So though FreeType is carefully written w.r.t. correctness, it was not meant to deal with malicious input and that robustness is hard to put in afterwards.
So though FreeType is carefully written w.r.t. correctness, it was not meant to deal with malicious input and that robustness is hard to put in afterwards.
1. Calling the input token sequence a "command". It probably only makes sense to think of this as a "command" on a model that's been fine-tuned to treat it as such.
2. Skipping over BPE as part of tokenization - but almost every transformer explainer does this, I guess.
3. Describing transformers as using a "word embedding". I'm actually not aware of any transformers that use actual word embeddings, except the ones that incidentally fall out of other tokenization approaches sometimes.
4. Describing positional embeddings as multiplicative. They are generally (and very counterintuitively to me, but nevertheless) additive with token embeddings.
5. "what attention does is it moves the words in a sentence (or piece of text) closer in the word embedding" No, that's just incorrect.
6. You don't actually need a softmax layer at the end, since here they're just picking the top token and they can just do that pre-softmax since it won't change. It's also weird how they talked about this here when the most prominent use of softmax in transformers is actually in the attention component.
7. Really shortchanges the feedforward component. It may be simple, but it's really important to making the whole thing work.
8. Nothing about the residual
'horseback riding' to me sounds like it's supposed to be a humourous phrase along the lines of 'driving a desk' (a sort of blue-collar self-deprecation for having moved up from driving whatever vehicle) - like you're still in training and haven't been given a live horse yet or something.
Definitely humorous: https://m.youtube.com/watch?v=5wSw3IWRJa0
We absolutely will not. The only reason people believe this is that they've forgotten how to do speaker-dependent recognition (SDR), which is more accurate and more secure anyway. We were doing SDR in the 80s with 1/1000 the CPU power and 1/1000 the memory.
SDR does require an initial training session, but once that's done any modern computer or smartphone should be able to handle it locally with no cloud server environment.
It is just an implementation issue how payments (through tax or otherwise) are routed. In a system that involves market forces, the key thing is that if everybody is insured, including the healthy, the cost per person comes down.
This is a different perspective from (most uses of) neural networks, which do not have this clear separation between the model and how to reason about it. It's funny that Chris Bishop in 1995 wrote the textbook "Neural Networks for Pattern Recognition" and now is effectively arguing against using neural networks.
You can use both by using neural networks as "factors" (the black squares) in probabilistic models.
Grammatically, in English the verb "swim" requires an "animate subject", i.e. a living being, like a human or an animal. So the question of whether a submarine can swim is about grammar. In Russian (IIRC), submarines can swim just fine, because the verb does not have this animacy requirement. Crucially, the question is not about whether or how a submarine propels itself.
Likewise, in English at least, the verb "think" requires an animate object. the question whether a machine can think is about whether you consider it to be alive. Again, whether or how the machine generates its output is not material to the question.