> Unlike widely used Reinforcement Learning (RL)-based and Search-based approaches, GPT-3.5 not only interprets scenarios and actions but also utilizes common sense to optimize its decision-making process
Relying on LLMs for reasoning seems dangerous due to the risk of hallucinations, especially in a safety-critical setting like self-driving. I have some other problems with this paper, for example, the comparison to RL is limited to zero-shot and this technique will struggle to run in real-time due to the slow inference speeds of LLMs.
Maybe there is some potential for LLMs to work as a fall-back mechanism in new situations or to help predict the behavior of humans and other cars, but I doubt that LLMs will become central to decision making in self-driving cars.
I really appreciate this point, and I wonder if this is a flaw of the continuously updated model of cloud-based software. While it integrates with AGILE and bugs are (in theory) quickly fixed, there's much harder to make breaking changes. Contrast that to the versioned software model where Microsoft Word could make major changes like introducing the ribbon. I wonder if it's a good idea to do some sort of versioning in the cloud-based setting.
I think you're severely underestimating the impact of California's investment in education. UC Berkeley and Stanford (private, but accepts state research grants) are top institutions which had for decades attracted some of the brightest minds (professors and students) to the area. These people helped shape the field, from early work on the ARPANET and Operating Systems to the Big Data and AI innovations of today. The expertise is passed on to students which creates not only a highly talented labor pool, but also trains the next generation of innovators.
Considering this concentration of talent, I think it's no surprise that the Bay Area is a hub for innovation, and the impressive list of companies founded by Berkeley [1] and Stanford [2] alumni is a testament to this. The concentration of talent is the environment that attracts funding and VCs, and will ensure that the Bay Area will remain the place to be in tech.
And Seattle, your "perfect counterfactual", has University of Washington, another state-funded top institution.
Yes, I agree that most major cities have these things, but I truly believe the SF Symphony is special, almost as brilliant as the NY Philharmonic.
They regularly host top performers from around the world -- Itzhak Perlman, Gustavo Dudamel, Yuja Wang, and so many more. And Michael Tilson Thomas is a treasure. Anyway, I can ramble about this forever and I appreciate the response :)
As we saw in Italy and New York, COVID-19 has the potential to overwhelm hospitals. Officially, almost 170,000 Americans have died. NYTimes estimates that COVID-19 already caused over 200,000 deaths by comparing against the expected number of deaths if we weren't in a pandemic. So yes, COVID-19 is a clear and present danger to our lives.
Wow, this is incredibly damning and all but blames the Boeing and ineffective FAA oversight for the tragic crashes.
I hope this leads to significant fines for Boeing and jailtime for the more egregious actors involved; according to the report, their negligence directly led to the loss of lives. Knowing how important Boeing is to national security/the economy, I'm skeptical that enough will be done...
Relying on LLMs for reasoning seems dangerous due to the risk of hallucinations, especially in a safety-critical setting like self-driving. I have some other problems with this paper, for example, the comparison to RL is limited to zero-shot and this technique will struggle to run in real-time due to the slow inference speeds of LLMs.
Maybe there is some potential for LLMs to work as a fall-back mechanism in new situations or to help predict the behavior of humans and other cars, but I doubt that LLMs will become central to decision making in self-driving cars.