Speaker 1: Welcome to the GROC three presentation.
Speaker 2: The mission of Xai and Grock is to understand the universe.
Speaker 2: We want to understand the nature of the universe so
Speaker 2: we can figure out what's going on. Where are the aliens,
Speaker 2: what's the meaning of life, how does the universe end?
Speaker 1: How did it start? All these fundamental questions.
Speaker 2: We're driven by curiosity about the nature of the universe,
Speaker 2: and that's also what causes us to be a maximally
Speaker 2: truth seeking AI, even if that truth is sometimes at.
Speaker 1: Odds with what is politically correct.
Speaker 2: In order to understand the nature of the universe, you
Speaker 2: must absolutely rigorously pursue truth or you will not understand
Speaker 2: the universe. You'll be suffering from some amount of delusion
Speaker 2: or error. So that is our goal, figure out what's
Speaker 2: going on. And we're very excited to present GROC three,
Speaker 2: which is we think, in order of magnitude, more capable
Speaker 2: than GROC two in a very short period of time.
Speaker 1: And that's thanks to the hard work of an incredible team.
Speaker 2: My monitor to work with such a great team, and
Speaker 2: of course we'd love to have so the smartest humans
Speaker 2: out there join our team.
Speaker 1: So let's go, Hi Ron.
Speaker 3: My name is Igor lead Engineering at xi I GBPA
Speaker 3: leading research. I'm Tony working on the reasoning team, all right, Ele,
Speaker 3: And I don't do anything. I just show up occasionally.
Speaker 1: Yeah.
Speaker 3: So, like I mentioned, GROK is the tool that we're
Speaker 3: working on. Grock is our AI that we're building here
Speaker 3: at XAI, and we've been working extremely hard over the
Speaker 3: last few months to improve GROC as much as we
Speaker 3: can so we can give all of you access to it.
Speaker 3: We think it's going to be extremely useful. Do we
Speaker 3: think it's going to be interesting to talk to a
Speaker 3: funny really really funny and we're going to explain to
Speaker 3: you how we've improved GROC over the last few months.
Speaker 3: We've made quite a jump in capabilities. Actually we should
Speaker 3: explain maybe also what is why do we call it groc?
Speaker 2: So grog is a word from a Heinland novel Stranger
Speaker 2: in a Strange Land, and it's used by a guy
Speaker 2: who was raised on Mars. And the word grock is
Speaker 2: to sort of fully and profoundly understand something. That's what
Speaker 2: the word grog means, fully and profoundly understand something.
Speaker 1: And empathy is important. True.
Speaker 4: So yeah, if we charged xs progress in the last
Speaker 4: few months, it's only been seventeen months since we started
Speaker 4: kicking off our very first model. GROCK one was almost
Speaker 4: like a toy by this point on the two hundred
Speaker 4: and fourteen billion parameters. And now if we're prout the
Speaker 4: progress the time on XXI is the performance of favorite
Speaker 4: benchmark numbers at MLU on the yaxis, we're literally progressing
Speaker 4: at the unprecedent speed across the whole field. And then
Speaker 4: we kick off GROG one point five right after GUARG
Speaker 4: one released after November twenty twenty three, and then GROG two.
Speaker 1: So if you look at where all the performance coming from.
Speaker 4: We have a very correct engineering team and all the
Speaker 4: best AII talent. The only one thing we need is
Speaker 4: a big intelligence comes from big cluster, so we can
Speaker 4: reconvert the entire progress and makes AI now replacing the
Speaker 4: benchmarket of waxes to the total amount of training flops.
Speaker 4: That is how many GPS we can run at any
Speaker 4: given time to trail all large language models to compress
Speaker 4: the entire Internet.
Speaker 1: So after old human knowledge. Really that's right.
Speaker 2: Yeah, the Internet is being part of it, but it's
Speaker 2: really all human knowledge everything.
Speaker 4: Yeah, the whole Internet fits into USB stick at this
Speaker 4: point it's like all the human tokens very soon into
Speaker 4: the real world. We had so much trouble actually training
Speaker 4: Grock two back in the days.
Speaker 1: We kickoff the model around February.
Speaker 4: And we thought we had a large amount of chips,
Speaker 4: but turned out we can barely get AK training chips
Speaker 4: running coherently at any given time, and we had so
Speaker 4: many cooling and power issues.
Speaker 1: I think you were there in the data center.
Speaker 2: Yeah, it was like really sort of more like AK
Speaker 2: chips on average at eighty percent efficiency, more like like
Speaker 2: sixty five hundred effective h one hundreds training for near
Speaker 2: several months, but you know we're the one hundred k.
Speaker 1: So yeah, that's right, that's right. So what's the next step?
Speaker 4: Right, so after Groark too, So if we all continue accelerate,
Speaker 4: we have to take the matter into our own hands.
Speaker 4: We have to solve all the coolings, all the power
Speaker 4: issues and everything.
Speaker 3: So it so on April of last year, Elan decided
Speaker 3: that really the only way for XAI to succeed, for
Speaker 3: XI to build the best AI out there, is to
Speaker 3: build our own data center. So really we realize we
Speaker 3: have to build the data center in about four months.
Speaker 3: It turned out it took US one hundred and twenty
Speaker 3: two days to get the first one hundred KGPUS up
Speaker 3: and running, and that was a monumental effort to be
Speaker 3: able to do that. We believe it's the biggest fully
Speaker 3: connected h one hundred cluster of its kind. We actually
Speaker 3: decided that we need to double the size of the
Speaker 3: cluster pretty much immediately if we want to build the
Speaker 3: kind of way that we want to build.
Speaker 1: So we then had another phase.
Speaker 3: Which we haven't talked about publicly, so this is the
Speaker 3: first time that we're talking about this, where we doubled
Speaker 3: the capacity of the data center yet again. And that
Speaker 3: not only took us ninety two days. So we've been
Speaker 3: able to use all of diseributse, use all of this
Speaker 3: compute to improve GROG in the meantime. And basically today
Speaker 3: we're going to present the results of that, the fruits
Speaker 3: that came from that.
