Superintelligence is within reach.
Building safe superintelligence (SSI) is the most important technical problem of our time.
We've started the world’s first straight-shot SSI lab, with one goal and one product: a safe superintelligence.
It’s called Safe Superintelligence Inc.
SSI is our mission, our name, and our entire product roadmap, because it is our sole focus. Our team, investors, and business model are all aligned to achieve SSI.
We approach safety and capabilities in tandem, as technical problems to be solved through revolutionary engineering and scientific breakthroughs. We plan to advance capabilities as fast as possible while making sure our safety always remains ahead.
This way, we can scale in peace.
Our singular focus means no distraction by management overhead or product cycles, and our business model means safety, security, and progress are all insulated from short-term commercial pressures.
We are an American company with offices in Palo Alto and Tel Aviv, where we have deep roots and the ability to recruit top technical talent.
We are assembling a lean, cracked team of the world’s best engineers and researchers dedicated to focusing on SSI and nothing else.
If that’s you, we offer an opportunity to do your life’s work and help solve the most important technical challenge of our age.
Now is the time. Join us.
Ilya Sutskever, Daniel Gross, Daniel Levy
June 19, 2024
Building safe superintelligence (SSI) is the most important technical problem of our time.
We've started the world’s first straight-shot SSI lab, with one goal and one product: a safe superintelligence.
It’s called Safe Superintelligence Inc.
SSI is our mission, our name, and our entire product roadmap, because it is our sole focus. Our team, investors, and business model are all aligned to achieve SSI.
We approach safety and capabilities in tandem, as technical problems to be solved through revolutionary engineering and scientific breakthroughs. We plan to advance capabilities as fast as possible while making sure our safety always remains ahead.
This way, we can scale in peace.
Our singular focus means no distraction by management overhead or product cycles, and our business model means safety, security, and progress are all insulated from short-term commercial pressures.
We are an American company with offices in Palo Alto and Tel Aviv, where we have deep roots and the ability to recruit top technical talent.
We are assembling a lean, cracked team of the world’s best engineers and researchers dedicated to focusing on SSI and nothing else.
If that’s you, we offer an opportunity to do your life’s work and help solve the most important technical challenge of our age.
Now is the time. Join us.
Ilya Sutskever, Daniel Gross, Daniel Levy
June 19, 2024
Forwarded from Nikkei Asia
China, Iran tap AI to influence U.S. election, warn experts
Deceptive online actors from Iran and China are trying to manipulate U.S. public opinion ahead of the presidential election, using artificial intelligence to craft appeals that sound more natural to the American audience, experts warn.
Read more here
Deceptive online actors from Iran and China are trying to manipulate U.S. public opinion ahead of the presidential election, using artificial intelligence to craft appeals that sound more natural to the American audience, experts warn.
Read more here
막상 온종일 대화 내용을 들어보면, 돈 버는 이야기는 하나도 없고, 회사들 어렵고, 망하고 있고, 곧 망할 것 같은 회사 대표들과 정신과 상담하듯 이야기하는 내용밖에 없어서 굉장히 놀라고 신기해하는 거 같다.
“오빠 투자해서 돈 버는 사람 아니었어? 무슨 119 소방대원 같은데?”라는 말을 할 정도로 요새 코로나바이러스가 몇몇 우리 투자사들에 주는 충격은 상당하다.
“When the Going Gets Tough, the Tough Get Going”이다. 노래 제목을 그대로 번역하면, “상황이 힘들어지면, 강인한 사람들은 더 강인해진다.”이다.
통제 못 하는 건 어쩔 수 없다. 통제 할 수 있는 거에만 집중하자. 그러다가 망할 수도 있지만, 그렇더라도 최선을 다해서 시원하게 한 번 싸워보자. 사업가답게, 대표답게, 용감하고 떳떳하게 온몸으로 부딪쳐보자. 결과와는 상관없이 모두가 다 나중에 술잔을 기울이면서 웃을 수 있도록.
https://m.youtube.com/watch?v=-n3sUWR4FV4&embeds_referring_euri=https%3A%2F%2Fwww.thestartupbible.com%2F&source_ve_path=OTY3MTQ
“오빠 투자해서 돈 버는 사람 아니었어? 무슨 119 소방대원 같은데?”라는 말을 할 정도로 요새 코로나바이러스가 몇몇 우리 투자사들에 주는 충격은 상당하다.
