https://youtu.be/ajkAbLe-0Uk

Major Takeaways:

Product Differentiation: Perplexity AI focuses on providing accurate and trustworthy search results with citations, thereby positioning itself as a superior alternative to AI models like ChatGPT and Bart in terms of search accuracy. They differentiate themselves further by leveraging reasoning engines in combination with a well-ranked index of relevant content to generate quick and accurate answers.

Technology Utilization and Development: Perplexity AI's strategy relies on utilizing well-established AI models such as ChatGPT and Bart, but also developing their own models to address specific aspects of their product. This allows them to create a competitive and unique search experience. Moreover, the company orchestrates various components in their backend to ensure they work together efficiently and reliably.

Business Model and Advertising: The company considers advertising within a chat interface, which could provide relevant and targeted ads based on user profiles and queries, as a promising potential business model. The need for transparency and ethical advertising practices is emphasized.

AI Integration: The future vision for Perplexity AI involves the seamless integration of language models into everyday devices, which will enable natural conversations and immediate responses. The speaker acknowledges the existing limitations but expresses confidence in the continual advancements of the technology.

Data Quality and Training: The quality of training data is highlighted as a key factor in achieving higher levels of reasoning and intelligence in AI models. This is seen as a factor contributing to the lead of OpenAI in the AI market.

Open-source vs. Closed Models: The speaker discusses the implications of open-source models and closed models like Google and OpenAI, noting that the progress in the field depends on algorithmic efficiencies and talented researchers. The dynamics of this will be influenced by whether organizations continue to publish their techniques or opt to stay closed.

Lessons for AI Startup Founders:

Differentiation is Key: In a competitive field, providing a unique value proposition is crucial. This might involve creating more accurate or trustworthy results, or delivering them in a more efficient manner.

Leverage and Develop Technology: While it's beneficial to leverage established AI models, developing your own models to address specific aspects of your product can create a competitive edge.

Backend Efficiency: The success of your startup doesn't only rely on the end product but also how well the backend processes and components are orchestrated.

Ethical Business Practices: In implementing advertising or other monetization methods, maintaining transparency and ethical practices is essential to avoid the risk of alienating users.

Quality of Training Data: As an AI startup, the quality of your training data is paramount. Efforts should be made to curate high-quality data to achieve superior models.

Open Source vs. Closed Debate: The choice between operating with open-source models or closed ones can have implications on your company's future. Founders should consider the pros and cons of each, taking into account factors such as collaboration, progress speed, and knowledge sharing.

Japan uses Chat GPt quite alot.

Based on the available data, the usage of ChatGPT in the selected countries is as follows:

1. United States: The United States accounts for 15.32% of the total audience using ChatGPT
2. India: India accounts for 6.32% of the total audience using ChatGPT.
3. Japan: Japan accounts for 3.97% of the total audience using ChatGPT.
4. Canada: Canada accounts for 2.74% of the total audience using ChatGPT.
5. Other countries: The rest of the world accounts for 68.36% of visits to ChatGPT's website.

Coming AI event in sf

💁‍♂️ How to Play Long Term Games:

Systems > Goals
Discipline > Motivation
Trust > Distrust
Principles > Tactics
Writing > Reading
Vulnerability > Confidence
North Stars > Low Hanging Fruit
Trends > News
Habits > Sprints
Questions > Answers
Problems > Solutions
People > Projects

AI가 게임의 제작부터 게임의 UI/UX까지 많은 부분을 변화시켜놓을 거라고 생각합니다.

지난 몇년간 AI 모델은 엄청난 속도로 변화해왔는데요. 가장 최신의 AI 모델의 발전 역사와 앞으로 예상되는 AI 연구주제를 바탕으로 미래의 게임을 상상해봅니다.

Stable Diffusion 모델이 빠르게 혁신하면서, 게임 아트와 관련해서 다양한 실험이 이루어지고 있습니다. 게임 아트를 기획하고 개발하는 과정에서 AI를 잘 사용한 프로세스는 뭘까요?

이 두가지 질문에 대해서 궁금증이 생기셨다면 아래 구글폼을 작성해주세요 🙂

https://forms.gle/RFJjwqELL9juekP66

I don't have to check hacker news on a daily basis anymore! Thanks for the service!

https://share.snipd.com/show/a7f48397-d9ed-458a-9bda-51b504acddee

What is going on in China?
https://36kr.com/

What era do we live in?

A wide range of AI tasks that used to take 5 years and a research team to accomplish in 2013, now just require API docs and a spare afternoon in 2023.

Not a single PhD in sight. When it comes to shipping AI products, you want engineers, not researchers.

Microsoft, Google, Meta, and the large Foundation Model labs have cornered scarce research talent to essentially deliver “AI Research as a Service” APIs. You can’t hire them, but you can rent them — if you have software engineers on the other end who know how to work with them. There are ~5000 LLM researchers in the world, but ~50m software engineers. Supply constraints dictate that an “in-between” class of AI Engineers will rise to meet demand.