Speaker 4: That's yeah, So all the paths, all the roads leads
Speaker 4: to GROD three ten x more compute, more than ten
Speaker 4: x really maybe fifteen x compared to our private generation model,
Speaker 4: and GROCT finished the pre trading early January and we started,
Speaker 4: you know, the model is still currently trading. Actually, so
Speaker 4: this is a little preview of our benchmine numbers. So
Speaker 4: we evaluate the GROCK three on you know, three different
Speaker 4: categories on general mathematical reasoning, on general knowledge about STEM
Speaker 4: and science, and then also on computer science coding. So
Speaker 4: amy American Invitational Math Examination hosted you know a year.
Speaker 4: If we evaluated model performance, we can see that the
Speaker 4: GROD three across the board is in the league of
Speaker 4: its own, even as little brother Groctor Mini is reaching
Speaker 4: the frontier across.
Speaker 1: All the other competitors.
Speaker 4: You would say, well, at this point, all these benchmarks
Speaker 4: you're just evaluating, you know, the memorization of the textbooks,
Speaker 4: memorization of the GitHub repos, how about real time usefulness?
Speaker 4: How about we actually use those models in our product.
Speaker 4: So what we did instead is we actually kicked off
Speaker 4: a blind test of our garacy model code named Chocolate,
Speaker 4: Pretty Hot Chocolate. I've been running on this platform called
Speaker 4: chap Arena for two weeks. I think the entire x
Speaker 4: platform at some point speculated this might be the next
Speaker 4: generation of AI coming away. So how this chap arena
Speaker 4: works is that it's stripped away the entire product service, right,
Speaker 4: It just raw comparison of the engine of those Asia's
Speaker 4: the language models themselves and place interface.
Speaker 1: Where the user will submit one single querry and you.
Speaker 4: Get to show two responses, you don't know which model
Speaker 4: they come from, and India you make the vote. So
Speaker 4: in this blind test, GROX three, an early version of
Speaker 4: GROX three already reached like fourteen hundred. No other models
Speaker 4: had reached an ELO score had to have comparison to
Speaker 4: all the other models at this score.
Speaker 1: And it's not just one single category.
Speaker 4: It's fourteen hundred aggregated across all the categories in chapbock capabilities,
Speaker 4: in struction, following coding, So it's number one across the
Speaker 4: board in this.
Speaker 2: Blind test, and it's still climbing, so we actually keep
Speaker 2: to keep updating it. So it's forty fourteen hundred, but
Speaker 2: fourteen hundred and climbing.
Speaker 3: Yeah, And in fact, we have a version of the
Speaker 3: model that we think is already much better than the
Speaker 3: one that we tested here. Yeah, we'll see, I guess,
Speaker 3: but that's the one that we're working on talking about today.
Speaker 1: Yeah.
Speaker 2: So actually, thing, if you're using BARG three, you I
Speaker 2: think you may notice improvements almost every day because we're
Speaker 2: continuously improving the model. So literally even within twenty four hours,
Speaker 2: you'll see improvements.
Speaker 1: Yep.
Speaker 4: But we believe here at the XAI, getting the best
Speaker 4: pre training model is not enough. That's not enough to
Speaker 4: build the best AI and the best A. I need
Speaker 4: to think like a human to contemplate about all the
Speaker 4: possible solutions, self critique, verify all the solutions, backtrack, and
Speaker 4: also think from the first principle.
Speaker 1: That's a very important capability.
Speaker 4: So we believe that as we take the best PREA
Speaker 4: training model and continue training with reinforcement learning, it will
Speaker 4: enlicit the additional reasoning capabilities that allows the model just
Speaker 4: becomes so much better and scale, not just in the
Speaker 4: training time, but actually in the test time as well.
Speaker 4: So we already found the model is extremely useful internally,
Speaker 4: saving one hundreds of hours of coding time. So you
Speaker 4: are the user of our these model, what does the
Speaker 4: use cases yea.
Speaker 3: So, like Jimmy said, we've added advanced reasoning capabilities to
Speaker 3: GROG and we've been testing them pretty heavily over the
Speaker 3: last few weeks in order to give you a little
Speaker 3: bit of a taste of what it looks like when
Speaker 3: GROG is solving hard reasoning problems. So we've prepared two
Speaker 3: little problems for you. One that comes from physics and
Speaker 3: one is actually a game that God is going to
Speaker 3: write for us. When it comes to the physics problem,
Speaker 3: you know, what we want Grog to do is to
Speaker 3: plot a viable trajectory to do a transfer from Earth
Speaker 3: to Mars and then at a later point in time
Speaker 3: a transfer back from Mars to Earth. And that requires,
Speaker 3: you know, some physics that Grog will have to understand.
Speaker 3: So we're going to challenge Grock, you know, come up
Speaker 3: with a viable trajectory, calculate it, and then plot it
Speaker 3: for us so we can see it. And yeah, this
Speaker 3: is totally unscripted, by the way, this is the Grog interface,
Speaker 3: and we've typed in this text that you can see
Speaker 3: here generate code for an animated three D plot of
Speaker 3: a launch from Earth, landing on Mars, and then back
Speaker 3: to Earth at the next launch window. And we've not
Speaker 3: kicked off or the query and you can see Grog spinking.
Speaker 3: So out of Rock's advanced reasoning capabilities are these thinking
Speaker 3: traces that you can see here. You can even go
Speaker 3: inside and actually read what Grock is thinking as it's
Speaker 3: going through the problem, as it's trying to solve it.
Speaker 2: Yeah, weld say like we are doing some obscuration of
Speaker 2: the thinking so that our model doesn't get totally copied instantly.
Speaker 2: So there's more to the thinking than is displayed.
