Amazon blew Alexa's shot to dominate AI, according to employees (2024)

Amazon's attempt to revamp its Alexa voice assistant with generative AI has been hindered by structural dysfunction, technological challenges, and a lack of access to sufficient data and specialized computer chips, causing significant delays. Despite a promising demo in September 2023, the new Alexa is still not ready for release, and the company is struggling to catch up with its Big Tech rivals, including Google, Microsoft, and Apple, which have made significant strides in integrating AI into their digital assistants.

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The 'dangerous' promise of a techno-utopian future

The US government has announced a $100 billion joint venture called Stargate to build the infrastructure for the next generation of AI, with plans to invest up to $500 billion and create 100,000 jobs. However, critics like philosopher Emile P. Torres warn that the push for AI development, driven by ideologies like transhumanism and longtermism, poses significant risks to humanity, including environmental degradation, job displacement, and the potential for artificial superintelligence to become a force beyond human control.

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You are using Cursor AI incorrectly

The author has been observing how software engineers use Cursor, a tool that can be used to automate tasks, and has identified several common mistakes, including using it as a replacement for Google Search and not utilizing its full potential. To use Cursor effectively, the author recommends building a "stdlib" of thousands of prompting rules and composing them together, starting with a rule that describes where to store the rules, such as in a specific directory with a standardized naming convention.

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Take-Two CEO Strauss Zelnick: 'there's no such thing' as artificial intelligence

Take-Two CEO Strauss Zelnick believes that artificial intelligence is an "oxymoron" and doesn't truly exist, instead considering it a digital tool that will increase efficiency and productivity in the gaming industry without reducing employment. Zelnick has consistently expressed this view, also highlighting the risk of copyright infringement associated with using large language models and emphasizing the importance of protecting intellectual property.

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Why AI Is a Philosophical Rupture

Tobias Rees, a philosopher and founder of an AI studio, believes that AI challenges traditional human understanding by defying fundamental concepts that have defined the modern period, such as the clear-cut distinction between humans and machines. He argues that AI is a form of intelligence, albeit different from human intelligence, and that its capabilities can be complementary to human intelligence, potentially allowing us to be "smarter together" by operating on scales and processing information in ways that are beyond human capability.

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STP: Self-Play LLM Theorem Provers with Iterative Conjecturing and Proving

The Self-play Theorem Prover (STP) addresses the challenge of limited training data in formal theorem proving by having two roles: a conjecturer that generates increasingly challenging conjectures and a prover that attempts to prove them. STP achieves state-of-the-art performance, doubling the previous best result in the LeanWorkbook dataset and outperforming other methods in various benchmarks, by iteratively training the conjecturer and prover to improve each other.

ArXiv

Leveraging Multimodal LLM for Inspirational User Interface Search

Inspirational search in mobile user interface design is hindered by existing AI-based methods that often miss crucial semantics and require limited metadata. A new approach using a multimodal large language model (MLLM) to extract and interpret semantics from mobile UI images has been developed, outperforming existing methods and offering a more enriched search experience for UI designers.

ArXiv

Test-time scaling new approach: extra test-time compute improves LLM reasoning

Researchers developed a simple approach to test-time scaling for language modeling, using a curated dataset and a technique called budget forcing to control test-time compute, which led to improved performance on math questions. Their model, s1-32B, outperformed OpenAI's o1 model by up to 27% on certain questions and was able to extrapolate beyond its initial performance without test-time intervention, with the model, data, and code made open-source.

ArXiv

Value-Based Deep RL Scales Predictably

Value-based off-policy reinforcement learning (RL) methods can be predictable in their performance despite their reputation for pathological behavior, with their data and compute requirements lying on a Pareto frontier controlled by the updates-to-data ratio. By estimating this frontier, it is possible to predict the required data and compute for a given performance level, allowing for optimal allocation of resources and hyperparameter tuning to maximize performance within a budget.

ArXiv

Bolt: Bootstrap long chain-of-thought in LLMs without distillation [pdf]

Large language models (LLMs) have shown impressive reasoning capabilities, particularly with the use of long chain-of-thought (LongCoT) methods, which enable them to analyze problems and devise plans. A new approach, called BOLT, has been introduced to enable LLMs to develop LongCoT capabilities without relying on knowledge distillation from existing models or expensive human annotations, and has achieved impressive results on various benchmarks.

ArXiv

Show HN: Daily-notes.nvim – fuzzy time journal and planning plugin

The daily-notes.nvim plugin is a Neovim plugin that enables creating periodic notes for journals and planning, inspired by Obsidian's feature and Journal.nvim. It allows users to create and manage daily, weekly, or monthly notes with customizable templates and date formats, and can be integrated with other plugins such as telescope-file-browser and zen-mode.

Repo

Show HN: Minimalist LLM Commandline Tool

The ai tool is a minimalist command-line utility that allows users to interact with various language models, such as GPT-4, by reading questions from standard input and writing responses to standard output. To use the tool, users must install it using go install, obtain API keys from model providers like OpenAI or Anthropic, and set these keys in their shell environment.

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Using VSCode to track and visualize AI experiments

The DVC Extension for Visual Studio Code allows users to run, compare, visualize, and track machine learning experiments directly within the VS Code environment, utilizing the open-source data versioning and ML experiment management tool DVC. This extension provides features such as experiment tracking, visualization, live tracking, reproducibility, and data management, all while keeping data under the user's control and utilizing existing Git hosting for sharing and collaboration.

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Show HN: Open-source self-hosted AI voice interviewer platform for Hiring

FoloUp is an open-source platform that utilizes AI to conduct voice interviews for hiring, allowing companies to generate tailored interview questions and analyze candidate responses. The platform can be set up locally by cloning the project, configuring environment variables, and installing necessary dependencies, and also offers options for self-hosting and contributing to the project.

Repo

Show HN: llm-fuse – Aggregate Repository Files for LLM Context

llm-fuse is a command-line tool that generates an aggregated text file from numerous files within a repository, allowing for local directory scanning, Git-tracked file filtering, and remote repository cloning. The tool provides various features such as file filtering, token counting, and content chunking, and can be installed globally using pip or pipx, with usage examples including processing local directories and remote repositories with customizable options.

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