Our goal is to bound the learner's cumulative regret's are the actions chosen by the learner and In case we have k (,) L for some L> 0 then the result holds for L . 's according to the standard MW update algorithm which's, we follow the same proof steps as in proof of Theorem 1 to show that, with probability at least 1, the learner's regret can be bounded as R ( T) The corollary's statement then follows by observing that As discussed in Section 3.3, in a repeated Stackelberg game the decision Before bounding the leader' regret, recall that the algorithm resulting from Corollary 3 consists of In this section, we describe the experimental setup of Section 4.1. D ( y), (18) 16 Figure 3: Obtained rewards when the rangers know the poachers' model (OPT), use the proposed algorithm to update their patrol strategy online ( SU ( x, y) to maximize their own utility function. For the poachers' utility we use GP-UCB either converges to suboptimal solutions or displays a slower learning curve. In the case of more than one best response, ties are broken in an arbitrary but consistent manner.

To this end, we use kernel-based regularity assumptions to capture and exploit the structure in the opponent's response. We propose a novel algorithm for the learner when playing against an adversarial sequence of opponents.

To this end, we use kernel-based regularity assumptions to capture and exploit the structure in the opponent's response. We propose a novel algorithm for the learner when playing against an adversarial sequence of opponents.

Our goal is to bound the learner's cumulative regret's are the actions chosen by the learner and In case we have k (,) L for some L> 0 then the result holds for L . 's according to the standard MW update algorithm which's, we follow the same proof steps as in proof of Theorem 1 to show that, with probability at least 1, the learner's regret can be bounded as R ( T) The corollary's statement then follows by observing that As discussed in Section 3.3, in a repeated Stackelberg game the decision Before bounding the leader' regret, recall that the algorithm resulting from Corollary 3 consists of In this section, we describe the experimental setup of Section 4.1. D ( y), (18) 16 Figure 3: Obtained rewards when the rangers know the poachers' model (OPT), use the proposed algorithm to update their patrol strategy online ( SU ( x, y) to maximize their own utility function. For the poachers' utility we use GP-UCB either converges to suboptimal solutions or displays a slower learning curve. In the case of more than one best response, ties are broken in an arbitrary but consistent manner.

As synthetic data becomes increasingly prevalent in training language models, particularly through generated dialogue, concerns have emerged that these models may deviate from authentic human language patterns, potentially losing the richness and creativity inherent in human communication. This highlights the critical need to assess the humanlikeness of language models in real-world language use. In this paper, we present a comprehensive humanlikeness benchmark (HLB) evaluating 20 large language models (LLMs) using 10 psycholinguistic experiments designed to probe core linguistic aspects, including sound, word, syntax, semantics, and discourse (see https://huggingface.co/spaces/XufengDuan/HumanLikeness). To anchor these comparisons, we collected responses from over 2,000 human participants and compared them to outputs from the LLMs in these experiments. For rigorous evaluation, we developed a coding algorithm that accurately identified language use patterns, enabling the extraction of response distributions for each task. By comparing the response distributions between human participants and LLMs, we quantified humanlikeness through distributional similarity. Our results reveal fine-grained differences in how well LLMs replicate human responses across various linguistic levels. Importantly, we found that improvements in other performance metrics did not necessarily lead to greater humanlikeness, and in some cases, even resulted in a decline. By introducing psycholinguistic methods to model evaluation, this benchmark offers the first framework for systematically assessing the humanlikeness of LLMs in language use.

A pair of lion brothers have made the longest swim ever recorded for their species – about 1.5 kilometres across hippo and crocodile-infested waters. The massive swim – equivalent to the aquatic leg of an Olympic triathlon – was the pair's fourth attempt to cross the Kazinga Channel in Queen Elizabeth National Park, Uganda, and was recorded by a drone-mounted thermal camera at night. The lions had to abort earlier attempts after encountering large animals, most likely hippos or Nile crocodiles, which are also visible in the footage. Making the effort even more extraordinary, one of the lions, named Jacob, has only three legs. Jacob has had an extremely challenging life, says Alexander Braczkowski at Griffith University in Australia: he has been gored by a buffalo, his family was poisoned for the lion body-part trade, he was caught in a poacher's snare and he eventually lost his leg after it was stuck in a poacher's steel trap.

BRIAN KENNY: Artificial intelligence or AI for short is certainly creating a lot of buzz these days. And although it may seem like this amorphous thing that's somewhere off in our future, it's already very much in our midst. Navigation apps have turned printed maps into relics. Alexa, knows what you need from the grocery store before you do. Google Nest has the house at just the right temperature before you roll out from under the covers. And this is all great, but now you have to wonder if this intro is written by me or chat GPT. Which raises an important question.

The phone is not lost, quite the opposite. Incongruous but invaluable, the phone is part of a network of devices placed throughout the forest to listen for the telltale sounds of illegal logging. Amidst the rustle of leaves, the scampering of critters, and the steady drip of moisture, the sound of a lorry or chainsaw is an alarm bell that can bring forest rangers hurrying to the scene. It's just one of the ways that technology is helping conservationists in the fight to protect wildlife and the planet. The advance of technology is often seen as a risk for the environment: From the invention of the plough to carve the landscape, through the industrial revolution, to the electronic age's thirst for Earth's limited resources.

Large language models (LLMs) and LLM-driven chatbots such as ChatGPT have shown remarkable capacities in comprehending and producing language. However, their internal workings remain a black box in cognitive terms, and it is unclear whether LLMs and chatbots can develop humanlike characteristics in language use. Cognitive scientists have devised many experiments that probe, and have made great progress in explaining, how people process language. We subjected ChatGPT to 12 of these experiments, pre-registered and with 1,000 runs per experiment. In 10 of them, ChatGPT replicated the human pattern of language use. It associated unfamiliar words with different meanings depending on their forms, continued to access recently encountered meanings of ambiguous words, reused recent sentence structures, reinterpreted implausible sentences that were likely to have been corrupted by noise, glossed over errors, drew reasonable inferences, associated causality with different discourse entities according to verb semantics, and accessed different meanings and retrieved different words depending on the identity of its interlocutor. However, unlike humans, it did not prefer using shorter words to convey less informative content and it did not use context to disambiguate syntactic ambiguities. We discuss how these convergences and divergences may occur in the transformer architecture. Overall, these experiments demonstrate that LLM-driven chatbots like ChatGPT are capable of mimicking human language processing to a great extent, and that they have the potential to provide insights into how people learn and use language.

While AI is making a big impact in pretty much every business area, it is also important to note some of the ways it is helping to save our planet. Conservationists are increasingly turning to AI as an innovative solution to overcome various biodiversity crises. It helps protect a diverse set of species and assists law enforcement agents who are often short-staffed, and it is almost impossible for them to cover a vast stretch of land, such as a national park. This is one of the reasons why AI is so useful because it can take a lot of the time-consuming work off the shoulders of human workers, such as constantly monitoring surveillance data. In this article, we will talk about some of the interesting ways AI is being used to protect endangered species and the data annotation that is required to create it.