Since the proposal of transformers (V aswani et al., 2017), these models have been limited to bounded input lengths, because of their need to attend to every token in the input.

Russian President Vladimir Putin has said that there has so far been no need to use nuclear weapons in Ukraine, expressing "hope" that they will not be required. Putin said his country had enough "strength and means" to bring the three-year war, sparked by Russia's 2022 invasion of Ukraine, to a "logical conclusion with the outcome Russia requires". His comments were part of a documentary marking his quarter century in power by state television channel Rossiya 1 that was released on Sunday. Responding to a question from journalist Pavel Zarubin about the Russian response to Ukrainian strikes on Russian territory, Putin said: "There has been no need to use those [nuclear] weapons … and I hope they will not be required." His comments came ahead of his unilaterally declared three-day ceasefire over May 8-10 to mark the 80th anniversary of the victory of the Soviet Union and its allies over Nazi Germany in World War II, an initiative that he claimed would test Kyiv's readiness for long-term peace.

Since the proposal of transformers, these models have been limited to bounded input lengths, because of their need to attend to every token in the input. In this work, we propose Unlimiformer: a general approach that wraps any existing pretrained encoder-decoder transformer, and offloads the cross-attention computation to a single k-nearest-neighbor (kNN) index, while the returned kNN distances are the attention dot-product scores. This kNN index can be kept on either the GPU or CPU memory and queried in sub-linear time; this way, we can index practically unlimited input sequences, while every attention head in every decoder layer retrieves its top-k keys, instead of attending to every key. We evaluate Unlimiformer on several long-document and book-summarization benchmarks, showing that it can process even 500k token-long inputs from the BookSum dataset, without any input truncation at test time. We demonstrate that Unlimiformer improves pretrained models such as BART and Longformer by extending them to unlimited inputs without additional learned weights and without modifying their code. We make our code and models publicly available at https://github.com/abertsch72/unlimiformer .

FOX News contributor Mollie Hemingway and Rebelle Communications founder and CEO Laura Fink discuss the U.S. media reporting that Israel was responsible for killing 500 people in a Gaza hospital bombing on'MediaBuzz.' JERUSALEM – Over the past two weeks, since Palestinian terrorist group Hamas carried out its deadly attack in southern Israel killing some 1,400 Israelis, there is a fear that a new front in the old war between Israelis and Palestinians could open up – in the digital realm. While doctored images and fake news have long been part of the Middle East wartime arsenal, with the arrival less than a year ago of easy-to-use artificial intelligence (AI) generative tools it seems highly probable that deepfake visuals will soon be making an appearance on the war front too. "Hamas and other Palestinian factions have already passed off gruesome images from other conflicts as though they were Palestinian victims of Israeli assaults, so this is not something unique to this theater of operations," David May, a research manager at the Foundation for Defense of Democracies, told Fox News Digital. He described how in the past, Hamas has been known to intimidate journalists into not reporting about its use of human shields in the Palestinian enclave, as well as staging images of toddlers and teddy bears buried in the rubble. Hamas killed at least 1,400 in a surprise terror attack that hit men, women, children and older civilians on Oct. 7. (Getty) "Hamas controls the narrative in the Gaza Strip," said May, who follows Hamas' activities closely, adding that "AI-generated images will complicate an Israeli-Palestinian conflict already rife with disinformation."

To be able to understand and predict human actions, artificial agents must be able to identify, comprehend, and reason about human emotions. Different formal models of human emotions have been studied in AI literature. Doyle, Shoham, and Wellman propose a logic of relative desire [1]. Lang, Van Der Torre, and Weydert introduce utilitarian desires [2]. Meyer states logical principles aiming at capturing anger and fear [3]. Steunebrink, Dastani, and Meyer expand this work to hope [4]. Adam, Herzig, and Longin propose formal definitions of hope, fear, relief, disappointment, resentment, gloating, pride, shame, admiration, reproach, gratification, remorse, gratitude, and anger [5].

Personally, I'm a firm believer and fan of Kaggle and definitely look at it as the home of Data Science. Kaggle Grandmasters are the heroes of Kaggle or definitely mine. I've been on a pursuit to depict and understand their journey into the field also if they're still humans or have passed onto an alternate reality (not still sure about that one). H2O World event recently had the biggest Kaggle Grandmaster Panel. This post will share my takeaways from the panel discussions along with a few notes from my previous interviews.