Continuous Learning_Startup & Investment
Idea #1: LLMs as Agents - LLMs have the potential to be powerful agents, defined as (1) choosing a sequence of actions to take - through reasoning/planning or hard-coded chains – and (2) executing that sequence of actions - @AndrewYNg and @hwchase17: some…
YouTube
What's next for AI agentic workflows ft. Andrew Ng of AI Fund
Andrew Ng, founder of DeepLearning.AI and AI Fund, speaks at Sequoia Capital's AI Ascent about what's next for AI agentic workflows and their potential to significantly propel AI advancements—perhaps even surpassing the impact of the forthcoming generation…
Excited to release DBRX, a 132 billion parameter mixture of experts language model with 36 billion active parameters.
It’s not only a super capable model, but has many nice properties at inference time because of its MoE architecture. Long context (up to 32K tokens), large batch size, and other compute bound workloads will especially benefit from the sparsity win over similarly sized dense models. Instead of going through all the parameters in the model, only the active parameters need to be passed through - a FLOPs win allowing for high model quality without compromising on inference speed.
It’s not only a super capable model, but has many nice properties at inference time because of its MoE architecture. Long context (up to 32K tokens), large batch size, and other compute bound workloads will especially benefit from the sparsity win over similarly sized dense models. Instead of going through all the parameters in the model, only the active parameters need to be passed through - a FLOPs win allowing for high model quality without compromising on inference speed.
Investing heavily in people is key to fostering the entrepreneurship that shapes our future.
Meta Pursues AI Talent With Quick Offers, Emails From Zuckerberg
Company has made job offers without interviewing candidates and relaxed its longstanding practice of not increasing compensation for employees threatening to leave.
To better compete for artificial intelligence researchers, Meta Platforms is making unconventional moves, including extending job offers to candidates without interviewing them and relaxing a longstanding practice of not increasing compensation for employees threatening to leave.
In a sign of how seriously the social media company is taking the competition for AI talent, CEO Mark Zuckerberg has personally written to researchers at Google’s DeepMind unit to recruit them, according to two people who viewed the emails. In some notes, Zuckerberg emphasized the importance of AI to Meta and said he hopes the recipient and the company will work together, one of those people said.
Meta’s intense efforts to recruit and retain employees come as it ramps up investment in AI and after several researchers who developed its large language models left for rivals, including DeepMind, OpenAI and French startup Mistral, two of whose founders came from Meta.
Zuckerberg’s interventions have helped with recruiting. In announcing his move to Meta as a principal Llama engineer in the generative AI group last week, former DeepMind researcher Michal Valko gave “massive thanks for a very personal involvement” to Meta’s senior AI leaders—and “Mark,” referring to Zuckerberg. Valko declined to comment. Zuckerberg typically isn’t involved in hiring individual contributors, a classification for most research scientists and engineers, a former employee said. Meta declined to comment.
Company has made job offers without interviewing candidates and relaxed its longstanding practice of not increasing compensation for employees threatening to leave.
To better compete for artificial intelligence researchers, Meta Platforms is making unconventional moves, including extending job offers to candidates without interviewing them and relaxing a longstanding practice of not increasing compensation for employees threatening to leave.
In a sign of how seriously the social media company is taking the competition for AI talent, CEO Mark Zuckerberg has personally written to researchers at Google’s DeepMind unit to recruit them, according to two people who viewed the emails. In some notes, Zuckerberg emphasized the importance of AI to Meta and said he hopes the recipient and the company will work together, one of those people said.
Meta’s intense efforts to recruit and retain employees come as it ramps up investment in AI and after several researchers who developed its large language models left for rivals, including DeepMind, OpenAI and French startup Mistral, two of whose founders came from Meta.
Zuckerberg’s interventions have helped with recruiting. In announcing his move to Meta as a principal Llama engineer in the generative AI group last week, former DeepMind researcher Michal Valko gave “massive thanks for a very personal involvement” to Meta’s senior AI leaders—and “Mark,” referring to Zuckerberg. Valko declined to comment. Zuckerberg typically isn’t involved in hiring individual contributors, a classification for most research scientists and engineers, a former employee said. Meta declined to comment.
Earlier this week, someone asked me about how poker has informed my view of business risk. In short, profoundly.
