DeepSeek’s New AI Is A Game Changer

[00:00] Hmm, why does this deep sea quirk exist?

[00:02] I mean, it adds vision capabilities to

[00:05] the deep sea AI system, but that's not

[00:07] new. A lot of other AI systems have

[00:10] vision capabilities. You just drop an

[00:12] image here and it works. Even video and

[00:15] even for open models. So, why do we need

[00:18] this paper? Well, they did something

[00:21] incredible here and it is an absolute

[00:24] game changer. Why? You see, if you ask a

[00:27] previous technique to count the number

[00:29] of people in this photo, it will think

[00:32] something like this. Okay, there are

[00:34] people on the upper left and a bunch of

[00:36] stripy guys in two rows. That is kind of

[00:40] three rows. Some of them are standing,

[00:42] some of them are sitting.

[00:44] Ah, it's just so confusing to just count

[00:46] them up using only words. Two problems

[00:49] with this one. One, this is prone to

[00:51] error. Two, you have to think a lot.

[00:55] Just describing stuff. Why? What would

[00:58] we, humans, do? Of course, we would use

[01:01] our finger and would point at the image.

[01:04] One, two, three, and so on.

[01:07] Done. Don't describe images like a poet.

[01:10] Point like a human. Now, that is exactly

[01:13] what this new technique does. It allows

[01:16] an AI system to point at things while

[01:19] thinking and it is absolutely brilliant.

[01:22] This makes it more accurate and it also

[01:25] makes it faster. In a world where

[01:27] hardware and tokens cost a fortune, it

[01:30] is fantastic to have something that

[01:33] gives us results faster and cheaper.

[01:35] But, it turns out thinking with visual

[01:38] primitives has even more advantages. It

[01:41] can also do topological reasoning. For

[01:43] instance, if you give it a maze with a

[01:46] start and end point, you not only get a

[01:49] correct answer to your questions, but

[01:51] you can also trace back the whole

[01:54] thought process visually.

[01:56] I love that. Also, here you can ask

[01:59] where the crown connects and look.

[02:02] To the octopus. Yeah, it answers

[02:05] correctly, but you can also see how it

[02:08] came to that conclusion. Now, make no

[02:11] mistake. These are simple examples. I'll

[02:14] show you in a moment if it is as good as

[02:16] these billion-dollar frontier models.

[02:19] Also, if something goes wrong, this will

[02:21] make it easier to find mistakes and fix

[02:24] them to create an even better model.

[02:26] This puts us one step closer to AI

[02:29] systems we can actually understand that

[02:32] do not just give us a soup of numbers.

[02:34] So good. So, how good is it? Well, hold

[02:38] on to your papers, fellow scholars, and

[02:41] I dropped my papers here. Look, it needs

[02:43] about 90% fewer visual tokens than most

[02:47] frontier models. Now, wait, wait, wait.

[02:50] It doesn't matter how little you think

[02:52] if you just say three as an answer

[02:55] without thinking. Thinking time doesn't

[02:57] matter if it is incorrect. So, how

[03:00] accurate is it?

[03:02] Are you kidding me? This free system

[03:04] matches or beats almost everything. And

[03:08] once again, we are talking about this,

[03:10] which is free, going up against

[03:12] billion-dollar systems here. Wow. Now,

[03:16] we are fellow scholars here, so at this

[03:18] point we ask, are these results real?

[03:21] You know, benchmarks are being gamed

[03:23] left and right. Now, here is what many

[03:26] people missed. Average over seven

[03:29] benchmarks, but in-house benchmarks

[03:31] excluded.

[03:33] That is the key. They did not rig their

[03:35] own benchmarks. You know why? Well,

[03:38] everyone loves it because it's one of

[03:40] the oldest tricks in the book. If you

[03:42] are not performing well, just create a

[03:45] new benchmark that fits you. Let's make

[03:47] a YUNUS benchmark. You will always be

[03:50] world first in being you. And this is

[03:53] not the case here. Amazing. This is free

[03:56] and open research. So, this technique

[03:58] can potentially be added to many

[04:00] existing models, including free ones.

