> Wilmore said he had no reason to doubt Musk’s claims about an offer to bring them home earlier, though Wilmore said he was not privy to such an offer.
Which pretty much amounts to "I don't know, but I can't see why Musk would lie."
Hard to say, as a native speaker eloquence here doesn’t seem to refer to the authors own eloquence but rather the amount of words and roundabout explanations they’ve dedicated to the topic. Ie, in contrast to a simple explanation for the obvious things. If anything it reads a touch self deprecating.
I think you are reading too much into my response. The list of events isn't an attempt to justify anything. It is mainly to establish that what happens in 1948 isn't coming out of nowhere, but rather has been brewing and building up for a while in the region. If someone is only familiar with 1948, they're going to really struggle to understand the motivations of either side and will likely end up with a simplistic view of one side or the other as evil depending on what information they get.
As for the colonialist interpretation... that confuses more than it clarifies. There is enough substance to the conflict itself that we don't need to resort to grand sweeping historical narratives.
Tbh starting the history doesn't at 1948 doesn't really help much. Around 1948 is when the Jewish and Palestinian narratives are already in full swing and have some of the strongest evidence for each side. On the Jewish side you'd have their invasion by the broader Arab world and their subsequent mentality that they have to defend themselves at all costs. On the Palestinian side you have the Nakba and the resulting trauma that forms their identity.
If you go back earlier you start to see the roots of the conflict more clearly. From Russian Jews being expelled out of Russia and looking towards the Ottoman empire and historic Judea as a place from their home, Ottoman policies around 1900s when they are hyper paranoid about quashing nationalism in their multi-ethnic empire and how that plays into their treatment of Jews they allow to immigrate into their lands, then the British control over Ottoman lands and how they tried to administer to it and designated Judea as a vague "Jewish National Home" which gave neither Jews nor Arabs clarity on the political reality of the region. Then there was the early immigration of Jews to Israel in the 1910s and 1920s which meets some Arab resistance and sparks violence, but the migration stops in the mid 1920s, so tensions cool a bit. Then in the 1930s the Nazis rise to power and immigration of Jews to Palestine _really_ starts. At this point the US has imposed strict quotas on Jews immigrating to the US (in around 1924), so for many Jews Palestine isn't their first choice, but it is a choice for them and the British seem serious about this "Jewish National Home" thing, so they go for it. Then comes the great Arab revolt in 1936 where Arabs in Palestine respond to the large influx of Jewish migrants, demand independence from Britain, and seek to end the Jewish National Home project. Somewhere around the early 1940s WW2 is in swing and the Nazis invade Egypt and establish a SS unit there whose purpose is the genocide the Jews in Palestine. This was a pretty close call, if it wasn't for a British victory in Egypt that held the Nazi's back, odds are the Middle East would look very different today.
And that brings us to 1948 when the British withdraw and the stage is set for the resulting chaos. So there you have it. On one side a population struggling to determine their national identity after the fall of a 600 year old empire and dealing with a large influx of immigrants that they don't want to their lands (and also still struggling to figure out what "their" means). On the other hand you have a group of people trying to flee their home country and having no option but this weird "Jewish National Home" that totally isn't a state according to the British despite it being maybe possible a state for Jews? Then in the middle of a clearly bad situation, Britain withdraws and the Jews and Palestinians have been dealing with the situation sense.
None of this is to say that one side is right or wrong. Rather, if you look around 1948 you're already missing a lot of the buildup to the conflict and the reason there is no trust between the two groups in 1948.
I disagree. In most subjects, recapitulating the historical development of a thing helps motivate modern developments. Eg
1. Start with bag of words. Represent words as all zero except one index that is not zero. Then a document is the sum (or average) of all the words in the document. We now have a method of embedding a variable length piece of text into a fixed size vector and we start to see how "similar" is approximately "close", though clearly there are some issues. We're somewhere at the start of nlp now.