Poker is a fundamentally defensive game when played at an elite level. A defensive game doesn’t mean you can’t generate huge profits. In fact, poker can yield enormous profits but the way it happens is unintuitive to most.
Maximum profits in poker, and other defensive games for that matter, occur when your error rate is less than your opponent’s error rate.
So their errors - your errors = your profit. If you minimize your errors, you maximize your potential profit.
This simple formula forces you to learn that a lot of the time, the biggest enemy of your success is you. By managing yourself in a predictable, reliable way, you give yourself time for your opponent to self-own themselves. This is true in poker, but it is even more true in business.
As an example, suppose you have an R&D budget and you’re trying to build a product. Once you have some initial product market fit, the most important thing to do is to allocate your remaining resources in a thoughtful way.
You should have many small bets that extend the product area. If any one of these fail, it won’t be life-threatening and you will have learned something that will reduce your future error rate. These small bets can then ladder into a few medium-sized bets which ultimately lead to a few large bets. In such a process, you’ve not only taken many bets, of various sizes, you’ve also done this over a long quantum of time. In that same time, a less organized competitor will eventually do something wrong/stupid/both.
Said differently, you’ve de-risked your error rate in a thoughtful methodical way and have evidence that things are working while giving your competitor enough time to flail and eventually fail.
In so many companies that I’ve invested in and companies that I’ve worked for, I’ve seen enormous bets being made too early, and mostly out of ego. These bets are rarely rooted in data and most have eventually been rolled back.
The second thing to understand in poker is that when you make many small bets, you can play more hands - and some of these can lead to huge pots. Some of the biggest pots I’ve won have been with 2-2 and 8-6 suited while some of the biggest pots I’ve lost are with A-A!
In business, as in poker, you have to make unconventional bets if you want to win huge pots. And the no-brainer bets are rarely big winners and can sometimes come back and sting you. So as an investor, by keeping my bets small I keep my errors small while giving myself a chance to win big by doubling and tripling down at the right time.
Poker is a fundamentally defensive game when played at an elite level. A defensive game doesn’t mean you can’t generate huge profits. In fact, poker can yield enormous profits but the way it happens is unintuitive to most.
Maximum profits in poker, and other defensive games for that matter, occur when your error rate is less than your opponent’s error rate.
So their errors - your errors = your profit. If you minimize your errors, you maximize your potential profit.
This simple formula forces you to learn that a lot of the time, the biggest enemy of your success is you. By managing yourself in a predictable, reliable way, you give yourself time for your opponent to self-own themselves. This is true in poker, but it is even more true in business.
As an example, suppose you have an R&D budget and you’re trying to build a product. Once you have some initial product market fit, the most important thing to do is to allocate your remaining resources in a thoughtful way.
You should have many small bets that extend the product area. If any one of these fail, it won’t be life-threatening and you will have learned something that will reduce your future error rate. These small bets can then ladder into a few medium-sized bets which ultimately lead to a few large bets. In such a process, you’ve not only taken many bets, of various sizes, you’ve also done this over a long quantum of time. In that same time, a less organized competitor will eventually do something wrong/stupid/both.
Said differently, you’ve de-risked your error rate in a thoughtful methodical way and have evidence that things are working while giving your competitor enough time to flail and eventually fail.
In so many companies that I’ve invested in and companies that I’ve worked for, I’ve seen enormous bets being made too early, and mostly out of ego. These bets are rarely rooted in data and most have eventually been rolled back.
The second thing to understand in poker is that when you make many small bets, you can play more hands - and some of these can lead to huge pots. Some of the biggest pots I’ve won have been with 2-2 and 8-6 suited while some of the biggest pots I’ve lost are with A-A!
In business, as in poker, you have to make unconventional bets if you want to win huge pots. And the no-brainer bets are rarely big winners and can sometimes come back and sting you. So as an investor, by keeping my bets small I keep my errors small while giving myself a chance to win big by doubling and tripling down at the right time.
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Today we are excited to announce the next chapter of Bezi, a fundamental shift in designing for 3D apps and games.
Introducing Bezi AI ✨
Designers can now ideate at the speed of thought with an infinite asset library.
With Bezi AI, you can:
✨ Generate 3D assets within seconds using text prompts.
🛠️ Simply drag and drop assets into the Bezi editor.
