AI에서 공간 지능의 진화

AI의 공간 지능은 로봇 학습을 촉진하여 기계가 3D 세계와 상호 작용하고 이해할 수 있도록 합니다.

### 시각의 진화와 공간 지능의 부상
* 🌌 5억 4천만 년 전 세상: **시력 부족으로 인한 암흑, 빛을 감지하는 최초의 생물인 삼엽충의 출현.**
* 🧠 다양한 동물 종들이 화석 기록에 등장하는 **캄브리아기 폭발**과 시각의 발달로 인한 **지능의 진화**는 보는 것에서 이해하고 행동하는 것으로 이어졌습니다.
* 👁 ️ 컴퓨터 비전의 발전과 사람이 입력한 문장으로 **사진과 동영상을 생성**할 수 있는 **생성형 AI 알고리즘의 개발.**
* 🤖 공간 지능의 발전으로 **2D 이미지를 3D 정보로 변환**하고 컴퓨터가 **3D 세계에서 보고, 배우고, 행동**할 수 있도록 가르칩니다.
* 🔬 공간 지능이 로봇 학습에 미치는 영향과 **구두 지시를 기반**으로 3D 환경에서 로봇이 행동하도록 훈련하는 방법을 알아보세요.
* 🏥 스마트 센서, 자율 로봇, 수술용 증강 현실, **로봇의 뇌파 제어** 등 의료 분야에 AI와 공간 지능을 적용하고 있습니다.
* 🌍 공간 지능을 갖춘 컴퓨터와 로봇이 **생산성과 인류를 향상시키는 신뢰할 수 있는 파트너**가 되는 미래의 비전입니다.

Let me show you something. To be precise, I'm going to show you nothing. This was the world 540 million years ago. Pure, endless darkness. It wasn't dark due to a lack of light. It was dark because of a lack of sight. Although sunshine did filter 1,000 meters beneath the surface of ocean, a light permeated from hydrothermal vents to seafloor, brimming with life, there was not a single eye to be found in these ancient waters. No retinas, no corneas, no lenses. So all this light, all this life went unseen. There was a time that the very idea of seeing didn't exist. It [had] simply never been done before. Until it was. So for reasons we're only beginning to understand, trilobites, the first organisms that could sense light, emerged. They're the first inhabitants of this reality that we take for granted. First to discover that there is something other than oneself.

A world of many selves. The ability to see is thought
to have ushered in Cambrian explosion, a period in which a huge
variety of animal species entered fossil records. What began as a passive experience, the simple act of letting light in, soon became far more active. The nervous system began to evolve. Sight turning to insight. Seeing became understanding. Understanding led to actions. And all these gave rise to intelligence. Today, we're no longer satisfied with just
nature's gift of visual intelligence. Curiosity urges us to create machines
to see just as intelligently as we can, if not better. Nine years ago, on this stage, I delivered an early progress report
on computer vision, a subfield of artificial intelligence. Three powerful forces
converged for the first time: Aa family of algorithms
called neural networks, fast, specialized hardware
called graphic processing units, or GPUs, and big data, like the 15 million images that my lab
spent years curating called ImageNet.

Together, they ushered in
the age of modern AI. We've come a long way. Back then, just putting labels on images
was a big breakthrough. But the speed and accuracy
of these algorithms just improved rapidly. The annual ImageNet
challenge, led by my lab, gauged the performance of this progress. And on this plot, you're seeing
the annual improvement and milestone models. We went a step further and created algorithms
that can segment objects or predict the dynamic
relationships among them in these works done by my students
and collaborators. And there's more. Recall last time I showed you
the first computer-vision algorithm that can describe a photo
in human natural language. That was work done with my brilliant
former student, Andrej Karpathy. At that time, I pushed my luck and said, Andrej, can we make computers
to do the reverse? And Andrej said,
Ha ha, that's impossible.

Well, as you can see from this post, recently the impossible
has become possible. That's thanks to a family
of diffusion models that powers today's
generative AI algorithm, which can take human-prompted sentences and turn them into photos and videos of something that's entirely new. Many of you have seen the recent
impressive results of Sora by OpenAI. But even without the enormous
number of GPUs, my student and our collaborators have developed a generative
video model called Walt months before Sora. And you're seeing some of these results. There is room for improvement. I mean, look at that cat's eye and the way it goes under the wave
without ever getting wet. What a cat-astrophe. (Laughter) And if past is prologue, we will learn from these mistakes
and create a future we imagine. And in this future, we want AI to do everything it can for us, or to help us.