“When the Going Gets Tough, the Tough Get Going”이다. 노래 제목을 그대로 번역하면, “상황이 힘들어지면, 강인한 사람들은 더 강인해진다.”이다.
통제 못 하는 건 어쩔 수 없다. 통제 할 수 있는 거에만 집중하자. 그러다가 망할 수도 있지만, 그렇더라도 최선을 다해서 시원하게 한 번 싸워보자. 사업가답게, 대표답게, 용감하고 떳떳하게 온몸으로 부딪쳐보자. 결과와는 상관없이 모두가 다 나중에 술잔을 기울이면서 웃을 수 있도록.
https://m.youtube.com/watch?v=-n3sUWR4FV4&embeds_referring_euri=https%3A%2F%2Fwww.thestartupbible.com%2F&source_ve_path=OTY3MTQ
YouTube
Billy Ocean - When the Going Gets Tough, the Tough Get Going (Official Video)
Listen on Spotify: http://smarturl.it/BilOceSpotify?IQid=BilOceWTG
Listen on Apple Music: http://smarturl.it/BilOceAppleM?IQid=BilOceWTG
Buy on iTunes: http://smarturl.it/BilOceTVBiTunes?IQid=BilOceWTG
Buy on Amazon: http://smarturl.it/BilOceTVBAmazon?IQid=BilOceWTG…
Listen on Apple Music: http://smarturl.it/BilOceAppleM?IQid=BilOceWTG
Buy on iTunes: http://smarturl.it/BilOceTVBiTunes?IQid=BilOceWTG
Buy on Amazon: http://smarturl.it/BilOceTVBAmazon?IQid=BilOceWTG…
👍1
In 2008, Fengji was leading "Asura" at Tencent, coincidentally another Monkey King game.
The game eventually lost it’s way as the focus shifted from fun to revenue.
Fengji believed that games should be “fun first.”
In 2014, Feng Ji left with 7 colleagues to form Game Science.
They started with mobile games to pay the bills.
Their first two games ended up as complete flops.
Feng Ji held onto his dream of creating a single-player masterpiece. He was just waiting for the right moment.
By 2016, Steam's data showed 1/3 of its active users were from China.
Development skills has caught on. Chinese gamers craved high-quality experiences.
It was clear the market was ready.
Feng Ji said, "We're willing to burn ourselves, but we're not moths flying to the fire.”
League of Legends saved Feng.
He forged a close bond with Daniel Wu. They became late-night gaming buddies.
In 2017, Wu bet big on Feng Ji's vision, buying 20% of Game Science for $8.5M.
Despite losses on earlier projects, Wu kept faith.
A true ride or die partner.
Feng Ji hated the mobile game model of in-app purchases and endless monetization.
He believed it killed the true essence of gaming.
This resulted to the company splitting into 2 teams.
One team to keep making mobile games, while he moved on to fulfill his single player dream.
On February 25, 2018, Game Science takes the plunge into AAA development.
The team went all in. They quit jobs, sold properties, and went 4-5 years without income.
Wu provided additional funding and contributed a "large chunk" of the $70M budget.
Black Myth: Wukong was born.
Feng Ji’s vision was clear: create a global game rooted in traditional Chinese culture.
- The team read the novel “Journey to the West” 100+ times.
- They visited countless cultural sites.
- Created 1.2 billion models for the Monkey King’s armor.
Authenticity was everything.
Creating China’s first AAA game was no joke:
The team grew from 7 to 30 and couldn’t find required talent.
They faced challenges in adapting new technologies such as the Unreal Engine.
Wu and Feng called themselves “two drowning rats.”
