EpiBrief-MMWR-LM
đź¦
Generate CDC-style epidemiological analysis
Bryan Tegomoh, MD, MPH PRO
BryanTegomoh
·
AI & ML interests
Medicine, Public Health, Biosecurity, Genomics Surveillance
Recent Activity
updated
a Space
about 2 months ago
BryanTegomoh/EpiBrief-MMWR-LM
updated
a dataset
about 2 months ago
BryanTegomoh/publichealth-bench
published
a dataset
about 2 months ago
BryanTegomoh/publichealth-bench
Organizations
None yet
updated
a
Space
about 2 months ago
updated
a
dataset
about 2 months ago
published
a
dataset
about 2 months ago
updated
a
dataset
about 2 months ago
published
a
dataset
about 2 months ago
updated
a
dataset
about 2 months ago
published
a
dataset
about 2 months ago
posted
an
update
about 2 months ago
Post
230
Why Artificial Superintelligence seems unreasonable with current Large Language Models...
Large Language Models (LLMs) are brilliant map readers, but not map makers.
They navigate existing human knowledge with amazing fluency, yet every discovery they make already lives somewhere inside their training data.
Einstein’s leap from Newtonian physics to relativity required inventing a new conceptual map, one that didn’t exist in any data before 1915.
A model trained only on pre-1915 text could never make that leap.
That’s the limitation of current transformer architectures: they interpolate, they don’t invent.
Transformers are optimized to predict, not to discover.
They work inside a fixed “context window”, a bubble of known tokens and learned patterns, and lose coherence when they step outside it.
They lack grounding in the real world, curiosity loops, and experimental feedback — the ingredients that fuel true scientific creation.
To reach Artificial Superintelligence (ASI), we’ll need new systems that:
Build and test causal models of reality
Learn continuously, not in frozen training runs
Possess memory, curiosity, and intrinsic goals
Combine neural, symbolic, and probabilistic reasoning
Until then, our smartest machines remain brilliant mirrors, reflecting the sum of human knowledge, not creating new universes of thought.
Large Language Models (LLMs) are brilliant map readers, but not map makers.
They navigate existing human knowledge with amazing fluency, yet every discovery they make already lives somewhere inside their training data.
Einstein’s leap from Newtonian physics to relativity required inventing a new conceptual map, one that didn’t exist in any data before 1915.
A model trained only on pre-1915 text could never make that leap.
That’s the limitation of current transformer architectures: they interpolate, they don’t invent.
Transformers are optimized to predict, not to discover.
They work inside a fixed “context window”, a bubble of known tokens and learned patterns, and lose coherence when they step outside it.
They lack grounding in the real world, curiosity loops, and experimental feedback — the ingredients that fuel true scientific creation.
To reach Artificial Superintelligence (ASI), we’ll need new systems that:
Build and test causal models of reality
Learn continuously, not in frozen training runs
Possess memory, curiosity, and intrinsic goals
Combine neural, symbolic, and probabilistic reasoning
Until then, our smartest machines remain brilliant mirrors, reflecting the sum of human knowledge, not creating new universes of thought.
updated
a
model
2 months ago
published
a
model
2 months ago
published
a
Space
2 months ago