It's over.
Google Veo 2 just wiped out the competition and became the new king of the AI media world.
No one can tell this is AI now.
10 wild examples:
@GjMcGowan essentially an interactive coupon code- there are a number of ways the LLM can be made to always refuse lowball offers, such as validation at checkout. I would be surprised if the final price output given by the LLM is plugged directly into their payment systems
Falcon 180B is out๐คฏ
- 180B params
- Trained on 3.5 trillion tokens+7 million GPU hours
- Quality on par with PaLM 2 outperforms Llama 2 and GPT-3.5 across 13 benchmarks
- 4bit and 8bit precision with similar quality
Demo: https://t.co/JCMYid7VYn
Blog: https://t.co/vb8ppRHgJ5
This is a baby GPT with two tokens 0/1 and context length of 3, viewing it as a finite state markov chain. It was trained on the sequence "111101111011110" for 50 iterations. The parameters and the architecture of the Transformer modifies the probabilities on the arrows.
E.g. we can see that:
- state 101 deterministically transitions to 011 in the training data, so the probability of that transition becomes higher (79%). Not near 100% because we only did 50 steps of optimization.
- state 111 goes to 111 and 110 with 50% probability each, which the model almost learns (45%, 55%).
- states like 000 are never encountered during training, but have relatively sharp transition probabilities, e.g. 73% of going to 001. This is a consequence of inductive biases in the Transformer. One might imagine wanting this to be 50%, except in a real deployment almost every input sequence is unique, not present in the training data verbatim.
Not really sure where I was going with this :D, I think it's interesting to train/study tiny GPTs because it becomes tractable to visualize and get an intuitive sense of the entire dynamical system. Play with here: https://t.co/8jdceMLpqy
in the future, we will not learn how to read and write, but how to deeply interpret seven plus/minus two dimensional embedding spaces, and by projecting the neural encoding back into the machine via neuralink, we will all learn how to be one with the machine
โ๏ธ๐๐ฟ๐ฎ๐๐น๐ต๐ฎ๐น๐น๐ฎ ๐ ๐๐ฎ๐๐๐น๐ฒ๐๐ฎ๐ป๐ถ๐ฎ ๐๐ฟ๐ผ๐๐๐ผ๐๐ฒ๐ฟ ๐๐ถ๐๐ฒ๐ฎ๐๐ฎ๐! โ๏ธ
x1 Alucard (Ezio)
x1 Simon Belmont (Jhala)
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