They were on the brink of failure.
August 2020 was the turning point.
A 13-minute gameplay trailer went viral.
2M views on YouTube, 25M on Bilibili.
• 10,000 job applications
• The team grew to 140 employees
• Tencent bought a 5% stake
What was meant to be a recruitment video became a global sensation.
"When you are at the peak of confidence, you are also staring at the valley of foolishness."
https://x.com/WillieChou/status/1832780019187228843
The game eventually lost it’s way as the focus shifted from fun to revenue.
Fengji believed that games should be “fun first.”
In 2014, Feng Ji left with 7 colleagues to form Game Science.
They started with mobile games to pay the bills.
Their first two games ended up as complete flops.
Feng Ji held onto his dream of creating a single-player masterpiece. He was just waiting for the right moment.
By 2016, Steam's data showed 1/3 of its active users were from China.
Development skills has caught on. Chinese gamers craved high-quality experiences.
It was clear the market was ready.
Feng Ji said, "We're willing to burn ourselves, but we're not moths flying to the fire.”
League of Legends saved Feng.
He forged a close bond with Daniel Wu. They became late-night gaming buddies.
In 2017, Wu bet big on Feng Ji's vision, buying 20% of Game Science for $8.5M.
Despite losses on earlier projects, Wu kept faith.
A true ride or die partner.
Feng Ji hated the mobile game model of in-app purchases and endless monetization.
He believed it killed the true essence of gaming.
This resulted to the company splitting into 2 teams.
One team to keep making mobile games, while he moved on to fulfill his single player dream.
On February 25, 2018, Game Science takes the plunge into AAA development.
The team went all in. They quit jobs, sold properties, and went 4-5 years without income.
Wu provided additional funding and contributed a "large chunk" of the $70M budget.
Black Myth: Wukong was born.
Feng Ji’s vision was clear: create a global game rooted in traditional Chinese culture.
- The team read the novel “Journey to the West” 100+ times.
- They visited countless cultural sites.
- Created 1.2 billion models for the Monkey King’s armor.
Authenticity was everything.
Creating China’s first AAA game was no joke:
The team grew from 7 to 30 and couldn’t find required talent.
They faced challenges in adapting new technologies such as the Unreal Engine.
Wu and Feng called themselves “two drowning rats.”
They were on the brink of failure.
August 2020 was the turning point.
A 13-minute gameplay trailer went viral.
2M views on YouTube, 25M on Bilibili.
• 10,000 job applications
• The team grew to 140 employees
• Tencent bought a 5% stake
What was meant to be a recruitment video became a global sensation.
"When you are at the peak of confidence, you are also staring at the valley of foolishness."
https://x.com/WillieChou/status/1832780019187228843
Forwarded from SNEW스뉴
😂허구헌날 VC에게 까이는 스타트업 창업자분들에게 힘이 될만한 옛날 얘기.
1976년 스티브 잡스가 애플을 창업한 후, 초기 투자금을 모으기 위해 누굴 만나 어떻게 까이고, 누굴 소개 받아 어떻게 투자로 연결되었는 지 자세히 도표로 정리.
여기 보면 별 시덥지않은 이유로 투자 거절을 하고, 평생 이불킥한 거물 VC 이름들이 주루룩 나온다.
스티브 잡스도 초기엔 개무시 당하고 까인 게 한 두번이 아닌데, 나 정도면 양호하다고 위로하는 밤이 되시길...^^
참, 최초 투자가 이뤄진 계기는, 자기는 투자를 안하지만 대신 다른 투자가를 소개해준 사람이 있었기에 가능했다. 그러니 까였다고 좌절 말고, 깐 사람과도 좋은 관계를 유지하시라. "꺼진 불도 다시 보자!"
1. 클라이너 퍼킨스의 톰 퍼킨스와 유진 클라이너
벤처 캐피털 업계의 전설적 인물들이지만, 이들은 잡스와의 만남조차 거부했다. 잡스의 비전을 알아보지 못한 것이다.
