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  • hace 12 horas
El servicio de robotaxis de Tesla está experimentando importantes problemas operativos en las ciudades de Texas donde se expandió recientemente. Pruebas realizadas por Reuters revelaron largos tiempos de espera, disponibilidad limitada de vehículos y problemas de navegación que socavan la visión del CEO Elon Musk de un gigante de la tecnología de conducción autónoma.
El servicio sigue limitado a tres ciudades de Texas, a pesar de que Musk predijo en julio pasado que los robotaxis darían servicio a la mitad de la población estadounidense para finales de 2025.
Los periodistas que probaron el servicio en Dallas, Houston y Austin se encontraron con esperas superiores a 30 minutos y frecuentes faltas de disponibilidad. En una prueba en Dallas, un viaje que normalmente dura 20 minutos tardó casi dos horas en completarse.
Gran parte del valor de mercado de Tesla, de 1,6 billones de dólares (más de cinco veces el de cualquier otro fabricante de automóviles), está ligado a la convicción de los inversores de que la compañía pronto desplegará una vasta flota de robotaxis. Musk ha afirmado que la tecnología de conducción autónoma de Tesla "funciona en cualquier lugar" y ha criticado el enfoque metódico de Waymo, de Alphabet, que realiza mapeo de alta definición y pruebas exhaustivas antes de entrar en nuevos mercados.
En julio pasado, Musk predijo que los robotaxis de Tesla darían servicio a la mitad de la población de EE. UU. para finales de 2025.

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00:26¡Gracias!
00:32I think at this point, with the technology being what it is, Tesla and Waymo included,
00:37I think we would all feel safer if there was still a person that could take manual control of the
00:41vehicle,
00:42especially in certain situations.
00:44So I wouldn't say that I support it by any means.
01:03People, at least so far, tend to be much better at unexpected situations
01:09and being able to reason about something that you've never seen before
01:15but be able to figure out on the fly what to do,
01:19especially if you're using a machine learning-based system.
01:22It relies critically on the training data that's been given,
01:27and if you come up with a completely what's known as out-of-distribution,
01:31which is sort of a code word for just new or novel situation,
01:37then it's going to be much more difficult for a software-based system to react than people are.
01:49It's partly the autonomous car side.
01:52It's partly also the human expectations.
01:55Just in the same way if you have an Alexa at home,
01:58you realize the first time that when you say something it doesn't understand you,
02:02then you figure out how to talk to it.
02:03You change your behavior in response to the technology.
02:06I think that will also happen with autonomous cars,
02:09that the people will change their behavior once they have a good model
02:12of what they do and don't understand.
02:23I think it's like mainly like a novelty.
02:27I don't think they're very necessary.
02:29It kind of just takes away jobs from people who need to be like ride-share drivers.
02:43I feel like a human may be like better at like creative thinking and stuff,
02:48like in situations where kind of normal road laws don't apply,
02:52like if there's a block up on the highway
02:54or if it's like the marathon coming up and like certain roads are closed off,
02:59a human might be able to like better navigate new situations
03:02versus a robot where it's kind of just relying on programming.
03:06They said, I have fallen for a co-work.
03:09And I think he has two because he tells me the sweetest things.
03:14and he says, I'm sorry, I don't know.
03:19I'm sorry, I'm sorry.
03:20I don't know.
03:22I do know.
03:22I don't know.
03:24I mean, we're not going to love for him in this neighborhood.
03:24So this thing I'm going to love forっぱい
03:27and it doesn't go up now and it's normal.
03:28Gracias.
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