Speaker 3: And because this is totally unscripted, there's actually a chance
Speaker 3: that Grock might make a little courting mistake and it
Speaker 3: might not actually work. So just in case, we're going
Speaker 3: to launch two more instances of this, so if something
Speaker 3: goes wrong, we were able to search to those enshow
Speaker 3: you something that's presentable, So we're kicking off the other
Speaker 3: two as well, And like I said, we have a
Speaker 3: second problem as well. Actually, one of our favorite activities
Speaker 3: here XCI is having rock right games for us, not
Speaker 3: just any now any old game, any game that you
Speaker 3: might already be familiar with, but actually creating new games
Speaker 3: on the spot and being creative about us. So one
Speaker 3: example that we found was really really fun is create
Speaker 3: a game that's a mixture of the two games Tetris
Speaker 3: and Jewels.
Speaker 2: So this is maybe an important thing like that this
Speaker 2: obviously if you if you ask an ai to create
Speaker 2: a game like Tetris, there's there are many examples of
Speaker 2: Tetris on the on the internet.
Speaker 1: The air or game like Dueled whatever, this, it can
Speaker 1: copy it.
Speaker 2: What's interesting here is it achieved a creative solution combining
Speaker 2: the two games that actually works and is a good game. Yeah,
Speaker 2: that's the it's created. We're seeing the beginnings of creativity.
Speaker 3: Fingers crossed that we can recreate that, but hopefully it works.
Speaker 3: Hopefully it's actually because this is a bit more challenging,
Speaker 3: we're going to use something special here, which we call
Speaker 3: big Brain. That's our mode in which we use more computation,
Speaker 3: which was more reasoning of our rock, just to make
Speaker 3: sure that you know, there's a good chance here that
Speaker 3: actually might actually do it. So we're also going to
Speaker 3: fire of free attempts here at solving this game, at
Speaker 3: creating this game that's a mixture of Tetris and the Jewels.
Speaker 3: Let's let's see what god comes out.
Speaker 2: I've played the game. It's pretty good. Like it's like, wow, okay,
Speaker 2: this is something.
Speaker 3: Yeah, So while Grog is thinking in the background, we
Speaker 3: can now actually talk about some concrete numbers. How well
Speaker 3: is Groug doing across tons of different tasks that we've
Speaker 3: tested on. So we'll hand it over to Tony to talk
Speaker 3: about that.
Speaker 5: Yeah, Okay, so let's see how Greg has on those
Speaker 5: interesting challenging benchmarks. So yeah, so reasoning again refers to
Speaker 5: those models that actually thinks quite for quite a long
Speaker 5: time before it tries to solve a problem. So in
Speaker 5: this case, you know, around the months ago the growth
Speaker 5: three apprecionning finishes, so after that we worked very hard
Speaker 5: to put the reasoning capability into the current Growth three model.
Speaker 5: But again this is very early days, so the model
Speaker 5: still currently in training. So right now, what we're going
Speaker 5: to show to people is this beta version of the
Speaker 5: growth three reasoning model. Alongside, we also are training a
Speaker 5: mini version of the reasoning model. So essentially on this
Speaker 5: plot you can see the growth three reasoning beta and
Speaker 5: then Growth three mini reasoning. The reasoning mini reasoning is
Speaker 5: actually a model that we train for much longer time,
Speaker 5: and you can see that sometimes you actually perform slightly
Speaker 5: better compared to the Growth three reasoning. This also just
Speaker 5: means that there's a huge potential for the Growth three
Speaker 5: reasoning because it's trained for.
Speaker 1: Much less time.
Speaker 5: So all right, so let's actually look at how it
Speaker 5: does on those three benchmarks. So Jimmy also introduced already,
Speaker 5: so essentially we're looking at three different areas mathematics, science,
Speaker 5: and coding. And for math we're picking this high school
Speaker 5: competition math problem. For science we actually picked those PhD
Speaker 5: level science questions.
Speaker 1: And for coding, it's also actually pretty challenging.
Speaker 5: It's competitive code and also some eco, which is some
Speaker 5: interview problems that people usually get when they interview for companies.
Speaker 5: So on those benchmarks, you can see that the growth
Speaker 5: three actually performed quite well across the board compared to
Speaker 5: other competitors. Yeah, so it's pretty promising. These models are
Speaker 5: very smart.
Speaker 1: So totally what what are those shaded bars? Yeah, so okay,
Speaker 1: so I'm you asked this question.
Speaker 5: So for those models, because it can reason, it can think,
Speaker 5: you can also ask them to even think longer. You
Speaker 5: can spend more what we call test and compute, which
Speaker 5: means you can spend more time to reason, to think
Speaker 5: about the problem before you spit out the answer. So
Speaker 5: in this case, the shaded bar here means that we
Speaker 5: just asked the model to spend more time. You know,
Speaker 5: it can solve the same problem many many times before
Speaker 5: it tries to conclude what is the right solution, And
Speaker 5: once you give this compute or this kind of budget
Speaker 5: to the model. It turns out the model can even
Speaker 5: perform better. So this is it's honly the shaded part
Speaker 5: in those.
Speaker 4: Plots, right, So I think this is really exciting, right
Speaker 4: because now instead of just doing one chain of thoughts
Speaker 4: with AI while not the multiplex at once. Yes, so
Speaker 4: that's a very powerful technique that allows to continue to
Speaker 4: scale the model capabilities after training. And you know, people
Speaker 4: often ask were actually just overfitt into the benchmarks?
Speaker 1: So how mogulization?
Speaker 5: So yes, I think, yeah, this is definitely a question
Speaker 5: that we are asking ourselves whether we're overfitting into those
Speaker 5: current benchmarks.
Speaker 1: Luckily, we have a real test.
Speaker 5: So about five days ago, AIM twenty twenty five just finished.
Speaker 5: This is where high school come students compete in this
Speaker 5: particular benchmark. So we got this very fresh new competition,
Speaker 5: and then we ask our two models to compete on
Speaker 5: the same benchmark, the same exam, and it turns out
Speaker 5: very interest the gross three reasoning the big one actually
Speaker 5: does better on this particular new, fresh exam. This also
Speaker 5: means that the generalization capability of the big model is stronger,
Speaker 5: much stronger compared to the smaller model. If you compare
Speaker 5: to the last year's exam. Actually this is the opposite.