2. 빌 드레이퍼
드레이퍼는 잡스와 워즈니악을 오만하다고 판단, 투자를 거절했다. 제품의 잠재력보다 개인의 성격에 집중한 결정이었다.
3. 피치 존슨
가정용 컴퓨터의 개념을 이해하지 못해 투자를 거절했다. "요리법을 저장하는 데 쓰려고?" 라고 물었다고 한다.
4. 스탠 베이트
1만 달러에 애플의 10%를 살 수 있는 기회를 거절했다. 잡스의 외모를 이유로 그를 신뢰하지 않았기 때문이다.
5. 놀란 부쉬넬
아타리의 창업자인 부쉬넬은 5만 달러에 애플의 3분의 1을 살 수 있는 기회를 거절했다. 하지만 잡스를 돈 발렌타인에게 소개했다.
6. 돈 발렌타인
세코이아 캐피털의 창립자인 발렌타인은 직접 투자하지는 않았지만, 잡스를 마이크 마쿨라에게 소개했다.
7. 마이크 마쿨라
마쿨라는 9만1000달러를 투자해 애플의 26%를 확보, 최초의 엔젤 투자자가 되었다. 그는 또한 레지스 매키나를 설득해 애플의 마케팅을 맡게 했다.
8. 레지스 매키나
애플의 상징적인 로고 제작에 참여했다.
9. 행크 스미스
벤록의 스미스는 30만 달러를 투자해 애플의 10%를 확보했다.
이 과정은 단순한 자금 조달 이상의 의미를 갖는다. 잡스의 성공은 끈질긴 인내와 네트워킹의 힘을 보여준다. 그는 수많은 거절 속에서도 포기하지 않고 계속해서 문을 두드렸고, 결국 그의 비전을 이해하는 사람들을 만나게 되었다.
모든 이가 당신의 아이디어를 이해할 필요는 없다. 중요한 것은 끊임없이 노력하고 네트워크를 확장하며, 당신의 비전을 공유할 수 있는 적임자를 찾는 것이다.
https://www.facebook.com/share/p/yYhPJkn3gJofbDSh/?
1976년 스티브 잡스가 애플을 창업한 후, 초기 투자금을 모으기 위해 누굴 만나 어떻게 까이고, 누굴 소개 받아 어떻게 투자로 연결되었는 지 자세히 도표로 정리.
여기 보면 별 시덥지않은 이유로 투자 거절을 하고, 평생 이불킥한 거물 VC 이름들이 주루룩 나온다.
스티브 잡스도 초기엔 개무시 당하고 까인 게 한 두번이 아닌데, 나 정도면 양호하다고 위로하는 밤이 되시길...^^
참, 최초 투자가 이뤄진 계기는, 자기는 투자를 안하지만 대신 다른 투자가를 소개해준 사람이 있었기에 가능했다. 그러니 까였다고 좌절 말고, 깐 사람과도 좋은 관계를 유지하시라. "꺼진 불도 다시 보자!"
1. 클라이너 퍼킨스의 톰 퍼킨스와 유진 클라이너
벤처 캐피털 업계의 전설적 인물들이지만, 이들은 잡스와의 만남조차 거부했다. 잡스의 비전을 알아보지 못한 것이다.
2. 빌 드레이퍼
드레이퍼는 잡스와 워즈니악을 오만하다고 판단, 투자를 거절했다. 제품의 잠재력보다 개인의 성격에 집중한 결정이었다.
3. 피치 존슨
가정용 컴퓨터의 개념을 이해하지 못해 투자를 거절했다. "요리법을 저장하는 데 쓰려고?" 라고 물었다고 한다.
4. 스탠 베이트
1만 달러에 애플의 10%를 살 수 있는 기회를 거절했다. 잡스의 외모를 이유로 그를 신뢰하지 않았기 때문이다.
5. 놀란 부쉬넬
아타리의 창업자인 부쉬넬은 5만 달러에 애플의 3분의 1을 살 수 있는 기회를 거절했다. 하지만 잡스를 돈 발렌타인에게 소개했다.
