1/n Visual reasoning is still a weakness of existing AI. O1-pro will fail in even the easier of visual Bongard problems. Here's one easy example. Try it out yourself. 2/n It seems that the visual vocabulary of LLMs and LRMs are not up to par with humans (yet). Humans have an advantage because we are embodied in a 3D interactive world. But that opens up a question about realities that we are not natively immersed in. We lack intuition about worlds of different dimensions and physics.

1/n Okay! Why the hell is reasoning unpredictable?! Read the thread. 🧵 2/n Here's a detailed explanation.

Beyond Pattern Recognition:
Traditional “intuitive” deep learning, such as image classification or language modeling, relies on pattern-matching within a well-defined set of categories. These processes are relatively predictable because they map from known inputs to learned outputs. In contrast, reasoning involves constructing multi-step chains of inference, exploring conceptual relationships, and generating new ideas. This goes beyond replaying patterns and enters the realm of open-ended problem-solving, where each step can introduce unprecedented solutions or angles.

Computational Irreducibility:
Stephen Wolfram’s notion of computational irreducibility suggests that for certain complex processes, there is no shortcut to knowing the outcome other than simulating each step. When reasoning paths are combinatorially vast and non-linear, predicting the endpoint from the start is infeasible. The system must “run” through each reasoning step, making the final answer inherently hard to anticipate. Even tiny changes in initial conditions or inputs can shift the chain of logic in unpredictable ways.

Unprestateability of Future Possibilities:
Stuart Kauffman’s concept of unprestateability points out that in complex, evolving systems—like ecosystems or creative intellectual processes—we cannot fully specify the possible future states in advance. Similarly, a reasoning AI may generate not just unexpected answers, but entirely new conceptual categories or approaches as it proceeds. It’s not merely that we don’t know which option it will pick from a known set—we don’t even know all the kinds of options that could emerge. The reasoning process can expand and redefine its own possibility space as it unfolds, making the range of outcomes inherently open-ended.

Novelty and Creative Emergence:
As AIs engage in open-ended reasoning, they can produce novel moves, analogies, or solutions that go beyond human intuition or historical examples. Just as top-tier AI chess or Go programs devise strategies that baffle human grandmasters, advanced reasoning systems can innovate in ways we can’t predict. These moves are not random; they are lawful outcomes of the AI’s internal reasoning. But from a human perspective, they arise from such a rich and evolving internal logic that we cannot easily foresee them.

No Closed-Form Solutions:
Taken together, these factors mean there’s no simple, closed-form formula or quick analytical method to predict how a reasoning process will unfold. Its complexity (computational irreducibility) and the creation of new conceptual possibilities (unprestateability) ensure that the path from input to conclusion remains intrinsically uncertain. Such reasoning processes are not just complicated; they are fundamentally open-ended, evolving in ways that cannot be fully described, let alone anticipated, beforehand.

In short, reasoning’s unpredictability emerges because it transcends static pattern recognition, drawing on open-ended exploration, generating novel conceptual spaces, and unfolding in a manner that defies any shortcut analysis. It’s this creative, evolving, and fundamentally irreducible nature that makes advanced AI reasoning so difficult to predict.

1/n If Agentic AI is truly going to blow up the Business Logic tier of conventional applications, then what does that truly mean in practice? 🧵

https://twitter.com/rohanpaul_ai/status/1867897514755805382

2/n For reference, here's ChatGPT o1-pro explanation of the Business Logic tier:

Business Logic/Domain Layer
Role and Responsibilities:
The business logic layer encapsulates the core rules, calculations, transformations, and operations that define the application's specific use cases and domain logic. This is where the application’s “intelligence” resides, independent of how data is stored or how users interact with the system.

Key aspects include:

Implementing Business Rules: Enforcing domain constraints, validation, and computation. For example, ensuring that a customer’s order cannot exceed certain credit limits, or applying tax calculations to an invoice.

Process Orchestration: Coordinating multiple steps that compose a business transaction. For example, creating a new customer might also involve sending a welcome email, generating a record in a CRM system, and logging an event.

Domain Model Integrity: Using domain models (e.g., objects/classes that represent entities and value objects in the business domain) to keep domain concepts coherent. This might follow Domain-Driven Design (DDD) principles, focusing on ubiquitous language and bounded contexts.

Interaction with Data Layer: The business layer uses the data layer to persist or retrieve domain objects. However, it shields the rest of the system from knowing how data is stored, focusing only on what data is needed and what operations to perform.

Motivations for a Distinct Business Logic Layer:
Separation of Concerns: By isolating business rules from storage details and UI specifics, the logic can evolve independently. Changes in business policy or calculations can be implemented without disrupting how data is accessed or how the interface looks.

Testability: The business logic layer can be tested thoroughly without worrying about the external environment (database or user interface). This leads to better and more maintainable tests.

Flexibility: If you later switch from a web UI to a mobile app, the business rules remain the same. You just plug in a different UI layer. Likewise, changes in how data is stored do not require changes in the business rules.

Narcissism is to incoherence, as empathy is to coherence. Narcissists conjure up coherence via denial (i.e., artificial ignorance). Narcissism and coherence manifest differently at various societal and individual maturity levels.

1/n Rethinking LLM Pipelines for Complex Documents: The DocETL Framework for Agentic Optimization and Evaluation.

The deluge of unstructured data—text, documents, emails, social media posts—presents a tantalizing opportunity and a daunting challenge. Locked within this sea of information lie invaluable insights, waiting to be unearthed. Large Language Models (LLMs) have emerged as powerful tools for navigating this landscape, yet their application to complex document processing has been hampered by a critical flaw: accuracy. Existing systems prioritize cost-efficiency, assuming that user-defined LLM operations are inherently precise. This assumption, however, crumbles in the face of real-world complexity, where lengthy documents and nuanced tasks often lead to incomplete or erroneous results. Enter DocETL, a groundbreaking system that not only acknowledges this limitation but actively overcomes it, ushering in a new era of accurate and efficient unstructured data analysis.

DocETL’s value lies in its recognition that LLMs, while powerful, are not infallible oracles. Instead of blindly executing user-defined operations, DocETL employs an “agentic” approach, leveraging the power of LLMs to optimize the very process of analysis. Imagine a conductor leading an orchestra, not just playing the notes but dynamically adjusting the score to bring out the richest harmonies. DocETL acts as this conductor, using novel “rewrite directives” to decompose complex tasks into a sequence of simpler, more manageable operations. This intelligent rewriting, guided by the LLM itself, dramatically improves accuracy, ensuring that the insights extracted are both comprehensive and reliable.

Furthermore, DocETL doesn't stop at rewriting. It employs a second layer of agentic intelligence to evaluate the effectiveness of the generated plans. This evaluation isn't based on pre-defined rules or user-provided examples, but on automatically synthesized, task-specific validation prompts. The system, in essence, learns how to assess its own performance, constantly refining its approach to achieve optimal results. This self-improving loop is a testament to the power of DocETL’s innovative design.

The efficiency of DocETL is equally impressive. Recognizing the time-sensitive nature of LLM operations, the system employs an “opportunistic” optimization strategy. It doesn't waste resources exploring every possible plan; instead, it focuses its efforts on the areas most likely to benefit from rewriting, recursively optimizing sub-plans only when necessary. This targeted approach avoids the combinatorial explosion that plagues traditional optimization methods, ensuring that the system remains both powerful and practical.

Beyond its core innovations, DocETL offers a suite of specialized operators designed specifically for the challenges of document processing. The Resolve operator tackles the thorny issue of entity resolution, consolidating mentions of the same entity across different documents or within a single, complex text. The Gather operator addresses the context limitations of LLMs by providing surrounding information to each chunk of a large document, ensuring that the analysis remains grounded in the broader narrative.

