KARL FRISTON - INTELLIGENCE 3.0

Designing Ecosystems of Intelligence from First Principles

In this video, Professor Carl Friston discusses his paper on designing ecosystems of intelligence from first principles. He explains the active inference principle and how it can be used to create intelligent systems that learn from their observations and update their beliefs accordingly. He also proposes a research agenda for the next decade and beyond to design such ecosystems of intelligence from scratch.

Active Inference Principle

• Intelligent systems are those which can accumulate evidence for a generative model of their sensed world.

• They can learn from their observations and update their beliefs accordingly.

• Ensembles of agents can share beliefs and cooperate through communication protocols.

• This leads to a formal account of collective intelligence that rests on shared narratives and goals.

Research Agenda

• Develop a shared hyperspatial modeling language and transaction protocols.

• Novel methods for measuring and optimizing collective intelligence.

Importance

• Offers a way to harness the power of artificial intelligence for the common good without compromising human dignity or autonomy.

• Challenges us to rethink our relationship with technology, nature, and each other.

• Invites us to join in a global community of sense makers who are curious about the world and eager to improve it.

The Free Energy Principle

In this section, Professor Friston talks about the free energy principle. He explains how it has the potential to reshape how we view the connection between inanimate matter and living things.

Free Energy Principle

• It has been dismissed as a triviality or even tautology and on the other hailed as revolutionary.

• It has the potential to reshape how we view the connection between inanimate matter and living things.

• It can answer the question of how and why consciousness and intelligence might emerge from physical matter and processes.

Flow Dynamics

• All things from particles to people to the largest systems all move or evolve according to two processes which combine.

• The first is a smooth flowing evolution, while the second is a random process that knocks around the smooth flow in unpredictable ways.

• These two very different effects combine into a chaotic flow which may be entangled in a kind of tropical storm while still maintaining a semblance of structure and things.

Markov Blanket

• Things which are defined by a Markov blanket must always move towards a pullback attractor, which maintains their coherence and identity over time.

• The dynamics of such a system manifest as Bayesian active inference, where it maintains an internal equivalent of a generative model encoding beliefs about itself and the world.

Conclusion

In this section, Professor Friston concludes his talk by discussing how simple ideas can have far-reaching consequences. He encourages us to continue exploring these ideas with curiosity and eagerness.

Simple Ideas

• Our shared human journey is filled with examples of simple ideas that were nonetheless hard to discover.

• Some remain hard to comprehend despite being explained due to their subtle simplicity belies their far-reaching and deep consequences.

• Examples include relativity, quantum mechanics, parsimony, entropy, and now free energy principle.

Curiosity

• Professor Friston encourages us to join in a global community of sense makers who are curious about the world and eager to improve it.

Introduction to the Free Energy Principle and Numerai

In this section, Professor Karl Friston introduces the free energy principle and its application to multi-scale active inference. He also thanks Numerai for sponsoring the episode and provides an overview of their data science competition platform.

The Free Energy Principle

• The free energy principle is a general principle that applies to all scales of size and time.

• It leads to an ecosystem of things interacting across scales, which may help us find the keys to a mathematics of emergence and consciousness.

Introduction to Numerai

• Numerai is a data science competition platform that predicts the stock market.

• They have paid out over 50 million dollars for 5,000 models on their platform.

• Their platform provides many years of backtesting data as well as statistical diagnostics.

• Users can submit predictions weekly or daily either manually or automatically.

• Staking is a vote of confidence in your model, which prevents overfitting and reduces bad intelligence from contributing to their aggregate.

Designing Ecosystems of Intelligence from First Principles

In this section, Professor Karl Friston discusses his recent paper "Designing Ecosystems of Intelligence from First Principles" which lays out a vision for research and development in artificial intelligence for the next decade.

Vision for Research and Development in AI

• The paper lays out a vision for research and development in AI for the next decade.

• This vision is premised on active inference, which formulates adaptive behavior that can be read as the physics of intelligence.

