Today, another AI assistant is joining the party with Alexa, Google Assistant, Siri, Viv, and the gang. Her name is Alice, and she comes from Russia. Yandex, the Russian internet giant, has big plans for the future and Alice is a key part of those. Read also: Russia sentences hackers from Humpty Dumpty ring Facebook, Google, Twitter execs to testify at Russia hearings Did Russia's election hacking break international law? Recently, Yandex celebrated its 20 years in Moscow, and the celebration was an opportunity to visit Yandex HQ, converse with some of its top minds, and get the lowdown of what's cooking and how things work behind the scenes.
Yandex recently announced its new search algorithm Palekh, which improves how Yandex understands the meaning behind every search query by using its deep neural networks as a ranking factor among others. Ultimately, the new algorithm helps Yandex improve its search results across the board but especially for long-tail search queries. As most State of Digital readers know, long-tail search queries are categorized by searches that the search engine very rarely processes. There is a correlation between the rarity of a query and the length of it. Typically, the shorter the query the more common it is and the longer it is the more rare it is.
The Artificial Intelligence market may seem broad and complex, but in reality, most of the market is filled with producers that are incapable of providing quality products. Most AI constructs offered on the market have very limited capabilities and their potential for additional learning has been exhausted.
As Russia's government develops a digital economy, organisations are stepping up the use of artificial intelligence (AI) and machine learning technologies. Every conference this year contains a dead human genius reincarnated as software system or a robot. Yes, there is a lot of hype, but there is real worth in AI and Machine Learning. Read our counseling on how to avoid adopting "black box" approach. You forgot to provide an Email Address.
We study the problem of ranking from crowdsourced pairwise comparisons. Answers to pairwise tasks are known to be affected by the position of items on the screen, however, previous models for aggregation of pairwise comparisons do not focus on modeling such kind of biases. We introduce a new aggregation model factorBT for pairwise comparisons, which accounts for certain factors of pairwise tasks that are known to be irrelevant to the result of comparisons but may affect workers' answers due to perceptual reasons. By modeling biases that influence workers, factorBT is able to reduce the effect of biased pairwise comparisons on the resulted ranking. Our empirical studies on real-world data sets showed that factorBT produces more accurate ranking from crowdsourced pairwise comparisons than previously established models.