2. One big issue is that there are a lot of common noisy words (like "good", "think", "said", etc.) that can make the embedding more similar than we feel they should be. So now we develop strategies for reducing the impact of those words one our vector. Remember how we just summed up the individual word vectors in 1? Now we'll scale each word vector by its frequency so that the more frequent the word in our corpus, the smaller we'll make the corresponding word vector. That brings us to tf-idf embeddings.
3. Another big issue is that our representation of words don't capture word similarity at all. The sentences "I hate when it rains" and "I dislike when it rains" should be more similar than "I hate when it rains" and "I like when it rains", but in our embeddings from (2) the similarity of the two pairs is going to be similar. So now we revisit our method of constructing word vectors and start to explore ways to "smear" words out. This is where things like word2vec and glove pop up as methods of creating distributed representation of words. Now we can represent documents by summing/averaging/tf-idfing our word vectors the same as we did in 2.
4. Now we notice there is an issue where words can have multiple meanings depending on their surrounding context. Think of things like irony, metaphor, humor, etc. Consider "She rolled her eyes and said, 'Don't you love it here?"'" and "She rolled the dough and said, 'Don't you love it here?'". Odds our, the similarity per (3) is going to be pretty similar, despite the fact that its clear these are wildly different meanings. The issue is that our model in (3) just uses a static operation for combining our words, and because of that we aren't capturing the fact that "Don't you love it here" shouldn't mean the same thing in the first and second sentences. So now we start to consider ways in which we can combine our word vectors differently and let the context affect the way in which we combine them.
5. And that brings us to now where we have a lot more compute than we did before and access to way bigger corpora so we can do some really interesting things, but its all still the basic steps of breaking down text into its constituent parts, representing those numerically, and then defining a method to combine various parts to produce a final representation for a document. The above steps greatly help by showing the motivation for each change and understanding why we do the things we do today.
Yes! And that’s an oversight on my part — word embeddings are interesting but I usually deal with documents when doing nlp work and only deal with word embeddings when thinking about how to combine them into a document embedding.
Give it a shot! I’d grab a corpus like https://scikit-learn.org/stable/datasets/real_world.html#the... to play with and see what you get. It’s not going to be amazing, but it’s a great way to build some baseline intuition for nlp work with text that you can do on a laptop.
Eh, I disagree. When I began working in ML everything was about word2vec and glove and the state of the art for embedding documents was adding together all the word embeddings and it made no sense to me but it worked.
Learning about BoW and simple ways of convert text to fixed length vectors that can be used in ML algos clarified a whole for me, especially the fact that embeddings aren’t magic they are just a way to convert text to a fixed length vector.
BoW and tf-idf vectors are still workhorses for routine text classification tasks despite their limitations, so they aren’t really a dead end. Similarity a lot of things that follow BoW make a whole lot more sense if you think of them as addressing limitations of BoW.
Heh -- my explanation isn't the clearest I realize, but yes, it is BoW.
Eg fix your vocab of 50k words (or whatever) and enumerate it.
Then to make an embedding for some piece of text
1. initialize an all zero vector of size 50k
2. for each word in the text, add one to the index of the corresponding word (per our enumeration). If the word isn't in the 50k words in your vocabulary, then discard it
3. (optionally), normalize the embedding to 1 (though you don't really need this and can leave it off for the toy example).
initialize an embedding (for a single text) as an all zero vector of size 50k
> I think the key property of embeddings is that the dimensions each individually mean/measure something, and therefore the dot product of two embeddings (similarity of direction of the vectors) is a meaningful similarity measure of the things being represented.
In this case each dimension is the presence of a word in a particular text. So when you take the dot product of two texts you are effectively counting the number of words the two texts have in common (subject to some normalization constants depending on how you normalize the embedding). Cosine similarity still works for even these super naive embeddings which makes it slightly easier to understand before getting into any mathy stuff.
You are 100% right this won't give you the word embedding analogies like king - man = queen or stuff like that. This embedding has no concept of relationships between words.
Yes! And the follow up that cosine similarity (for BoW) is a super simple similarity metric based on counting up the number of words the two vectors have in common.