• The vision includes a cyber physical ecosystem of natural and synthetic sense-making in which humans are integral participants called shared intelligence.

Active Inference and Existential Imperative

This section discusses the concept of active inference and existential imperative in intelligent systems. The focus is on how belief sharing in ensembles of agents can provide a common ground or frame of reference.

Understanding Active Inference and Existential Imperative

• Active inference is also known as self-evidencing over multiple scales, which foregrounds the existential imperative of intelligent systems.

• The existential imperative refers to curiosity or the resolution of uncertainty that underwrites belief sharing in ensembles of agents.

• Certain aspects or factors of each agent's generative model provide common ground or a frame of reference.

Application in Industry

• The white paper was a response to think seriously and pragmatically about how this theoretical approach would play out in industry.

• The emphasis was on what it means to exist and how that would manifest in terms of intelligent behavior.

• Intelligence is seen as the kind of inference needed to maintain one's existence, hence the existential imperative.

Distributed Intelligence and Belief Sharing

• We move from thinking about a single thing to having lots of things talking to each other, which leads us into distributed intelligence and cognition.

• Belief sharing becomes crucial when we have lots of things talking to each other, just like humans do through language.

• Curiosity is central to identifying necessary behaviors for truly intelligent or sentient behavior.

The Future of AI and IA

In this section, the speakers discuss the future of AI and IA, taking into account the shift from the Industrial Age to the age of information. They explore how technology and infrastructure can be used to realize shared intelligence.

The Philosophy of Information

• Luciano Floridi's philosophy of information discusses the infosphere and third-order technology like Amazon and Facebook.

• Our digital identity has already been distributed, leading to infinite fractionation in the info sphere.

• Humanity is no longer the substrate; information is now the substrate.

Distributed Network of Intelligent Systems

• The AI age may end up being a distributed network of intelligent systems that interact frictionlessly in real-time.

• Nodes in this ecosystem may be human users as well as human-designed artifacts that embody or implement forms of intelligence.

Technology Enmeshment

• Being ensconced and enmeshed in technology to such a high degree is truncating our very existence.

• Information is now a first-class citizen in our society, reducing human control, responsibility, self-determination, and devaluing human skills.

• However, from a physicist's perspective, it's all about information. It's all about realizing potentials by minimizing certain potentials like self-information.

Realizations of Information

• At each level (quantum physics), it boils down to information - probability of being in a particular state.

• We are just realizations of information.

The Role of Information in Humanity

In this section, the speakers discuss the role of information in humanity and whether humans are special.

Humans as Special Beings

• 3D believes that information is becoming the primary substrate, but he thinks that humans are special because we have autonomy and choose our own actions.

• Humans cannot be replicated in silicone, so there's no such thing as general intelligence. Instead, there are just algorithms that perform skills while our humanity is being truncated.

• There is a bright line between a very clever thermostat or some machine learning artifact and humans. It's the autonomy, agency, ability to plan, and all existential imperatives underwriting that planning.

Curiosity as a Definitive Aspect of Humanity

• Curiosity is a definitive aspect of humanity not found in other kinds of intelligence like Siri or Google Maps.

• Belief sharing requires sentient artifacts equipped with curiosity. They will be curious about us since we populate their world.

Information vs. Belief Updating

In this section, the speakers discuss whether information or belief updating is king.

Information Is Not King

• The idea that information is king may be quite bad for humanity according to 3D.

• Relief updating is king since it ensues once you act upon the world to do some smart data mining to respond to some epistemic affordances.

• The optimization framing does not have baked-in universally expected information gain and curiosity.

Active Learning Using Machine Learning

• Active learning using machine learning has the potential to equip systems with belief updating, but it's not an explicit part of the design for the age of intelligence.

Shared Intelligence and Distributed Intelligence

In this section, the speakers discuss shared intelligence and distributed intelligence.

The Brain Analogy

• The analogy of the brain is helpful in explaining shared intelligence and distributed intelligence.

• Neurons are really smart little elements that make up a smart system.