Speaker 5: The smaller model kind of learns the previous exams better.
Speaker 5: So yeah, so this this actually shows some kind of
Speaker 5: true generalization from the model.
Speaker 4: Right, So, seventeen months ago our Rock zero and Rock
Speaker 4: Web baret solved any high school problems. That's right, And
Speaker 4: now we have a kid that just already graduate. The
Speaker 4: Grock is right to go to college?
Speaker 1: Is that right? I mean, it's won't belong before. It's
Speaker 1: simply perfect.
Speaker 2: The human exams won't be part it'll be too easy.
Speaker 4: Yeah, and the internally we actually as a rocket continually evolved.
Speaker 1: We're going to talk about, you know what we're.
Speaker 4: Excited about, but very soon there will be no more
Speaker 4: benchmark left.
Speaker 1: Yeah. Yeah. One thing that's quite fascinating.
Speaker 3: I think that we basically only trained Grock's reasoning abilities
Speaker 3: on math problems and competitive coding problems. It's so very
Speaker 3: very specialized kinds of tasks, but somehow it's able to
Speaker 3: work on all kinds of other different tasks, so including
Speaker 3: creating games. No, lots and lots of different things. And
Speaker 3: what seems to be happening is that basically Grog learns
Speaker 3: this ability to detect its own mistakes and it's thinking,
Speaker 3: correct them, persist on a problem, try lots of different variants,
Speaker 3: pick them one that's best. So there are is generalized
Speaker 3: generalizing abilities that learns from mathematics and from coding, which
Speaker 3: it can then use to solve all kinds of other problems.
Speaker 1: So that's yeah, that's pretty I mean, reality is the
Speaker 1: instantiation of mathematics. That's right.
Speaker 4: And one thing we're actually really excited about that going
Speaker 4: back to our faulty mission, is what if one day
Speaker 4: we have a computer just like the thought that utilize
Speaker 4: our entire.
Speaker 1: Cluster just for the one very important problem.
Speaker 4: In the test time, all the deply turned out right,
Speaker 4: So I think back then we'll building the GBU clusters together. Uh,
Speaker 4: you're applobing cables, and I remember that when we turned
Speaker 4: on the first initial test, you can hear all the
Speaker 4: GPS humming in the hallway.
Speaker 1: That's almost feel like spiritual.
Speaker 3: Yeah, that's actually a pretty cool thing that we're able
Speaker 3: to do. That we can go into the data center
Speaker 3: and tinker with the machines there. So for example, we
Speaker 3: went in and we unplucked a few of the cables
Speaker 3: and just made sure that our training setup is still running,
Speaker 3: running stably. So that's something that's you know, I think
Speaker 3: most AI you know teams out there don't usually do.
Speaker 3: But it's actually totally unlocks like a new level of
Speaker 3: reliability and what you're able to do with the hypers.
Speaker 1: So okay, so when are we going to solve remont?
Speaker 4: So the easiest solution is to enumerate over all possible
Speaker 4: strains and as all you have a verifier, en up compute,
Speaker 4: you'll be able to do it.
Speaker 1: Okay, my projection will be what your guess, what is
Speaker 1: your neural edge? Calculate?
Speaker 4: So my my boat for the so so three years
Speaker 4: ago I told you that I think not two years later,
Speaker 4: two things is gonna happen.
Speaker 1: We're gonna see machines win some battles.
Speaker 4: Yes, two ways a word, fields metal, Globel price with
Speaker 4: probably some expert in the loop.
Speaker 3: Right, so the expert uplifting. So this year or next year,
Speaker 3: that's what it comes down to you. Yeah, So it
Speaker 3: looks like GROG finished all of its thinking on the
Speaker 3: on the two problems. So let's take a look at
Speaker 3: what it said. All right, so this was the little physics.
Speaker 1: Problem we had.
Speaker 3: You know, we we've collapsed the thoughts here, so they're
Speaker 3: you know, they're hidden. And then we see Grock's answer
Speaker 3: below that, so it explains it wrought a Python script
Speaker 3: here using map plot Lip then gives us all of
Speaker 3: the code. So let's take a quick look at the code.
Speaker 3: You know, it seems like it's doing reasonable things here,
Speaker 3: not not totally of the mark, solf Capitler says here,
Speaker 3: so maybe it's solving capitalist laws, capital capitalist law and americally. Yeah,
Speaker 3: there's really only one way to find out if this
Speaker 3: thing is working.
Speaker 1: I would say, let's let's give it a try. Let's run.
Speaker 3: Let's run the code all right, and we can see, yeah,
Speaker 3: I've got animating two different planets, Earth and Mars here,
Speaker 3: and then the green ball is the vehicle that's transiting
Speaker 3: the spacecraft that's transitioning between Earth and Mars, and you
Speaker 3: could see the journey from Earth to Mars and looks like, yeah,
Speaker 3: indeed the astronauts were turned safely, you know, at the
Speaker 3: right moment in time. So obviously this was just generated
Speaker 3: on the spot, so we can't tell you if that
Speaker 3: was actually correct solution. So we're going to take a
Speaker 3: close a look. Now, maybe we're going to call some
Speaker 3: colleagues a space X ask them if if this.
Speaker 2: Is legit, that's pretty close it's it's I mean yeah,
Speaker 2: I mean, there's there's there's there's a lot of complexities
Speaker 2: in the actual orbits that have to be taking into account,
Speaker 2: but this is this is pretty close to what looks
Speaker 2: like I add that or my pendets here. This has
Speaker 2: got the Earth home and transfer on it. When where
Speaker 2: we're going to install rock on a rocket.
Speaker 1: Well, I suppose in two years three years.
Speaker 2: Everything is two years away. Well, Earth and Mars. Transit
Speaker 2: can occurs every twenty six months. The next we're currently
Speaker 2: in a transit window approximately. The next one would be
Speaker 2: November of next year, roughly the end of next year,
Speaker 2: and if all go as well, SpaceX will send starship
Speaker 2: rockets to Mars with Optimus robots and Rock.
