vicentwu

I want buy one.

It's absurd. let's mark it down.

All the ai search platform have to find a way to share value back to content owners, or the whole eco system will collapse.

Good work! Context-awareness has huge potential. I don't think this demo hit the right mark, but it definitely shed some light.

Good read.

cool

Genuis!

I like the CRT-like filter effect.

Hmm... The key is to successfully decompose a big, hard problem into easier atomic sub-problems. However, the decomposition process itself is difficult, and this paper is not about that. They decompose a task using a human-written prompt.

wow....

I like the vibe.

Prompts are really an interesting way of programming, and we can actually express logic containing abstract adjectives like ‘happy’ and ‘unsatisfied’ in a somewhat arbitrary way.

Great!

"As an analogy, imagine that you could put your dog or cat into hibernate mode whenever you left on a trip. Your dog or cat might not notice, but even if they did, they might not mind. Now imagine that you could put your child into hibernate mode whenever you were too busy to spend time with them. Your child would absolutely notice, and even if you told them it was for their own good, they would make certain inferences about how much you valued them. That’s the situation the human characters in the story find themselves in." Fascinating.

RL doesn't need that much static data, it needs a lot of "good" tasks/challenges and computation.

Trained with 300 raw pairs directly from the ARC training set without using any data augmentation process, such as generating many more pairs with some kind of ARC generator? That's amazing.

"Note on "tuned": OpenAI shared they trained the o3 we tested on 75% of the Public Training set. They have not shared more details. We have not yet tested the ARC-untrained model to understand how much of the performance is due to ARC-AGI data."

Really want to see the number of training pairs needed to achieve this socre. If it only takes a few pairs, say 100 pairs, I would say it is amazing!

Off the topoc. I think, in the long-term , inference should be done along with some kind of training.

Past efforts leds to today's products. We need to wait to see the real imapct on the ability to ship.

It's amazing!

Fasciating. Language is a type of action evoloved for information exchaging, which maps latent "video", "audio" and "thoughts" into "sentences" and vice versa.

Cool! The real-time feedback will have enormous ramifications on the art creation workflows.

Asking models to do math is kind of an effecitve way to measure their capabilities, especially in reasoning and abstraction, which are quite important for problem solving.

The chain-of-thought works quite like the "System 2" introduced in <<Thinking, Fast and Slow>>, which is slower, more deliberative, and more logical.

I think the last paragraph quite makes sense. It seems "true" that some kind of reasoning capability emerges as LLMs get bigger, which makes those LLMs quite useful and blows a lot of people's minds at the beginning. But, I think, essentially, the fundamental training goal of LLMs--guessing what the next word should be--pushes the model into a kind of reasonable nonsense generator, and the reasoning capability emerges because it can help the model to make stuff up. Therefore, we should be cautious about the result generated by these LLMs. They might be reasonable, but to make up the next word is their real top priority.

Maybe in the future there might be some kind of "forbidden to use in commercial AI models" policy on websites in the near future, just like what the art community is doing now.

I can't wait to see the model's ability of saying "They don't know", which I think is an important feature if it serves as a search engine, because it can reduce the amount of generated ramblings which it's actually really good at.

So i think the ability of the search engine to say "I don't know" is very important, and most of current chatgpt like models in the market don't have this feature.

It's fascinating to think about the future landscape of the search and web.

Some assumptions: 1. Url-based web will not wither away. 2. Asking questions in the chat-like mode is more natural to people. 3. Generated answers cost more when longer. 4. Generated answers are some kind of distilled knowledge and can't be right all the time. 4. People don't like long answers and prefer the concise one. 5. Sources and citations make generated answers more credible. 6. Fully exploring a question needs a lot of information from different views. 7. Generated answers

some simple thoughts: The search behavior would hugely be two main steps: 1.getting some concise answers from the AI model directly through a chat, which might be enough for 90% use cases. 2.some more extensive search just like how people are searching today, which might be a kind of niche.

For websites, being cited in the generated answers will be the new kind of SEO things, and it would be a good strategy to producing some newest, deep or long-tail knowledge and information, which leads to a more traditional way of search because AI model doesn't have enough data to generate a good answer.

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The video is a kine of misleading, which should told you that the driving in the video was a vision of the future rather than a capability it already had or would soon have.