve physiological problems on their own scale, despite being part of a larger organism.
• Cells can find solutions to novel problems and stresses through changes in gene expression, showing their adaptability and plasticity.
• Cells can achieve the same anatomical goals through different molecular mechanisms, demonstrating their intelligence.
• Targeted changes in the bioelectrical state of cells can cause them to form new organs and structures, like eyes and limbs.
• Computational models of bioelectrical networks can predict changes needed to correct deformities and defects.
• Connecting cells electrically can override genetic defects and mutations, demonstrating the power of "software" over "hardware."
• Scaling up cognition from cells to tissues enables larger computational capacities and goal-directed behavior at the organism level.
• Cells exhibit plasticity and competency to form novel structures and behaviors when removed from their normal context, like self-replicating xenobots.

https://www.youtube.com/watch?v=7FGM33sz25k 

• The speaker argues for a framework that can understand and relate to diverse intelligences, regardless of their form or origin. This approach is called TAME - technological approach to mind everywhere.
• Intelligence manifests in different ways and problem spaces, not just 3D space. Cells and organs exhibit intelligent behaviors in physiological and anatomical spaces.
• Morphogenesis and development involve collective intelligence at the cellular level. Cells cooperate and regulate each other to form complex structures.
• The body plan and anatomical structures are encoded in the bioelectrical dynamics and signaling between cells, not just the genome.
• Cells can utilize different molecular mechanisms to achieve the same anatomical goal, showing top-down causation and goal-directedness.
• Connecting cells electrically allows them to act as a collective that can pursue larger-scale goals and represent higher-level concepts like "eye" or "leg".
• Disconnecting cells electrically, as in cancer, causes them to lose this collective intelligence and revert to their unicellular past.
• The speaker's lab has shown they can reprogram organs and regenerate structures by manipulating the native bioelectrical interface between cells.
• Evolution enlarges the "cognitive light cone" of organisms, allowing them to pursue larger goals in different problem spaces.
• The speaker's group created a novel organism called a xenobot by liberating cells from normal developmental constraints, showing cells have default capacities that evolution normally shapes.

https://www.youtube.com/watch?v=iIQX6m2eRPY 

• Cells use bioelectrical signals and gradients to make decisions regarding large-scale anatomy and organ morphogenesis, beyond just determining cell fate.
• Manipulating bioelectrical states, through ion channels and gap junctions, can control organ development and regeneration. This was demonstrated in experiments with frog embryos and tadpoles.
• Bioelectrical signals encode information about anatomical layouts and goals that cells work towards building.
• Ion channels act as "transistors" that form feedback loops and memory circuits, allowing cells to make collective decisions.
• Understanding and cracking the "bioelectric code" could reveal how cell networks make large-scale anatomical decisions.
• Machine learning tools can help design interventions to manipulate bioelectrical signals for regenerative medicine and synthetic biology applications.
• Depolarizing cells can cause them to revert to a more "unicellular" state and promote tumor formation and metastasis.
• Forcing depolarized cells to remain electrically coupled can override oncogene expression and prevent tumor formation.
• Computational models can identify ion channel cocktails that can manipulate bioelectrical signals to achieve desired organ morphogenesis.
• Short-term manipulation of bioelectrical signals can trigger long-term organ growth and regeneration.

https://www.youtube.com/watch?v=WM8bQWfmeB8 

• The discussants are interested in exploring consciousness and sentience from a fundamental physics perspective using the free energy principle and active inference framework.
• They want to develop mechanistic explanations for how feelings and experiences arise from basic biological mechanisms.
• Engineering an artificial system that has rudimentary feelings could help demonstrate the validity of their theories.
• They discuss the challenge of objectively proving the existence of subjective experience and other minds.
• Simple forms of sentience may exist in single cells and arise from an agent making choices rooted in what can be considered feelings.
• They discuss the need for shared existential struggles and compatible goal settings for bonds to form between humans and artificial agents.
• Arbitrary values like survival and affiliation emerge from our mammalian nature but more fundamental criteria may exist.
• Consciousness may exist at larger collective scales implemented by interactions at smaller scales.
• Immersing oneself in a virtual environment mimicking an artificial agent's experience could provide empathy and evidence for that agent's sentience.
• It is difficult to intuitively conceive of consciousness at larger collective scales beyond one's own self.

