Autonomous driving vehicles have been of keen interest ever since automation of various tasks started. Humans are prone to exhaustion and have a slow response time on the road, and on top of that driving is already quite a dangerous task with around 1.35 million road traffic incident deaths each year. It is expected that autonomous driving can reduce the number of driving accidents around the world which is why this problem has been of keen interest for researchers. Currently, self-driving vehicles use different algorithms for various sub-problems in making the vehicle autonomous. We will focus reinforcement learning algorithms, more specifically Q-learning algorithms and NeuroEvolution of Augment Topologies (NEAT), a combination of evolutionary algorithms and artificial neural networks, to train a model agent to learn how to drive on a given path. This paper will focus on drawing a comparison between the two aforementioned algorithms.

As data-driven systems are increasingly deployed at scale, ethical concerns have arisen around unfair and discriminatory outcomes for historically marginalized groups that are underrepresented in training data. In response, work around AI fairness and inclusion has called for datasets that are representative of various demographic groups. In this paper, we contribute an analysis of the representativeness of age, gender, and race & ethnicity in accessibility datasets - datasets sourced from people with disabilities and older adults - that can potentially play an important role in mitigating bias for inclusive AI-infused applications. We examine the current state of representation within datasets sourced by people with disabilities by reviewing publicly-available information of 190 datasets, we call these accessibility datasets. We find that accessibility datasets represent diverse ages, but have gender and race representation gaps. Additionally, we investigate how the sensitive and complex nature of demographic variables makes classification difficult and inconsistent (e.g., gender, race & ethnicity), with the source of labeling often unknown. By reflecting on the current challenges and opportunities for representation of disabled data contributors, we hope our effort expands the space of possibility for greater inclusion of marginalized communities in AI-infused systems.

Recent work in reinforcement learning has focused on several characteristics of learned policies that go beyond maximizing reward. These properties include fairness, explainability, generalization, and robustness. In this paper, we define interventional robustness (IR), a measure of how much variability is introduced into learned policies by incidental aspects of the training procedure, such as the order of training data or the particular exploratory actions taken by agents. A training procedure has high IR when the agents it produces take very similar actions under intervention, despite variation in these incidental aspects of the training procedure. We develop an intuitive, quantitative measure of IR and calculate it for eight algorithms in three Atari environments across dozens of interventions and states. From these experiments, we find that IR varies with the amount of training and type of algorithm and that high performance does not imply high IR, as one might expect.

Pre-trained language models have made great progress on dialogue tasks. However, these models are typically trained on surface dialogue text, thus are proven to be weak in understanding the main semantic meaning of a dialogue context. We investigate Abstract Meaning Representation (AMR) as explicit semantic knowledge for pre-training models to capture the core semantic information in dialogues during pre-training. In particular, we propose a semantic-based pre-training framework that extends the standard pre-training framework (Devlin et al., 2019) by three tasks for learning 1) core semantic units, 2) semantic relations and 3) the overall semantic representation according to AMR graphs. Experiments on the understanding of both chit-chats and task-oriented dialogues show the superiority of our model. To our knowledge, we are the first to leverage a deep semantic representation for dialogue pre-training.

One of the key goals of brain-inspired computing is to develop methods that draw inspiration from computational neuroscience and cognitive science to build effective adaptive and efficient agents that are capable of intelligently interacting with their environment. Notably, brain-inspired computational research seeks to develop intelligent systems that are capable of circumventing the current limitations of modern-day approaches [1, 2], such as deep neural networks trained by the popular backpropagation of errors (or backprop)[3]. This goal is complementary to (and, to an extent, even a precursor to some elements of) the domain of neurorobotics [4, 5], which focuses on designing robotic devices that contain control systems based on or are inspired by principles of animal/human nervous systems and/or brain structures guided by the key premise that (neural) models are embodied in a body and an environment. While the gap between neurorobotics and many brain-inspired approaches largely is largely divided between focus on real-world hardware (the former) or software simulation (the latter), one pathway to bridging this gap might lie in developing powerful brain-inspired approaches that scale up to and operate robustly on problems that may ultimately be tackled by embodied robotic systems as well as using higher-quality, more realistic simulation platforms (as we do in this work). It is along this path that this work takes a step forward by developing a neurobiologically-grounded neural circuit that is used to craft a complete agent that can tackle extremely sparse reward learning control problems (tested on a more realistic, higher quality robotic system simulator), a problem that many robotic systems must ultimately face, much as humans and animals do in the real world. To build such building neural blocks and an agent system, we start from two neurocognitive theoretical foundations, predictive processing (or coding) and planning-as-inference. With respect to predictive coding, which views the brain as a type of hierarchical, pattern-creation engine [6] that engages in continual self-correction [7], we implement a fundamental circuit where each of its levels/regions are implemented by clusters of neurons that attempt to predict the state of other neural clusters/regions and adjust their synapses based on how different their predictions were from observed signals. This allows us to sidestep many of the key issues central to backprop, such as the vanishing/exploding gradient problems [8], the requirement for a long, unstable credit assignment feedback pathway [9], forward and backward locking problems [10], and the need for differentiability [11, 9]. On the other hand, motivated by planningarXiv:2209.09174v1