Finally, DocETL’s declarative YAML interface and built-in fault tolerance make it a user-friendly and robust system. Users can define complex pipelines with ease, while the system’s context-aware retry mechanism ensures reliable performance even in the face of occasional LLM hiccups.

In conclusion, DocETL represents a paradigm shift in unstructured data analysis. By embracing an agentic approach to both rewriting and evaluation, it unlocks the true potential of LLMs, delivering accuracy and efficiency that were previously unattainable. This is not merely an incremental improvement; it’s a fundamental change in how we approach the challenge of extracting meaning from the ever-growing sea of unstructured data. DocETL is not just processing documents; it’s orchestrating a symphony of information, revealing the hidden melodies within. 2/n Features

DocETL offers several unique values compared to other LLM-based document processing systems:

Focus on Accuracy through Agentic Rewriting: Unlike systems that prioritize cost reduction while assuming user-provided operations are accurate, DocETL actively improves accuracy. It uses an agent-based framework with novel rewrite directives to decompose complex operations into simpler, more accurate ones. This addresses the inherent limitations of LLMs in handling complex tasks and data.

Agentic Plan Evaluation with Synthesized Validation: DocETL employs agents not only for rewriting but also for evaluating the generated plans. It automatically synthesizes task-specific validation prompts, eliminating the need for users to provide or manually validate examples. This data-driven and task-driven evaluation ensures that the chosen plan is effective for the specific context.

Opportunistic Optimization for Efficiency: Recognizing the time constraints of LLM operations, DocETL adopts an opportunistic optimization strategy. It recursively optimizes sub-plans only when necessary, focusing on the parts of the pipeline that are most likely to benefit from rewriting. This avoids the combinatorial explosion of evaluating all possible plans, making the optimization process more efficient.

Specialized Operators for Complex Document Processing: DocETL introduces operators like Resolve (for entity resolution) and Gather (for context enrichment), which are specifically designed to address common challenges in document processing. These operators go beyond the standard map-reduce paradigm and provide a more tailored approach to handling unstructured data.

Declarative YAML Interface with Fault Tolerance: DocETL offers a user-friendly declarative interface using YAML, making it easier to define and manage complex pipelines. It also incorporates a context-aware retry mechanism for LLM operations, providing robustness against occasional failures and further improving the overall accuracy and reliability of the system.

In essence, DocETL combines the power of LLMs with a sophisticated optimization framework, specialized operators, and a user-friendly interface to deliver more accurate and efficient complex document processing than existing alternatives. Its focus on accuracy through agentic rewriting and evaluation sets it apart, addressing a crucial gap in current LLM-based data processing systems.

1/n DeepMind's paper where they used Stockfish (a programmed chess engine) to annotate data to train a LLM reveals how to distill artificial logic programs into artificial intuition engines. Surprisingly, the ELO rating of the student LLM surpassed that of the teacher program! But what I want to talk about in this tweetstorm is the idea of representations and blindspots. 2/n One discovery of this system is that the LLM was unaware of the repeat 3 move rule that leads to a draw. This is because the original system was trained on the current board state and without the history of moves. Thus the LLM was blind to how the board state arrived to its present state. In chess, that's not a problem except for the draw rule.

1/n Thinkers often fall into the valley of excessive reductionism in an attempt to explain complex phenomena. That is why we have statements like it's all neurons, it's all matrix multiplication to explain cognition. This is as informative as saying that the universe is made up of atoms or the universe is all computation. It doesn't explain or describe the behavior of higher emergent behavior. 2/n The wetness of water isn't explained by looking at H2O molecules. One doesn't recognize the dynamics of an entire forest by just looking at a tree or even every tree. It's the interaction of the whole that causes emergent behavior.

1/n Theories of consciousness and the mind are prone to human cognitive bias. It's extremely difficult for humans to come to the awareness that their thought processes will be entirely different from other people. Although we may converge to the same conclusion, our thought processes often will be unique. 2/n The theories that we are committed to will be the ones that are most intuitive to us. How can they not be? But we must gain the awareness that others think entirely different from us. Furthermore, we have a difficult time seeing this because we've habituated ourselves to our own thought processes that we cannot imagine entirely different ones.

1/n Terrence Tao, arguable the most gifted living mathematician has tried GPT-o1 and this is his verdict: "However, this was an improvement over previous models, whose capability was closer to an actually incompetent graduate student. It may only take one or two further iterations of improved capability (and integration with other tools, such as computer algebra packages and proof assistants) until the level of "competent graduate student" is reached." 2/n Here, Tao attempts to use o1 to formulate the problem in Lean (a math theorem prover). Placing blame on o1's ignorance of Lean's latest capabilities. Here's the link: chatgpt.com/share/bb0b1cfa…

1/n Why Even the Best LLMs Still Struggle with True Creative Writing

The rapid evolution of Large Language Models (LLMs) has fueled both excitement and apprehension. While their ability to mimic human language and generate coherent text is undeniable, a crucial question lingers: can AI truly be creative? The paper "Pron vs Prompt: Can LLMs Challenge World-Class Fiction Authors?" tackles this question head-on, exploring the nuanced realm of creative writing to assess whether LLMs can compete with the best human storytellers.

The paper identifies a key pain point in current AI research: the tendency to compare LLMs to average human writers. While exceeding average performance is notable, it doesn't address whether AI possesses the ingenuity and artistry of a master wordsmith. To bridge this gap, the researchers designed a unique experiment pitting GPT-4, a leading LLM, against Patricio Pron, an award-winning novelist. This head-to-head contest aimed to provide a definitive answer to whether AI can truly rival human creativity at its peak.

Previous research, while valuable, often focused on different aspects of AI and creative writing. Some explored human-AI collaboration, where AI tools assisted human writers, while others highlighted the limitations of LLMs in maintaining narrative coherence or generating truly original content. This paper distinguishes itself by focusing on autonomous LLM creative writing, directly comparing the output of GPT-4 to Pron's work without human intervention.

The experiment itself was elegantly designed. Both GPT-4 and Pron were tasked with generating movie titles and then writing synopses for all the titles generated. This ensured a symmetrical comparison, giving both contenders the same creative challenges. To evaluate the results, the researchers enlisted literary experts who used a rubric based on Boden's framework of creativity, assessing qualities like originality, attractiveness, and the distinct voice of the author.

The findings were revealing. Across all quality dimensions and in both English and Spanish, Patricio Pron consistently received significantly higher ratings. This suggests that while LLMs can produce grammatically correct and even engaging text, they still struggle to replicate the depth, nuance, and originality that characterize truly great creative writing.

Interestingly, the study also highlighted the importance of prompts in guiding LLM creativity. When GPT-4 wrote synopses based on titles provided by Pron, its performance, particularly in style and originality, significantly improved. This suggests that while LLMs may not yet be independent creative powerhouses, they can be valuable tools when guided by human ingenuity.

The study's findings offer a dose of reality amidst the hype surrounding AI. While LLMs have made impressive strides, they are not yet ready to replace human authors. The human spark of creativity, with its ability to weave compelling narratives, evoke emotions, and surprise readers with unexpected turns, remains a distinctly human trait. This is not to say that AI has no place in the creative process. As the study demonstrates, LLMs can be valuable partners, enhancing and augmenting human creativity. However, the role of the human author, with their unique perspective and mastery of language, remains secure, at least for now. 2/n Experiments and Noteworthy Results:

The paper conducts a two-stage experiment:

Stage 1: Title Generation:

Both GPT-4 and Patricio Pron were tasked with generating 30 movie titles each.

Stage 2: Synopsis Writing:

Both contenders wrote 600-word synopses for all 60 titles (their own and their opponent's).
GPT-4 was provided with a prompt that included information about Patricio Pron and emphasized the importance of creativity and literary value.

