QFM017: Machine Intelligence Reading List May 2024

Everything that I found interesting in May 2024 about machines behaving intelligently

Matthew Sinclair
12 min readJun 5, 2024
Source: Photo by Michael Dziedzic on Unsplash

In this month’s edition of the Quantum Fax Machine’s Machine Intelligence Reading List, we explore a range of topics in machine intelligence and generative AI:

We start with Anthropic — Prompt Engineering, an in-depth interactive tutorial on crafting effective prompts for Claude language models. This guide is useful for anyone looking to refine their prompt engineering skills, covering everything from basic techniques to advanced strategies for avoiding hallucinations and optimising role prompting.

For those interested in cutting-edge retrieval augmented generation (RAG), a State-of-the-Art Exact Binary Vector Search for RAG in 100 lines of Julia provides a demonstration of achieving state-of-the-art performance with minimal code. The article emphasises the efficiency and cost benefits of binary vector spaces, making it a good resource for anyone looking to push ahead their RAG pipelines.

Exploring the broader implications of AI, Malleable software in the age of LLMs speculates on a future where large language models democratise software development. This potential shift could empower everyday users to create and modify software tools, fundamentally changing software production and distribution.

Regarding safety and ethics, Refusal in LLMs is mediated by a single directive that examines how refusal behaviour in large language models can be manipulated by altering specific activation directions. This research underscores the fragility of safety fine-tuning and highlights the need for robust AI safety mechanisms.

On the industry front, OpenAI’s anticipated challenge to Google with its own search engine is detailed in OpenAI to Challenge Google with Its Own Search Engine in May. This move could disrupt the search engine market, perhaps offering a new paradigm in web search.

Finally, AI Copilots Are Changing How Coding Is Taught explores the integration of generative AI into coding education. This article discusses how AI tools reshape curricula, aid problem-solving, and focus on higher-level thinking despite challenges like AI hallucinations and ethical considerations.

And many more!

The Quantum Fax Machine Propellor Hat Key will guide your browsing. Enjoy!

Anthropic — Prompt Engineering (thenameless.net): The Anthropic Prompt Engineering Interactive Tutorial provides a step-by-step guide for individuals with a basic understanding of AI to master the art of crafting effective prompts for Claude language models. The course covers essential techniques from basic prompt structuring to advanced methods like avoiding hallucinations and using role prompting, ensuring participants can build and troubleshoot prompts for various applications.

#AI #PromptEngineering #Claude #TechTutorial #LanguageModels

State-of-the-Art Exact Binary Vector Search for RAG in 100 lines of Julia (domluna.com): The article demonstrates how to achieve state-of-the-art exact binary vector search for Retrieval Augmented Generation (RAG) using only 100 lines of Julia code. It highlights the benefits of binary vector spaces, such as reduced server costs and improved in-memory retrieval feasibility, and provides benchmarking results showing the efficiency and speed of this approach compared to traditional methods.

#BinaryVectorSearch #RAG #JuliaProgramming #MachineLearning #DataScience

Malleable software in the age of LLMs (geoffreylitt.com): The article explores the potential impact of large language models (LLMs) like GPT-4 on software development, suggesting that soon all computer users may be able to create and modify small software tools. This shift could lead to significant changes in software production and distribution, enabling users to perform tasks such as creating one-off scripts and GUIs, developing custom in-house software, and extending existing applications without traditional programming skills.

#LLMs #SoftwareDevelopment #EndUserProgramming #AI #GPT4

5 Big Myths Of AI and Machine Learning Debunked (splunk.com): Artificial intelligence (AI) and machine learning (ML) are often misunderstood, leading to misconceptions about their capabilities and limitations. This article debunks five common myths, clarifying that AI and ML are not the same, are not magic solutions, do not require advanced degrees to understand, will not replace all jobs, and do not need perfect data to be effective.

#AI #MachineLearning #TechMyths #BusinessInnovation #FutureTech

Ilya 30u30 AI papers for John Carmack (arc.net): Ilya Sutskever provided John Carmack with a curated list of around 30 research papers, suggesting that mastering this selection would cover 90% of the essential knowledge in the field of artificial intelligence today.

#AI #Research #JohnCarmack #IlyaSutskever #TechReading

John Carmack’s ‘Different Path’ to Artificial General Intelligence (dallasinnovates.com): John Carmack, a renowned tech innovator, is independently pursuing artificial general intelligence (AGI) through his startup Keen Technologies, aiming to achieve human-level AI by 2030. Carmack believes his unique approach and extensive background in gaming, aerospace, and virtual reality position him well to contribute significantly to this challenging goal.

