Communication is an essential part of the human experience. Being such an instrumental aspect of life, it's only natural that we expand upon it, making it more comfortable, more efficient, and give us the ability to communicate with anyone, anywhere on the globe. Technology has long been a driving factor behind making communication much more natural and far-reaching, such as the telephone and fax machines. However, more recent technologies are radically changing the way that we communicate with each other. AI and related technologies -- IoT, machine learning, automation, etc. -- have found their way into almost every industry, with benefits too valuable to be ignored.

In the end, it came down to 213 thousandths of a second! That was the difference between the two best times in the finale of the first AWS AWS DeepRacer Student Wildcard event hosted in Ottawa, Canada this May. I watched in awe as 13 students competed in a live wildcard race for the AWS DeepRacer Student League, the first global autonomous racing league for students offering educational material and resources to get hands on and start with machine learning (ML). Students hit the starting line to put their ML skills to the test in Canada's capital where members of parliament cheered them on, including Parliamentary Secretary for Innovation, Science and Economic Development, Andy Fillmore. Daphne Hong, a fourth-year engineering student at the University of Calgary, won the race with a lap time of 11:167 seconds.

Three Carnegie Mellon University researchers in the School of Computer Science recently earned Faculty Early Career Development Program (CAREER) awards from the National Science Foundation. The awards are the foundation's most prestigious for young faculty researchers. An assistant professor in the Computer Science and Electrical and Computer Engineering departments, Weina Wang received $500,000 to develop algorithms that guarantee ultra-low latency in edge computing, which supports emerging applications such as autonomous driving, augmented reality and automated mobile robots. This work will establish algorithms to optimize the time it takes for data to travel from one point to another and for the corresponding computation to be done without lag, even with a high volume of users in those systems. In addition to this research, Wang will also use the grant to continue expanding STEM outreach activities for K-12 students -- mentoring students from underrepresented groups, promoting the visibility of researchers from underrepresented groups and initiating online outreach seminars for the general public.

Farmers worldwide face the problem of improper storage and transportation of agrochemicals, which leads to many harmful side effects. Sumit's goal was to bring a secure, decentralized, tamper-proof blockchain ledger with a secure authentication device protocol to streamline all manufacturers' and logistics data. His solution was developed by creating a tiny machine learning model with Neuton.AI and embedding it into an Arm MCU. Arm and Neuton.AI's goal is to democratize AI and enable developers to solve real-world problems and use AI for good. Join this talk to learn more about how you can get started!

When looking at how automation has improved over the years, its usage has become integral to many career fields. One that may not immediately come to mind is Education. Automation is an oddity in education as people would rather learn from a human teacher than from an emotionless machine. A teacher is by far the better choice for the classroom and when paired with an AI to oversee the room, it might become even better. There are a few ways that AI has and will help teachers become more efficient in the coming years.

Formwelt, OpenAI Codex, Github Co-Pilot and other Artificial Intelligence projects will make the SourceLess Platform usable by absolutely anyone, being able to create anything just by using words (written or spoken). For example, by using the Formwelt language, anyone, regardless of nationality, can communicate in a direct and semantically correct way with OpenAI Codex and create anything in the digital world; you can create a complete and complex website in less than an hour. All these AI systems will be implemented inside the SourceLess Platform, thus everyone can have access to all the facilities of the new Web through a single domain (eg: str.domain). Education, Technology & Innovation -- these three pillars of the future are the foundations of the SourceLess Platform. The purpose of education in the Sourceless project is to transmit knowledge or foster skills and character traits. These aims may include the development of understanding, rationality, kindness, and honesty.

Natural Language Processing (NLP) refers to building machines that can understand and respond to voice data with their own text and speech. Natural Language Processing falls under the umbrella of Artificial Intelligence (AI) and recent models like the Bidirectional Encoder Representations from Transformers (BERT), Generative Pre-Trained Transformer 3 (GPT-3) and Pathways AI Language Models (PaLM) have made accurate human-machine communication possible. These Large language Models (LLMs) are trained on massive volumes of text with billions of parameters and are able to understand and answer reading comprehension questions as well as generating new text such as a summary. Put simply, LLMs are trained to predict the next words in a sentence, such as by extending the autocomplete feature in messaging applications. But they can do much more, for example question answering, translation, image captioning, human-level dialogue agents, entity linking, or even data cleaning (for mixes of structured and unstructured data). NLP is already being used to automate some human tasks (RPA – robotic process automation), however the breath-taking advances in the last 3 years, NLP open new potential for businesses to digitize company knowledge and disrupting incumbent business models.

This online training provides a comprehensive list of analytical skills designed for students and researchers interested to learn applied statistics and data science to tackle common and complex real world research problems. This training covers end-to-end guide from basic statistics such as Chi-square test and multi-factorial ANOVA, to multivariate statistics such as Structural equation modeling and Multilevel modeling. Similarly, you will also learn powerful unsupervised machine learning techniques such as Apriori algorithm and tSNE, to more complex supervised machine learning such as Deep Learning and Transfer Learning. Whether you are a beginner or advanced researcher, we believe there is something for you! This workshop helps you better understand complex constructs by demystifying data science and statistical concepts and techniques for you.

The transformation towards intelligence in various industries is creating more demand for intelligent and flexible products. In the field of robotics, learning-based methods are increasingly being applied, with the purpose of training robots to learn to deal with complex and changing external environments through data. In this context, reinforcement learning and imitation learning are becoming research hotspots with their respective characteristics. However, the two have their own limitations in some cases, such as the high cost of data acquisition for reinforcement learning. Moreover, it is difficult for imitation learning to provide perfect demonstrations. As a branch of imitation learning, interactive imitation learning aims at transferring human knowledge to the agent through interactions between the demonstrator and the robot, which alleviates the difficulty of teaching. This thesis implements IIL algorithms in four simulation scenarios and conducts extensive experiments, aiming at providing exhaustive information about IIL methods both in action space and state space as well as comparison with RL methods.

In this work, we introduce a new perspective for learning transferable content in multi-task imitation learning. Humans are able to transfer skills and knowledge. If we can cycle to work and drive to the store, we can also cycle to the store and drive to work. We take inspiration from this and hypothesize the latent memory of a policy network can be disentangled into two partitions. These contain either the knowledge of the environmental context for the task or the generalizable skill needed to solve the task. This allows improved training efficiency and better generalization over previously unseen combinations of skills in the same environment, and the same task in unseen environments. We used the proposed approach to train a disentangled agent for two different multi-task IL environments. In both cases we out-performed the SOTA by 30% in task success rate. We also demonstrated this for navigation on a real robot.