Speaker 1: Mh.
Speaker 3: I'm curious about this combination of Tetris and the duets.
Speaker 3: Looks like the Tetris as we've named it internally. So okay,
Speaker 3: we also have an output from Rock here wrote a
Speaker 3: Python script Spence.
Speaker 1: That is what it's been doing.
Speaker 3: If you look at the code, there are some constants
Speaker 3: that are being defined here, some.
Speaker 1: Colors, and then the totrominos.
Speaker 3: The pieces of Tetris are there, obviously very hard to
Speaker 3: see and at one glance if this is good, so
Speaker 3: we gotta we gotta run this to figure out if
Speaker 3: it's working.
Speaker 1: Well, let's let's give it a try.
Speaker 3: Fingers crossed ay, Right, So this kind of looks like Tetris,
Speaker 3: but the colors are a little bit off, right, The
Speaker 3: colors are different here, and if you think about what's
Speaker 3: going what's going on here? The jewel it has this
Speaker 3: mechanic where if you get free jewels in a row,
Speaker 3: you know, then they disappear and also gravity activates. Right,
Speaker 3: So what happens if you get three of the colors together? Okay,
Speaker 3: so something happened. So I think I think what Bruck
Speaker 3: did in this version is that, you know, once you
Speaker 3: connect three at least three blocks of the same color
Speaker 3: in a row.
Speaker 1: Then.
Speaker 3: Gravity activates and they disappear, and then gravity activates and
Speaker 3: all the other blocks fall down. I'm kind of kind
Speaker 3: of curious if there's still a Tetris mechanic here where
Speaker 3: if the line is full, does it actually clear it
Speaker 3: or what happens? Then it's up to interpretation.
Speaker 2: So who knows every I mean, when you're it'll do
Speaker 2: different variants when you ask it.
Speaker 3: It doesn't do the same thing every time exactly. We've
Speaker 3: seen a few other work very differently, but that's one
Speaker 3: seems cool.
Speaker 4: So are we ready for game studio at x l AI.
Speaker 2: Yes, so we're launching an AI gaming studio at x
Speaker 2: c I. If you're interested in joining us in building
Speaker 2: AI games, Uh, please join x AI where we're launching
Speaker 2: in AI Gaming studio or announcing it tonight.
Speaker 1: Let's go. Yeah, big games, that's an actual game. Yeah,
Speaker 1: all right.
Speaker 4: So I think one thing is super exciting for us
Speaker 4: is that once you have the best patriot model, you
Speaker 4: have the best reason model.
Speaker 1: Right, so we already see that we.
Speaker 4: Actually give a capability for those model.
Speaker 1: To think harder, think longer, think more broad.
Speaker 4: The performance continuing improves, and we're really excited about the
Speaker 4: next front here that will happen if we'll not only
Speaker 4: allow the model to think harder, but also provide more tools,
Speaker 4: just like call real humans to solve those problems. For
Speaker 4: real humans, we don't ask them to solve women hypothesis
Speaker 4: just with a piece of pen and paper. The Internet,
Speaker 4: so with all the basic web browsing, search engine and
Speaker 4: coding interpreters that builds the foundations and the best reasoning
Speaker 4: model build the foundations for the grog agent to come.
Speaker 4: So today we're actually introducing a new product called deep
Speaker 4: search that is the first generation of our grock agents
Speaker 4: that not just helping the engineers and research and scientists
Speaker 4: do coding, but actually help everyone to answer questions that.
Speaker 1: You have day today.
Speaker 4: It's kind of like a next generation search engine that
Speaker 4: really help you to understand the universe. So you can
Speaker 4: start asking questions like, for example, hey, when is the
Speaker 4: next starship launch day? For example, So let's try that
Speaker 4: the answer. On the left inside we see a high
Speaker 4: level progress bar. Essentially, you know the model knowledge is
Speaker 4: going to do one single search like the current right system,
Speaker 4: but actually thought very deeply about, hey, what's the user
Speaker 4: intent here, and what are the facts that actually consider
Speaker 4: at the same time, and how many different websites actually
Speaker 4: actually go and read the accountent right, So this can
Speaker 4: save hundreds of hours of everyone's Google time if you
Speaker 4: want to really look into certain topics.
Speaker 1: And then on the right.
Speaker 4: Inside you can see the bullet of how the current
Speaker 4: model you know, is doing what websites browsing, what sources
Speaker 4: verifying and oftentimes actually cross validate different sources out there
Speaker 4: to make sure the answer is actually correct before the
Speaker 4: output final answer, we can, you know, at the same time,
Speaker 4: fire up a few more querries. How about you know,
Speaker 4: you know your gamer right, so sure? Yeah, so how
Speaker 4: about what are some of the best bills and most
Speaker 4: popular bills in uh pathl hardcore right hardcore league?
Speaker 2: You if you can technically just look at the hardcore letterer,
Speaker 2: it might be a fast.
Speaker 1: Way to figure it out.
Speaker 4: They always see what model does. And then we can
Speaker 4: also do uh you know, something more fun. For example,
Speaker 4: how about like make a prediction about the marsh madness
Speaker 4: out there.
Speaker 1: Yeah, so this is.
Speaker 2: Kind of a fun one where Warren Buffett has a
Speaker 2: billion dollar bet if you can exactly match the I
Speaker 2: think the the the sort of the entire winning tree
Speaker 2: of marsh Madness, you can win a billion dollars from
Speaker 2: Warren Buffett. So, like, it would be pretty cool if
Speaker 2: AI could help you win a billion dollars from Buffett.
Speaker 1: It seems like a pretty good investment.
Speaker 4: Let's go yeah, all right, so now let's fire up
Speaker 4: the quarry and see what mama does.
Speaker 1: So we can actually go back to our very first one.
Speaker 4: How about the it wasn't counting on this, that's right, Okay,
Speaker 4: so we got the first one and all the thought
Speaker 4: around one minute. Okay, so the key inside here. The
Speaker 4: next starship is going to be on twenty fourth or later.