👥 Collaborate in real-time on a 3D canvas.
Created by designers for designers, Bezi empowers you and your team to create faster, together.
Get started today 👉 bezi.com/ai
Introducing Bezi AI ✨
Designers can now ideate at the speed of thought with an infinite asset library.
With Bezi AI, you can:
✨ Generate 3D assets within seconds using text prompts.
🛠️ Simply drag and drop assets into the Bezi editor.
👥 Collaborate in real-time on a 3D canvas.
Created by designers for designers, Bezi empowers you and your team to create faster, together.
Get started today 👉 bezi.com/ai
https://slideshare.net/slideshow/llm-zero-training-largescale-diffusion-model-from-scratch/267015845
SlideShare
LLM에서 배우는 이미지 생성 모델 ZERO부터 학습하기 Training Large-Scale Diffusion Model from Scratch
LLM에서 배우는 이미지 생성 모델 ZERO부터 학습하기 Training Large-Scale Diffusion Model from Scratch - Download as a PDF or view online for free
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Continuous Learning_Startup & Investment
Earlier this week, someone asked me about how poker has informed my view of business risk. In short, profoundly. Poker is a fundamentally defensive game when played at an elite level. A defensive game doesn’t mean you can’t generate huge profits. In fact…
이번 주 초에 누군가 제게 포커가 비즈니스 위험에 대한 제 관점에 어떤 영향을 미쳤는지 물어왔습니다. 간단히 말해서, 매우 큰 영향을 미쳤습니다.
포커는 엘리트 수준에서 플레이할 때 근본적으로 방어적인 게임입니다. 방어적인 게임이라고 해서 큰 수익을 창출할 수 없다는 의미는 아닙니다. 사실 포커는 엄청난 수익을 창출할 수 있지만 그 방식은 대부분의 사람들에게 직관적이지 않습니다.
포커를 비롯한 다른 방어 게임에서 최대 수익은 내 실수율이 상대의 실수율보다 낮을 때 발생합니다.
따라서 상대의 실수 = 나의 실수 = 나의 이익입니다. 오류를 최소화하면 잠재적 이익을 극대화할 수 있습니다.
이 간단한 공식을 통해 많은 경우 성공의 가장 큰 적은 바로 자신이라는 사실을 깨닫게 됩니다. 예측 가능하고 신뢰할 수 있는 방식으로 자신을 관리하면 상대방이 스스로를 관리할 시간을 벌 수 있습니다. 이는 포커에서도 마찬가지지만 비즈니스에서는 더욱 그렇습니다.
예를 들어 R&D 예산이 있고 제품을 개발하려고 한다고 가정해 보겠습니다. 초기 제품이 어느 정도 시장에 적합하다고 판단되면 가장 중요한 것은 남은 자원을 신중하게 배분하는 것입니다.
제품 영역을 확장하는 작은 베팅을 많이 해야 합니다. 이 중 하나라도 실패하더라도 생명을 위협할 정도는 아니며 향후 오류율을 줄일 수 있는 교훈을 얻게 될 것입니다. 이러한 작은 베팅은 몇 번의 중간 규모의 베팅으로 이어져 궁극적으로 몇 번의 큰 베팅으로 이어질 수 있습니다. 이러한 과정에서 여러분은 다양한 규모의 많은 베팅을 했을 뿐만 아니라 오랜 시간 동안 이를 수행한 것입니다. 같은 시간 동안 덜 조직적인 경쟁자는 결국 뭔가 잘못하거나 어리석거나 둘 다 할 것입니다.
다르게 말하면, 사려 깊고 체계적인 방법으로 오류율을 낮추고 경쟁업체가 허둥대다가 결국 실패할 수 있는 충분한 시간을 주면서 일이 제대로 작동하고 있다는 증거를 확보한 것입니다.
제가 투자했던 많은 회사와 제가 근무했던 회사에서 너무 일찍, 그리고 대부분 자존심 때문에 막대한 베팅을 하는 것을 보았습니다. 이러한 베팅은 데이터에 근거한 경우가 거의 없으며 대부분 결국 롤백되었습니다.
