Replying to @tallinzen
I think your statement made an implicit assumption that literary values are positively correlated to scientific values — my own experience of reading philosophy has made me realizing that I need to treat them with separate half brains 😁
Precisely ! I found the theory and finding here interesting - but if those are first principle, new systems could have built with them, without appealing to current modeling or architectural substrates.
Replying to @AlexGDimakis @minilek
Perhaps the likebood function is the culprit - instead of the algorithm for doing posterior inference ( or not doing ).
A genuine scientist needs to have a healthy skepticism - that is the type of DNA that is most valuable.
Mike was my colleague at Berkeley, and he's been fairly skeptical of deep learning's trajectory for years. Most scientists are skeptics, and it can take a big shock to get someone to consider a possibility that goes against the anchoring effect coded in our DNA.
I too was somewhat of a skeptic until ~6 months ago, and people ask me all the time why I joined Anthropic.
It started in April, when a friend I've known for 20 yrs messaged me saying he'd be in the Bay Area in a week, and he suggested we meet up. I invited him to my house for dinner, and he told me he had just joined Anthropic. He said the models were becoming increasingly more capable, and he believed both that they'd soon be smarter than him and also that all intellectual work would be highly devalued in the near future, and lastly that society was not at all ready for what's coming.
I'd of course heard claims similar to this before, but not from a friend I'd known for this long, and who I trusted, at my dining room table. It was the initial shock needed to destabilize my own anchoring. Also, I've been fortunate to work with and/or mentor many smart people in my life (e.g. Fields medalists, a 4x Putnam Fellow, IOI first place winners, etc.), so I feel pretty calibrated in recognizing very smart people, and this was someone who is definitely very smart and with impressive such credentials. So, it was hard to ignore this friend saying the models would be smarter than even him in particular.
After that dinner, I decided to do an informal survey, calling and meeting up with a lot of my friends who now work on improving model capabilities at these frontier labs. They all agreed with that first friend. These were all people I'd known 10, 15, 20 years, who mentored me, or I mentored them, or we met professionally in academia. These are people I trust, and their beliefs on this topic were consistent.
I then spent a whole lot of time understanding the missions of different companies, and talking to current and former employees and people in leadership. I read Dario's (long) essays and watched videos on YouTube of company leaders being interviewed. Anthropic's stated mission "to ensure that the world safely makes the transition through transformative AI" felt like exactly the right guiding principle to handle such a society-disrupting technology, and everyone I spoke to at the company was very aligned with this mission. It's easily the most mission-focused place I've ever been at, and everyone takes the work extremely seriously and with a profound sense of responsibility.
And now here I am. And being on the inside, the pace of progress has surprised me more than I first imagined. The moment that pure mathematicians are in, to me, is just a microcosm of what I predict a much larger of society will be grappling with in the not too distant future, and I really hope our leaders and the general public internalize sooner than later just how significant a moment we're in.
Replying to @CsabaSzepesvari @rsalakhu
Let me make a proposal to that effect: just like we forecast global warming with an interval - especially if we think we are to be wiped out - let us do the same for the P(doom) which seems also having that psychological effect 😆
Replying to @tdietterich @deltaVee42
@tdietterich there is a bit issue that even with ai help, dealing with the notebooks are slower and you would need human to verify the “assisted dealing with” - the foundation issue is that the generation is “free” without a strong value preference which humans do.
Replying to @andrewgwils
Whether you want to hear from a (particular) human or agent could also be a load bearing question too! 😄
Perhaps this is for the long term benefit - we certainly do not want recursive self improvement picks up those behavior - now who gets to define what is desired or not ?
I don't usually comment on these types of things, but I can't believe that people are actually upset or think it's insane that Anthropic would push back against sustained and needlessly abusive behaviour. I totally support them in this. Regardless of whether you think the models have feelings, it is an interactive service, and it's obviously their prerogative to prohibit abusive conduct on their servers, and totally understandable that they wouldn't want to be an outlet for that behaviour.
Is this kind of statement even SCIENTIFIC? Wanting to prove every model has problems, list all. Wanting to prove your model is immune, show proof rigorously - we all know too well cherry-picking. What is ur TRUE intention? @AnthropicAI
Replying to @yoavgo
I was not referring to token perplexity per se ( hence the quotation marks ). I was mentally modeling pieces of reasoning traces assembling together - a long chain of reasonable human aligned small ones would be more probably than a chain with a few non human aligned ones.
Replying to @yoavgo
Ah,an unstated assumption in my theory. The “perplexity” of those nuanced strings of tokens following each other are not as high as you thought ? ( in other words you are not exposed to as many corpus material so unencountered co occurrences appears to be high unlikely?)
While we might imagine ranking function need to consider arbitrarily long future tokens but I suspect the past ( generated tokens ) heavily condition the future generation so a few steps look ahead might be enough ( high quality but shorter reasoning steps )
Replying to @yoavgo
My theory is that the autoregressive generation uses the (increasing) KV caches as working memories and doing bread first search over possible completion paths while adaptively suppressing low probability ones. This does not explain how this ranking function is learned though.
Replying to @tallinzen
I am not sure that is a sound advice : has geoff been using matlab for the last xx years - maybe he has switched to python? No matter — we are all using natural language to code today!
Replying to @andrewgwils
As you know the objectives are easier to state but constraints always get in the way of reaching the optimal - so what are the constraints ? 😄
I am trying to grasp the missing link between THAT purpose and a compelling reason to justify the expense of human (mathematicians and society at large) are we cyclicly defending that the knowledge of creating something beyond our knowledge realm is worthwhile ? @ChrSzegedy