You're right that what I described isn't what people commonly think about as embeddings (given we are more advanced now the above description), but broadly an embedding is anything (in nlp at least) that maps text into a fixed length vector. When you make embedding like this, the nice thing is that cosine similarity has an easy to understand similarity meaning: count the number of words two documents have in common (subject to some normalization constant).
Most fancy modern embedding strategies basically start with this and then proceed to build on top of it to reduce dimensions, represent words as vectors in their own right, pass this into some neural layer, etc.
One straightforward way to get started is to understand embedding without any AI/deep learning magic. Just pick a vocabulary of words (say, some 50k words), pick a unique index between 0 and 49,999 for each of the words, and then produce embedding by adding +1 to the given index for a given word each time it occurs in a text. Then normalize the embedding so it adds up to one.
Presto -- embeddings! And you can use cosine similarity with them and all that good stuff and the results aren't totally terrible.
The rest of "embeddings" builds on top of this basic strategy (smaller vectors, filtering out words/tokens that occur frequently enough that they don't signify similarity, handling synonyms or words that are related to one another, etc. etc.). But stripping out the deep learning bits really does make it easier to understand.
Depends on what "AI" means here. There is a spectrum of "we have a bunch of data in a database and some folks hand tuning queries" to "we built a deep learning network to predict XYZ." In the middle of that spectrum lie things like decision trees which provide explainable results.
If by "conventional neural network" you mean a stack off fully connected layers, then yes, in theory one of those could learn a similar mechanism because of the universal approximation theorem. However, training one might be intractable.
It's good to ignore self-attention for a moment and take a look at a convolutional network (a CNN). Why is a CNN more effective than just stacks of fully connected layers? Well, instead of just throwing data at the network and telling it to figure out what to do with it, we've built in some prior knowledge into the network. We tell it "you know, a cup is going to be a cup even if it is 10 pixels up or 10 pixels down; even if it is in the upper right of the image or the lower left." We also tell it, "you know, the pixels near a given pixel are going to be pretty correlated with that pixel, much more so than pixels far away." Convolutions help us express that kind of knowledge in the form of a neural network.
Self-attention plays a similar role. We are imbuing our network with an architecture that is aware of the data it is about to receive. We tell it "hey, elements in this sequence have a relation with one another, and that relative location might be important for the answer". Similar to convolutions, we also tell it that the location of various tokens in a sequence is going to vary: there shouldn't be much difference between "Today the dog went to the park" and "The dog went to the park today." Like convolutions, self-attention builds in certain assumptions we have about the data we are going to train the network on.
So yes, you are right that fully-connected layers can emulate similar behavior, but training them to do that isn't easy. With self-attention, we've started with more prior knowledge about the problem at hand, so it is easier to solve.
If anything, it seems like this was an effective means to introduce a lot of people to possible liabilities they have under GDPR/CCPA (or why they are not applicable to them).
This is partly because the first course in real analysis (and usually the first upper division linear algebra course) is the first proof oriented course most students face. The material isn't necessarily the hardest, but the proof stuff is new to many people.
The linked graph shows Sweden at 7x norway's rate, 5x finland's rate and 3x denmarks rate. Am I reading that right? How are you concluding that Sweden's strategy was clearly superior?
That's a fair criticism. I always rewrite & revise the important notes as I go forward, so I never end up looking back. I can imagine that it is super frustrating to find things that aren't near the end or on a known page number -- their page scrolling functionality is clunky to say the least.
> The basic problem with the Remarkable is that it's useless without the $8/month "cloud" plan because that's the only plan that enables its primary feature: handwriting recognition.
Are you saying the key feature for the remarkable is handwriting recognition?
I have a remarkable 2 and I love it, but the whole appeal is to replace the endless paper notebooks and loose sheets that I use. It is great for that. Since I've bought it they've added a bunch of new stuff and the subscription stuff as well. I'm not sure how I feel about it, but then again I have no need for those features. It sounds like most people want a general purpose e-ink tablet whereas the remarkable is basically e-ink paper.
> Wilmore said he had no reason to doubt Musk’s claims about an offer to bring them home earlier, though Wilmore said he was not privy to such an offer.
Which pretty much amounts to "I don't know, but I can't see why Musk would lie."