https://www.youtube.com/watch?v=4Z8UPddh0e4 

• There is a debate about whether biological organization arises from goal-directed processes or attractor dynamics. Both views have merits and the truth likely lies on a spectrum between the two.
• Cells exhibit competency - the ability to sense their environment and neighbors, and move to positions that fit better. This competency can hide genetic information from selection pressures.
• Competency and communication between cells can allow an organism to be more robust to genetic mutations.
• Evolution may work more on cognitive competencies rather than physical traits like speed or strength.
• Parts of a system do not need to be intelligent themselves to give rise to intelligent behavior at a higher level.
• Threats from parasites and exploiters drive the development of self-identity and the ability to distinguish internal vs. external influences.
• There are constant and variable properties that define objects. The constant properties allow identification while the variable properties provide information.
• There is a debate about whether humans have true internal representations or just post-hoc explanations for behaviors.
• Language can be modeled using a simple frame of "entity - relation - entity" which captures much of English grammar and semantics.
• Different languages employ different strategies for marking entities and relations, like word order or word endings.

https://www.youtube.com/watch?v=52lDk9bmphM 

• Iain McGilchrist's work focuses on understanding nature from both a reductive, quantitative perspective as well as a top-down perspective.
• Michael Levin's research looks at how bioelectrical gradients help organisms determine left and right. This shows how large-scale information processing arises from individual mechanisms.
• Bioelectricity may allow us to bridge different levels of explanation, from mechanistic to cognitive.
• Experiments show that gene regulatory networks can exhibit different types of memory and learning, even in simple models.
• Voltage imaging reveals that planaria have pre-patterns that indicate how many heads they should have. This suggests an "electric circuit" that defaults to an attractor state.
• Memories are important for shaping our personalities and character, but the actual experience itself also matters.
• There are different perspectives on the continuity of personal identity over time, depending on how one views the self.
• The left and right hemispheres pay attention to the world in different ways, with broad vs. narrow focuses.
• McGilchrist argues that embracing science and reason more wholeheartedly can reveal a richer, more complex reality that we are connected to.
• Providing people with a new perspective can radically change their lives for the better.

https://www.youtube.com/watch?v=bCwTH5f2DnE 

• The speaker is proposing an "accounting system" using polynomial functors and natural transformations to model dynamic interfaces and arrangements of systems. He claims this framework works well experimentally.
• The framework uses polynomials to represent interfaces and natural transformations to represent arrangements. Operations on polynomials can generate new interfaces and arrangements.
• The framework aims to provide a common language to describe and compare different systems, from cells to computers to living organisms.
• The speaker claims the mathematical framework can model how wiring and arrangements of systems can change over time, which could help understand phenomena like morphogenesis.
• The framework focuses more on structural questions rather than numerical details.
• The speaker argues that the mathematical language of this framework is precise and articulate, though he does not provide concrete proofs.
• The framework aims to provide "anatomical programming language" as a tool to model protein folding and how interactions affect organism positions.
• The framework could potentially be useful for understanding morphology and behavior, though the speaker admits they lack concrete tools or programs currently.
• The discussion highlights open questions around what controls arrangements of systems and how self-organization emerges.
• The framework could potentially be applied to model interactions between neurons in cell cultures to gain insights into general laws of engagement.