[04:03] This paper does not have a model

[04:05] attached that I know of. It describes

[04:07] the concept of how to do it in detail.

[04:10] It's a blueprint, if you will. More

[04:12] intelligence for all of us for free.

[04:16] The world needs more papers like this.

[04:18] Love it. But, this all sounds like

[04:21] magic. How did they do this? Well, look,

[04:23] this is their own policy distillation

[04:26] objective. We need exactly this. You

[04:29] see, normally, we have a bunch of expert

[04:32] AI models. Now, at the risk of

[04:34] simplifying things, imagine that one of

[04:37] these guys is great at boxes. Nobody

[04:40] does boxes better than this guy. The

[04:42] other one is great at tracing mazes with

[04:45] points. But, that's not what we want.

[04:48] What we want is one AI that can do all

[04:51] of these things. And that is where this

[04:53] comes into play. We train a student

[04:56] model that learns from all of these

[04:58] teachers. It says what it would try to

[05:01] do, then the teachers say, "Okay, here's

[05:04] what I would have done." Do this enough

[05:06] and the student will be pretty good at

[05:09] all of these different kinds of visual

[05:11] thinking. This is why they used the name

[05:13] distilling the knowledge of a bunch of

[05:15] expert teachers into a student. So,

[05:19] where does this put us? Okay, so here's

[05:21] what I think. Dear fellow scholars, this

[05:23] is Two Minute Papers with Dr. Károly

[05:25] Zsolnai-Fehér. You know, we always

[05:27] thought that we would make AI systems

[05:29] smarter by giving it higher resolution

[05:32] images to train on. More pixels, more

[05:34] smarts. It turns out not true.

[05:37] Sometimes, that's not what we need at

[05:39] all. Deep Seek just cut down those

[05:42] visual tokens by 90% and still beat

[05:45] frontier models. Less is more. Now, is

[05:48] this perfect? All problems solved? No.

[05:52] Limitations. One, the AI does not

[05:55] automatically do this kind of pointy

[05:57] thinking. It needs a word as a cue for

[06:00] this kind of thinking. Two, bounding

[06:03] boxes are nice for people, but if you

[06:05] are counting blades of grass or strands

[06:08] of hair, now, in this case, not having

[06:11] those in very high resolution is a

[06:13] problem.

[06:15] >> [laughter]

[06:15] >> Yep, once again, the two-minute papers

[06:18] special, thin structures. Every time,

[06:22] man. It's so painful. And three, this

[06:25] kind of topological reasoning does not

[06:27] generalize as well as we'd like. It

[06:30] might not be as robust when you show it

[06:32] something completely new. So, careful

[06:35] with the misleading media headlines,

[06:37] careful with the hype everywhere. There

[06:39] is still plenty to improve here. But, I

[06:42] feel that this might be a breakthrough.

[06:44] And that makes it

[06:46] maybe the third one this month in AI

[06:48] research. What a time to be alive. Also,

[06:52] with large AI companies going to IPO,

[06:55] they are about to become ventures that

[06:57] look to maximize their profits. More

[06:59] money needed every quarter. So, it's

[07:02] going to become more and more crucial to

[07:05] own your own AI systems with free open

[07:07] weights models. And this one makes them

[07:10] better.

[07:11] Love it. Here you see me running the

[07:13] full DeepSeek AI model through Lambda

[07:16] GPU Cloud. 671

[07:20] billion parameters running super fast

[07:22] and super reliably. This is insane. I

[07:26] love it and I use it on a regular basis.

[07:29] Lambda provides you with powerful Nvidia

[07:32] GPUs to run your own chatbots and

[07:35] experiments. Seriously, try it out now

[07:37] at lambda.ai/papers

[07:40] or click the link in the description.