A review exploring how the current artificial intelligence revolution is driven more by capital interests and hype than technological necessity. The piece examines Cory Doctorow’s argument that technology development is often steered toward maximizing investor returns rather than human empowerment, leading to a phenomenon known as "reverse centaurs" where workers lose autonomy and skill to machines. It critiques the industry's perceived inevitabilism and its similarities to the process of enshittification seen in other tech sectors.
* The economic motivations behind artificial intelligence hype
* Concept of reverse centaurs versus automation theory
* Critique of Big Tech business models
* Impact of capitalism on technological progress
* The economic motivations behind artificial intelligence hype
* Concept of reverse centaurs versus automation theory
* Critique of Big Tech business models
* Impact of capitalism on technological progress
As artificial intelligence continues to advance and outperform humans in specific tasks like mathematics or complex gaming, the question arises whether human cognition will remain unique. Tom Griffiths argues that intelligence is not a single linear scale but a multifaceted trait shaped by different constraints. While AI excels at processing vast amounts of data using scalable hardware, human intelligence is uniquely defined by biological limitations such as short lifespans and limited neural capacity. These constraints have forced humans to develop specific strengths in pattern recognition, social cooperation, and efficient learning from minimal experience. Ultimately, rather than seeing AI as a direct rival on all fronts, we should view it as a different kind of entity with its own set of capabilities and weaknesses.
- Intelligence is multifaceted rather than a single scale like height.
- Human intelligence is shaped by biological constraints such as lifespan and brain size.
- AI intelligence is driven by data volume, scalability, and machine communication.
- Different underlying architectures lead to different methods of problem-solving.
- Humans and AI are likely to be companions with distinct capabilities rather than total competitors.
- Intelligence is multifaceted rather than a single scale like height.
- Human intelligence is shaped by biological constraints such as lifespan and brain size.
- AI intelligence is driven by data volume, scalability, and machine communication.
- Different underlying architectures lead to different methods of problem-solving.
- Humans and AI are likely to be companions with distinct capabilities rather than total competitors.
Driven by labor shortages, Japan is leading the "Physical AI" sector by integrating AI with its advanced mechatronics and hardware expertise. Supported by significant government funding, the nation is moving from experimental trials to practical deployments in logistics, manufacturing, and defense, aiming for global market dominance by 2040.
This article features Noyuri Mima, professor at Hakodate Future University, and explores her journey in the field of science and technology. Miwa recounts a pivotal moment in her youth when she visited the Japan IBM headquarters in 1977 and witnessed a computer instantly print a calendar based on her birthdate. This experience ignited her passion for computers. The article delves into her background, including her education at Toyo English Women's College and her early interest in mathematics and science, highlighting her as a pioneer for women in STEM.
Amazon outages linked to rapid AI integration were discussed in a recent internal meeting. AI glitches in algorithms managing infrastructure caused disruptions (e.g., issues viewing product details, Freevee streaming). While Amazon is aggressively using AI, sources say the speed is creating instability. The company is focused on reliability amidst growing AI competition. Amazon declined to comment specifically but affirmed commitment to customer experience
NVIDIA GTC is the premier AI conference and exhibition. Learn about the latest advancements in AI, deep learning, and accelerated computing. Includes keynote speakers, sessions, workshops, and an exhibit hall.
For some, artificial intelligence tools answer questions and make life more efficient. But for others, AI has become a form of companionship – a virtual friend, a therapist, even a romantic partner. Is AI a cure for loneliness? Or is this a symptom of something gone very wrong? Horizons moderator William Brangham explores AI relationships with Sherry Turkle, Justin Gregg and Nick Thompson.
In this tutorial, we build a hierarchical planner agent using an open-source instruct model. We design a structured multi-agent architecture comprising a planner agent, an executor agent, and an aggregator agent, where each component plays a specialized role in solving complex tasks. We use the planner agent to decompose high-level goals into actionable steps, the executor agent to execute those steps using reasoning or Python tool execution, and the aggregator agent to synthesize results into a coherent final response. By integrating tool usage, structured planning, and iterative execution, we create a fully autonomous agent system that demonstrates how modern AI agents reason, plan, and act in a scalable and modular manner.
Permacomputing is a concept and community of practice oriented around resilience and regenerativity in computer and network technology, inspired by permaculture. It encourages a sustainable approach to computing, maximizing hardware lifespans, minimizing energy use, and focusing on existing resources.
Announcement of the SF Permacomputing Club, a meetup group discussing and working with the principles of permacomputing, focusing on resilience, regenerativity, and alternatives to mainstream computing infrastructure.
