Top 7 from (502 pages of) Stanford’s 2024 AI Index Report
Interesting report. https://aiindex.stanford.edu/report/
It is only 502 pages long. But my blog is much shorter 😇
And, am sure you will be relieved to hear that no Generative AI LLMs were harmed during the creation of the blog …
This is from a quick review — I plan to go over one more time (at least) for a deep dive …
1 AI beats humans on some tasks, but not on all
- Nothing new. The Google AGI levels is of relevance here.
2 Frontier models get way more expensive
- According to AI Index estimates, the training costs of state-of-the-art AI models have reached unprecedented levels. For example, OpenAI’s GPT-4 used an estimated $78M worth of compute to train, while Google’s Gemini Ultra costs $191M
3 Robust and standardized evaluations for LLM responsibility are seriously lacking
- New research from the AI Index reveals a significant lack of standardization in responsible AI reporting. Leading developers, including OpenAI, Google, and Anthropic, primarily test their models against different responsible AI benchmarks. This practice complicates efforts to systematically compare the risks and limitations of top AI models
- MACHIAVELLI Benchmark — an interesting framework for assessing traits such as deception, morality, and power-seeking in sophisticated AI systems ! — Interesting read
4 People across the globe are more cognizant of AI’s potential impact — and more nervous
- Keep this in mind as we develop Generative AI systems
5 Thanks to LLMs, robots have become more flexible (and interactive)
- Robots become very interactive - Beyond their improved robotic capabilities, these models can ask questions, which marks a significant step toward robots that can interact more effectively with the real world
6 More technical research in agentic AI
- Yep, LAMs (Large Agent Models) will rule …
7 Political deepfakes are easy to generate and difficult to detect
Good, I have exactly 7 ! What caught your attention ?
