QFM049: Machine Intelligence Reading List January 2025
Everything that I found interesting last month about machines behaving intelligently.
Tags: qfm, machine, intelligence, reading, list, january, 2025
Source: Photo by Michael Dziedzic on Unsplash
QFM049: Machine Intelligence Reading List January 2025
This month’s Machine Intelligence Reading List tracks the ongoing realignment of the AI landscape, with a particular focus on employment shifts, the maturation of AI development practices, and the increasing presence of open-source challengers. It also highlights the philosophical and structural consequences of AI’s advance, both in the workforce and the economy.
AI’s effect on employment remains a focal point, with a World Economic Forum survey indicating that 41% of employers worldwide anticipate reducing staff due to AI by 2030. This aligns with concerns about automation replacing specific job categories while potentially creating new ones, though which industries will experience net growth remains uncertain. Similarly, Dustin Ewers argues that AI will not eliminate software development as an industry but rather reshape it, making development more efficient and shifting the skills in demand.
Open-source AI development continues to disrupt established players, particularly in the case of DeepSeek, a Chinese AI company whose open-source models rival OpenAI’s best at significantly lower costs. The rapid rise of DeepSeek has already caused shifts in market valuations, with Nvidia’s stock price taking a hit due to concerns about cheaper alternatives disrupting AI chip demand. Meanwhile, Berkeley researchers have demonstrated that replicating state-of-the-art AI models at a fraction of the cost is feasible, suggesting a broader shift towards commoditised AI.
On the technical front, infrastructure and platform design for AI systems remain key areas of discussion. Chip Huyen’s overview of generative AI platforms details how modular architectures, caching, and model orchestration ensure scalable, efficient AI applications. The AI Video Starter Kit offers a practical example, providing an open-source toolkit for integrating AI-driven video capabilities into web applications. Elsewhere, a shift toward AI-assisted software development is evident in the rise of Chat-Oriented Programming (CHOP), a methodology that re-frames coding as an interactive, AI-assisted process rather than a static workflow.
Beyond technical considerations, AI’s role in strategic decision-making and economic positioning is a recurring theme. Lukas Petersson’s two-part series, “AI Founder’s Bitter Lesson” and “No Power”, explores how vertical AI solutions struggle to establish defensible market positions, while horizontal AI models benefit from greater flexibility and scalability. This echoes Hamilton Helmer’s “7 Powers” framework, where only a few AI-driven businesses establish durable competitive advantages. Meanwhile, the strategic implications of AGI are explored in “Capital, AGI, and Human Ambition”, which questions whether advanced AI will entrench existing power structures or redistribute resources through mechanisms like universal basic income.
Finally, the role of AI in human creativity and cognitive augmentation remains an area of active debate. Brian Eno offers a critique, arguing that AI-generated content lacks the intentionality and provenance of human creativity. This stands in contrast to works like “The day I taught AI to read code like a Senior Developer”, which demonstrates how AI can enhance human expertise rather than replace it.
As always, the Quantum Fax Machine Propellor Hat Key will guide your browsing. Enjoy!
41% of Employers Worldwide Say They’ll Reduce Staff by 2030 Due to AI: A World Economic Forum survey highlights the impact of AI technology on global employment, revealing that 41% of companies expect to reduce staff by 2030. The report, which included feedback from 1,000 employers representing 14 million workers across 22 industries, noted the rise of AI and big data as top skills for the future. Despite potential job losses, a net growth in job numbers is anticipated as AI evolves, though certain roles such as graphic designers and legal secretaries may decline.
#AI #Employment #FutureOfWork #Technology #Innovation
Building A Generative AI Platform: Chip Huyen discusses the key components and strategies for building a generative AI platform. The post explores the platform’s architecture, starting from basics and progressively adding features like context enhancement, guardrails, model routers, gateways, caching, and orchestration for observability and robustness. It emphasizes the importance of modularity in system design and addresses challenges such as security, cost, and performance scalability.
