They've got thunderbird which is as far as I know the only serious alternative to outlook.
Same issue here.
including the Slogan: 'From the river to the sea – Palestine shall be free'
As it should, this phrase and it's Israeli counterpart "between the sea and the Jordan there will be only Israeli sovereignty" are often accompanied by calls for mass deportation at best and genocide at worst.
These sentences are not bad on their own, but the parties from which they originate (Hamas and Likud) have transparent desires for war crimes and genocide.
“They had Polish refugee status certificates for the adults and Polish ID cards for the children. These people were taken to the police station for further investigation,” said von Vegesack. He added that, after their legal situation was confirmed, the family had to be sent back to Poland.
According to the spokesman, German officials informed the Polish border guard about the situation through the Polish-German Centre for Cooperation of Border, Police and Customs Services in Świecko.
“Since there was no reaction from the Polish side for several hours, the officers decided to take the family to the Polish-German border…in order to release them to Poland,” said von Vegesack.
The article above should be more "polish refugees left alone in Poland by German police" rather than the current one which suggests that Germany is getting rid of its migrants illegally.
Huh, your comment made look if there were personalized recommendation algorithms running on lemmy. From what I found, it appears that Lemmy does not use personalized recommendation algorithms : https://join-lemmy.org/docs/contributors/07-ranking-algo.html
The specific function used for ranking is here : https://github.com/LemmyNet/lemmy/blob/4ba6221e04ab3e186669aeaa890d23b1e3f3d1a9/crates/db_schema/replaceable_schema/utils.sql#L18
I'm wondering how hard it would be to adapt the code to customize the score for every user, instead of it being global.
I makes it look like the advancement here is finding methods to efficiently use sets of graphs which are an order of magnitude larger than prior methods could use for training? They also seem to have used more sets of graphs than prior models across a wider set of domains. Am I reading this correctly?
I find it challenging to gauge the paper's impact fully, as this isn't my area of expertise. However, the ability to use diverse graphs in a single model surprised me and seemed worth sharing.
Separately, Jong has also alleged that Apple subjected her to a hostile work environment after a senior member of her team, Blaine Weilert, sexually harassed her. After she complained, Apple investigated and Weilert reportedly admitted to touching her "in a sexually suggestive manner without her consent," the complaint said. Apple then disciplined Weilert but ultimately would not allow Jong to escape the hostile work environment, requiring that she work with Weilert on different projects. Apple later promoted Weilert.
As a result of Weilert's promotion, the complaint said that Apple placed Weilert in a desk "sitting adjacent" to Jong's in Apple’s offices. Following a request to move her desk, a manager allegedly "questioned" Jong's "willingness to perform her job and collaborate" with Weilert, advising that she be “professional, respectful, and collaborative,” rather than honoring her request for a non-hostile workplace.
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French here we use both the middle and the left. It depends on the group of friends.
Now instead of just querying the goddamn database, a one line fucking SQL statement, I have to deal with the user team
Exactly, you understand very well the purpose of microservices. You can submit a patch if you need that feature now.
Funnily enough I'm the technical lead of the team that handles the user service in an insurance company.
Due to direct access to our data without consulting us, we're getting legal issues as people were using addresses to guess where people lived instead of using our endpoints.
I guess some people really hate the validation that service layers have.
You are in a bubble. A neo nazi march was banned two weeks ago in France before being allowed again by the judicial system. The exact same scenario has been repeating for pro-palestine protests.
At least in France, the scenario seems to be that the government wants to ban any controversial march and is being kept under control by the justice system.
I also have a similar experience, I was mugged at knife point and spit on by two adolescents. After that I was jumpy around groups of teens.
That said , I do not think my fear of teens was rational, neither was it healthy. Only a small minority of teens will mug people. Fearing a whole group for the actions of the few is in human nature, but it is something we must fight against.
I mean what is the end goal if women are in fear of men ? You can probably reduce violent crime even more, but it remains a rare event. Only 31 out of 1000 people were victims of a violent crime in the UK in 2010. If that doesn't work, what remains? Sex segregation ?
I'm afraid that would not be sufficient.
These instructions are a small part of what makes a model answer like it does. Much more important is the training data. If you want to make a racist model, training it on racist text is sufficient.
Great care is put in the training data of these models by AI companies, to ensure that their biases are socially acceptable. If you train an LLM on the internet without care, a user will easily be able to prompt them into saying racist text.
Gab is forced to use this prompt because they're unable to train a model, but as other comments show it's pretty weak way to force a bias.
The ideal solution for transparency would be public sharing of the training data.
Paper here : https://arxiv.org/pdf/2312.00752.pdf
Abstract :
Foundation models, now powering most of the exciting applications in deep learning, are almost universally based on the Transformer architecture and its core attention module. Many subquadratic-time architectures such as linear attention, gated convolution and recurrent models, and structured state space models (SSMs) have been developed to address Transformers’ computational inefficiency on long sequences, but they have not performed as well as attention on important modalities such as language. We identify that a key weakness of such models is their inability to perform content-based reasoning, and make several improvements. First, simply letting the SSM parameters be functions of the input addresses their weakness with discrete modalities, allowing the model to selectively propagate or forget information along the sequence length dimension depending on the current token. Second, even though this change prevents the use of efficient convolutions, we design a hardware-aware parallel algorithm in recurrent mode. We integrate these selective SSMs into a simplified end-to-end neural network architecture without attention or even MLP blocks (Mamba). Mamba enjoys fast inference (5× higher throughput than Transformers) and linear scaling in sequence length, and its performance improves on real data up to million-length sequences. As a general sequence model backbone, Mamba achieves state-of-the-art performance across several modalities such as language, audio, and genomics. On language modeling, our Mamba-3B model outperforms Transformers of the same size and matches Transformers twice its size, both in pretraining and downstream evaluation.
It's great for making images to associate with language learning flashcards.