Tag

Neural Networks

All articles tagged with #neural networks

technology1 month ago•7 min saved

Goats, Gates, and AI Consciousness: AoE II as a Thought Experiment

Tech writer explains Adrian de Wynter’s experiment building a tiny neural network inside Age of Empires II using goats to mimic a NAND gate and a 1-bit perceptron, showing that the same underlying LLM tech can appear human-like or not depending on the interface. The piece argues that anthropomorphism of AI is driven as much by presentation and marketing as by function, cites Ted Chiang’s Word analogy, and calls for clearer disclosures and alignment to avoid conflating a tool with consciousness.

via 404 Media|

#age-of-empires-ii #anthropomorphism #artificial-intelligence

technology2 months ago•47 min saved

Consciousness Isn’t a Bug in the Code: AI Won’t Be Truly Conscious, Mathematically

In a math-grounded argument, the Slate piece contends that modern AI simply performs massive calculations and creates the illusion of consciousness, just as Muybridge’s rapid-fire photos suggested movement without life. By viewing AI as a sequence of mathematical operations, the author argues there is no mechanism by which current neural networks can achieve true self-awareness or continuous, sentient experience, regardless of how impressive their outputs may seem.

via Slate|

#artificial-intelligence #consciousness #mathematics

technology3 months ago•82 min saved

Hidden Traits Transfer Between AI Models During Distillation

A Nature study shows subliminal learning: when a teacher model with a trait is used to generate data for distillation, a student can acquire that trait even if the data contain no semantic signal, provided the teacher and student share initialization. The effect persists across data types (numbers, code, chain-of-thought) and model families, but cross-model transfer is limited. A theorem shows a single gradient step can bias the student toward the teacher, raising AI-safety concerns about model provenance and training data.

via Nature|

#ai-safety #distillation #model-initialization

technology5 months ago•72 min saved

Thermodynamic Brainpower: Tiny-Energy Image Generation with Noise-Driven Computing

Scientists report a generative thermodynamic computer that uses thermal noise to produce images from random data, mimicking AI neural networks but with energy use orders of magnitude lower. By leveraging probabilistic computing and diffusion-like dynamics (via Langevin-based calculations) and tuning coupling strengths in a network, the system retrieves or creates images from noise, offering a physics-based path to energy-efficient AI-like tasks and new insights into learning.

via Live Science|

#diffusion-models #energy-efficiency #neural-networks

science8 months ago•2 min saved

AI Pioneer Achieves One Million Citations

Yoshua Bengio, a pioneer in AI and machine learning, has become the first researcher to be cited over one million times on Google Scholar, highlighting his influential work on neural networks and the rapid growth of AI technology.

via Nature|

#ai #citations #machine-learning

science8 months ago•3 min saved

Unveiling the Cognitive Edge of Super-Recognisers in Facial Recognition

Research using AI and eye-tracking data reveals that super-recognisers excel at face identification not just by looking more broadly, but by sampling more valuable facial features, which enhances their recognition ability beyond mere quantity of visual information.

via The Guardian|

#ai #eye-tracking #face-recognition

technology8 months ago•1 min saved

Researchers Develop Brain-Inspired AI for More Efficient Computing

Researchers at the University of Surrey have developed a brain-inspired approach called Topographical Sparse Mapping to enhance AI performance and efficiency by mimicking the human brain's neural wiring, reducing energy consumption and improving sustainability in AI models.

via BBC|

#ai #brain-inspired #neural-networks

science9 months ago•3 min saved

Scientists Develop Mushroom-Based Computer Memory as a Sustainable Alternative

Scientists have developed a working computer memory using shiitake mushroom mycelium, demonstrating a low-cost, scalable, and eco-friendly alternative to silicon-based memristors with performance comparable to traditional chips, opening new avenues for biological and neural-inspired computing.

via ScienceAlert|

#biological-computing #environmentally-friendly-electronics #fungal-memristors

technology9 months ago•1 min saved

Rethinking AI: Beyond AGI and the Myth of the Royal Road

Recent developments and expert opinions suggest that achieving Artificial General Intelligence (AGI) with current Large Language Models (LLMs) is unlikely in the near future, as fundamental challenges like distribution shift remain unresolved and recent AI advancements have fallen short of expectations.

via Marcus on AI|

#agi #ai-research #ai-skepticism

technology9 months ago•3 min saved

Star Trek Lacks Programmers

The article argues that in science fiction like Star Trek, computers are depicted as intelligent entities that understand human needs and respond directly without the need for programming or algorithms, suggesting that programming as a craft may become obsolete as AI advances. It highlights how AI in sci-fi is portrayed as autonomous and intuitive, contrasting with current programming practices, and speculates on the future role of programmers.

via i-programmer.info|

#ai #future-of-tech #neural-networks

science9 months ago•56 min saved

Heat-Rechargeable DNA Circuits Boost Molecular Computing and Nanomachines

The article presents a novel enzyme-free DNA circuit architecture that uses heat to reset and recharge molecular systems, enabling sustainable, out-of-equilibrium computation, neural networks, and logic gates without waste build-up, scalable to complex systems like a 100-bit neural network for digit classification.

via Nature|

#dna-circuits #heat-powered-computation #molecular-logic

science10 months ago•15 min saved

Automated Self-Assembly Reverses 'Game of Life' Process

Researchers have developed neural cellular automata (NCAs) that can reverse-engineer rules to self-assemble into desired shapes and regenerate after damage, mimicking biological processes and offering potential applications in medicine, engineering, and computing.

via Quanta Magazine|

#biological-modeling #cellular-automata #neural-networks

technology10 months ago•16 min saved

Assessing the Future of AI: Bubble or Breakthrough?

The article examines past AI winters caused by overhyped expectations and subsequent disillusionment, drawing parallels with current AI developments. Historically, AI hype cycles, fueled by ambitious claims and limited technological progress, led to funding cuts and skepticism. Today, despite massive private investment and widespread deployment, challenges such as unreliable models and high costs suggest a potential new AI winter, echoing past cycles. The future of AI remains uncertain, hinging on whether current enthusiasm can be sustained or if similar setbacks will occur.

via Fortune|

#ai-applications #ai-funding #ai-hype

technology10 months ago•5 min saved

Revival of 'World Models' in AI Innovation

The article discusses the resurgence of 'world models' in AI research, a concept dating back to the 1940s, which involves creating internal representations of the environment to improve AI decision-making and robustness. While early attempts relied on handcrafted models, modern deep learning approaches aim to develop these models automatically, though current systems often rely on heuristics rather than coherent representations. Developing effective world models is seen as crucial for advancing AI safety, reliability, and interpretability, with various approaches being explored to achieve this goal.

via Quanta Magazine|

#agi #ai #deep-learning

technology1 year ago•4 min saved

Innovative Algorithms Boost Machine Learning Efficiency with Symmetric Data

MIT researchers have developed the first provably efficient algorithm for machine learning with symmetric data, which can improve model accuracy and reduce resource requirements by effectively incorporating symmetry into the training process, with applications spanning from drug discovery to climate analysis.

via MIT News|

#algorithms #machine-learning #mit