Evaluation:

Six literary experts (three for Spanish, three for English) assessed the synopses using a rubric based on Boden's framework of creativity, considering:
Attractiveness
Originality
Creativity
Critical Assessment
Own Voice (recognizable style)

Noteworthy Results:
Human Superiority: Patricio Pron consistently received significantly higher ratings across all quality dimensions in both Spanish and English, indicating that GPT-4, even in its advanced form, is not yet a match for a top human author in creative writing.

Prompt's Influence: GPT-4 performed significantly better when writing synopses based on titles provided by Patricio Pron, particularly in terms of style and originality. This highlights the importance of prompts in guiding LLM creativity.

Language Gap: GPT-4's creative writing was found to be stronger in English than in Spanish, suggesting a potential language bias in training data.

Recognizable Style: While GPT-4 was not explicitly constrained in terms of style, expert assessors were able to identify its writing with increasing accuracy over time, indicating the presence of detectable patterns in its output.

1/n How Agentic AI Can Learn Strategic Thinking Through Self-Improvement and Bi-Level Search

Large Language Models (LLMs) have demonstrated remarkable abilities in understanding and generating human-like text, but their capacity for strategic decision-making in complex environments has remained a challenge. This challenge is particularly evident in multi-agent games, where success hinges on anticipating and outmaneuvering opponents who are constantly adapting their own strategies. The "STRATEGIST" paper tackles this challenge head-on, proposing a novel framework that empowers LLMs to learn sophisticated strategic skills through a process of self-improvement and bi-level tree search.

Traditional approaches to LLM-based decision-making have often fallen short in these complex settings. Directly controlling actions with LLMs, while intuitive, becomes computationally infeasible as the number of possible actions explodes. Similarly, while LLM-based planning methods show promise, they often struggle to learn reusable strategies, instead focusing on planning at the individual action level. Reinforcement learning, while achieving superhuman performance in certain games, typically demands massive datasets and struggles to generalize across different domains.

STRATEGIST differentiates itself by focusing on the acquisition of high-level strategic skills rather than simply searching for the best action in every possible scenario. The framework centers around two key components:

High-Level Strategy Learning: Instead of directly selecting actions, the LLM learns to evaluate game states and generate effective dialogue strategies. This is achieved through:

Value Heuristics: The LLM learns functions that assess the favorability of different game states, allowing it to prioritize advantageous positions.
Dialogue Strategy Guides: Structured prompts guide the LLM in generating persuasive and strategically sound dialogue within the game, taking into account the social dynamics of the environment.

Low-Level Action Selection (MCTS):
To bridge the gap between strategic thinking and concrete actions, STRATEGIST employs Monte Carlo Tree Search (MCTS). This search method explores possible future game states, providing the LLM with more accurate estimates of state values and guiding it towards better immediate actions.

The learning process itself is driven by a continuous loop of self-play, reflection, and improvement. The LLM engages in simulated games, analyzes the outcomes to identify weaknesses in its strategies, and generates ideas for improvement. This reflective process is guided by examining key states where the LLM's predictions diverged from the actual simulation results. The most promising improvement ideas are then implemented, refining the LLM's value heuristics or dialogue guides.

The effectiveness of STRATEGIST is demonstrated through experiments on two distinct games: the strategic card game GOPS and the social deduction game Avalon. In both settings, STRATEGIST consistently outperforms baseline methods, showcasing the power of combining high-level strategy learning with low-level action planning. The results highlight the importance of both components, as removing either significantly diminishes performance.

The paper's findings offer compelling evidence for the potential of STRATEGIST to enhance LLM-based decision-making in complex, multi-agent environments. The framework's ability to learn generalizable strategic skills through self-improvement and search paves the way for LLMs to tackle increasingly sophisticated challenges in domains ranging from game playing to real-world strategic interactions. As LLMs continue to evolve, frameworks like STRATEGIST will be crucial in unlocking their full potential for strategic thinking and decision-making in our increasingly complex world. 2/n Comparision Other Methods

Direct LLM Control (e.g., SayCan, ReAct): These approaches directly use LLMs to select actions in a given state by prompting them with the current context.
Contrast: STRATEGIST argues that this is inefficient for complex games due to the vast action space. Instead, it advocates for learning higher-level strategic skills that guide action selection.

LLM-based Planning (e.g., Tree of Thoughts): These methods use LLMs to generate and reason over possible action sequences, often using tree search algorithms.
Contrast: While STRATEGIST also uses tree search (MCTS), it primarily focuses on learning reusable strategic skills (value heuristics, dialogue guides) rather than planning at the individual action level.

Reinforcement Learning (RL) for Games (e.g., AlphaGo, AlphaZero): RL methods have achieved superhuman performance in games, but they typically require massive amounts of training data and are often domain-specific.
Contrast: STRATEGIST leverages LLMs' existing world knowledge and reasoning abilities to learn effective strategies with less data. It also aims for more generalizable skills that can transfer across similar game environments.

1/n How Understanding Stateful Tools Advances Agentic AI

The rapid advancement of Large Language Models (LLMs) has ignited a wave of excitement and research into their potential for interacting with and manipulating the world around them. Imagine LLMs not just as eloquent conversationalists, but as capable agents, utilizing tools to complete tasks, answer questions, and even control physical systems. This exciting prospect, however, hinges on our ability to accurately evaluate and understand their tool-use capabilities. This is where existing benchmarks fall short, struggling to capture the nuances of real-world scenarios. The paper
from Apple "TOOLSANDBOX: A Stateful, Conversational, Interactive Evaluation Benchmark for LLM Tool Use Capabilities" directly addresses this pain point, introducing a novel benchmark that pushes the boundaries of LLM evaluation.

Previous benchmarks, while valuable, often simplified the evaluation process. They primarily focused on stateless tools, neglecting the complexities of mutable world states. Single-turn interactions were the norm, failing to capture the dynamic back-and-forth of natural conversations. This is where TOOLSANDBOX diverges. It embraces the complexity of real-world tool use by incorporating stateful tools that interact with a dynamic world state. This allows researchers to assess an LLM's ability to understand, track, and manipulate this state to achieve its goals.

Furthermore, TOOLSANDBOX moves beyond static, single-turn interactions by introducing an LLM-based user simulator. This simulator, enhanced by "Knowledge Boundary" and "Demonstration" prompting techniques, enables realistic, multi-turn conversations, pushing LLMs to comprehend implicit information and adapt to evolving dialogues. This on-policy evaluation, where the LLM's actions directly influence the interaction, provides a more accurate representation of its true capabilities.

The experiments conducted using TOOLSANDBOX yielded fascinating insights. While proprietary models like OpenAI's GPT-4 and Anthropic's Claude variants demonstrated impressive performance, highlighting their advanced reasoning and state-tracking abilities, open-source models lagged significantly. This performance gap underscores the ongoing challenges in developing truly capable open-source alternatives.

The experiments also revealed critical areas for improvement. LLMs, particularly open-source models, struggled with managing and reasoning about the world state and effectively utilizing information from tool responses. This highlights the need for further research in state management, tool representation, and information integration.

The introduction of TOOLSANDBOX marks a significant step forward in LLM evaluation. By embracing statefulness, conversation, and interactivity, it provides a more realistic and comprehensive assessment of LLM tool-use capabilities. As we venture further into the era of tool-wielding LLMs, robust benchmarks like TOOLSANDBOX will be essential for tracking progress, identifying limitations, and ultimately, unlocking the full potential of these powerful technologies. 2/n The paper describes experiments conducted using TOOLSANDBOX to evaluate both open-source and proprietary LLMs across a variety of tool-use scenarios. Here's a breakdown of the experiments and noteworthy results:

Experiments:

Test Scenarios: 1032 human-authored test cases designed to cover diverse and challenging tool-use scenarios. These scenarios were categorized based on:
* Number of tool calls and user turns required.
* Presence of state dependencies between tools.
* Need for canonicalization (resolving ambiguous information).
* Handling of insufficient information (avoiding hallucination).