#ArtificialIntelligence #JohnCarmack #TechInnovation #AGI #FutureTech

Diffusion Models — Paper Explanation — Math Explained (youtube.com): This article provides an in-depth explanation of diffusion models, covering their mathematical foundations and operational principles in generating data. It details how these models function by iteratively refining noise into coherent data through a reverse diffusion process.

#MachineLearning #AI #DiffusionModels #DataScience #Mathematics

I’m Bearish OpenAI (stovetop.substack.com): The author argues that OpenAI is unlikely to surpass Apple, Google, and Meta in software or hardware and must be the first to achieve Artificial General Intelligence (AGI) to remain competitive. However, due to significant brain drain and a focus on consumer products that waste valuable computing resources, the author doubts OpenAI’s ability to reach AGI soon.

#OpenAI #ArtificialIntelligence #TechIndustry #AGI #BigTech

I Want Flexible Queries, Not RAG (win-vector.com): John Mount argues that while retrieval augmented generation (RAG) attempts to address the limitations of large language models (LLMs) by supplementing them with external references, the true value of LLMs lies in their flexible natural language query interface rather than their generative capabilities, which often produce unreliable results. He illustrates this with a personal anecdote where a traditional search engine, despite its flaws, provided a more satisfactory outcome than a purely generative AI.

#AI #NaturalLanguageProcessing #MachineLearning #SearchEngines #GenerativeAI

Introduction to gpt-4o (openai.com): GPT-4o integrates text, audio, and visual inputs into a single model, enhancing its ability to process and generate multimodal outputs more cohesively. Currently, the API supports text and image inputs, with audio capabilities expected soon.

#AI #MachineLearning #TechInnovation #OpenAI #GPT4

Diffusion Models | Paper Explanation | Math Explained (youtube.com): The video provides a detailed explanation of diffusion models, covering their fundamental concepts, mathematical derivations, and comparisons with other methods, aiming to make the topic accessible and understandable.

#DiffusionModels #MachineLearning #AI #DeepLearning #MathExplained

Ways to think about AGI (ben-evans.com): The article discusses the long-standing debate over the potential and implications of Artificial General Intelligence (AGI), referencing historical predictions, current developments in AI, and differing expert opinions on its feasibility and risks. It highlights how AGI, if achieved, could fundamentally change automation and poses significant ethical and existential considerations.

#AGI #ArtificialIntelligence #AIResearch #TechDebate #FutureTech

AI and problems of scale (ben-evans.com): The article discusses how generative AI enables the automation of tasks at a massive scale, fundamentally changing their nature and raising new ethical and practical concerns. It illustrates this through examples like surveillance, face recognition, and deepfakes, emphasizing that these issues are often rooted in societal and cultural perceptions rather than the technology itself.

#AI #Automation #Surveillance #Ethics #Technology

What I mean when I say that machine learning in Elixir is production-ready (cigrainger.com): The article argues that machine learning in Elixir is not only ready for production but is highly efficient due to its integration with the BEAM and OTP, providing robust, fault-tolerant systems. The Nx library’s architecture and capabilities, such as multi-stage compilation and hardware agnosticism, further enhance Elixir’s suitability for machine learning tasks.

#Elixir #MachineLearning #BEAM #Nx #ProductionReady

How LLMs Work, Explained Without Math (miguelgrinberg.com): The article explains how Large Language Models (LLMs) like GPT work without using advanced mathematics, describing their core functionality of predicting the next token in a text sequence based on the input provided. It covers the concepts of tokenization, probability prediction, and the use of neural networks with a focus on the Transformer architecture and its attention mechanism.

#GenerativeAI #LLMs #MachineLearning #AIExplained #Tokenization

Apple to unveil AI-enabled Safari with iOS 18 & macOS 15 (appleinsider.com): Apple plans to launch a new AI-powered version of Safari with iOS 18 and macOS 15, featuring advanced content blocking, an “Intelligent Search” tool for summarising web pages, and a “Web Eraser” for removing unwanted page elements, all accessible through an updated user interface.

#Apple #Safari #AI #iOS18 #macOS15

alessiodm-drl-zh — Deep Reinforcement Learning — Zero to Hero (github.com): The “Deep Reinforcement Learning: Zero to Hero” course provides a practical introduction to foundational deep reinforcement learning algorithms, guiding participants to write and understand key algorithms such as DQN, SAC, and PPO, while training AI to play Atari games and perform tasks like landing on the Moon.