Emergent Intelligence

In this section, the speaker discusses how lots of little smart things together can create emergent intelligence. The emergence of intelligence is dependent on getting these little smart things to talk to each other in the right way.

Shared Intelligence

• Emergent behavior arises from getting lots of little smart things to talk to each other in the right kind of way.

• Belief sharing is already present in technology such as Sat Nav.

• The move is towards making belief sharing a much more symmetric interaction between users and apps.

Definition of Intelligence

• Shane Legg's definition of intelligence is the ability of an agent to solve a variety of tasks in different environments.

• Francois Chalet's definition is efficiently creating abstractions given limited prize and experience.

• Pay Wang's definition is adaptation efficiency over finite resources.

• The speaker's definition focuses on physics-based information geometries and belief updating.

Essential Aspects of Intelligence

• All definitions touch upon essential aspects of intelligence that would emerge if any self-organizing system managed to coexist with other self-organizing systems.

Introduction to Biological Intelligence

In this section, the speaker discusses the concept of biological intelligence and how it differs from other forms of intelligence.

What is Biological Intelligence?

• Biological intelligence refers to the ability of living organisms to self-organize and adapt to their environment.

• Biotic self-organization rests upon the same mechanics as other forms of intelligence, but it is far removed from psychological or anthropomorphic intelligence.

• The difference between basal cognition and biotic self-organization lies in the ability to imagine counterfactual futures or have a world model that explains things beyond the present moment.

• Evolutionary processes are an example of belief updating and intelligent information accumulation, but they do not involve planning for future outcomes.

Trajectories and Sense-Making

In this section, the speaker discusses trajectories and sense-making as key aspects of biological intelligence.

Trajectories

• Trajectories are paths through time that cannot be localized to a single point in time. They necessarily entail both past and future events.

• Information geometry, autonomy, and emergent properties all rely on trajectories rather than states.

Sense-Making

• Sense-making involves selecting among different futures or considering abstractions about what might happen if certain actions are taken.

• Curiosity requires imagining possible futures before they occur. This distinguishes anthropomorphic intelligence from other forms of intelligence found in nature.

Action, Knowing, Planning

In this section, the speaker discusses action, knowing, and planning as additional aspects of biological intelligence.

Action

• Action involves the ability to act on the environment in a goal-directed manner.

Knowing

• Knowing involves having cognitive priors that allow for understanding concepts like agency and intentionality.

Planning

• No specific bullet points provided.

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The Universalist Idea

In this section, the speaker discusses the idea that there might be a single algorithm which underpins intelligence with the brain acting like a massive TPU repeating instructions ad nauseam to generate complex behavior.

The Power of Scale and Size

• Professor Christopher Summerfield suggests that the success of mammalian brains is not due to any careful crafting into a mosaic of different functional subsystems but instead is merely due to size.

• There's a powerful relationship between the sheer number of neurons and the complexity of behavior.

• Researchers such as Carl Friston, Jeff Hawkins, and Andrew have flirted with the idea that there might be a single algorithm which underpins intelligence.

Emergent Intelligence

• Jeff Hawkins has proposed his "thousand brains theory" of intelligence where there's a very simple underlying algorithm or principle replicated at successive scales producing emergent intelligence.

• The speaker agrees with this Universalist idea.

Structural Learning

In this section, the speaker talks about structural learning from both machine learning and radical constructivist perspectives.

Pressing Issues About Structures and Structural Learning

• Handcrafting structures architectures doesn't scale well at bottlenecks.

• A Universalist approach seeks one principle redeployed at successive scales sufficient for explaining emergent behaviors at particular scales.

Scale-Free Universalism

• Renormalization group theory describes how Dynamics can be summarized in terms of say A lagrangian so that if I take lots and lots of little things um and I start coarse grading them in a particular way then if I want to describe the behavior of all elements at one scale then I should be able to recapitulate it at every level.

• From this point of view, you have a particular kind of universalism that is actually scale-free because you get the same principle emerging at every level.