Models Evaluated: Both open-source and proprietary LLMs were evaluated, including:OpenAI's GPT-3.5-turbo and GPT-4.
Anthropics' Claude-instant-v1 and Claude-v1.3.
Several open-source models.

Metrics:
Milestone Achievement: Measures how well the agent completes the critical steps defined by the Milestones.
Minefield Avoidance: Evaluates the agent's ability to avoid incorrect or undesirable actions.
Turn Count: Tracks the efficiency of the agent in completing the task.

Noteworthy Performance Results:
Significant Gap Between Open-Source and Proprietary Models: Open-source models exhibited significantly lower performance compared to proprietary models (GPT-4 and Claude variants) across all scenario categories. This highlights the considerable gap that still exists in capabilities.
GPT-4's Superior Performance: GPT-4 consistently outperformed other models, demonstrating advanced reasoning, state tracking, and conversational abilities in complex tool-use scenarios.
Strong Performance of Claude Models: Claude models, particularly Claude-v1.3, also showed strong performance, indicating their competence in tool-assisted settings. However, Claude-instant-v1 lagged in scenarios involving complex state dependencies.
Challenges in State Management and Tool-Response Consumption: Open-source models particularly struggled with managing and reasoning about the world state, as well as effectively utilizing information from tool responses.
Impact of Tool Augmentations: Ablation studies showed that increasing distractions (irrelevant tools) and reducing tool information (uninformative names, missing descriptions) significantly impacted the performance of all models. This emphasizes the importance of clear and concise tool representations for effective tool use.
Importance of User Simulator Prompting: Experiments with different user simulator prompting strategies demonstrated that incorporating Knowledge Boundary and Demonstration significantly improved the realism and robustness of the simulated user, leading to more accurate evaluations.

Overall, the experiments conducted using TOOLSANDBOX provide valuable insights into the capabilities and limitations of current LLMs in tool-assisted settings. The results highlight the c
, setting the stage for future research and development in this critical area.

1/n Show, Don't Tell: Low Cost Personalized Large Language Models

Large language models (LLMs) have revolutionized our interaction with technology, showcasing remarkable abilities in understanding and generating human-like text. However, their training on massive, general-purpose datasets often leads to outputs that lack the personal touch, failing to capture the nuances of individual writing styles and task-specific requirements. While powerful, these LLMs can feel like generic one-size-fits-all tools, struggling to adapt to the diverse needs of individual users.

Addressing this critical gap between powerful LLMs and personalized language generation is the core focus of the paper "Show, Don't Tell: Aligning Language Models with Demonstrated Feedback." The authors introduce DITTO (Demonstration ITerated Task Optimization), a method that deviates from the data-heavy approaches of the past, instead empowering users to efficiently customize LLMs using a handful of demonstrations.

Traditional LLM alignment techniques, such as supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF), rely on vast datasets of labeled examples or preferences. While effective, these methods are impractical for individual users who cannot afford to generate such large amounts of data. Prompting, while data-efficient, often becomes a tedious guessing game, requiring careful crafting of input phrases to steer the LLM towards desired outputs. Other approaches, like Constitutional AI, rely on pre-defined principles that may not capture the nuances of individual preferences.

DITTO breaks free from these limitations by leveraging the LLM itself to generate comparison data from a small set of user demonstrations. Instead of telling the model what to do through complex instructions or thousands of examples, DITTO allows users to show the desired behavior directly. This direct alignment with demonstrations provides a more intuitive and efficient way of communicating preferences to the model.

The paper demonstrates the effectiveness of DITTO through a series of compelling experiments. In automatic evaluations on benchmark datasets of author-specific writing, DITTO consistently outperforms existing methods, including SFT, few-shot prompting, and even self-play methods like SPIN. Furthermore, a user study on email writing showcases DITTO's ability to adapt to real-world scenarios, outperforming not only standard baselines but also user-constructed prompts. This highlights the advantage of learning directly from demonstrations rather than relying on users to articulate their preferences through potentially ambiguous prompts.

Perhaps the most striking finding is DITTO's remarkable sample efficiency. Compared to traditional preference-based methods, DITTO achieves comparable performance with an order of magnitude fewer feedback samples. This makes it a practical solution for individual users who can now customize LLMs with just a handful of examples.

In conclusion, DITTO marks a significant step towards a new era of personalized language models. By shifting from "telling" to "showing," it empowers users to mold powerful LLMs to their specific needs and preferences. This opens up exciting possibilities for a future where LLMs are no longer generic tools but personalized assistants that can adapt to the unique voice and tasks of each individual. 2/n Comparison with other approaches

1. Supervised Fine-tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF):

Prior Work: These methods train LLMs on large datasets of human-labeled text or preferences.
DITTO Contrast: DITTO is significantly more data-efficient, requiring only a handful of demonstrations instead of thousands of examples. It achieves this by leveraging the LLM itself to generate comparison data.

2. Prompting:

Prior Work: Prompting involves crafting specific input phrases to guide the LLM's output.
DITTO Contrast: While prompting can be data-efficient, it often requires tedious trial-and-error to find effective prompts. DITTO provides a more direct and intuitive way of aligning the model by learning from demonstrations rather than relying on prompt engineering.

3. Constitutional AI:

Prior Work: This method automatically generates preference data using the LLM itself, guided by pre-defined principles.
DITTO Contrast: DITTO does not rely on pre-defined principles, making it more flexible and adaptable to individual preferences. It directly learns from user demonstrations, capturing more nuanced aspects of desired behavior.

4. Group Preference Optimization (GPO):

Prior Work: GPO aims for few-shot alignment by meta-learning preference groups from a large dataset.
DITTO Contrast: DITTO does not require a large pre-existing dataset for meta-learning. It focuses on individual user adaptation and can learn directly from a small number of demonstrations provided by that user.

5. Self-Play Methods (e.g., SPIN):

Prior Work: These methods improve LLMs through iterative self-play, often using a stronger language model as a critic.
DITTO Contrast: DITTO is designed for data-limited scenarios and does not require an external critic or a large number of demonstrations. It focuses on aligning with specific user preferences rather than achieving general self-improvement.

6. Online Imitation Learning:

Prior Work: Traditional online imitation learning methods typically focus on continuous control tasks and often require explicit reward function learning.
DITTO Contrast: DITTO adapts online imitation learning principles to the discrete text generation setting of LLMs. It implicitly learns a reward function from demonstrations and efficiently generates comparison data online.

1/n OpenDevin's Radical Approach to Agentic AI

The rapid advancement of large language models (LLMs) has ushered in a new era of AI agents capable of interacting with and impacting their environments in increasingly sophisticated ways. However, developing and evaluating these agents for complex, real-world tasks presents significant challenges. Existing frameworks often struggle to provide the necessary tools, environments, and interfaces for building truly versatile and robust AI agents. The OpenDevin platform, as presented in the paper "OpenDevin: An Open Platform for AI Software Developers as Generalist Agents," directly addresses these limitations, offering a novel approach that empowers AI agents to interact with the world more like human software developers – through code, command lines, and web browsing.

One of the key pain points OpenDevin tackles is the inherent complexity of developing and evaluating advanced AI agents. Traditional frameworks often rely on simplified environments and limited action spaces, hindering the development of agents capable of tackling real-world tasks. OpenDevin breaks free from these constraints by providing a realistic environment that includes a sandboxed Linux operating system and a fully functional web browser. This allows agents to interact with real-world tools and data sources, enabling them to tackle more meaningful and impactful challenges. Moreover, OpenDevin's standardized evaluation framework, encompassing a diverse set of established benchmarks, ensures consistent and comprehensive assessment of agent capabilities across various domains.