#DeepReinforcementLearning #AI #MachineLearning #Python #Coding

Daniel Dennett ‘Where Am I?’ (thereader.mitpress.mit.edu): Daniel Dennett’s essay “Where Am I?” explores the philosophical and practical implications of a brain transplant, where his brain is removed and placed in a vat while his body operates remotely via radio links. This story delves into questions of identity, consciousness, and the nature of the self, illustrating the complexities and paradoxes that arise when considering where “self” truly resides.

#Philosophy #Consciousness #Identity #CognitiveScience #BrainInAVat

AlphaFold 3 predicts the structure and interactions of all of life’s molecules (blog.google): Google DeepMind and Isomorphic Labs have introduced AlphaFold 3, an advanced AI model that predicts the structure and interactions of a wide range of biomolecules with unprecedented accuracy, aiming to transform biological research and drug discovery. The model is available for free to scientists via the AlphaFold Server, enhancing the accessibility and speed of molecular predictions critical for understanding diseases and developing new treatments.

#AI #Biotechnology #DrugDiscovery #MolecularBiology #AlphaFold3

OpenAI Releases Its First AI-Made Music Video (techchilli.com): OpenAI has released its first AI-made music video, “The Hardest Part,” directed by Paul Trillo and produced using their new text-to-video generator, Sora, for the artist Washed Out. This launch aims to attract collaborations with major studios and media executives while sparking debate over the impact of AI on the creative industry.

#OpenAI #AIgenerated #MusicVideo #TechInnovation #ArtificialIntelligence

GenAI in 2024 — Another Decade in One Year? (medium.com): The article discusses how the rapid adoption of generative AI technologies in 2023, driven by tools like ChatGPT, has accelerated advancements typically seen over a decade into a single year. It outlines three major trends for 2024: the evolution of model orchestration, the transition from prototyping to production with a focus on data utilization, and the emphasis on return on investment (ROI) for AI investments, predicting continued innovation and practical applications across various industries.

#GenAI #AI2024 #MachineLearning #AITrends #TechInnovation

I will never go back — Ontario family doctor says new AI notetaking saved her job (globalnews.ca): Dr. Rosemary Lall, a family physician in Ontario, credits new AI note-taking software with saving her job by significantly reducing the time spent on administrative tasks, thus restoring her work-life balance and joy in her profession. The AI Scribe technology, which transcribes and organizes patient notes in real time, is being piloted with promising early results but raises concerns about data privacy and patient confidentiality.

#AI #Healthcare #TechInnovation #Ontario #MedicalTechnology

Better & Faster Large Language Models via Multi-token Prediction [2404.19737] (arxiv.org): Training large language models to predict multiple future tokens at once enhances sample efficiency, leading to improved performance on generative tasks and faster inference times without additional training overhead.

#LanguageModels #AI #MachineLearning #AIResearch #TokenPrediction

Refusal in LLMs is mediated by a single direction (lesswrong.com): The article discusses a study finding that refusal behavior in large language models (LLMs) is controlled by a single direction in the model’s activation space. By modifying this direction, researchers can either bypass or induce the refusal of harmful or harmless instructions, demonstrating the fragility of safety fine-tuning in open-source chat models. This method of controlling refusal behaviors validates interpretability results and highlights potential vulnerabilities in LLMs’ safety mechanisms.

#AI #MachineLearning #LLM #TechResearch #CyberSecurity

OpenAI to Challenge Google with Its Own Search Engine in May (beebom.com): OpenAI is reportedly planning to launch a search engine in May, potentially to coincide with Google’s annual developer conference, as suggested by a subdomain and SSL certificate for search.chatgpt.com. Speculations are based on information shared by Jimmy Apples and comments by OpenAI’s CEO Sam Altman, who hinted at a new approach to web search rather than merely replicating Google.

#OpenAI #Google #SearchEngine #AI #TechNews

AI Copilots Are Changing How Coding Is Taught (spectrum.ieee.org): Generative AI is reshaping how coding is taught by aiding students in understanding complex concepts and problem-solving, while educators strive to balance this technology with foundational computer science education. The integration of AI tools into coursework is evolving the curriculum to focus more on higher-level thinking and problem decomposition, despite challenges like AI hallucinations and ethical considerations.

#AI #CodingEducation #GenerativeAI #FutureOfLearning #TechInEducation

Regards,
M@

[ED: If you’d like to sign up for this content as an email, click here to join the mailing list.]

Originally published on quantumfaxmachine.com.

You can also check out the Slideshare version here:

--

--