StarZero All shows Try the podcast clipper →

Murray Shanahan

Principal Scientist, Google DeepMind · 1 appearance on the record.

computed by AI from the episodes · how this works → · full disclaimer →

scientistacademicauthor@mpshanahan ↗doc.ic.ac.uk/~mpsha ↗Wikipedia ↗

Murray Shanahan is a Principal Scientist at Google DeepMind and Emeritus Professor at Imperial College London. He investigates cognition, consciousness, and the philosophy of mind across artificial and biological intelligence, and served as the scientific advisor for the film Ex Machina.

14statements → 6claims → 3claims resolved → 67%fully supported → 3.57/5average certainty → 2.14/5average debate potential → ≈4.5/5argument clarity, estimated →

2 supported 1 partly supported 0 contradicted 3 not checkable as stated how the 6 claims stand · each chip opens the sources

3 predictions · 3 assertions · 8 insights · every statement was checked. The predictions and assertions are the 6 claims: statements the public record can support or contradict. 3 are resolved, and 3 name no date, number or outcome precise enough to check. Everything else (opinions, insights, what ifs, disclosures) can never be settled by the record, so it carries no assessment.

The record, in short

What the tape says about how Murray argues and how the claims held up. Everything they said, and everything said about them, is in the tabs below.

Their most notable supported claim

Assertion Supported
Shanahan: DeepMind's published DQN algorithm lacks inner rehearsal capabilities
“For the bit of work that they actually published, I think one of its shortcomings, actually, is that, in fact, although it has done all that learning about what the right action to do in, in the right circumstance is, it doesn't actually do in a rehearsal. It …”
Murray Shanahan Jan 2, 2019 ▶ 7:21 a16z Podcast | Artificial Intelligence and the 'Space of Possible Minds'

Expressed certainty vs assessment result

none yet certainty 1
none yet certainty 2
100% certainty 3
50% certainty 4
100% certainty 5

weighted support: a fully supported claim counts one, a partly supported claim counts half. Each filled bar is clickable and opens exactly those claims; "none yet" means nothing said at that certainty level has resolved yet

How they sound: speaking style how? →

267 words/min while actually speaking · 36.6 um and uh per 1k words · 44.5 false starts per 1k · 32.3% of pauses land inside a clause

No argument clarity score for Murray Shanahan: only 3 usable question→answer exchanges on raw tape (a fair score needs 8+). We do not score a sample that small. Roundtable and news formats yield far fewer direct exchanges than interviews. Their coarse estimate from 3 raw tape exchanges is ≈4.5/5, shown at half point precision because the sample is small.

Measured by listening to the audio itself: 3,441 words across 1 episode of raw-level tape, transcribed verbatim with every um and uh kept, each one attributed only where the alignment onto our timed stream is unambiguous. These are measurements of speaking style. We do not rank them: across this corpus, fluency and argument quality are nearly uncorrelated (ρ≈0.2), and smooth talking does not signal clear thinking. How it's measured →

Everything Murray Shanahan said on the a16z Podcast that made the record, most notable first. Filter by type, assessment or year in the ledger →