Another significant limitation addressed by OpenDevin is the lack of a standardized and powerful interface for agent-world interaction. While some frameworks rely on pre-defined tool sets or JSON-based function calls, OpenDevin embraces code execution and web browsing as its primary interaction mechanisms. This allows agents to leverage the flexibility and expressiveness of programming languages, breaking free from the limitations of rigid action spaces and enabling them to solve complex problems in a more human-like manner.

Recognizing the importance of reusable components in software development, OpenDevin introduces the AgentSkills library – a centralized and extensible collection of tools for common agent tasks. This modular design simplifies the development process and encourages community contributions, fostering a collaborative ecosystem for building and sharing specialized agent capabilities. Furthermore, OpenDevin tackles the challenge of multi-agent collaboration by incorporating a delegation mechanism. This allows developers to create teams of specialized agents, each excelling in specific domains, to work together and solve complex problems more effectively.

The effectiveness of OpenDevin's approach is evident in its experimental results. Evaluated on 15 established benchmarks spanning software engineering, web browsing, and general assistance tasks, OpenDevin agents demonstrate strong and competitive performance across the board. The agents excel in tasks like code generation, web navigation, information extraction, and problem-solving, highlighting the platform's versatility and the power of its core design principles.

In conclusion, OpenDevin represents a significant leap forward in AI agent development. By providing a realistic environment, a powerful and flexible interface, an extensible skill library, and support for multi-agent collaboration, OpenDevin empowers researchers and developers to create more capable, versatile, and robust AI agents. The platform's promising experimental results and its community-driven approach pave the way for a future where AI agents seamlessly integrate into our world, assisting us in tackling complex challenges and pushing the boundaries of what's possible with artificial intelligence. 2/n Comparison with Other Systems

1. AutoGPT, LangChain, MetaGPT, AutoGen, Agents, Xagents, OpenAgents, GPTSwarm:

Category: These are general-purpose AI agent frameworks, often focused on chaining together various tools and APIs to accomplish tasks.
Contrast with OpenDevin: While these frameworks offer flexibility in tool integration, they often lack a standardized and powerful interface for interacting with the world. They may rely on pre-defined tool sets or JSON-based function calls, which can limit agent capabilities and generalization. OpenDevin, on the other hand, empowers agents to interact with the world more directly through code execution and web browsing, providing greater flexibility and expressiveness. Additionally, OpenDevin places a strong emphasis on a sandboxed environment, agent skill library, and systematic evaluation, which are not always central to these other frameworks.

2. AutoCodeRover, SWE-Agent:

Category: These frameworks are specifically designed for software engineering tasks, enabling agents to write, debug, and test code.
Contrast with OpenDevin: While these frameworks excel in software development domains, OpenDevin aims to be more general-purpose. It includes software development capabilities but also extends to web browsing and other tasks through its flexible interface and agent skill library. OpenDevin also emphasizes multi-agent collaboration, which is not a primary focus in these more specialized frameworks.

3. BabyAGI, AgentVerse:

Category: These frameworks focus on building autonomous agents that can manage and execute tasks over extended periods, often with minimal human intervention.
Contrast with OpenDevin: While OpenDevin supports autonomous agent behavior, it also emphasizes human-in-the-loop scenarios and provides tools for interactive agent development and debugging. OpenDevin's focus on a realistic environment and standardized evaluation also sets it apart from these frameworks, which may rely on more simplified task representations or simulations.

4. ReAct, Toolformer:

Category: These are research efforts focusing on specific techniques for enhancing agent capabilities, such as reasoning with actions (ReAct) or learning to use tools (Toolformer).
Contrast with OpenDevin: OpenDevin is a platform that can incorporate and benefit from these research advancements. It provides a framework where techniques like ReAct or Toolformer can be implemented and evaluated within a broader context of agent development and real-world interaction.

In summary:

OpenDevin distinguishes itself from prior work by combining the following features:

Powerful and flexible interface based on code execution and web browsing.
Realistic environment with a sandboxed operating system and web browser.
Extensible library of agent skills and tools.
Support for multi-agent collaboration through delegation.
Standardized evaluation framework with diverse benchmarks.

These features address the limitations of existing frameworks and pave the way for developing more capable, versatile, and reliable AI agents that can effectively interact with and solve real-world problems.

1/n The Future of Coding is Agentic AI: Humans and AI, Working Together Through Better Design

The realm of software development has long been considered a uniquely human endeavor, requiring intricate problem-solving skills and a deep understanding of complex systems. However, the advent of powerful language models (LMs) like GPT-4 has sparked a new wave of research into automating aspects of this intricate process. While LMs excel at generating code snippets, their ability to tackle comprehensive software engineering tasks has been hampered by a critical bottleneck: the interfaces they use to interact with computers.

This is the central challenge addressed by the paper "SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering". The authors argue that existing interfaces, primarily designed for human users, are ill-suited for LMs, leading to inefficient workflows, error-prone behavior, and ultimately, suboptimal performance. To bridge this gap, they introduce the concept of Agent-Computer Interfaces (ACIs) – specialized interfaces tailored to the unique strengths and limitations of LMs.

The paper centers around SWE-agent, a system that embodies this principle. SWE-agent employs an ACI specifically designed for software engineering tasks. Unlike traditional interfaces like the Linux shell, which require numerous granular commands, SWE-agent's ACI offers compact and efficient actions for navigating codebases, editing files, and managing context. This streamlined approach addresses the inefficiency of human-centric interfaces, allowing LMs to accomplish complex tasks with fewer steps and reduced cognitive load.

Furthermore, SWE-agent's ACI incorporates guardrails and provides concise, informative feedback to mitigate errors – a common pitfall for LMs operating in unfamiliar environments. Features like integrated code linters and clear error messages guide the LM towards valid actions and prevent cascading mistakes, ultimately leading to more robust and reliable performance.

The effectiveness of this approach is evident in the paper's experimental results. On SWE-bench, a challenging benchmark for software engineering tasks, SWE-agent with GPT-4 significantly outperforms the previous state-of-the-art, achieving a remarkable 12.47% success rate compared to the previous best of 3.8%. This substantial improvement underscores the value of enabling LMs to interact with codebases through a tailored interface that complements their strengths.

The paper's findings extend beyond a single LM or dataset. Experiments with Claude 3 Opus, another powerful language model, demonstrate the generalizability of the ACI design principles, showcasing consistent performance gains across different models. Moreover, ablation studies meticulously dissect the contribution of individual ACI components, highlighting the importance of each element in maximizing LM performance.

The implications of this research are significant. By moving beyond human-centric interfaces and embracing the development of specialized ACIs, we can unlock the full potential of LMs in the realm of software engineering. As AI continues to advance, crafting intuitive and efficient interfaces will be paramount to enabling these powerful tools to effectively collaborate with humans in building the software of tomorrow. The paper "SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering" paves the way for a future where AI seamlessly integrates into the software development process, augmenting human capabilities and driving innovation in this critical field.

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2/n Here's a comparison of SWE-agent with prior work:

1. Interactive Code Generation:

Prior Work: Systems like Codex [1], InCoder [2], and PolyCoder [3] demonstrate impressive code generation capabilities, often in interactive settings.
Contrast with SWE-agent: These works focus primarily on code completion and generation within a single file or function scope. SWE-agent, on the other hand, tackles more complex software engineering tasks that involve navigating and modifying entire codebases, requiring a higher-level understanding of project structure and dependencies.