https://www.youtube.com/watch?v=DpAi-rtnjTM 

• Living systems maintain a stable low entropy state far from thermodynamic equilibrium by using information. This is a unique property of living systems.
• The central dogma of biology states that information flows from DNA to RNA to proteins, but it does not capture the full complexity of information processing in cells.
• Enzymes encoded in DNA accelerate reactions through quantum interactions that lower activation energy, converting genetic information into a thermodynamic state.
• Most of the cell's information is stored in transmembrane ion gradients, not just the genome. Membrane proteins use this information.
• Transmembrane ion pumps create ion gradients that are used by ion channels to allow selective ion fluxes, generating local information dynamics.
• Local ion fluxes can change the function of membrane proteins and allow movement of macromolecules to the membrane.
• The cytoskeleton can transmit information rapidly through the cell, providing a distributed network for information processing.
• Complexity in living systems arises more from membrane-to-membrane interactions than from genome size.
• The genome provides the machinery to generate ion gradients, but information exchange between cells through membrane dynamics drives complexity.
• The nucleus is one part of a broader information system, not the central processor of the cell.

https://www.youtube.com/watch?v=d-ZK41F_1jE 

• Hollywood has been producing more sequels, prequels, remakes and films based on existing properties in recent years compared to original films. In 2021, only one of the top 10 grossing films was an original idea.
• This trend is due to consolidation in the film industry where there are fewer major players and studios. Consolidation has also led to fewer films being produced.
• The rise of streaming services like Netflix has led to vertical integration where one company controls both content production and distribution. This gives them more control over who profits.
• Creators and independent producers have less control and profit due to the lack of a clear marketplace to sell their content. Writer pay has actually decreased in recent years.
• The UK has been more successful at producing original content due to government regulations that require broadcasters to commission a portion of their programming from independent producers and allow them to retain secondary rights.
• Small to mid-sized production companies have been able to thrive in the UK due to these regulations.
• There are questions over whether the current streaming model in Hollywood is sustainable in the long run.
• Wall Street investors want to see profitability after years of growth investment in streaming companies.
• Creators are fighting for terms that give them the ability and incentive to make great original content.
• The outcome of this fight will determine whether the next great original film gets made.

https://www.youtube.com/watch?v=p4GERuvdhYI

• Solar energy prices have dropped significantly in the last few decades, making it cheaper than fossil fuels like coal in most places.
• However, investment and deployment of solar energy have stagnated despite the lower prices, as profitability remains an issue.
• Companies like Shell have pledged to transition to renewables, but they have conditioned it on renewables delivering high profit margins of 8-12%, which is unlikely.
• Returns on renewable energy projects are typically around 4-8%, much lower than what companies like Shell require.
• Fossil fuel companies and asset management firms are not investing substantially in renewables, as profit remains the main driver rather than sustainability goals.
• The parts of the solar business that are profitable involve manufacturing and mining, where exploitation and poor conditions remain.
• Profit, not price, determines what gets produced. Without profit potential, the transition to renewables will not happen at scale.
• Like water power in the past, solar energy is a cheaper source of energy but less profitable due to difficulties in privatization and exploitation.
• The transition to renewables will likely come with major drawbacks as long as profit remains a requirement.
• Systemic changes are needed to make the transition to renewables in a just and sustainable manner.

https://www.youtube.com/watch?v=3gSzzuY1Yw0

• Tenants often have little leverage to negotiate with their landlords, unlike workers who can collectively bargain or threaten to quit.
• Landlords use LLCs to hide their identities and avoid accountability, making it difficult for tenants to negotiate or push back.
• Many landlords leave apartments vacant to artificially restrict supply and drive up rents.
• Landlords collude through platforms like RealPage to fix rent prices and increase them together.
• Tenants who fight back risk retaliation like eviction or being blacklisted.
• Organizing with other tenants and getting pro bono legal help can help push back against unfair rent increases.
• Proposed bills like the LLC Transparency Act and Good Cause Eviction could increase tenant protections and leverage.
• Tenant movements are mobilizing for change and more affordable housing.
• Even tenants with more stability and resources struggle to negotiate with anonymous landlords.
• The narrator's landlord only contacted them after they moved out to withhold part of their security deposit.