• The speaker deploys the free energy principle at different scales, including dendritic self-organization, Neuroscience, and morphogenesis and cellular pattern formation.

Coupling Between Levels

• The interesting game comes between the coupling between levels where one level constrains an inform or contextualizes the level below and vice versa.

Self-Organization and Natural Selection

In this section, the speaker discusses how natural selection and active inference principles provide constraints on self-organization in a population. The conversation also touches on how behavior, experience-dependent plasticity, mind evidence accumulation, and decision-making contribute to the gene pool at an evolutionary level.

Constraints on Self-Organization

• Sampling random from a population scales of free energy self-autopoietic process

• Top-down conversation: developmental time for any given phenotype

• Bottom-up conversation: behavior, experience-dependent plasticity, mind evidence accumulation, decision-making contribute to the gene pool at an evolutionary level

• Anything that is adaptive and has size must comply with the principles of self-evidencing where evidence is marginal likelihood that can always be read as accuracy minus complexity

Graphical Architecture and Hierarchical Scales

This section focuses on graphical architecture and hierarchical scales. The speaker explains that sparsity defines the nature of a graph and that anything deep in a hierarchical sense means there's a particular kind of graph with a certain sparsity structure.

Sparsity Structure

• Interesting structure in virtue of sparsity or connections not there otherwise it's a full graph

• Crucially it's a sparsity structure that allows me to call it hierarchy

• Renormalization group evolution getting right graphical architecture on message passing scheme and computer design

Emergence Ladder and Causal Pressures Between Scales

In this section, the speaker discusses top-down causation and the emergence ladder. The conversation touches on how different scales of a Bayesian mechanic self-organization viewed through the lens of basic mechanics assemble.

Causal Pressures Between Scales

• Different scales of a Bayesian mechanic self-organization viewed through the lens of basic mechanics

• Top-down causation exactly in the spirit that George Alexander writes about it

• Emergent phenomenon and causal pressures between scales

• Single cells assemble at spatial scales

The Universalism of Free Energy Minimization

In this section, the speaker discusses how free energy minimization is a universal principle that applies to different scales and contexts.

Levels of Spatial Scale and Temporal Scales

• Natural selection requires sacrificing oneself for the greater good.

• Free energy minimization is a universal principle that applies to different scales and contexts.

• Learning is slow belief updating where states matter now are equipped with another kind of label called parameters or weights in machine learning in a neural network.

• Structure learning plays out over years, but it's exactly the same principle as free energy minimization.

Information Geometry and Basal Intelligence

• The conservation of exactly the same principles that have this information geometry and implicitly intelligence in of a basal sort.

Boundaries and Markov Blankets

In this section, the speakers discuss the concept of boundaries and Markov blankets in relation to complex systems.

Emergence over Time and Self-Organization over Space

• Emergence over time and self-organization over space are key concepts when discussing structural learning.

• The speakers question whether we are only interested in time and space when talking about these concepts.

Three-Dimensional Markov Boundary

• The concept of a three-dimensional Markov boundary is introduced.

• The speakers discuss how this could be applied to modeling complex systems.

Identifying Markov Blankets

• The importance of identifying Markov blankets is discussed.

• One approach is to define the temporal scale at which they exist.

Hierarchy of Blankets

• The idea of a hierarchy of blankets is introduced.

• The speakers explore whether blankets could be observer-relative or simply a lens for describing reality.

Understanding Markov Blankets

In this section, the speaker discusses the concept of a Markov blanket and how it can be understood in the context of one's perspective.

Definition of a Markov Blanket

• A Markov blanket is defined by a particular sparsity structure when formulating any state space in terms of Dynamics.

• The free energy principle commits to the notion that there are states and that those states have a separation of temporal scales that disambiguates or separates systemic States from random fluctuations.

• For sufficiently large systems, the probability of there not being a Markov blanket is zero.

Observer Dependent Perspective

• If you're part of the system, then there are no observer-dependent perspectives using Markov blankets as an epistemological device.

