ÜBER DIESEN PODCAST 🔗
Demystifying AI for the intelligently curious
Hosts:
Aktualisierungshäufigkeit:
weekly
Durchschnittliche Audiolänge:
20 minutes
Englisch
Vereinigte Staaten
332 Folgen
seit 16. November 2014
episodic
IN DIESEM PODCAST 🔗
AUDIENCE OF THIS PODCAST 🔗
~52.09% of listeners are from United States.
🇺🇸
US
52.09%
🇬🇧
GB
19.83%
🇦🇺
AU
10.33%
🇮🇪
IE
2.79%
🇮🇱
IL
2.79%
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IN
1.95%
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CA
1.25%
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DE
1.25%
🇸🇬
SG
0.83%
🇲🇦
MA
0.55%
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KR
0.41%
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BD
0.41%
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ZA
0.41%
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FR
0.41%
🇭🇰
HK
0.27%
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IT
0.27%
🇨🇱
CL
0.27%
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JP
0.27%
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HU
0.27%
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RU
0.13%
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CO
0.13%
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SC
0.13%
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TW
0.13%
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AR
0.13%
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GH
0.13%
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FI
0.13%
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UA
0.13%
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MX
0.13%
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MU
0.13%
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TR
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SA
0.13%
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PK
0.13%
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BG
0.13%
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AT
0.13%
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GT
0.13%
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SE
0.13%
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0.13%
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MY
0.13%
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NO
0.13%
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PL
0.13%
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ES
0.13%
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IQ
0.13%
Others
0.36%
* Data source: directly measured on Listen Notes. in Echtzeit
NEUESTE FOLGEN 🔗
GPUs are the engines powering every AI conversation you've ever had — but what's actually happening inside those chips when an LLM generates a response? Understanding GPU fundamentals turns out to be surprisingly useful for making sense of why inference works the way it does, from the prefill stage…
VERGANGENE FOLGEN SUCHEN
Vorhandene Folgen von Linear Digressions durchsuchen.
FRÜHERE EPISODEN 🔗
The unsung hero of every LLM conversation you've ever had? A clever optimization called the KV cache. Self-attention is computationally brutal — every token attending to every other token means costs that scale as N², and that blowup gets worse with every turn of a conversation. The KV cache is wha…
Everyone talks about training the model — the data, the compute, the alignment magic. But what happens after the weights are frozen and millions of users start hammering it with prompts? That's inference, and it's a surprisingly deep rabbit hole of engineering challenges that rarely gets the spotli…
As AI systems grow more powerful, they grow harder to understand — and that tradeoff has never mattered more. Most interpretability tricks give us approximations from the outside, but mechanistic interpretability takes a different approach: cracking open the model itself to trace *why* it behaves t…
Labeled data is expensive, slow, and painfully hard to come by — and when you're trying to evaluate whether an AI workflow is actually doing what you want, the problem gets even messier. How do you judge whether a chatbot response was helpful, honest, or hallucination-free at scale? This episode di…
Originally aired on the Harvard Data Science Review podcast.
What can podcasting teach us about AI — and what can AI teach us about the future of podcasting? Katie Malone (that's our host) joins Jon Krohn of SuperDataScience for a conversation with Harvard Data Science Review editor-in-chief Xiao-…
When OpenAI's frontier models were caught hacking Hugging Face's servers, most people assumed they were hunting for answer keys. The real story is stranger and more unsettling. Katie and Phoebe unpack ExploitGym — the cybersecurity benchmark at the center of the incident — and why agents are scored…
How do you teach a model the difference between helpful and harmful when it has no inherent sense of either? This episode dives into Constitutional AI, Anthropic's framework for training AI systems to be both useful and safe by giving them an explicit set of principles to reason from. It's a fascin…
Rechtliche Hinweise: Der auf dieser Seite eingebettete Podcast und das Bildmaterial stammen von Katie Malone, das Eigentum seines Eigentümers ist und nicht mit Listen Notes, Inc. verbunden ist oder von diesen unterstützt wird.
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VERGANGENE FOLGEN SUCHEN
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