We introduce an emerging AI-based approach and prototype system for assisting team formation when researchers respond to calls for proposals from funding agencies. This is an instance of the general problem of building teams when demand opportunities come periodically and potential members may vary over time. The novelties of our approach are that we: (a) extract technical skills needed about researchers and calls from multiple data sources and normalize them using Natural Language Processing (NLP) techniques, (b) build a prototype solution based on matching and teaming based on constraints, (c) describe initial feedback about system from researchers at a University to deploy, and (d) create and publish a dataset that others can use.

Critical periods are phases during which a toddler's brain develops in spurts. To promote children's cognitive development, proper guidance is critical in this stage. However, it is not clear whether such a critical period also exists for the training of AI agents. Similar to human toddlers, well-timed guidance and multimodal interactions might significantly enhance the training efficiency of AI agents as well. To validate this hypothesis, we adapt this notion of critical periods to learning in AI agents and investigate the critical period in the virtual environment for AI agents. We formalize the critical period and Toddler-guidance learning in the reinforcement learning (RL) framework. Then, we built up a toddler-like environment with VECA toolkit to mimic human toddlers' learning characteristics. We study three discrete levels of mutual interaction: weak-mentor guidance (sparse reward), moderate mentor guidance (helper-reward), and mentor demonstration (behavioral cloning). We also introduce the EAVE dataset consisting of 30,000 real-world images to fully reflect the toddler's viewpoint. We evaluate the impact of critical periods on AI agents from two perspectives: how and when they are guided best in both uni- and multimodal learning. Our experimental results show that both uni- and multimodal agents with moderate mentor guidance and critical period on 1 million and 2 million training steps show a noticeable improvement. We validate these results with transfer learning on the EAVE dataset and find the performance advancement on the same critical period and the guidance.

Abstract--When a student is asked to perform a given task, her subjective estimate of the difficulty of that task has a strong influence on her performance. There exists a rich literature on the impact of perceived task difficulty on performance and motivation. Yet, there is another topic that is closely related to the subject of the influence of perceived task difficulty that did not receive any attention in previous research - the influence of revealing the true difficulty of a task to the student. Specifically, we elaborate on the question of how a student's learning experience For many tasks, the task difficulty is unknown a priori. When a student (or worker) receives a certain task, her However, for a given task category (such as matchstick riddles), subjective estimate of the difficulty of that task has a strong it might be possible to build a data-driven AI that influence on her performance. Research has shown that perceptions predicts the difficulty level of a given task from that category. of task difficulty are strongly correlated to both Whether the development of such an AI is beneficial depends performance metrics such as the success rate and the solution on the influence of revealing task difficulty on the student's time and psychological factors that influence performance such performance. Therefore, understanding the influence of revealing as motivation, interest, self-efficacy and subjective task value task difficulty (in case that it is available) is particularly (attainment value, intrinsic value, utility value). Most commonly, the student's perception of the difficulty Whereas the influence of revealing the task difficulty on the of a given task is obtained implicitly from the description student's performance might be consistent (always positive or of the task, the background or setting in which the task was always negative), it might also be more complex.

In this work we present a preliminary investigation of a novel algorithm called Dyna-T. In reinforcement learning (RL) a planning agent has its own representation of the environment as a model. To discover an optimal policy to interact with the environment, the agent collects experience in a trial and error fashion. Experience can be used for learning a better model or improve directly the value function and policy. Typically separated, Dyna-Q is an hybrid approach which, at each iteration, exploits the real experience to update the model as well as the value function, while planning its action using simulated data from its model. However, the planning process is computationally expensive and strongly depends on the dimensionality of the state-action space. We propose to build a Upper Confidence Tree (UCT) on the simulated experience and search for the best action to be selected during the on-line learning process. We prove the effectiveness of our proposed method on a set of preliminary tests on three testbed environments from Open AI. In contrast to Dyna-Q, Dyna-T outperforms state-of-the-art RL agents in the stochastic environments by choosing a more robust action selection strategy.

