Abstract-- We propose a hybrid approach aimed at improving thesample efficiency in goal-directed reinforcement learning. We do this via a two-step mechanism where firstly, we approximate a model from Model-Free reinforcement learning. Then, we leverage this approximate model along with a notion of reachability using Mean First Passage Times to perform Model-Based reinforcement learning. Built on such a novel observation, we design two new algorithms - Mean First Passage Time based Q-Learning (MFPT-Q) and Mean First Passage Time based DYNA (MFPT-DYNA), that have been fundamentally modified from the state-of-the-art reinforcement learning techniques. Preliminary results have shown that our hybrid approaches converge with much fewer iterations than their corresponding state-of-the-art counterparts and therefore requiring much fewer samples and much fewer training trials to converge. I. INTRODUCTION Reinforcement Learning (RL) has been successfully applied to numerous challenging problems for autonomous agents to behave intelligently in unstructured real-world environment. One interesting area of research in RL which motivates this work is goal-directed reinforcement learning problem (GDRLP) [1] [2]. In GDRLP, the learning process takes place in two stages.

We propose a novel online learning algorithm, called SpCoSLAM 2.0, for spatial concepts and lexical acquisition with high accuracy and scalability. Previously, we proposed SpCoSLAM as an online learning algorithm based on unsupervised Bayesian probabilistic model that integrates multimodal place categorization, lexical acquisition, and SLAM. However, our previous algorithm had limited estimation accuracy owing to the influence of the early stages of learning, and increased computational complexity with added training data. Therefore, we introduce techniques such as fixed-lag rejuvenation to reduce the calculation time while maintaining an accuracy higher than that of the previous algorithm. The results show that, in terms of estimation accuracy, the proposed algorithm exceeds the previous algorithm and is comparable to batch learning. In addition, the calculation time of the proposed algorithm does not depend on the amount of training data and becomes constant for each step of the scalable algorithm. Our approach will contribute to the realization of long-term spatial language interactions between humans and robots.

Current state of-the-art systems struggle to understand nuances of human language. Machine Reading (MR) technology helps you bridge the gap. Download "Machine Reading Comprehension" to learn how MR addresses these problems and paves the way for smoother human-computer interaction.

In one of the iconic scenes from the teen movie "Fast Times at Ridgemont High," sun-baked stoner Jeff Spicoli has a double cheese and sausage pizza delivered to his classroom, boldly interrupting his uncompromising instructor mid-lecture. Spicoli was considered a mischievous airhead for flouting early-1980s dining etiquette, but he may actually have been way ahead of his time. More than three decades later, a California campus is embracing a kind of food delivery -- via robot. On Wednesday, students at University of the Pacific in Stockton, Calif., will be able to order snacks and beverages for the first time from a bright-colored roving robot on wheels known as the "Snackbot." Its stout body perched atop six small wheels, the electric Snackbot resembles some combination of an Igloo cooler and a Volkswagen Microbus.

Before getting into neural networks, let's complete the last section for logistic regression -- Multi-class Logistic Regression. This series of exercise make use of a handwritten digits dataset that consists of 5000 training examples, where each example is a 20 pixel by 20 pixel grayscale image of the digit. Since the dataset was given in .mat The official documentation can be found here. To better understand the dataset, having the shape of the data tells us the dimension of the data.

Aiming to make school students well-versed in technologies shaping the future, the Central Board of Secondary Education (CBSE) has decided to introduce artificial intelligence as an elective subject. "The decision to introduce artificial intelligence as a skill subject was taken at a recent meeting of the board's governing body. It has been decided that the subject would be introduced in classes 8, 9 and 10 as a skill subject," a member of the board's governing body said. Artificial intelligence is the ability of a machine to think, learn and perform tasks normally requiring human intelligence, such as visual perception, speech recognition and decision-making skills. Capabilities demonstrated by machines, including computers, from playing chess to operating cars and beyond, fall within the domain of artificial intelligence. With technologies like artificial intelligence, data analytics and big data making a huge impact globally, it is important that the board modernises its curriculum to stay abreast of the latest developments, the governing body member said.

No, emerging technologies such as artificial intelligence and automation are not taking away jobs. They are generating more jobs which are quite lucrative too. Emerging technologies poses a big threat to traditional business of the companies so they are forced to look for employees who are conversant with the new technological trends. As the tech business is being redrawn, it has generated demand for employees in key domains related to emerging technology. According to a report by Simplilearn, one of the leading global e-learning companies, a large number of jobs have been generated in 2018 in the emerging technologies.

My course does exactly what the title describes in a simple, relatable way. I help you to grasp the complete start to end concepts of fundamental deep learning. On your own it can be quite confusing, difficult and frustrating. I've been through the process myself, and with the help of lifelong ... I want to share this with my fellow beginners, developers, AI aspirers, with you. I will give you straightforward examples, instructions, advice, insights and resources for you to take simple steps to create your own neural networks from scratch.

Artificial Intelligence has moved from the big wide screens from films such as Terminator. It has, in fact, moved into our daily life. AI has been a success with improvements in various fields such as mechanical engineering, agriculture and many more. Even healthcare, which stayed a little far away from the digital revolution has embraced AI in tasks that involved low level manual labour. In this article, let us discuss on the advantages of Artificial Intelligence in Education sector.

Despite so, there is a lack of direct access to relevant ML education for clinicians and biomedical researchers. Various factors attribute to the failure of ML to be integrated within undergraduate and graduate medical education training. At present, there is no accreditation requirements related to retain curricular hours in the present schema with the emerging biomedical knowledge and demands for new content segments. In the United States, assessment in undergraduate medical education, places great emphasis on the preparation of licensing exams and a recent competency focus on entrustable professional activities (EPA's), none of which involves AI. In addition, medical schools fall short of faculty expertise needed to teach this content which is mainly conducted in computer science, mathematics and engineering faculties.