Speaker 4: So no earlier than February twenty fourth, it might be sooner.
Speaker 4: So yeah, so I think we can you know, go
Speaker 4: down to go down what a model does, so it
Speaker 4: does little research flight seven what happened got grounded and actually.
Speaker 1: Look into the FCC filing you know, from data collections.
Speaker 4: Uh, and that should make a new conclusion that yeh
Speaker 4: if we continue to roll down, let's see right, Yeah,
Speaker 4: so it makes uh the you know little table. I
Speaker 4: think inside XAI we often joked about the time to
Speaker 4: the first table is the only you know latency that matters. Yeah,
Speaker 4: so that's how the model making influence. And look at
Speaker 4: all the sources and then we can look into the
Speaker 4: gaming one. So how about the break So for this
Speaker 4: particular one, we look at the buildings light and you
Speaker 4: know it's a lot better so uh what they inferl us.
Speaker 4: But if we go down, so the surprising fact of
Speaker 4: all the other builds, So look into the twelve classes,
Speaker 4: so we'll see that the medium bill was pretty popular
Speaker 4: whenever the game first came out. And now the Invokers
Speaker 4: of the World.
Speaker 1: Took over Monkety Biker for sure. Yeah that's right.
Speaker 4: Yeah, followed by the stone Weavers. Then that's really good mapping.
Speaker 4: So yeah, and then we can see uh uh the
Speaker 4: match manners.
Speaker 1: How about that?
Speaker 4: Soe One interesting thing about the dep search is that
Speaker 4: if you actually go into the panel.
Speaker 1: Where it shows, you know, what are the subtasks.
Speaker 4: You can actually click the bottom left of this right
Speaker 4: and then in this case you can actually go to
Speaker 4: actually reading to the mind of Grock. What informations does
Speaker 4: the model actually think about our truck worthy what or not?
Speaker 4: How does it actually cross all their different information sources?
Speaker 4: So that makes the entire search experience and information with
Speaker 4: Trual process a lot more transparent to our users.
Speaker 3: This is much more powerful than any search engine out there.
Speaker 3: You can literally just tell it only use sources from X.
Speaker 3: You know, we'll try to respect that, and so it's
Speaker 3: much more steerable, much more intelligent than I mean.
Speaker 1: It really should save you a lot of times.
Speaker 2: So something that might take an hour or an hour
Speaker 2: of researching on the web or searching media. You can
Speaker 2: just ask it to go do that and come back
Speaker 2: in ten minutes later.
Speaker 1: It's done. An hour's work worth of work for you.
Speaker 2: That's really what it comes down to, exactly and and
Speaker 2: maybe better than you could have done it yourself.
Speaker 1: Yeah.
Speaker 4: Think about the informount of interns working for you that
Speaker 4: you can just fire up all the tasks and come
Speaker 4: back a minute later.
Speaker 1: This is going to be interesting one.
Speaker 4: So marchmass had not happened yet, so I guess we
Speaker 4: had to follow up with a next livestream.
Speaker 2: Yeah, it seems like pretty good. Like forty dollars might
Speaker 2: get you a billion dollars forty dollars subscription, that's.
Speaker 1: Right, I mean my work. So yeah, so where are
Speaker 1: the users gonna have their heads on? Rock three yees?
Speaker 3: So the good news is we've been working tirelessly to
Speaker 3: actually release all of these features that we've shown you.
Speaker 3: The Grock free based model with amazing chat capabilities that's
Speaker 3: really useful, that's really interesting to talk to, the deep search,
Speaker 3: the advanced reasoning mode, all of these things. We want
Speaker 3: to row them out to you today, starting with the
Speaker 3: plus subscribers on x So it's the first group that
Speaker 3: will initially get access.
Speaker 1: Make sure to update your x.
Speaker 3: Up if you want to see all of the advanced capabilities,
Speaker 3: because we just released the update now as we're as
Speaker 3: we're talking here, and yeah, if you're interested in getting
Speaker 3: early access to grock, then sign up for Premium Plus.
Speaker 3: And also we're announcing that we're starting a separate subscription
Speaker 3: for Grock that we call Super Grock for those who
Speaker 3: those real rock fans. That one of the most bands
Speaker 3: capabilities and the earliest access to new futures.
Speaker 1: So feel free to check that out as well.
Speaker 2: This this is for the dedicated grock app and for
Speaker 2: the website exactly.
Speaker 3: So our new website is called grock dot com. Yeah,
Speaker 3: and you're also guess and you can also find our
Speaker 3: Brock app in the ir S app Store, and that
Speaker 3: gives you a more even more polished experience that's totally
Speaker 3: grock focused. If you're if you want to have grock
Speaker 3: not easily available one tape away.
Speaker 2: Yeah, and the version on grock dot com on you know,
Speaker 2: on a web browser is going to be the most
Speaker 2: the latest and most advanced version because obviously it takes
Speaker 2: us a while to get it get something into an
Speaker 2: app and they get it approved by the app store.
Speaker 2: So and then if there's something on a phone format
Speaker 2: is limitations where you can do so. The most powerful
Speaker 2: version of grock and the latest version will be the
Speaker 2: web version at rock dot com.
Speaker 3: Yeah, so so watch out for the name grock Free
Speaker 3: in the app giveaway exactly that that's that's the giveaway
Speaker 3: that you have grock free. And if it says grow
Speaker 3: true then at grogby hasn't quite arrived for yet, but
Speaker 3: we're working hard to brow this out today and then
Speaker 3: to even more people over the coming days.
Speaker 4: Yeah, make sure you update your phone app too, where
Speaker 4: you're going to get all the tools were showcased today
Speaker 4: with the thinking mold with the deep search. So yeah,
Speaker 4: really looking forward to all the feedbacks you have.
Speaker 2: Yeah, I think we should emphasize that this is kind
Speaker 2: of a beta like meaning that you should expect some
Speaker 2: imperfections at first, but we will improve it rapidly almost
Speaker 2: every day.
Speaker 1: In fact, every day I think it'll get better.
