Embedding undirected graphs in a Euclidean space has many computational benefits. FastMap is an efficient embedding algorithm that facilitates a geometric interpretation of problems posed on undirected graphs. However, Euclidean distances are inherently symmetric and, thus, Euclidean embeddings cannot be used for directed graphs. In this paper, we present FastMap-D, an efficient generalization of FastMap to directed graphs. FastMap-D embeds vertices using a potential field to capture the asymmetry between the to-and-fro pairwise distances in directed graphs. FastMap-D learns a potential function to define the potential field using a machine learning module.

Video games have proved to be a very defying laboratory to study machine-learning techniques, such as Deep Reinforcement Learning (DRL) algorithms. This paper presents a new approach for a DRL-based agent trained through Deep Q-Network (DQN) technique to perform free kicks in FIFA 18 game. The main motivation for choosing this case study is the fact that, like in many situations of the real life, FIFA represents a stochastic environment. Coping with this task, the main contributions of the present paper consist on: inspired on the OpenAI Gym and on the OpenAI Universe platforms, implementing a new user-friendly interface (in terms of portability and use simplicity) to connect the learning module with the 3D FIFA's game environment; implementing a DRL-based agent for free kicks in FIFA that uses two distinct data representations retrieved from lower cost computational procedures. The results were validated through two evaluative parameters: score of well succeed kicks and training time.

Welcome to experience a mind-blowing "Artificial Intelligence Expert Course" in 2022. Artificial Intelligence Expert Course: Platinum Edition - The course has now launched. Artificial Intelligence (AI) seems to be a unique technology of making a machine, a robot fully autonomous. AI is an analysis of how the machine is thinking, studying, determining, and functioning when it is trying to solve problems. These kinds of problems are present in all fields, the most emerging ones, and even beyond.

This workshop aims at promoting and exploring the possibilities for research and practical applications involving natural language processing (NLP) and games.

Often, artificial intelligence (AI) is used broadly to describe all types of systems that seem to make decisions we do not quite understand. But while many reasonably complex systems make decisions like this, it does not immediately make them "intelligent." For example, I might not understand how my "smart" oven thermometer seems to know when my roast beef will be perfectly done, or how my garden light knows when to turn on, but the engineers putting together the (not-too-complex) mathematical equation do. There are many other systems that, at first glance, look intelligent--but they are just constructed by smart people. We should not label these as "intelligent" because that suggests they are making their own decisions instead of simply following a human-designed path. A better way to distinguish (artificially) intelligent systems from those that just follow human-made rules is to look for the person who can explain the systems' inner workings (i.e., the person ultimately responsible for what the systems do).

Sydney, Australia, 04 February, 2022 – Tata Consultancy Services (TCS) has entered a strategic partnership with DeakinCo., a division of Deakin University, to co-design a series of corporate learning programs to meet the growing demand of talent in emerging technologies such as machine learning, artificial intelligence, data analytics and robotics. The programs aim to help address the digital skills gap and accelerate the economic growth of Australia. The new partnership brings together Deakin's academic excellence and TCS' extensive industry networks and experience. The first program, to be piloted in early 2022, will focus on machine learning, which consists of three streams enabling senior executives, mid-management and practitioners to leverage the power of this emerging technology in their chosen profession. Each stream will be facilitated by academics and industry experts. The programs are designed to address specific capability gaps for businesses and will provide learners with an engaging experience that goes to the heart of the skills and knowledge required in these dynamic fields.

This Women in AI Podcast episode is with Juliet Waters, Chief Knowledge Officer at Kids Code Jeunesse, a Canadian charity with a mission to give every Canadian child access to digital skills education, with a focus on girls and underserved communities. KCJ teaches kids and their educators about topics including algorithm literacy and artificial intelligence, and how these integrate with the UN's Sustainable Development Goals to give kids the confidence and creative tools they need to build a better future. Listen to the podcast here. Thank you so much for joining us for the Woman in AI Podcast today. You're currently Chief Knowledge Officer at Kids Code Jeunesse so I wanted to, first of all, for any of our listeners that are not maybe familiar with KCJ, ask if you could share a brief overview. Sure, so we started a Canadian charity in around 2013, working alongside teachers in classrooms, trying to help develop some viable lesson plans that would help to bring computer programming into the classroom.

To build a web app you'd typically use such Python web frameworks as Django and Flask. But the steep learning curve and the big time investment for implementing these apps present a major hurdle. Streamlit makes the app creation process as simple as writing Python scripts! In this article, you'll learn how to master Streamlit when getting started with data science. The data science process boils down to converting data to knowledge/insights while summarizing the conversion with the CRISP-DM and OSEMN data frameworks.

This course is for every beginner in the data science space. We have been there before and we understood what your learning challenges are. This short course will focus on showing you end to end what it takes to build and deploy a simple machine learning solution. You will be able to deploy this solution using the flask framework as an API and also as a Platform. We will also introduce you to libraries that make it easy to quickly explore, build, and deploy a machine learning solution.

Artificial Intelligence (AI) is becoming an important component of various sectors and in decision-making in various domains. Research in AI has seen tremendous growth, thanks to big data, escalated processing speed, and innovations in AI-based models. McKinsey Global Institute predicts that by 2030, at least 70 percent of companies will have to adopt at least one type of AI technology and around 60 percent of the current occupations will be automated in the next ten years. Recognizing the importance of AI in almost every field, many countries have regarded AI as a national priority. To promote AI and the research involved, the USA launched the American Artificial Intelligence Initiative in 2019.