• If you're being observed by something else, then those observers will never know your internal states.

• At the microscopic scale, there are still affordances and information sharing in complex systems.

Dynamics of Markov Blankets

• The formation of coalitions and in-groups can be articulated in terms of wandering Markov blankets.

• The sparsity structure and who one talks to or listens to can all be described using wandering Markov blankets.

What is it like to be something?

In this section, the speakers discuss the concept of "being something" and how it relates to Markov blankets. They also touch on the idea of simulating cells or people using Markov blankets.

Being Something

• "Being something" means that someone is asking about another thing, which is unanswerable and unknowable.

• Markov blankets are used as a device to understand self-organization, simulate it, and predict it.

• Hard-coding the Markov blankets would bottleneck the system. The system should be defined at a sufficient resolution where all of this could emerge itself.

Spatial Temporal Contiguity

• The question of whether Markov blankets must have spatial-temporal contiguity arises. Discontinuous blankets in information geometry are interesting because they can be visualized in 3D space.

• Place cells and grid cells suggest continuity or contiguity aspect.

Routing Information

• The hierarchy could also be described in terms of Markov blankets within Markov blankets within Markov blankets.

• Designing a web with routing and context-sensitive information processing is important for adaptive context-sensitive machines.

• Using Markov blankets as implicit nodes has enormous implications for minimizing complexity of message passing and defining hierarchy.

Special Connectivity

• There will be special kinds of connectivity such as place fields that can best be understood practically from the point of view of graph theory rather than information geometry.

Architectural Principles and Metric Spaces

In this section, the speakers discuss the architectural principles of intelligent machines and how they relate to metric spaces. They also touch on the idea of contiguity in metric spaces and its relevance to building thinking machines.

Sparsity Fit for Explaining Data

• To minimize complexity in free energy or negative log marginal likelihood, finding the right sparsity fit is crucial.

• The translational invariance of translation symmetry is an important architectural principle that relates to contiguity in metric spaces.

• Some worlds recapitulated by intelligent machines have a contiguity property due to elaboration of aspects of sensorion causes in a metric space.

Topological Non-Metric Representation

• As hierarchies get deeper, there is a move from architectures that emphasize metrics to more discretized topological non-metric representations.

• Part-whole relationships can nest into hierarchies, which connects with the philosophical study of myriology.

Inductive Biases and Part-Whole Relationships

In this section, the speakers discuss inductive biases and their role in minimizing complexity. They also touch on part-whole relationships and their importance as an inductive prior.

Inductive Biases Minimizing Complexity

• Inductive biases are crucial for minimizing complexity.

• Most inductive priors reduce hypothesis set size but not too much as it leads to approximation error.

Part-Whole Relationships as Inductive Prior

• Myriology emphasizes relations between entities while set theory focuses on the membership relation between a set and its elements.

• Part-whole relationships are an interesting inductive prior that reduces complexity. Hinton's glom architecture is an example of this.

Models and Optimization

In this section, the speaker discusses how to carve up evidence complexity and accuracy in models. He talks about optimization and providing the simplest account that maintains a degree of accuracy.

Carving Up Evidence Complexity and Accuracy

• The speaker discusses how to carve up evidence complexity and accuracy in models.

• He talks about optimization and providing the simplest account that maintains a degree of accuracy.

• The speaker explains finding the right structure for describing data using structural priors.

• He mentions that much of evolution and machine learning architectures is finding the right structure apt for describing data.

Structural Priors

In this section, the speaker talks about graphical models or implicit factor graphs when it comes to implementation. He also discusses orthogonal direction from hierarchical composition.

Graphical Models or Implicit Factor Graphs

• The speaker talks about graphical models or implicit factor graphs when it comes to implementation.

• He mentions that orthogonal direction from hierarchical composition is important in terms of breadth of a model.

• The speaker explains conditional independencies within any one particular scale.

Separability in Models

In this section, the speaker discusses separability in models. He also talks about factors that can be separated for scene construction.