Despite achieving accuracies higher than 90% on recognizing upper-limb movements through sEMG (surface Electromyography) signal with the state of art classifiers in the laboratory environment, there are still issues to be addressed for a myo-controlled prosthesis achieve similar performance in real environment conditions. Thereby, the main goal of this review is to expose the latest researches in terms of strategies in each block of the system, giving a global view of the current state of academic research. A systematic review was conducted, and the retrieved papers were organized according to the system step related to the proposed method. Then, for each stage of the upper limb motion recognition system, the works were described and compared in terms of strategy, methodology and issue addressed. An additional section was destined for the description of works related to signal contamination that is often neglected in reviews focused on sEMG based motion classifiers. Therefore, this section is the main contribution of this paper. Deep learning methods are a current trend for classification stage, providing strategies based on time-series and transfer learning to address the issues related to limb position, temporal/inter-subject variation, and electrode displacement. Despite the promising strategies presented for contaminant detection, identification, and removal, there are still some factors to be considered, such as the occurrence of simultaneous contaminants.

Tom Mustill was kayaking with his friend Charlotte in Monterey Bay, California, when an animal three times the size of the largest Tyrannosaurus Rex hurtled from the water and crashed down on their tiny craft. As the flying humpback whale fell upon them and their kayak was sucked beneath the waves, Mustill assumed he would die. Miraculously he and Charlotte found themselves gasping for breath, clinging to their capsized kayak. How had they survived a smash with a creature three times the weight of a double-decker bus? What happened next was almost as weird.

Artificial Intelligence Software Market is projected to grow to Multimillion by 2026 from USD million in 2021, at a Impressive CAGR during the forecast period. Google, Baidu, IBM, Microsoft, SAP, Intel, Salesforce, Brighterion, KITT.AI, IFlyTek, Megvii Technology, Albert Technologies, H2O.ai, Brainasoft, Yseop, Ipsoft, NanoRep(LogMeIn), Ada Support, Astute Solutions, IDEAL.com, This report focuses on the Artificial Intelligence Software in global market, especially in North America, Europe and Asia-Pacific, South America, Middle East and Africa. This report categorizes the market based on manufacturers, regions, type and application. The worldwide market for Artificial Intelligence Software is expected to grow at a CAGR of roughly xx% over the next five years, will reach xx million US$ in 2024, from xx million US$ in 2017.

Transductive Few-Shot learning has gained increased attention nowadays considering the cost of data annotations along with the increased accuracy provided by unlabelled samples in the domain of few shot. Especially in Few-Shot Classification (FSC), recent works explore the feature distributions aiming at maximizing likelihoods or posteriors with respect to the unknown parameters. Following this vein, and considering the parallel between FSC and clustering, we seek for better taking into account the uncertainty in estimation due to lack of data, as well as better statistical properties of the clusters associated with each class. Therefore in this paper we propose a new clustering method based on Variational Bayesian inference, further improved by Adaptive Dimension Reduction based on Probabilistic Linear Discriminant Analysis. Our proposed method significantly improves accuracy in the realistic unbalanced transductive setting on various Few-Shot benchmarks when applied to features used in previous studies, with a gain of up to $6\%$ in accuracy. In addition, when applied to balanced setting, we obtain very competitive results without making use of the class-balance artefact which is disputable for practical use cases. We also provide the performance of our method on a high performing pretrained backbone, with the reported results further surpassing the current state-of-the-art accuracy, suggesting the genericity of the proposed method.

Models of acoustic word embeddings (AWEs) learn to map variable-length spoken word segments onto fixed-dimensionality vector representations such that different acoustic exemplars of the same word are projected nearby in the embedding space. In addition to their speech technology applications, AWE models have been shown to predict human performance on a variety of auditory lexical processing tasks. Current AWE models are based on neural networks and trained in a bottom-up approach that integrates acoustic cues to build up a word representation given an acoustic or symbolic supervision signal. Therefore, these models do not leverage or capture high-level lexical knowledge during the learning process. In this paper, we propose a multi-task learning model that incorporates top-down lexical knowledge into the training procedure of AWEs. Our model learns a mapping between the acoustic input and a lexical representation that encodes high-level information such as word semantics in addition to bottom-up form-based supervision. We experiment with three languages and demonstrate that incorporating lexical knowledge improves the embedding space discriminability and encourages the model to better separate lexical categories.