Does current AI represent a dead end?

Professor Eerke Boiten believes that current AI systems, based on large neural networks, are unmanageable and irresponsible to use in serious applications due to their emergent behavior and lack of compositionality. This makes it difficult to develop, reuse, and verify these systems, as their internal structure does not relate meaningfully to their functionality, and they do not create explicit models of knowledge.

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Building AI Products–Part I: Back-End Architecture

The authors developed an AI-powered Chief of Staff for engineering leaders, which gained 10,000 users within a year, but then pivoted to create Outropy, a developer platform for building AI products. They share lessons learned from building the AI assistant, including structuring AI applications, using inference pipelines and agents, and the importance of understanding the differences between agents and microservices.

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Show HN: I made a web app to bring children's drawings to life

Doodle Dreams is a platform that transforms children's drawings into animated videos using AI technology. Users can upload a photo or scan of a drawing, and then download or share the resulting animation, with pricing plans available for 5, 25, or 50 animations.

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Spotify is full of AI music

Spotify is facing criticism for allowing AI-generated music on its platform, with some users and artists claiming it's ruining the experience. Bands like Jet Fuel & Ginger Ales, which has gained hundreds of thousands of listeners, are suspected to be AI-generated due to their lack of online presence and suspicious traits such as "uncanny valley vocals."

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OpenAI is Lehman Brothers: A crash is coming

The 2007 subprime mortgage crisis, fueled by a herd mentality and over-optimism, shares similarities with the current hype surrounding generative AI, which is being propped up by massive investments despite its lack of practical applications and significant problems. The unsustainable business model of companies like OpenAI, which are burning billions of dollars to "scale," may eventually lead to a collapse, causing a ripple effect throughout the tech industry and resulting in a significant loss of value.

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Language Model as Visual Explainer

LVX is a method that interprets vision models using a tree-structured linguistic explanation, combining the strengths of vision models and large language models (LLMs) to craft hierarchical visual attribute explanations. This approach allows for dynamic pruning and growth of the explanation tree, providing human-understandable explanations and enabling the refinement of the vision model's knowledge of visual attributes.

ArXiv

Invariants: Computation and Applications

Invariants, which remain unchanged under transformations, are a fundamental concept in mathematics with numerous applications. Research on invariants, particularly in differential and algebraic invariant theories, continues to be an active area, with recent work focusing on developing algorithms for computing invariants and solving equivalence problems in geometry and algebra.

ArXiv

Explaining Large Language Models Decisions Using Shapley Values

Large language models (LLMs) have potential applications in simulating human behavior, but their validity is uncertain due to divergences from human processes and sensitivity to prompt variations. A novel approach using Shapley values from cooperative game theory reveals "token noise" effects, where LLM decisions are disproportionately influenced by tokens with minimal informative content, raising concerns about the robustness of insights obtained from LLMs.

ArXiv

Are Language Models Actually Useful for Time Series Forecasting?

Large language models (LLMs) do not significantly improve time series forecasting performance, and in some cases, removing or replacing them can even lead to better results. Pretrained LLMs also fail to outperform models trained from scratch and do not effectively represent sequential dependencies in time series data.

ArXiv

Syntax error recovery in parsing expression grammars (PEG parsers)

Parsing Expression Grammars (PEGs) lack a good error recovery mechanism, making them unsuitable for use in Integrated Development Environments (IDEs) that require building syntactic trees for incomplete or syntactically invalid programs. A proposed extension to PEGs, using labeled failures and recovery expressions, adds a syntax error recovery mechanism, allowing for more robust parsing and demonstrated effectiveness in a Lua language parser implementation.

ArXiv

Show HN: Aicmt – Auto-split Git changes

Aicmt is an AI-powered Git commit assistant that generates commit messages and automatically splits code changes into multiple well-organized commits following best practices. It can be installed using pip or brew and allows users to control the number of commits generated, or let AI decide the optimal number.

Repo

Show HN: I made an Neuroscience-Inspired Memory Layer for LLM Applications

HawkinsDB is a neuroscience-inspired memory layer for large language model (LLM) applications, designed to store and recall information in a more human-like way. It's based on Jeff Hawkins' Thousand Brains Theory and supports multiple types of memory, including semantic, episodic, and procedural, allowing for more nuanced and context-aware queries.

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Agentarium: Creating social simulations with AI Agents

Agentarium is a Python framework for managing and orchestrating AI agents, providing a flexible and intuitive way to create, manage, and coordinate interactions between multiple AI agents in various environments. It features advanced agent management, robust interaction management, and a checkpoint system, among others, and is designed for efficiency and scalability.

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Show HN: OSS Ecosystem for WhatsApp Marketing, Sales and Transactional Comms

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Source to Prompt: Turn code projects into LLM prompts

Your Source to Prompt is a single HTML file that runs in a browser, allowing users to select code files and combine them into a single text output for Large Language Models, with features like local security, no dependencies, and preset management. It stands out from existing tools by addressing pain points such as working with private repositories and repetitive file selection.

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