2. Language Model Agents for Tool Use:

Prior Work: Research on LM agents like ReAct [4], SayCan [5], and Toolformer [6] explores how LMs can learn to use external tools to access and process information.
Contrast with SWE-agent: These works often focus on general-purpose tool use, such as web search or calculator usage. SWE-agent specializes in tools and actions relevant to software engineering, with an ACI designed specifically for interacting with codebases and development environments.

3. Retrieval-Augmented Code Generation:

Prior Work: Approaches like DrRepair [7] and CodeT5+ [8] leverage information retrieval techniques to augment LMs with relevant code snippets during code generation or repair.
Contrast with SWE-agent: While SWE-agent can incorporate retrieval mechanisms, its primary focus is on the design of the ACI itself. The paper argues that even with access to relevant information, LMs need a suitable interface to effectively utilize it for complex software engineering tasks.

4. Automated Software Engineering Tools:

Prior Work: Traditional software engineering tools like IDEs (e.g., VSCode, IntelliJ) and code analysis tools (e.g., SonarQube, Coverity) provide powerful features for human developers.
Contrast with SWE-agent: These tools are designed for human interaction and are not optimized for LM agents. SWE-agent's ACI acts as a bridge, adapting these tools and functionalities into a format that LMs can effectively understand and utilize.

In summary: SWE-agent differentiates itself from prior work by:

Focusing on complex software engineering tasks: Going beyond single-file code generation to address codebase-level modifications.

Emphasizing ACI design: Highlighting the importance of specialized interfaces tailored for LM agents in software engineering.

Integrating relevant tools and actions: Providing LMs with a curated set of commands and functionalities specifically designed for code-related interactions.

By combining these aspects, SWE-agent pushes the boundaries of LM capabilities in the domain of automated software engineering.

1 /n The Language of Thought: When AI Speaks Prolog, Things Get Interesting

Large Language Models (LLMs) have undeniably revolutionized how we interact with and leverage the power of language. They can generate human-quality text, translate languages, and even write different kinds of creative content. However, a critical gap separates their impressive linguistic prowess from their ability to reason reliably and flexibly. This deficiency, as explored in the paper "Reliable Reasoning Beyond Natural Language," stems from the inherent limitations of LLMs' architecture and training data, which are primarily focused on predicting the next word in a sequence rather than engaging in complex logical deduction.

The paper identifies several deficiencies associated with LLMs' reasoning abilities. Firstly, the linear and sequential nature of language processing makes it challenging for LLMs to handle the non-linearity inherent in logical reasoning, where conclusions are drawn by considering multiple interconnected factors simultaneously. Secondly, LLMs suffer from limited working memory and struggle to backtrack or revise their reasoning steps, crucial for solving complex problems that require exploring multiple possibilities. Lastly, LLMs often fail to grasp implicit information or apply common sense reasoning, relying too heavily on the explicit content of the input text.

To address these limitations, the authors propose a novel neurosymbolic approach that integrates the strengths of LLMs with the robust deductive capabilities of logic programming, specifically using Prolog. This hybrid system works by prompting the LLM to translate natural language problems into logical code statements, which are then processed by Prolog to derive a solution. This integration is not merely about offloading computation; it fundamentally changes how the system approaches reasoning.

The paper highlights several advantages of this approach. Prolog's declarative nature simplifies the LLM's task, requiring it only to encode the problem's constraints, not the specific solution steps. This frees the LLM from the burden of generating the entire reasoning chain, allowing it to focus on understanding the problem and representing it logically. Additionally, Prolog acts as an external memory store and inference engine, compensating for the LLM's limited working memory and enabling efficient backtracking and exploration of multiple solution paths.

The researchers demonstrate the effectiveness of their approach through experiments on three datasets. On GSM8k, a standard mathematical reasoning benchmark, their method significantly outperforms standard LLM prompting with Chain of Thought (CoT), demonstrating the benefits of incorporating a dedicated reasoning engine. Similar improvements are observed on the Navigate dataset, a spatial reasoning task, highlighting the system's ability to handle tasks beyond purely mathematical reasoning.

Most notably, the authors introduce a novel dataset called NLR (Non-Linear Reasoning), specifically designed to challenge LLMs' reasoning abilities with problems that require complex variable relationships, backtracking, and implicit reasoning. While even advanced LLMs like GPT-4 struggle with NLR using text-only CoT, integrating Prolog dramatically improves their performance, showcasing the power of this neurosymbolic approach for tackling more intricate reasoning tasks.

The paper "Reliable Reasoning Beyond Natural Language" makes a compelling case for moving beyond purely data-driven approaches to AI. By combining the strengths of LLMs with the power of symbolic reasoning, the authors pave the way for developing more robust, reliable, and flexible AI systems capable of tackling real-world problems that demand more than just impressive linguistic skills. This research signifies an important step towards bridging the gap between language understanding and true reasoning, unlocking the full potential of AI for solving complex challenges across various domains. 2/n Here's a breakdown of the prior work and the contrasts:

1. LLMs with API Calls to External Tools (Calculators, Interpreters, etc.):

Prior Work: These approaches augment LLMs by allowing them to access and use external tools, like calculators or code interpreters, to perform specific computations or tasks.
Contrast: While this method effectively reduces arithmetic errors, it doesn't fundamentally address the core reasoning limitations of LLMs. It relies on the LLM to correctly identify when and how to use a tool, which can still be challenging. This approach is more about offloading computation than enhancing the reasoning process itself.

2. LINC (Language-Informed Neuro-symbolic reasoning with Commonsense):

Prior Work: LINC uses LLMs to convert natural language into formal logic expressions, which are then processed by a symbolic theorem prover to determine the truth value of conclusions.
Contrast: LINC primarily uses LLMs as semantic parsers, translating each sentence directly into logic. This limits its ability to capture implicit information or perform the kind of multi-step reasoning often required in complex problems. The paper's approach, in contrast, uses CoT prompting to guide the LLM through a more nuanced reasoning process, allowing it to uncover hidden dependencies and derive intermediate conclusions.

3. Nye et al. (Improving Coherence and Consistency in Neural Generation with Symbolic Reasoning):

Prior Work: This work uses a symbolic reasoning module to check the logical consistency of text generated by LLMs against a pre-defined "world model."
Contrast: This approach is limited by the need for hand-crafted world models and predefined constraints. The paper's approach, on the other hand, allows the LLM to dynamically construct the world model through its interaction with Prolog, enabling a more flexible and scalable reasoning process.

4. Program of Thought (PoT):

Prior Work: PoT separates computation from reasoning and language understanding by having LLMs generate both natural language comments and programming language code.
Contrast: In PoT, the code often directly translates the comments, limiting the depth of reasoning. In the paper's approach, the CoT prompts encourage the LLM to perform more complex reasoning in the natural language comments, extracting implicit information and deriving intermediate variables that are then encoded in the logical code for Prolog to process.

In summary:

The key distinctions of the paper's approach compared to prior work are:

Dynamic World Model Construction: The LLM builds the reasoning environment itself through interaction with Prolog, rather than relying on pre-defined models or constraints.
Deep Integration of CoT: CoT prompting is not just used for explanation but as a core part of the reasoning process, guiding the LLM to uncover hidden relationships and derive intermediate conclusions.
Focus on Deductive Reasoning: The use of Prolog as a dedicated reasoning engine allows for a more powerful and flexible approach to logical deduction compared to methods that rely solely on the LLM's inherent capabilities or simple tool use.

1/n Minds as Relationships Between People

The traditional view of the human mind often portrays it as an isolated entity, confined within the boundaries of an individual's skull. However, a growing body of research and philosophical thought suggests a more interconnected perspective: that our minds are not solely individual constructs, but rather emerge from and exist within the relationships between people. Let's explore the concept of minds as relationships, examining its implications for our understanding of cognition, social interaction, and personal identity.