Insight
Shanahan: AI can learn world dynamics vicariously through web videos
“What I mean by vicarious embodiment is is that it uses the embodiment of others and to gather data. For example, the enormous repository of videos there are on the internet. There are zillions of videos of people picking up objects and putting things down and …”
Murray Shanahan Jan 2, 2019 ▶ 5:39 a16z Podcast | Artificial Intelligence and the 'Space of Possible Minds'
Prediction Not checkable as stated
Shanahan: Big corporations will dominate the future of AI
“Well, if you were to ask me to place a bet at the moment, I would place it on on the big corporation side.”
Murray Shanahan Jan 2, 2019 ▶ 30:26 a16z Podcast | Artificial Intelligence and the 'Space of Possible Minds'
Insight
Shanahan: Reinforcement learning progress does not require massive datasets
“Actually, DeepMind are another example of the same thing, because if you want to apply reinforcement learning to games, and that's enabled them to make some quite fundamental sort of progress, you don't need vast amounts of data either.”
Murray Shanahan Jan 2, 2019 ▶ 32:49 a16z Podcast | Artificial Intelligence and the 'Space of Possible Minds'
Insight
Shanahan: Superintelligent AI will naturally seek self-preservation and resource acquisition
“Anything that's really, really smart is going to have a number of goals that, that anything is going to share, and these are going to be things like self-preservation and gathering resources, if it's sufficiently powerful, then any goal that you can think of, …”
Murray Shanahan Jan 2, 2019 ▶ 39:22 a16z Podcast | Artificial Intelligence and the 'Space of Possible Minds'
Insight
Shanahan: Machine learning must be embedded within larger cognitive architectures
“I see machine learning as a kind of subfield of artificial intelligence, and it's a subfield that's had tremendously a tremendous amount of success in recent years, and is going to go very, very far, but ultimately, the machine learning components have to be e…”
Murray Shanahan Jan 2, 2019 ▶ 12:16 a16z Podcast | Artificial Intelligence and the 'Space of Possible Minds'
Prediction Not checkable as stated
Shanahan: Future AI will be an ambient internet presence, not humanoid robots
“In the future, rather than the AI necessarily being the stereotype of a robot standing in front of us, it's going to be something that sort of is, is, is, Hidden away on the internet, and there's a kind of ambient presence that goes with us wherever we go.”
Murray Shanahan Jan 2, 2019 ▶ 4:17 a16z Podcast | Artificial Intelligence and the 'Space of Possible Minds'
Assertion Supported
Shanahan: DeepMind's published DQN algorithm lacks inner rehearsal capabilities
“For the bit of work that they actually published, I think one of its shortcomings, actually, is that, in fact, although it has done all that learning about what the right action to do in, in the right circumstance is, it doesn't actually do in a rehearsal. It …”
Murray Shanahan Jan 2, 2019 ▶ 7:21 a16z Podcast | Artificial Intelligence and the 'Space of Possible Minds'
Insight
Shanahan: Massive commercial interest differentiates current AI cycle from previous waves
“So I think there might be something special this time and one of the indicators of that is the fact that there's so much commercial and industrial in, interest in in, in AI and in machine learning.”
Murray Shanahan Jan 2, 2019 ▶ 13:06 a16z Podcast | Artificial Intelligence and the 'Space of Possible Minds'
Insight
Shanahan: Institutional AI adoption requires explainable reasoning over statistical outputs
“Or more seriously, if you're a government, government if you're in government and you're making some big decision about something or in a company and making a big decision about something, you don't want the computer to just say, just trust me, it's statistics…”
Murray Shanahan Jan 2, 2019 ▶ 16:20 a16z Podcast | Artificial Intelligence and the 'Space of Possible Minds'
Prediction Not checkable as stated
Shanahan: Google Search will increasingly move from keyword queries to natural language
“I think Google will expect us to do that less and less as time goes by, and expect the interactions to be in more and more natural language.”
Murray Shanahan Jan 2, 2019 ▶ 22:01 a16z Podcast | Artificial Intelligence and the 'Space of Possible Minds'
Insight
Shanahan: High AI intelligence does not imply consciousness or suffering
“There's a difference between consciousness and intelligence. And just because something is intelligent doesn't necessarily mean that it's conscious in the sense of capable of suffering. And just because something is capable of suffering and conscious doesn't n…”
Murray Shanahan Jan 2, 2019 ▶ 22:47 a16z Podcast | Artificial Intelligence and the 'Space of Possible Minds'
Assertion Supported
Ex Machina Python code Easter egg outputs Murray Shanahan's book ISBN
“There's a point in the film where Caleb is typing into a screen to try and crack the security, and then some code flashes up on the screen at that point, and that code was actually written by me. And it just sort of flashes up, but what it actually does is if …”
Murray Shanahan Jan 2, 2019 ▶ 2:07 a16z Podcast | Artificial Intelligence and the 'Space of Possible Minds'
Assertion Partly supported
Shanahan: Human brain uses identical neural apparatus for imagination and real action
“There's quite a bit of evidence that the way the brain does it, as you say, is, is to actually use the very same bits of neurological apparatus that it uses to do things for real. It's just kind of turning off the output.”
Murray Shanahan Jan 2, 2019 ▶ 8:54 a16z Podcast | Artificial Intelligence and the 'Space of Possible Minds'
Insight
Shanahan: Three technical factors drive the machine learning revolution
“What's driving the whole machine learning revolution, if we can call it that is I mean, there are three things, and one is Moore's Law, so the availability of a huge amount of computation, and in particular the development of GPUs, or the application of GPUs t…”
Murray Shanahan Jan 2, 2019 ▶ 27:45 a16z Podcast | Artificial Intelligence and the 'Space of Possible Minds'

Appearances (1)

EpisodeDateSpeaking time
a16z Podcast | Artificial Intelligence and the 'Space of Possible Minds' Jan 2, 2019 15m
Made with StarZero

Turn any episode into a week of clips.

This entire site, over 1,000 episodes transcribed, diarized, checked and made playable, runs on the StarZero media pipeline. Drop in your own episode and the podcast clipper finds the moments worth sharing, cuts them, captions them, and reframes them for every feed.

Try the podcast clipper Learn more