From: redpointai
AI models, particularly those developed by OpenAI, are showing significant potential for advancing scientific research and exploring social sciences [00:42:10]. These models are transitioning from being broadly capable to potentially surpassing expert humans in various domains [00:42:26].
Current Capabilities and Applications
OpenAI’s O1 model is at the forefront of reasoning in Large Language Models (LLMs) [00:36:03]. It is described as “more intelligent” and “extremely good” for tackling very hard problems [00:15:24].
Accelerating Scientific Research
O1 can tackle hard research questions that would normally require someone with a PhD to handle [00:15:40]. The hope is that these models can act as partners to researchers, enabling tasks that were previously impossible or doing them much faster [00:42:51].
Specific areas where O1 has shown impressive results include:
- Math [00:43:59]: The model can multiply large numbers by working through arithmetic steps like carrying digits [00:20:15]. However, it’s more optimal for it to use a calculator tool or write a Python script for such tasks [00:20:31].
- [[AI advancements in coding and software engineering | Coding]] [00:43:59]: O1 is expected to perform much better in coding than its preview version, with the potential to significantly change the field [00:22:27]. Internally, it is used for difficult coding tasks or when a large amount of code needs to be written [00:23:04].
While it’s currently unclear if O1 can improve the state of chemistry, biology, or theoretical mathematics research, releasing the model to experts in these fields will provide valuable feedback [00:43:24].
Exploring Social Sciences
AI models like O1 are promising for social science experiments and neuroscience research [00:36:09]. They can provide insights into human behavior by imitating humans, which can be more scalable and cheaper than using human subjects [00:36:17].
An example of this application is in Game Theory:
- Models can be used for experiments like the Ultimatum Game [00:38:01], where participants decide on splitting money [00:38:04].
- Historically, such experiments at higher stakes involved significant costs or ethical concerns, sometimes requiring studies in very poor communities [00:38:59]. AI models could offer insights into how people might react in cost-prohibitive or ethically sensitive situations [00:39:19].
- The ability to quantify how closely models match human behavior in these settings will be important [00:39:56]. As models become more capable, they are expected to better imitate human actions [00:40:08].
AI in Education and Human Interaction
The development of LLMs has effectively solved the problem of AI communication, as they possess a language that humans also use [00:40:43]. This means AI agents can now interact and negotiate with other AI agents and humans [00:40:55]. This capability has implications for AI in education and legal applications, though these are not explicitly detailed here.
Overcoming Challenges in AI Research
Previously, the hardest unsolved research question was finding a general way to scale inference compute [00:00:11]. This was expected to take at least a decade but was achieved in 2-3 years [00:01:15]. This breakthrough is largely attributed to advancements in “test time compute” [00:03:15].
Nome, a research scientist at OpenAI, was a key part of the work on O1 [00:00:26]. His background in search and planning for games like poker and diplomacy influenced this approach [00:00:33]. The shift in mindset was from extending specific algorithms to more domains to starting with a general domain (like language) and figuring out how to scale test-time compute for it [00:18:34].
This approach has led to models like O1 capable of emergent behaviors [00:14:04], such as trying different strategies, breaking down problems, recognizing mistakes, and correcting them, simply by being allowed to “think for longer” [00:13:43]. This qualitative change in behavior gave conviction that the approach would be significant [00:14:38].
Challenges and Future Directions
The “Soft Wall” of Pre-training
Scaling pre-training models further incurs increasing costs, moving from thousands to hundreds of millions of dollars [00:02:07]. While models continue to improve with more resources, there’s an eventual “soft wall” where the cost becomes economically intractable, reaching billions or tens of billions of dollars [00:02:37].
Importance of Test-Time Compute
Test-time compute is seen as having significant “runway” for further scaling [00:03:37]. The potential for algorithmic improvements in this area is substantial [00:03:42].
- Current ChatGPT queries cost around a penny [00:03:33].
- For highly important problems, people might be willing to pay millions of dollars per query [00:04:48], indicating about eight orders of magnitude of room to push test-time compute further [00:05:00].
The Bitter Lesson and Scaffolding
Richard Sutton’s “Bitter Lesson” is cited, suggesting that techniques that scale well with more compute and data will ultimately succeed over human-coded knowledge or “scaffolding” [00:26:05].
- While scaffolding and prompting tricks can push models slightly further in the short term, they don’t scale well with more data and compute [00:26:48].
- Models like O1, which scale well with more data and compute, are expected to make these scaffolding techniques obsolete in the long run [00:27:15]. This poses a challenge for startups that might invest heavily in such temporary solutions [00:27:37].
Role of Academia in AI Research
Academia faces challenges in competing with industry labs due to the dependence on data and compute resources [00:29:04].
- There’s an incentive to add “clever prompting or tricks” to achieve marginal performance gains for papers, which may not lead to impactful long-term research [00:29:20].
- It’s suggested that academia should focus on investigating novel architectures or approaches that demonstrate promising scaling trends with more data and compute, even if they don’t achieve state-of-the-art performance immediately [00:30:21]. Industry labs are interested in such foundational work [00:31:09].
Multimodal Models and Hardware
O1 accepts images as input [00:36:38], and there are no perceived blockers to making these models as multimodal as others like GPT-4o [00:16:48].
The development of O1 signals a shift in hardware thinking, moving from a focus on massive pre-training runs to optimizing for inference compute [00:35:09]. This creates an opportunity for hardware innovation to adapt to this new paradigm [00:35:27].
Outlook
Progress in AI research is expected to accelerate [00:45:29]. The shift in perspective regarding the rapid progress of AI, particularly with the general scaling of test-time compute, has increased optimism about achieving highly intelligent models sooner than previously thought [00:34:12].