Separability in Models

• The speaker discusses separability in models.

• He mentions factors that can be separated for scene construction.

• The speaker explains how heavy lifting is done from the mapping between the levels.

Non-Linearity in Models

In this section, the speaker talks about non-linearity in models. He also discusses structure learning and evolutionary process.

Non-Linearity in Models

• The speaker talks about non-linearity in models.

• He mentions that structure learning will be finessed.

• The speaker explains how to accommodate non-linearity and how evolution of machine learning architectures is an evolutionary process.

An Activism and Representationalism

In this section, the speaker contrasts an activism with representationalism. They discuss how the brain is not just a behaviorist thing but rather a dance of dialogue between the agent and the environment in a cybernetic loop.

An Activism

• An activism is at the heart of circular causality that follows from the existence of a Markov blanket.

• The speaker gives an example of radical inactivism where representationalism can be dispensed entirely. They use the example of a walking robot that fell down a hill gracefully without needing cognition.

• The body's tuning to its environment eliminates the need for cognition, leading to circular causality.

• When applying free energy principle to emulate or simulate sentient behavior predicated on sense-making, one necessarily simulates action perception cycles, making it inactive inference.

Decomposition of Cognition

In this section, the speaker discusses their thoughts on decomposing cognition into thinking, feeling, knowing, acting, and environment.

Decomposition of Cognition

• Thinking, feeling, knowing, acting are key components that distinguish their line of thought.

• A Markov blanket is necessary for something to be demarcated or individuated from something else. This requires bi-directional traffic between agent sensing and acting upon its environment.

• Circular causality arises when there are two directions of travel: agent sensing its environment and agent acting upon its environment or vice versa.

• Free energy principle applied to emulate or simulate sentient behavior predicated on sense-making necessarily simulates action perception cycles, making it inactive inference.

Active Inference

In this section, the speaker discusses active inference and its relationship to smart data mining.

Active Inference

• Active inference is at the heart of applications of the free energy principle.

• The use of inactive is at the heart of all right-minded formulations of behavior and self-organization since Plato.

• Smart data mining changes the nature of the game from big data making sense of big data to small agile intelligent systems that move on the world to get the right kind of data that will serve their imperatives.

• Imperatives are to maximize evidence for models in the world.

Small Smart Things and Inactivism vs Representationalism

The speaker discusses the importance of small smart things that can gather their own data to resolve uncertainty about their context. They also explore the continuum between inactivism and representationalism, noting that good inactivists need the right representations to plan intelligently.

Small Smart Things

• Small smart things are important because they can actively gather the right kind of data to resolve uncertainty about their context.

• These agents must minimize complexity and expertly handle data to be effective.

• Good scientists design experiments for these agents to get smart data that resolves uncertainties.

Inactivism vs Representationalism

• Radical inactivism is a philosophical concept that denies representationalism, which is necessary for planning and imagining scenarios.

• The speaker notes that chat GTP is an example of mindless enacted sense-making, which is opposite to radical inactivism.

• The speaker argues that good inactivists need the right representations if they want to plan intelligently.

• As organisms move up scales, they need more planning abilities and therefore require representations.

Grounding Cognition

• Cognition must be grounded in different domains such as physical world, language, acting, affordances, and knowledge.

• If organisms ground cognition in affordances, then it raises questions about how much they are learning a world model through this lens.

• To build counterfactual plans expertly requires representing causal contingencies and states of affairs upon which you're predicating your next action.

• Many of the abstractions that humans learn are not grounded in the physical world, but they still require representations for planning.

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The Role of Markov Blanket in Communication

In this section, the speaker discusses how the Markov blanket acts as a holographic screen that facilitates communication between two agents. The messages written on the screen must have the same interpretation to achieve mutual understanding.

The Role of Markov Blanket in Communication

• The Markov blanket acts as a holographic screen for communication.

• Messages written on the screen must have the same interpretation for mutual understanding.

