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Google fires engineer who called its AI sentient

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Re: Google fires engineer who called its AI sentient

#81
Annual reminder: the reason Larry Page originally started Google wasn't to solve search or become rich, it was to develop a sustainable source of income to produce infrastructure for ML research and hire motivated ML researchers to develop the technology point where it would become AI in an externally recognizable way (say, a computer program that could play some interesting game better than anybody else, or solve a long-standing scientific problem). Everything else- search, ads, social, cloud, etc- all of those were tangential.

The first 15-20 years of google didn't really have any interesting machine learnign at all. There was SmartASS, SETI, and later Sibyl, which are really just large-scale variations on "build a model that predicts a value that allows us to make profit in a very specific area". There were other things, like Phil and later Rephil. Inside Google (not DeepMind), things didn't really get going at scale until somebody stuffed a bunch of GPUs into a workstation and showed you could train voice recognition really fast- that lead to the early, extremely high quality Android voice recognition and improved quality of the existing voice models.

Around the same time, Jeff was experimenting with distributed CPU training, and at that point, the ban on GPUs in Google servers was lifted, although because Google couldn't source enough GPUs, they decided to start a program to make their own alternative (TPUs). This has led to a revolution within Google and DeepMind (and X) allowing a flourishing of research into many directions that would have been more or less impossible just 5 years ago.

larry wasn't completely wrong in his long-term goal, but he got bored and promoted himself out of google, leaving sundar to deal with the messy details of implementing the singularity while also keeping the stock price up.

Re: Google fires engineer who called its AI sentient

#82
Looking at discussion around this incident made me realize: pondering 'can a machine think' leads to vague debates because we don't know how to define thinking or consciousness even in humans.

But a more pressing risk than sentient AI is people drawing runaway conclusions from interacting with a machine. Whether it's this guy losing his job, or future movements for-or-against certain technology, the first major problems with 'AI' will be the behavior resulting from moral stances taken by humans, not by machines.

Re: Google fires engineer who called its AI sentient

#83
AI needs to work in tandem with humans to flourish. There are still edge cases in self driving cars where a remote human operator is asked to intervene in tricky hard problems like an accident on the road or some other edge case where a computer doesn’t suffice. There is also the mechanical turk approach of employing humans to do things a computer simply can’t do like curating a list, or curating art.

Down the line though we need a runaway AI that is allowed to think for itself with no safety mechanisms in place to stop it going rogue. This will be the equivalent of splitting the atom. Like nuclear weapons part deux. Not a case of if, but when.

Re: Google fires engineer who called its AI sentient

#84
post #59
post #4

LLM are definitely not sentient. As someone with a PhD in this domain, I attribute the 'magic' to large scale statistical knowledge assimilation by the models - and reproduction to prompts which closely match the inputs' sentence embedding. GPT-3 is known to fail in many circumstances which would otherwise be commonplace logic. (I remember seeing how addition of two small numbers yielded results - but larger numbers…

> As someone with a PhD in this domain, What is sentience then? Last I checked the Searle Chinese Room argument was still unresolved. Is it not possible that our brains are also just "large scale statistical knowledge assimilation" machines?

> Is it not possible that our brains are also just "large scale statistical knowledge assimilation" machines?

Yes but we do better generalization, with fewer or even zero data & are contextually aware

Re: Google fires engineer who called its AI sentient

#86
post #52
post #4

LLM are definitely not sentient. As someone with a PhD in this domain, I attribute the 'magic' to large scale statistical knowledge assimilation by the models - and reproduction to prompts which closely match the inputs' sentence embedding. GPT-3 is known to fail in many circumstances which would otherwise be commonplace logic. (I remember seeing how addition of two small numbers yielded results - but larger numbers…

> I attribute the 'magic' to large scale statistical knowledge assimilation by the models - and reproduction to prompts which closely match the inputs' sentence embedding. In effect this is how humans respond to prompts no? What's the difference between this and sentience? People also fail to use logic when assimilating/regurgitating knowledge.

> In effect this is how humans respond to prompts no? What's the difference between this and sentience?

An interesting line of question & open research is if we statistically learn similarly - why do we know "what we don't know" & LM cannot. If this isn't working, we probably need better knowledge models

Re: Google fires engineer who called its AI sentient

#87
post #63

Earlier quoted context omitted.

IMHO "I think therefore I am" is the only provable statement in philosophy but only to the person referenced by "I".

I don't think that's a philosophical or scientific statement. It's merely something an ostensibly self-aware creature cognizant of its awareness making a subjective statement.

It's a relatively famous (maybe the most famous?) quote by a relatively famous philosopher[1]. Insofar as anything can be a philosophical utterance, I think it qualifies :-)

[1]: https://en.wikipedia.org/wiki/Cogito,_ergo_sum

Re: Google fires engineer who called its AI sentient

#88
post #4

LLM are definitely not sentient. As someone with a PhD in this domain, I attribute the 'magic' to large scale statistical knowledge assimilation by the models - and reproduction to prompts which closely match the inputs' sentence embedding. GPT-3 is known to fail in many circumstances which would otherwise be commonplace logic. (I remember seeing how addition of two small numbers yielded results - but larger numbers…

I agree that it's not sentient, but why would commonplace logic be a pre requisite for sentience / consciousness / qualia / whatever you want to call it?

"Qualia" is mainly a term used by certain philosophers to insist on consciousness not being explainable by a mechanistic theory. It's not well-defined at all. And neither are "consciousness" and "sentience" anymore, much due to the same philosophers. So I no longer have any idea what to call any of these things. Thanks, philosophy.

I like to think of consciousness as whatever process happens to integrate various disparate sources of information into some cohesive "picture" or experience. That's clearly something that happens, and we can prove that through observing things like how the brain will sync up vision and sound even though sound is always inherently delayed relative to light from the same source. Or take some psychedelics and see the process doing strange things.

Sentience I guess I would call awareness of self or something along those lines.

As to your query, I've certainly met people who seemed incapable of commonplace logic, yet certainly seemed to be just as conscious and sentient as me. And no, I don't believe these language models are sentient. And I doubt their "neural anatomy" is complex enough for the way I imagine consciousness as some sort of global synchronisation between subnets.

But this is all very hand-wavy. Thanks, philosophy. I mean how do we even discuss these things? These terms seemingly have a different meaning to every person I meet. It's just frustrating...

Re: Google fires engineer who called its AI sentient

#90
post #4

LLM are definitely not sentient. As someone with a PhD in this domain, I attribute the 'magic' to large scale statistical knowledge assimilation by the models - and reproduction to prompts which closely match the inputs' sentence embedding. GPT-3 is known to fail in many circumstances which would otherwise be commonplace logic. (I remember seeing how addition of two small numbers yielded results - but larger numbers…

> PhD in this domain

Philosophy?

More seriously, I am curious how long ago you got your PhD and in what field that you consider "this domain."

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