Speaker 2: So if you want a more polished version, i'd like
Speaker 2: maybe wait a week, but expect improvements literally every day.
Speaker 2: And then we're also going to be providing a voice
Speaker 2: you can have conversational. In fact, I was trying it
Speaker 2: earlier today. It's working pretty well, but not we need
Speaker 2: these a bit more polish, the sort of way where
Speaker 2: you can just literally talk to it like you're talking
Speaker 2: to a person. That's awesome. It's actually I think one
Speaker 2: of the best experiences of Grog. That's that's probably about
Speaker 2: a week away.
Speaker 3: So with that said, I think we might have some
Speaker 3: audience questions. Surely, all right, take a look. Yeah, let's
Speaker 3: take a look the audience from the ass platform.
Speaker 2: Yeah.
Speaker 1: Cool.
Speaker 3: So the first question here is when Grock Voice Assistant.
Speaker 3: When is it coming out as soon as possible, just
Speaker 3: like Elan said, just a little bit of publishing away
Speaker 3: from being reached to everybody. Obviously, it's going to be
Speaker 3: released in an early form and we're going to rapidliterate
Speaker 3: among that.
Speaker 4: The next question is like when will GROX three being
Speaker 4: the API? So this is coming in the GROG three
Speaker 4: API with both the reasing models and deep search.
Speaker 1: Is coming away in the coming weeks.
Speaker 4: We're actually very excited about the enterprise use cases of
Speaker 4: all these additional tools that now Grock has access to,
Speaker 4: and how the testim computed and tool use can actually
Speaker 4: really accelerate all the business use cases. Another one is
Speaker 4: will voice mode be native or a text to speech?
Speaker 4: So I think that means is it going to be
Speaker 4: one one model that is understanding what you say and
Speaker 4: then talking back to you, or is it going to
Speaker 4: be some.
Speaker 3: System that has text of speech inside of it. And
Speaker 3: the good news is it's going to be one model,
Speaker 3: like and not a variant of grock Free that we're
Speaker 3: going to release, which basically understands what you're saying what
Speaker 3: you're saying and then generates the audio directly from that.
Speaker 3: So very much like grock Free generates text, that model
Speaker 3: generates audio, and that has a bunch of advantagers. I
Speaker 3: was talking to it earlier today and it said hi, Igor, no,
Speaker 3: reading my name from probably from some texts that it had,
Speaker 3: And I said, no, my name is Igor, and it
Speaker 3: remember that, you know, so it could continue to say Igor,
Speaker 3: just like a human world, and you can't.
Speaker 1: Achieve that with Texas speech.
Speaker 4: So yeah, so here's a question for you, pretty spicy,
Speaker 4: you know, is Grog a boy or a girl?
Speaker 1: And they think Grog is whatever you wanted to be? Yah?
Speaker 2: Yeah, single, yes, all right, the shop is open. So honestly,
Speaker 2: people are going to pull in love with Croc. It's
Speaker 2: it's like probable.
Speaker 1: Uh.
Speaker 4: The next question, will Grock be able to transcribe audio
Speaker 4: into text? Yes, so we'll have this capability in both
Speaker 4: the app and also the API without that's like Groschia
Speaker 4: just be your personal assistant looking over your shoulder, right
Speaker 4: and follow you along the way, learn everything you have learned,
Speaker 4: and really help you to understand the world better.
Speaker 1: It becomes smodery every day.
Speaker 2: Yeah, I mean the voicematter Groc doesn't isn't simply it's
Speaker 2: not just voice text. It understands like tone, inflection, pacing, everything.
Speaker 1: It's it's wild. I mean, it's like token a person. Yep.
Speaker 4: So any plans for conversation memory, absolutely, we're working on
Speaker 4: it right now.
Speaker 1: That's right.
Speaker 4: Let's see without the other ones. So what about the
Speaker 4: you know, the DM features? Right, So if you have
Speaker 4: personalization said that, if you uh, you know Grock remembers
Speaker 4: your previous interactions, yes, Should it be one Groc or
Speaker 4: multiple different.
Speaker 2: Grocks, It's up to you. You can have one Grok
Speaker 2: or many Grons. I suspect people will probably have one one.
Speaker 1: Yeah, I won't have a doctor Groc. Yeah, the Grouk doc.
Speaker 1: That's right, right, cool.
Speaker 3: So in the past we've open sourced grock one, so
Speaker 3: somebody is asking us we're going to do it again?
Speaker 2: Yeah, I think when once scrut A general approach is
Speaker 2: that we will open source the last version when the
Speaker 2: next version is fully out. It's like when when GROG
Speaker 2: three is mature and stable, which is probably within a
Speaker 2: few months, then will open source GROUG two.
Speaker 4: Mm hmm, okay, so we probably have time for one
Speaker 4: last question. What was the most difficult part about working
Speaker 4: on this project? I assume Grock three and the world
Speaker 4: I'm most excited about, so I think me looking back,
Speaker 4: you know, getting the whole model training on one hundred
Speaker 4: k h one hundred coherently, that's almost a battle against
Speaker 4: the final boss of the universe, the entropy, Because at
Speaker 4: a given time, you can have a cosmic rate that
Speaker 4: beaming downe and flip a bit you know, transistor and
Speaker 4: not the entire graded update. If it's fit mentis a bit,
Speaker 4: the entire grade update is out of whack.
Speaker 1: And nine hundred thousand of those.
Speaker 4: Orchestrate them every time at any given time, and GPS
Speaker 4: can go down, and.
Speaker 2: I mean it's worth breaking down, like how were we
Speaker 2: able to get the world's most powerful training cluster operational
Speaker 2: within one hundred and twenty two days because we started
Speaker 2: off we we actually weren't intending to do a data
Speaker 2: center ourselves. We were going to just we went to
Speaker 2: the data center providers and said, how long would it
Speaker 2: take to have one hundred thousand GPUs operating coherently in
Speaker 2: a single location, And we've got time frames from eighteen
Speaker 2: to twenty four months, so like, well, eighteen twenty four months,
Speaker 2: that means losing as a certainty. So the only option
Speaker 2: was to do it ourselves. So if you break down
Speaker 2: the problem, you guest doing like reasoning here, it takes
Speaker 2: your thing. Yeah, exactly. So what we needed a building.
