u/Tobio-Star

TLDR: A group of researchers attempted to replicate the brain's plasticity by designing a neural network with real-time self-organization abilities, where neural connections change continuously as new data is processed. They bet on generalization emerging from continual adaptation

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➤Key quotes:

>Researchers have designed a new type of large language model (LLM) that they propose could bridge the gap between artificial intelligence (AI) and more human-like cognition.

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>Called "Dragon Hatchling," the model is designed to more accurately simulate how neurons in the brain connect and strengthen through learned experience, according to researchers from AI startup Pathway.

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>They described it as the first model capable of "generalizing over time," meaning it can automatically adjust its own neural wiring in response to new information. Dragon Hatchling is designed to dynamically adapt its understanding beyond its training data by updating its internal connections in real time as it processes each new input, similar to how neurons strengthen or weaken over time.

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>Unlike typical transformer architectures, which process information sequentially through stacked layers of nodes, Dragon Hatchling's architecture behaves more like a flexible web that reorganizes itself as new information comes to light. Tiny "neuron particles" continuously exchange information and adjust their connections, strengthening some and weakening others.

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>Over time, new pathways form that help the model retain what it's learned and apply it to future situations, effectively giving it a kind of short-term memory that influences new inputs.

➤IMPORTANT CAVEAT

>In tests, Dragon Hatchling performed similarly to GPT-2 on benchmark language modeling and translation tasks — an impressive feat for a brand-new, prototype architecture, the team noted in the study.

>Although the paper has yet to be peer-reviewed, the team hopes the model could serve as a foundational step toward AI systems that learn and adapt autonomously.

TLDR: Google is launching a $200K Kaggle competition to build better benchmarks inspired by cognitive science (neuroscience + psychology). They define 10 dimensions of intelligence observed in humans including unusual categories like metacognition and attention. The idea is to make AI evaluation a more rigorous science, grounded in proven cognitive science, and maybe less susceptible to benchmaxxing.

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➤Key quotes:

> Tracking progress toward AGI will require a wide range of methods and approaches, and we believe cognitive science provides one important piece of the puzzle.

> Our framework draws on decades of research from psychology, neuroscience and cognitive science to develop a cognitive taxonomy. It identifies 10 key cognitive abilities that we hypothesize will be important for general intelligence in AI systems:

1. Perception: extracting and processing sensory information from the environment
2. Generation: producing outputs such as text, speech and actions
3. Attention: focusing cognitive resources on what matters
4. Learning: acquiring new knowledge through experience and instruction
5. Memory: storing and retrieving information over time
6. Reasoning: drawing valid conclusions through logical inference
7. Metacognition: knowledge and monitoring of one's own cognitive processes
8. Executive functions: planning, inhibition and cognitive flexibility
9. Problem solving: finding effective solutions to domain-specific problems
10. Social cognition: processing and interpreting social information and responding appropriately in social situations

> We propose a three-stage evaluation protocol [for each ability] : evaluate AI systems across a broad suite of cognitive tasks → collect human baselines for the same tasks → compare each AI system’s performance relative to human performance

> To put this theory into practice, we are launching a new Kaggle hackathon. The hackathon encourages the community to design evaluations for five cognitive abilities where the evaluation gap is the largest: learning, metacognition, attention, executive functions and social cognition.