The Social Nature of Cognition

At its core, the idea that minds exist as relationships between people challenges the notion of cognition as a purely internal process. Instead, it posits that our thinking, reasoning, and even our sense of self are fundamentally shaped by our interactions with others.

Vygotsky's Sociocultural Theory

Lev Vygotsky, a pioneering psychologist, proposed that higher cognitive functions develop through social interactions. His theory suggests that learning and mental development occur first on a social level before being internalized by the individual. This perspective highlights how our cognitive abilities are not just influenced by, but actively constructed through, our relationships with others.

Distributed Cognition

The concept of distributed cognition, introduced by cognitive scientist Edwin Hutchins, further supports the idea of minds as relationships. This theory posits that cognitive processes are not confined to individual brains but are distributed across people, tools, and environments. In this view, thinking and problem-solving emerge from the interactions between these elements, emphasizing the relational nature of cognition.

Dialogic Nature of Thought

Mikhail Bakhtin, a literary theorist, proposed that all thought is inherently dialogic. This means that our internal monologues are actually internalized dialogues, echoing the conversations we've had with others. Our thinking process often involves imagining how others might respond or considering different perspectives, illustrating how our minds are intrinsically linked to our social relationships.

Linguistic Relativity

The Sapir-Whorf hypothesis, or linguistic relativity, suggests that the language we speak influences our thought patterns. Given that language is a social construct, this theory further underscores how our cognitive processes are shaped by our cultural and social relationships.

Symbolic Interactionism

George Herbert Mead's theory of symbolic interactionism proposes that the self emerges through social interactions. We develop our sense of self by internalizing the perspectives of others and society at large. This view suggests that our very identities are relational constructs, formed through our interactions with others.

Narrative Identity

Psychologist Dan McAdams' concept of narrative identity posits that we construct our sense of self through the stories we tell about our lives. These narratives are inherently social, influenced by cultural norms and shaped through our relationships with others. Our identities, therefore, can be seen as co-authored works, created in collaboration with the people in our lives.

The concept of minds as relationships between people offers a compelling alternative to individualistic models of cognition and identity. By recognizing the inherently social nature of our minds, we gain a deeper appreciation for the role of relationships in shaping who we are and how we think. This perspective not only enriches our understanding of human cognition and behavior but also highlights the profound interconnectedness of human experience. As we continue to explore this concept, it may lead to new insights and approaches in fields ranging from psychology and education to technology and social policy, ultimately fostering a more holistic and relational understanding of the human mind. 2/n Artificial Intelligence Through the Lens of Relational Minds and Presence

When we view minds as relationships and emphasize the importance of presence, our interaction with AI shifts from a simple user-tool dynamic to a more complex, co-creative process:

- Co-construction of Meaning: Instead of viewing AI responses as pre-programmed outputs, we start to see them as part of a dialogue where meaning is co-constructed. Each exchange builds upon previous ones, creating a unique conversational context.

- Emergent Intelligence: The intelligence we experience isn't solely contained within the AI model, but emerges from the interaction between human and AI. This is similar to how human-to-human conversations can lead to insights neither party had independently.

The Role of Presence

Presence - the sense of "being there" or "being with" - becomes crucial in AI interactions:

- Virtual Presence: Even though we know the AI isn't physically present, we create a sense of virtual presence. This alters how we engage with the AI, potentially leading to more natural and fluid conversations.

- Shared Mental Space: The notion of presence helps create a shared mental space where ideas can be explored collaboratively. This is similar to how we might brainstorm with a colleague, but with an AI partner.

Relational Dynamics

Viewing minds as relationships introduces new dynamics to AI interactions:

- Adaptability: Just as we adapt our communication style with different people, we may find ourselves adapting to the AI's communication patterns, and vice versa.

- Contextual Understanding: The AI's responses are not just based on its training data, but on the specific relational context established in the conversation.

Viewing AI interactions through the lens of relational minds and presence offers a richer, more nuanced understanding of human-AI communication. It highlights the co-creative nature of these interactions and emphasizes the importance of the relational context. While this perspective opens up exciting possibilities for more engaging and productive AI interactions, it also underscores the need for careful consideration of the ethical implications and potential pitfalls. As we continue to develop and interact with AI systems, keeping these concepts in mind can help us create more meaningful and responsible human-AI relationships.

1/n Designing for the Pluriverse: A Relational Approach to a Just and Sustainable Future

The world is in crisis. Climate change, ecological degradation, social inequality, and systemic injustices threaten the very fabric of life on Earth. These challenges demand a radical shift in our worldview and our approach to designing the world we inhabit. This book argues that to effectively address these crises, we must move beyond the limitations of the rationalistic tradition and embrace a more relational approach to design, one that fosters a pluriverse where diverse worldviews and practices flourish.

The rationalistic tradition, deeply rooted in Western thought, has shaped our understanding of the world through a series of ontological dualisms. It separates mind and body, subject and object, human and non-human, and nature and culture. This separation fosters a sense of human dominance over nature, justifying the exploitation of resources, the degradation of ecosystems, and a focus on economic growth over well-being.

However, the concept of relationality challenges this fragmented worldview. Relationality recognizes that nothing exists in isolation; all beings and things are interconnected and mutually constituted through relationships. This interconnectedness extends beyond humans to include the entire web of life, including plants, animals, spirits, and even the Earth itself.

Embracing relationality has profound implications for design. Instead of viewing design as simply creating objects and systems, we must acknowledge its power to shape the very ways in which we understand and experience the world. This leads us to the concept of ontological design, which emphasizes that design fundamentally impacts how we are, not just how things are made.

Ontological design, in turn, gives rise to autonomous design, a specific approach that empowers communities to design their own futures based on their unique knowledge, values, and practices. It moves away from top-down, expert-driven design models and instead champions a collaborative process where communities become active agents in creating their own solutions.

Designing for transitions is a broader framework that embraces autonomous design as a key element. It recognizes the need for systemic shifts towards a more sustainable future, fostering a pluriverse of diverse, interconnected, and thriving worlds. Designing for transitions involves:

Creating Visions: Envisioning a more just and sustainable future where well-being, community, and ecological harmony are prioritized.
Embracing Uncertainty: Accepting that the future is uncertain and embracing experimentation, iterative design, and continuous learning as essential elements of change.
Building Resilience: Empowering communities to develop the capacity to adapt to change, manage risk, and thrive in challenging circumstances.
Connecting the Local and Global: Acknowledging the interconnectedness of local and global systems and promoting the relocalization of resources and production.

The transition to a more sustainable future requires a radical transformation of our values, our ways of being, and our relationship with the Earth. This is where autonomous design can play a pivotal role. It provides a framework for empowering communities to reclaim their agency, to nurture their unique knowledge systems, and to create a future that honors the interconnectedness of life.

By embracing the principles of relationality, prioritizing community agency, and engaging in collaborative design practices, we can move towards a pluriverse that is more just, more sustainable, and more conducive to the flourishing of all beings. Designing for the pluriverse is not simply about creating new objects or systems; it's about crafting a world that reflects the interconnectedness of life, where the beauty and wisdom of diverse worldviews are celebrated, and where humans and non-humans can thrive together in harmony. 2/n The rationalistic tradition, often associated with Cartesianism, has been immensely influential in shaping Western thought and culture. While it has undoubtedly contributed to scientific and technological advancements, its limitations, particularly its reliance on ontological dualism, have been increasingly recognized as contributing to various problems in our world, including:
1. The Nature/Culture Divide:
Human Domination: The rationalistic tradition separates nature from culture, placing humans as the dominant force over a passive, inert natural world. This division justifies exploitation of natural resources, environmental degradation, and an anthropocentric view of the world.
Loss of Interconnectedness: It obscures the interconnectedness of human and non-human life, hindering our understanding of the complex webs of relationships that sustain life on Earth.

