All topics / AI applications
Topic
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AI applications
1
episodes
1
thinkers
3h
of conversation
12
books & papers
5
terms defined
The neighbourhood: AI applications and the ideas it travels with. Drag to roam, click a star for the episode, click a neighbour to travel.
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From foundational to frontier
Climb the spectrum. The most accessible conversations come first.
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ACCESSIBLECOREFRONTIER
The lexicon
Every term the guests lean on, in plain language. Read one in full, or filter to find it.
What the corpus says
The throughline across every conversation that touches this idea.
Self-supervised learning mimics human observational learning without explicit task reinforcement, offering more efficient learning than supervised or reinforcement methods.
Predicting future events from video using self-supervised learning is complex due to the multitude of plausible continuations.
Contrastive learning requires positive and negative pairs, while non-contrastive methods focus on maximizing mutual information between outputs.
AI systems like Tesla's autopilot use multitask learning to manage over a hundred tasks simultaneously, enhancing system performance.
AI can potentially solve global challenges like climate change by designing new materials and stabilizing plasma for fusion reactors.
Voices on AI applications
4 standout quotes from across the corpus.
Go read
12 books and papers cited across these episodes.
For the specialist
What experts find new
3 expert-level takeaways for a specialist reader.
At the frontier
Still unresolved
2 open questions flagged across these conversations.
The thinkers
Who takes this idea on, by how often they return to it.