For years, I have been saying that taking a picture is not the same
as seeing and understanding. Today, I would like to add to that. Simply seeing is not enough. Seeing is for doing and learning. When we act upon this world
in 3D space and time, we learn, and we learn
to see and do better. Nature has created this virtuous cycle
of seeing and doing powered by “spatial intelligence.” To illustrate to you what your spatial
intelligence is doing constantly, look at this picture. Raise your hand if you feel
like you want to do something. (Laughter) In the last split of a second, your brain looked
at the geometry of this glass, its place in 3D space, its relationship with the table, the cat, and everything else. And you can predict
what's going to happen next. The urge to act is innate to all beings
with spatial intelligence, which links perception with action. And if we want to advance AI
beyond its current capabilities,

we want more than AI
that can see and talk. We want AI that can do. Indeed, we're making exciting progress. The recent milestones
in spatial intelligence is teaching computers to see, learn, do, and learn to see and do better. This is not easy. It took nature millions of years
to evolve spatial intelligence, which depends on the eye taking light, project 2D images on the retina, and the brain to translate
these data into 3D information. Only recently, a group
of researchers from Google are able to develop an algorithm
to take a bunch of photos and translate that into 3D space, like the examples we're showing here. My student and our collaborators
have taken a step further and created an algorithm
that takes one input image and turn that into 3D shape. Here are more examples. Recall, we talked about computer programs
that can take a human sentence and turn it into videos. A group of researchers
in University of Michigan

have figured out a way to translate
that line of sentence into 3D room layout, like shown here. And my colleagues at Stanford
and their students have developed an algorithm
that takes one image and generates infinitely plausible spaces for viewers to explore. These are prototypes of the first
budding signs of a future possibility. One in which the human race
can take our entire world and translate it into digital forms and model the richness and nuances. What nature did to us implicitly
in our individual minds, spatial intelligence
technology can hope to do for our collective consciousness. As the progress of spatial
intelligence accelerates, a new era in this virtuous cycle
is taking place in front of our eyes. This back and forth
is catalyzing robotic learning, a key component for any
embodied intelligence system that needs to understand
and interact with the 3D world. A decade ago, ImageNet from my lab enabled a database of millions
of high-quality photos.

to help train computers to see. Today, we're doing the same
with behaviors and actions to train computers and robots
how to act in the 3D world. But instead of collecting static images, we develop simulation environments
powered by 3D spatial models so that the computers can have
infinite varieties of possibilities to learn to act. And you're just seeing
a small number of examples to teach our robots in a project led by my lab
called Behavior. We’re also making exciting progress
in robotic language intelligence. Using large language model-based input, my students and our collaborators
are among the first teams that can show a robotic arm
performing a variety of tasks based on verbal instructions, like opening this drawer
or unplugging a charged phone. Or making sandwiches,
using bread, lettuce, tomatoes, and even putting a napkin for the user. Typically I would like
a little more for my sandwich, but this is a good start. (Laughter)

In that primordial ocean,
in our ancient times, the ability to see and perceive
one's environment kicked off the Cambrian explosion
of interactions with other life forms. Today, that light
is reaching the digital minds. Spatial intelligence is allowing machines to interact not only with one another, but with humans, and with 3D worlds, real or virtual. And as that future is taking shape, it will have a profound
impact to many lives. Let's take health care as an example. For the past decade, my lab has been taking
some of the first steps in applying AI to tackle challenges
that impact patient outcome and medical staff burnout. Together with our collaborators
from Stanford School of Medicine and partnering hospitals, we're piloting smart sensors that can detect clinicians
going into patient rooms without properly washing their hands. Or keep track of surgical instruments. Or alert care teams when
a patient is at physical risk, such as falling. We consider these techniques
a form of ambient intelligence.

