Human-level power efficiency in robotics will take a very long time to achieve because a working robot is always a system problem integrating hardware, software, and countless details, and we should take a measured, realistic approach rather than over-promising.

33:08efficiency of a human being when it comes to to menial tasks? So let's say just basically you know minimum wage or something like that like how far away are we from this? Is this like 5 years or this is like never I think it's going to take a very long time. So if you really think about like a robots in the real environment in the end it it will

33:28always be a system. So every working robot in the real environment is a system work. It's you need to be very mindful and thoughtful about how the system are coming together. the hardware, the software, the brain, even like to the details of for example what's the friction coefficients of your of your fingers. So there's a lot of things you have to consider to make these things uh a reality and it will

33:48take iterations. But what I am excited about is that I have always been at the state of the arts of robot learning and also trying to push the state of the art forward. Yeah. >> But the state-ofthe-arts always moving faster than I expected. So what's I'm focusing on and trying to investigate right now is very different from for them when I started my PhD. So this is a

34:10speak to how fast the whole ecosystem has been evolving and all the moving pieces started coming together or building this robotic systems but we also have to be like calibrated about our predictions. So we will see a lot of like a progress but to achieve for example human level efficiency and capabilities it will take uh longer. Martin, the hardest thing in today's AI is to have the right measured optimism,

34:36[laughter] >> right? >> That's right. It's totally true. Yeah. >> I mean, even LLM does not have human brain efficiency. Human brain operates on 30 watts. >> Yeah, that's true. >> That's so we are far from that. So, >> but that but I mean performance to power it may be close, right? in narrow task like software >> like generating an image or or software engineering than it is right. Yeah, I

35:02think so. I don't think we're anywhere close when it comes to robotics. Does this change how you think about your um like strategically the level of ambition that your team can go after? I mean does it is it changed that or is it still very much in line with what you expected to do when you started? >> It definitely changed the trajectories in a very profound manners. So we see a

35:22lot of unlock in be able to do this whole process do the modeling of the environments yeah much more efficient and much more scalable manners especially in partner together with word labaps and I also want to add to f if you think about for example the current states of the language models >> so those are models that's with incredible capabilities >> but still you don't just blind trust it