Transformers seem to be the successor to conv nets. But in my direct experience advocating for them, it's amazing how reluctant industry peeps were to trying them because they associated all the limitations of LSTM networks with them for a long time. YMMV, but that's how it went with me.
I even predicted DeepMind's CASP 14 network would be transformer-based back in 2018, but I couldn't have told you the details of that transformer, just that it was a no-brainer to move from fixed width convolutions to arbitrary width attention sums because sequence motifs and long-range interactions are of arbitrary width in the sequence.
All that seems to have changed with AlphaFold 2 because unlike GPT-XXX, this isn't a parlor trick with memorized text. This is actually useful and the FOSSing of the network will spawn all sorts of new applications of the approach.
So now I wonder what will replace Transformers because nothing lasts forever and there are a lot of smart people trying all sorts of new ideas.
I even predicted DeepMind's CASP 14 network would be transformer-based back in 2018, but I couldn't have told you the details of that transformer, just that it was a no-brainer to move from fixed width convolutions to arbitrary width attention sums because sequence motifs and long-range interactions are of arbitrary width in the sequence.
All that seems to have changed with AlphaFold 2 because unlike GPT-XXX, this isn't a parlor trick with memorized text. This is actually useful and the FOSSing of the network will spawn all sorts of new applications of the approach.
So now I wonder what will replace Transformers because nothing lasts forever and there are a lot of smart people trying all sorts of new ideas.