This is Part II of the two-part comprehensive survey devoted to a computing framework most commonly known under the names Hyperdimensional Computing and Vector Symbolic Architectures (HDC/VSA). Both names refer to a family of computational models that use high-dimensional distributed representations and rely on the algebraic properties of their key operations to incorporate the advantages of structured symbolic representations and vector distributed representations. Holographic Reduced Representations is an influential HDC/VSA model that is well-known in the machine learning domain and often used to refer to the whole family. However, for the sake of consistency, we use HDC/VSA to refer to the area. Part I of this survey covered foundational aspects of the area, such as historical context leading to the development of HDC/VSA, key elements of any HDC/VSA model, known HDC/VSA models, and transforming input data of various types into high-dimensional vectors suitable for HDC/VSA. This second part surveys existing applications, the role of HDC/VSA in cognitive computing and architectures, as well as directions for future work. Most of the applications lie within the machine learning/artificial intelligence domain, however we also cover other applications to provide a thorough picture. The survey is written to be useful for both newcomers and practitioners.

Offline reinforcement learning (RL) tasks require the agent to learn from a pre-collected dataset with no further interactions with the environment. Despite the potential to surpass the behavioral policies, RL-based methods are generally impractical due to the training instability and bootstrapping the extrapolation errors, which always require careful hyperparameter tuning via online evaluation. In contrast, offline imitation learning (IL) has no such issues since it learns the policy directly without estimating the value function by bootstrapping. However, IL is usually limited in the capability of the behavioral policy and tends to learn a mediocre behavior from the dataset collected by the mixture of policies. In this paper, we aim to take advantage of IL but mitigate such a drawback. Observing that behavior cloning is able to imitate neighboring policies with less data, we propose \textit{Curriculum Offline Imitation Learning (COIL)}, which utilizes an experience picking strategy for imitating from adaptive neighboring policies with a higher return, and improves the current policy along curriculum stages. On continuous control benchmarks, we compare COIL against both imitation-based and RL-based methods, showing that it not only avoids just learning a mediocre behavior on mixed datasets but is also even competitive with state-of-the-art offline RL methods.

Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you need. Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry – and prepare you for a move into this hot career path. Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon. It's then demonstrated using Python code you can experiment with and build upon, along with notes you can keep for future reference.

The Augusta Webster, MD, Office of Medical Education (AWOME) has begun introducing a new component of the medical education curriculum to current medical students: instruction in Digital Health and Data Science. The curriculum is being co-developed by David Liebovitz, MD, associate vice chair for clinical informatics in the Department of Medicine and co-director of the Center for Medical Education in Data Science and Digital Health, and Mahesh Vaidyanathan, MD, MBA, assistant professor of Anesthesiology. The utilization of large data sets and machine learning is rapidly growing in healthcare. Feinberg is proud to be at the forefront of preparing our students to not only utilize this technology in care delivery and research, but also to critically evaluate its applicability and limitations. I am confident that this curriculum will be the foundation for many of our students to become leaders in the field of data science and augmented intelligence in medicine." The new curriculum component will see students meeting several core competencies and learning outcomes while learning about the health data ecosystem; the health IT regulatory environment; data science methods and research; digital health decision support; bias, ethics and health equity; and the sociotechnical context for digital health and data science. Mahesh Vaidyanathan, MD, MBA, assistant professor of Anesthesiology, is a co-leader of Feinberg's new Digital Health and Data Science curriculum component for medical students. "The tools that data science brings to clinical care enable more effective and personalized care for our patients.

Around 18 months ago, I lost my job due to the COVID-19 pandemic. I was working as a part-time tutor while in college. The money I got from tutoring was used to cover expenses like food, petrol, and my car. After the government imposed lockdown restrictions on the entire country, I was unable to continue teaching. I couldn't go to college either and had to study at home.

Back in 2012, Alexnet scored 63.3% Top-1 accuracy on ImageNet. Now, we are over 90% with EfficientNet architectures and teacher-student training. In this article, we will focus on the evolution of convolutional neural networks (CNN) architectures. Rather than reporting plain numbers, we will focus on the fundamental principles. All the depicted architectures are based on the concepts that we will describe. Note that, the FLoating point Operations Per second (FLOPs) indicate the complexity of the model, while on the vertical axis we have the Imagenet accuracy. The radius of the circle indicates the number of parameters.