#GenerativeAI #AIPlatform #MachineLearning #AIDevelopment #TechBlog
Things we learned about LLMs in 2024: In 2024, multiple organisations developed Large Language Models (LLMs) surpassing GPT-4’s capabilities, with some models now operable on personal devices. LLM costs decreased due to competition and efficiency improvements, while context lengths expanded, enabling more comprehensive inputs. Multimodal functionalities, including video and audio processing, became more prevalent, though fully autonomous AI agents remain unrealised. The environmental impact of LLMs showed mixed trends, and the need for improved critical evaluation of these models persists.
#LLMs #AI #GPT4 #ArtificialIntelligence #Technology
Agents Are Not Enough: The paper titled ‘Agents Are Not Enough’ explores the evolving role of autonomous agents in the field of Artificial Intelligence. Despite the appeal of generative AI, the authors argue that additional components like Sims, representing user preferences, and Assistants, coordinating user tasks, are crucial for creating an effective ecosystem. This ecosystem approach moves beyond traditional agent models to ensure sustainability and success.
#ArtificialIntelligence #AI #AutonomousAgents #Technology #Innovation
Why Everyone in AI is Freaking Out About DeepSeek: DeepSeek, a Chinese AI company, has emerged rapidly as a notable competitor to OpenAI with the release of its open-source language model DeepSeek-R1. This model matches and even exceeds the capabilities of OpenAI’s best models at a fraction of the cost, challenging established norms in AI development. The release has triggered widespread discussions, highlighting DeepSeek’s ability to break through U.S. export controls and offer powerful AI tools, raising questions about the competitive landscape and ethical considerations of AI technology and open-source distribution.
#AI #DeepSeek #OpenSource #TechInnovation #AIResearch
AI Founder’s Bitter Lesson. Chapter 1 - History Repeats Itself: In “AI Founder’s Bitter Lesson: Chapter 1 - History Repeats Itself”, the article delves into recurring patterns in the AI industry, highlighting the persistent success of general approaches over specific strategies. It emphasises that AI founders today are making similar mistakes to past researchers by investing in specialised domain knowledge, which is often surpassed by solutions leveraging broader computational power. As AI models improve, the significance of engineering effort in developing AI products diminishes, leading to a potential shift towards more flexible, general-purpose solutions. The article uses historical and current examples to illustrate the ongoing impact of this “bitter lesson.” It serves as a cautionary message for AI startups to focus on future-ready, scalable models while adapting to evolving industry standards.
#AI #Startups #BitterLesson #TechTrends #Innovation
AI Founder’s Bitter Lesson. Chapter 2 - No Power: In this article by Lukas Petersson, the focus is on the competitive dynamics between vertical and horizontal AI products. While vertical AI solutions were initially first to market and use domain-specific knowledge constraints, they risk being outperformed by more flexible, horizontal AI models as computing power evolves. The major challenge for verticals lies in their inability to establish sturdy market defenses within Hamilton Helmer’s 7 Powers framework, with cornered resources being a rare exception that could provide a competitive advantage.
#AI #TechStrategy #Startups #Innovation #ArtificialIntelligence
The day I taught AI to read code like a Senior Developer: The article describes a transformative experiment where the author taught AI to analyze code with the mindset of a senior developer rather than a novice. Initially, the AI struggled with complex codebases, producing unhelpful and basic insights. By adjusting the AI’s approach to focus on understanding code context, architecture, and historical evolution like experienced developers do, its analysis improved significantly, even identifying potential issues and understanding the broader impact of code changes.
#AI #MachineLearning #CodeAnalysis #DeveloperMindset #TechInnovation
Chat-oriented Programming (CHOP) in Action: The concept of “chat-oriented programming” (CHOP) is described as a transformative approach in software development, enhancing the traditional programming workflow by integrating AI coding assistants to provide contextual help and streamline coding processes. CHOP leverages iterative prompt refinement, aiding developers in understanding and navigating codebases more efficiently, which subsequently improves code writing and debugging through natural language interactions. This new paradigm could significantly shift how developers approach coding tasks, utilizing AI tools like Cody to expedite and optimize the software development lifecycle.