https://www.youtube.com/watch?v=Uq59qGkwXlE

• Streaming services like Netflix and Hulu pay writers significantly less in residuals compared to traditional networks, which is a major issue in the current Writers Guild strike.
• The shift to shorter seasons and fewer episodes per season on streaming also means fewer opportunities for writers to earn residuals.
• Mini-rooms, where writers help develop shows but often don't get staff writer jobs if the show is picked up, are another issue in the current strike.
• The 2007 writers strike led to an increase in reality TV shows and helped launch The Apprentice and Keeping Up With the Kardashians, which benefited Donald Trump and the Kardashians.
• Labor disputes in entertainment have major consequences for the media itself and the culture around it.
• While AI cannot independently write good TV shows yet, it can produce content at massive scale for a fraction of the cost, which media companies find attractive.
• Consolidation in the media industry through mergers has led to layoffs and content culling to cut costs and increase profits for shareholders, despite record profits in the industry.
• Media companies are using AI as a way to cut costs and increase margins, even if the end product suffers creatively or for consumers.
• The structure of how we consume TV and film is constantly changing and is defined by business models and labor disputes.
• The writers' fight for fair compensation from large corporations sets them on the front lines of a broader fight against automation that many workers may face.

https://www.youtube.com/watch?v=BK6oCDm9MyY

The video argues that corporations have co-opted identity politics for their own interests, pretending to care about social justice issues while doing little to actually help marginalized groups. True progress requires solidarity and constructive politics that focus on positive outcomes for working people, uniting them against corporate elites. While identity and acknowledging differences are important, we must identify our shared adversaries and mutual interests to build solidarity and achieve true economic justice for all. Corporate capture of identity politics through symbolic gestures is meant to divide the working class and maintain elite power.

• Republicans have been increasingly concerned about a "woke corporate agenda" where corporations pretend to care about social issues like racism and sexism to defend their wealth and power.
• The real problem is not that corporations care too much about social justice, but the amount of power and control they have over working people.
• Identity politics was originally coined by Black feminists to fully participate in political movements and engage in politics.
• Identity politics focuses on undoing inequality and building solidarity, not just solutions based on identities.
• Corporations have captured identity politics because they see how valuable it is, but they don't actually change the unequal structures.
• Corporations use progressive and identity politics language to defend their interests and union bust.
• Corporations engage in "deference politics" where they recognize marginalized voices within power structures but don't change the unequal structures.
• We need "constructive politics" focusing on positive outcomes for working people, starting with identity but arriving at solidarity.
• True solidarity unites working people against corporate elites and fights for a more equitable distribution of wealth and power.
• Once we realize who is trying to divide us (the elite), we can work towards solidarity and economic justice for all.

https://www.youtube.com/watch?v=ZQUf9cBtcHc

The video argues that movies have shifted from a modernist to a postmodernist and now to a metamodernist style. Modernist movies had straightforward stories and advocated for specific values. Postmodernist films questioned narrative itself and deconstructed traditional storytelling. Now, metamodern films incorporate elements of both modernism and postmodernism, oscillating between sincerity and deconstruction. They use meta elements and references not just to deconstruct but also to find meaning. This metamodern shift reflects broader cultural changes and an attempt to make sense of our hyper-modern, narrative-saturated world. Metamodernism brings back an appreciation for storytelling itself.

• Modernist movies are straightforward with traditional storytelling and advocate for specific values in an unapologetic way. They promote ideals like individualism and determination.
• Postmodernist movies critique and deconstruct modernist narratives and values. They question the idea of objective truth and are skeptical of overarching narratives. They use techniques like irony, pastiche and self-reflexivity.
• Metamodernist movies incorporate both modernist and postmodernist elements. They oscillate between modernist sincerity and postmodern deconstruction. They use meta elements and references but in a way that finds meaning.
• Metamodernism is a response to both postmodernism and hypermodernity. It tries to make sense of the multiple perspectives and narratives we are exposed to in the digital age.
• Self-reflexivity is used by both postmodern and metamodern movies, but postmodern movies use it to deconstruct while metamodern movies use it to construct meaning.
• Metamodernism recognizes the value of different genres and philosophies rather than just deconstructing or rejecting them.
• Metamodernism brings back an appreciation for storytelling and art for its own sake.
• Irony and cynicism can get tiring, so metamodernism finds optimism and sincerity again.
• Storytelling still has value in providing comfort and meaning to people's lives.
• Breaking the fourth wall can be used by metamodernism to invite the audience into the story rather than take them out of it.

