/explore
|
You are here 
|
vintagedata.org | ||
| | | | |
amatria.in
|
|
| | | | | 2024 has been an intense year for AI. While some argue that we haven't made much progress, I beg to differ. It is true that many of the research advances from 2023 have still not made it to mainstream applications. But, that doesn't mean that research is not making progress all around! | |
| | | | |
darioamodei.com
|
|
| | | | | [AI summary] The text discusses the implications of China's advancements in AI, particularly through the development of DeepSeek models, and the role of export controls on AI chips. It highlights the ongoing trend of companies investing heavily in training powerful AI models, which is becoming increasingly expensive despite cost reductions due to efficiency gains. The author argues that export controls are crucial to prevent China from acquiring the necessary chips to develop advanced AI systems, which could lead to a bipolar world with significant global implications. The text also addresses the performance of DeepSeek, noting that their achievements are not due to the lack of export controls but rather their resources and talent. The author emphasizes the ... | |
| | | | |
www.lesswrong.com
|
|
| | | | | This isn't really a "timeline", as such - I don't know the timings - but this is my current, fairly optimistic take on where we're heading. ... | |
| | | | |
sophiabits.com
|
|
| | | [AI summary] The article argues that Large Language Models are best suited for generative tasks like creating content, but traditional supervised learning methods are superior for non-generative tasks such as classification and named entity recognition due to better accuracy, lower latency, and reduced costs. | ||
