In 2009, her school district introduced an algorithmic method to measure teacher performance, with the plan of terminating the bottom 2% of the district's staff. By 2011, Wysocki had gotten the boot. Because the computer had identified her a bad teacher, despite the raves of parents and administrators. The district's algorithm was based on year-over-year improvements on standardized test scores, and teachers knew this. What became largely apparent was that some teachers, anxious not to be in that bottom 2%, had cheated to inflate scores on their students' behalf.

It's a common sentiment that Deep Learning is only good for images and language models. This post is about using Deep Learning on tabular data, for both Regression and Classification problems. We will use fastai library for creating our deep learning models. We will use Kaggle competitions as benchmarks to see how our solutions compares to other solutions using traditional ML models. If you haven't watched fastai tutorials already, please visit this link for the awesome and free tutorials.

We introduce a new online learning framework where, at each trial, the learner is required to select a subset of actions from a given known action set. Each action is associated with an energy value, a reward and a cost. The sum of the energies of the actions selected cannot exceed a given energy budget. The goal is to maximise the cumulative profit, where the profit obtained on a single trial is defined as the difference between the maximum reward among the selected actions and the sum of their costs. Action energy values and the budget are known and fixed. All rewards and costs associated with each action change over time and are revealed at each trial only after the learner's selection of actions. Our framework encompasses several online learning problems where the environment changes over time; and the solution trades-off between minimising the costs and maximising the maximum reward of the selected subset of actions, while being constrained to an action energy budget. The algorithm that we propose is efficient and general in that it may be specialised to multiple natural online combinatorial problems.

We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence. In this report, we set out to capture a snapshot of the exponential progress in AI with a focus on developments in the past 12 months. Consider this report as a compilation of the most interesting things we've seen with a goal of triggering an informed conversation about the state of AI and its implication for the future. This edition builds on the inaugural State of AI Report 2018, which can be found here: www.stateof.ai/2018 We consider the following key dimensions in our report: - Research: Technology breakthroughs and their capabilities.

The weekend felt like an eternity for the 13-year-old. So when she handed back her "robot baby" it was with great relief. She had taken part in a program launched by the Caldas municipality in Colombia to try to tackle the problem of teenage pregnancies. "This experience was pretty tough, it's not easy being a mommy or a daddy," Ortegon said. The baby's cries were so loud that they even bothered her parents.

Based on its core scientist team's top-level R&D strength, as well as technological innovation and breakthroughs, Squirrel AI Learning started holding four "man-machine competitions" in Zhengzhou, Chengdu and Dongying in October 2017 in a bid to identify any difference between its adaptive learning system and human teaching. Dr. Kalns demonstrated to the RE-WORK audience the results of the four competitions: surprisingly, machine teaching outperformed human teaching in all the four competitions. Taking the fourth competition, which unfolded in one hundred cities, as an example, students at the same intellectual level were divided into two groups and received human teaching and Squirrel AI Learning respectively. Every student in the machine teaching group learned 42 knowledge points on the average, while every student in the human teaching learned 28 knowledge points on the average; in terms of average scoring in the core part of the competition, the students in the AI teaching group had their scores increased by 5.4 on the average, while the students in the human teaching group just had their scores increased by 0.7 on the average, suggesting that machine teaching enabled students to take a firmer grasp of knowledge points than human teaching and improved the learning efficiency more significantly than human teaching. According to the results, Squirrel AI Learning is basically the same as or better than individualized human teaching.

Ludii is a general game system being developed as part of the ERC-funded Digital Ludeme Project (DLP). While its primary aim is to model, play, and analyse the full range of traditional strategy games, Ludii also has the potential to support a wide range of AI research topics and competitions. This paper describes some of the future competitions and challenges that we intend to run using the Ludii system, highlighting some of its most important aspects that can potentially lead to many algorithm improvements and new avenues of research. We compare and contrast our proposed competition motivations, goals and frameworks against those of existing general game playing competitions, addressing the strengths and weaknesses of each platform.

We deal with the \textit{selective classification} problem (supervised-learning problem with a rejection option), where we want to achieve the best performance at a certain level of coverage of the data. We transform the original $m$-class classification problem to $(m+1)$-class where the $(m+1)$-th class represents the model abstaining from making a prediction due to uncertainty. Inspired by portfolio theory, we propose a loss function for the selective classification problem based on the doubling rate of gambling. We show that minimizing this loss function has a natural interpretation as maximizing the return of a \textit{horse race}, where a player aims to balance between betting on an outcome (making a prediction) when confident and reserving one's winnings (abstaining) when not confident. This loss function allows us to train neural networks and characterize the uncertainty of prediction in an end-to-end fashion. In comparison with previous methods, our method requires almost no modification to the model inference algorithm or neural architecture. Experimentally, we show that our method can identify both uncertain and outlier data points, and achieves strong results on SVHN and CIFAR10 at various coverages of the data.

In an unusual combination of disciplines, a basketball-shooting robot created by Japan's leading automaker helped students at a Tokyo elementary school on Friday to learn math. The physically active math lesson was joined by professional players from the B. League's Alvark Tokyo basketball team as well as Cue3, a humanoid robot made by one of the team's major sponsors, Toyota Motor Corp. The special class was part of Tokyo 2020 Math Drill, a learning program that incorporates 55 official sports from the Tokyo Olympics and Paralympics into math classes to provide fun learning opportunities. Sixth-graders at Fuchu Elementary School No. 10 in the city of Fuchu were divided into groups of 13 to 17 students. Each student shot the ball once and calculated the success rates for each group, making it an exercise in using fractions. The group that scored highest got to compete against players Daiki Tanaka and Joji Takeuchi.

College students lead stressful lives. They've got assignments to complete and extracurriculars to attend, not to mention tests to prepare for and job applications to submit. Unfortunately for them, the negative health effects of stress are well-documented. If left untreated, it can cause cardiovascular diseases, affect memory and cognition, and even suppress the immune system. To help suss out the outsized contributors to social and academic pressure, researchers at the College of Computer Science at the University of Massachusetts turned to AI, which they used to predict stress levels -- below median, median, or above median -- from questionnaire and smartphone sensor data.