Do you mean that his algorithms don’t converge?

Flo-rida and the intel nerds trying to be cool unveiling what looked like a GPU was definitely a low point

Which are the best parties this year?

You need to do feature computation and find correspondences. If you’re using a learned feature extractor, that will be twice as expensive as the monocular model. But let’s say you’re using a classical feature extractor. You still need to do feature matching. For dense depth maps, both of these stages can be as expensive, if not more, than a single forward pass through a highly optimized mobile NN architecture.

You can put that compute on the glasses. The real problem is heat dissipation. It is what killed google glass.

One camera is cheaper than two, though. Cheaper in every sense (compute, memory, network bandwidth, energy consumption, parts cost, etc).

Don’t forget to add the people who got into the war and will want to pull out when it gets too expensive.

Ask your friends who were laid off at Meta, in the past couple months, who is hiring them with the same compensation they were getting at Meta, or better.

“A bit more competitive” is an understatement. There’s an increased supply of workers and an increasing number of them are lowering the expectations about compensation. Companies that are hiring will have many options, and they’ll likely chose the option that costs them less.

I THOUGHT REWARD WAS ALL YOU NEED

What are those errors you have observed in the datasets like the Pile?

They know deepmind papers bring in readers.

My first ML paper