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.

Artificial Intelligence (AI) revolution is here! "Artificial Intelligence market worldwide is projected to grow by US$284.6 Billion driven by a compounded growth of 43. Deep Learning, one of the segments analyzed and sized in this study, displays the potential to grow at over 42. AI is a broader general field that entails several subfield such as machine learning, robotics, and computer vision. For companies to become competitive and skyrocket their growth, they need to leverage Artificial Intelligence (AI) power to improve processes, reduce cost and increase revenue. AI is broadly implemented in many sectors nowadays and has been transforming every industry from banking to healthcare, transportation and technology. The demand for AI talent has exponentially increased in recent years and it's no longer limited to Silicon Valley! According to Forbes, AI Skills are among the most in-demand for 2020 [2]. The purpose of this course is to provide you with knowledge of key aspects of modern Artificial Intelligence applications in a practical, easy and fun way. The course provides students with practical hands-on experience using real-world datasets. One key unique feature of this course is that we will be training and deploying models using Tensorflow and AWS SageMaker. In addition, we will cover various elements of the AI/ML workflow covering model building, training, hyper-parameters Deploy Emotion AI-based model using Tensorflow 2.0 Serving and use the model to make inference. Understand the concept of Explainable AI and uncover the blackbox nature of Artificial Neural Networks and visualize their hidden layers using GradCam technique. Develop Deep Learning model to automate and optimize the brain tumor detection processes at a hospital. Build and train AI model to detect and localize brain tumors using ResNets and ResUnet networks (Healthcare applications). Build, train, deploy AI models in business to predict customer default on credit card using AWS SageMaker XGBoost algorithm. Develop, train, and test State-of-the art DeepDream algorithm to create AI-based art masterpieces using Keras API in TF 2.0. Develop ANNs models and train them in Google's Colab while leveraging the power of GPUs and TPUs. Artificial Intelligence (AI) revolution is here! "Artificial Intelligence market worldwide is projected to grow by US$284.6 Billion driven by a compounded growth of 43.

Digitization is penetrating more and more areas of life. Tasks are increasingly being completed digitally, and are therefore not only fulfilled faster, more efficiently but also more purposefully and successfully. The rapid developments in the field of artificial intelligence in recent years have played a major role in this, as they brought up many helpful approaches to build on. At the same time, the eyes, their movements, and the meaning of these movements are being progressively researched. The combination of these developments has led to exciting approaches. In this dissertation, I present some of these approaches which I worked on during my Ph.D. First, I provide insight into the development of models that use artificial intelligence to connect eye movements with visual expertise. This is demonstrated for two domains or rather groups of people: athletes in decision-making actions and surgeons in arthroscopic procedures. The resulting models can be considered as digital diagnostic models for automatic expertise recognition. Furthermore, I show approaches that investigate the transferability of eye movement patterns to different expertise domains and subsequently, important aspects of techniques for generalization. Finally, I address the temporal detection of confusion based on eye movement data. The results suggest the use of the resulting model as a clock signal for possible digital assistance options in the training of young professionals. An interesting aspect of my research is that I was able to draw on very valuable data from DFB youth elite athletes as well as on long-standing experts in arthroscopy. In particular, the work with the DFB data attracted the interest of radio and print media, namely DeutschlandFunk Nova and SWR DasDing. All resulting articles presented here have been published in internationally renowned journals or at conferences.

Online learning via Bayes' theorem allows new data to be continuously integrated into an agent's current beliefs. However, a naive application of Bayesian methods in non stationary environments leads to slow adaptation and results in state estimates that may converge confidently to the wrong parameter value. A common solution when learning in changing environments is to discard/downweight past data; however, this simple mechanism of "forgetting" fails to account for the fact that many real-world environments involve revisiting similar states. We propose a new framework, Bayes with Adaptive Memory (BAM), that takes advantage of past experience by allowing the agent to choose which past observations to remember and which to forget. We demonstrate that BAM generalizes many popular Bayesian update rules for non-stationary environments. Through a variety of experiments, we demonstrate the ability of BAM to continuously adapt in an ever-changing world.