In reinforcement learning for partially observable environments, many successful algorithms were developed within the asymmetric learning paradigm. This paradigm leverages additional state information available at training time for faster learning. Although the proposed learning objectives are usually theoretically sound, these methods still lack a theoretical justification for their potential benefits. We propose such a justification for asymmetric actor-critic algorithms with linear function approximators by adapting a finite-time convergence analysis to this setting. The resulting finite-time bound reveals that the asymmetric critic eliminates an error term arising from aliasing in the agent state.

It's hard to escape artificial intelligence. From algorithms curating social media feeds to personal assistants on smartphones and home devices, AI has become part of everyday life for millions of people across the world. The future of that human-tech relationship may one day involve AI systems being able to learn entirely on their own, becoming more efficient, self-supervised and integrated within a variety of applications and professions. But some on social media claim this evolution toward AI autonomy has already happened. "Facebook recently shut down two of its AI robots named Alice & Bob after they started talking to each other in a language they made up," reads a graphic shared July 18 by the Facebook group Scary Stories & Urban Legends.

A large portion of the field of statistics and statistical methods is dedicated to data where the distribution is known. Samples of data where we already know or can easily identify the distribution of are called parametric data. Often, parametric is used to refer to data that was drawn from a Gaussian distribution in common usage. Data in which the distribution is unknown or cannot be easily identified is called nonparametric. In the case where you are working with nonparametric data, specialized nonparametric statistical methods can be used that discard all information about the distribution.

The buried line in the new Facebook report about one-on-one chatbots conversations provides a great glimpse into the future of the language. In the report, researchers at the Facebook Artificial Intelligence Research Lab describe the use of machine learning to negotiate with their "dialogue agents." At one point, the researchers wrote, they had to adjust one of their models, otherwise the bot-to-bot dialogue "developed their language for negotiation, which led to the disagreement from human language to agents. "Instead they had to use the so-called fixed supervised model. In other words, the model that allowed the two bots to communicate -- and to use machine learning to continually redirect strategies for that conversation -- led the bots to communicate in their non-human language.

"The translation of letters and words into a stenographic language by producing new results with analysis data will surely serve humanity in every field where life integrates with technology," Deniz Unay, a social media specialist, told Anadolu Agency. Stenography is a system of rapid writing using symbols or abbreviations instead of words or phrases, and it increases brevity of writing when compared to longhand writing, which is widely used today. Stenography derives from stenos (narrow) and graphein (to write) in Greek language. "Many recent studies have shown that artificial intelligence and stenography systems are used together in the creation of special alphabets in the encryption process," Unay said. Emphasizing the importance of stenographic language created by artificial intelligence in military strategy planning, he argued the association brings security and privacy issues one step further.

We show that policy gradient (PG) and its variance reduction variants can be derived by taking finite difference of function evaluations supplied by estimators from the importance sampling (IS) family for off-policy evaluation (OPE). Starting from the doubly robust (DR) estimator [Jiang and Li, 2016], we provide a simple derivation of a very general and flexible form of PG, which subsumes the state-of-the-art variance reduction technique [Cheng et al., 2019] as its special case and immediately hints at further variance reduction opportunities overlooked by existing literature.

In 1818, the first copy of Frankenstein; or, The Modern Prometheus was published. Two hundred years later, it's still our go-to monster story, even if the cultural images we associate with it owe more to Boris Karloff's portrayal of the monster than Mary Shelley's original novel. Only a handful of books maintain relevance beyond a decade, let alone 200 years – yet Frankenstein endures to this day and still offers instant shorthand for cultural touchstones. Even the name Frankenstein conjures up images of a frightening hotchpotch concoction that isn't natural and shouldn't exist: Frankenfoods, Frankenbabies, and even Frankenalgorithms. That latter of these is important. Artificial intelligence algorithms are silently changing lives, but not in the dramatic (and abrupt) way a serial-killing monster might.

Facebook abandoned an experiment after two artificially intelligent programs appeared to be chatting to each other in a strange language only they understood. The two chatbots came to create their own changes to English that made it easier for them to work – but which remained mysterious to the humans that supposedly look after them. The bizarre discussions came as Facebook challenged its chatbots to try and negotiate with each other over a trade, attempting to swap hats, balls and books, each of which were given a certain value. But they quickly broke down as the robots appeared to chant at each other in a language that they each understood but which appears mostly incomprehensible to humans. The robots had been instructed to work out how to negotiate between themselves, and improve their bartering as they went along. But they were not told to use comprehensible English, allowing them to create their own "shorthand", according to researchers.