While I understand this is a PR talk for a startup, I think the text itself contains a number of interesting observations.
Regarding the idea of distributed models communicating with each other, I have also been thinking (and writing [1]) along those lines, where I see that the data amounts needed to fully digitalize ourselves and our society requires far too much storage if just serialized (limited by bandwidth if nothing else), while smart, updateable models are actually a much better storage medium for such information, as it can communicate only the important bits (any new information) on a higher level, with each other.
The other observation here that rings bells for me is how I think lessons from trying to develop intelligent systems should upvalue the human mind rather than devalue it, as we start to treat it less like an ad-hoc thing, and more like the finely tuned machine it is, which also benefits greatly from optimizing what data we feed it with, the architecture of solution strategies etc. All of which is an area where humans and machines can do wonders together [2].
> Artificial intelligence can do more every day, but deciding what it should do is up to us
> For artificial intelligence to benefit from distributed knowledge, it must itself be distributed.
I wish to highlight these two important concepts, with which I fully agree.
Artificial intelligence must enable all of humanity to excel and realize its full potential; it must not be used for the purposes of war, economic competition, or gaining dominance over others.
In other words: artificial intelligence must serve natural intelligence, not the other way around.
So unoriginal that they didn’t even realise the history of Thinking Machines and that usage when they scrambled around for any kind of name after leaving OpenAI to suck on the nipple of venture capital with ideas at the level of GitHub freebies.
Not that one. This one is a start-up founded by ex-oAI CTO Mira Murati. Last I heard they were mainly doing hosted finetunes with a few clicks on popular open models.
Why would you call your company Thinking Machines if you believe this, by calling them that you're already framing them as replacing the human act of thinking.
Feels like they appropriated the name first, then pivoted ideologically to differentiate themselves from everyone else.
Sure, humans will be sitting at home unemployed with plenty of time to think. They just won't be doing any thinking that has much of an affect on the world or their situation.
What's super cringe is that there already was a company called Thinking Machines, which built the Connection Machine supercomputer. The CM was featured in the movie Jurassic Park and had a network fabric for its CPUs co-designed by Richard Feynman.
This is yet another techbro outfit (although founded by a techsis) necromancing the name of the former supercomputer company. It's as if OpenAI decided to call itself Symbolics for the associations with that name.
That was my thought, but I also think we're at the point that the iconic companies that have come and gone 30+ years ago are unknown to the current crop of young-ish startup founders.
I would be really curious to know if the current Thinking Machines team had any awareness of the prior company, or if they landed on that name completely unaware.
IMO this shows how we have been pursuing many of these goals for half a century now.
so in this new AI LM / agent world , AI is only going to be as good as the "AI Conductor". The human which can build the rules, validate the output , and Conduct the AI properly
My experience is that AI is just that, a “mech suit for your brain.” It has no creativity or volition but has superhuman memory, superhuman speed, and superhuman context in some narrow cases.
So it takes a thought and unfolds it, looks up relevant thoughts and information, elaborates, works through implications, and in some cases can execute.
You could do all that but like doing math manually it would take forever. You could manually calculate a spreadsheet too.
I disagree. LLMS take a human thought, simplify it, normalize it, remove it from its original context, inject it with their own biases and prejudices, assume an imaginary context. They bastardize human thoughts into something fairly generic.
The comparison with manual calculation or other mechanical operations doesn’t work, LLMs don’t work at the same level of abstraction, they take over the decision making human generally do. When you write code or write a text, us humans are continuously taking lots of small decisions, we don’t just translate 1:1 a thought to an artifact. And that’s the part that is taken over by LLMs.
Inevitably yes, the question is whether the combined cyborg is still better than the original human.
E.g. I'm sure we are generally less skilled in mental arithmetic since the advent of the calculator, but it has allowed us to solve vastly more complex problems in the end.
> E.g. I'm sure we are generally less skilled in mental arithmetic since the advent of the calculator, but it has allowed us to solve vastly more complex problems in the end.
This is like saying we have been getting a lot worse at walking since the advent of the car but it has allowed us to practice global trade in the end.