6. 돈 발렌타인
세코이아 캐피털의 창립자인 발렌타인은 직접 투자하지는 않았지만, 잡스를 마이크 마쿨라에게 소개했다.
7. 마이크 마쿨라
마쿨라는 9만1000달러를 투자해 애플의 26%를 확보, 최초의 엔젤 투자자가 되었다. 그는 또한 레지스 매키나를 설득해 애플의 마케팅을 맡게 했다.
8. 레지스 매키나
애플의 상징적인 로고 제작에 참여했다.
9. 행크 스미스
벤록의 스미스는 30만 달러를 투자해 애플의 10%를 확보했다.
이 과정은 단순한 자금 조달 이상의 의미를 갖는다. 잡스의 성공은 끈질긴 인내와 네트워킹의 힘을 보여준다. 그는 수많은 거절 속에서도 포기하지 않고 계속해서 문을 두드렸고, 결국 그의 비전을 이해하는 사람들을 만나게 되었다.
모든 이가 당신의 아이디어를 이해할 필요는 없다. 중요한 것은 끊임없이 노력하고 네트워크를 확장하며, 당신의 비전을 공유할 수 있는 적임자를 찾는 것이다.
https://www.facebook.com/share/p/yYhPJkn3gJofbDSh/?
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https://openai.com/index/learning-to-reason-with-llms/
We trained these models to spend more time thinking through problems before they respond, much like a person would. Through training, they learn to refine their thinking process, try different strategies, and recognize their mistakes.
In our tests, the next model update performs similarly to PhD students on challenging benchmark tasks in physics, chemistry, and biology. We also evaluated o1 on GPQA diamond, a difficult intelligence benchmark which tests for expertise in chemistry, physics and biology. In order to compare models to humans, we recruited experts with PhDs to answer GPQA-diamond questions. We found that o1 surpassed the performance of those human experts, becoming the first model to do so on this benchmark. These results do not imply that o1 is more capable than a PhD in all respects — only that the model is more proficient in solving some problems that a PhD would be expected to solve.
Chain of Thought
Similar to how a human may think for a long time before responding to a difficult question, o1 uses a chain of thought when attempting to solve a problem. Through reinforcement learning, o1 learns to hone its chain of thought and refine the strategies it uses. It learns to recognize and correct its mistakes. It learns to break down tricky steps into simpler ones. It learns to try a different approach when the current one isn’t working. This process dramatically improves the model’s ability to reason. To illustrate this leap forward, we showcase the chain of thought from o1-preview on several difficult problems below.
We trained these models to spend more time thinking through problems before they respond, much like a person would. Through training, they learn to refine their thinking process, try different strategies, and recognize their mistakes.
In our tests, the next model update performs similarly to PhD students on challenging benchmark tasks in physics, chemistry, and biology. We also evaluated o1 on GPQA diamond, a difficult intelligence benchmark which tests for expertise in chemistry, physics and biology. In order to compare models to humans, we recruited experts with PhDs to answer GPQA-diamond questions. We found that o1 surpassed the performance of those human experts, becoming the first model to do so on this benchmark. These results do not imply that o1 is more capable than a PhD in all respects — only that the model is more proficient in solving some problems that a PhD would be expected to solve.
Chain of Thought
Similar to how a human may think for a long time before responding to a difficult question, o1 uses a chain of thought when attempting to solve a problem. Through reinforcement learning, o1 learns to hone its chain of thought and refine the strategies it uses. It learns to recognize and correct its mistakes. It learns to break down tricky steps into simpler ones. It learns to try a different approach when the current one isn’t working. This process dramatically improves the model’s ability to reason. To illustrate this leap forward, we showcase the chain of thought from o1-preview on several difficult problems below.
Openai
Learning to reason with LLMs
We are introducing OpenAI o1, a new large language model trained with reinforcement learning to perform complex reasoning. o1 thinks before it answers—it can produce a long internal chain of thought before responding to the user.