Fire, ready, aim. Instead of requiring data scientists/ML engineers do a laborious data collection exercise before training a single domain specific model that is then put into production, a product manager/software engineer can prompt an LLM, and build/validate a product idea, before getting specific data to finetune.

Let’s say there are 100-1000x more of the latter than the former, and the “fire, ready, aim” workflow of prompted LLM prototypes lets you move 10-100x faster than traditional ML. So AI Engineers will be able to validate AI products say 1,000-10,000x cheaper. It’s Waterfall vs Agile, all over again. AI is Agile.

🆕 post: You Are Not Too Old
(To Pivot Into AI)

https://twitter.com/swyx/status/1641849911326150661

새로운 것이 등장하면 그 누구도 전문가가 될 수 없는 시기가 있습니다. 그저 관심 있는 사람들만 관심을 갖고 가지고 놀며 서로 이야기할 뿐입니다. 하지만 결국에는 그 일이 성숙해지고 그 창이 닫힙니다. 진입 장벽이 훨씬 높아진 후에는요.

당신은 AI로 전환하기 위해 너무 늙지 않았습니다.

https://www.latent.space/p/not-old

AI x Design: https://www.figma.com/blog/ai-the-next-chapter-in-design/

혹시 Design 쪽 커리어를 가져가고 있는 분들중 실력과 관심 두가지가 다 있는 지인 분들이 있으실까요?~ ㅎㅎ
5명정도만 모여도 재밌는 이야기 많이 할 수 있을 것 같은데요!

Vercel also started AI accelerator.

https://vercel.com/blog/vercel-ai-accelerator

We need to understand function call Open AI recently announced.

https://www.latent.space/p/function-agents#details

Wow he is earning meony $1 mrr bwith two ai services.

📸 http://PhotoAI.com $62K MRR
🖼 http://InteriorAI.com $52K MRR

I found GitHub to be the best organizer of AI-related newsletters and podcasts. Eureka!!!

https://github.com/swyxio/ai-notes/blob/main/Resources/Good%20AI%20Podcasts%20and%20Newsletters.md

오늘 발견한 재밌는 깃헙. 재밌는 게 너무 많네 🤣

AI 블로그 운영자이자 팟캐스트 호스트 https://latent.space/

AI note: https://github.com/swyxio/ai-notes/tree/main
- 활용사례
- 초심자/중급자/고수가 읽을 거리
- 커뮤니티
- People
- Reality & Demotivations
- Legal, Ethics, and Privacy
- Alignment, Safety

Good AI Podcasts and Newsletters: https://github.com/swyxio/ai-notes/blob/main/Resources/Good%20AI%20Podcasts%20and%20Newsletters.md

Funding news_Foundational models
https://www.newcomer.co/p/former-github-cto-jason-warner-raises
https://reka.ai/announcing-our-58m-funding-to-build-generative-models-and-advance-ai-research/

지난 며칠동안 프랑스 부르고뉴 지방서 유명한 와이너리 오너들과 시음하고, 식사하고 또 음악을 즐기는 시간을 가졌다. 특별한 인연때문 아니라 그냥 아는 사람이 소수인원 (8명) 가는 일정을 만들었는데 거기 끼여서 갔다.

태국기업이지만 세계적으로 사업하는 사람, 호주 대기업 대표, 또 상장기업 투자가 그리고 나... 다들 부부 동반으로 참가했는데 기대보다 훨씬 값진 시간 이였다.

와이너리 오너들하고 같이 즐기면서 대화 하는게 우리가 투자한 회사들과 보내는 시간들하고 비슷했다. 날씨에 대한 고민부터 AI 도 고민하고 있다. 참고로 그들은 보통 새벽 6시부터 밤 10시까지 일한다. 그리고 peak season 에는 잠도 거의 못자면서 일한다. 그들에게도 역시 많은 것을 배우고 감탄하게 되었다.

그중 인상 깊었던 말들:

"음식 먹기전 (배가 고플때) 와인은 더 느끼고 맛있다. 사람의 본능이다."

"이제 30년째 하는데 어떻게 해야되는지 조금 알아가는것 같다. 변수가 많아서 계속 실험을 해야된다."

"우린 열심히 한다. 어느 누구보다도. 그렇지만 결국 하늘이 많은것을 좌우하기때문 기도도 많이 한다."

"다들 특유의 방법이 있다. 물론 남들이 뭘 어떻게 하는지 관심있게 보지만...그리고 실헙도 하지만 우리만의 방식대로 한다."

"모든 와인은 마실때마다 다르다. 각 사람마다 좋아하는 향이 있고 맛이 있는거기 때문 어떤 와인이 절대적이라고 할수 없다. 그리고 똑같은 와인도 병마다 조금씩 다르고 또 그때 기분/분위기 따라서 다른맛을 느낀다." -- (로마네꽁티 오너).

마지막 귀절을 그분에게 듣고... 이제는 편히 쫄지않고 와인 즐길수 있게되서 좋았다.