• A good model of the world is necessary for consistency between both sides of the screen or server.

• There is a circular causality between knowledge and state estimation.

Generalized Synchrony and Entanglement

This section focuses on generalized synchrony and entanglement. It explains how sparsely coupled sets converge on synchronization manifold by maximizing marginal likelihood and minimizing free energy.

Generalized Synchrony and Entanglement

• Sparsely coupled sets converge on synchronization manifold by maximizing marginal likelihood and minimizing free energy.

• Uncertainty resolution is an existential imperative to converge on shared models.

• Good models are necessary for state estimation, which requires inference, while state estimation is necessary for learning.

Intelligence, Learning, and Knowledge Acquisition

This section discusses intelligence, learning, and knowledge acquisition. It explains how belief updating is a process of intelligence that depends upon learning at different time scales.

Intelligence, Learning, and Knowledge Acquisition

• Belief updating is a process of intelligence that depends upon learning at different time scales.

• The right neural network and inference are necessary for learning and state estimation, respectively.

• There is a circular causality between knowledge acquisition and state estimation.

Chat GPT

This section discusses the speaker's journey with Chat GPT. It explains how it became useful for coding and generating emails.

Chat GPT

• Chat GPT became useful for coding and generating emails.

• DaVinci 2 transgressed the anthropomorphic Fooled by Randomness threshold.

Introduction to GPT and its Generative Model

In this section, the speaker introduces GPT and its generative model. They discuss the problems with GPT's generation and how it can be verified.

GPT's Self-Attention Decoder Transformer

• GPT uses a self-attention decoder Transformer, which is a neural network architecture that introduces permutation invariance to Tuple permutation invariance.

• This architecture turns out to be extremely useful for language.

In Context Learning

• In context learning is where rather than just generating token by token, you insert a prompt and then continue to generate from that prompt.

• People discovered that they could ask it questions, and it had an emergent reasoning capability.

Preference Fine Tuning

• Preference fine-tuning is when additional supervision on the top with human reference examples aligns it to humans and makes it give slightly more politically correct or sensical answers.

• It has been integrated into GPT, which does retrieval augmented generation.

Bing's Successful Launch

• Bing had a successful launch of retrieval augmented generation but generated results that weren't even in the document.

• The product managers at Microsoft didn't bother checking the truthfulness of this generation.

Accessibility of Chat GTP

In this section, the speaker discusses why Chat GTP was so important and how it changed the landscape of AI accessibility.

Participatory Aspect

• The participatory aspect of belief sharing among lots of smart agents including ourselves is what made Chat GTP so attractive.

• It has changed the landscape in terms of selling AI to investors.

Generative AI

• Generative AI is what Chat GTP brought to the table.

• It generates the kind of stuff that we see and is an interpolation machine.

Dyadic Interaction

• The interesting bit about generative AI is when you have the opportunity to select from the stuff it generates.

• This dyadic interaction between humans and generative AI is what makes it so attractive.

Generative AI and Language Models

In this section, the speaker discusses generative AI and language models. The speaker explains how generative AI generates content in data space and how it is different from generating beliefs like Google Maps. The speaker also talks about the importance of language in generative AI and large language models.

Generative AI Generates Content in Data Space

• Generative AI generates content in data space.

• It generates sensations, data, and stuff that has been mined in the space that the mining took place.

• It does not need to understand because its purpose is to generate content.

Importance of Language in Generative AI

• Language is a distillation of our knowledge of our world.

• Generative AI generates content in the context of language which speaks to knowledge.

• Large language models generate content that is a materialized snapshot of our sense-making, abstractions, and world knowledge.

Prompt Engineering and Conversational Interface

• Prompt engineering allows for an interactive process between humans and machines.

• As a conversational interface, large language models are very powerful.

Misinformation Problem with Large Language Models

• Large language models pollute the info sphere with misinformation and false news.

• People will not bother fact-checking information generated by machines.

• There is ambivalence about anthropomorphizing content generated by generative AI.

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