#ChatOrientedProgramming #AI #CodingAssistants #SoftwareDevelopment #ProgrammingParadigm
AI Video Starter Kit: The AI Video Starter Kit is a comprehensive toolkit designed to facilitate the creation of AI-powered video applications in the browser. It leverages Next.js and Remotion for seamless video processing, allowing for integration with advanced AI models like Minimax, Hunyuan, and LTX through fal.ai. The kit supports features such as browser-native video processing, multi-clip composition, and audio integration, making it ideal for developers looking to incorporate AI-driven video functionalities into their projects. It provides utilities like metadata encoding and a video processing pipeline, and supports deployment via Vercel.
#AIVideo #NextJS #Remotion #OpenSource #VideoProcessing
AI’s Walking Dog: Brian Eno critiques AI’s role in creative processes, arguing that its profit-driven development prioritises market share over social value and security. He contends that AI-generated content lacks intentionality and provenance, essential elements that imbue human creations with meaning. Eno warns that AI may encourage individuals to live in isolation from each other and their inner lives, diminishing the transformative magic of play inherent in human creativity.
#AI #Creativity #BrianEno #Technology #Society
Ignore the Grifters - AI Isn’t Going to Kill the Software Industry: In the article ‘Ignore the Grifters - AI Isn’t Going to Kill the Software Industry’, Dustin Ewers argues against the doomsayers who claim that AI will eliminate software developer jobs. He highlights that while AI will change the nature of these jobs, it will also create more opportunities by making software development more efficient and unlocking new projects with better ROI. The article discusses economic principles like Jevons Paradox and Comparative Advantage to explain why the software industry is likely to thrive rather than diminish with the growth of AI.
#AI #SoftwareDevelopment #Economics #FutureOfWork #Innovation
NeuralSVG: An Implicit Representation for Text-to-Vector Generation: NeuralSVG is a novel method for generating vector graphics from text prompts using implicit neural representations. By building on Vision-Language and diffusion models, NeuralSVG encodes graphics into a small MLP network allowing dynamic adjustments like color palette and aspect ratio while preserving a layered SVG structure. The approach optimizes for efficient, structured, and flexible outputs that prioritize practical usability in design fields and enable substantial control during the inference time.
#NeuralSVG #AI #VectorGraphics #MachineLearning #SVG
Nvidia shares sink as Chinese AI app spooks markets: Nvidia experienced a significant financial hit with a decline in stock value exceeding a sixth, driven by the sudden rise in popularity of DeepSeek, a Chinese artificial intelligence chatbot. This app, which rapidly became the top free download in the US, is said to be developed at a fraction of the cost associated with its competitors in the AI space. The market impact was not limited to Nvidia; other prominent tech firms with stakes in AI, including Microsoft and Google, also saw substantial decreases in their stock values.
#Nvidia #AI #StockMarket #DeepSeek #Technology
25 AI Predictions for 2025, from Marcus on AI: The article presents a series of predictions about AI and its development by the end of 2025, authored by Gary Marcus. He anticipates that despite the hype, artificial general intelligence (AGI) will not manifest this year, and AI models will continue to face challenges like reliability and hallucinations. While AI will not drastically alter the job market, it will influence various sectors through new tools and technologies. The piece also reviews Marcus’ past predictions, highlighting that while there has been slow progression in certain areas, other predicated issues like scaling challenges have come to light.
#AI #Predictions #TechFuture #GaryMarcus #ArtificialIntelligence
The 2025 AI Engineer Reading List: “The 2025 AI Engineer Reading List” offers a comprehensive guide for those looking to deepen their understanding of AI engineering. Covering 10 fields such as language models, vision, and code generation, the list curates around 50 essential papers, models, and blogs that are considered must-reads for the year. It emphasizes practical insights, highlighting why each paper is important and how it applies in real-world scenarios, catering to both beginners and seasoned engineers.