포커에서 두 번째로 이해해야 할 것은 작은 베팅을 많이 하면 더 많은 핸드를 플레이할 수 있고, 이 중 일부는 큰 팟으로 이어질 수 있다는 것입니다. 제가 이긴 가장 큰 팟 중 일부는 2-2와 8-6 수트였고, 제가 잃은 가장 큰 팟 중 일부는 A-A였습니다!
포커와 마찬가지로 비즈니스에서도 큰 돈을 따려면 파격적인 베팅을 해야 합니다. 그리고 당연한 베팅은 큰 돈을 따는 경우가 드물고 때때로 돌아와서 당신을 괴롭힐 수 있습니다. 따라서 투자자로서 저는 베팅을 작게 유지함으로써 오류를 최소화하는 동시에 적절한 타이밍에 두 배, 세 배로 늘려 큰 수익을 올릴 수 있는 기회를 마련합니다.
포커는 엘리트 수준에서 플레이할 때 근본적으로 방어적인 게임입니다. 방어적인 게임이라고 해서 큰 수익을 창출할 수 없다는 의미는 아닙니다. 사실 포커는 엄청난 수익을 창출할 수 있지만 그 방식은 대부분의 사람들에게 직관적이지 않습니다.
포커를 비롯한 다른 방어 게임에서 최대 수익은 내 실수율이 상대의 실수율보다 낮을 때 발생합니다.
따라서 상대의 실수 = 나의 실수 = 나의 이익입니다. 오류를 최소화하면 잠재적 이익을 극대화할 수 있습니다.
이 간단한 공식을 통해 많은 경우 성공의 가장 큰 적은 바로 자신이라는 사실을 깨닫게 됩니다. 예측 가능하고 신뢰할 수 있는 방식으로 자신을 관리하면 상대방이 스스로를 관리할 시간을 벌 수 있습니다. 이는 포커에서도 마찬가지지만 비즈니스에서는 더욱 그렇습니다.
예를 들어 R&D 예산이 있고 제품을 개발하려고 한다고 가정해 보겠습니다. 초기 제품이 어느 정도 시장에 적합하다고 판단되면 가장 중요한 것은 남은 자원을 신중하게 배분하는 것입니다.
제품 영역을 확장하는 작은 베팅을 많이 해야 합니다. 이 중 하나라도 실패하더라도 생명을 위협할 정도는 아니며 향후 오류율을 줄일 수 있는 교훈을 얻게 될 것입니다. 이러한 작은 베팅은 몇 번의 중간 규모의 베팅으로 이어져 궁극적으로 몇 번의 큰 베팅으로 이어질 수 있습니다. 이러한 과정에서 여러분은 다양한 규모의 많은 베팅을 했을 뿐만 아니라 오랜 시간 동안 이를 수행한 것입니다. 같은 시간 동안 덜 조직적인 경쟁자는 결국 뭔가 잘못하거나 어리석거나 둘 다 할 것입니다.
다르게 말하면, 사려 깊고 체계적인 방법으로 오류율을 낮추고 경쟁업체가 허둥대다가 결국 실패할 수 있는 충분한 시간을 주면서 일이 제대로 작동하고 있다는 증거를 확보한 것입니다.
제가 투자했던 많은 회사와 제가 근무했던 회사에서 너무 일찍, 그리고 대부분 자존심 때문에 막대한 베팅을 하는 것을 보았습니다. 이러한 베팅은 데이터에 근거한 경우가 거의 없으며 대부분 결국 롤백되었습니다.
포커에서 두 번째로 이해해야 할 것은 작은 베팅을 많이 하면 더 많은 핸드를 플레이할 수 있고, 이 중 일부는 큰 팟으로 이어질 수 있다는 것입니다. 제가 이긴 가장 큰 팟 중 일부는 2-2와 8-6 수트였고, 제가 잃은 가장 큰 팟 중 일부는 A-A였습니다!
포커와 마찬가지로 비즈니스에서도 큰 돈을 따려면 파격적인 베팅을 해야 합니다. 그리고 당연한 베팅은 큰 돈을 따는 경우가 드물고 때때로 돌아와서 당신을 괴롭힐 수 있습니다. 따라서 투자자로서 저는 베팅을 작게 유지함으로써 오류를 최소화하는 동시에 적절한 타이밍에 두 배, 세 배로 늘려 큰 수익을 올릴 수 있는 기회를 마련합니다.
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