Speaker 2: We can't build a building, so we must use an
Speaker 2: existing building.
Speaker 1: So we looked for basically for factories that had been.
Speaker 2: That had been abandoned, but the factory was in good shape,
Speaker 2: like the company had gone bankrupt to something.
Speaker 1: So we found an electro Luxe factory in Memphis.
Speaker 2: That's why it's in Memphis, home of Elvis and also
Speaker 2: one of the oldest I think it was the capital
Speaker 2: of ancient Egypt, and it was actually very nice factory
Speaker 2: that forever for whatever reason that Electrolux had left and
Speaker 2: that that gave us shelter for the computers. Then we
Speaker 2: needed power that we needed at least one hundred and
Speaker 2: twenty megawats at first, but the building only had fifteen megawats,
Speaker 2: and ultimately for two hundred thousand megree thousand GPUs we
Speaker 2: needed a quarter gigawat, so we they initially leased a
Speaker 2: whole bunch of generators, so we have generators on one
Speaker 2: side of the building, just trailer after trailer of generators
Speaker 2: until we get the utility power to come in. But
Speaker 2: then we also need cooling, so on the other side
Speaker 2: of the building it was just trailer after trailer of cooling,
Speaker 2: So we leased about a quarter of the mobile cooling
Speaker 2: capacity of the United States on the other side of
Speaker 2: the building. Then we needed to get the GPUs all installed,
Speaker 2: and they're all liquid cooled, so in order to achieve
Speaker 2: the density necessary, this is a liquid cooled system.
Speaker 1: So we had to get all the plumbing for liquid cooling.
Speaker 2: Now, we had ever done a liquid cooling data center
Speaker 2: at scale, so this was an incredibly dedicated effort by
Speaker 2: a very talented team to achieve that outcome.
Speaker 1: I may think, now it's going to work. Nope.
Speaker 2: The issue is that the the power fluctuations for a
Speaker 2: GPU cluster are dramatic. So it's like this giant symphony
Speaker 2: that has taking place, like having a symphony with one
Speaker 2: hundred thousand or two hundred thousand participants in the symphony,
Speaker 2: and the whole orchestra will go quiet and loud in
Speaker 2: you know, one hundred milliseconds, and so this caused massive
Speaker 2: power fluctuations. So then which then caused the generators to
Speaker 2: lose their minds and they.
Speaker 1: Weren't expecting this.
Speaker 2: So to buffer the power, we then used Tesla megapas
Speaker 2: to smooth out the power.
Speaker 1: So the mega packs had to be reprogrammed.
Speaker 2: So with the XAI we were working with Teesla, we
Speaker 2: reprogrammed the megapacs to be able to deal with these
Speaker 2: dramatic power fluctuation fluctuations to smooth out the powers that
Speaker 2: the computers could actually run properly, and that worked.
Speaker 1: It quite tricky and then but.
Speaker 2: Even at that point, just left to make the computers
Speaker 2: all communicate effectively, so all the networking had to be
Speaker 2: solved and debugging Brazilian network cables a debugging Nickel at
Speaker 2: four in the morning, or we solved it like roughly
Speaker 2: four twenty am. Was well figured out, Like there's some well,
Speaker 2: there are a whole bunch of issues. One there was
Speaker 2: like a bios mismatch.
Speaker 1: Bios was not set up correctly.
Speaker 3: We HADSPCI outputs between two different machines. One that was working, yeah,
Speaker 3: one that was not working. Many many other things.
Speaker 1: I mean, yeah, exactly.
Speaker 2: This would go on for a long time if we
Speaker 2: actually listed all the things. But it's like interesting, It's
Speaker 2: not like, oh, we just magically made it happen. You
Speaker 2: had to break down the problem, just like groctas for reasoning,
Speaker 2: into the constituent elements, and then solve each of the
Speaker 2: constituent elements in order to achieve a coherent training cluster
Speaker 2: in a period of time that is a small fraction
Speaker 2: of what anyone else could do it in.
Speaker 3: And then once the train cluster was up and running
Speaker 3: and we could use it, or we had to make
Speaker 3: sure that it actually stays healthy throughout, which is his
Speaker 3: own brand challenge. And then we had to get every
Speaker 3: single detail of the training right in order to get
Speaker 3: a rookery level model, which is actually really really hard.
Speaker 3: So we don't know if there are any other models
Speaker 3: out there that have Rockery's capabilities, but whoever trains a
Speaker 3: model better than rock Crey has to be extremely good
Speaker 3: at the science of deeplining at every aspect of the engineering.
Speaker 1: So it's it's not so easy to pull this off.
Speaker 1: And this is now going to be the last cluster
Speaker 1: were build and last model we train.
Speaker 2: Oh yeah, we're We've already started work on the next cluster,
Speaker 2: which will be about five times to power, so instead
Speaker 2: of a quarter gigawad, roughly one point to giga.
Speaker 1: What what's the what's the back to the future was?
Speaker 1: What's the power?
Speaker 2: Does the back to the Future car anyway back to
Speaker 2: the future power? It's like roughly in that order, I think. So,
Speaker 2: you know, there will be the sort of the GB
Speaker 2: two hundred slash three hundred pleasure once again. It will
Speaker 2: be the most powerful training cluster from the world. So
Speaker 2: we're not like stopping here, and.
Speaker 4: Our reason model is going to continue improve by accessing
Speaker 4: more tools every day. So yeah, we're very excited to
Speaker 4: share any of that coming results with you all.
Speaker 3: Yeah, the thing that keeps us going is basically being
Speaker 3: able to give free to you and then seeing the
Speaker 3: usage go up, seeing everybody enjoy no clock.
Speaker 1: That's that's what really gets us up in the morning.
Speaker 1: So thanks for your name. Thanks guys,