like extra pairs of eyes
that do make a difference. But I would like more interactive help
for our patients, clinicians, and caretakers who desperately
also need an extra pair of hands. Imagine an autonomous robot
transporting medical supplies while caretakers focus on our patients, or augmented reality,
guiding surgeons to do safer, faster, and less invasive operations. Or imagine patients with severe paralysis
controlling robots with their thoughts. That's right, brainwaves,
to perform everyday tasks that you and I take for granted. You're seeing a glimpse of that future
in this pilot study from my lab recently. In this video, the robotic arm is cooking
a Japanese sukiyaki meal controlled only by the brain's
electrical signal, non-invasively collected
through an EEG cap. (Applause) Thank you. The emergence of vision
half a billion years ago turned a world of darkness upside down. It set off the most profound
evolutionary process:

the development of intelligence
in the animal world. AI's breathtaking progress
in the last decade is just as astounding. But I believe the full potential
of this digital Cambrian explosion won't be fully realized until we power
our computers and robots with spatial intelligence, just like what nature did to all of us. It’s an exciting time to teach
our digital companion to learn to reason and to interact with this beautiful
3D space we call home, and also create many more new worlds
that we can all explore. To realize this future won't be easy. It requires all of us
to take thoughtful steps and develop technologies
that always put humans in the center. But if we do this right, the computers and robots
powered by spatial intelligence will not only be useful tools but also trusted partners to enhance and augment
our productivity and humanity while respecting our individual dignity and lifting our collective prosperity. What excites me the most in the future is a future in which that AI
grows more perceptive.

insightful and spatially aware, and they join us on our quest to always pursue a better way
to make a better world. Thank you. (Applause)

With Spatial Intelligence, AI Will Understand the Real World | Fei-Fei Li | TED

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시각의 진화: 삼엽충에서 현대 AI까지

5억 4천만 년 전, 빛을 감지할 수 있는 최초의 생물로 삼엽충이 등장하기 전까지 세상은 어둡고 앞을 볼 수 없었습니다.

삼엽충의 시각 능력은 캄브리아기 폭발로 이어졌고, 시각은 이해력과 지능으로 발전하여 결국 현대 인공지능의 탄생으로 이어진 것으로 추정됩니다.

컴퓨터 비전의 발전은 단순히 이미지에 라벨을 붙이는 것에서 동적 관계를 예측하고 인간의 언어로 사진을 설명하는 것까지 빠르게 발전해 왔습니다.

소라나 월트와 같은 생성형 AI 알고리즘은 이제 텍스트 프롬프트를 새로운 이미지와 동영상으로 변환할 수 있습니다.

지각과 행동을 연결하는 공간 지능은 단순히 보고 말하는 것을 넘어 AI를 발전시키는 데 매우 중요합니다.

연구자들은 컴퓨터가 3D 공간을 이해하도록 가르치는 데 있어 상당한 진전을 이루었으며, Google과 미시간 대학교가 그 선두에 서 있습니다.

공간 지능이 미래에 미치는 영향

연구원들은 이미지를 3D 공간 배치로 변환하는 알고리즘을 개발하여 세계를 디지털 방식으로 모델링할 수 있는 길을 열었습니다.

공간 지능의 발전은 3D 공간 모델을 사용하여 컴퓨터와 로봇을 훈련시키는 로봇 학습의 새로운 시대를 이끌고 있습니다.

로봇 언어 지능 분야에서도 흥미로운 진전이 이루어지고 있으며, 이제 로봇 팔이 구두 지시에 따라 작업을 수행하는 모습을 보여줄 수 있는 팀이 등장하여 이 분야의 상당한 발전을 예고하고 있습니다.

공간 지능이 의료 분야에 미치는 영향

공간 지능은 기계와 사람, 그리고 3D 세계가 상호 작용할 수 있게 해주며 의료 분야에 큰 영향을 미치고 있습니다.

AI와 스마트 센서는 손 위생 준수 여부를 감지하고, 수술 도구를 추적하며, 환자의 낙상을 방지하는 데 사용되어 주변 환경 인텔리전스 시스템으로 활용되고 있습니다.

공간 지능이 적용된 의료 서비스의 미래에는 의료용품을 운반하는 자율 로봇, 외과의를 안내하는 증강 현실, 환자가 뇌파로 로봇을 제어할 수 있는 기능 등이 포함되며, 최근 파일럿 연구에서 입증된 바 있습니다.

AI는 지난 10년간 눈부신 발전을 거듭해왔지만, 그 잠재력은 공간 지능을 통해 실현될 것입니다.

공간 지능으로 컴퓨터와 로봇을 강화하면 유용한 도구가 될 뿐만 아니라 인간의 생산성과 인류애를 향상시키는 신뢰할 수 있는 파트너가 될 것입니다.

미래에는 지각력과 통찰력, 공간 인식 능력이 더욱 향상되어 더 나은 세상을 만들기 위한 인간의 노력에 동참하는 AI의 흥미로운 전망이 펼쳐질 것입니다.