#AI #Engineering #ReadingList #Tech2025 #MachineLearning
Capital, AGI, and Human Ambition: This article explores the implications of Artificial General Intelligence (AGI) on the economy, particularly how it might shift the balance between human and non-human factors in production. It suggests that AI advancements could reduce the need for human labor, leading to a society that cares less about human welfare as existing power structures become more entrenched. The piece also debates whether post-AGI wealth disparities would exacerbate inequalities or lead to a redistribution of resources through measures like UBI. Ultimately, it questions if human ambition will be stifled in a future dominated by AI, potentially leading to a static and inert society.
#AGI #ArtificialIntelligence #FutureSociety #Economy #HumanAmbition
Plentiful, high-paying jobs in the age of AI: The article discusses the potential for high-paying jobs in the age of AI, examining both optimistic and pessimistic views. It tackles widespread concerns that AI will eventually replace human jobs, causing wage depreciation; however, the discussion also highlights the concept of comparative advantage, suggesting that humans might still retain jobs and even thrive economically. It weighs the impact of AI as a general-purpose technology, debating how constraints in computation and energy might influence AI’s role and the sustained value of human labor.
#AI #Careers #FutureOfWork #ComparativeAdvantage #Economy
What is an AI-first startup?: The article explores the concept of AI-first startups, which are envisioned to be fundamentally different from existing tech companies, akin to the difference between Uber and traditional taxi firms. These startups are enabled and driven by advancements in AI and will naturally benefit from and adapt to such progress. Key topics include the potential economic shifts driven by AI, the challenges traditional businesses may face from AI-first models, and a look into hypothetical future businesses leveraging AI. There is an emphasis on AI’s ability to make intelligence more accessible, redefining roles within businesses and the broader economy.
#AIstartups #innovation #AIprogress #futuretech #AIeconomy
Where will AI be at the end of 2027? A bet: In a compelling article, Gary Marcus and Miles Brundage engage in a fascinating bet regarding the future of AI by 2027. They predict whether AI systems will be able to accomplish 8 out of 10 specified complex tasks, with the wager reflecting their opposing views—Marcus as a skeptic of generative AI and Brundage as an advocate. The tasks outlined include capabilities such as writing NYT-quality obituaries, generating Nobel-caliber scientific discoveries, and more. The outcome of this bet will provide a measurable test for AI progress towards achieving Artificial General Intelligence.
#AI #FutureTech #ArtificialIntelligence #GeniusBet #TechDebate
The Future is Now, If You Want It: This article delves into the rapid expansion of artificial intelligence (AI) as a crucial technology, highlighting its role as a deflationary force that makes things cheaper. The author encourages readers to consider personal preferences on whether they want more of the same for less cost or the same for a lower price. As technology costs approach zero, the landscape of content production and marketing is set to shift dramatically, creating unique individualized experiences while posing challenges like targeting phishing attacks.
#AI #Technology #FutureTech #Innovation #DigitalMarketing
Markdown for the AI Era with AgentMark: AgentMark introduces a toolset for developing type-safe prompts and agents specifically tailored for markdown and JSX. It brings a seamless integration of features such as custom models, streaming, and observability, enhancing productivity for developers in the AI domain. Users can benefit from VSCode extension support, and compatibility with various languages, although full support is still under development for Python and Java.
#AgentMark #Markdown #AI #TechIntegration #JSX
DeepSeek R1 Replicated for $30 | Berkley’s STUNNING Breakthrough Sparks a Revolution.: Wes Roth discusses recent advancements in AI technology, focusing on Large Language Models (LLMs) and the anticipated arrival of Artificial General Intelligence (AGI). The video highlights Berkeley’s creation of the DeepSeek R1, an innovative AI model that was replicated at a fraction of the original cost, sparking potential widespread adoption. Stay informed with the latest updates from major AI players like OpenAI and Google, as they continue to lead the conversation in this rapidly-evolving field.
#AI #LLM #AGI #DeepSeek #OpenAI
Regards, M@
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Originally published on quantumfaxmachine.com and cross-posted on Medium.
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