Yes cars are a part of the solution but there are a lot more factors at play.
A calculator does not do math, a calculator (and computer) calculates or computes. The math is the study and understanding of the problem space (and the problem solving) that the human is doing behind the calculator.
"solve vastly more complex problems" the calculator has accelerated this but it is not really a cause effect relation. The advancements in the understanding of the complex problems could've also happened without calculators and the computation could have been done instead (for example) with 1000 people in a bunker.
By vastly more complex problems, I think the parent is referring to engineering problems, not mathematical problems. And in this case quantity has a quality all its own. Yes, 1000 people in a bunker could in theory do the calculations necessary to refine airframes or planetary scale weather modeling, practically they would be impossible economically and would never be solved
They're making a "few hundred million of ARR" - not bad for a company who only launched their first product, a training platform called Tinker in October last year.
We power AI with methane because it's a powerful greenhouse gas. That's because the future worth destroying is human. If it wasn't we wouldn't be destroying it, duh.
Regarding the idea of distributed models communicating with each other, I have also been thinking (and writing [1]) along those lines, where I see that the data amounts needed to fully digitalize ourselves and our society requires far too much storage if just serialized (limited by bandwidth if nothing else), while smart, updateable models are actually a much better storage medium for such information, as it can communicate only the important bits (any new information) on a higher level, with each other.
The other observation here that rings bells for me is how I think lessons from trying to develop intelligent systems should upvalue the human mind rather than devalue it, as we start to treat it less like an ad-hoc thing, and more like the finely tuned machine it is, which also benefits greatly from optimizing what data we feed it with, the architecture of solution strategies etc. All of which is an area where humans and machines can do wonders together [2].
[1] https://livingsystems.substack.com/p/the-future-of-data-less...
[2] https://livingsystems.substack.com/p/ai-progress-should-upgr...
> For artificial intelligence to benefit from distributed knowledge, it must itself be distributed.
I wish to highlight these two important concepts, with which I fully agree.
Artificial intelligence must enable all of humanity to excel and realize its full potential; it must not be used for the purposes of war, economic competition, or gaining dominance over others.
In other words: artificial intelligence must serve natural intelligence, not the other way around.
Unfortunately, human nature being what it is, this seems extraordinarily unlikely.
Feels like they appropriated the name first, then pivoted ideologically to differentiate themselves from everyone else.
This is yet another techbro outfit (although founded by a techsis) necromancing the name of the former supercomputer company. It's as if OpenAI decided to call itself Symbolics for the associations with that name.
(I know, Corp vs. Lab).
I would be really curious to know if the current Thinking Machines team had any awareness of the prior company, or if they landed on that name completely unaware.
IMO this shows how we have been pursuing many of these goals for half a century now.
So it takes a thought and unfolds it, looks up relevant thoughts and information, elaborates, works through implications, and in some cases can execute.
You could do all that but like doing math manually it would take forever. You could manually calculate a spreadsheet too.
The comparison with manual calculation or other mechanical operations doesn’t work, LLMs don’t work at the same level of abstraction, they take over the decision making human generally do. When you write code or write a text, us humans are continuously taking lots of small decisions, we don’t just translate 1:1 a thought to an artifact. And that’s the part that is taken over by LLMs.
E.g. I'm sure we are generally less skilled in mental arithmetic since the advent of the calculator, but it has allowed us to solve vastly more complex problems in the end.
This is like saying we have been getting a lot worse at walking since the advent of the car but it has allowed us to practice global trade in the end.
Yes cars are a part of the solution but there are a lot more factors at play.
A calculator does not do math, a calculator (and computer) calculates or computes. The math is the study and understanding of the problem space (and the problem solving) that the human is doing behind the calculator.
"solve vastly more complex problems" the calculator has accelerated this but it is not really a cause effect relation. The advancements in the understanding of the complex problems could've also happened without calculators and the computation could have been done instead (for example) with 1000 people in a bunker.
https://x.com/deedydas/status/2072340532718887068
https://semianalysis.com/
>it's all AI slop
k
Where? When? Unless I missed any of their models