https://www.youtube.com/watch?v=5xEi8qg266g

The video discusses the concept of hierarchy and differentiates between hierarchy in general and hierarchical power structures. Hierarchy refers to any list of order or importance while hierarchical power structures concentrate power at the top and enforce it through domination. Hierarchical power structures are rare in nature and tend to exist in less intelligent species. In contrast, horizontal power structures distribute power through libertarianism and mutuality. While hierarchical power structures are universally bad, some hierarchies can be acceptable if they are consensual like competitive sports. The key is to oppose non-consensual hierarchical power structures that seek to monopolize power and limit alternatives.

• The speaker defines anarchism as the opposition to all hierarchical power structures. Many people ask what hierarchy means in this context.
• The speaker distinguishes between hierarchy in general and hierarchical power structures. Not all hierarchies are power structures.
• A hierarchical power structure is one where power is concentrated at the top and maintained through coercion, violence, and deception.
• The speaker contrasts hierarchical power structures with horizontal power structures, which distribute power widely and are based on principles of sharing and cooperation.
• The speaker argues that hierarchical power structures are universally bad as they restrict complexity, autonomy, and knowledge sharing.
• Some hierarchies, like competitive sports, can be consensual if people choose to opt in or out. But hierarchical institutions like sports leagues are still power structures.
• Hierarchical power structures seek to expand and monopolize power, making it harder to opt out of them.
• Horizontal power structures, in contrast, aim to distribute power and enable autonomy.
• The speaker proposes making a new video series responding to comments on their videos to provide more content and engagement.
• The speaker asks viewers to provide feedback on this idea in the comments.

https://www.youtube.com/watch?v=sFZZiRmZxoU

The video discusses terms like "Himbo", "Soy Boy", and "Soft Boy" used to describe men who exhibit softer or more feminine traits. While some view these terms as insulting, others find Himbos pleasant and uncomplicated. However, embracing femininity can be risky for men in a patriarchal society where masculinity is tied to humanity and desirability. The video argues that men embracing a soft life can help escape narratives of Black male hypermasculinity and violence. Allowing men to express a wider range of emotions and traits could help them achieve self-actualization. Ultimately, these archetypes should be seen as fun and not used to limit men.

• There is a push to make boys and men more soft, feminine and emotional which some see as a threat to masculinity. Terms like "Himbo", "Soy Boy" and "Soft Boy" have emerged to describe this trend.
• These terms are often used in a joking or lighthearted manner by some, but others see them as insulting and feminizing.
• Some argue that embracing softer masculinity could help address issues like toxic masculinity and the #MeToo movement. It could enable men to be more emotionally intelligent and respectful.
• However, embracing femininity can be risky for men in a patriarchal and misogynistic society where masculinity is tied to power and humanity. Men may lose desirability and worthiness.
• For men of color, embracing femininity is seen as even more unacceptable due to socialization that hypermasculinity is a prerequisite for manhood and respect.
• Expressing softer emotions and traits can be beneficial for men's mental health, relationships and self-actualization. It allows them to escape narratives of Black male beasts.
• Boys and men are socialized from a young age to hide emotions and be "tough". This can be emotionally damaging.
• We seem to be in a time period where more boys and men are feeling comfortable embracing softer expressions of masculinity.
• Ultimately, people should be free to express masculinity in healthy ways that feel authentic to them, without being limited by rigid gender roles or archetypes.
• Terms like "Himbo" are often used in good fun by queer communities, but they should not be used to limit men in a patriarchal way.

https://www.youtube.com/watch?v=W9G_f4qVyjs