How many times have I stated in this group that big language modelers bragging about their parameters counts worse than cottoncandyfluffpuffery? Reducing the parameter counts by a factor of 1000 and achieving comparable performance on just about _any_ plausible language benchmark is damning of such hypercottoncandyfluffpuffery.
On Wed, Oct 7, 2020 at 4:08 AM <[email protected]> wrote: > James, if theirs is trained on enwik9 and if GPT was too, then we can > compare. Remember, if both models reached a training level where both > really need not eat more data, you'd see the smarter one is better no > matter how much data the other eats. So let's say GPT scores better than > theirs, it's ok if it has a big model, because the other can't reach its > accuracy even if eats to get a as big model as GPT's. Big model is only a > problem if can't run it. That requires optimization before moving on. One > day when we reach the seemingly best AI, we can then start worrying about > model size and speed as its the only gain left. > > And if I'm wrong, then my first thought on this thread is correct. > *Artificial General Intelligence List <https://agi.topicbox.com/latest>* > / AGI / see discussions <https://agi.topicbox.com/groups/agi> + > participants <https://agi.topicbox.com/groups/agi/members> + delivery > options <https://agi.topicbox.com/groups/agi/subscription> Permalink > <https://agi.topicbox.com/groups/agi/T6761a13445e5864b-Mc32deb96ba6f64b000912289> > ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/T6761a13445e5864b-M387f0bb3b56686149ff2cce3 Delivery options: https://agi.topicbox.com/groups/agi/subscription
