Unprecedented advancements in technology and the growing complexity of the world's research challenges demand novel approaches to discovery and innovation. One way for leaders in STEM to stay ahead of this curve is by attending the nation's top tech conferences. These conferences are an excellent chance for STEM professionals to develop valuable connections, exchange groundbreaking ideas, share best practices, and learn new skills while staying abreast of emerging trends and practices in the ever-evolving technology landscape. However, with dozens of conferences to choose from, it can be challenging to select the right one for your organization. And, with the emergence of the COVID-19 global pandemic, it has become difficult to accurately plan for future events.

Sami Haddadin runs a'robot kindergarten' where intelligent machines learn from each other.Credit: Technical University of Munich "AI and robotics development pull us right into the heart of what it is to be human," says Sami Haddadin, founding director of the Munich School of Robotics and Machine Intelligence (MSRM) at TUM. "We're not looking to usher in an'age of automatons'. Rather, we hope to enable a smooth transition to an age of human- machine interaction." MSRM's research agenda covers the understanding of humans in order to develop intelligent machines that can, in turn, help humans. Haddadin gives an example: give a young child a key and, within around 20 tries, they can unlock a door. A child's intuitive ability to manipulate a tool is one aspect, but they also watch and learn from adults.

It should come as no surprise that many people have been exercising at home since the outbreak of Covid-19. As gyms have shut their doors and the CDC has suggested we stay in our homes many exercise enthusiasts have taken to working out inside. In order to capitalize on this shift, many companies have been creating online workouts in the form of internet videos, on-demand classes, interactive classes, and exercise apps. So much traffic has shifted to these online services that we could be seeing a permanent change in the industry. The popularity of the home workout can be seen by looking at some quick data on Google trends.

AI World School (AIWS) is launching its remote self-learning platform providing AI and Coding technology education to students from ages 7 to 18. AIWS announces the global launch of its self-paced online learning platform providing AI learning experiences to students at home, to homeschoolers and in K12 schools. The world is changing faster than ever amid these testing times of COVID. Children safety is paramount more than ever, and they need to face these challenges and navigate them successfully in the future. To combat this, AIWS believes that innovation, creativity & STEM skills will be essential to prepare one's' child to become future-ready. AI & Coding provides a competitive advantage when applying to colleges, internships, and for career opportunities.

With a lot of hype going on with the data science field, most of us jump directly into machine learning models and algorithms to make business decisions. All the online courses available fail to teach the very basics of decision making. Hypotheses testing is one of the basic building blocks of decision making and oldest. The earliest use of hypotheses testing was in the 1700s by John Arbuthnot to test whether male and female births are equally likely to occur. In this article, we will be discussing everything about hypotheses testing at the beginner level along with python code making use of the SciPy package.

Although learning R is not the main focus of this course, but we will implicitly learn R by diving deep into statistical concepts. The Crucial advantage of this course is not learning algorithms and machine learning but rather developing our critical thinking and understanding what the outcomes of these models represent. The course is designed to take you to step by step in a journey of R and statistics, It is packed with templates, Exercises, quizzes, and resources that will help you understand the core R language and statistical concepts that you need for Data Science and business analytics.

This review aims to present the current knowledge of how genetic fingerprints contribute to an individual's nutritional status. The results show: "Based on the outcomes of candidate genes, genome-wide-association studies, and meta-analyses thereof, we have identified several single nucleotide polymorphisms (SNPs) involved in the vitamins' metabolic pathways. Polymorphisms in genes encoding proteins involved in vitamin metabolism and transport are reported to have an impact on vitamin D status; while genetic variants of vitamin D receptors were most frequently associated with health outcomes. Genetic variations that can influence vitamin E status include SNPs involved in its uptake and transport, such as in SCAR-B1 gene, and in lipoprotein metabolism. Variants of the genes encoding the sodium-dependent vitamin C transport proteins are greatly associated with the body's status on vitamin C. Regarding the vitamins of the B-complex, special reference is made to the widely studied variant in the MTHFR gene".

Learning about many things can provide numerous benefits to a reinforcement learning system. For example, learning many auxiliary value functions, in addition to optimizing the environmental reward, appears to improve both exploration and representation learning. The question we tackle in this paper is how to sculpt the stream of experience—how to adapt the learning system’s behavior—to optimize the learning of a collection of value functions. A simple answer is to compute an intrinsic reward based on the statistics of each auxiliary learner, and use reinforcement learning to maximize that intrinsic reward. Unfortunately, implementing this simple idea has proven difficult, and thus has been the focus of decades of study. It remains unclear which of the many possible measures of learning would work well in a parallel learning setting where environmental reward is extremely sparse or absent. In this paper, we investigate and compare different intrinsic reward mechanisms in a new bandit-like parallel-learning testbed. We discuss the interaction between reward and prediction learners and highlight the importance of introspective prediction learners: those that increase their rate of learning when progress is possible, and decrease when it is not. We provide a comprehensive empirical comparison of 14 different rewards, including well-known ideas from reinforcement learning and active learning. Our results highlight a simple but seemingly powerful principle: intrinsic rewards based on the amount of learning can generate useful behavior, if each individual learner is introspective.

A fundamental question at the intersection of learning theory and game theory is as follows: how should individually rational agents act when they have to learn about the consequences of their actions in the same uncertain environment? The emerging research area of multiplayer learning-- which explores this basic question and is motivated by a broad range of modern applications, from modeling competition among firms [Mansour et al., 2018, Aridor et al., 2019] to implementing protocols for wireless networks [Liu and Zhao, 2010, Cesa-Bianchi et al., 2016, Shahrampour et al., 2017]--has been of increasing interest. A particularly salient application is the online marketplace, which can often be modeled as a two-sided matching market with uncertainty. Examples include online labor markets (Upwork and TaskRabbit for freelancing, Handy for housecleaning), online crowdsourcing platforms (Amazon Mechanical Turk), and peer-to-peer platforms (Airbnb). The multi-armed bandit is a core learning problem in which a player is faced with a choice among K actions, each of which is associated with a reward distribution, and the goal is to learn which action has the highest reward, doing so as quickly as possible so as to be able to reap rewards even while the learning process is underway. To introduce a game-theoretic aspect into the bandit problem, it is natural to place the problem into the context of a two-way matching market, where the choices faced by the players are identified with the entities on the other side of the market, and where the need to realize a matching imposes economic constraints and incentives. Such a blend of bandit learning with two-sided matching markets was introduced by Liu et al. [2020], who formulated a problem in which the players and the arms form the two sides of the market, and each side has preferences over the other side.

Humans can leverage hierarchical structures to split a task into sub-tasks and solve problems efficiently. Both imitation and reinforcement learning or a combination of them with hierarchical structures have been proven to be an efficient way for robots to learn complex tasks with sparse rewards. However, in the previous work of hierarchical imitation and reinforcement learning, the tested environments are in relatively simple 2D games, and the action spaces are discrete. Furthermore, many imitation learning works focusing on improving the policies learned from the expert polices that are hard-coded or trained by reinforcement learning algorithms, rather than human experts. In the scenarios of human-robot interaction, humans can be required to provide demonstrations to teach the robot, so it is crucial to improve the learning efficiency to reduce expert efforts, and know human's perception about the learning/training process. In this project, we explored different imitation learning algorithms and designed active learning algorithms upon the hierarchical imitation and reinforcement learning framework we have developed. We performed an experiment where five participants were asked to guide a randomly initialized agent to a random goal in a maze. Our experimental results showed that using DAgger and reward-based active learning method can achieve better performance while saving more human efforts physically and mentally during the training process.