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.
FreeType was written when fonts were local, trusted, resources, and it was written in low-level C to be fast. The TrueType/OpenType format is also made for fast access, e.g. with internal pointers, making validation a pain.
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.
TrueType also just has way too much complexity accumulated into it. The character to glyph mapping table alone has nine different encoding formats. I was only writing a TTF file instead of reading it and the complexity was still impressive.
If you think FreeType is bad, wait until you find out win32k.sys used to parse TrueType fonts directly in the kernel. https://msrc.microsoft.com/blog/2009/11/font-directory-entry... (that’s just one of a million vulnerabilities thanks to kernel mode font parsing.)
That makes it sound like it’s a choice, which it isn’t really. The way to look at it is from a probabilistic perspective: with the fix, you maximise the probability of the data. Without the fix, you fairly arbitrarily raise some probabilities to a power greater than one, and some to a power less than one.
Isn’t the main problem the cost of heating? In the UK at least a kWh of electricity cost 4 times what a kWh of gas costs. Even if a heat pump is twice as efficient as gas heating, the cost of heating is still twice as high.
I agree except for (6). A language model assigns probabilities to sequences. The model needs normalised distributions, eg using a softmax, so that’s the right way of thinking about it.
This is true in general but not in the use case they presented. If they had explained why a normalized distribution is useful it would have made sense - but they just describe this as pick-the-top-answer next-word predictor, which makes the softmax superfluous.
Training a speaker-specific recogniser that improves over a generic recogniser requires a lot more data nowadays. First, generic systems are a lot better and trained on a lot more data nowadays. Second, speaker adaptation worked better for the Gaussian mixture models from the late nineties (don’t know about the eighties) than for neural networks.
This is how healthcare is set up in Switzerland and the Netherlands.
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.
The "model" in the title is the model of the world, as a probabilistic model. The good thing about such a model is that it explicitly states your beliefs about the world. Once you've defined it, in theory reasoning about it is straightforward. (In practice a lot of papers get written about how to do approximate inference.) It's also straightforward to do unsupervised learning.
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.
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.
I haven't read "Neural Networks for Pattern Recognition", but his "Pattern Recognition and Machine Learning"[1] is the text for ML work including Bayesian approaches.
I don't think one should view this as "arguing against" neural networks - it's more that Bayesian approaches give you something different.
One of the most popular ways of using techniques like this is the "Variational Autoencoder". I've been working on using some alternate distributions with them as of late - it's very interesting, and quite powerful.
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.