Northeast Wisconsin Technical College is hosting an Artificial Intelligence Bootcamp in partnership with the Mark Cuban Foundation. Cuban, an American billionaire entrepreneur and television personality, created an AI bootcamp offered in select cities across the county to train the next generation of AI leaders. NWTC is the only college in the country to be part of this bootcamp. During the course of the week, high school students will learn about AI, its ethical implications and where they already interact with it in their own lives. "I didn't know any of this stuff even existed in life," said Kennedy Raboin, a student participant.

Recent years have seen stagnating improvements to branch predictor (BP) efficacy and a dearth of fresh ideas in branch predictor design, calling for fresh thinking in this area. This paper argues that looking at BP from the viewpoint of Reinforcement Learning (RL) facilitates systematic reasoning about, and exploration of, BP designs. We describe how to apply the RL formulation to branch predictors, show that existing predictors can be succinctly expressed in this formulation, and study two RL-based variants of conventional BPs.

Stochastic nested optimization, including stochastic compositional, min-max and bilevel optimization, is gaining popularity in many machine learning applications. While the three problems share the nested structure, existing works often treat them separately, and thus develop problem-specific algorithms and their analyses. Among various exciting developments, simple SGD-type updates (potentially on multiple variables) are still prevalent in solving this class of nested problems, but they are believed to have slower convergence rate compared to that of the non-nested problems. This paper unifies several SGD-type updates for stochastic nested problems into a single SGD approach that we term ALternating Stochastic gradient dEscenT (ALSET) method. By leveraging the hidden smoothness of the problem, this paper presents a tighter analysis of ALSET for stochastic nested problems. Under the new analysis, to achieve an $\epsilon$-stationary point of the nested problem, it requires ${\cal O}(\epsilon^{-2})$ samples. Under certain regularity conditions, applying our results to stochastic compositional, min-max and reinforcement learning problems either improves or matches the best-known sample complexity in the respective cases. Our results explain why simple SGD-type algorithms in stochastic nested problems all work very well in practice without the need for further modifications.

The large-scale data stream problem refers to high-speed information flow which cannot be processed in scalable manner under a traditional computing platform. This problem also imposes expensive labelling cost making the deployment of fully supervised algorithms unfeasible. On the other hand, the problem of semi-supervised large-scale data streams is little explored in the literature because most works are designed in the traditional single-node computing environments while also being fully supervised approaches. This paper offers Weakly Supervised Scalable Teacher Forcing Network (WeScatterNet) to cope with the scarcity of labelled samples and the large-scale data streams simultaneously. WeScatterNet is crafted under distributed computing platform of Apache Spark with a data-free model fusion strategy for model compression after parallel computing stage. It features an open network structure to address the global and local drift problems while integrating a data augmentation, annotation and auto-correction ($DA^3$) method for handling partially labelled data streams. The performance of WeScatterNet is numerically evaluated in the six large-scale data stream problems with only $25\%$ label proportions. It shows highly competitive performance even if compared with fully supervised learners with $100\%$ label proportions.

While work in fields of CSCW (Computer Supported Collaborative Work), Psychology and Social Sciences have progressed our understanding of team processes and their effect performance and effectiveness, current methods rely on observations or self-report, with little work directed towards studying team processes with quantifiable measures based on behavioral data. In this report we discuss work tackling this open problem with a focus on understanding individual differences and its effect on team adaptation, and further explore the effect of these factors on team performance as both an outcome and a process. We specifically discuss our contribution in terms of methods that augment survey data and behavioral data that allow us to gain more insight on team performance as well as develop a method to evaluate adaptation and performance across and within a group. To make this problem more tractable we chose to focus on specific types of environments, Alternate Reality Games (ARGs), and for several reasons. First, these types of games involve setups that are similar to a real-world setup, e.g., communication through slack or email. Second, they are more controllable than real environments allowing us to embed stimuli if needed. Lastly, they allow us to collect data needed to understand decisions and communications made through the entire duration of the experience, which makes team processes more transparent than otherwise possible. In this report we discuss the work we did so far and demonstrate the efficacy of the approach.

Breakthroughs in machine learning in the last decade have led to `digital intelligence', i.e. machine learning models capable of learning from vast amounts of labeled data to perform several digital tasks such as speech recognition, face recognition, machine translation and so on. The goal of this thesis is to make progress towards designing algorithms capable of `physical intelligence', i.e. building intelligent autonomous navigation agents capable of learning to perform complex navigation tasks in the physical world involving visual perception, natural language understanding, reasoning, planning, and sequential decision making. Despite several advances in classical navigation methods in the last few decades, current navigation agents struggle at long-term semantic navigation tasks. In the first part of the thesis, we discuss our work on short-term navigation using end-to-end reinforcement learning to tackle challenges such as obstacle avoidance, semantic perception, language grounding, and reasoning. In the second part, we present a new class of navigation methods based on modular learning and structured explicit map representations, which leverage the strengths of both classical and end-to-end learning methods, to tackle long-term navigation tasks. We show that these methods are able to effectively tackle challenges such as localization, mapping, long-term planning, exploration and learning semantic priors. These modular learning methods are capable of long-term spatial and semantic understanding and achieve state-of-the-art results on various navigation tasks.

Despite the visibility and energy surrounding artificial intelligence, higher education is taking measured steps toward incorporating this technology. EDUCAUSE is helping institutional leaders, IT professionals, and other staff address their pressing challenges by gathering and sharing data. This report is based on an EDUCAUSE QuickPoll. QuickPolls enable us to rapidly gather, analyze, and share input from our community about specific emerging topics.Footnote1 We are on the verge of peak hype about how artificial intelligence (AI) can (and will) transform our lives. No fewer than seven emerging AI technologies were prominently featured on "The Gartner Hype Cycle for Emerging Technologies, 2020."Footnote2

Green Bay high school students are getting a hands-on lesson in Artificial Intelligence. Northeast Wisconsin Technical College is hosting an Artificial Intelligence Bootcamp, in partnership with the Mark Cuban Foundation. NWTC is the only college in the country a part of the program. Northeast Wisconsin high school students are getting the opportunity to learn about computer science and engineering. "What we're doing is helping expose them to a bunch of different parts of artificial intelligence," says Jill Thede, Associate Dean of Trades and Engineering Technologies.

Understanding machine learning and deep learning concepts is essential, but if you're looking to build an effective AI career, you need production engineering capabilities as well. Earlier last month, DeepLearning.AI launched a much anticipated Machine Learning Engineering for Production (MLOps) Specialization. It covers how to conceptualize, build, and maintain integrated systems that continuously operate in production. In this Specialization, you will learn how to use well-established tools and methodologies for doing all of this efficiently, as well as important concepts in the emerging fields of MLOps and data-centric AI. To celebrate the launch of the new program, we are pleased to invite you to join us on June 30 for our live virtual event where our instructors for the MLEP Specialization are joined with industry speakers to talk about machine learning engineering for production, as well as a sneak peek of the MLEP Specialization.

AI can point out places where courses need to improve. Identifying the gaps in educational programs ensures that learning institutions do more to prepare students for the job market. Now, even online course providers are using AI to improve their offerings. An excellent way of determining that a course needs to be more extensive is when a large number of students always seem to submit the wrong answer to specific homework assignments. If a learning institution has an AI system in place, it will notify the teachers about this problem.