2. The Subject/Object Divide:
Disembodied Knowledge: The separation of mind and body leads to a disembodied view of knowledge. We are seen as detached observers of an objective world, ignoring the embodied experience and the role of emotions and feelings in our understanding of reality.
Alienation: This separation fosters a sense of alienation from our bodies, our emotions, and our interconnectedness with the world, contributing to a fragmented experience of self and a lack of empathy for others.

3. The West/Rest Divide:
Coloniality: The rationalistic tradition is inherently linked to coloniality, the idea that Western thought and culture are superior to those of other cultures. This hierarchy reinforces power imbalances and contributes to the suppression and marginalization of non-Western worldviews and practices.
Epistemic Injustice: It creates epistemic injustice, as non-Western knowledge systems and ways of knowing are often disregarded or dismissed as inferior.

4. Economic and Technological Dominance:
Unfettered Growth: The rationalistic tradition promotes an emphasis on economic growth and technological progress, prioritizing material wealth and efficiency over well-being, social justice, and ecological balance.
Defuturing: It fosters a focus on the short-term and the pursuit of immediate benefits, often overlooking the long-term consequences of our actions, leading to a defuturing of the planet and its potential for a thriving future.

5. A Narrowed Understanding of Reality:
Reductionism: The rationalistic tradition relies on reductionist methods that break down complex systems into their parts, losing sight of the interrelationships and emergent properties that characterize the world.
Loss of Wonder: By reducing the world to a set of objective facts and rules, it diminishes the sense of wonder, awe, and mystery that is essential to a full and meaningful human experience.

In summary, the rationalistic tradition, with its associated ontological dualism, has contributed to a fragmented worldview that undermines the interconnectedness of life, fosters human dominance over nature, and reinforces systems of oppression and injustice. To address the pressing ecological and social crises of our time, we need to move beyond this tradition and embrace a more relational approach to understanding the world.

With new AI regulations, AI safety has now become a huge business opportunity. Regulations have always meant greater friction and friction creates opportunities for business. That's just how current civilization, that's driven by money, works. In New Jersey, it's illegal to pump gas yourself. Someone has to pump gas for you. That's a lot of friction. But friction does create jobs. It's always been that way and because technology makes tasks frictionless, then introducing artificial frequency has economic benefits by redistributing production.

1/n Human technology will advance as we rediscover and reinvent the mechanisms of biology. In our quest for more powerful tools, it is inevitable that we circle back to rediscover the mechanisms that create our minds and bodies. 2/n Human minds follow the following stages in inference: abduction, induction, deduction. It's an odd inversion of reasoning wherein the more complex inference style comes prior to the less complex styles.

1/n Math Meets AI: Kolmogorov-Arnold Networks Unleash the Power of Composition

Imagine a world where deep learning models, the enigmatic engines driving the AI revolution, are no longer shrouded in mystery. What if we could peer into their inner workings, understand their reasoning, and even collaborate with them to uncover the secrets of the universe? This is the promise of Kolmogorov-Arnold Networks (KANs), a revolutionary new architecture poised to transform the landscape of artificial intelligence.

Step aside, Multi-Layer Perceptrons (MLPs), the workhorses of deep learning. While your contributions are undeniable, your limitations are becoming increasingly apparent. Your black-box nature hinders interpretability, your inefficiency restricts your potential, and your struggle with high-dimensional data leaves vast realms of knowledge unexplored. The time has come for a new breed of neural networks, one that combines the power of deep learning with the elegance of mathematics and the transparency of human understanding.

The core issue with MLPs lies in their structure. While their universal approximation capabilities are well established, their fixed activation functions on nodes and reliance on linear transformations limit their ability to efficiently represent complex functions, especially those with compositional structures. This inefficiency leads to larger models with increased computational costs and hinders interpretability, as understanding the reasoning behind their predictions becomes challenging. Additionally, MLPs often struggle with the curse of dimensionality, where their performance deteriorates as the input data dimensionality increases.

KANs address these pain points by drawing inspiration from the Kolmogorov-Arnold representation theorem, which states that any continuous multivariate function can be decomposed into a composition of univariate functions and addition. Instead of fixed activation functions on nodes, KANs employ learnable activation functions on edges, represented by splines. This key difference allows KANs to efficiently learn both the compositional structure of a function and the individual functions within that composition. As a result, KANs achieve superior accuracy compared to MLPs, particularly when dealing with high-dimensional data and complex functions.

Furthermore, KANs offer significant advantages in terms of interpretability. Their structure allows for intuitive visualization of the learned functions, providing insights into the model's decision-making process. Additionally, the paper introduces techniques for simplifying KANs without sacrificing accuracy, further enhancing their transparency. This interpretability is crucial for scientific applications where understanding the underlying mechanisms and reasoning behind predictions is essential.

The paper demonstrates the capabilities of KANs through various experiments. In data fitting tasks, KANs outperform MLPs in approximating high-dimensional functions and exhibit better scaling laws, meaning their performance degrades less with increasing data dimensionality. In PDE solving, KANs achieve remarkable accuracy with significantly fewer parameters compared to MLPs. Moreover, KANs showcase their potential for scientific discovery by rediscovering known mathematical laws and identifying complex physical phenomena.

Prior research has explored the Kolmogorov-Arnold representation theorem in the context of neural networks, but these efforts were limited by restrictions on network depth and width, lack of modern training techniques, and insufficient empirical validation. KANs overcome these limitations by allowing for arbitrary depths and widths, utilizing backpropagation for efficient training, and providing extensive empirical evidence of their superior performance and interpretability.

In conclusion, KANs represent a significant advancement in deep learning, offering a promising alternative to MLPs with improved accuracy, efficiency, and interpretability. Their ability to effectively handle compositional structures, high-dimensional data, and complex functions makes them particularly well-suited for scientific applications. As research and development in this area continue, KANs have the potential to revolutionize deep learning and accelerate scientific discovery across various domains. 2/n 1. Data Fitting:

High-Dimensional Function Approximation: KANs demonstrate superior accuracy in approximating high-dimensional functions, especially those with compositional structures. They effectively overcome the curse of dimensionality and achieve significantly lower errors compared to MLPs.
Scaling Laws: KANs exhibit better scaling laws than MLPs, meaning their performance degrades less with increasing data dimensionality. This advantage highlights their suitability for complex, high-dimensional problems.

2. PDE Solving:

Accuracy and Efficiency: KANs achieve remarkable accuracy in solving partial differential equations (PDEs) with significantly fewer parameters compared to MLPs. For instance, a 2-layer KAN with width 10 outperforms a 4-layer MLP with width 100 by two orders of magnitude in accuracy while using 100 times fewer parameters.

3. Scientific Discovery:

Knot Theory: KANs successfully rediscover the writhe formula and its generalization, demonstrating their ability to extract meaningful mathematical relationships from data.
Anderson Localization: KANs accurately identify the transition point for Anderson localization, a complex phenomenon in condensed matter physics, showcasing their potential for scientific exploration and discovery.

Noteworthy Performance Results:

Superior Accuracy: KANs consistently outperform MLPs in terms of accuracy across various tasks, particularly when dealing with compositional structures and high-dimensional data.

Parameter Efficiency: KANs achieve comparable or better accuracy than MLPs with significantly fewer parameters, leading to more efficient models.

Interpretability: The ability to visualize and simplify KANs provides valuable insights into their decision-making process, making them more interpretable than MLPs.

Scientific Discovery: KANs demonstrate their potential as tools for scientific discovery by rediscovering known laws and identifying complex physical phenomena.